During geomagnetic storms, which are a result of solar wind interaction with the Earth’s magnetosphere, geomagnetically induced currents (GICs) begin to flow in the long-distance high-voltage power grids on the Earth’s surface. It causes a number of negative phenomena that affect the normal operation of the entire electric power system. To investigate the nature of the phenomenon and its possible effects on transformers, a GIC monitoring system was created in 2011, the devices of which were installed at five substations of the Kola-Karelian power transit in northwestern Russia. Over 12 years of operating the system a large amount of data has been accumulated, which cannot be analyzed manually within a reasonable amount of time. To analyze the constantly growing volume of recorded data effectively, a method for automatic classification of GIC in autotransformer neutral was proposed. The method is based on a continuous wavelet transform of the neutral current data combined with a convolutional neural network (CNN) to classify the obtained scalogram images. As the result of comparing four CNNs with different architectures, a model that showed excellent GIC classification performance on the validation set (100.00% accuracy and loss of 0.0115) was chosen.

Audio classification using deep learning models, essential for applications like voice assistants and music analysis, faces challenges when deployed on edge devices due to their limited computational resources and memory. Achieving a balance between performance, efficiency, and accuracy is a significant obstacle in optimizing these models for such constrained environments. In this investigation, we evaluate diverse deep learning architectures, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), for audio classification tasks on the ESC 50, UrbanSound8k, and Audio Set datasets. Our empirical findings indicate that Mel Spectrograms outperform raw audio data, attributing this enhancement to their synergistic alignment with advanced image classification algorithms and their congruence with human auditory perception. To address the constraints of model size, we apply model compression techniques, notably magnitude pruning, Taylor Pruning, and 8-bit quantization. The research demonstrates that a hybrid pruned model achieves a commendable accuracy rate of 89 percent, which, although marginally lower than the 92 percent accuracy of the uncompressed CNN, strikingly illustrates an equilibrium between efficiency and performance. Subsequently, we deploy the optimized model on Raspberry Pi 4 and NVIDIA Jetson Nano platforms for audio classification tasks. These findings highlight the significant potential of model compression strategies in enabling effective deep learning applications on resource-limited devices, with minimal compromise on accuracy.

Hyperspectral (HS) imaging plays a pivotal role in various fields, including medical diagnostics, where precise human vein detection is crucial. Hyperspectral image data are very large and can cause computational complexities. Dimensionality reduction techniques are often employed to streamline HS image data processing. This paper investigates the effectiveness of three dimensionality reduction techniques, namely: Principal Component Analysis (PCA), Folded PCA (FPCA), and Ward’s Linkage Strategy using Mutual Information (WaLuMI) for vein detection using HS images. A HS image dataset, encompassing left and right-hand images captured from 100 subjects with varying skin tones was created and annotated using anatomical data to represent vein and non-vein areas within the images. To generate experimental results, the HS image dataset was divided into train and test datasets. Optimum performing parameters for each of the dimensionality reduction techniques in conjunction with The Support Vector Machine binary classification were determined using the Training dataset. The performance of the three dimensionality reduction based vein detection methods was then assessed and compared using the test image dataset. Results show that the FPCA-based method outperforms the other two methods in terms of accuracy. For visualization purposes, the classification prediction image for each technique is post-processed using morphological operators, and results show the significant potential of HS imaging in vein detection.

OSI model used to be common network model for years. In the case of ad hoc networks with dynamic topology and difficult radio communications conditions, gradual departure is happening from the classical kind of OSI network model with a clear delineation of layers (physical, channel, network, transport, application) to the cross-layer approach. The layers of the network model in ad hoc networks strongly influence each other. Thus, the cross-layer approach can improve the performance of an ad hoc network by jointly developing protocols using interaction and collaborative optimisation of multiple layers. The existing cross-layer methods classification is too complicated, because it’s based on the whole manifold of network model layers combinations, regardless their importance. In this work, we review ad hoc networks cross-layer methods, propose the new useful classification of cross-layer methods, and show future research directions in ad hoc networks cross-layer methods development. The proposed classification can help to simplify the goal-oriented cross-layer protocol development.

This paper considers the direction of arrival (DOA) estimation problem of phase ambiguity for unfolded coprime linear array (UCLA). The existing most common stacking subarray-based method can tackle the phase ambiguity problem. However, the method is not always true. For a given DOA, it has its corresponding steering vector, but if there are two DOAs which have the same steering vectors with the given DOA for different subarrays, the phase ambiguity problem still arises. A modified method, which defines a decision variable and uses the classic beamforming technique to estimate the DOAs is proposed. However, this method needs additional algorithm to distinguish the true DOAs besides multiple signal classification (MUSIC) spectrum. And sometimes this method is not reliable if the decision variable is not chosen appropriately. Therefore, based on the UCLA, we reconstruct the array to design an improved UCLA called IUCLA. With moving the reference element, the linear relationship among the directional vectors can be broken. In this way, we can directly utilize the MUSIC algorithm to estimate with no additional algorithm. The proposed method can solve the phase ambiguity problem and it does not demand other technique to decrease the complexity. Moreover, the Cramer-Rao Bound (CRB), as the lower bound of the unbiased estimation, is provided and numerical simulations are given to demonstrate the effectiveness and superiority of the proposed method.

Antimicrobial peptides (AMPs) are essential components of the innate immune system, present across a spectrum of organisms. Their versatility lies in their ability to effectively inhibit a wide range of pathogens including bacteria, fungi, parasites, and viruses. As the challenge of antibiotic resistance escalates and concerns surrounding antibiotic use grow, AMPs have emerged as promising alternatives. Their applications span diverse domains such as medicine, agriculture, food production, animal husbandry, and aquaculture, primarily due to their efficacy against resistant pathogens, signifying their crucial role in combating infections. Beyond their antimicrobial properties, research illuminates their intricate involvement in the immune response network. These peptides impact crucial processes including cytokine release, chemotaxis, wound healing, angiogenesis and the activation of the adaptive immune system. This comprehensive review delves into the multifaceted realm of AMPs, encompassing their classification, varied applications across industries and notably, their significance in diverse disease conditions like infections, skin diseases, cancer, oral diseases, lung diseases and cardiovascular ailments. Special attention is directed towards current advancements in clinical applications of peptide-based therapeutics, offering invaluable insights into their potential future integration within clinical settings.

We performed arthroscopically assisted coracoclavicular (CC) stabilization using a suture button device for modified Neer type IIb lateral clavicle fractures accompanied by CC ligament injury. The purpose of the present study was to evaluate the radiological and clinical outcomes of this procedure. Six consecutive patients with modified Neer type IIb fractures were treated with the technique and followed for 12 months. Postoperative ranges of motion (ROMs) and X-rays were evaluated every 3 months. Shoulder functional scores (University of California Los Angeles score, Japanese Orthopedics Association score), visual analog scale (VAS) scores for pain at rest, at night, and during motion, and VAS score for satisfaction were analyzed at 12 months after surgery. For the clinical outcomes, early ROM recovery and excellent outcomes were achieved. For the radiological outcomes, all patients accomplished bone union, slight superior displacement was noted in the CC distance and Dog Bone distance, and bone hole dilation was observed in the superior coracoid and inferior clavicle, but there were no critical complications. The present findings indicate that arthroscopically assisted CC stabilization with a suture button device for unstable lateral clavicle fractures can produce satisfactory radiological and clinical results.

The soils of the high-altitude mountains along the East African Rift Valley are poorly understood. Understanding the potential of soils for agriculture, climate change mitigation, and environmental functioning necessitates an understanding of their relationship to soil-forming factors. Therefore, this study focuses on the volcanic soils of Mount Guna. Eighty-five soil profiles, between 3000 and 4120 m a.s.l., were described and sampled along seven topographic transects. The samples were analyzed for physicochemical characteristics using standard methods and classified according to WRB 2022. The clay mineralogy of six profiles was analyzed with X-ray diffraction. The first four-factor axes, which are related to elevation, parent material, climate, and land use were found by factor analysis, explaining more than 60% of the total variation. The clay portions are primarily composed of trioctahedral chlorite, trioctahedral mica (phlogopite or biotite), vermiculite, kaolinite, some quartz, some amorphous silicates (most likely pyroclastic glass), and minor feldspar. The presence of weatherable minerals (biotite, amphibole, feldspars, and so on) suggests that these soils have not been weathered extensively. The dominant Reference Soil Groups found in the study area are Andosols, Phaeozems, Leptosols, Regosols, Cambisols, Luvisols, and Vertisols. As a result, our findings suggest that altitudinal variation, climate, lithology, and their contributions to the variability of soil characteristics and development along the toposequence cannot be separated in this study; more similar studies in other high-elevation/altitude mountains are required. There have been no other studies of high-altitude mountains in East Africa where so many soil profiles have been examined.

This research presents a novel Customised Load Adaptive Framework (CLAF) for fault classifica-tion in Induction Motors (IMs), utilizing the Machinery Fault Prevention Technology (MFPT) Bearing Dataset. CLAF represents a pioneering approach that extends traditional fault classification methodologies by accounting for load variations and dataset customization. Through a meticulous two-phase process, it unveils load-dependent fault subclasses that have not been readily identified in traditional approaches. Additionally, new classes are created to accommodate the dataset's unique characteristics. Phase 1 involves the exploration of load-dependent patterns in time and frequency domain features using one-way Analysis of Variance (ANOVA) ranking and validation via bagged tree classifiers. In Phase 2, CLAF is applied to identify mild, moderate, and severe load-dependent fault subclasses through optimal Continuous Wavelet Transform (CWT) selection through Wavelet Singular Entropy (WSE) and CWT energy analysis. The results are compelling, with a 96.3% classification accuracy achieved when employing a wide neural network to classify proposed load-dependent fault subclasses. This underscores the practical value of CLAF in en-hancing fault diagnosis in IMs and its future potential in advancing IM condition monitoring.

Use of fingerprints found at a crime scene is a common practice for identifying suspects in criminal investigations. Over the past two decades, attempts have been made to obtain additional information from fingerprints, beyond locating suspects as part of an investigation. This includes gender, age and nationality. Researchers demonstrated 75%-90% accuracy in gender classification based on fingerprint images. Nonetheless, despite promising results, these studies have several significant shortcomings with respect to their practical feasibility. First, they ignore the low quality and quantity of fingerprints collected from the crime scene since typically the scene of a crime has only one fingerprint collected, and the fingerprint might be partially or of poor quality as well. Second, as most results are based on a single database, public or private, it is difficult to generalize the most suitable method. Third, studies miss the untapped potential of Data-Centric AI (DCAI) approaches for improving results. The aim of this study was to compare, for the first time, the gender classification from a fingerprint using several datasets and with varying fingerprint image quality. The results from four databases are compared, three public and one internal private database. In addition, we utilize the latest Data-Centric AI (DCAI) approaches for improving classification results. The results demonstrate that a conservative Convolutional Neural Networks (CNN) such as VGG is sufficient for this task. Classification accuracy ranges from 80% to 95% depending on the quality of the fingerprint, with DCAI approaches adding 1%-4% improvement. For partially or low-quality fingerprint images, the periphery of a fingerprint is the most significant area for determining gender. The source code is also provided here for practical application.

Our daily activities hinge on the flexibility of our fingers, and a fractured finger can significantly disrupt these routines. The finger bones enable us to bend and fold the fingers and thumb to pick up or grasp objects and do all of our daily activities. A broken finger can cause adverse effects on our daily life activities. It is important to treat broken finger as soon as possible. Swift and precise treatment begins with capturing multiple X-rays, followed by the critical step of fracture detection in these images. Relying on the naked eye for this task carries the risk of overlooking small fractures. To address this issue an automated diagnoses of fractured fingers from images is required for which the current research employs advanced deep learning models—ResNet34, ResNet50, ResNet101, ResNet152, VGG-16, and VGG-19—to classify finger images from the Musculoskeletal Radiographs (MURA) dataset as either fractured or non-fractured. The results emphasize the consistently strong performance of ResNet models, attaining an impressive accuracy of 81.9%. This surpasses VGG models by 3.4% and establishes ResNet as the new benchmark for state-of-the-art accuracy.

Chinese calligraphy, revered globally for its therapeutic and mindfulness benefits, encompasses styles such as Regular (Kai Shu), Running (Xing Shu), Official (Li Shu), and Cursive (Cao Shu) scripts. Beginners often start with Regular script, advancing to more intricate styles like Cursive. Each style, marked by unique historical calligraphers' contributions, requires learners to discern distinct nuances. The integration of AI in calligraphy analysis, collection, recognition, and classification are pivotal. This study introduces an innovative Convolutional Neural Network (CNN) architecture, pioneering the application of CNN in the classification of Chinese calligraphy. Focusing on the four principal calligraphers' styles from the Tang dynasty (690-907 A.D), this research spotlights the era when the traditional regular script font (Kai Shu) was refined. A comprehensive dataset of 8282 samples from these calligraphers, representing the zenith of regular style, was compiled for CNN training and testing. The model distinguishes personal styles for classification, showing superior performance over existing networks. Achieving 89.5-96.2% accuracy in calligraphy classification, our approach underscores the significance of CNN in both font and artistic style categorization. This research paves the way for advanced studies in Chinese calligraphy and its cultural implications.

In recent years, there has been significant growth in the ubiquity and popularity of three dimensional(3D) point clouds, with an increasing focus on the classification of 3D point clouds. To extract richer features from point clouds, many researchers have turned their attention to various point set regions and channels within irregular point clouds. However, this approach has limited capability in attending to crucial regions of interest in 3D point clouds and may overlook valuable information from neighboring features during feature aggregation. Therefore, this paper proposes a novel 3D point cloud classification method based on global attention and adaptive graph convolution. The method consists of two main branches: the first branch com-putes attention masks for each point, while the second branch employs adaptive graph convolu-tion to extract global features from the point set. It dynamically learns features based on point interactions, generating adaptive kernels to effectively and precisely capture diverse relationships among points from different semantic parts. Experimental results demonstrate that the proposed model achieves 93.8% in overall accuracy and 90.8% in average accuracy on the ModeNet40 dataset.

In recent years, ozone (O3) pollution has been rapidly spreading, restricting further improvement of air quality in China. Investigating the interaction of O3 concentration and identifying their driven cities are important for the prevention and control of O3 pollution in China. However, the complex interaction between O3 pollution between the cities and their driven cities has not yet been revealed. In this study, we address this gap based on complex network methods. Specifically, an ozone relational network is constructed using an association calculation method. The driven nodes and spatial clusters were analyzed based on the maximum matching algorithm and the Louvain algorithm. The findings of the study reveal an aggregation phenomenon in the ozone network concerning distance. Furthermore, as the threshold Tc varies, the proportion of driven nodes exhibits a positive correlation. Moreover, a closer threshold value corresponds to a higher coincidence ratio of the driven nodes. The results provide scientific guidance for national O3 pollution prevention and regional synergy formatting. Furthermore, the introduced network-based approaches offer a mythological framework for the study of air pollution in key cities and clusters.

Gelephu, located in the Himalayan region, has experienced one of the strongest development activities due to the suitable topography and geographic location, which has led to rapid urbanization. The main objective of the study is to perform land use land cover (LULC) mapping for Gelephu for 2016 and 2023 by a Random Forest (RF) classifier, using the Semi-automatic Classification Plugin (SCP) in QGIS and identify LULC changes. Furthermore, the study assessed the vegetation change dynamics within the study area by analysing the Normalized Difference Vegetation Index (NDVI) for 2016 and 2023. Additionally, the study characterized the resulting LULC change for Gelephu Thromde, a sub-administrative municipal entity, as a result of the notable intensity of the infrastructure development activities. The current study used a framework to collect Sentiniel-2 satellite data, which was then used for pre- and post-processing to create LULC and NDVI maps. The classification model achieved high accuracy, with an area under the curve (AUC) of up to 0.89. The corresponding LULC and NDVI statistics were analysed to determine the current status of the LULC and vegetation indices, respectively. The LULC change analysis reveals urban growth of 5.65% and 15.05% for Gelephu and Gelephu Thromde, respectively. The NDVI assessment shows significant deterioration in vegetation health with a 75.11% loss of healthy vegetation in Gelephu between 2016 and 2023. This research provides the first version of LULC and NDVI maps of Gelephu. The analysis results indicate possible future implications on sustainable development management in Gelephu Bhutan.

Memristive spiking neural networks are a promising emerging technology in the field of deep learning. These models can be implemented using neuromorphic hardware and therefore offer low power consumption and low latency, crucial to edge-computing applications. These systems, however, pose several challenges, including on-chip training and connectivity reduction. The latter is essential to ease the manufacturing complexity of the system. On-chip training can be realized using synaptic plasticity enabled by memristors. In this work, we compare two methods of connectivity reduction applicable to memristive spiking networks: an ensemble-based approach and a probabilistic sparse connectivity approach. We evaluate both of these methods in conjunction with a three-layer spiking neural network on the handwritten and spoken digits classification tasks using two memristive plasticity models and a classical time-dependent plasticity rule. On the handwritten digits recognition task, both methods achieve the F1-score of 0.89–0.93, and yield the 0.80–0.96 F1-score on the spoken digits recognition task.

(1) Background: Transcription factors (TFs) are main regulators of eukaryotic gene expression. The cooperative binding of at least two TFs to genomic DNA is the widespread mechanism of transcription regulation. Massive analysis of co-occurrence of overrepresented pairs of motifs (composite elements, CEs) for different target TFs studied in ChIP-seq experiments can clarify the mechanisms of TF cooperation. (2) Methods: We focused on homotypic CEs representing the same motif models and considered only CEs with spacers. We improved the capability of the Motifs Co-Occurrence Tool (MCOT) to predict asymmetric homotypic CEs in which one of par-ticipating motifs has higher conservation than the other. We categorized the target TFs from the data of M. musculus ChIP-seq and A. thaliana ChIP-seq/DAP-seq according to the structure of DNA-binding domains (DBDs) into classes. (3) Results: For all TF classes the predominance of asymmetric CEs over both symmetric and those with no enrichment in both directions was re-vealed. Target TFs from classes Basic helix-loop-helix factors (bHLH) and Basic leucine zipper factors (bZIP) showed the highest fractions of datasets with asymmetric CEs. We showed that pioneer TFs, despite their DBD types, have a higher significance of asymmetry within homotyp-ic CEs compared to other TFs. (4) Conclusion: Asymmetry within homotypic CEs is a promising new feature decrypting the mechanisms of gene transcription regulation.

In this paper, we demonstrate that fixed-weight layers, generated from random distribution or logistic functions, can effectively extract significant features from input data, resulting in high accuracy on a variety of tasks, including Fisher’s iris, Wisconsin Breast Cancer, and MNIST datasets. We have observed that logistic functions yield high accuracy with less dispersion in results. We have also assessed the precision of our approach under conditions of minimizing spikes number generated in the network, it is a practically useful for reducing energy consumption in spiking neural networks. Our findings reveal that the proposed method demonstrates a highest accuracy on Fisher’s iris and MNIST datasets with logistic regression. Furthermore, they surpass the accuracy of the conventional (non-spiking) approach using logistic regression in the case of Wisconsin Breast Cancer. We have also investigated the impact of non-stochastic spike generation on accuracy.

Artificial intelligence techniques are now widely used in various agricultural applications, including the detection of devastating diseases such as late blight (Phytophthora infestans) and early blight (Alternaria solani) affecting potato (Solanum Tuberorsum L.) crops. In this paper, we present a mobile application for potato crop pest detection based on deep neural networks. The images were taken from the PlantVillage dataset with a batch of 1000 images for each of the three identified classes. An exploratory analysis of the architectures used for early and late blight diagnosis in potatoes was performed, achieving an accuracy of 98.76%, with MobileNetv2. Based on the results obtained, an offline mobile application was implemented, supported on devices with Android 4.1 or later, also featuring an information section on the 27 pests affecting potato crops and a gallery of symptoms. For future work, segmentation techniques will be used to highlight the damaged region in the potato leaf by evaluating its extent and possibly identifying different types of pests affecting the same plant.

Text classification is one of the most important task in natural language processing, In this research, we carried out several experimental research on three (3) of the most popular Text classification NLP classifier in Convolutional Neural Network (CNN), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVN). In the presence of enough training data, Deep Learning CNN works best in all parameters for evaluation with 77% accuracy, followed by SVM with accuracy of 76%, and multinomial Bayes with least performance of 69% accuracy. CNN has the best performance in the presence of large enough training dataset because of the presence of filter/ kernels which help to indentify patterns in text data regardless of their position in the sentence. We repeated the training again with just one-third of our data, at this point SVM comes with the best performance, the performance of CNN noticeably drops but still better than Multinomial Naive Bayes, the reason why SVM works best when we reduce the training data was because of its ability to look for a hyper-plane that creates a boundary between different classes of data so as to properly classify them, so we believed that getting the hyper-plane was more efficient when we reduce the dataset, hence reason for the good performance. Multinomial Naive Bayes have the least performance which we attributed to its assumption of independency between the features which sometimes does not hold true. We concluded that availability of data should be an important factor when choosing classifier for Natural Language Processing Text Classification task. CNN should be use in the presence of enough dataset, and SVM should be use when data is not enough. Multinomial Naive Bayes must not be trusted with state of the art NLP task due to its assumption of independency between the features.

This proposed research explores a novel approach to image classification by deploying a complex-valued neural network (CVNN) on a field-programmable gate array (FPGA), specifically for classifying 2D images transformed into polar form. The aim of this research is to address the limitations of existing neural network models in terms of energy and resource efficiency, by exploring the potential of FPGA-based hardware acceleration in conjunction with advanced neural network architectures like CVNNs. The methodological innovation of this research lies in the Cartesian to polar transformation of 2D images, effectively reducing the input data volume required for neural network processing. Subsequent efforts focused on constructing a CVNN model optimized for FPGA implementation, emphasizing the enhancement of computational efficiency and overall performance. The experimental findings provide empirical evidence supporting the efficacy of the image classification system developed in this study. One of the developed models, CVNN_128, achieves an accuracy of 88.3% with an inference time of just 1.6ms and a power consumption of 4.66mW for the classification of the MNIST test dataset consists of 10,000 frames. While there is a slight concession in accuracy compared to recent FPGA implementations that achieve 94.43%, our model significantly excels in classification speed and power efficiency—surpassing existing models by more than a factor of 100. In conclusion, the paper demonstrates the substantial advantages of FPGA-implementation of CVNNs for image classification tasks, particularly in scenarios where speed, resource, and power consumption are critical. The study’s reproducible results and corresponding code are available on GitHub at the following link: https://github.com/mahmad2005/CVNNonFPGA

Human-to-human communication via the computer is mainly done using a keyboard or microphone. In the field of Virtual Reality (VR), where the most immersive experience possible is desired, the use of a keyboard contradicts this goal, while the use of a microphone is not always desirable (e.g. silent commands during task force training) or simply not possible (e.g. if the user has a hearing loss). Data gloves help to increase immersion within the VR as they correspond to our natural interaction. At the same time, they offer the possibility to accurately capture hand shapes, such as those used in non-verbal communication (e.g. thumbs up, okay gesture, ...) and in sign language. In this paper, we present a hand shape recognition system using Manus Prime X data gloves, including data acquisition, data preprocessing, and data classification to enable nonverbal communication within VR. We investigate the impact on accuracy and classification time of using an Outlier Detection and a Feature Selection approach in our data preprocessing. To obtain a more generalized approach, we also studied the impact of artificial Data Augmentation, i.e., we create new artificial data from the recorded and filtered data to augment the training dataset. With our approach, 56 different hand shapes could be distinguished with an accuracy of up to 93.28%. With a reduced number of 27 hand shapes, an accuracy of up to 95.55% could be achieved. Voting Meta-Classifier (VL2) has proven to be the most accurate, albeit slowest, classifier. A good alternative is Random Forest (RF), which was even able to achieve better accuracy values in a few cases and was generally somewhat faster. Outlier Detection has proven to be a effective approach, especially in improving classification time. Overall, we have shown that our hand shape recognition system using data gloves is suitable for communication within VR.

Ransomware attacks have rapidly proliferated, inflicting severe financial damages on businesses and individuals. Machine learning approaches to automate ransomware detection have shown promise but grapple with challenges like limited training data. This study introduces a novel deep learning model for few-shot ransomware classification. The model employs entropy features derived directly from malware binaries coupled with a twin neural network architecture utilizing transfer learning. Tests on over 1000 samples across 11 families demonstrate a weighted F1-score of 85.8\%, surpassing existing methods. The approach mitigates biases in limited training data and preserves intricacies lost in image-based features. It exhibits precise classification capabilities even with sparse samples of new ransomware variants. The research highlights the potential of entropy-driven deep learning to equip defenses against emerging zero-day ransomware strains.

This study provides a comprehensive assessment of Derawan Island's coral reefs over two decades (2003, 2011, and 2021) from a machine learning classification perspective. Employing non-parametric algorithms like Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART), our analysis primarily focused on spatial and temporal changes in coral habitats. RF emerged as the most accurate method, demonstrating an accuracy of 71.43% with Landsat 9, 73.68% with Sentinel 2, and 78.28% with Multispectral Aerial Photos. We found that the accuracy of our classification results was significantly influenced by the geographic resolution, as well as the quality of field and satellite/aerial image data. Through spatial clustering, the coral habitats exhibited an Nearest Neighbor Index (NNI) value of 0.8727, indicating specific patterns of distribution. The analysis revealed a decrease in coral reef extent from 2003 to 2011, shrinking to 16 hectares with varying densities, followed by a slight area increase but with more heterogeneous densities between 2011 and 2021. This study not only highlights the dynamic nature of coral reef habitats over two decades but also underscores the critical role of machine learning in environmental monitoring and conservation efforts.

During a multi-detector computed tomography (MDCT) examination, it is crucial to efficiently organize, store, and transmit medical images in DICOM standard, which requires significant hardware resources and memory. Our project processed large amounts of DICOM images by classifying them based on cross-section views that may carry important information about a possible diagnosis. We ensured that images were retained and saved in PNG format to optimize hardware resources while preserving patient confidentiality. Furthermore, we have developed a graphical, user-friendly interface that allows physicians to visualize specific regions of interest in a patient's brain where changes may indicate disease. Our proposed method enables quick classification of medical images into predefined classes of confirmed diseases of brain parenchyma, contributing to swift decision-making for further diagnosis for more precisely evaluating and characterizing brain changes, and it can lead to the rapid application of adequate therapy, which may result in better outcomes.

Software-Defined Networking (SDN) is a network implementation paradigm of great importance, as it has a profound impact on the pace of technological progress. While SDN doesn’t directly address the technical complexities of routing, congestion control, traffic engineering, security, mobility, reliability, or real-time communication, it paves the way for innovative solutions to emerge for these and similar challenges. Security is of utmost importance in SDN with Distributed Denial of Service (DDoS) being an attack which creates large scale problems. DDoS creates malicious traffic that resembles normal traffic in order to create service problems. As such, mechanisms that distinguish between benign and malicious traffic is essential, since this is the first step to mitigate the problem of DDoS. In this paper, we take a dataset onboard which exhibits benign and malicious traffic in SDN. There are 23 features that are used for classification purposes. Here we utilise classification procedure based on three methods based Grammatical Evolution applied on the aforementioned data. We provide results that show the efficiency of our approach and show that all three methods exhibits satisfactory results.

The agricultural sector is currently confronting multifaceted challenges such as an increased food demand, a slow adoption of sustainable farming, a need for climate-resilient food systems, resource inequity, and protection of the small-scale farmers’ practices, all issues integral to food security and environmental health. Remote sensing technologies can assist precision agriculture to effectively address these complex problems, by providing farmers with a high-resolution lens. The use of vegetation indices (VIs) is an essential component of remote sensing, which combine the variability of spectral reflectance value (derived from remote sensing data) with the growth stage of crops. Currently a wide array of VIs is available that could be used to provide a classification and an evaluation of the state and health of crops. However precisely this high number leads to difficulties in selecting the best VI and combination of VIs for a specific objective. Without a thorough documentation and analysis of appropriate VIs, users might be confronted with difficulties in using remote sensing data or even with a very low accuracy of the results. Thus, the objective of this review is to conduct a critical analysis of the existing state of the art on the most important features related to the effective use of VIs for the discrimination and monitoring of the most important agricultural crops (wheat, corn, sunflower, soybean, rape, potatoes, and forage crops), grasslands and meadows. This data could be highly useful for all the stakeholders involved in agricultural activities (from farmers, researchers up to institutions dealing with the centralization and monitoring of agricultural crops).

In this paper, we redefine the boundaries of blood cell classification, expanding from 5 to 11 distinct blood cell types using the challenging 11-class PBC dataset. This shift enables a deeper understanding of blood cell diversity, overcoming previous limitations in medical image analysis. Our approach combines state-of-the-art deep learning techniques, including pre-trained ConvNets, ViTb16 models, and custom CNN architectures. We employ transfer learning, fine-tuning, and ensemble strategies, such as CBAM and Averaging ensembles, to achieve unprecedented accuracy and interpretability. Our fully fine-tuned EfficientNetV2 B0 model sets a new standard, with a macro-average precision, recall, and F1-score of 91%, 90%, and 90%, respectively, and an average accuracy of 93%. This breakthrough underscores the transformative potential of 11-class blood cell classification for more precise medical diagnoses. Moreover, our groundbreaking "Naturalize" augmentation technique produces remarkable results. The 2K-PBC dataset generated with "Naturalize" boasts a macro-average precision, recall, and F1-score of 97%, along with an average accuracy of 96% when leveraging the fully fine-tuned EfficientNetV2 B0 model. This innovation not only elevates classification performance but also addresses data scarcity and bias in medical deep learning. Our research marks a paradigm shift in blood cell classification, enabling more nuanced and insightful medical analyses. The "Naturalize" technique’s impact extends beyond blood cell classification, emphasizing the vital role of diverse and comprehensive datasets in advancing healthcare applications through deep learning.

Numerous companies frequently engage in collaborative cooperation to exchange intelligence rather than resorting to competing strategies to achieve higher levels of intelligence. \textit{Intelligence} is essential in determining \textit{collaborative intelligence} . Intelligence can be discerned by a range of behaviors and actions that go beyond the mere individual observable activity. Collaborative intelligence is often defined as the combined actions of individuals (such as humans and machines) working towards a common goal. It encompasses more than just behaviour and includes other crucial elements such as the sharing of intelligence. The ontological view concerns the system's understanding and representation of information, data, and the fundamental reality it aims to capture and manage. This paper aims to expand upon our previous research on Intelligence in decentralized environments. The methodology utilized in this paper involves employing an ontological view to derive intelligence, encompassing its various forms, such as data, information, and knowledge. The paper explores the fundamental significance of intelligence and semantic integration in supporting the collaborative intelligence framework. Semantic integration is crucial to establishing a shared understanding of individuals' data, information, and concepts. It is fundamental in facilitating effective communication, intelligence sharing, and decision-making within a collaborative environment.

In the realm of the five-category classification endeavor, there has been limited exploration into applied techniques for classifying Arabic text. These methods have primarily leaned on singletask learning, incorporating manually crafted features that lack robust sentence representations. Recently, the Transformer paradigm has emerged as a highly promising alternative. However, when these models are trained using single-task learning, they often face challenges in achieving outstanding performance and generating robust latent feature representations, especially when dealing with small datasets. This issue is particularly pronounced in the context of the Arabic dialect, which has a scarcity of available resources. Given these constraints, this study introduces an innovative approach to dissecting sentiment in Arabic text. This approach combines Inductive Transfer (INT) with the Transformer paradigm to augment the adaptability of the model and refine the representation of sentences. By employing self-attention SE-A and feed-forward sub-layers as a shared Transformer encoder for both the five-category and three-category Arabic text classification tasks, this proposed model adeptly discerns sentiment in Arabic dialect sentences. The empirical findings underscore the commendable performance of the proposed model, as demonstrated in assessments of the Hotel Arabic-Reviews Dataset, the Book Reviews Arabic Dataset, and the LARB dataset.

The health and stability of trees are essential information for the safety of people and property in urban greenery, parks or along roads. The stability of the trees is linked to root stability but essentially also to trunk decay. Currently used internal tree stem decay assessment methods, such as tomography or penetrometry, are reliable but usually time-consuming and unsuitable for large-scale surveys. Therefore, a new method based on close-range remote sensed data, specifically close-range photogrammetry and iPhone LiDAR, was tested to detect decayed standing tree trunks automatically. The proposed study used the PointNet deep learning algorithm for 3D data classification. It was verified in three different datasets consisting of pure coniferous trees, pure deciduous trees or mixed data to eliminate the influence of the detectable symptoms for each group and species itself. The mean achieved validation accuracies of the models were 65.5 % for Coniferous trees, 58.4 % for Deciduous trees and 57.7 % for Mixed data classification. The accuracies indicate promising data, which can be either used by practitioners for preliminary surveys or for other researchers to acquire more input data and create more robust classification models.

Photoelectric smoke detectors are the most cost-effective devices for very early fire alarms. However, due to different light intensity response values for different fire smoke and interference from interferential aerosols, they have a high false alarm rate, which limits their popularity in Chinese homes. To address these issues, an embedded spatial-temporal convolutional neural network (EST-CNN) model is proposed for real fire smokes identification and aerosols (fire smokes and interferential aerosol) classification. EST-CNN consists of three modules including information fusion, scattering feature extraction, and aerosol classification. Moreover, a two dimensional spatial-temporal scattering (2D-TS) matrix is designed to fuse the scattered light intensities in different channels and adjacent time slices, which is the output of the information fusion module and the input of the scattering feature extraction module. EST-CNN is trained and tested with experimental data measured on the established fire test platform using the developed dual-wavelength dual-angle photoelectric smoke detector. The optimal network parameters are selected through extensive experiments resulting in an average classification accuracy of 95.6% for different aerosols with only 66 kB network parameters. The experimental results demonstrate the feasibility of the designed EST-CNN model to be directly installed in existing commercial photoelectric smoke detectors to realize aerosol classification.

This study investigates the spatio-temporal variability of the onset, offset, and Length of the rainy season in Senegal over the 1981-2018 period. The onset, offset dates, and length of the rainy season are crucial parameters for agricultural planning in West Africa, which exhibits high interannual and spatial variability in rainfall. The objective is to detect and spatially classify these phenological parameters across Senegal using different approaches. Daily precipitation data from the Climate Hazards group InfraRed Precipitation with Stations (CHIRPS) dataset over the study period and ERA5 reanalysis were used. The method of was applied to detect the onset, offset dates, and length of the rainy season. The Kohonen algorithm was then used to spatially classify these parameters on topological maps. A hierarchical ascendant classification was also performed to reduce the number of classes obtained. Finally, a composite analysis of moisture transport before the onset and end of the 1984 and 2007 seasons was conducted with ERA5 data. The results show a meridional gradient of the start of the season, progressively later from southeast to northwest, while the end follows a north-south gradient. The duration of the rainy season ranges from 45 days in northern Senegal to 150 days in the south. The maximum number of dry days also decreases from north to south. The Kohonen self-organizing maps allow spatial prediction of the onset, end date, and duration of the rainy season across Senegal. The dynamics analysis reveals that early onset years are associated with stronger and earlier northward moisture transport over West Africa, underscoring the role of this parameter in triggering the rainy season. These promising results can provide an early warning to policymakers and farmers on the start and end dates of the season in each area of Senegal.

Academic researchers publish their work in various formats, such as papers, patents, and research reports, on different academic sites. When searching for a particular researcher's work, it can be challenging to pinpoint the right individual, especially when there are multiple researchers with the same name. In order to handle this issue, we propose a name disambiguation scheme of researchers with the same name based on heterogeneous academic sites. The proposed scheme collects and integrates research results from these varied academic sites, focusing on attributes crucial for disambiguation. It then employs clustering techniques to identify individuals who share the same name. Additionally, the proposed rule-based algorithm name disambiguation method and the existing deep learning-based identification method. This approach allows for the selection of the most accurate disambiguation scheme, taking into account the metadata available in the academic sites, using a multiclass classification approach. To demonstrate the effectiveness of the proposed method, we conduct various performance evaluations, measuring accuracy, recall, and the F1-measure, highlighting the scheme's superior performance in the name disambiguation.

The accurate classification of landfill waste diversion plays a critical role in efficient waste management practices. Traditional approaches, such as visual inspection, weighing and volume measurement, and manual sorting, have been widely used but suffer from subjectivity, scalability, and labour requirements. In contrast, machine learning approaches, particularly Convolutional Neural Networks (CNN), have emerged as powerful deep learning models for waste detection and classification. This paper analyses VGG-16, InceptionResNetV2, DenseNet121, Inception V3, and MobileNetV2 models to classify real-life waste when trained on pristine and unadulterated materials, versus samples collected at a landfill site. When training on DiversionNet, the unadulterated material dataset with labels required for landfill modelling, classification accuracy was limited to 49.69% in the real environment. Using real-world samples in the newly formed RealWaste dataset showed that practical applications for deep learning in waste classification is possible, with Inception V3 reaching 89.19% classification accuracy on the full spectrum of labels required for accurate modelling.

Historic sites are important components of every city's cultural history because they preserve rich historical knowledge and distinctive values passed down from previous generations to the present. Due to the progress of urbanization and modernization, many historic sites face pressure from damage and transformation. In this paper, a method for assessing cultural heritage damage was developed to measure the extent of spatial damage in historic sites. Using sample data obtained in Xiyang, Qixian, and Xiaoyi, all historic cities in Shanxi Province, Mainland China, and combined weights were estimated using the Delphi technique and the CRITIC weight method. Following this, the Spatial Damage Degree Model (SDDM) based on K-means cluster analysis and K-nearest neighbor (KNN) classification was developed. The findings show that the model efficiently solves the problem of assessing spatial damage levels in historic sites. Through multiple linear regression analysis, it was shown that the damage to historic sites was predominantly caused by three factors: natural erosion, construction damage, and planning and policy. SDDM was used to calculate the spatial damage levels of historic sites, allowing conservators to fully comprehend the features and concerns related to historic sites. As a result, more scientific and rational preservation approaches may be developed, improving the efficiency of historic site restoration and conservation and encouraging the sustainable development of urban and rural heritage.

This research paper presents a deep learning approach to early detection of skin cancer using image augmentation techniques. The authors propose a two-stage image augmentation technique that involves the use of geometric augmentation and generative adversarial network (GAN) to classify skin lesions as either benign or malignant. This research utilized the public HAM10000 dataset to test the proposed model. Several pre-trained models of CNN were employed, namely Xception, Inceptionv3, Resnet152v2, EfficientnetB7, InceptionresnetV2, and VGG19. Our approach achieved accuracy, precision, recall, and F1-score of 96.90%, 97.07%, 96.87%, 96.97%, respectively, which is higher than the performance achieved by other state-of-the-art methods. The paper also discusses the use of SHapley Additive exPlanations (SHAP), an interpretable technique for skin cancer diagnosis, which can help clinicians understand the reasoning behind the diagnosis and improve trust in the system. Overall, the proposed method presents a promising approach to automated skin cancer detection that could improve patient outcomes and reduce healthcare costs.

One of the most important applications in the wireless sensor networks (WSN) is to classify mobile targets in the monitoring area. In this paper, a multi-DBN weighted voting classification algorithm is proposed on the basis of the Deep Belief Network (DBN) classifier and combined with the idea of voting method, which is implemented on the nodes of the WSN monitoring system by means of "upper training, lower transplantation" appraoch. The performance of the algorithm is verified by using real-world experimental data, and the results show that the proposed method has a higher accuracy in classifying the target signal features, achieving an average classification accuracy of 84.63% across four different types of moving targets. The experiment reveals that the multi-DBN weighted voting algorithm enhances the target classification accuracy by approximately 5% in comparison to the single DBN classifier, but the memory and computation time required for the algorithm to run are also increased at the same time. Compared to the FFNN classifier, which exhibited the highest classification accuracy among the four selected methods, the algorithm achieves an improvement of approximately 8.8% in classification accuracy. However, it incurs greater time overhead to run.

For the territory of the Republic of Tatarstan (Russia), a spatial model of forest communities was built as a basis for assessing the sequestration potential of ecosystems. Combination of multi-temporal and multi-spectral satellite imagery from the Landsat 8 and Landsat 9 platform as input data and Google Earth Engine cloud platform were used. The set of 292 vegetation indices and metrics computed from the pre-processed imagery, were combined into dataset. The Weka X-Means clustering algorithm was trained and applied to study area. The unsupervised classification was carried out by vegetation classes in the Braun-Blanquet system. The results of unsupervised classification were verified using data from more than 17,000 relevés with geographic references from the Flora database. For automatic classification, the EuroVeg Checklist expert system in the JUICE 7.1 package was used. The proposed methodology for obtaining initial data and unsupervised classification, supported by an automated expert system, made it possible to obtain a picture of the vegetation distribution in the study area with sufficient accuracy, and in the future, it will be used to assess the sequestration potential of the ecosystems of the region under study.

Computer vision is a powerful technology that has enabled solutions in various fields by analyzing visual attributes in images. One field that has taken advantage of computer vision is agricultural automation, which promotes high-quality crop production. The nutritional status of a crop is a crucial factor in determining its productivity. This status is mediated by approximately 14 chemical elements acquired by the plant, and their determination plays a pivotal role in farm management. To address the timely identification of nutritional disorders, this study focuses on the classification of three levels of phosphorus deficiencies through individual leaf analysis. The methodological steps include: (1) generating a database with laboratory-grown maize plants that were induced to total phosphorus deficiency, medium deficiency, and total nutrition, using different capture devices; (2) processing the images with state-of-the-art transfer learning architectures (i.e. VGG16, ResNet50, GoogLeNet, DenseNet201, and MobileNetV2); and (3) evaluating the classification performance of the models using the created database. The results show that the VGG16 model achieves superior performance, with 98% classification accuracy. However, the other studied architectures also demonstrate competitive performance and are considered state-of-the-art automatic leaf deficiency detection tools. The proposed method can be a starting point to fine-tune machine vision-based solutions tailored for real-time monitoring of crop nutritional status.

This paper first introduces the main classification of brain diseases and the main causes of these diseases. It is followed by wavelet analysis, which is a mathematical function or wave-like pattern used to transform data, which can decompose the signal into different wavelet functions, each of which is related to a specific scale or frequency. Wavelet analysis has been applied in many fields such as image processing, data analysis and engineering. In addition, wavelet is also used in the analysis of biological signals. Wavelet analysis breaks down brain signals, such as EEG or fMRI data, into various frequency components at different scales. Wavelet analysis provides time-frequency localization. Pattern recognition can be enhanced by isolating salient features in the data. Wavelet denoising can effectively separate noise from underlying brain signals. Wavelet denoising usually adopts threshold method. Wavelet analysis can enhance pattern recognition by isolating salient features in the data. Researchers can train machine learning models based on these wavelet-derived features to recognize specific patterns associated with different neurological disorders. Wavelet analysis can track these changes by continuously evaluating the frequency content of the neuroimage data over time. Through continuous efforts, wavelet theory and technology have become valuable tools in the field of neuroscience and brain disease research.

Chronic rhinosinusitis (CRS) can have a significant impact on quality of life. With persistent symptoms and the failure of initial medical treatments, surgical management is indicated. Despite the excellent results of endoscopic sinus surgery for persistent CRS, it is quite a challenging procedure for frontal sinusitis given the complex anatomy and location of the frontal sinus. Frontal sinus cells significantly contribute to the complexity of the frontal sinus, and numerous studies have sought to establish their association with sinusitis. This review offers a comprehensive understanding of the frontal sinus cells, their different classifications, their prevalence among different populations, and their relationship to sinusitis. After an extensive review of the current literature, the International Frontal Sinus Anatomy Classification (IFAC) is the most recent classification method and a preferred practical preoperative assessment tool. Although the Agger Nasi Cell is the most prevalent cell among all reported populations, ethnic variations are still influencing the other cells' distribution. Studies are inconsistent in reporting a relationship between frontal sinus cells and sinusitis, and that is mainly because of the difference in the classification method used. More research using a standardized classification method is needed to understand the association between frontal sinus cells and sinusitis.

Website defacement is an illegal electronic act of changing a website. In this paper, robust machine learning classifiers’ capabilities were exploited to select the best input feature set for evaluating a website’s defacement risk. Zone-H, a private organization, offered us a defacement mining dataset. A sample of 93644 datapoints was concisely pre-processed and used for modelling purposes. Considering multidimensional features as input, reason and hackmode variables were chosen as outputs. Massive machine learning models were examined; however, decision tree (DT), k-nearest neighbour (k-NN), and random forest (RF) were the most powerful algorithms used to predict the target model. The input variables 'domain', 'system', 'web_server','redefacement', 'type', 'def_grade', and 'reason/hackmode' were tested and used to shape the final model. Using the cross-validation (CV) technique, the model’s key performance factors were calculated and reported. After calculating the average scores for the hyperbolic metrics (i.e., max-depth, min-sample-leaf, weight, max-features, and CV), both targets were evaluated, and the learning algorithms were ranked as RF, DT, and k-NN. The reason and hackmode variables were thoroughly analysed. The average score accuracies for the reason and hackmode targets were 0.85 and 0.585, respectively. The results showed a significant development in terms of modelling and optimizing website defacement risk. The study also successfully addresses the main cybersecurity concerns, in particular website defacement.

Deep Learning (DL) has made significant advances in computer vision with the advent of Vision Transformers (ViT). Unlike Convolutional Neural Networks (CNNs), ViTs use self-attention to extract both local and global features from image data, and then use residual connections to feed these features directly into a fully networked multilayer perceptron head. In hospitals, hematologists prepare peripheral blood smears (PBSs) and read them under a medical microscope to detect abnormalities in blood counts such as leukemia. However, this task is time-consuming and prone to human error. This study investigates the transfer learning process of Google ViT and ImageNet CNNs to automate the reading of PBSs. The study used two online PBS datasets, PBC and BCCD, and transferred them into balanced datasets to investigate the influence of data amount and noise immunity on both neural networks. The PBC results show that Google ViT is an excellent DL neural solution for data scarcity. The BCCD results show that Google ViT is superior to ImageNet CNNs in dealing with unclean, noisy image data because it is able to extract both global and local features and use residual connections, despite the additional time and computational overhead.

Transportation tasks in nursing are common and shorten the time for direct patient care in times of nursing shortage and an aging society. There is a lack of research on transportation needs, with most studies focusing on overall nursing task analysis without a deeper analysis of transportation tasks. In July and September 2023, we conducted weekly observations of nurses to assess clinical transportation needs. We aim to understand the economic impact and the methods nurses use for transportation tasks. We conducted a participant observation using a standardized app-based form over a seven-day observation period in two rural clinics. N=1,830 transports were made by nurses and examined by descriptive analysis. Non-medical supplies account for 27.05 % (n=495) of all transports, followed by medical supplies at 17.32 % (n=317), pharmacotherapy at 14.10 % (n=258) and other other categories like meals or drinks contributing 12.68 % (n=232). Most transports had a factual transport time of under a minute, with patient transport and lab samples displaying more variability. In total, 77.15 % of all transports were made by hand. Requirements to collect items or connect transports with patient care were included in 5 % of all transports. Our economic evaluation spotlighted meals as the most costly transport, with 9,596.16€ p.a. in the observed clinics. Budget-friendly robots would amortize over one year by transporting meals. We support understanding nurses’ transportation needs via further research on assistive robots to validate our findings and determine the feasibility of transport robots.

TThe following three objectives were tested in this study: (1) investigate the utility of low-altitude remote sensing using UAS technology to compare the effects of different N application systems in rice production; (2) use spatial extrapolation to scale up plot-level generated to farmer field rice yield data based on crop spectral signatures, and (3) predict and map out rice productivity as a function of N placement systems. Images were captured on a UAV platform at midseason of the rice crop. Orthomosaics were developed for selected fields in rice-producing zones. Grain yields were assessed from low, medium, and high crop health plots delineated based on NDVI values. On the plot scale, UDP outyielded non-UDP by 0.84%. Individual plot yield data were scaled up to the farmer field level through Jenks natural breaks classification and es-tablishing an empirical relationship between OSAVI and plot yields. Assessment of the scaled-up field levelfield-level data also confirmed the superiority of UDP N man-agement over the non-UDP systems in promoting rice yields. Scaling up plot scale da-ta to whole field levels also facilitated generating and mapping expected yield maps for individual farmer fields in the three zones studied. This study has established a tangible simple but tangible protocol protocol for predicting and mapping rice yields in small-scale farmer fields using UAS data.

Neutral facial expression recognition is of great importance in various domains and applications. This study introduces a data-centric approach for neutral facial expression recognition, presenting a comprehensive study that explores different methodologies, techniques, and challenges in the field to foster a deeper understanding. The results show that data augmentation plays a crucial role in improving dataset performance. Additionally, the study investigates different model architectures and training techniques to identify the most effective approach, with the InceptionV3 model achieving the highest accuracy of 72%. Furthermore, the research examines the influence of preprocessing methods on the performance of both InceptionV3 and a simplified CNN model. Interestingly, the results indicate that preprocessing techniques positively affect the performance of the simpler CNN model but negatively impact the InceptionV3 model. The implemented system, used to evaluate the findings, demonstrates promising results, correctly classifying 77% of neutral expressions. However, there are still areas for improvement. Creating a specialized dataset that includes both neutral and non-neutral expressions would greatly enhance the accuracy of the system. By addressing limitations and implementing suggested improvements, neutral facial expression recognition can be significantly enhanced, leading to more effective and accurate results.

Several factors impact the durability of concrete bridge decks, including traffic loads, fatigue, temperature changes, environmental stress, and maintenance activities. Detecting problems such as corrosion, delamination, or concrete degradation early on can lower maintenance costs. Nondestructive evaluation (NDE) techniques can detect these issues at early stages. Each NDE method, meanwhile, has limitations that reduce the accuracy of the assessment. In this study, multiple NDE technologies were combined with machine learning algorithms to improve the interpretation of half-cell potential (HCP) and electrical resistivity (ER) measurements. A parametric study was performed to analyze the influence of five parameters on HCP and ER measurements, such as degree of saturation, corrosion length, delamination depth, concrete cover, and moisture condition of delamination. The results were obtained through finite element simulations and used to build two machine learning algorithms, a classification algorithm and a regression algorithm, based on the Random Forest methodology. The algorithms were tested using data collected from a bridge deck in the BEAST® facility. Both machine learning algorithms were effective in improving the interpretation of ER and HCP measurements using data from multiple NDE technologies.

In August 2020, the World Health Assembly launched a global initiative to eliminate cervical cancer by 2030, setting three primary targets. One key goal is to achieve a 70% screening coverage rate for cervical cancer, primarily relying on the precise analysis of Papanicolaou (Pap) or digital Pap smears. However, the responsibility of reviewing Pap smear samples to identify potentially cancerous cells primarily falls on pathologists, a task known to be exceptionally challenging and time-consuming. This paper proposes a solution to address the shortage of pathologists for cervical cancer screening. It leverages the OpenAI-GYM API to create a deep reinforcement learning environment utilizing liquid-based Pap smear images. By employing the Proximal Policy Optimization algorithm, autonomous agents navigate Pap smear images, identifying cells with the aid of rewards, penalties, and accumulated experiences. Furthermore, the use of a pre-trained convolutional neuronal network like Res-Net54 enhances the classification of detected cells based on their potential for malignancy. The ultimate goal of this study is to develop a highly efficient, automated Papanicolaou analysis system, ultimately reducing the need for human intervention in regions with limited pathologists.

Lung cancer (LC) stands as the foremost cause of cancer-related fatality rates worldwide. Early diagnosis significantly enhances patient survival rate. Nowadays, low dose computed tomography (LDCT) is widely employed on chest as a tool for large-scale lung cancer screening. Nonetheless, a large amount of chest radiographs causes an onerous burden on radiologists. Some computer-aided diagnostic (CAD) tools can provide insights to medical images for diagnosis and augmenting diagnostic speed. However, due to variation in parameter settings across different patients, substantial discrepancies in image voxels persist. We find different voxel-size causing a compromise between model generalization and diagnostic efficacy. This study investigates the performance disparities of diagnostic models trained on original images and LDCT images reconstructed to different voxel-sizes but isotropic. The application is to differentiate a nodule to be benign or malignant. Using 11 features, a support vector machine (SVM) is trained on LDCT images with an isotropic voxel having side-length of 1.5 mm for 225 patients in house. The result yields a favorable model performance with an accuracy of 0.9596 and an area under the receiver operating characteristic curve (ROC/AUC) of 0.9855. In addition to furnish CAD tools for clinical applications, future research is encouraged to include LDCT images from multi-centers.

Osteoarthritis (OA) is a prevalent disease and the leading cause of pain, disability, and quality of life deterioration. Our study sought to evaluate the image quality and dose of cone-beam com-puted tomography arthrography (CBCT-A) and compare them to digital radiography (DR) for OA diagnosis. Overall, 32 cases of CBCT-A and DR with OA met inclusion criteria and were prospectively analyzed. The Kellgren and Lawrence classification (KLC) stage, sclerosis, osteo-phytes, erosions, and mean joint width (MJW) were compared between CBCT-A and DR. Image quality was excellent in all CBCT-As, with excellent inter-observer agreement. OA un-der-classification was noticed with DR for MJW (p=0.02), osteophyte detection (<0.0001), and KLC (P<0.0001). The HU values obtained for the CBCT did not correspond to the values for MDCT, with a greater mean deviation obtained with the MDCT HU for MBIR1 than MBIR2. CBCT-A has been found to be more reliable for OA diagnosis than DR as revealed by our results using three-point rating scale for the qualitative image analysis, with higher quality and an ac-ceptable dose. Moreover, the use of this imaging technique permits the preoperative assessment of extremities in OA diagnosis, the upright position and bone microarchitecture analysis being two other advantages of CBCT-A.

Antimicrobial resistance (AMR) poses a global public health threat, with rates directly linked with consumption. The World Health Organization (WHO) AWaRе-classification aims to guide antibiotic use, which is influenced by drug availability, affordability, and economic policies. In Jordan, a high proportion of consumed antibiotics belong to the 'Watch' category. Data from the WHO-AWaRе classification, the Essential Medicines List, and the Jordan Food and Drug Administration were analyzed. Antibiotics for adults were classified and their cost per Defined Daily Dose (DDD) was determined, then affordability was assessed. In 2023, 43 injectable and 43 oral antibiotics for adults were registered in Jordan. 'Watch' antibiotics were the most common. 'Access' antibiotics had the lowest cost/DDD. 'Rеsеrvе' antibiotics were the most expensive with few generics. Injectable antibiotics had a negative correlation between cost and the number of alternatives. Affordability was higher for oral antibiotics compared to injectable ones. 'Rеsеrvе' antibiotics were generally unaffordable. This study highlights the need to promote 'Access' antibiotics over other categories by encouraging registration of missing ‘Access’ antibiotics and adjusting 'Watch' and 'Rеsеrvе' antibiotic prices. Competition among generics can lead to lower prices, increasing affordability and accessibility. We emphasize the importance of the AWaRе-classification in guiding antibiotic use in Jordan.

Hepatitis C presents a significant global health challenge, necessitating early diagnosis and precise severity classification for timely medical intervention. This study explores the application of machine learning techniques to predict the severity of hepatitis C, leveraging an extensive dataset. Our approach encompasses rigorous data preprocessing, advanced model development, and fine-tuned hyperparameter optimization to ensure accurate and reliable predictions. We evaluated four classification models: Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Machine (SVM), comparing their accuracy in classifying patients. The results showed that the Random Forest and Gradient Boosting models outperformed the others with an accuracy of approximately 93.50%, demonstrating their potential in assisting Hepatitis C diagnosis. Further model enhancements through hyperparameter tuning and feature engineering can improve the precision of Hepatitis C diagnosis, contributing to better patient care.

Perceptual and statistical evidence has underlined voice characteristics of individuals affected by genetic syndromes different from that of normophonic subjects. In this paper we propose a procedure for the systematic collection of such pathological voices and the development of AI-based automatic tools to support differential diagnosis. Guidelines are provided concerning most suitable recording devices, vocal tasks and acoustical parameters, in order to simplify, speed up and make the whole procedure homogenous and replicable. The proposed procedure was applied to a set of 56 subjects, affected by Costello syndrome (CS), Down syndrome (DS), Noonan syndrome (NS) and Smith-Magenis syndrome (SMS). The whole database has been divided into three groups, respectively called: paediatric subjects (PS, individuals &lt; 12 years of age), female adults (FA) and male adults (MA) subjects. In line with literature results, the Kruskal-Wallis test and post-hoc analysis with Dunn-Bonferroni test highlighted several significant differences in acoustical features not only between healthy subjects and patients, but also across syndromes within PS, FA and MA groups. Machine learning provided for the PS group a k-nearest neighbour classifier with 75% accuracy, for the FA group a support vector machine (SVM) model with 84% accuracy and for the MA group a SVM model with 97% accuracy. These preliminary results suggest that the proposed procedure, based on acoustical analysis and AI, might be helpful for an effective non-invasive automatic characterization of genetic syndromes.

The management and classification of solid waste is one of the most important challenges around the world, in order to sustain economic growth and preserve the environment. The objective is to propose the use of Smart Ecological Points as a strategy to address the problem related to the solid waste management system at the source, which has become one of the biggest problems globally and Colombia is no exception. The article describes the current state of the problem in the country and in turn, presents the possibility of developing a prototype corresponding to a low-cost Smart Ecological Point supported by the use of an experimental capacitive sensor and Machine Learning algorithms, which will reduce the time necessary for the classification of recyclable and non recyclable waste, minimizing the health risks and increasing the percentage of waste that can be reused, by reducing the probability of being contaminated at the source, an aspect that is very common when done manually. According to the results obtained, it was evident that the proposed prototype made an adequate classification of waste, generating the possibility of being manufactured with existing technology in the environment, in order to promote adequate waste classification at the source.

In this study, we introduce and advanced multi-feature selection technique for bacterial classifi-cation employing the Salp Swarm Algorithm (SSA). We enhance SSA’s effectiveness by incorpo-rating the Opposition-Based Learning (OBL) strategy and the Local Search (LSA) algorithm. The proposed technique encompasses three key stages, streamlining the automated categorization of bacteria based on their distinctive features. The research adopts a multi-feature selection approach bolstered by an enhanced iteration of the Salp Swarm Algorithm (SSA). Enhancements include the utilization of Opposition-Based-Learning (OBL) to increase population diversity during search and Local Search Algorithms (LSA) to tackle local optimization challenges. The ISSA algorithm is designed to optimize the multi-feature selection by increasing the number of selected feature and improving classification accuracy. This study compares its performance with several other algo-rithms across ten distinct test datasets. The comparison results show that ISSA has better perfor-mance in terms of classification accuracy on 3 datasets consisting of 19 features, with a value reaching 73.75%. Additionally, ISSA excels in determining the optimal feature count and producing a better-fit value with a classification error rate of 0,249. Thus, the ISSA technique is expected to make a significant contribution to solving feature selection problems in bacterial analysis

This research was carried out to know the species composition of the Arbuscular Micorrhizal Fungi (AMF) communities native to two contrasting soils, and to establish the development and stability of aggregates in those soils contained in pots, as substrates for corn plants in the greenhouse. These soils were inoculated with allochthonous AMF. The experiment had three factors: Soil (S with two levels [S1 and S2]); AMF (A with three levels: without application [A0], with application of Claroideoglomus claroideum [A1] and with application of a consortium [A2]) and Fertilization (f with two levels (without fertilization [f0] and with fertilization [f1]); which generated 12 treatments, each with five replicates (60 experimental units [EU]); the EU consisted of a pot with a plant of corn; with a completely random distribution. The results showed that the Typic Ustifluvent presented nine species of native AMF, while the Typic Dystrustert had only three species. The native AMF in each soil influenced the activity of the allochthonous AMF, with differences being found in the stability of the macro-sized aggregates (0.5 to 2.0 mm) of each soil when they did not receive fertilization

The Supersymmetry Genetic code (SSyGC) table is based on five physicochemical symmetries: 1) double mirror symmetry on the principle of the horizontal and vertical mirror symmetry axis between all bases (purines [A, G) and pyrimidines (U, C)], and 2) of bases in form of codons 3) direct – complement like codon/anticodon symmetry in the sixteen alternating boxes of the genetic code columns, 4) A+T rich and C+G rich alternate codons in the same row between both columns of the genetic code, 5) the same position between divided and undivided codon boxes in relation to horizontal mirror symmetry axes. The SSyGC table has a unique physicochemical purine – pyrimidine symmetry net which is as the core symmetry common for all, more than thirty different nuclear and mitochondrial genetic codes. This net is present in the SSyGC table of all RNA and DNA living species. None of these symmetries is present in the Standard Genetic Code (SGC) table which is constructed on the alphabetic horizontal and vertical U-C-A-G order of bases. Here we show that the free energy value of each codon incorporated as fundamentally mapping the “energy code” in the SSyGC table is compatible with mirror symmetry. On the other hand, in the SGC table the same free energy values of codons are dispersed and a mirror symmetry between them is not recognizable. At the same time the mirror symmetry of the SSyGC table and the DNA quadruplets together with our classification of codons/trinucleotides are perfectly imbedded in the mirror symmetry energy mapping of codons/trinucleotides and point out in favor of maintaining the integrity of the genetic code and DNA genome. We also argue that physicochemical symmetries of the SSyGC table in the manner of the purine – pyrimidine symmetry net, the quadruplet symmetry of DNA molecule and the free energy of codons have remined unchanged during all of evolution. The unchangeable and universal symmetry properties of the genetic code, DNA molecule and energy code are decreasing disorder between codons/trinucleotides and shed a new light on evolution. Diversity in all living species on Earth is broad, but the symmetries of the Supersymmetry Genetic Code as the code of life and the DNA quadruplets related to the” energy code” are unique, unchangeable and have the power of natural laws.

Deep learning methods have been widely used in PolSAR image classification. To learn the polarimetric information, many deep learning methods expect to learn high-level semantic features from original PolSAR data. However, only original data cannot learn multiple scattering features and complex structures for extremely heterogeneous terrain objects. In addition, deep learning methods always cause edge confusion due to the high-level features. To overcome these shortages, we propose a double-channel CNN network combined with an edge-preserving MRF model(DCCNN-MRF) for PolSAR image classification. Firstly, to combine complex matrix data and multiple scattering features together, a double-channel convolution network(DCCNN) is developed, which consists of a Wishart-based complex matrix and multi-feature subnetworks. The Wishart-based complex matrix network can learn the statistical characteristics and channel correlation well, and the multi-feature network can learn high-level semantic features well. Then, a unified network framework is designed to fuse two kinds of features to enhance advantageous features and reduce redundant ones. Finally, an edge preserving MRF model is designed to combine with the DCCNN network. In the MRF model, a sketch map-based edge energy function is designed by defining adaptive weighted neighborhood for edge pixels. Experiments are conducted on four real PolSAR data sets with different sensors and bands. Experimental results demonstrate the effectiveness of the proposed DCCNN-MRF method.

The increasing prevalence of periprosthetic femoral fractures, specifically in the vicinity of the hip, has emerged as a significant issue in recent times. Consequently, there is a need for a thorough examination to enhance the effectiveness of management and treatment approaches. The findings emphasized a significant disparity in the occurrence and characteristics of these fractures, as multiple studies have highlighted the efficacy of various treatment strategies, such as open reduction and internal fixation, as well as the utilization of cortical strut allografts. Furthermore, the research has identified potential risk factors that have an impact on the characteristics of fractures, providing valuable insights that could be crucial in the development of preventive strategies. This research provides a thorough examination of periprosthetic femoral fractures, highlighting the importance of a cohesive treatment algorithm to improve the handling of such fractures. Moreover, it promotes the need for a collaborative endeavor in conducting research in this field, cultivating a more profound comprehension that has the potential to drive progress in therapeutic approaches, ultimately enhancing patient results over an extended period of time. It is crucial that forthcoming research endeavors persist in expanding upon these discoveries, striving towards a unified methodology in tackling this substantial clinical obstacle.

Image classification in Remote Sensing and Geographic Information Systems (GIS) containing various land-cover classes is essential for efficient and sustainable land-use estimation, and other tasks like object detection, localization and segmentation. Deep Learning (DL) techniques have shown a tremendous potential in the GIS domain. While Convolutional Neural Networks (CNN) have dominated most of the image analysis domain, a new architecture called transformers have proved to be a unifying solution for several AI-based processing pipelines. Vision Transformers (ViT), a variant of transformers can have comparable and in some cases better accuracy than a CNN. However, they suffer from a significant drawback associated with an excessive use of training parameters. In this research we explore several modifications in the vision transformer architectures, especially MobileViT that can be optimized while boosting accuracy. To verify our proposed approach these new architectures are trained on four land-cover datasets AID, EuroSAT, UC-Merced, and WHU-RS19. Experiments reveal that combination of lightweight convolutional layers including ShuffleNet along with depthwise separable convolutions and average pooling can reduce the trainable parameters by 17.85% and yet achieve higher accuracy than the base MobileViT. It is also observed that utilizing a combination of convolution layers along with multi-headed self attention layers in MobileViT variants provide better performance in capturing local and global features unlike the standalone ViT architecture that utilizes almost 95% more parameters than the proposed MobileViT variant.

Mycoplasma synoviae (MS) is a highly contagious bacteria that can cause significant harm in commercial poultry populations while not prevented. Rapid detection of its presence in a flock is crucial from the perspective of animals' health and economic income. Authors propose spectral measurements strongly backed up by the AI data processing algorithms for classifying egg origin: from healthy hens or MS-infected ones. The newest obtained classification factors are F-scores for white eggshells 99% and for brown eggshells 99%—all data used for classification were taken by the portable multispectral fibre-optics reflectometer.

Breast cancer is a common malignant tumour and studies have shown that early and accurate detection is crucial for patients. With the maturity of medical imaging and deep learning development, significant progress has been made in breast cancer classification, which greatly improves the accuracy and efficiency of classification. This review focuses on deep learning, migration learning, GAN, and lifelong learning to elaborate and summarise the important roles arising from breast cancer detection. This review also examines the dataset and labeling issues required for breast cancer classification. In conclusion, at the end of the article, we look at future directions for breast cancer classification research, including cross-migration learning, multimodal data fusion, model interpretability, and lifelong learning, and also explore how to provide personalized treatment plans for patients.

Despite the impressive performance of deep neural networks on many different vision tasks, they have been known to be vulnerable to intentionally added noise to input images. To combat these adversarial examples (AEs), improving the adversarial robustness of models has emerged as an important research topic, and research has been conducted in various directions including adversarial training, image denoising, and adversarial purification. Among them, this paper focuses on adversarial purification, which is a kind of pre-processing that removes noise before AEs enter a classification model. The advantage of adversarial purification is that it can improve robustness without affecting the model’s nature, while another defense techniques like adversarial training suffer from a decrease in model accuracy. Our proposed purification framework utilizes a Convolutional Autoencoder as a base model to capture the features of images and their spatial structure. We further aim to improve the adversarial robustness of our purification model by distilling the knowledge from teacher models. To this end, we train two Convolutional Autoencoders (teachers), one with adversarial training and the other with normal training. Then, through ensemble knowledge distillation, we transfer the ability of denoising and restoring of original images to the student model (purification model). Our extensive experiments confirm that our student model achieves high purification performance(i.e., how accurately a pre-trained classification model classifies purified images). The ablation study confirms the positive effect of our idea of ensemble knowledge distillation from two teachers on performance.

Requirements Engineering (RE) is an important step in the whole software development lifecycle. The problem in RE is to determine the class of the software requirements as functional and non-functional. Proper and early identification of these requirements is vital for the entire development cycle. On the other hand, manual government of requirement classes is a timewasting task, and it needs intensive effort. Automatic requirement classification techniques through advanced Machine Learning (ML) strategies are started to be developed to address this problem. Basically, software requirements are generated as natural language explanations and therefore requirement classification may be handled as a particular Natural Language Processing (NLP) problem. From ML point of view, classification strategies require dataset to be used in the training/learning phase. For this goal, we generated a unique Turkish dataset having collected the requiremenst from real-world software projects with 4600 samples. Of these requirements, 3000 and 1600 were labeled as functional and non-functional, respectively. The data set has been evaluated using (i) traditional machine learning algorithms, (ii) deep learning algorithms, and (iii) transformer models respectively. As a result BERTurk was found to be the most successful algorithm to discriminate functional and non-functional requirements in terms of 95% f-score metric.

Facial emotion detection is a challenging task that deals with emotion recognition. It has applications in various domains, such as behavior analysis, surveillance systems and human-computer interaction (HCI). Numerous studies have been implemented to detect emotions, including classical machine learning algorithms and advanced deep learning algorithms. For the machine learning algorithm, the hand-crafted feature needs to be extracted, which is a tiring task and requires human effort. Whereas in deep learning models, automated feature extraction is employed from samples. Therefore, in this study, we have proposed a novel and efficient deep learning model based on Neural Architecture Search Network utilizing superior artificial networks such as RNN and child networks. We performed the training utilizing the FER 2013 dataset comprising seven classes: happy, angry, neutral, sad, surprise, fear, and disgust. Furthermore, we analyzed the robustness of the proposed model on CK+ datasets and comparing with existing techniques. Due to the implication of reinforcement learning in the network, most representative features are extracted from the sample network. It extracts all key features without losing the key information. Our proposed model is based on one stage classifier and performs efficient classification. Our technique outperformed the existing models attaining an accuracy of 98.14%, recall of 97.57%, and precision of 97.84%.

Accurate and efficient medicinal plant image classification is of utmost importance as these plants produce a wide variety of bioactive compounds that offer therapeutic benefits. With a long history of medicinal plant usage, different parts of plants, such as flowers, leaves, and roots, have been recognized for their medicinal properties and are used for plant identification. However, leaf images are extensively used due to their convenient accessibility and are a major source of information. In recent years, transfer learning and fine-tuning, which use pre-trained deep convolutional networks to extract pertinent features, has emerged as an extremely effective approach for image identification problems. This study is leveraging the power of three component deep convolutional neural networks, namely VGG16, VGG19 and DenseNet201, to derive features from the input images of the medicinal plant dataset, containing leaf images of 30 classes. The models were compared and ensembled to make four hybrid models to enhance the predictive performance by utilizing the averaging and weighted averaging strategies. Quantitative experiments were carried out to evaluate the models on the Mendeley medicinal leaf dataset. The resultant ensemble of VGG19+DensNet201 with fine-tuning showcases enhanced capability in identifying medicinal plant images with an improvement of 7.43% and 5.8% compared with VGG19 and VGG16. Furthermore, VGG19+DensNet201 can outperform its standalone counterparts by achieving an accuracy of 99.12% on the test set. A thorough assessment with metrics such as accuracy, recall, precision, and f1-score firmly establishes the effectiveness of the ensemble strategy.

This paper presents a two-stage hierarchical neural network using image classification and object detection algorithms as key building blocks for a system that automatically detects a potential design right infringement. This neural network is trained to return the Top-N original design right records that highly resemble the input image of a counterfeit. Design rights specify the unique aesthetic characteristics of a product. Due to the rapid change of trends, new design rights are continuously generated. This work proposes an Ensemble Neural Network (ENN), an artificial neural network model that aims to deal with a large amount of counterfeit data and design right records that are frequently added and deleted. At first, we performed image classification and objection detection learning per design right using the existing models with a proven track record of high accuracy. The distributed models form the backbone of the ENN and yield intermediate results aggregated at a master neural network. This master neural network is a deep residual network paired with a fully connected network. This ensemble layer is trained to determine the sub-models that return the best result for a given input image of a product. In the final stage, the ENN model multiples the inferred similarity coefficients to the weighted input vectors produced by the individual sub-models to assess the similarity between the test input image and the existing product design rights to see any sign of violation. Given 84 design rights and the sample product images taken meticulously under various conditions, our ENN model achieved average Top-1 and Top-3 accuracies of 98.409% and 99.460%, respectively. Upon introducing new design rights data, a partial update of the inference model was done an order of magnitude faster than the single model. ENN maintained a high level of accuracy as it scaled out to handle more design rights. Therefore, the ENN model is expected to offer practical help to the inspectors in the field, such as the customs at the border that deal with a swarm of products.

Fish feeding behavior analysis plays a critical role in aquaculture, marine fisheries, and ecological monitoring. At present, the accuracy of current fish feeding behavior analysis is easily affected by the environment, and the hypoxia state in water and high temperature have a huge impact on behavior. In addition, the poor stability, and the speed is affected by the calculation amount of the model, so it cannot be applied to aquaculture ponds. In order to solve the above problems, we propose an improved RepVGG-based method for fish feeding behavior analysis to identify nine behavioral states of fish populations, including hypoxia, hot, and normal states of none, medium, and strong feeding. Firstly, our method adds the identity and residual branches to the block of the VGG network, focusing on the acceleration operation to ensure accuracy. At the same time, the Efficient Channel Attention (ECA) module is added to reduce dimensionality to balance speed and accuracy. To evaluate the effectiveness of the method, it is validated on a constructed fish feeding behavior dataset and compared with a Convolutional Neural Network (CNN) including AlexNet, VGG16, ResNet34, MobileNet V3, and RepVGG. The experimental results show that after 15 epochs of training, the average precision of proposed method reached 97%. Compared with the basic classification algorithm, our method can increase by at least 3% while the inference speed exceeds 85 Frames Per Second (FPS). This study can be integrated into the aquaculture vision system to guide users to plan feeding strategies, and provide new ideas and methods for water quality monitoring.

Keywords: Electrical power systems, Support vector machines, Random Forest, Machine learning, Wavelet transform, Transmission lines fault, Electrical power quality, Short circuit, Classification of faults, Localization of faults, Decision trees, Ensemble learning, K-nearest neighbors.

Accurate fuel mapping plays a crucial role in fire detection and management strategies. This paper presents a method for discriminating between wildfire fuel types by exploiting together remote sensing data and Convolutional Neural Networks (CNN). Specially, a CNN-based classification approach that leverages Sentinel-2 imagery is exploited to accurately classify fuel types into seven preliminary main classes (conifers, broadleaf, shrubs, grass, bare soil, urban areas, and water bodies) with an high accuracy of 0.99$\%$. To further refine the fuel mapping results, subclasses were generated from the seven principals by using biomass and bioclimatic maps. These additional maps provide complementary information about vegetation density and climatic conditions, respectively. By incorporating this information, we align our fuel type classification with the widely used Scott/Burgan fuel classification system. This refinement step allows for a more detailed and comprehensive assessment of fuel types, enhancing the accuracy and effectiveness of fire management efforts, which can be utilized by fire management agencies, policymakers, and researchers for improved fire behavior prediction and mitigation practices. The proposed approach presents a valuable tool for enhancing fire management, contributing to more effective wildfire prevention and mitigation efforts.

In recent years web applications and on-line business transactions have grown many folds. Consequently, also cyberattacks have increased and represent a serious threat to the pervasive digital services upon which our society relies. To mitigate cyberattacks, many countermeasures are deployed on computing nodes (e.g., anti-malware software) as well as on network devices to detect and possibly block malicious packets in transit; these monitoring devices broadly go under the name of firewalls. Firewalls are designed according to two main architectural approaches: software running on a standard or embedded computer, or purposedly designed hardware, e.g., ASICs. Software-based solutions have the advantage of high flexibility and can be ported on easily upgradable hardware. However, hardware implementation represents the only viable solution for high data rates. On the market, very fast devices of the latter kind are available, but their cost is typically very high, especially considering that their ultra-optimized design makes updating them very difficult, with the consequence of a rather short lifespan. As a more balanced alternative, we wanted to investigate the use of an FPGA architecture, , which is significantly easier to update than custom-built chips, and features low-latency and high-throughput characteristics concurrently, making it preferrable to other programmable systems based on GPUs or microcontrollers. In this paper a packet sniffer that has been designed on FPGA with a 1 Gbit/s data transfer rate is presented. The system is implemented on the FPGA development board KC705 by Xilinx, can analyze Ethernet frames, checking the frame fields against a set of rules defined by the user and calculates statistics of the received Ethernet frames over time. The designed packet sniffer has been successfully tested both with Ethernet frames ad hoc generated using a packets generator, and with real web traffic by connecting the packet sniffer to the internet.

One of the most prevalent brain diseases, epilepsy is characterized by recurring seizures that happen quite frequently. During seizures, a patient suffers uncontrollable muscle contractions that cause loss of motion and balance, which could lead to harm or even death. Establishing an automatic method for warning patients about impending seizures requires extensive research. It is possible to anticipate seizures by analyzing the Electroencephalogram (EEG) signal from the scalp region of the human brain. Time domain-based features such as Hurst exponent (Hur), Tsallis Entropy (TsEn), improved permutation entropy (impe), and amplitude-aware permutation entropy (AAPE) were extracted from EEG signals. In order to diagnose epileptic seizure children from normal children automatically, this study conducted two sessions, in the first session, the extracted features from the EEG dataset were classified using three machine learning (ML)-based models including support vector machine (SVM), K nearest neighbor (KNN), or decision tree (DT), while in the second session, the dataset was classified using three deep learning (DL)-based recurrent neural network (RNN) classifiers including a gated recurrent unit (GRU), long short-term memory (LSTM), and bidirectional (BiLSTM). The EEG dataset obtained from the Neurology Clinic at the Ibn-Rushd Training Hospital. In this regard, detailed explanations and research from the time domain and entropy characteristics show that using GRU, LSTM, and BiLSTM RNN deep learning classifiers on the All−time−entropy fusion feature improves the final classification results.

Using traditional methods based on detection rules written by human security experts, there is a significant challenge in accurately detecting many network threats that are increasingly becoming sophisticated. In order to deal with the limitation of the traditional methods, network threat detection techniques utilizing artificial intelligence technologies such as machine learning are being extensively studied. Research has also been conducted on analyzing various string patterns in network packet payloads through natural language processing techniques to detect attack intent. However, due to the nature of packet payloads containing binary data as well as text data, a new approach is needed that goes beyond typical natural language processing techniques. In this paper, we study a token extraction method optimized for payloads using n-gram and byte pair encoding techniques. Furthermore, we generate embedding vectors that can understand the context of the packet payload using algorithms such as Word2Vec and FastText. We also compute the embedding of various header data associated with packets, such as IP addresses and ports. Given the combination of these features, we ensemble a text 1D-CNN and a multi-head attention network in a novel fashion. We validated the effectiveness of our classification technique with the CICIDS2017 open dataset and over half a million data collected by The Education Cyber Security Center (ECSC) currently operating in South Korea. The proposed model showed remarkable progress compared to previous studies, demonstrating a highly accurate classification performance with an F1-Score of 0.998. Our model can also preprocess and classify 150,000 network threats per minute to help security agents in the field divert their time to analyzing more complex attack patterns.

Domain-specific systems-on-chip (DSSoCs) aim to narrow the gap between general-purpose processors and application-specific designs. CPU clusters enable programmability, while hardware accelerators tailored to the target domain minimize task execution times and power consumption. Traditional operating system (OS) schedulers can diminish the potential of DSSoCs as their execution times can be orders of magnitude larger than the task execution time. To address this problem, we propose a dynamic adaptive scheduling (DAS) framework that combines the advantages of a fast, low-overhead scheduler and a sophisticated, high-performance scheduler with a larger overhead. We present a novel runtime classifier that chooses the better scheduler type as a function of the system workload, leading to improved system performance and energy-delay product (EDP). Experiments with five real-world streaming applications indicate that DAS consistently outperforms fast, low-overhead, and slow, sophisticated schedulers. DAS achieves a 1.29x speedup and 45% lower EDP than the sophisticated scheduler under low data rates and a 1.28x speedup and 37% lower EDP than the fast scheduler when the workload complexity increases. Furthermore, we demonstrate that the superior performance of the DAS framework also applies to hardware platforms, with up to a 48% and 52% reduction in execution time and EDP, respectively.

Agriculture is one of the indispensable fields for the survival of mankind. Potatoes also have a significant role in the field of agriculture. The quality and quantity of potatoes are significantly impacted by several diseases, such as early blight and late blight, and manual interpretation of these leaf diseases is time-consuming and inconvenient. Fortunately, leaf appearance is used to detect diseases in potato plants. Productivity will considerably rise if the infections are detected early. Different types of image processing methods and machine learning methods are used for early recognition of those diseases from leaf images so that the product losses are decreased significantly. For the incredible and marvelous performance of CNN, it is the most popular deep learning method that is used immensely for the recognition of leaf diseases from images. Several pretrained deep learning models, such as VGG16, ResNet50, InceptionV3, MobileNetV2, Xception, and a deep learning model developed using CNN, are employed for potato leaf disease classification and recognition on the same dataset. The transfer learning technique is applied to the pretrained model and the data augmentation technique is applied to the Proposed CNN model for potato disease classification from leaf samples. Compared to pretrained models, the proposed CNN model offers the lowest loss and highest accuracy for potato leaf disease detection while using fewer parameters and layers. It achieves the best performance with a test accuracy of 99.33% compared to other Pretrained models used in the diagnosis of potato leaf disease.

The efficient recognition and classification of personal protective equipment are essential for en-suring the safety of personnel in complex industrial settings. Using the existing methods, manually performing macro-level classification and identification of personnel in intricate spheres is tedious, time-consuming, and inefficient. The availability of several artificial intelligence models in recent times presents a new paradigm shift in object classification and tracking in complex settings. In this study, several compact and efficient deep learning model architectures are explored, and a new efficient model is constructed by fusing the learning capabilities of the individual, efficient models for better object feature learning and optimal inferencing. The new model construct follows the contributory learning theory whereby each fussed model brings its learned features, which are then combined to obtain a more accurate and rapid model using normalized quantization-aware learning. During the investigation, a separable convolutional driven model was constructed as a base model, and then the various efficient architectures combined for the rapid identification and classification of the various hardhat classes used in complex industrial settings. A remarkable rapid classification and accuracy were recorded with the new resultant model.

"Finding fresh water in the ocean of data." is a challenge that all deep learning domains struggle with, especially in the area of hyperspectral image analysis. As hyperspectral remote sensing technology advances by leaps and bounds, there are increasing amounts of hyperspectral images(HSIs) can be available. Whereas in fact, these unlabeled HSIs are powerless to be used as material to driven a supervised learning task due to the extremely expensive labeling costs and some unknown regions. Although learning-based methods have achieved remarkable performance due to their superior ability to represent features, at the cost, these methods are complex, inflexible and tough to carry out transfer learning. In this paper, we propose the "Instructional Mask AutoEncoder"(IMAE), which is a simple and powerful self-supervised learner for HSI classification that uses a transformer-based mask autoencoder to extract the general features of HSIs through a self-reconstructing agent task. Moreover, we utilize the metric learning to perform an instructor which can direct the model focus on the human interested region of the input so that we can alleviate the defects of transformer-based model such as local attention distraction, lack of inductive bias and tremendous training data requirement. In downstream forward propagation, instead of global average pooling, we employ a learnable aggregation to put the tokens into fullplay. The obtained results illustrate that our method effectively accelerates the convergence rate and promotes the performance in downstream task.

Recently a measure termed F* was described, as an interpretable transformation of the F measure, also known as the Dice coefficient. Using elementary mathematical methods, it is shown that F* is in fact identical to a previously described measure, monotonically related to the F measure, and variously termed in previous publications, dating from the late 19^th to the late 20^th century, as the ratio of verification, the Jaccard similarity measure or index, the threat score, the Tanimoto index, and the critical success index. The origins of these different terms in different disciplines (weather forecasting, ecology, machine learning) may explain the repeated independent redescription of this measure.

An automatic recognizing system of white blood cells can assist hematologists in the diagnosis of many diseases, where accuracy and efficiency are paramount for computer-based system. In this paper, we present a new image processing system to recognize the five types of white blood cells in peripheral blood with marked improvement in efficiency when juxtaposed against mainstream methods. The prevailing deep learning segmentation solutions often utilize millions of parameters to extract high-level image features and neglect the incorporation of prior domain knowledge, which consequently consume substantial computational resources and increase the risk of overfitting, especially when limited medical image samples are available for training. To address these challenges, we propose a novel memory-efficient strategy that exploits graph structures derived from the images. Specifically, we introduce a lightweight superpixel-based Graph Neural Network (GNN) and break new ground by introducing superpixel metric learning to segment nucles and cytoplasm. Remarkably, our proposed segmentation model (SMGNN) achieves state-of-the-art segmentation performance while utilizing at most 10000$\times$ less than the parameters compared to existing approaches. The subsequent segmentation-based cell type classification processes show satisfactory results that such automatic recognizing algorithms are accurate and efficient to execeute in hematological laboratories.

Abstract: In recent years, low frequency (LF) electroencephalogram (EEG) signals have been decoded to obtain kinematic trajectories with the aim of achieving closed loop natural control. However, decoded trajectories suffer variability and low correlation against the measured movement. This paper suggests that if EEG features of motor intents are directly associated with fixed points along a given range of motion, then the natural kinematic parameters can be quantified. A novel classification method is proposed to evaluate this hypothesis along with equations for obtaining the movement parameters (velocity, acceleration). Furthermore, this implies that the force of an end-effector and work done in moving the end-effector may be computed, leading to natural control of EEG-based robotic arms and neuroprostheses. Finally, expectations are that combining the proposed method with existing classification methods will increase the accuracy and scalability of brain-computer interface (BCI) applications.

Infection of liver flukes (Opisthorchis viverrini) is partly due to their suitability for habitats in sub-basin areas, which causes the intermediate host to remain in the watershed system in all seasons. Spatial monitoring of fluke infection at the small basin analysis scale is important because this can enable analysis at the level of the spatial factors involved and influencing infections. The spatial mathematical model was weighted by the nine spatial factors by dividing the analysis into two levels. 1) sub-basin boundary level analyzed with ordinary least square (OLS) model used to analyze spatial factors of liver fluke aimed at analyzing spatial factors related to human liver fluke infection according to sub-basin boundaries, and 2) infection risk positional analysis level with machine learning-based forest classification and regression (FCR) and displaying predictive results of infection risk locations along stream lines. The analysis results show 4 prototype models that import different independent variable factors. The results show that Model-1 and Model-2 give the most AUC = 0.964 and the variables that influence infection risk the most were distance to stream lines, and distance to water bodies, NDMI and NDVI factors rarely affect accuracy. This FCR machine learning application approach can be applied to the analysis of infection risk areas at the sub-basin level, but independent variables must be screened with a preliminary mathematical model weighted to the spatial units in order to obtain the most accurate predictions.

Background: Pancreatic cancer (PC) is a disease with poor prognosis and survival rate. There is a pertinent need to identify the risk factors of this disease. The purpose of this study is to identify a subset of factors (a.k.a. features) as predictors of PC from the Prostate, Lung, Colorectal and Ovarian (PLCO) cancer dataset consisting of responses to 65 questions about demographics, cancer and health history, medication usage, and smoking habits from 154,897 participants. Method: There are two challenges to selecting the subset of features that predict PC with highest probability: the problem is computationally intractable, and the PLCO dataset is highly imbalanced. We use an innovative method to use the dataset in a balanced way, without involving up- or down-sampling. We use nine feature selection methods to select the optimal subset of features from the preprocessed and balanced dataset. Results: Our preprocessed dataset consists of 32 risk factors (8 demographics, 5 cancer history, 13 health history, 2 medication usage, 4 smoking habits). Risk factors belonging to cancer and health history, followed by smoking habits, were consistently chosen by the feature selection methods. We also discuss findings in the medical sciences literature that corroborate our findings. Conclusions: The study found that risk factors belonging to cancer and health history are the most prominent ones for PC. In particular, previously diagnosed with PC is chosen as the most prominent risk factor by majority of methods. While most of our findings are consistent with the literature, some of our findings shed light on novel factors that may not have received their due attention by the research community.

This paper presents a novel method for automatic modulation classification (AMC) for cognitive radio (CR) networks based on a simple classifier that is trained with high-order cumulant. The proposed method focuses on the statistical behavior of modulated signals and includes analog modulation and digital schemes, which received less attention in the literature. The effectiveness of the proposed method is demonstrated through simulation results using high-quality generated signals under different signal-to-noise ratios (SNRs) and channel conditions. The classification performance achieved by the proposed method is superior to that of the more complex deep learning methods, making it well-suited for deployment in end units of CR networks, particularly in military and emergency service applications. The proposed method offers a cost-effective, high-quality solution for AMC that meets the stringent requirements of these critical applications.

Water quality assessments are crucial for human health and environmental safeguards. The utilization of a subset of artificial intelligence such as Machine Learning (ML) presents significant impacts to enhance the prediction and classification of water quality. In this research, a set of diverse ML algorithms was evaluated to handle a comprehensive dataset of water quality measurements over an extended period. The aim was to develop a robust approach for accurately forecasting water quality. This approach employed machine learning classifiers such as Logistic Regression (LR), Support Vector Machine (SVM), Stochastic Gradient Descent (SGD), K-Nearest Neighbors (KNN), Gaussian Process Classification (GPC), Gaussian Naive Bayes (GNB), Random Forest (RF), Decision Tree (DT), XGBoost, and Multilayer Perceptron (MLP). The water quality parameters assessed for pH, hardness, solids, chloramines, sulfate, conductivity, organic carbon, trihalomethanes and turbidity. The XGBoost model exhibited the highest accuracy of 89.47% among the classifiers and Stacked Ensemble Classifiers (SEC) improved the prediction further to 92.98%. The findings suggest that XGBoost and the SEC hold promise as reliable approaches for water quality assessments in contrast of artificial intelligence.

It is crucial to select the right machine learning classifier for image classification and face recog-nition. This study examines the effectiveness of four different face recognition classifiers - Support Vector Machines (SVM), Random Forest, K-Nearest Neighbors (KNN), and Neural Networks. An analysis of the Large Faces in the Wild (LFW) dataset was carried out using Principal Component Analysis (PCA). Classifiers are rigorously trained and evaluated based on the extracted features. Comparison of classifier performance is an insightful way to figure out their strengths and weaknesses. Having a visual representation of the classifier's performance gives a complete understanding of its capabilities. Through the selection of the most appropriate classifier, study results contribute to advancements in image classification, recognition, and biometric identification. The comparison study demonstrated that the Neural Network classifier was exceptionally accurate and proficient in recognizing faces from the LFW dataset when used in conjunction with PCA for feature extraction. According to the comparative analysis, the Neural Network classifier proved exceptionally accurate and proficient at identifying faces from the LFW dataset when combined with PCA for feature extraction.

Objective: To evaluate the existing meta-analytic evidence of associations between exposure to ultra-processed foods, as defined by the Nova food classification system, and adverse health outcomes. Design: Systematic umbrella review of existing meta-analyses. Data Sources: MEDLINE, PsycINFO, Embase, and the Cochrane Database of Systematic Reviews, as well as manually searching reference lists from 2009 to June 2023. Inclusion Criteria: Systematic reviews and meta-analyses of cohort, case-control, and/or cross-sectional study designs that evaluated the associations between exposure to ultra-processed foods and adverse health outcomes in humans across the lifespan. Results: The search identified 45 unique pooled analyses, including 13 dose-response and 32 non-dose-response associations (n=9,888,373). Overall, direct associations were found between exposure to ultra-processed foods and 32 (71%) health outcome domains spanning cardiometabolic, gastrointestinal, and respiratory conditions, cancer, mental health, and mortality. Based on a pre-specified evidence classification criteria, convincing evidence (Class I) supported direct associations between greater ultra-processed food exposure and higher risks of incident cardiovascular disease-related mortality (RR: 1.50; 95%CIs: 1.37 to 1.63), type two diabetes (dose-response RR: 1.12; 95%CIs: 1.11 to 1.13), and depressive outcomes (HR: 1.21; 95%CIs: 1.16 to 1.28), as well as higher risks of prevalent anxiety outcomes (OR: 1.48; 95%CIs: 1.37 to 1.59) and combined common mental disorder outcomes (OR: 1.53; 95%CIs: 1.43 to 1.63). Highly suggestive (Class II) evidence indicated that greater exposure to ultra-processed foods was directly associated with higher risks of incident all-cause mortality (RR: 1.21; 95%CIs: 1.15 to 1.27), heart disease-related mortality (dose-response HR: 1.66; 95%CIs: 1.51 to 1.84), and type two diabetes (OR: 1.40; 95%CIs: 1.23 to 1.59), together with higher risks of prevalent obesity (OR: 1.55; 95%CIs: 1.36 to 1.77), adverse sleep-related outcomes (OR: 1.41; 95%CIs: 1.24 to 1.60), and wheezing (RR: 1.40; 95%CIs: 1.27 to 1.55). Out of the remaining 34 pooled analyses, 21 were graded as suggestive or weak strength (Class III-IV) and 13 were graded as no evidence (Class V). Using the GRADE framework, 22 pooled analyses were rated as "Low" quality, with 19 rated as "Very Low” and four rated as "Moderate" quality. Conclusions: Higher exposure to ultra-processed food was associated with a greater risk of adverse health outcomes, especially cardiometabolic, common mental disorder, and mortality outcomes. These findings provide a rationale to evaluate the effectiveness of using population-based measures to target and reduce dietary exposure to ultra-processed foods for improved human health. Systematic Review Registration: PROSPERO CRD42023412732.

Transportation authorities aim to boost road safety by identifying risky locations and applying suitable safety measures. The Highway Safety Manual (HSM) is a vital resource for US transportation professionals, aiding in the creation of Safety Performance Functions (SPFs), which are predictive models for crashes. These models rely on Negative Binomial distribution-based regression and misinterpreting them due to unmet statistical assumptions can lead to erroneous conclusions, including inaccurately assessing crash rates or missing high-risk sites. The Florida Department of Transportation (FDOT) has introduced context classifications to HSM SPFs, complicating assumption violation identification. This study, part of an FDOT-sponsored project, investigates established statistical diagnostic tests to identify model violations and proposes a novel approach to determine optimal spatial regions for Empirical Bayes adjustment. This adjustment aligns HSM-SPFs with regression assumptions. The study employs a case study involving Florida roads. Results indicate that a 20-mile radius offers an optimal spatial sample size for modeling crashes of all injury levels, ensuring accurate assumptions. For severe injury crashes, which are less frequent and harder to predict, a 60-mile radius is suggested to fulfill statistical modeling assumptions. This methodology guides FDOT practitioners in assessing the conformity of HSM-SPFs with intended assumptions and determining appropriate region sizes.

Immunostaining of transcription factors allows a more exact classification of pituitary neuroendocrine tumours (PitNETs), but not a better prediction of their clinical behaviour. This retrospective, single-centre study aims to classify a series of PitNETs using Trouillas et al.’s clinicopathological classification from 2013. We analysed 166 patients undergoing PitNET surgery in 2013–2023. Tumours were identified according to the gene and immunohistochemical expression of PitNET transcription factors plus adenohypophyseal hormones. Tumours were classified according to a grading system based on MRI invasion and Ki-67 index. Eighty-one (48.8%) patients had grade 2a tumours; 71 (42.8%), grade 1a; 8 (4.8%), 2b; and 6 (3.6%), 1b. At a mean follow-up of 57.8 (standard deviation 30) months, 13.9% (n=23) showed recurrence/progression; independent predictors of recurrence were tumour volume (p=0.031) and T2 signal intensity ratio (SIR) (p&lt;0.001). This risk was 18.6-fold higher for a T2 SIR of 2 or more. Grade 2a and 2b tumours, T2 SIR, and silent corticotroph adenomas (SCAs) were associated with lower progression-free survival. Our results add more evidence to the prognostic value of the five-grade PitNET classification and suggest higher clinical surveillance of patients with SCAs is warranted. The MRI findings highlight the increasing value of radiological evaluation for managing PitNETs.

This study focused on demonstrating the potential of classification algorithm in the chemical composition characterization of transiting exoplanets. The Python-based module PLATON 5.3 for forward modelling of transiting planet spectra was used to simulate a set of transmission spectra of an exoplanet with size R_p = 1.40*R_jupiter, and mass M_p = 0.73*M_jupiter orbiting around the host star of size M_s = 1.16*M_sun and surface temperature of 1200 Kelvin. The gas composition of the exoplanet atmosphere was varied at low and high levels of 3-gas mix of CO₂, O₂, N₂ and CH₄ resulting to eight classes of spectra. The transit spectra were then used as input data to a forward neural network classifier with the eight gas composition classes as target outputs. The trained classifier achieved at most 97.9% overall accuracy.

. Identifying students who might have difficulty in their course of studies ahead of time is crucial. There can be many reasons for performance issues, such as personality, family, social, and/or economic. We advocate that educational systems should use machine learning to predict students’ performance based on performance factors. This would allow educational professionals and institutions to put in place a preventive plan to help students towards achievements of their educational goals and success. In this chapter, we propose a student performance prediction method and evaluate its performance. We provide a taxonomy of performance factors that help to gauge students performance from different perspectives and give insights on the categories and features that have more significant impact on students’ performance. The results of this work can be used by education institutions to put in place a student-centric approach to tackle performance issues before they create long-term effects on student’s life. In addition, it will help education policymakers to introduce a tailored approach for the population in specific areas.

Identification of plants is a challenging task which aims to identify the family, genus, and species level according to morphological features. Automated deep learning-based computer vision algorithms are widely used for identifying plants and can help users to narrow down the possibilities. However, numerous morphological similarities between and within species make the classification difficult. In this paper, we tested a custom convolution neural network (CNN) and vision transformer (ViT) based models using the PyTorch framework to classify plants. We used a large dataset of 88K and 16K images for classifying plants at genus and species levels respectively. Our results show that for classifying plants at the genus level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420, and other state-of-the-art CNN-based models suggested in previous studies on a similar dataset. The ViT model achieved top accuracy of 83.3% for classifying plants at the genus level. ViT models also perform better for classifying plants at the species level compared to CNN-based models ResNet50 and ResNet-RS-420, with a top accuracy of 92.5%. We show that the correct set of augmentation techniques plays an important role in classification success.

Abstract: The traditional model of closed circuit ball milling systems has been used for several decades, however, if the classifier of the closed circuit ball mill system performs the duties of both pre-classification and check-classification, the characterization error of the traditional model is large. To address this problem, a new model is obtained by modifying and supplementing the traditional one. The most important feature of the new model compared to the traditional model is that the circulating load is considered to be the fresh feed for the entire system rather than the ball mill. The error between the slurry level height calculated using the traditional model and the actual is 52.31%, which is 13.77 times higher than that of the new model. When the relative capacity varies greatly, that of the ball mill calculated by the traditional model fluctuates between 0.95 and 1.15, while the new model is 0.6 to 1.4. The new model has a higher accuracy than the traditional model in characterizing the production status of the grinding system, which is of some significance for industrial production.

Background: In this secondary analysis of the Supporting the Modification of Lifestyle In Lowered Emotional States (SMILES) randomised controlled trial, we investigated if the beneficial effects of a dietary intervention on clinical depression were driven, in part, by reducing the consumption of foods classified as ultra-processed.Methods: The SMILES trial enrolled 67 adults with major depressive disorder, randomly assigning them to either a 12-week modified Mediterranean dietary intervention or a social support control. Our analysis included 44 participants with non-missing dietary data and at least one valid Montgomery–Åsberg Depression Rating Scale (MADRS) assessment at 12 weeks of follow-up. The Nova food classification system was used to estimate the proportion of ultra-processed foods in the overall diet (percentage of grams) based on data from seven-day food diaries. We fitted linear regression models under blinded conditions to determine whether ultra-processed food intake-change from baseline to 12 weeks moderated the dietary intervention effects on depressive symptoms. We estimated mean differences in depressive symptoms along with ninety-five per cent confidence intervals (95%CIs).Results: For participants in the dietary intervention, there was an additional 2.5-point improvement in MADRS scores for each 10% reduction in the dietary share of ultra-processed foods compared to participants in the control group (between-group mean differences in depressive symptoms: −2.46, 95%CIs −4.71 to −0.20, p=0.039, η _p ² of 0.10).Conclusions: These preliminary findings suggest that the therapeutic benefit of a dietary intervention for depression may stem, at least in part, from reductions in the dietary share of ultra-processed foods.

Diabetes mellitus is a chronic disease that affects millions of people worldwide. Article focuses on detecting the presence of diabetes in patients using microarray gene data obtained from the pancreas. Handle the high-dimensional nature of the data, four different dimensionality reduction techniques, namely Bessel Function, Discrete Cosine Transform (DCT), Least Square Linear Regression (LSLR), and Artificial Algae Algorithm (AAA) are used. After reduced the data, Meta-heuristic algorithms are applied like Dragonfly Optimization Algorithm (DOA) and Elephant Herding Optimization Algorithm (EHO) for feature selection. Ten classification techniques are using to classify the data in both the format like without feature selection method and with feature selection method. The classification techniques are Non-Linear Regression (NLR), Linear Regression (LR), Gaussian Mixture Model (GMM), Expectation Maximum (EM), Bayesian Linear Discriminant Classifier (BLDC), Logistic Regression (LoR), Softmax Discriminant Classifier (SDC), Support Vector Machine (SVM) with linear kernel, Support Vector Machine (SVM) with polynomial kernel, and Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. Results showed that the AAA with SVM(RBF) achieved an accuracy of 90% without feature selection. However, when feature selection was applied, with EHO of AAA with SVM(RBF) exhibited the highest accuracy of 95.714%, followed closely by with DOA of AAA with SVM (RBF) at 94.28%.

The healthy function of the vestibular system (VS) is of vital importance for individuals to carry out their daily activities independently and safely. This study carries out Tsallis entropy (TE)-based analysis on insole force sensor data in order to extract features to differentiate between healthy and VS-diseased individuals. Using a specifically developed algorithm, we detrend the acquired data to examine the fluctuation around the trend curve in order to consider the individual’s walking habit and thus increase the accuracy in diagnosis. It is observed that the TE-value increases for diseased people as an indicator of the problem of maintaining balance. As one of the main contributions of this study, in contrast to studies in the literature that focus on gait dynamics requiring extensive walking time, we directly process the instantaneous pressure values, enabling to reduce the data acquisition period significantly. The extracted feature set is then inputted into fundamental classification algorithms, with the Support-Vector-Machine (SVM) demonstrating the highest performance, achieving an average accuracy of 95%. This study constitutes a significant step of a large project aiming to identify specific VS disease together with its stage. The performance achieved in this study provides a strong motivation to further explore this topic.

It is important for food recognition to separate each ingredient within a food image at the pixel level. In this paper, we propose a new approach to segment ingredients by utilizing a CNN-based Single-Ingredient Classification Model. In detail, we firstly introduce a standardized biological-based hierarchical ingredient structure and construct a single-ingredient image dataset based on this structure. Then, we build a single-ingredient classification model based on a novel convolutional neural network (CNN) architecture that utilizes an attention mechanism. Afterwards, we propose a new framework for segmentation using the above single-ingredient classification model as the backbone. In this framework, two methods are involved in segmenting ingredients in the food images. We introduce five evaluation metrics (IoU, Dice, Purity, Entirety, Loss of GTs) to assess the performance of ingredient segmentation in terms of ingredient classification. Extensive experiments demonstrate the effectiveness of the proposed method, achieving an maximal mIoU of 0.65, mDice of 0.77, mPurity of 0.83, mEntirety of 0.80, and mLoGTs of 0.06 on the FoodSeg103 dataset. The results confirm that our CNN-based architecture achieves higher segmentation performance compared to ResNet18 and EfficientNet-B0 when used as the backbone for ingredient segmentation. We believe that our ingredient segmentation approach lays the foundation for subsequent ingredient recognition.

Satellite-based data classification performance remains a challenge for research community in the field of land use/land cover mapping. Here we investigated supervised per-pixel classifications performance under different scenarios, based on single and seasonal multispectral data combi-nations of different sensors (Landsat-8 OLI and Sentinel-2 MSI). In case of Landsat, seasonal spectral indices (EVI and NDMI) were included. A typical Mediterranean watershed with a complex landscape comprised of various forest and wetland ecosystems, crops, artificial surfaces, and lake water was selected to test our approach. All available geospatial data from national databases (Forest Map, LPIS, Natura2000 habitats, cadastral parcels, etc.) are used as ancillary data for clas-sification training and validation. We examined and compared the performance of ML, RF, KNN and SVM classifiers under different scenarios for land use/land cover mapping, according to Copernicus Land Cover (CLC2018) nomenclature. In total, eight land use/land cover classes were identified in Landsat-8 OLI and nine in Sentinel-2a MSI for an acceptable overall accuracy over 85%. A comparison of the overall classification accuracies shows that Sentinel-2a overall accuracy was slightly higher than Landsat-8 (96.68% vs. 93.02%). Respectively, the best-performed algorithm was ML in Sentinel-2 while in Landsat-8 was KNN. However, machine-learning algorithms have similar results regardless the type of sensor. We concluded that best classification performances achieved using seasonal multispectral data. Future research should be oriented towards inte-grating time-series multispectral data of different sensors and geospatial ancillary data for land use/land cover mapping.

Sequence analysis is a robust methodological framework that has gained popularity in various fields, including transportation research. It provides a comprehensive approach to understanding the dynamics and patterns of individual behaviors over time. In the context of the Metropolitan Region of Barcelona, applying sequence analysis to the mobility surveys offers valuable insights into the sequencing and order of travel activities and modes, shedding light on the complex interrelationship between individuals, their travel choices, and the built environment. The Barcelona travel surveys collect detailed data on individuals' travel behavior, such as trip purpose, duration, mode of transportation, and origin-destination pairs. Sequence analysis allows for examining travel behaviors as dynamic processes, unveiling travel patterns' underlying structure and evolution in a day. A data analytics methodological approach is described; it enables the identification of common travel patterns and the exploration of variations across different demographic groups or geographical regions. Sequence analysis reveals insights into the factors influencing mode choice and potential opportunities for sustainable transport interventions. The paper proposes a methodological approach to discover homogeneous travel behavioral segments from diaries included in travel surveys in order to refine transport policies to selected segments by transportation planners and authorities

Background: The level of food processing has gained interest for a potential determinant of human health. The aim of this study was to assess the relation between level of food processing and prostate cancer severity. Methods: A sample of 120 consecutive patients were examined for their dietary habits assessed through validated food frequency questionnaires, their dietary intake of food groups categorized according to the NOVA classification, and the severity of prostate cancer according to the European Association of Urology (EAU) guidelines groups risk. Uni- and multivariate logistic regression analyses were performed to test the association between the variables of interest. Results: Individuals reporting higher consumption of unprocessed/minimally processed foods were less likely to have worse prostate cancer severity than lower consumers in the energy-adjusted model [odds ratio (OR) = 0.38, 95% confidence interval (CI): 1.17-0.84, P = 0.017 and OR = 0.33, 95% CI: 0.12-0.91, P = 0.032 for medium/high vs. low grade and high vs. medium/low grade prostate cancers, respectively); however, after adjusted for potential confounding factors, the association was no more significant (Table 4). A borderline association was also found between higher consumption of UPF and worse prostate cancer severity in the energy-adjusted model (OR = 2.11, 95% CI: 0.998-4.44; P = 0.051), but again the association was no more significant after adjusting for the other covariates. Conclusions: The level of food processing seems not to be independently associated with prostate cancer severity, while potentially related to other factors that need further investigation.