Space exploration presents vast prospects for scientific, industrial, and economic progress. This paper introduces the MeSat mission as a pioneering approach to Mars exploration. MeSat aims to deepen our understanding of Martian conditions and resources by employing an optimized Earth-to-Mars trajectory, enabling a comprehensive study of the Martian atmosphere and surface. The mission comprises a Cargo Microsatellite hosting three 6U CubeSats and two 3U CubeSats, deployed into four separate Mars orbits forming a constellation. Each CubeSat carries distinct payloads: a THz radiometer for water detection, a high-resolution surface camera, a top-tier spectrometer, and a Fourier Transform Spectrometer (FTS) for wind speed readings. This paper focuses on two pivotal innovations: the development of a 183GHz radiometer payload for detecting water on Mars and a optimal mission design algorithm that analyzes a fuel-efficient low-thrust trajectory from Earth to Mars. Regarding the THz payload for water detection, a subharmonic Schottky diode-based mixer is meticulously designed to efficiently down-convert radio signals. The down-converted signals are carefully analyzed using a customized Do-It-Yourself (DIY) spectrum meter, which facilitated the capture and processing of acquired data. To provide the necessary Local Oscillator signals for the mixer, a Tripler employing three-anode Schottky diodes is developed. Additionally, the Tripler requires a signal generator for its entry frequency, which is addressed by developing a DIY signal generator. Together, these components, comprising the Schottky diode mixer, Tripler, and DIY circuits, collectively form the complete THz payload, demonstrating exceptional power efficiency with a total consumption of only 3.7W. The study employs a dual-step hybrid optimization algorithm (PSO-Homotopy) to analyze fuel-efficient low-thrust trajectories from Earth to Mars, incorporating the Ephemeris dynamics model to account for gravitational perturbations in the entire solar system. In practical mission design, crucial factors like hyperbolic excess velocity, diverse opportunities for Earth launch and Mars rendezvous, varied propulsion systems, and Time of Flight (TOF) play vital roles in trajectory optimization.

The rapid growth of equipment tasks in 5G large-scale machine-type communication scenarios presents challenges due to user equipment’s (UE) limited computing power and power constraints. To address these limitations, task offloading to unmanned aerial vehicles (UAVs) has gained attention. However, traditional cloud computing centres far from the UE fail to meet latency requirements. Mobile Edge Computing (MEC) emerges as a solution by deploying computing servers at the edge of the cellular network to enhance service responsiveness. However, traditional MEC solutions need more flexibility. This paper explores the advantages of UAVs, including flexibility and rapid deployment, and considers UAV power constraints. It proposes a joint optimization approach for user offloading strategies and UAV trajectories to minimize the overall energy consumption of UE. By leveraging the benefits of UAVs and MEC, the proposed method aims to improve task execution efficiency in energy-constrained 5G machine-type communication scenarios.

The introduction of non-conventional renewable energies (photovoltaic and wind, in the residential case) demand new proposals and opportunities to obtain a domestic energy management system (HEMS), which allows reducing the use of electrical energy. The HEMS incorporates artificial intelligence (AI) techniques to respond to energy demand (DR), which can control, switch, turn on and off, modifying the consumption profile, reducing monthly billing, which as a consequence brings improvement. of the quality of life at home. Based on what has been described, a model for identifying electrical appliances and determining consumption patterns for the home is proposed, using intrusive measurement/actuator equipment called Smart Socket. The Smart Socket measures the electrical variables of voltage and current in real time, calculating the powers and active-reactive-apparent energies of each household appliance (or the most relevant ones). The information provided by the Smart Socket is used as a basis to configure the HEMS, with the aim of optimizing energy use using artificial intelligence techniques and tools, thus reducing energy consumption, CO2 emissions and the user's monthly billing. residential.

The content of this paper is a very significant contribution to the heritage regional reconstruction and conservation interventions. The results about the iconographic analysis of the Early-Renaissance frescoes of St. Leocadia Church, in Chaves, in the north-east of Portugal, were presented for the first time. This paper aims to study facial similarity analysis applied to identification of the personages represented in the 1511-1513 mural paintings of the apse of St. Leocadia Church, in Chaves Municipality (North of Portugal) and those ones in the oil paintings by the Proto-Renaissance Portuguese painter Nuno Gonçalves. In this work, it was explored the feasibility of face recognition technologies in answering many ambiguities concerning Manueline stylistic identity and iconography. A proposal was made to identify the characters, fundamental to the meaning of the frescoes. It has been experimentally proved on 7 characters of St. Leocadia Church’s paintings, in comparison with the referred old master portraits. It was used an in-house trained deep face recognition model to extract facial features from images of 15th and 16th century portraits to compare their degree of similarity. It was undertaken facial similarity analysis of the faces in the formidable portrait gallery of the 15th century Portuguese society represented in the Panels of St. Vicent, which has been subject of national and international research for 130 years. On the other, in the oil painting of St. Peter and St. Paul and Infanta St. Joana, of the same old master. The iconographic interpretation of the mural paintings of St. Leocadia, based in the deep facial features of the images, is plausible: a catechetical purpose; the ritual practices of royal ancestor worship in royal portrait apses of the churches; the Portuguese maritime expansion and the macro-imperial ideology of D. Manuel I.

: Cybersecurity has become a critical area in the digital field in recent years. The expansion of networks has revolutionised the way network structures are organised and managed. However, with increased connectivity and the growing complexity of modern networks, the threat of cyber-attacks has become more intense. As technology continues to advance, it brings both opportunities and challenges. One of the major challenges is the need to secure networks and sensitive data from various malicious activities. Traditional networks have evolved to include Software Defined Network (SDN), which offers a more flexible and programmable framework. Researchers should focus on detecting attacks in SDN because SDN networks are becoming more popular and attractive targets for clients due to their programmability and dynamic nature. The centralised controller, known as the backbone of an SDN, becomes a single point of failure and a potential target for attackers if not properly secured. Researchers need to emphasise the detection of attacks in SDN in order to mitigate these risks. By understanding the potential vulnerabilities and attack methods specific to SDN, researchers can develop effective detection approaches and propose countermeasures. This methodology helps protect the network from potential threats and minimises the impact of successful attacks. Therefore, this survey paper provides a review of Intrusion Detection Systems (IDSs) in Software-Defined Networks (SDN) to provide a thorough understanding of SDN security issues.

The pharmaceutical industry is one of the largest pollutants and generator of toxic solvent resi-dues for the environment and human beings. In the last decade, there has been a huge evolution in the design and development of new environmentally friendly products and processes, in re-sponse to the global environmental concern and the growing demand for products with quality ingredients and manufacturing processes aimed at achieving sustainability. Consumers are looking for products with the benefits of natural products, with sustainable and environmentally friendly formulations with few synthetic molecules as possible. Thus, based on the origins of the pharmaceutical industry, it is important to return to Nature and enjoy the benefits that natural products bring to us. Deep eutectic solvents appear as an alternative to toxic solvents commonly used to extract bioactive compounds. These solvents are considered green solvents because they are made up of natural compounds such as sugars, amino acids, and carboxylic acids. In addition, they are simple to prepare, with an atomic economy of 100%, with attractive physicochemical properties. This paper is a comprehensive review in which is described the main properties, characteristics, and production methods of deep eutectic solvents as well as its application to extract from natural sources bioactive compounds with pharmaceutical interest.

Machine learning is a fundamental aspect of artificial intelligence that involves the development of algorithms and models that allow computers to learn and make predictions or decisions without explicit programming. With the development of neural networks, back-propagation algorithms and deep learning, machine learning has made breakthroughs in the fields of image recognition, natural language processing and handwriting recognition using machine learning techniques. The advent of deep learning has revolutionized the field of handwriting recognition, using convolutional neural networks, recurrent neural networks, and sequence-to-sequence models to provide solutions that go beyond machine learning methods and significantly improve the accuracy and robustness of handwriting recognition systems. But challenges remain, including the need for large labelled datasets, computational resources and addressing potential biases. As research in deep learning techniques continues to drive handwriting recognition closer towards realisability, machine learning approaches remain at the forefront.

In this study, tree selection/plantation DSS (Decision Support Systems) were reviewed and assessed based on essential objectives in the available literature. We reviewed existing DSS using multiple data sources and available online resources such as web interfaces. We compared the existing DSS, focusing primarily on five primary objectives in this study, which DSS may address when it comes to the tree selection, including a) Climate resilience, b) Infrastructure/Space Optimization, c) Agroforestry, d) Ecosystem services, e) Urban sustainability. Climate resilience of tree species and urban sustainability is addressed relatively less in existing systems, which can be holistically internalized in future DSS tools Based on this review, DNN (Deep Neural Networks) is recommended to address achieving trade-offs between complex objectives such as maximizing ecosystem services, climate resilience of tree species, maintaining Agroforestry, and other benefits.

The advent of AI in medicine has transformed various medical specialties, including orthodontics. AI has shown promising results in enhancing the accuracy of diagnoses, treatment planning, and predicting treatment outcomes. With the growing number of AI applications and commercially available tools, there is an increase in their usage in orthodontic practices worldwide. This review aims to explore the principles of artificial intelligence (AI), its applications in the diagnostic process of modern orthodontic practices, and concerns associated with the implementation of AI algorithms in clinical practice. A comprehensive review of the literature was conducted, focusing on five categories where AI has been applied in orthodontics: dental diagnostics, cephalometric evaluation, skeletal age determination, temporomandibular joint (TMJ) evaluation, and extraction decision making. AI has demonstrated high efficacy in all those fields. However, variations in performance and the necessity of manual supervision indicate that AI should be used cautiously in clinical settings. Nevertheless, the high complexity and potential unpredictability of AI algorithms call for cautious implementation and regular manual validation of results. Continuous AI learning, proper governance, and addressing privacy and ethical concerns are crucial for the successful integration of AI into orthodontic practice.

African swine fever virus (ASFV) is the causative agent of African swine fever, an economically important disease of pigs, often with a high case fatality rate. ASFV has demonstrated low genetic diversity among isolates collected within Eurasia. To explore the influence of viral variants on clinical outcomes and infection-dynamics in pigs experimentally infected with ASFV, we have designed a deep sequencing strategy. Variant analysis revealed 3 distinct SNPs present in the virus inoculum (at 2.4%, 0.7%, and 13% frequency, respectively) that were maintained within all infected pigs (1-6 % frequency, and the latter at 16-21%). Several pigs displayed other unique SNPs at <10% frequency. In addition, a deletion of 10487 bp (resulting in the complete loss of 21 genes) was present at nearly 100% frequency in the ASFV DNA from one pig at position 6362-16849. This deletion was also found to be present at low levels in the virus inoculum and in two other infected pigs. The current methodology can be used for the currently circulating Eurasian ASFVs and also adapted to other ASFV strains and genotypes. Comprehensive deep sequencing is critical for following ASFV molecular evolution, especially for identification of modifications that affect virus virulence.

In the wake of increasing digital fraud, this paper introduces an innovative application of Artificial Intelligence (AI) in detecting fraudulent activities across finance, healthcare, and e-commerce sectors. It presents a detailed analysis of machine learning methodologies, specifically focusing on the advantages of supervised, unsupervised, and deep learning techniques. The paper addresses the challenges such as data imbalance, model interpretability, and ethical implications in AI-based fraud detection. It also discusses the necessity of high-quality datasets and advocates for the integration of traditional and advanced machine learning methods to enhance accuracy and adaptability in fraud identification. However, it acknowledges the limitations including computational demands and overfitting risks. The study underscores the importance of collaborative efforts between AI experts and industry professionals to develop ethical, efficient, and reliable AI solutions for fraud detection.

Venous thromboembolism (VTE) is a prevalent medical condition with high morbidity, mortality, and associated cost. Anticoagulation remains the main treatment for VTE, though the decision on when, how, and for how long to administer anticoagulants is increasingly complex. This review highlights the different phases of VTE management, special circumstances for consideration such as antiphospholipid syndrome, coronary artery disease, cancer associated thrombus and COVID, and future anticoagulation options. Anticoagulation management will continue to be a complex decision, applying evidence-based medicine to individual patients with the hope to maximize effectiveness while minimizing risks.

To support pathologists in breast tumor diagnosis, deep learning plays a crucial role in the development of histological whole slide image (WSI) classification methods. However, automatic classification is challenging due to the high-resolution data and the scarcity of representative training data. To tackle these limitations, we propose a deep learning-based breast tumor gigapixel histological image multi-classifier integrated with a high-resolution data augmentation model to process the entire slide by exploring its local and global information and generating its different synthetic versions. The key idea is to perform classification and augmentation in feature latent space, reducing the computational cost while preserving the class label of the input. We adopt a deep learning-based multi-classification method and evaluate the contribution given by a Conditional Generative Adversarial Network-based data augmentation model on the classifier’s performance for three tumor classes in the BRIGHT Challenge dataset. The proposed method has allowed us to achieve an average F1 equal to 69.5, considering only the WSI dataset of the Challenge. The results are comparable to those obtained by the Challenge winning method (71.6), also trained on the annotated tumor region dataset of the Challenge.

In the era of digital advancements, the escalation of credit card fraud necessitates the development of robust and efficient fraud detection systems. This paper delves into the application of machine learning models, specifically focusing on ensemble methods, to enhance credit card fraud detection. Through an extensive review of existing literature, we identified limitations in current fraud detection technologies, including issues like data imbalance, concept drift, false positives/negatives, limited generalisability, and challenges in real-time processing. To address some of these shortcomings, we propose a novel ensemble model that integrates a Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Bagging, and Boosting classifiers. This ensemble model tackles the dataset imbalance problem associated with most credit card datasets by implementing under-sampling and the Synthetic Over-sampling Technique (SMOTE) on some machine learning algorithms. The evaluation of the model utilises a dataset comprising transaction records from European credit card holders, providing a realistic scenario for assessment. The methodology of the proposed model encompasses data pre-processing, feature engineering, model selection, and evaluation, with Google Colab computational capabilities facilitating efficient model training and testing. Comparative analysis between the proposed ensemble model, traditional machine learning methods, and individual classifiers reveals the superior performance of the ensemble in mitigating challenges associated with credit card fraud detection. Across accuracy, precision, recall, and F1-score metrics, the ensemble outperforms existing models. This paper underscores the efficacy of ensemble methods as a valuable tool in the battle against fraudulent transactions. The findings presented lay the groundwork for future advancements in the development of more resilient and adaptive fraud detection systems, crucial as credit card fraud techniques continue to evolve.

Sustainable agriculture is at the forefront of modern farming practices, with a growing emphasis on optimizing crop production while minimizing environmental impact. The choice of fertilizer application technology plays a critical role in achieving these objectives. Soybean (Glycine max (L.) Merr.), a valuable crop globally, serves both as a source of human nutrition and livestock feed. To address the challenges of enhancing soybean production while minimizing ecological harm, this study evaluates and compares the performance of different fertilizer application technologies. In this study we combined remote and ground surveys to allow a comprehensive understanding of the impact of these technologies on soybean cultivation. The study focuses on deep placement of slow-release nitrogen fertilizers with coated urea at a 20 cm depth, conventional nitrogen fertilizer application, and manure organic fertilizer application. Remote surveys are conducted using advanced smart farming tools such as Unmanned Aerial Vehicle (UAV), ground surveys such as soil plant analysis development SPAD measurements, and soybean parameters to assess the impact of fertilizer application. This study revealed that deep placement fertilizer demonstrated better performance compared to other fertilizer application technologies for soybean cultivation. The findings from this study contribute to the advancement of sustainable agricultural practices and empower farmers with knowledge to make informed decisions for optimizing green soybean production.

A high-resolution catalog for the 2014 Ms6.5 Ludian aftershocks was constructed based on the deep learning phase-picking model (CERP) and the seismic-phase association technology (PALM). A specific training strategy, which combines the advantages of the conventional short-long window average energy ratio algorithm (STA/LTA) and AI algorithm, is employed to retrain the CERP model. The P & S phases were accurately detected and picked on continuous seismic waveforms by the retained AI model. Hypoinverse and HypoDD were utilized for precise location of 3286 events. Compared to the previous results, our new catalog exhibits superior performances in terms of location accuracy and the number of aftershock events, thereby enabling a more de-tailed depiction of the deep-seated tectonic features. According to the distribution of aftershocks, it can be inferred that, (1) the seismogenic fault of Ludian earthquake is the NW-trending Baogunao-Xiaohe Fault. (2) the Ludian aftershocks interconnected the discontinuous NW-trending Baogunao-Xiaohe Fault, and intersected with the Zhaotong-Ludian Fault. (3) It suggests that the NE-trending Zhaotong-Ludian Fault may have been intersected by the NW-trending Baogunao -Xiaohe Fault, indicating that the Baogunao-Xiaohe Fault is likely a rela-tively young Neogene fault.

The peculiar physicochemical features of deep eutectic solvents (DESs), in particular their tunability, make them ideal media for various applications. Despite their ability to solubilize metal oxides, their use as rust removers from valuable substrates has not yet been thoroughly investigated. In this study we chosen three known DESs, consisting of choline chloride and acetic, oxalic or citric acid for evaluating their ability to remove corrosion products from a cellulose-based material as linen fabric and two different lithotypes, as travertine and granite. The artificial staining was achieved by placing a rusty iron grid on their surfaces. The DESs were applied by means of cellulose poultice on the linen fabrics, while on the rusted stone surfaces with a cotton swab. Macro- and microscopic observations, colorimetry and SEM/EDS analysis were employed to ascertain the cleaning effectiveness and the absence of side effects on the samples after treatment. Oxalic acid-based DES was capable of removing rust stains from both stone and cellulose-based samples, while choline chloride/citric acid DES was effective only on stone specimens. The results suggest a new practical application of DESs for the elimination of rust from lithic and cellulosic substrates of precious and artistic value.

This paper introduces an innovative approach to lipreading, leveraging a web application designed to generate subtitles for videos where the speaker's mouth is visible and a comprehensive literature review that precedes the discussion, encompassing a thorough examination of various lipreading methods employed over the past decade. Our method employs a powerful deep learning model, featuring a 3D-convolution network and bidirectional LSTM, enabling accurate sentence-level predictions based solely on visual lip movements. With an impressive accuracy of 97%, our model is trained using pre-segmented lips regions, transformed into animated GIFs for effective pre-training. This work stands as a significant contribution to the evolving landscape of lipreading research, offering a practical and accurate solution for real-world applications.

This paper investigates the adaptability of four state-of-the-art Artificial Intelligence (AI) models to the Australian mammographic context through transfer learning, explores the impact of image enhancement on model performance and analyses the relationship between AI outputs and histopathological features for clinical relevance and accuracy assessment. A total of 1712 screening mammograms (n=856 cancer cases and n=856 matched normal cases) were used in this study. The 856 cases with cancer lesions were annotated by two expert radiologists and the level of concordance between their annotations was used to establish image subsets. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of Globally aware Multiple Instance Classifier (GMIC), Global-Local Activation Maps (GLAM), I&H and End2End AI models, both in the pre-trained and transfer learning modes, with and without applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. The four AI models with and without transfer learning in the high-concordance subset outperformed those in the entire dataset. Applying the CLAHE algorithm to mammograms improved the performance of the AI models. In the high-concordance subset with transfer learning and CLAHE algorithm applied, the AUC of the GMIC model was highest (0.912), followed by GLAM model (0.909), I&H (0.893) and End2End (0.875). There were significant differences (P<0.05) in the performances of the four AI models between high-concordance subset and entire dataset. The AI models demonstrated significant differences in malignancy probability concerning different tumour size categories in mammograms. The performance of AI models was affected by several factors such as concordance classification, image enhancement and transfer learning. Mammograms with strong concordance of radiologists’ annotations, applying image enhancement and transfer learning could enhance the accuracy of AI models.

While machine learning (ML) has been quite successful in the field of structural health monitoring (SHM), its practical implementation has been limited. This is because ML model training requires data containing a variety of distinct damaged instances captured from a real structure and the experimental generation of such data is challenging. One way to tackle this issue is by generating training data through numerical simulations. However, simulated data cannot capture the bias and variance of experimental uncertainty. To overcome this problem, this work proposes a deep learning-based domain transformation method for transforming simulated data to the experimental domain. Use of this technique has been demonstrated for debonding location and size prediction of stiffened panels using a vibration-based method. The results are satisfactory for both, debonding location and size prediction. This domain transformation method can be used in any field where experimental data for training machine learning models is scarce.

In the evolving landscape of Portfolio Management (PM), the fusion of advanced machine 1 learning techniques with traditional financial methodologies has opened new avenues for innovation. 2 Our study introduces a cutting-edge model combining Deep Reinforcement Learning (DRL) with 3 a Non-stationary Transformer architecture. This model is specifically designed to decode complex 4 patterns in financial time series data, enhancing portfolio management strategies with deeper insights 5 and robustness. It effectively tackles the challenges of data heterogeneity and market uncertainty, 6 key obstacles in PM. Our approach integrates key macroeconomic indicators and targeted news 7 sentiment analysis into its framework, capturing a comprehensive picture of market dynamics. This 8 amalgamation of varied data types addresses the multifaceted nature of financial markets, enhancing 9 the model’s ability to navigate through the complexities of asset management. Rigorous testing 10 demonstrates the model’s efficacy, highlighting the benefits of blending diverse data sources and 11 sophisticated algorithmic approaches in mastering the nuances of PM.12

X-band marine radar captures signal reflected from the sea surface. Theoretical studies indicate that the initial unfiltered signal contains meaningful information including parameters related to wind waves. Traditional methods for estimating significant wave height (SWH) rely on the physical laws describing signal reflection from rough surfaces. However, recent studies suggest the feasibility of employing artificial neural networks (ANNs) for SWH approximation. Both classical and ANN-based approaches necessitate costly in situ data. In this study, we propose generating synthetic radar images with specified wind wave parameters using Fourier-based approach and Pierson–Moskowitz wave spectrum as a viable alternative. We generate synthetic images and use them for unsupervised learning approach to train a convolutional component of an ANN. After that, we train a regression ANN based on the previous convolutional component to obtain SWH back from synthetic images. Then, we apply preliminary trained weights for the same regression model to train SWH approximation on the dataset of real sea clutter images. In this study, we demonstrate the increase in the accuracy of SWH estimation from radar images with the preliminary training on synthetic data.

Background: Genome-wide association studies (GWAS) have shed light on various complex diseases and traits, by detecting more than 400,000 associated genetic loci. This number is expected to drastically increase because of the use of novel artificial intelligence methods offering new ways to study the effects of variants. Deep learning using artificial neural networks (ANN) is a sub-field of artificial intelligence, which simulates how the human brain learns. We aimed at assessing the potential of deep learning in human genetic studies of Alzheimer’s Disease (AD) and how these compare to the traditional statistical methods used in GWAS, by simultaneously testing the two approaches on the same dataset, while discovering new genetic loci associated to AD. Methods: To address this aim, phenotypic and genome-wide SNP data from the UK Biobank was analysed on a binary outcome, AD diagnosis, in two different data balance options, of one-to-one and one-to-two case-control datasets, using 2,764 cases vs 2,764 controls and 5,528 controls respectively matched on gender, age, ethnicity, PC1-20 and genotyping array. Genetic data handling and GWAS were performed using PLINK, whereas neural networks were trained using GenNet, a new ANN tool, with the same datasets, separated into training (60%), validation (20%) and test (20%) sets. Neural network layers were determined using biological knowledge, by annotating SNPs to genes and genes to AD related pathways, using ANNOVAR annotations followed by GeneSCF and KEGG. Results: Significant associations were detected between four SNPs linked to two different genes and AD for the 1 to 1 case-control study design and six SNPs linked to four different genes for the 1 to 2 case-control study design by using PLINK. All identified regions have been previously associated to AD. GenNet identified twelve SNPs on seven genes to be associated with AD, all with biological plausibility, achieving an AUC of 0.80 when using three biologically determined layers and 0.73 when using two layers at the neural networks. No common top SNPs were identified between the machine learning and GWAS models. Conclusion: This is one of the first studies attempting to compare the traditional GWAS to more sophisticated state-of-art methods for understanding the genetic architecture of complex phenotypes using the same dataset. More systematic comparisons with such approaches on real data are needed to enable best practises for machine learning in the analysis of genome-wide genetic data.

Membrane filtration is a popular choice in the treatment of wastewater for reuse. However, when used to produce clean water, these devices are subject to concentration polarization and fouling, which inhibit their ability to operate efficiently and provide high-quality permeate streams. Patterned membrane surfaces offer a hydrodynamic approach to mitigating concentration polarization and the surface fouling that follows. However, when subject to a steady feed flow rate, surface patterns generally produce stagnant zones in cavity-like spaces where particulate from the bulk flow recirculates in stationary vortices. To prevent aggregation in these recirculation zones, and therefore the onset of surface fouling, we study a rapidly pulsed feed flow. When combined with cavity-like geometries, such as the valleys present in membrane surface patterns, a rapidly pulsed flow generates mixing mechanisms (i.e., the deep sweep and the vortex ejection) that disrupt otherwise stagnant regions. First, we confirm the existence of these mechanisms, which are typically induced in impermeable cavities at relatively low Reynolds numbers, for near transitional feed Reynolds numbers in permeable cavity spaces via numerical simulation of the flow field. Following this confirmation, we demonstrate the ability of these mechanisms to remove particles trapped in recirculation zones via massless particle tracking studies. The results of this work suggest that when combined with a rapidly pulsed inlet flow, patterned membrane surfaces can not only alleviate concentration polarization and the surface fouling that follows but also reduce the need for traditional cleaning methods, which require operational downtime and often involve the use of abrasive chemical agents.

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.

In recent years, Edge Intelligence (EI) has emerged, combining edge computing with AI, specifically deep learning, to run AI algorithms directly on edge devices. In practical applications, EI faces challenges related to computational power, power consumption, size, and cost, with the primary challenge being the trade-off between computational power and power consumption. This has rendered traditional computing platforms unsustainable, making heterogeneous parallel computing platforms a crucial pathway for implementing EI. In our research, we leveraged the Xilinx Zynq 7000 heterogeneous computing platform, employed High-Level Synthesis (HLS) for design, and implemented two different accelerators for LeNet-5 using loop unrolling and pipelining optimization techniques. The experimental results show that when running at a clock speed of 100 MHz, the PIPELINE accelerator, compared to the UNROLL accelerator, experiences an 8.09% increase in power consumption but speeds up by 14.972 times, making the PIPELINE accelerator superior in performance. Compared to the CPU, the PIPELINE accelerator reduces power consumption by 91.37% and speeds up by 70.387 times, while compared to the GPU, it reduces power consumption by 93.35%.This study provides two different optimization schemes for edge intelligence applications through design and experimentation, and demonstrates the impact of different quantization methods on FPGA resource consumption. These experimental results can provide a reference for practical applications, thereby providing a reference hardware acceleration scheme for edge intelligence applications.

PurposeThe purpose of this research was the development and evaluation of an intelligent assistive classification system for the recognition of endometriosis and the improvement of the accuracy of laparoscopic imaging in diagnosis. The research was developed with the use of Deep Learning approaches. Methods Data from 4448 laparoscopy images were used in a retrospective chart analysis. The data were divided into two folders, healthy and pathological including 2157 healthy and 2291 pathological images. Based on simple clinical and imaging information and criteria such as the diagnosis of endometriosis (included in an open-source dataset GLENDA of Kaggle repository), data mining algorithms were used to improve laparoscopic imaging accuracy. Results The final developed computer system based on the ResNet50 algorithm predicted the best outcome for all participants who had laparoscopic surgical therapy. The Keras tool was used and the generated code was implemented in Python programming language providing a mean accuracy of > 95 %. Conclusion The intelligent approach revealed better performance than the commonly used imaging criteria in predicting endometriosis improving the time and the total accuracy of diagnostic approaches.

Building deep learning models proposed by third parties can become a simple task when specialized libraries are used. However, a great deal of mystery still surrounds the design of new models or the modification of existing ones. These tasks require in-depth knowledge of the different components or building blocks as well as their dimensions. This information is limited and broken up in different literature. In this article, we collect and explain in-depth the building blocks to design deep learning models, starting from the artificial neuron to the concepts involved in building deep neural networks. Furthermore, the implementation of each building block is exemplified using the Keras library.

Industrial radiography is a pivotal non-destructive testing (NDT) method that ensures quality and safety in a wide range of industrial sectors. Conventional human-based approaches, however, are prone to challenges in defect detection accuracy and efficiency, primarily due to the high inspection demand from manufacturing industries with high production throughput. To solve this challenge, numerous computer-based alternatives have been developed, including Auto-mated Defect Recognition (ADR) using deep learning algorithms. At the core of training, these algorithms demand large volumes of data that should be representative of real-world cases. However, the availability of digital X-ray radiography data for open research is limited by non-disclosure contractual terms in the industry. This study presents a pipeline that is capable of modeling synthetic images based on statistical information acquired from X-ray intensity distribution from real digital X-ray radiography images. Through meticulous analysis of the intensity distribution in digital X-ray images, the unique statistical patterns associated with the exposure conditions used during image acquisition, type of component, thickness variations, beam divergence, anode heel effect, etc., are extracted. The realized synthetic images were utilized to train deep learning models, yielding an impressive model performance with mean intersection over union (IoU) of 0.93, and mean dice coefficient of 0.96, on real unseen digital X-ray radiography images. This methodology is scalable and adaptable, making it suitable for diverse industrial applications.

This paper introduces a novel ensemble adjustment Kalman filter (EAKF) that integrates a machine-learning approach. The approach employs nonlinear variable relationships established by a deep neural network (DNN) during the analysis stage of the EAKF. This process nonlinearly projects observation increments into the state variable space. The newly developed DNN-EAKF algorithm can be applied to coupled data assimilation using coupled ocean-atmosphere models. It enhances cross-component updates in strongly coupled data assimilation (SCDA) by diminishing errors in estimating cross-component error covariance arising from insufficient ensemble members, thereby improving the SCDA analysis. This paper employs a conceptual model to conduct twin experiments, validating the DNN-EAKF’s capability to utilize cross-component observation information in SCDA effectively. The approach is anticipated to offer insights for future methodological integrations of machine learning and data assimilation and provide methods for SCDA applications in coupled general circulation models.

The stability of deep soft rock tunnels is of particular concern under seepage conditions. Aiming at thoroughly discussing the seepage actions and their consequences on the support scheme of such structures, the host rocks of the Weilai Tunnel situated in Guangxi province of China are taken as the research substance. Emphasizing on seepage conditions, stresses, displacements and plastic zone radii along the surrounding rocks of such tunnel are adequately examined taking into consideration the Mogi-Coulomb strain criterion and the elastic-plastic theory. Subsequently, based on strain-softening model, the coupled actions of seepage and softening on the rocks surrounding the tunnel are studied. According to the results, it is revealed that the support scheme of deep soft rock tunnels must be of highest resistance possible to better decrease the plastic zone and the tangential stress along the host rocks. Moreover, throughout the surrounding rocks, the dissemination of pore water pressure is strongly affected by the uneven permeability coefficient under anisotropic seepage states. Combined effects of softening and seepage are very dangerous for the surrounding rocks of deep-buried tunnels. To better guarantee reasonable longevity of such tunnels, long-term monitoring of their support structures with reliable remote sensors is strongly recommended.

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.

It is time-consuming for acquiring complete data by fully phase encoding in two orthogonal directions along with one frequency encoding direction. Undersampling in the 3D k-space is promising in accelerating such 3D MRI process. Though 3D undersampling can be conducted according to predefined probability density, the density based method is not optimal. Because of the large amount of 3D data and computational cost, it is challenging to perform data-driven and learning-based 3D undersampling and subsequent 3D reconstruction. To tackle this challenge, this paper proposes a deep neural network called EEUR-Net, realized by optimizing specific undersampling patterns for the fully-sampled 3D k-space data. Innovatively, our undersampling algorithm employs an end-to-end deep learning approach to optimize phase encoding patterns and uses a 3D U-Net for image reconstruction of undersampled data. Through end-to-end training, we obtain an optimized 3D undersampling pattern, which significantly enhances the quality of the reconstructed image under the same acceleration factor. A series of experiments on a knee MRI dataset demonstrated that, in comparison to standard random uniform, radial, Poisson and equispaced Cartesian undersampling schemes, our end-to-end learned undersampling pattern considerably improves the reconstruction quality of undersampled MRI images.

Today, specific CNN models assigned to specific tasks are often used. In this article, the authors have investigated three models MobileNet, EfficientNetB0 and InceptionV3. Artificial intelligence methods for deep networks will be presented but implemented in a limited computer resource. Three types of training bases were investigated, starting with a simple base verifying five colors, followed by recognition of two different orthogonal elements, and then more complex images discriminating between elements. The research aimed to demonstrate the models' capabilities based on training base parameters such as the number of images and epoch types. The architectures proposed by the authors in these cases were chosen based on simulation studies conducted on a virtual machine with limited hardware parameters. The proposals present the advantages and disadvantages of different models based on TensorFlow, Keras libraries in the Jupiter environment written based on the Python language. The authors' proposal of the selected AI model enables further work on e.g. image classification, but in a limited computer resource for industrial implementation e.g. on a PLC.

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.

Crack inspection in railway sleepers is crucial for ensuring rail safety and avoiding deadly accidents. Traditional methods for detecting cracks on railway sleepers are very time-consuming and lack efficiency. Therefore nowadays, researchers are paying attention to the vision-based algorithm, especially Deep Learning algorithms. In this work, we adopted the U-net for the first time for detecting cracks on a railway sleeper and proposed a modified U-net architecture named Dense U-net for segmenting the cracks. In the Dense U-net structure, we established several short connections between the encoder and decoder blocks, which enabled the architecture to obtain better pixel information flow. Thus, the model extracted the necessary information in more detail to predict the cracks. We collected images from railway sleepers, processed them in a dataset, and finally trained the model with the images. The model achieved an overall F1-score, precision, Recall, and IoU of 86.5% 88.53%, 84.63%, and 76.31% respectively. We compared our suggested model with the original U-net, and the results demonstrate that our model outperformed the U-net in both quantitative and qualitative results. Moreover, we considered the necessity of crack severity analysis and measured a few parameters of the cracks (e.g., length, maximum width, area, density). The engineers must know the severity of the cracks to have an idea about the most severe locations and take the necessary steps to repair the badly affected sleepers.

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.

This paper presents a model for archival and retrieval of the videos of natural flowers. To design an efficient video retrieval system the stages namely, keyframe selection, feature extraction, feature dimensionality reduction and indexing are essential for fast browsing and accessing of videos. Three different keyframe selection approaches are proposed using clustering algorithms after segmenting flower regions from its background. Deep Convolutional Neural Network is used as a feature extractor. After keyframe selection, a video is represented with a set of keyframes. To reduce the feature dimension of a video, two feature selection methods are utilized. For an efficient archival and fast retrieval of flower videos an indexing method called KD-tree is recommended. For a given query video, similar videos are retrieved both in relative and absolute search modalities. An extensive experimentation conducted on a relatively large flower video dataset. The data set consists of 7788 videos of 30 different species of flowers. The videos are captured with three different devices in different resolutions. The comparative study reveals proposed keyframe selection approaches gives better results. It has also been observed that the videos retrieved in absolute approach with features selected from Binormal separation metric and indexing gives good results.

Skin cancer is one of the widespread diseases that typically develop on the skin due to continuous exposure to sunlight. Although cancer can appear on any part of the human body, skin cancer reports account for over half of all cancer occurrences worldwide. There are substantial obstacles to the precise diagnosis and classification of skin lesions because of the morphological variety and indistinguishable characteristics across skin malignancies. Recently, Deep Learning models have been used in the field of image-based lesion diagnosis, and it has demonstrated diagnostic efficiency on par with that of dermatologists. To increase classification efficiency and accuracy for skin lesions, a cutting-edge multi-layer deep Convolutional Neural Network (CNN) termed SkinLesNet has been built in this study. The ResNetV50 and VGG16 models have been carefully compared to review the performance of the proposed model. The dataset used in this study, PAD-UFES-20, contains 1314 samples in total and includes three common forms of skin lesions. The proposed approach, SkinLesNet, significantly outperforms the well-known compared models in the given conditions

Natural fibers have attracted increasing interest as an alternative to produce environmentally friendly and sustainable materials. Particularly, hemp fibers have revealed to be widely used in various industrial applications due to their extremely unique properties. However, hemp can generate a large amount of agro-waste, and results in an attractive source of biopolymers for the development of low-cost materials in alternative to the raw materials and conventional petroleum-based plastics. In addition, deep eutectic solvents (DESs), a new type of truly green solvents, have been tested to remove gums, lignin, and other non-cellulosic components from hemp fibers. Reusing these components dissolved into the DESs to fabricate new materials directly by electrospinning is a very attractive but still unexplored endeavor. Thus, this innovative research to venture new upcycling pathways is focused on the fabrication of composite nanofibers by electrospinning of a gel-based blend of Poly(vinyl alcohol) (PVA) and hemp agro-waste (HW) dissolved into choline chloride (ChCl) : Glycerol (1:2) and ChCl : Urea (1:2) DES mixtures. The results obtained revealed that the produced nanofibers displayed uniform appearance with diameters ranging from 257.72 ± 65.63 nm to 380.78 ± 133.98 nm. In addition, the mechanical properties of the electro-spun composite nanofibers produced from the gel-based blends of HW dissolved in DESs and PVA (HW-DESs_PVA) were found to be superior, resulting in an enhanced tensile strength and Young’s modulus. Furthermore, the incorporation of HW into the nanofibers was able to provide bioactive antioxidant and antibacterial properties. Overall, this study demonstrated a promising, more sustainable, and eco-friendly way to produce electrospun composite nanofibers using HW in a circular economy perspective.

This paper presents an approach to enhancing the clarity and intelligibility of speech in digital communications compromised by various background noises. Utilizing deep learning techniques, specifically a Variational Autoencoder (VAE) with 2D convolutional filters, we aim to suppress background noise in audio signals. Our method focuses on four simulated environmental noise scenarios: storms, wind, traffic, and aircraft. Training dataset has been obtained from public sources (TED-LIUM 3 dataset, which includes audio recordings from the popular TED-TALK series) combining with these background noises. The audio signals were transformed into 2D power spectrograms, upon which our VAE model was trained to filter out the noise and reconstruct clean audio. Our results demonstrate that the model outperforms existing state-of-the-art solutions in noise sup-pression. Although differences in noise types were observed, it was challenging to definitively conclude which background noise most adversely affects speech quality. Results have been assessed with objective methods (mathematical metrics) and subjective (listening to a set of audios by humans). Notably, wind noise showed the smallest deviation between the noisy and cleaned audio, perceived subjectively as the most improved scenario. Future work involves refining the phase calculation of the cleaned audio and creating a more balanced dataset to minimize differences in audio quality across scenarios. Additionally, practical ap-plications of the model in real-time streaming audio are envisaged. This research contributes significantly to the field of audio signal processing by offering a deep learning solution tailored to various noise conditions, enhancing digital communication quality.

Measuring Marginal Reflex Distance-1 (MRD-1) is a crucial clinical tool used to evaluate the position of the eyelid margin in relation to the cornea. Traditionally, this assessment has been conducted manually by plastic surgeons, ophthalmologists, or trained technicians. However, with the advancements in Artificial Intelligence (AI) technology, there is a growing interest in the development of automated systems capable of accurately measuring MRD-1.In this context, we introduce novel MRD-1 measurement methods based on deep learning algorithms that can simultaneously capture images and compute the results. This prospective observational study involved 154 eyes of 77 patients aged over 18 years who visited Chungnam National University Hospital between September 1, 2023, and July 29, 2023. We collected four different MRD1 da-tasets from patients using three distinct measurement methods, each tailored to the individual patient. The mean MRD1 values, measured through the manual method using a penlight, the deep learning method, ImageJ analysis from RGB eye images, and ImageJ analysis from IR eye images in 56 eyes of 28 patients, were 2.64 ± 1.04 mm, 2.85 ± 1.07 mm, 2.78 ± 1.08 mm, and 3.07 ± 0.95 mm, respectively. Notably, the strongest agreement was observed between MRD1_deep learning (DL) and MRD1_IR (0.822, p < 0.01). In the Bland-Altman plot, the smallest difference was observed between MRD1_DL and MRD1_IR ImageJ, with a mean difference of 0.0611 and △LOA (limits of agreement) of 2.5162, which is the smallest among the other groups. In conclusion, this novel MRD1 measurement method, based on an IR camera and deep learning, demon-strates statistical significance and can be readily applied in clinical settings.

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.

Accurate short-term solar irradiance forecasting is crucial for the efficient operation of solar energy driven photovoltaic (PV) power plants. In this research, we introduce a novel hybrid ensemble forecasting model that amalgamates the strengths of machine learning tree-based models and deep learning neuron-based models. The hybrid ensemble model integrates the interpretability of tree-based models with the capacity of neuron-based models to capture complex temporal dependencies within solar irradiance data. Furthermore, stacking and voting ensemble strategies are employed to harness the collective strengths of these models, significantly enhancing prediction accuracy. This integrated methodology is enhanced by incorporating pixels from satellite images provided by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). These pixels are converted into structured data arrays and employed as exogenous inputs in the algorithm. The primary objective of this study is to improve the accuracy of short-term solar irradiance predictions, spanning a forecast horizon up to 6 hours ahead. The incorporation of EUMETSAT satellite image pixels data enables the model to extract valuable spatial and temporal information, thus enhancing the overall forecasting precision. This research also includes detailed analysis of the derivation of GHI using satellite images. The study was carried out and the models tested across three distinct locations in Austria. A detailed comparative analysis was carried out for traditional Satellite (SAT) and Numerical Weather Prediction (NWP) models with hybrid models. Our findings demonstrate a higher skill score for all of the approaches compared to smart persistent model and consistently highlight the superiority of the hybrid ensemble model for short-term prediction window of 1 to 6 hours. This research underscores the potential for enhanced accuracy of the hybrid approach to advance short-term solar irradiance forecasting, emphasizing its effectiveness at understanding the intricate interplay of the meteorological variables affecting solar energy generation worldwide. The results of this investigation carry noteworthy implications for advancing solar energy systems, thereby supporting the sustainable integration of renewable energy sources into the electrical grid.

Estimating Visual Quality of Street Space (VQoSS) is pivotal for urban design, environmental sustainability, civic engagement, etc. Recent advancements, notably in deep learning, have enabled large-scale analysis. However, traditional deep learning approaches are hampered by extensive data annotation requirements and limited adaptability across diverse VQoSS tasks. Multimodal Large Language Models (MLLMs) have recently demonstrated proficiency in various computer vision tasks, positioning them as promising tools for automated VQoSS assessment. In this paper, we pioneer the application of MLLMs to VQoSS change estimation, with our empirical findings affirming their effectiveness. In addition, we introduce Street Quality GPT (SQ-GPT), a model that distills knowledge from the current most powerful but inaccessible (not free) GPT-4, requiring no human efforts. SQ-GPT approaches GPT-4’s performance and is viable for large-scale VQoSS change estimation. In a case study of Nanjin, we showcase the practicality of SQ-GPT and knowledge distillation pipeline. Our work promises to be a valuable asset for future urban studies research.

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.

Augmented reality (AR) provides an opportunity for mobile fitness applications to show users real-time feedback on their current fitness activity. For such applications, it is essential to accurately track the user’s current fitness activity using available mobile sensors, such as inertial measurement units (IMUs). Convolutional neural networks (CNNs) have been shown to produce strong results in different time series classification tasks, including the recognition of activities of daily living. However, fitness activities can present unique challenges to the human activity recognition task (HAR), including greater similarity between individual activities and fewer available data for model training. In this paper, we evaluate the applicability of CNNs to the fitness activity recognition task (FAR) using IMU data and determine the impact of input data size and sensor count on performance. For this purpose, we adapted three existing CNN architectures to the FAR task and designed a fourth CNN variant, which we call the scaling fully convolutional network (Scaling-FCN). We designed a preprocessing pipeline and recorded a running exercise data set with 20 participants, in which we evaluated the respective recognition performances of the four networks, comparing them with three traditional machine learning methods commonly used in HAR. On our data set, all CNN architectures significantly outperformed traditional machine learning methods, reaching up to 97.14±1.36% classification accuracy on the test set for ResNet. Whereas a reduced input data size invariably led to a performance loss, removing specific sensors improved the performance of all CNN architectures, with our Scaling-FCN reaching the highest accuracy of 99.86±0.11%. Our results suggest that CNNs are generally well suited for fitness activity recognition, and noticeable performance improvements can be achieved if sensors are dropped selectively.

In the field of maneuvering target tracking, the combined observations of azimuth and Doppler may cause weak observation or non-observation in the application of traditional target tracking algorithms. Additionally, traditional target-tracking algorithms require pre-defined multiple mathematical models to accurately capture the complex motion states of targets, while model mismatch and unavoidable measurement noise lead to significant errors in target state prediction. To address those above challenges, in recent years, the target-tracking algorithms based on neural networks, such as recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and Transformer architectures, have been widely used for their unique advantages to achieve accurate predictions. To better model the nonlinear relationship between the observation time series and the target state time series, as well as the contextual relationship among time series points, we present a deep learning algorithm called recursive downsample-convolve-interact neural network (RDCINN) based on convolutional neural network (CNN) that downsamples time series into sub-sequences and extracts multi-resolution features to enable the modeling of complex relationships between time series, which overcomes the shortcomings of traditional target-tracking algorithms in using observation information inefficiently due to weak observation or non-observation. The experimental results show that our algorithm outperforms other existing algorithms in the scenario of strong maneuvering target tracking with the combined observations of azimuth and Doppler.

Capsizing accidents are regarded as marine accidents with a high rate of casualties per accident. Approximately 89% of all such accidents occur in small ships (vessels with gross tonnage less than 10 tons). Stability calculations are critical for assessing the risk of capsizing incidents and evaluating a ship's seaworthiness. Despite the high frequency of capsizing accidents involving small ships, they are generally exempt from adhering to stability regulations, thus remaining systemically exposed to the risk of capsizing. Moreover, the absence of essential design documents complicates direct ship stability calculations. This study utilizes hull form feature data—obtained from the general arrangement of small ships—as input for a deep learning model. The model is structured as a multilayer neural network and aims to infer hydrostatic curves, which is required data for stability calculations.

Availability and continuous operation under critical conditions are very important in electric machine drive systems. However, such systems, are suffering from several type of failures that affect the electric machine or the associated voltage source inverter. Therefore, fault diagnosis and fault tolerance are highly required. This paper presents a new robust deep learning-based approach to diagnosis multiple open-circuit fault in three phase two level voltage source inverter for induction motor drive applications. The proposed approach uses fault diagnosis variables obtained from sigmoid transformation of the motor stator currents. The open-circuit fault diagnosis variables are then introduced to a Bidirectional Long Short-Term Memory algorithm to detect the faulty switch(es). Several simulation and experimental results are presented to show the proposed fault diagnosis algorithm effectiveness and robustness.

The importance of radar-based human activity recognition increased significantly over the last two decades in safety and smart surveillance applications due to its superiority towards vision-based sensing in the presence of poor environmental conditions, like illumination, increased radiative heat, occlusion, and fog. An increased public sensitivity for privacy protection, and the progress of cost-effective manufacturing, led to a higher acceptance and distribution. Deep learning approaches proved that the manual feature extraction that relies heavily upon process knowledge can be avoided by its hierarchical, non-descriptive nature. On the other hand, ML techniques based on manual feature extraction provide a robust, yet empirical based approach where the computational effort is comparatively low. This review outlines the basics of classical ML- and DL-based human activity recognition and its advances while taking recent progress of both categories into regard. For every category, state-of-the-art methods are introduced, briefly explained and discussed in terms of their pros, cons and gaps. A comparative study is performed to evaluate the performance and computational effort based on a benchmarking data set to provide a common basis for the assessment of the techniques’ degree of suitability.

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.

Several studies in computer vision have examined specular removal, which is crucial for object detection and recognition. This research has traditionally been divided into two tasks: specular highlight removal, which focuses on removing specular highlights on object surfaces, and reflection removal, which deals with specular reflections occurring on glass surfaces. In reality, however, both types of specular effects often coexist, making it a fundamental challenge that has not been adequately addressed. Recognizing the necessity of integrating specular components handled in both tasks, we constructed a Specular-Light (S-Light) DB for training single-image-based deep learning models. Moreover, considering the absence of benchmark datasets for quantitative evaluation, the Multi-Scale Normalized Cross Correlation (MS-NCC) metric, which considers the correlation between specular and diffuse components, was introduced to assess the learning outcomes.

Chest X-ray (CXR) is the first tool globally employed to detect cardiopulmonary pathologies. These acquisitions are highly affected by scattered photons due to the large field-of-view required. Scatter in CXRs introduces background in the images, which reduces their contrast. We developed three deep learning-based models to estimate and correct the scatter contribution to CXRs. We used a Monte Carlo (MC) ray-tracing model to simulate CXRs from human models obtained from CT scans using different configurations (depending on the availability of dual-energy acquisitions). The simulated CXRs contained the separated contribution of direct and scattered X-rays in the detector. These simulated datasets were then used as the reference for the supervised training of several NN. Three NN models (single and dual energy) were trained with the MultiResUNet architecture. The performance of the NN models was evaluated on CXRs obtained with the MC code from chest CTs of patients affected by COVID-19. The results showed that the NN models were able to estimate and correct the scatter contribution to CXRs with an error &lt;5%, being robust to variations in the simulation setup and improving the contrast in soft tissue. The single-energy model was tested with real CXRs, providing robust estimations of the scatter-corrected CXRs.

With the development of remote sensing satellite technology for Earth observation, remote sensing stereo images have been used for three-dimensional reconstruction in various fields, such as urban planning and construction. However, remote sensing images often include noise, occluded regions, weakly textured areas and repeated textures, which can lead to reduced accuracy in stereo matching and affect the quality of the 3D reconstruction results. To reduce the impact of complex scenes in remote sensing images on stereo matching and to ensure both speed and accuracy, we propose a new end-to-end stereo matching network based on convolutional neural networks (CNNs). The proposed stereo matching network can learn features at different scales from the original images and construct cost volumes with varying scales to obtain richer scale information. Additionally, when constructing the cost volume, we introduce negative disparity to adapt to the common occurrence of both negative and nonnegative disparities in remote sensing stereo image pairs. For cost aggregation, we employ a 3D convolution-based encoder-decoder structure that allows the network to adaptively aggregate information. Before feature aggregation, we also introduce an attention module to retain more valuable feature information, enhance feature representation, and obtain a higher-quality disparity map. By training on the publicly available US3D dataset, we obtain the accuracy that 1.115 pixel in end-point error (EPE) and 5.32% in the error pixel ratio (D1) on the test dataset, and the inference speed is 92 ms. Comparing our model with existing state-of-the-art models, we achieve higher accuracy, and the network is beneficial for the three-dimensional reconstruction of remote sensing images.

The rising severity and frequency of wildfires in recent years in the United States have raised numerous concerns regarding the improvement in wildfire emergency response management and decision-making systems, which require operational high temporal and spatial resolution monitoring capabilities. Satellites are one of the tools that can be used for wildfire monitoring. However, none of the currently available satellite systems provide both high temporal and spatial resolution. For example, GOES-17 geostationary satellite fire products have high temporal (1-5 min) but low spatial resolution (2 km), and VIIRS polar orbiter satellite fire products have low temporal (~12 h) but high spatial resolution (375 m). This work aims to leverage currently available satellite data sources, such as GOES and VIIRS, along with Deep Learning (DL) advances to achieve an operational high-resolution, both spatially and temporarily, wildfire monitoring tool. Specifically, this study considers the problem of increasing the spatial resolution of high temporal but low spatial resolution GOES-17 data products using low temporal but high spatial resolution VIIRS data products. The main idea is using an Autoencoder DL model to learn how to map GOES-17 geostationary low spatial resolution satellite images to VIIRS polar orbiter high spatial resolution satellite images. In this context, several loss functions and DL architectures are implemented and tested to predict both the fire area and the corresponding brightness temperature. These models are trained and tested on wildfire sites from 2019 to 2021 in the western U.S. The results indicate that DL models can improve the spatial resolution of GOES-17 images, leading to images that mimic the spatial resolution of VIIRS images. Combined with GOES-17 higher temporal resolution, the DL model can provide high-resolution near-real-time wildfire monitoring capability as well as semi-continuous wildfire progression maps.

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.

Olive fruits at different ripening stages give rise to various table olive products and oil qualities. Therefore, developing an efficient method for recognizing and sorting olive fruits based on their ripening stages can greatly facilitate postharvest processing. This study introduces an automatic computer vision system that utilizes deep learning technology to classify the `Roghani` Iranian olive cultivar into five ripening stages using color images. The developed model employs convolutional neural networks (CNN) and transfer learning based on the Xception architecture and ImageNet weights as the base network. The model was fine-tuned by testing multiple configurations of well-known CNN layers. To minimize overfitting and enhance model generality, data augmentation techniques were employed. By considering different optimizers and two image sizes, four final candidate models were generated. These models were then compared in terms of loss and accuracy on the test dataset, classification performance (classification report and confusion matrix), and generality. All four candidates exhibited high accuracies ranging from 86.93% to 93.46% and comparable classification performance. In all models, at least one class was recognized with 100% accuracy. However, by taking into account the risk of overfitting, two models were discarded. Finally, a model with an image size of 224 × 224 and an SGD optimizer, which had a loss of 1.23 and an accuracy of 86.93%, was selected as the preferred option. The results of this study offer robust tools for automatic olive sorting systems, simplifying the differentiation of olives at various ripening levels for different post-harvest products.

The artificial intelligence (AI) is a machine or computing platform that is capable of making intelligent decision similarly to the human mind. The AI could improve diagnosis, treatment prognosis, and clinical workflow, particularly in the field of radiology. Concerning acute respiratory distress syndrome (ARDS) is a rather heterogeneous syndrome characterized by an inflammatory lung edema leading to an increased lung weight, decreased lung aeration with the presence of alveolar collapse and interstitial opacities mainly in the dependent area. Thus, lung imaging is an essential tool to assess not only the morphology but also the mechanical characteristics of ARDS patients. Chest computed tomography (CT) and ultrasound have a higher sensitivity and specificity than conventional chest radiography. This narrative review summarizes the state of art of AI in the field of lung imaging, focusing on CT and ultrasound technique in ARDS patients. A total number of 18 articles were retrieved. The application of AI in lung imaging was mainly devoted to assess the prediction of ARDS, the quantification of alveolar recruitment, the possible alternative diagnosis and the outcome. Although the presence of a physician is still essential to ensure a high quality of examinations the AI could assist the clinical team to provide the best possible care.

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.

Objective: To investigate whether impaired plantar cutaneous vibration perception contributes to axial motor symptoms in Parkinson’s disease (PD) and whether anti-parkinsonian medication and subthalamic nucleus deep brain stimulation (STN-DBS), show different effects. Methods: Three groups were tested: PD patients on medication (PD-MED), PD patients on medication and additional STN-DBS (PD-MED–DBS), and healthy subjects (HS) as reference. Motor performance was analyzed using a pressure distribution platform. Plantar cutaneous vibration perception thresholds (VPT) were investigated using a customized vibration exciter at 30 Hz. Results: Motor performance of PD-MED and PD-MED–DBS was characterized by greater postural sway, smaller limits of stability ranges, and slower gait, due to shorter strides, fewer steps per minute, and broader stride widths compared to HS. Comparing patient groups, PD-MED–DBS showed better overall motor performance than PD-MED, particularly for the functional limits of stability and gait. VPTs were significantly higher for PD-MED compared to HS, which suggests impaired plantar cutaneous vibration perception in PD. However, PD-MED–DBS showed less impaired cutaneous vibration perception than PD-MED. Conclusion: PD patients suffer from poor motor performance compared to healthy subjects. Anti-parkinsonian medication in conjunction with STN-DBS seems to be superior for normalizing axial motor symptoms compared to medication alone. Plantar cutaneous vibration perception is impaired in PD patients, whereas anti-parkinsonian medication together with STN-DBS is superior for normalizing tactile cutaneous perception compared to medication alone. Consequently, based on our results and the findings of the literature impaired plantar cutaneous vibration perception might contribute to axial motor symptoms in PD.

Since the advent of the Internet, there has been a significant shift from wired to wireless communication between devices. The volume of data transmitted has surged, primarily driven by the exponential growth of network services, Internet of Things (IoT) devices, and online users. This trend became especially pronounced with the onset of the COVID-19 pandemic, as the Internet's role and significance expanded dramatically. People began to spend more time online, engaging in activities like e-learning, remote work, and online shopping. Concurrently, concerns about the security of wireless communication channels have escalated. As more data is stored and transmitted over the Internet, cybercriminals are actively seeking unauthorized access to it to further their malicious objectives. Through a comprehensive literature review in the realm of wireless network security and privacy, it has become evident that several factors render these networks susceptible to vulnerabilities. The aim of this paper is to shed light on the major and common security and privacy challenges faced by existing wireless networks. Additionally, it seeks to demonstrate the array of techniques available to mitigate these issues. Ultimately, this paper will culminate in an in-depth analysis of cybersecurity measures to address the issues in wireless networks.

With the rapid development of space programs in various countries, the number of satellites in space is increasing, resulting in an increasingly complex space environment. Therefore, improving space object identification technology has become highly important. We proposes a method of applying deep learning to intelligent detection of space object. We utilize 49 authentic 3D satellite models including 16 scenarios to generate a dataset comprising 17,942 images, which contains over 500 actual satellite photos. Additionally, we acquired a substantial amount of annotated data using a semi-automatic labeling method, which resulted in significant labor cost savings, and obtained a total of 39,000 labels. We validate the feasibility of the dataset using YOLOv3 and YOLOv7 models. What's more, we optimize the YOLOv7 model by integrating deformable convolution RepPoint into the YOLOv7 backbone to obtain the YOLOv7-R model. Through training with these two models, experimental results show that YOLOv3 achieves an accuracy of 0.927, YOLOv7 reaches an accuracy of 0.964, and YOLOv7-R achieves the highest accuracy at 0.983. This provides an effective solution for intelligent space object detection.

The ever-evolving landscape of technology continually drives the demand for more robust and secure systems. Intrusion Detection and Prevention Systems (IPS) play a pivotal role in safeguarding digital infrastructures. IPS harnesses a blend of cutting-edge technologies, specifically Network-Based, Wireless-Based, and Host-Based Intrusion Detection Systems (IDS), to fortify network security. This multifaceted approach enhances the system's capacity to scrutinize incoming data and network traffic, effectively reducing the risk of intrusion. In the realm of malicious activity detection, IPS employs a diverse array of techniques and mechanisms. Our proposed IPS integrates both anomaly-based and signature-based analysis approaches. In signature-based analysis, intrusions are identified by matching data collected from various activities with pre-defined signatures, employing rule-based methods to detect anomalies effectively. In the anomaly-based analysis approach, the system juxtaposes current activities with a baseline of normal behaviour to flag any deviations. This is achieved through distance-based methods, ensuring a well-rounded approach to threat detection. Our comprehensive prevention system encompasses a suite of security measures, including anti-virus software, Deep Packet Inspection, and Quarantine. These components work in synergy to detect and thwart malicious activities effectively. Even when concealed or hidden, the IPS remains a reliable sentinel, ensuring the integrity of your digital ecosystem. This review delves into the intricate web of terminologies and concepts surrounding Intrusion Detection and Prevention Systems, shedding light on the evolving technology landscape and the critical role IPS plays in bolstering cybersecurity.

Natural deep eutectic solvents (NADES) have been shown as selective and environmentally friendly solvents for the extraction of bioactive compounds. However, studies on the solubility of low molecular weight carbohydrates (LMWC) in NADES are scarce. In this work, new solubility data of LMWC in NADES are provided and a new approach based on the use of these solvents for the efficient fractionation of bioactive carbohydrates was explored for the first time. Several mono- and disaccharides and three NADES based on choline chloride (ChCl) and different donors [2-ethylene glycol (EtG), glycerol (Gly) and ethanedioic acid dihydrate (Eth)] were considered. While degradation of carbohydrates, mainly ketoses, was detected with ChCl:Eth due to its acidic nature, ChCl:EtG and ChCl:Gly were found to be useful alternatives for selectively separating bioactive ketoses and their corresponding aldoses (e.g. lactulose/lactose and tagatose/galactose) present in equimolar binary mixtures. In addition, the usefulness of ChCl:EtG for the selective enrichment of lactulose to be used as food ingredient or nutraceutical was proven (from a 25% in the reaction mixture to a 56% in the purified sample). NADES could be used for the selective fractionation of value-added carbohydrates from interfering sugars for several applications including food science, engineering or pharmaceuticals.

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.

Abstract: Botnets pose a grave cybersecurity threat, enabling widescale malicious activities through networks of compromised devices. Detecting botnets is challenging given their frequent use of evasion techniques like encryption. Traditional signature-based methods fail against modern botnets capable of zero-day attacks. This paper surveys recent advances applying machine learning for botnet detection based on analysis of network traffic payloads, flows, DNS data, and hybrid feature fusion. Core machine learning models include support vector machines, neural networks, random forests, and deep learning architectures, which extract patterns to separate benign and botnet behaviors automatically. Results demonstrate machine learning's capabilities in identifying heterogeneous botnets using artefacts in network streams. However, challenges remain around limited labeled data, real-time streaming, adversarial evasion, and model interpretability. Promising directions involve semi-supervised learning, adversarial training, scalable analytics, and explainable AI to address these gaps. Beyond the technical aspects, responsible development and deployment of botnet detection systems raise ethical considerations around privacy, transparency, and accountability. With diligent cross-disciplinary collaboration, machine learning promises enhanced, generalizable, and trustworthy techniques to combat the serious threat posed by continuously evolving botnets across the digital ecosystem.

In this perspective article, we ponder topographical enhancements of artificial neural networks. In our recent paper in JMLR, we proved a quasi-equivalence between the network width and depth and also discussed the power of intra-links, which can be viewed as network height. In 1982, Hopfield published a network to model human associative memory, which contains many loops for dynamic evolution toward fixed points. Based on noising-denoising loops, diffusion models are recently developed to enable Bayesian modeling and inference with big data. Furthermore, we envision development of multi-AI-agent systems through “netware” engineering as a quantum leap of software engineering for emergent behaviors and autonomous AI at individual and population levels. We believe that the novel use of links and loops in space and time via multi-scale coupling would catalyze the next-generation neural networks.

Single Image Super Resolution (SSIR) is a problem in computer vision where the goal is 1 to create high-resolution images from low-resolution ones. It has important applications in fields 2 such as medical imaging and security surveillance. While traditional methods such as interpolation 3 and reconstruction-based models have been used in the past, deep learning techniques have recently 4 gained attention due to their superior performance and computational efficiency. This article proposes 5 an Autoencoder based Deep Learning Model for SSIR, in particular, a light model that uses fewer 6 parameters without compromising performance. The down-sampling part of the Autoencoder 7 mainly uses 3 by 3 convolution and has no subsampling layers. The up-sampling part uses transpose 8 convolution and residual connections from the down sampling part. The model is trained using a 9 subset of the VILRC ImageNet database. The model is evaluated using quantitative metrics PSNR, 10 SSIM as well as qualitative measures such as perceptual quality. PSNR and SSIM figures as high as 11 76.06 and 0.93 are reported.

3D human pose estimation has made significant advancements through the integration of deep learning techniques. This survey provides a comprehensive review of recent 3D human pose estimation methods, with a focus on monocular images, videos, and multi-view cameras. Our approach stands out through a systematic literature review methodology, ensuring an up-to-date and meticulous overview. Unlike many existing surveys that categorize approaches based on learning paradigms, our survey offers a fresh perspective, delving deeper into the subject. For image-based approaches, we not only follow existing categorizations but also introduce and compare significant 2D models. Additionally, we provide a comparative analysis of these methods, enhancing the understanding of image-based pose estimation techniques. In the realm of video-based approaches, we categorize them based on the types of models used to capture inter-frame information. Furthermore, in the context of multi-person pose estimation, our survey uniquely differentiates between approaches focusing on relative poses and those addressing absolute poses. Our survey aims to serve as a pivotal resource for researchers, highlighting state-of-the-art deep learning strategies and identifying promising directions for future exploration in 3D human pose estimation.

A partial or complete pulpotomy is a type of vital pulp therapy (VPT) that aims to remove the in-flamed, infected pulp, leaving behind healthy, vital pulp that is capable of healing. VPT has gained renewed popularity as a treatment option in permanent mature posterior teeth with irre-versible/moderate-severe pulpitis, its high success rates matching that of root canal treatment (RCT). There is currently no consensus regarding diagnostic and prognostic predictors of success of pulpotomies for managing such cases. Therefore, we conducted a scoping review to identify and analyze how these factors affect the outcome of treatment. A literature search using the PRISMA guidelines was undertaken using PubMed and Scopus on July 7, 2023. A total of 22 studies met the inclusion criteria and were qualitatively analyzed by two reviewers. The follow-ing diagnostic and prognostic factors were recognized and discussed; presenting signs and symptoms, periapical diagnosis, bleeding time, indicators of inflammation (bleeding time, con-centration of inflammatory biomarkers), patient age and medical status, the depth, activity and location of caries, and restorative factors. Based on the studies assessed, there is limited evidence to support their prognostic value. Further research is necessary to identify solid predictors of outcome.

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.

Generative AI has gained enormous interest nowadays due to new applications like chatGPT, DALL E, Stable Difussion and Deep Fake. Particularly DALL E, Stable Difussion and others (Adobe Firefly, ImagineArt...) are able to create images from a text prompt and are also able to recreate real photographs. Due to this fact, intense research has arisen to create new image forensics applications able to distinguish between real captured images and videos and artificial ones. Detecting forgeries made with Deep Fake is one of the most researched issues. This paper is about another kind of forgery detection. The purpose of this research aims to detect photo realistic AI created images versus real photos coming from a physical camera. For this purpose, techniques that perform a pixel level feature extraction are used. First one is Photo Response Non-Uniformity (PRNU). PRNU is a special noise due to imperfections on the camera sensor that is used for source camera identification. The underlying idea is that AI images will have a different PRNU pattern. Second one is Error level analysis (ELA). This is other type of feature extraction traditionally used for detecting image editions. In fact, ELA is being used nowadays by photographers to detect manually AI created images. Both kinds of features are used to train Convolutional Neural Networks to differentiate between AI images and real photographs. Good results are obtained achieving accuracy rates over 95%. Both extraction methods are carefully assessed by computing precision/recall and F1-score measurements.

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.

In the realm of autonomous vehicle environment perception, the primary objective of point cloud target detection is to swiftly and accurately identify three-dimensional objects from point cloud data. To meet this requirement, the prevalent network architecture employed in the industry is the voxel-based PointPillars model. Nonetheless, this model faces challenges in maintaining detection accuracy when objects are obscured or diminutive in size. In response to this issue, we introduce AGPNet, a novel model that seamlessly integrates four key modules: Data Augmentation, Dynamic Graph CNN, Pillar Feature Net, and Detection Head (SSD). The Data Augmentation module enhances the adaptability of point cloud data to complex and ever-changing real-world environments. The Dynamic Graph CNN module endows the network structure with geometric features, which encapsulate not only the point itself but also its adjacent points. The Pillar Feature Net module translates three-dimensional point cloud data into pseudo-image data through the utilization of voxels. Subsequently, the Detection Head (SSD) module leverages this pseudo-image data to conduct target detection of three-dimensional objects. Our experiments, conducted on the KITTI dataset, demonstrate that our proposed method boosts object detection accuracy by 6-7 percentage points compared to the PointPillars model, while maintaining similar detection times.

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.

Next-generation cancer and oncology research needs to take full advantage of the multi-modal structured, or graph, information, with the graph datatypes ranging from molecular structures to spatially resolved imaging and digital pathology to biological networks to knowledge graphs. Graph Neural Networks (GNNs) efficiently combine the graph structure representations with the high predictive performance of deep learning, especially on the large multi-modal datasets. In this review article, we survey the landscape of recent (2020-present) GNN applications in the context of cancer and oncology research, and delineate six currently predominant research areas. Subsequently, we identify the most promising directions for future research. We compare GNNs with graphical models and "non-structured" deep learning, and devise the guidelines for cancer and oncology researchers or physician-scientists asking the question of whether they should adopt the GNN methodology in their research pipelines.

Accurate climate data at fine spatial resolution are essential for scientific research and the development and planning of crucial social systems, such as energy and agriculture. Among them, sea surface temperature plays a critical role as the associated El Niño-Southern Oscillation (ENSO) is considered a significant signal of global interannual climate system. In this paper, we propose an implicit neural representation-based interpolation method with temporal information (T_INRI) to reconstruct climate data of high spatial resolution, with sea surface temperature as the research object. Traditional deep learning models for generating high-resolution climate data are only applicable to fixed resolution enhancement scales. In contrast, the proposed T_INRI method is not limited to the enhancement scale provided during the training process and its results indicate that it can enhance low-resolution input by arbitrary scale. Additionally, we discuss the impact of temporal information on the generation of high-resolution climate data, specifically, which month the low-resolution sea surface temperature data is from. Our experimental results indicate that T_INRI is advantageous over traditional interpolation methods under different enhancement scales, and the temporal information can improve T_INRI performance for a different calendar month. We also examined the potential capability of T_INRI in recovering missing grid value. These results demonstrate that the proposed T_INRI is a promising method for generating high-resolution climate data and has significant implications for climate research and related applications.

This study is to explore the feasibility using neural network (NN) and deep learning to diagnose three common respiratory diseases with only few symptom words. These three diseases are nasopharyngitis, upper respiratory infection, and bronchitis/bronchiolitis. Through natural language processing, the symptom word vectors are encoded by GPT-2 and classified by the last linear layer of the NN. The experimental results are promising, showing that this model achieves a high performance in predicting all these three diseases. They reach 90% in accuracy, which suggests the implications of the developed model, highlighting its potential use in assisting patients understanding their conditions via a remote diagnosis. Unlike previous studies that focus on extracting various categories of information from medical records, this study directly extracts sequential features from unstructured text data, reducing the effort required for data pre-process.

Changes in sea level exhibit nonlinearity, nonstationarity, and multivariable characteristics, making traditional time series forecasting methods less effective in producing satisfactory results. To enhance the accuracy of the predictions of changes in sea level, this study introduced an improved VMD–EEMD–LSTM hybrid model. This model decomposes satellite altimetry data from near the Dutch coast using VMD, resulting in components of the Intrinsic Mode Function (IMF) with various frequencies and a residual sequence. EEMD further dissects the residual sequence obtained from VMD into second-order components. These IMFs decomposed by VMD and EEMD are utilized as features in the LSTM model for making predictions, culminating in the final forecasted results. The experimental results demonstrated significant improvements in the predictive performance compared with the VMD–LSTM model. The RMSE (root mean square error) decreased by an average of 58.68%, the MAE (mean absolute error) reduced by an average of 59.96%, and the R2 (coefficient of determination) increased by an average of 49.85% compared with the VMD–LSTM model. In comparison with the EEMD–LSTM model, the RMSE decreased by an average of 26.95%, the MAE decreased by an average of 28.00%, and the R2 increased by an average of 6.53%. The VMD–EEMD–LSTM model exhibited significantly improved predictive performance.

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.

This paper presents a deep learning-based multi-guidance line detection approach, enabling an autonomous four-wheeled agricultural robot to navigate strip farmland. The integration of Proportional-Integral-Derivative (PID) and Fuzzy Logic Control (FLC) systems optimizes the velocity and heading angle of robot, facilitating smooth navigation along identified guidance line. Enhance cornering maneuverability with real-time kinematics (RTK)-assisted Global Navigation Satellite System (GNSS) (RTK-GNSS) positioning. Additionally, the spraying system combined with deep learning can effectively identify weeds and crop nutrient deficiencies to achieve precise spraying. The guidance system prioritizes irrigation lines for navigation, with additional guidance from crop and furrow lines. Trigonometric analysis determines the angular deviation between the identified guidance line and the vertical line of the top view image. Experiments under diverse weather conditions demonstrate the stable navigation of robot at 12.5 cm/s, achieving up to 90% accuracy in weed and nutrient deficiency identification. The spraying accuracy for weeds and deficiencies averages 87% and 73%, respectively, underscoring the system's contribution to sustainable and precision horticulture practices.

Accurate prediction of the degree of airport delays under the influence of convective weather is crucial for collaborative traffic management implementation and improving the efficiency of airport operations. However, existing studies usually only consider numerical-type quantitative features of weather-affected traffic in their models, and lack the introduction of spatial information to comprehensively portray the traffic operation scenarios under the influence of weather. In order to overcome this problem, this paper firstly designs a new image representation of weather-affected air traffic, and constructs a multi-channel traffic and weather scene image (MTWSI) by populating the airspace two-dimensional grid with traffic and meteorological information to represent the overall traffic operation situation in the terminal area under the influence of convective weather; then, a deep convolutional neural network-based airport delay prediction model ( ADLCNN), which takes MTWSI images as input and uses a specific CNN model to extract the deep features that affect traffic operation in it to input into the subsequent classification algorithm to predict the flight delay level; finally, a series of comparative experiments are carried out on the operational data of Guangzhou Baiyun Airport, and the experimental validation shows that, compared with the traditional machine learning methods, the proposed CNN-based airport delay prediction The experimental validation shows that the proposed CNN-based airport delay prediction model has satisfactory prediction performance compared with the traditional machine learning methods, which also proves that the proposed MTWSI method can more comprehensively respond to the real traffic conditions.

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.

The rising number of unknown-type attacks on the Internet emphasizes the significance of developing efficient intrusion detection systems, even if machine learning-based techniques can detect unknown types of attacks. The necessity for innovative techniques is highlighted by the possibility that traditional machine learning techniques will not be sufficient for identifying these unknown types of attacks. In this research, we address this difficulty by proposing a deep learning-based solution: the log-cosh variational autoencoder (LVAE). When it comes to understanding intricate data distributions and creating freshly reconstructed data, LVAE inherits the strong modeling skills of the variational autoencoder (VAE). To better imitate discrete features of actual attacks and generate unknown types of attacks, this study develops an effective reconstruction loss term employing the logarithmic hyperbolic cosine (log-cosh) function in the log-cosh variational autoencoder (LVAE). When compared to conventional VAEs, LVAE exhibits promising potential for effectively generating data that closely resembles an unknown attack, a crucial capability for increasing the unknown attack detection rate. To categorize the generated unknown-type data, eight feature extraction and classification techniques were used. Using the most recent CICIDS2017 dataset, numerous experiments were carried out, training under varying amounts of real and unknown-type attacks. Our optimal experimental results outperformed several state-of-the-art techniques with accuracy and average F1 scores of 99.89% and 99.83%, respectively. Outstanding results were also shown by the suggested LVAE strategy for producing unknown attack data. In general, our work sets a strong basis for the accurate and efficient identification of unknown types of attacks and contributes to the development of intrusion detection techniques.

The Mahuagou gold deposit is among the most important gold deposits in the core of Huangling Anticline. However, the geochemical exploration on the surface of the mining area presents challenges due to the thin overburde.This paper focuses on the overburden soil of Fengxiangshugou (FXS) – Mahuanggou (MHG) section as the research object, utilizes chemical form analysis of gold, soil halogen survey, and heat-released mercury survey to determine the key deep-penetrating geochemical methods for mining area. The results indicated that Si and Al components of samples exhibit minimal variation, suggesting that drift loads did not influence the overburden soil. Based on the systematic clustering, As, Sb, Mo, Bi, W and Hg emerge as ore-body or ore-belt front elements of hydrothermal gold deposits. In the study area, the predominant chemical form of gold in soil is the strong organic bond. Compared to the total amount, strong organic bound gold and heat-released mercury show higher anomaly contrasts, making them crucial indicators of faults, intrusions and hidden ore bodies. Consequently, chemical form analysis of gold and heat-released mercury surveys can enhance the anomaly contrast, proving beneficial for geochemical prospecting for weak anomalies in this area.

In this paper, we introduce an innovative approach to distinguish Gliosarcoma (GSM) from Glioblastoma (GBM). Our method combines causal fuzzy logic rules with the Big Bird architecture, a Transformer-based Deep Learning algorithm. Unlike prior research, which often relied on statistical models to reduce dataset dimensions before causal analysis, our approach harnesses the complete dataset in tandem with our causal fuzzy Big Bird architecture. Additionally, we benchmark our results not only against previous Gliosarcoma/Glioblastoma studies but also against GPT-2 for a comprehensive evaluation.

Computed tomography (CT) offers detailed insights into the internal anatomy of patients, partic-ularly for spinal vertebrae examination. However, CT scans are associated with higher radiation exposure and cost compared to conventional X-ray imaging. In this study, we applied a Genera-tive Adversarial Network (GAN) framework to reconstruct 3D spinal vertebrae structures from synthetic biplanar X-ray images, specifically focusing on anterior and lateral views. The synthetic X-ray images were generated using the DRRGenerator module in 3D Slicer, by incorporating segmentations of spinal vertebrae in CT scans for the region of interest focussing. The approach leverages a novel feature fusion technique based on X2CT-GAN to combine information from both views and employs a combination of Mean Squared Error (MSE) loss and adversarial loss to train the generator, resulting in high-quality synthetic 3D spinal vertebrae CTs. A total of n=440 CT data were processed. We evaluated the performance of our model using multiple metrics, in-cluding Mean Absolute Error (MAE) (for each slice of the 3D volume [MAE0] and for the entire 3D volume [MAE]), Cosine Similarity, Peak Signal-to-Noise Ratio (PSNR), 3D Peak Sig-nal-to-Noise Ratio (PSNR-3D), and Structural Similarity Index (SSIM). The average PSNR was 28.394 dB, PSNR-3D was 27.432, SSIM was 0.468, cosine similarity was 0.484, MAE0 was 0.034, and MAE was 85.359. The results demonstrated the effectiveness of the approach in reconstruct-ing 3D spinal vertebrae structures from biplanar X-rays, although some limitations in accurately capturing the fine bone structures and maintaining the precise morphology of the vertebrae were present. This technique has the potential to enhance the diagnostic capabilities of low-cost X-ray machines while reducing radiation exposure and cost associated with CT scans, paving the way for future applications in spinal imaging and diagnosis.

CagY is the largest and most complex protein from Helicobacter pylori's type IV secretion system (T4SS) and may participate in the modulation of gastric tissue inflammation. A three-dimensional structure has been reported for only two segments of the protein. To build a more complete model, particularly the region that spans between the outer membrane (OM) and the inner membrane (IM), we employed different approaches, including homology modeling, ab initio, and deep learning techniques. For the long-middle repeat region (MRR), modeling was performed using deep learning techniques and Molecular Dynamics. The modeled segments were assembled into a chain of 1595 aa, and a 14-chain CagY multimer structure was composed by structural alignment. The final multimer structure correlated with previously published struc-tures and allows to show how the multimer may form the T4SS channel through which CagA and other molecules are translocated to gastric epithelial cells. The model further confirmed that MRR, the most polymorphic and complex region of CagY, presents numerous cysteine residues forming disulfide bonds that stabilize the protein and suggest this domain probably functions as a contractile region that may play an essential role in the modulatory activity of CagY on tissue inflammation.

Conservation tillage and deep side-fertilization both hold the potential to reduce nitrogen leaching and improve grain yield and nitrogen use efficiency in fragrant rice cultivation practices. However, the combined impact of different tillage practices with deep side-fertilization on nitrogen leaching remains uncertain. Therefore, this study conducted on-site experiments for four rice growing seasons in both early and late seasons in 2018 and 2019, using the fragrant rice varieties "Meixiangzhan 2"(MX) and "Xiangyaxiangzhan"(XY). The four experimental treatments included:conventional tillage with regular fertilization (T1), conventional tillage with simultaneous deep fertilization (T2), reduced tillage with simultaneous deep fertilization (T3) , and no-tillage with simultaneous deep fertilization (T4). Our results indicate that the T4 treatment exhibited higher nitrogen leaching rates and potential nitrogen losses throughout the entire rice growth cycle, with a 4.51% increase in total nitrogen leaching and a 1.86% increase in potential nitrogen leaching compared to T1. In contrast, the T2 treatment demonstrated the lowest nitrogen leaching rate, resulting in a 6.01% reduction in total nitrogen leaching and a 9.57% decrease in potential nitrogen leaching compared to T1, demonstrating the most optimal performance. For MX, the T1 treatment resulted in lower daily grain outputs compared to the other treatments, with disparities ranging from 5.35% to 9.94%. Similarly, for the XY, the T1 treatment yielded significantly lower daily grain outputs compared to the other treatments, with discrepancies ranging from 6.26% to 10.81% during the late season of 2019. Overall, this study shows that conventional tillage with deep fertilization effectively reduces nitrogen leaching and boosts fragrant rice yields. Minimizing nitrogen leaching enhances nitrogen utilization efficiency. These insights highlight the significance of strategic fertilization and cultivation for environmental conservation and agricultural sustainability.

Tropical cyclones (TC) are dangerous weather events and accurate monitoring and forecasting can provide significant early warning to reduce loss of life and property. However, the study of tropical cyclone intensity remains challenging, both in terms of theory and forecasting. ERA5 reanalysis is benchmark data set for tropical cyclone studies, yet the maximum wind speed error is very large (68 kts) and still 19 kts after simple linear correction even in the better sampled North Atlantic. Here, we develop an adaptive learning approach to correct the intensity in the ERA5 reanalysis, by optimising the inputs to overcome the problems because of the poor data quality and updating the features to improve the generalisability of the deep learning-based model. Specifically, we use TC knowledge to increase the representativeness of the inputs so that the general features can be learned with deep neural networks in the sample space, and then use domain adaptation to update the general features from the known domain with historical storms to the specific features for the unknown domain of new storms. This approach can reduce the error to only 6 kts which is within the uncertainty of the best track data in IBTrACS in the North Atlantic. The method may have wide applicability, such as extending it to the correction of intensity estimation from satellite imagery and intensity prediction from dynamical models.

Robotics and autonomous technologies enable space travel. Robotics change space colonization. We study space-based robotics. Human-centric missions need robotics. Vision, decision-making, movement, and manipulation follow. These developments influence resource utilization, extravehicular activities, planetary surface exploration, long-duration space missions, and colonization. Robots improve research, productivity, and astronaut safety but present problems. Highlighting research and knowledge gaps. Space ethics, human-robot interaction, and failure recovery are gaps. This extensive study showed how robots and autonomous systems may transform space exploration and colonization. Researchers, engineers, and mission planners may benefit. Robotics and autonomy will improve space exploration.

Entrapment neuropathies of the lower limb are a misunderstood and underdiagnosed group of disorders, characterized by pain and dysesthesia, muscular weakness, and specific provoking movements on physical examination. The most frequent of these syndromes encountered in clinical practice are fibular nerve entrapment, proximal tibial neuropathy, sural nerve neuropathy, deep gluteal syndrome or sciatic nerve entrapment and lateral femoral cutaneous nerve entrapment, also known as meralgia paresthetica. They are commonly mistaken for lumbar plexopathies, radiculopathies, and musculotendinous diseases, which appear even more frequently and have overlapping clinical presentations. A comprehensive anamnesis, physical examination, and electrodiagnostic studies should help clarify the diagnosis. If the diagnosis is still unclear or a secondary cause of entrapment is suspected, magnetic resonance neurography, MRI or ultrasonography should be conducted to clarify the etiology, rule out other diseases and confirm the diagnosis. Moreover, clinicians should gain familiarity with this disease and their diagnostic confidence should increase, leading to early diagnosis of nerve damage and prevention of muscle atrophy. The reviewed and updated epidemiology, anatomy, pathophysiology, etiology, clinical presentation and EDX technique and interpretation of the entrapment neuropathies of the lower limb are further described in this article.

Scoring targets in shooting sports is a crucial and time-consuming task that relies on manually counting bullet holes. This paper introduced an automatic score detection model using object detection techniques. The performance of seven models belonging to two different architectural setups was compared. Models like YOLOv8n, YOLOv8s, YOLOv8m, RetinaNet-50, and RetinaNet-101 are single-shot detectors, while Faster RCNN-50 and Faster RCNN-101 belong to the two-shot detectors category. The dataset was manually captured from the shooting range and expanded by generating more versatile data using Python code. Before the dataset was trained to develop models, it was resized (640x640) and augmented using Roboflow API. The trained models were then assessed on the test dataset, and their performance was compared using matrices like mAP50, mAP50-90, precision, and recall. The results showed that YOLOv8 models can detect multiple objects with good confidence scores. Among all, YOLOv8m performed the best with the highest mAP50 value of 96.7%, followed by the performance of YOLOv8s with the mAP50 value of 96.5%. It is suggested that if the system is to be implemented in a real-time environment, YOLOv8s is a better choice since it took significantly less inference time (2.3ms) than YOLOv8m (5.7ms) and yet generated the competitive mAP50.

To identify objects in images, a complex set of skills is needed that includes understanding the context and being able to determine the borders of objects. In computer vision, this task is known as semantic segmentation and it involves categorizing each pixel in an image. It is crucial in many real-world situations: for autonomous vehicles, it enables the identification of objects in the surrounding area; in medical diagnosis, it enhances the ability to detect dangerous pathologies early, thereby reducing the risk of serious consequences. In this study, we compare the performance of various ensembles of convolutional and transformer neural networks. Ensembles can be created, e.g, by varying the loss function, data augmentation method or the learning rate strategy. Our proposed ensemble, which is based on the simple average rule, demonstrates exceptional performance on several datasets. All the resources used in this study are available online at the following GitHub repository: https://github.com/LorisNanni.

Osteoporosis is a common problem in orthopedic medicine, and it has become an important medical issue in orthopedics as Taiwan is gradually becoming an aging society. In the diagnosis of osteoporosis, the bone mineral density (BMD) derived from dual-energy X-ray absorptiometry (DXA) is the main criterion for orthopedic diagnosis of osteoporosis, but due to the high cost of this equipment and the lower penetration rate of the equipment compared to the X-ray images, the problem of osteoporosis has not been effectively solved for many people who suffer from osteoporosis, however, at present, in clinical diagnosis, doctors are not yet able to accurately interpret X-ray images for osteoporosis manually, and must rely on the data obtained from DXA. In recent years, with the continuous development of artificial intelligence, especially in machine learning and deep learning, significant progress has been made in image recognition. Therefore, it is worthwhile to revisit whether it is possible to use a convolutional neural network model to read a hip X-ray image and then predict the patient's BMD. In this study, we proposed a hip X-ray image segmentation model and a hip X-ray image recognition classification model. First, we used the U-Net model as a framework to segment the femoral neck, greater trochanter, wards triangle, and the total hip in the hip X-ray images. We then performed image matting and data augmentation. Finally, we constructed a predictive model for osteoporosis using deep learning algorithms. In the segmentation experiments, we used intersection over union (IoU) as the evaluation metric for image segmentation, and both the U-Net and U-Net++ models achieved segmentation results greater than or equal to 0.5. In the classification experiments, using the T-score as the classification basis, the total hip using the DenseNet121 model has the highest accuracy of 74%.

Wearable devices have become ubiquitous, collecting rich temporal data that offers valuable insights into human activities, health monitoring, and behavior analysis. Leveraging this data, researchers have developed innovative approaches to classify and predict time-based patterns and events in human life. Time-based techniques allow the capture of intricate temporal dependencies, which is the nature of the data coming from wearable devices. This paper focuses on predicting well-being factors, such as stress, anxiety, positive and negative affect, on the Tesserae dataset collected from office workers. We examine the performance of different methodologies, including deep learning architectures, LSTM, ensemble techniques, Random Forest (RF) and XGBoost and compare their performances for time-based and non-time-based versions. In time-based versions, we investigate the effect of previous records of well-being factors on the upcoming ones. The overall results show that time-based LSTM performs the best among conventional (non-time-based) RF, XGBoost, and LSTM. The performance even increases when we consider a more extended previous period, in this case, 3 past-days rather than 1 past-day to predict the next day. Furthermore, we explore the corresponding biomarkers for each well-being factor using feature ranking. The obtained rankings are compatible with the psychological literature. In this work, we validated them based on device measurements rather than subjective survey responses.

Self-supervised learning is a method that learns general representation from unlabeled data. Masked image modeling (MIM), one of the generative self-supervised learning methods, has drawn attention showing state-of-the-art performance on various downstream tasks, though showing poor linear separability resulting from the token-level approach. In this paper, we propose a contrastive learning-based multi-view masked autoencoder for MIM, exploiting an image-level approach by learning common features from two different augmented views. We strengthen MIM by learning long-range global patterns from contrastive loss. Our framework adopts simple encoder-decoder architecture, learning rich and general representation by following a simple process: 1) two different views are generated from an input image with random masking and by contrastive loss, we can learn semantic distance of the representations generated by an encoder. By applying a high mask ratio, 80%, it works as strong augmentation and alleviates the representation collapse problem. 2) With reconstruction loss, decoder learns to reconstruct an original image from the masked image. We assess our framework by several experiments on benchmark datasets of image classification, object detection, and semantic segmentation. We achieve 84.3% fine-tuning accuracy on ImageNet-1K classification and 76.7% in linear probing, exceeding previous studies and show promising results on other downstream tasks. Experimental results demonstrate that our work can learn rich and general image representation by applying contrastive loss to masked image modeling.

All kinds of vessels consist of dozens of complex machineries with rotating parts and electric motors that operate continuously in a harsh environment with excess temperature and humidity, vibration, fatigue and load. A breakdown or malfunction in one of these machineries can significantly impact the vessel’s operation and safety and consequently, the safety of the crew and the environment. To maintain operational efficiency and seaworthiness, the shipping industry invests substantial resources in preventive maintenance and repairs. This research presents the economic and technical benefits of predictive maintenance over to traditional preventive maintenance, and repair by replacement approaches in the maritime domain. By leveraging modern technology and Artificial Intelligence, we can analyze real-time operating conditions of machinery, enabling early detection of potential damages and allowing for effective planning of future maintenance and repair activities. In this paper, we propose and develop a Convolutional Neural Network that is fed with raw vibration measurements acquired in a laboratory environment from the ball bearings of a motor. Then, we investigate whether the proposed network can accurately detect the functional state of ball bearings and categorize any possible failures present, contributing to improved maintenance practices in the shipping industry.