The application of deep learning algorithms to predict the molecular profiles of various cancers from digital images of hematoxylin and eosin (H&E)-stained slides has been reported in recent years, mainly in gastric and colon cancers. In this study, we investigated the potential use of H&E-stained endometrial cancer slide images to predict the associated mismatch repair (MMR) status. H&E-stained slide images were collected from 127 cases of the primary lesion of endometrial cancer. After digitization using a Nanozoomer virtual slide scanner (Hamamatsu Photonics), we segmented the scanned images into 5,397 tiles of 512 × 512 pixels. The MMR proteins (PMS2, MSH6) were immunohistochemically stained, classified into MMR proficient/deficient, and annotated for each case and tile. We trained several neural networks, including convolutional and attention-based networks, using tiles annotated with the MMR status. Among the tested networks, ResNet50 exhibited the highest area under the receiver operating characteristic curve (AUROC) of 0.91 for predicting the MMR status. The constructed prediction algorithm may be applicable to other molecular profiles and useful for pre-screening before implementing other, more costly genetic profiling tests.

The orexin/hypocretin neuropeptide family has emerged as a focal point of neuroscientific research following the discovery that this family plays a crucial role in a variety of physiological and behavioral processes. These neuropeptides serve as powerful neuromodulators, intricately shaping autonomic, endocrine, and behavioral responses across species. Notably, they serve as master regulators of vigilance and stress responses; however, their role in complex processes such as food intake, metabolism, and thermoregulation warrant further investigation. This narrative review provides a journey through the evolution of our understanding of the orexin system, from its initial discovery to the promising progress made in developing orexin derivatives. It goes beyond conventional boundaries, striving to synthesize the multifaceted activities of orexins. Special emphasis is placed on domains such as stress response, fear, anxiety, and learning, in which the authors contributed to the literature with original publications. This article demonstrates the orchestrating role of the orexins in several intricate and integrative physiological processes. It also overviews the advancement of orexin pharmacology, which has already yielded some promising successes, particularly in the treatment of sleep disorders. This interdisciplinary approach to studying neuropeptides unveils their significance as vital connectors between transient functional changes and lasting structural adaptations. Ultimately, it advances our understanding of the intricate physiological and behavioral contributions of the orexin/hypocretin neuropeptide family.

The growing shift toward Internet of Things (IoT)-based solutions expands the attack surface of systems by connecting an extensive network of heterogeneous devices and technologies. The heterogeneity of IoT devices and the scale of the network often make conventional security measures impractical. Therefore, recent research efforts have focused on machine learning (ML)-based device-agnostic IoT security solutions. However, most proposed solutions have focused solely on detecting malicious traffic in IoT networks. While this is important, further information about the attack is needed to provide a comprehensive defense before, during, and after the incident. To address this gap, we proposed an anomaly-based intrusion detection model that detects malicious traffic and identifies the attack category and subcategory with high accuracy, recall, precision, and F1-score. The proposed intrusion detection model is an ensemble model that integrates multiple ML models to produce more robust and reliable detections. For evaluation, we performed three sets of experiments: (1) a binary classification to detect malicious traffic; (2) a multi-class classification to detect the attack category; and (3) a multi-class classification to detect the attack subcategory. In the experiments, our model achieved an accuracy, recall, precision, and F1-score of 100% for the binary detection; a precision, recall, and F1-score of 99% and an accuracy of 100% for the multi-class category classification; and an accuracy, recall, precision, and F1-score of 88% for the multi-class subcategory classification. These results suggest that the proposed model can reliably detect anomalies in IoT devices.

Accurate diagnosis for the 400 million people with rare diseases is critical for healthcare decisions, prognosis, understanding disease mechanisms, and identification of treatments. Despite advances in genome sequencing, barriers such as high interpretation costs, diagnostic expertise, throughput associated delays, and uncertain variant classifications persist, with demand exceeding capacity. Many variant classification methods focus narrowly on specific consequences, leading to the use of complex integrative pipelines that often overlook transcript variability and lack prediction transparency. To overcome these limitations, we introduce DITTO. This transparent, transcript-aware machine-learning method demonstrates superior overall performance in accuracy, recall, and precision when compared to existing tools. DITTO is publicly available at https://github.com/uab-cgds-worthey/DITTO

The use of Information Communication Technologies (ICT) in education brings up new possibilities of promoting the learning and health experiences. In this sense, education contexts of 21st century must consider these two areas of knowledge, especially their integration. This article presents learning practices developed with mobile devices and games, in order to improve learning and well-being in children and adolescents undergoing cancer treatment in non-formal educational setting. The methodology is based on qualitative case studies with content-based data analysis, involving informal interviews and observation methods. The study considers data from 5 patients who participated in the research between 2015 and 2019. The results demonstrate a positive influence of the practices with mobile technologies and games in terms of learning and in the well-being feeling of patients during the treatment.

Vibration monitoring is a critical aspect of assessing the health and performance of machinery and industrial processes. This study explores the application of machine learning techniques, specifically the Random Forest (RF) classification model, to predict and classify chatter—a detrimental self-excited vibration phenomenon—during machining operations. While sophisticated methods have been employed to address chatter, this research investigates the efficacy of a simplified RF model. The study leverages simulated vibration data, bypassing resource-intensive real-world data collection, to develop a versatile chatter detection model applicable across diverse machining configurations. The feature extraction process combines time-series features and Fast Fourier Transform (FFT) data features, streamlining the model while addressing challenges posed by feature selection. By focusing on the RF model’s simplicity and efficiency, this research advances chatter detection techniques, offering a practical tool with improved generalizability, computational efficiency, and ease of interpretation. The study demonstrates that innovation can reside in simplicity, opening avenues for wider applicability and accelerated progress in the machining industry.

Over the past decade, research in the field of Deep Learning has brought about novel improvements in image generation and feature learning; one such example being a Generative Adversarial Network. However, these improvements have been coupled with an increasing demand on mathematical literacy and previous knowledge in the field. Therefore, in this literature review, I seek to introduce Generative Adversarial Networks (GANs) to a broader audience by explaining their background and intuition at a more foundational level. I begin by discussing the mathematical background of this architecture, specifically topics in linear algebra and probability theory. I then proceed to introduce GANs in a more theoretical framework, along with some of the literature on GANs, including their architectural improvements and image-generation capabilities. Finally, I cover state-of-the-art image generation through style-based methods, as well as their implications on society.

Shared public buildings have become centers of innovation, integrating advanced technologies to meet evolving societal needs. With a heightened emphasis on occupants’ health and well-being, these buildings serve as hubs for technological convergence, facilitating seamless connectivity and intelligent data analysis and management. Within this context, environmental monitoring emerges as a foundational element, pivotal to all aspects of building management. This article provides findings from the nationally funded RE-START project, which focuses on shared public buildings, with special regard to educational and medical facilities. The project explores enhanced indoor air quality monitoring, focusing in particular on CO2 concentration that is directly correlated with occupancy, as a fundamental element for developing health and safety protocols, energy efficiency strategies, integration of smart building technologies, and data-driven energy management. The intersection of environmental monitoring, energy efficiency, security, and IoT technologies in in indoor spaces are relevant. The outcomes of the study reveal the delicate nature of all the involved components, that need to be carefully developed in an integrated manner.

This study was to identify clinical cancer biomarkers for recurrent gastric cancer survivors using artificial intel-ligence. From a hospital-based cancer registry database in Taiwan, the datasets of the incidence of recurrence and clinical risk features were included in 2476 gastric cancer survivors. We benchmarked Random forest with MLP, C4.5, AdaBoost, and Bagging algorithms on metrics and leveraged synthetic minority oversampling technique (SMOTE) for imbalanced dataset issues, cost-sensitive learning for risk assessment, and SHapley Additive ex-Planations (SHAP) for feature importance analysis in this study. Our proposed Random forest outperformed the other models with an accuracy of 87.9%, a recall rate of 90.5%, an accuracy rate of 86% and F1 of 88.2% on re-current category by a 10-fold cross-validation in a balanced dataset. We identified clinical features of recurrent gastric cancer, which are the top five features, stage, number of regional lymph node involvement, Helicobacter pylori, BMI(body mass index), and gender, which significantly affect the prediction model's output, are worth paying attention to in the following causal effect analysis. Using an artificial intelligence model, the risk factors for recurrent gastric cancer could be identified and cost-effectively ranked according to their feature importance. In addition, they should be crucial clinical features to provide physicians with the knowledge to screen high-risk patients in gastric cancer survivors as well.

Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today's Fourth Industrial Revolution (4IR or Industry 4.0). Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various application areas like healthcare, visual recognition, cybersecurity, and many more. However, building an appropriate DL model is a challenging task, due to the dynamic nature and variations in real-world problems and data. Moreover, the lack of core understanding turns DL methods into black-box machines that hamper development at the standard level. This article presents a structured and comprehensive view on DL techniques including a taxonomy considering various types of real-world tasks like supervised or unsupervised. In our taxonomy, we take into account deep networks for supervised or discriminative learning, unsupervised or generative learning as well as hybrid learning and relevant others. We also summarize real-world application areas where deep learning techniques can be used. Finally, we point out ten potential aspects for future generation DL modeling with research directions. Overall, this article aims to draw a big picture on DL modeling that can be used as a reference guide for both academia and industry professionals.

The conventional machine learning (ML) algorithms are continuously advancing and evolving at a fast-paced by introducing the novel learning algorithms. ML models are continually improving using hybridization and ensemble techniques to empower computation, functionality, robustness, and accuracy aspects of modeling. Currently, numerous hybrid and ensemble ML models have been introduced. However, they have not been surveyed in a comprehensive manner. This paper presents the state of the art of novel ML models and their performance and application domains through a novel taxonomy.

Hybrid forecasting methods have emerged as a solution surpassing the limitations of both statistical and deep learning approaches. While the first emphasize the significance of variables, they often produce worse forecasting results when compared to newer techniques. In contrast, deep learning models remain enigmatic "black boxes" in terms of interpretability, although achieving better results in forecasting. This article introduces the Comprehensive Cross-Correlation and Lagged Linear Regression Deep Learning (CCLR-DL) framework, designed to harness the best of both approaches, enhancing forecasting accuracy while retaining model interpretability through a feature selection process. CCLR-DL blens cross-correlation analysis, lagged multiple linear regression and granger's causality procedures with deep learning architectures based on LSTM. In a practical demonstration, CCLR-DL was applied to a real database of clinical visits associated to diagnoses in Catalonia, Spain (tracking a population of 6.3 million patients during 10 years). Predicting visits enables the healthcare managers to be ready for future demand shifts. Results demonstrate a consistent and substantial improvement over standalone statistical and deep learning methods when predicting healthcare demand. This hybrid approach not only showcases its efficacy but also offers a promising solution to the challenge of balancing predictive accuracy with model explicability. In this context, this work aims to design and validate a method for feature selection and forecasting of multivariate high dimensional time series datasets not only to improve prediction accuracy but also to model transparency by identifying a subset of variables that improve predictions and G-cause the target variable.

Byers Peninsula (62°34’-62°40’S-60°54’61°13’W), 60 km2 in size, is considered one of the largest ice-free areas in Antarctica. Since 2006, the Spanish Polar Program has taken part in a large number of environmental studies involving effects of climate changes, limnology and microbiology, live cycles, but not albedo. Surface albedo is one of the key physical parameters in the surface energy budget of polar regions. Most of Antarctica is covered by ice sheets; only about 0.44% of the area is permanently ice-free. However, in maritime Antarctica, the ice-free areas, corresponding to small islands, peninsulas, and coastal beaches, account for about 3% of the territory. To incorporate the contribution of these areas into global albedo models, it is necessary to relate the soil properties of the ice-free areas to the surface albedo response. Also, mapping soil properties and albedo have and special interest in these ice-free areas. Image classification using machine learning methods trained with georeferenced soil data could be useful for mapping soil properties and albedo in multispectral optical satellite images. A shallow neural network implemented using the Keras Python module was used to define and train models of soil properties using 15 explanatory variables corresponding to bands and spectral indices of a Sentinel image and a population of 49 soil samples taken from the top 5 centimeters of the soil profile. The soil samples were analyzed in the laboratory and a spectral library in the Vis-Nir range (350-2500 nm) was created. The albedo of the samples was integrated from the ADS spectra. At the same time, a linear regression model of albedo using the soil properties as explanatory variables was performed. The R2 fit of this new model was about 0.82 and the error of estimation was 4.1. The model was extended to the entire Byers Peninsula using the ML soil property models as explanatory variables. The RMSE of the extended models increased up to 8.2. We think that the main cause of this increase (2.6 points) is related to the error propagation, phenomenon due to the use of models as explanatory variables. However, another important part of the error increase could be due, among other reasons, to the use of an image that, although corrected, is not completely free of clouds.

Recent advances in artificial intelligence combined with behavioral sciences have led to the development of cutting-edge tools for recognizing human emotions based on text, video, audio, and physiological data. However, these data sources are expensive, intrusive, and regulated, unlike plants which have been shown to be sensitive to human steps and sounds. A methodology is proposed to use plants as human emotion detectors. Electrical signals from plants are tracked and labeled based on video data. Labeled data are then used for classification. MLP, biLSTM, MFCC-CNN, MFCC-ResNet, and additionally, Random Forests, 1-Dimensional CNN, and biLSTM without windowing models are set using a grid search algorithm with cross-validation. Finally, best-parameterized models are trained and used with the test set for classification. The performance of this methodology is measured with a case study with 54 participants, who were watching an emotionally charged video, as ground truth their facial emotions were simultaneously measured using face emotion analysis. The Random Forest model shows the best performance, particularly in recognizing high-arousal emotions, achieving an overall weighted accuracy of 55.2% and demonstrating high weighted recall in emotions such as fear 61.0% and happiness 60.4%. The MFCC-ResNet model offers decently balanced results, with Accuracy(MFCC-ResNet)=0.318 and Recall (MFCC-ResNet)=0.324. With the MFCC-ResNet model fear and anger are recognized with 75% and 50% recall respectively. Thus, using plants as an emotion recognition tool seems worth investigating, addressing both cost and privacy concerns.

This study evaluated the using of machine vision in combination with deep learning to identify weeds in real-time for wheat production system. PMAS-Arid Agriculture university research farm were selected for collection of images (6000 total images) of weeds and wheat crops under different weather condition. During growing season, the database was constructed to identify the weeds. For this study two framework were used TensorFlow and PyTorch under CNNs and Deep learning. Deep learning perfromed better with in PyTourch value as compared to another model in Tensorflow. comparing with other networks such as YOLOv4, we concluded that our network reaches a better result between speed and accuracy. Specifically, the maximum precision of weed and wheat plants were 0.89 and 0.91 respectively with 9.43 ms and 12.38 ms inference time per image (640 × 640) NVIDIA RTX2070 GPU.

Using machine learning or deep learning to solve the problem of garbage recognition and classification is an important application in computer vision, but due to the incomplete establishment of garbage datasets and the poor performance of complex network models on smart terminal devices, the existing garbage classification models The effect is not good.This paper presents a waste classification and identification method base on transfer learning and lightweight neural network. By migrating the lightweight neural network MobileNetV2 and rebuild it, The reconstructed network is used for feature extraction, and the extracted features are introduced into the SVM to realize the identification of 6 types of garbage. The model was trained and verified by using 2527 pieces of garbage labeled data in the TrashNet dataset, which ultimately resulted in a classification accuracy of 98.4% of the method, which proves that the method can effectively improve the classification accuracy and time and overcome the problem of weak data and less labeling. The over-fitting phenomenon encountered by small data sets in deep learning makes the model robust.

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

Using data-driven models to solve predictive maintenance problems has been prevalent for original equipment manufacturers (OEMs). However, such models fail to solve two tasks that OEMs are interested in: (1) Making the well-built failure prediction models working on existing scenarios (vehicles, working conditions) adaptive to target scenarios. (2) Finding out the failure causes, furthermore, determining whether a model generates failure predictions based on reasonable causes. This paper investigates a comprehensive architecture towards making the predictive maintenance system adaptive and interpretable by proposing (1) an ensemble model dealing with time-series data consisting of a long short-term memory (LSTM) neural network and Gaussian threshold to achieve failure prediction one week in advance and (2) an online transfer learning algorithm and a meta learning algorithm, which render existing models adaptive to new vehicles with limited data volumes. (3) Furthermore, the Local Interpretable Model-agnostic Explanations (LIME) interpretation tool and super-feature methods are applied to interpret individual and general failure causes. Vehicle data from Isuzu Motors, Ltd., are adopted to validate our method, which include time-series data and histogram data. The proposed ensemble model yields predictions with 100% accuracy for our test data on engine stalling problem and is more rapidly adaptive to new vehicles with smaller error following application of either online transfer learning or the meta learning method. The interpretation methods help elucidate the global and individual failure causes, confirming the model credibility.

The COVID-19 pandemic has led to the growth of hybrid and online learning environments and the trend to introduce more technology into the classroom. One such change would be the use of smart synchronous hybrid learning environments (SSHLE), which are settings with both in-person and online students concurrently, and in which technology plays a key role in sensing, analyzing, and reacting throughout the teaching and learning process. These changing environments and the incorporation of new technologies can place a greater orchestration load on participants and a reduction in teacher agency. In this context, the aim of this paper is to analyse the orchestration load and teacher agency in different SSHLEs. The NASA-TLX model was used to measure the orchestration load in several scenarios. Questionnaires and interviews were used to measure teacher agency. The results obtained indicate that the orchestration load of the teacher tends to be high (between 60 and 70 points out of 100 of the NASA-TLX workload), especially when they lack experience in synchronous hybrid learning environments, and the orchestration load of the students tends to have average values (between 50 and 60) in the SSHLEs analysed. Meanwhile, the teacher agency does not appear to be altered but shows potential for improvement.

Lead optimization in drug discovery is a crucial phase where initial hits are refined into compounds with improved pharmacological properties. While traditional methods rely on manual experimentation and modifications, AI-driven techniques have revolutionized this process by leveraging big data and predictive modeling. This review explores how AI-driven approaches accelerate lead optimization, showcasing examples like deep neural networks and reinforcement learning. Integration of multi-omic data and experimental validation further enhances AI-driven strategies. The future lies in refining these AI methods, democratizing tools, and interdisciplinary collaboration to streamline drug discovery and address medical needs efficiently.

Modelling both long and short-term user interests from historical data is crucial for accurate recommendations. However, unifying these metrics across multiple application domains can be challenging, and existing approaches often rely on complex, intertwined models which can be difficult to interpret. To address this issue, we propose a lightweight, plug-and-play interest enhancement module that fuses interest vectors from two independent models. After analyzing the dataset, we identify deviations in the recommendation performance of long and short-term interest models. To compensate for these differences, we use feature enhancement and loss correction during training. In the fusion process, we explicitly split long-term interest features with longer duration into multiple local features. We then use a shared attention mechanism to fuse multiple local features with short-term interest features to obtain interaction features. To correct for bias between models, we introduce a comparison learning task that monitors the similarity between local features, short-term features, and interaction features. This adaptively reduces the distance between similar features. Our proposed module combines and compares multiple independent long-term and short-term interest models on multiple domain datasets. As a result, it not only accelerates the convergence of the models but also achieves outstanding performance in challenging recommendation scenarios.

Reinforcement Learning (RL) has demonstrated considerable potential in solving challenges across various domains, notably in autonomous driving. Nevertheless, implementing RL in autonomous driving comes with its own set of difficulties, such as the overestimation phenomenon, extensive learning time, and sparse reward problems. Although solutions like Hindsight Experience Replay (HER) have been proposed to alleviate these issues, the direct utilization of RL in autonomous vehicles remains constrained due to the intricate fusion of information and the possibility of system failures during the learning process. In this paper, we present a novel RL-based autonomous driving system technology that combines Obstacle Dependent Gaussian (ODG) RL, Soft Actor-Critic (SAC), and meta-learning algorithms. Our approach addresses key issues in RL, including the overestimation phenomenon and sparse reward problems, by incorporating prior knowledge derived from the ODG algorithm. We evaluated our proposed algorithm on official F1 circuits, using high-fidelity racing simulations with complex dynamics. The results demonstrate exceptional performance, with our method achieving up to 89% faster learning speed compared to existing algorithms in these environments.

The importance of energy systems and its role in economics and politics is not hidden for anyone. This issue is not only important for the advanced industrialized countries, which are major energy consumers, but is also important for oil-rich countries. In addition to the nature of these fuels which contains polluting substances, the issue of their ending up has aggravated the growing concern. Biofuels can be used in different fields for energy production like electricity production, power production or for transportation. Various scenarios have been written about the estimated biofuels from different sources in the future energy system. The availability of biofuels for the electricity market, heating and liquid fuels is very important. Accordingly, the need for handling, modelling, decision making and future forecasting for biofuels can be one of the main challenges for scientists. Recently, machine learning and deep learning techniques have been popular in modeling, optimizing and handling the biodiesel production, consumption and its environmental impacts. The main aim of this study is to evaluate the ML and DL techniques developed for handling biofuels production, consumption and environmental impacts, both for modeling and optimization purposes. This will help for sustainable biofuel production for the future generations.

The prevalence of differential equations as a mathematical technique has refined the fields of control theory and constrained optimization due to the newfound ability to accurately model chaotic, unbalanced systems. However, in recent research, systems are increasingly more nonlinear and difficult to model using Differential Equations only. Thus, a newer technique is to use policy iteration and Reinforcement Learning, techniques that center around an action and reward sequence for a controller. Reinforcement Learning (RL) can be applied to control theory problems since a system can robustly apply RL in a dynamic environment such as the cartpole system (an inverted pendulum). This solution successfully avoids use of PID or other dynamics optimization systems, in favor of a more robust, reward-based control mechanism. This paper applies RL and Q-Learning to the classic cartpole problem, while also discussing the mathematical background and differential equations which are used to model the aforementioned system.

Carbon Capture and Storage (CCS) stands as a pivotal technological stride toward a sustainable future, with the practice of injecting supercritical CO2 into subsurface formations having already been an established practice for enhanced oil recovery operations. The overarching objective of CCS is to protract the operational viability and sustainability of platforms and oilfields, thereby facilitating a seamless transition towards sustainable practices. This study introduces a comprehensive framework for optimizing well placements in CCS operations, employing a derivative-free method known as Bayesian optimization. The development plan is tailored for scenarios featuring aquifers devoid of flow boundaries, incorporating production wells tasked with controlling pressure buildup and injection wells dedicated to CO2 sequestration. Notably, the wells operate under group control, signifying predefined injection and production targets and constraints that must be adhered to throughout the project’s lifespan. As a result, the objective function remains invariant under specific permutations of the well locations. Our investigation delves into the efficacy of Bayesian optimization under the introduced permutation invariance. The results reveal that it demonstrates critical efficiency in handling the optimization task extremely fast. In essence, this study advocates for the efficacy of Bayesian optimization in the context of optimizing well placements for CCS operations, emphasizing its potential as a preferred methodology for enhancing sustainability in the energy sector.

A long time has passed since COVID-19 was discovered and widely disseminated. Both machine learning and deep learning have moved towards the research of diagnosing COVID-19. Compared to deep learning, traditional machine learning-based methods can also achieve good diagnostic results if they are improved based on innovative points. And deep learning remains a more popular research object. Based on the deep neural network in the field of deep learning, the convolutional neural network, recurrent neural network, long and short-term memory network, and Transformer model have been extended. These models improve the performance and processing power of neural networks by introducing new structures and algorithms. This paper will introduce the basic concepts of machine learning and deep learning, as well as the details of using the relevant methods of both to help diagnose COVID-19.

We discuss the implementation of a suite of virtual diagnostics at the FACET-II facility currently under commissioning at SLAC National Accelerator Laboratory. The diagnostics will be used for prediction of the longitudinal phase space along the linac, spectral reconstruction of the bunch profile and non-destructive inference of transverse beam quality (emittance) using edge radiation at the injector dogleg and bunch compressor locations. These measurements will be folded in to adaptive feedbacks and ML-based reinforcement learning controls to improve the stability and optimize the performance of the machine for different experimental configurations. In this paper we describe each of these diagnostics with expected measurement results based on simulation data and discuss progress towards implementation in regular operations.

The objective of this study was to analyze the mental workload using EEG and eye tracking data and classify it using machine learning algorithms. The machine learning model was developed based on the simultaneous recording of eye tracking and EEG measurements during the experimental process. The experiments involved 15 university students, consisting of 7 women and 8 men. Throughout the experiments, the researchers utilized the n-back memory task and the NASA-Task Load Index (TLX) subjective rating scale to assess various levels of mental workload. The findings revealed that as the task difficulty increased, there was an increase in the diameter of both the right and left pupils, the number of fixations, the number and duration of saccades, and the number and duration of blinks. Conversely, variables related to fixation duration decreased. The EEG results indicated that theta power in the prefrontal, frontal, and front central regions increased with task difficulty. Additionally, alpha power increased in the frontal regions but decreased in the temporal, parietal, and occipital regions as the task became more challenging. Furthermore, low beta power significantly decreased in almost all brain regions as the task difficulty increased. In terms of the four-class classification problem, the mental workload level can be predicted with an accuracy rate of 76.59% using 34 selected features. This study has made a significant contribution to the literature by presenting a four-class mental workload estimation model that utilizes different machine learning algorithms.

Increased proliferation of IT services in all sectors has reinforced the adoption and diffusion across all levels of education and training institutions. However, lack of awareness of and knowledge about the key challenges and opportunities of elearning, seem to allude policymakers, resulting in low adoption or increased failure rate of many e-learning projects. Our study tries to address this problem through a review of relevant literature in e-learning. Our goal was to draw from the existing literature, insights into the opportunities and challenges of e-learning diffusion, and the current state-of-research in the field. To do this, we employed a systematic review of literature on some of the salient opportunities and challenges of e-learning innovation for educational institutions. These results aimed to inform policymakers and suggest some interesting issues to advance the research and adoption and diffusion of e-learning. Moreover, the bibliometric analysis shows that the field is experiencing high research attraction among scholars. However, several research areas in the field witnessed relatively low research paucity. Based on these findings, we discussed topics for possible future research.

Globally, zoonotic diseases have been on the rise in recent years. Predictive modelling approaches have been successfully used in the literature to identify the underlying causes of these zoonotic diseases. We examine the latest research in the field of predictive modeling that verifies the growth of zoonotic pathogens and assesses the factors associated with their spread. The results of our survey indicate that popular mathematical models can successfully be used in modeling the growth rate of these pathogens under varying storage temperatures. Additionally, some of them are used for the assessment of the inactivation of these pathogens based on various conditions. Based on the results of our study, machine learning models and deep learning are commonly used to detect pathogens within food items and to predict the factors associated with the presence of the pathogens.

Cardiovascular diseases (CVDs) remain the leading cause of mortality. Traditional approaches to diagnosing and treating CVDs often involve interpretation, which can lead to errors. The emergence of Artificial Intelligence (AI) and Machine Learning aids the management of CVDs. AI examines amounts of data and identifies patterns offering solutions for disease detection, risk prediction, and patient care. The American Heart Association's 2023 Scientific Sessions (AHA23) showcased studies that highlight AIs potential to revolutionize cardiac healthcare. This article presents a review of these cutting-edge studies exploring how AI is applied in health, the outcomes of these applications, and their implications for clinical practice. The insights gained from this review will contribute to the discussion on AI's role in healthcare and its capacity to enhance outcomes.

The rapid advancement of technology has revolutionized language learning, introducing innovative methods that depart from traditional instructional approaches. This study employs a mixed-methods research design to examine the impact of chatbot-based language learning support, adaptive learning algorithms, and virtual reality language immersion on the language learning proficiency and self-regulated learning skills of English as a Foreign Language (EFL) learners. The research design includes quantitative analysis of language proficiency scores and qualitative exploration of learner experiences with the technological interventions. The theoretical implications of this study are rooted in constructivist and socio-cultural learning theories, which underpin the design and implementation of the technological interventions. The triangulation of qualitative and quantitative data revealed that the participants' positive perceptions of the effectiveness of chatbot-based language learning support were supported by the quantitative results, with the variable "chat-bot based support" demonstrating a substantial mean difference compared to other groups. This convergence of findings reinforces the positive influence of chatbot-based support on language learning outcomes and highlights the importance of integrating theoretical perspectives with empirical evidence to gain a comprehensive understanding of the impact of technological interventions on language learning outcomes for EFL learners. The study's findings provide insights into the potential of these technological interventions to optimize language learning outcomes for EFL learners and promote autonomous learning behaviors.

Education setting evolved from historical open learning system to traditional classroom set-up to distance learning modality. Teaching-Learning practice is transformed with an evolution of teaching-learning materials. With technological advancement in progressive manner and it’s increasing use in academic setting, distance learning has been the on-demand and on-debate topic in current educational discourse. Comparatively fresh topic in Nepali academic setting, this paper intended to analyze the perception of Nepali students towards online modality in Nepali academic setting. This paper further analyzed the student’s preference towards distance learning in current Nepali academic setting. Research findings were analyzed based on data collected through literature review, interview with students and professor and quantitative data collection through use of google form. Study identified opportunities as revenue generation; continuation of academic career from any part of country; increase learning outcome among jobholders. Study identified challenges as unequal access and quality of internet facilities; affordability of laptops/computers; limited interaction; and frequent disturbances. Seeing the better prospects, study strongly supported the need of shift in academic shift from traditional setting to non-traditional setting in Nepali context to meet the global needs of competitive and quality education.

The recent past has witnessed a growing interest in technologies for creating chatbots. Advances in Large Language Models for natural language processing are underpinning rapid progress in chatbot development, and experts predict revolutionary changes in the labour market as many manual tasks are replaced by virtual assistants in a range of business functions. As the new technology becomes more accessible and advanced, more companies are exploring the possibilities of implementing virtual assistants to automate routine tasks and improve service. This article reports on qualitative inductive research undertaken within a chatbot development team operating in a major international enterprise. The findings identify critical success factors for chatbot projects, and, based on the in-depth case study, a model is put forward to aid the manageability of chatbot implementation. The presented model can serve as an exemplar guide for researchers and practitioners working on the design, development and implementation of virtual assistants. It is flexible and applicable in a wide range of business contexts, linking strategic business goals with execution steps. It is particularly applicable for teams with no experience of chatbot implementation, reducing uncertainty and managing decisions and risks throughout the project lifecycle, thereby increasing the likelihood of project success.

Noninvasive brain stimulation technique can be used for modulation of motor behaviors such as reaction time (RT). The main aim of this study was to investigate whether RT during a sequential visual isometric pinch task (SVIPT) can be affected by anodal transcranial direct current stimulation (a-tDCS) over three different stimulation sites such as dorsolateral prefrontal cortex (DLPFC), primary motor cortex (M1) or posterior parietal cortex (PPC). We also aimed to assess if stimulation of these cortical sites affect the transfer of learning during SVIPT. A total of 48 right-handed healthy participants were randomly assigned to one of the four a-tDCS groups: 1) left M1, 2) left DLPFC, 3) left PPC and 4) sham. A-tDCS was applied concurrent with SVIPT in which the participants were precisely controlled their forces to reach seven different target forces from 10 to 40% of maximum voluntary contraction (MVC) presenting on a computer screen with right dominant hand. Four test blocks were randomly performed at baseline and 15 min after the intervention including sequence and random blocks with either hand. Our results showed significant differences between a-tDCS groups on ratio RT in the sequence blocks of both the right trained (P = .041) and left untrained hands (P = .023) for target forces of 15% and 30 % of MVC, respectively. In sequence blocks, M1 showed significant elongation in ratio RT for the target force of 15% and 30 % MVC compared to PPC and Sham groups in the right hand. In sequence blocks with left hand, this negative effect were found between M1-DLPFC and M1-Sham groups. In random blocks, no significant differences were found among four groups at each target forces for the left hand while there were significant differences for 15% (P =. 04) and 40% MVC (P =. 035) in the right hand. Our findings suggest that three different stimulation sites (DLPFC, M1 or PPC) were differentially affected by a single session of a-tDCS at some target forces during SVIPT. The effects of a-tDCS on ratio RT were transferred in the left untrained hand for sequence blocks but not random blocks. Further research is needed to understand fundamental aspects of these main areas of the brain on temporal processing in a precision control task such as SVIPT.

Artificial Intelligence (AI) is becoming increasingly important, especially in the medical field. While AI has been used in medicine for some time, its growth in the last decade has been remarkable. Specifically, Machine Learning (ML) and Deep Learning (DL) techniques in medicine have been increasingly adopted thanks to the growing abundance of health-related data, improved suitability of such techniques for managing large data-sets, and more computational power. The Italian scientific community has been instrumental in advancing this research area. This article aims to conduct a comprehensive investigation of the ML and DL methodologies and applications used in medicine by the Italian research community in the last five years.

Effective sports training should be attuned to the athlete's specific conditionings and characteristics. In motor learning research, two often neglected factors that influence this resonance are the learner's athletic background and the structural diversity of exercises (e.g., relative similarity). This study examines the effects of three learning approaches (i.e., contextual interference (CI), differential learning (DL), and free-play control condition (CO)) on the parallel learning of Handball (HB), Volleyball (VB), and Basketball (BB) skills, considering participants' prior sport backgrounds. Forty-five males (15 HB, 15 VB, and 15 BB players) with a mean age of 22±1.4 years and at least 6 years of experience in the mastered discipline voluntarily participated in this study. A pre-post-retention test design including a 6-week-intervention program was employed. In terms of short-term (pre-post) gain, only the DL- group significantly improved their performance in both non-mastered disciplines (p=0.03 for the BB free-throw and p=0.05 for the HB shooting tests), with a trend (ES=0.53) towards an improvement in the performance of the mastered VB underarm-pass skill. In terms of relatively permanent gains, the CI group significantly improved their performances from pre- to retention-test only in the non-mastered BB free-throw skill (p=0.018). In contrast, the DL group significantly improved their performance at retention compared to the pre-test in both non-mastered BB (p=0.004) and HB (p=0.003) skills, with a trend (ES=0.4) towards improvement in the mastered VB test. In both short, and relatively permanent terms, higher composite score gains were observed in DL compared to CI (p=0.001 and 0.002) and CO (p=0.001 and <0.0001). In conclusion, the present findings provide additional support for the potential advantages of the DL model over those of CI. These findings can serve as the basis for tailored training and intervention strategies and provide a new perspective for addressing various issues related to individual and situational learning.

Recent advancements in artificial intelligence (AI) and deep learning have revolutionized the field of protein engineering, particularly in the area of de novo protein design. This review article explores the impact of AI-driven approaches on protein design, with a specific focus on the role of structure prediction neural networks, such as AlphaFold and RoseTTAFold. The article discusses the paradigm shift brought about by these networks, which have enabled the design of proteins with unique structures and functions that are not found in nature. The review covers various aspects of AI-driven protein design, including the use of protein language models to harness evolutionary information, the development of de novo protein design workflows utilizing deep learning, and the application of generative models like RFdiffusion and RoseTTAFold All-Atom. The article also highlights the successes and applications of AI-driven protein design across diverse domains, such as enzyme engineering, antibody design, and vaccine development. Additionally, the review identifies current challenges and future directions in the field, emphasizing the need to address limitations in modeling conformational dynamics and designing proteins for in vivo functionality. The article concludes by underscoring the potential of AI-driven protein design to transform various aspects of science and technology, while also acknowledging the importance of interdisciplinary collaboration and the development of robust pipelines for the validation and optimization of designed proteins. Overall, this comprehensive review serves as a valuable resource for researchers and practitioners interested in understanding the current state and future prospects of AI-driven protein design.

Educational institutions must identify students who are academically struggling to provide them with necessary support to improve their performance. In this context, recommendation systems powered by deep learning techniques are vital for detecting and categorizing such students. These systems help students plan their future by uncovering patterns in their historical academic data. This study introduces a new deep learning model designed to classify academically underperforming students in educational settings. The model incorporates a Gated Recurrent Neural Network (GRU) and includes specific neural network features like a dense layer, max-pooling layer, and the ADAM optimization algorithm. The model's training and evaluation were conducted using a dataset comprising 15,165 student assessment records from various academic institutions. The performance of the developed GRU model was benchmarked against other educational recommendation systems, including Recurrent Neural Network models, AdaBoost, and the Artificial Immune Recognition System v2. The proposed GRU model demonstrated remarkable accuracy, achieving an overall rate of 99.70%.

Groundwater represents a pivotal asset in conserving natural water reservoirs for potable consumption, irrigation, and diverse industrial uses. Nevertheless, human activities intertwined with industry and agriculture contribute significantly to groundwater contamination, highlighting the critical necessity to appraise water quality for safe drinking and effective irrigation. This research primarily focused on employing the Water Quality Index (WQI) to gauge water's appropriateness for these purposes. However, the generation of an accurate WQI can prove time-intensive owing to potential errors in sub-index calculations. In response to this challenge, an artificial intelligence (AI) forecasting model was devised, aiming to streamline the process while mitigating errors. The study collected 422 data samples from Mirpurkash, a city nestled in the province of Sindh, for a comprehensive exploration of the region's WQI attributes. Furthermore, the study probed into unraveling the interdependencies amidst variables in the physiochemical analysis of water. Diverse machine learning classifiers were employed for WQI prediction, with findings revealing that random forest and gradient boosting eclipsed other algorithms, achieving an accuracy rate of 99%. In close pursuit were SVM and XGBoost, registering accuracy scores of approximately 95% and 93%, respectively, while KNN and Decision Trees garnered accuracy rates of 88% and 87%, respectively. In addition to WQI prediction, the study conducted an uncertainty analysis of the models using the R-factor, providing insights into the reliability and consistency of predictions. This dual approach, combining accurate WQI prediction with uncertainty assessment, contributes to a more comprehensive understanding of water quality in Mirpurkash and enhances the reliability of decision-making processes related to groundwater utilization

Plasma technology shows tremendous potential for revolutionizing oncology research and treatment. Reactive oxygen and nitrogen species, electromagnetic emissions generated through gas plasma jets, have attracted significant attention due to their selective cytotoxicity towards cancer cells. To leverage the full potential of plasma medicine, researchers have explored the use of mathematical models and various subsets of machine learning, such as reinforcement learning, and deep learning. This review emphasizes the significant application of AI algorithms in the adaptive plasma system, paving the way for precision and dynamic cancer treatment. Realizing the full potential of AI in plasma medicine, requires research efforts, data sharing and interdisciplinary collaborations. Unravelling the complex mechanisms, developing real-time diagnostics, and optimizing AI models will be crucial to harness the true power of plasma technology in oncology. The integration of personalized and dynamic plasma therapies, alongside AI and diagnostic sensors, presents a transformative approach to cancer treatment with the potential to improve outcomes globally.

One of the most important challenges in the Machine and Deep Learning areas today is to build good models using small datasets, because sometimes it is not possible to have large ones. Several techniques have been proposed in the literature to address this challenge. This paper aims at studying the different available Deep Learning techniques and performing a thorough experimentation to analyze which technique or combination thereof improves the performance and effectiveness of the models. A complete comparison with classical Machine Learning techniques was carried out, to contrast the results obtained using both techniques when working with small datasets. Thirteen algorithms were implemented and trained using three different small datasets (MNIST, Fashion MNIST, and CIFAR-10). Each experiment was evaluated using a well-established set of metrics (Accuracy, Precision, Recall, F1, and the Matthews correlation coefficient). The experimentation allowed concluding that it is possible to find a technique or combination of them to mitigate a lack of data, but this depends on the nature of the dataset, the amount of data, and the metrics used to evaluate them.

This study explores the dependence of drone acoustic detection systems performance on distance, using learning machines with different complexities, from simple linear discriminants, to deep neural networks, such as YAMNet, which exploits transfer learning. Other machine learning systems studied are the multilayer perceptron, support vector machines and random forest. These methods have been evaluated with a meticulously selected database that includes a wide variety of drone and interference sounds, previously processed by array signal processing and affected by ambient noise. Two strategies for training learning machines are considered. In the first one, they are trained with unattenuated signals, trying to preserve the information generated by the sound sources, but they are tested considering the attenuation with distance, achieving effective detection at distances up to 200 metres with some methods, especially linear discriminant. In all cases, the existence of interferences that hinder detection is considered. In the second strategy, the systems are trained and tested with attenuated signals as a function of distance. The effective detection range increases up to 300 metres for most methods and up to 500 metres for the YAMNet based detector. In addition, this second approach raises the possibility of developing specialised detectors for each distance range, thus significantly extending the effective detection range. These results underline the promising potential of acoustic detection of drones at various distances, encouraging further exploration in this research area.

In this article, we analyse the computer aid screening method of COVID19 using Xray and CT scan images. The main objective is to set an analytical closure about the computer aid screening of COVID19 among the X-ray image and CT scan image. The computer aid screening method includes deep feature extraction, transfer learning and traditional machine learning image classification approach. The deep feature extraction and transfer learning method considered 13 pre-trained CNN model. The machine learning approach includes three sets of features and three classifiers. The pre-trained CNN models are alexnet, googlenet, vgg16, vgg19, densenet201, resnet18, resnet50, resnet101, inceptionv3, inceptionresnetv2, xception, mobilenetv2 and shufflenet. The features and classifiers in machine learning approaches are GLCM, LBP, HOG and KNN, SVM, Naive bay’s respectively. In addition, we also analyse the different paradigms of classifiers. In total, the comparative analysis is carried out in 65 classification models, i.e. 13 in deep feature extraction, 13 in transfer learning and 39 in machine learning approaches. Finally, all the classification models perform better in X-ray image set compare to CT scan image set.

Sleep disorder is a disease that can be categorized as both an emotional and physical problem. It imposes several difficulties and problems, such as distress during the day, sleep-wake disorders, anxiety, and several other problems. Hence, the main objective of this research is to utilize the strong capabilities of machine learning in the prediction of sleep disorders. In specific, this research aims to meet three main objectives. These objectives are to identify the best regression model, the best classification model, and the best learning strategy that highly suits sleep disorder datasets. Considering two related datasets and several evaluation metrics that are related to the tasks of regression and classification, the results revealed the superiority of the MultilayerPerceptron, SMOreg, and KStar regression models compared with the other twenty-three regression models. Also, IBK, RandomForest, and RandomizableFilteredClassifier showed superior performance compared with other classification models that belong to several learning strategies. Finally, the Function learning strategy showed the best predictive performance among the six considered strategies in both datasets and with respect to the most evaluation metrics.

In this paper, we have investigated the impact of an early learning assessment on students' motivation for improving their performance throughout the semester. An observation analysis was conducted on an entry level mechanical engineering course in which students are enrolled in during their first semester of engineering work. This study analyzes the effect that a first exam, with an average below a passing grade, has on student's outcome in the course. It was hypothesized that students were motivated to achieve their desired grade outcomes following inadequate performance on the first exam. This was investigated by diving into the results of the course and referencing initial performance to the remaining exam and assessment outcomes. Students were placed into grade bands ranging from 0 to 100 in 20% increments. Their results were tracked and it was shown that for the second mechanics exam, averages jumped 43.333%, 35.35%, and 30.055% for grade bands of 0 to 20, 20 to 40, and 40 to 60 respectively. Assessment grades increased as well with the remaining assessments being averaged to a score of 91.095%. Variables contributing to student performance came from both with-in and outside the classroom. Learning communities, material differentiation, and student and professor adaptation all contributed to the rise in performance. It was concluded that the internal and external variables acted in combination with one another to increase student dedication to achieve success.

Identifying dust aerosols from passive satellite images is of great interest for many applications. In this study, we developed 5 different machine-learning (ML) and deep-learning (DL) based algorithms, including Logistic Regression, K Nearest Neighbor, Random Forest (RF), Feed Forward Neural Network (FFNN), and Convolutional Neural Network (CNN), to identify dust aerosols in the daytime satellite images from the Visible Infrared Imaging Radiometer Suite (VIIRS) under cloud-free conditions on a global scale. In order to train the ML and DL algorithms, we collocated the state-of-the-art dust detection product from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) with the VIIRS observations along the CALIOP track. The 16 VIIRS M-band observations with the center wavelength ranging from deep blue to thermal infrared, together with solar-viewing geometries and pixel time and locations, are used as the predictor variables. Four different sets of training input data are constructed based on different combinations of VIIRS pixel and predictor variables. The validation and comparison results based on the collocated CALIOP data indicates that the FFNN method based on all available predictor variables is the best performing one among all methods. It has an averaged dust detection accuracy of about 81 %, 89 % and 85 % over land, ocean and whole globe, respectively, compared with collocated CALIOP. When applied to off-track VIIRS pixels, the FFNN method retrieves geographical distributions of dust that are in good agreement with on-track results as well as CALIOP statistics. For further evaluation, we compared our results based on the ML and DL algorithms to NOAA’s Aerosol Detection Product (ADP) , which is a product that classifies dust, smoke and ash using physical-based methods. The comparison reveals both similarity and differences. Overall, this study demonstrates the great potential of ML and DL methods for dust detection and proves that these methods can be trained on the CALIOP track and then applied to the whole granule of VIIRS granule.

Byers Peninsula is considered one of the largest ice-free areas in Maritime Antarctica. Since 2006, the Spanish Polar Program has taken part in a large number of environmental studies involving effects of climate changes in lives cycles, limnology and microbiology. Soils from maritime Antarctica are generally weakly developed and have chemical, physical and morphological characteristics strongly influenced by the parent material. However, biological activity during the short Antarctic summer promotes intense transference of nutrients and organic matter in areas occupied by different species of birds and marine mammals. To mapping and monitoring those areas with high biological occupation, could be very useful to have models of edaphic properties prepared from satellite images. In this approach, Deep Learning and Linear Regression models of soil properties and spectral indexes as explicative variables were performed. We training models of soil properties closely related to biological activity such as Dissolved Organic Carbon (DOC) and the iron fraction associated with organic matter (Fe). We tested the best approach to model the spatial distribution of DOC, Fe and pH by training models of Linear Regression and Deep Learning over Sentinel2 and WorldView2 images. The most robust models were used to track possible areas with ornithogenic soils as well as areas of the Byers Peninsula that could support the highest biological development.

Pandemic scenario has significantly changed several factors of life, including teaching, and learning at university. The development of suitable teaching materials to support university studies is mandatory to overcome distance learning difficulties and improve traditional teaching methodologies. This work explains a novel method for the preparation of teaching materials for medical sciences, but also plausible for other learning fields. An encephalon was extracted and prepared by using this methodology for teaching purposes. More than 200 students evaluated several factors of the material prepared this way, indicating a better understanding (up to 80%) of theoretical contents related to this human section, together with a high usability and good physical appearance

The value of landscape, as part of collective heritage, can be acquired by GIS due to the multilayer approach of the spatial configuration. Proficiency in geospatial technologies in order to collect, process, analyze, interpret, visualize and communicate geographic information is being increased by undergraduate and graduate students, but in particular by those who are training to become geography teachers at secondary education. This training can be carried out through personalized learning and distance learning methodology. Personalized GIS education aims to integrate students and enhance their understanding of landscape. Some teaching experiences are shown whereby opportunities offered by WebGIS will be described, through quantitative tools and techniques that will allow this modality of learning and improve its effectiveness. Results of this research show that students, through geospatial technologies, learn landscape as a diversity of elements but also the complexity of physical and human factors involved. Several conclusions will be highlighted: i) the contribution of geospatial training to education for sustainable development; ii) spatial analysis as a mean of skills acquisition about measures for landscape conservation; iii) expanding and applying acquired knowledge to other geographic spaces and different landscapes.

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.

Biofuels construct an essential pillar of energy systems. Biofuels are considered as a popular resource for electricity production, heating, household, and industrial usage, liquid fuels, and mobility around the world. Thus, the need for handling, modeling, decision-making, demand, and forecasting for biofuels are of utmost importance. Recently, machine learning (ML) and deep learning (DL) techniques have been accessible in modeling, optimizing, and handling biofuels production, consumption, and environmental impacts. The main aim of this study is to review and evaluate ML and DL techniques and their applications in handling biofuels production, consumption, and environmental impacts, both for modeling and optimization purposes. Hybrid and ensemble ML methods, as well as DL methods, have found to provide higher performance and accuracy in modeling the biofuels.

Text annotation is an essential element of the natural language processing approaches. The manual annotation process performed by humans has several drawbacks, such as subjectivity, slowness, fatigue, and possibly carelessness. In addition, annotators may annotate ambiguous data. So, we developed the concept of automated annotation to get the best annotations using several machine-learning approaches. The proposed approach is based on an ensemble algorithm of meta-learners and meta-vectorizer techniques. The approach employs a semi-supervised learning technique for automated annotation, aimed at detecting hate speech. This involves leveraging various machine learning algorithms, including Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbors (KNN), and Naive Bayes (NB), in conjunction with Word2Vec and TF-IDF text extraction methods. The annotation process is performed using 13,169 Indonesian YouTube comments data. The proposed model used a Stemming approach using data from Sastrawi and also new data of 2,245 words. Semi-supervised learning uses 5%, 10%, and 20% of labeled data as compared to performing labeling based on 80% of the datasets. In semi-supervised learning, the model learns from the labeled data, which provides explicit information, and the unlabeled data, which offers implicit insights. This hybrid approach enables the model to generalize and make informed predictions even when limited labeled data is available, ultimately enhancing its ability to handle real-world scenarios with scarce annotated information. In addition, the proposed method uses a variety of thresholds for matching words labeled with hate speech ranging from 0.6, 0.7, 0.8, and 0.9. The experiment showed that the KNN-Word2ec model has the best accuracy value of 96.9% with a scenario of 5%:80%:0.9. However, several other methods have also accuracy above 90%, such as SVM and DT based on both text extraction methods in several test scenarios.

Deep learning is an emerging area of machine learning (ML) research. It comprises multiple hidden layers of artificial neural networks. The deep learn- ing methodology applies nonlinear transformations and model abstractions of high level in large databases. The recent advancements in deep learning architec- tures within numerous fields have already provided significant contributions in artificial intelligence. This article presents a state of the art survey on the contri- butions and the novel applications of deep learning. The following review chron- ologically presents how and in what major applications deep learning algorithms have been utilized. Furthermore, the superior and beneficial of the deep learning methodology and its hierarchy in layers and nonlinear operations are presented and compared with the more conventional algorithms in the common applica- tions. The state of the art survey further provides a general overview on the novel concept and the ever-increasing advantages and popularity of deep learning.

Citizen science substantially contributes to large-scale biodiversity monitoring and the enhancement of public literacy. While both of the contributions largely depend on the public’s self- regulated learning. In this study, we employed a mix- method approach, incorporating both a questionnaire survey of 133 participants and interviews with 12 volunteers, aiming to explore the feature of self- regulated learning in citizen science, as well as identify the main factors impacting it. The volunteers are all different ages, from different jobs, with varying educational backgrounds. The findings revealed that the educational background of volunteers did not significantly impact their self-regulated learning. However, an increased frequency of participation was linked to higher levels of self-regulated learning. Furthermore, achievement goal orientation mediates the prediction of self-regulation by scientific epistemology. These insights underscore the importance of enhancing citizen science's impact on scientific literacy by fostering self-regulated learning among participants. This can be achieved through explicit instruction in scientific epistemology and by fostering a sense of achievement to boost achievement goal orientations. Additionally, our study advocates for the promotion of social interaction between students and out- of- school citizen scientists in school-based citizen science projects. The main innovation of this study is to consider volunteers as learners, not merely as contributors of data.

Unmanned aerial vehicle (UAV) photogrammetry allows the generation of orthophoto and digital surface model (DSM) rasters of a terrain. However, DSMs of water bodies mapped using this technique often reveal distortions in the water surface, thereby impeding the accurate sampling of water surface elevation (WSE) from DSMs. This study investigates the capability of deep neural networks to accommodate the aforementioned perturbations and effectively estimate WSE from photogrammetric rasters. Convolutional neural networks (CNNs) were employed for this purpose. Two regression approaches utilizing CNNs were explored: direct regression employing an encoder and solution based on prediction of the weight mask by an encoder-decoder architecture, subse-quently used to sample values from the photogrammetric DSM. The dataset employed in this study comprises data collected from five case studies of small lowland streams in Poland and Denmark, consisting of 322 DSM and orthophoto raster samples. Each sample corresponds to a 10 by 10 meter area of the stream channel and adjacent land. A grid search was employed to identify the optimal combination of encoder, mask generation architecture, and batch size among multiple candidates. Solutions were evaluated using two cross-validation methods: stratified k-fold cross-validation, where validation subsets maintained the same proportion of samples from all case studies, and leave-one-case-out cross-validation, where the validation dataset originates entirely from a single case study, and the training set consists of samples from other case studies. The proposed solution was compared with existing methods for measuring WSE in small streams using a drone. The results indicate that the solution outperforms previous photogrammetry-based methods and is second only to the radar-based method, which is considered the most accurate method available. The solution is characterized by a high degree of explainability and generalization.

This research investigated the significance of learning dispositif (LD) and emotional attachment (EA) on perceived learning success (LS) across a diaspora of Western, Russian, Asian, Middle Eastern and Chinese student cohorts. Foucault’s LD captures the disparate socio-cultural contexts, institutional milieus and more or less didactic teaching styles that moderate learning. EA is a multi-dimensional notion involving affective bonds that emerged in child psychology and spread to marketing and other fields. The sequential explanatory research reviewed the learning and EA literatures and generated an LD–EA framework to structure the quantitative phase of its mixed investigations. In 2017 and 2018, the research collected 150 responses and used a range of statistical techniques for quantitative analysis. It found that LS varied significantly across cohorts, intimating that dispositifs influence learning. Nonparametric analysis suggested that EA also influenced learning, but regressions were inconclusive. Exploratory techniques hint at a dynamic mix of emotional or cognitive motivations during the student learning journey, involving structural breaks in student/instructor relationships. Cluster analysis identified distinct student groupings, linked to years of learning. Separately, qualitative analysis of open-ended survey questions and expert interviews intimates that frequent teacher interactions can increase EA. The synthesis of quantitative with qualitative results and pedagogical reflection suggests that LD and EA both influence learning in a complex, dynamic system. The key constituents for EA are Affection, Connection, Social Presence (SP), Teaching Presence (TP) and Flow but student emotional engagement is conditioned by the socio-cultural milieu (LD) and associated factors like relationships and trust. Unlike in the Community of Learning framework, in the EA framework Cognitive Presence (CP) is an outcome of the interaction between these EA constituents, associated factors and the socio-cultural milieu. Finally, whilst awareness of culture and emotions is a useful pedagogical consideration, learning mainstays remain inclusive educational systems that identify student needs and support well-designed programmes. Within these, scaffolded modules should include a variety of engaging learning activities with non-threatening formative and trustworthy summative feedback. We acknowledge some statistical study limitations, but its tentative findings make a useful preliminary contribution

This article focuses on developing a continuous authentication system using behavioral biometrics to recognize users accessing computing devices. The user’s distinct behavioral biometric is captured through keystroke dynamics, and reward-based reinforcement learning (RL) ideas are applied to recognize them throughout the session. The suggested system adds an extra layer of security to traditional authentication methods, forming a robust continuous authentication system that can be added to static authentication systems. The methodology involves training a RL model to detect unusual user typing patterns and flag suspicious activity. Each user has an agent trained on their historical data, which is preprocessed and used to create episodes for the agent to learn from. The environment involves fetching observations and randomly corrupting them to learn out-of-order behavior. The observation vector includes both running features and summary features. The re-ward function is binary and minimalistic. The Principal Component Analysis (PCA) model is used to encode the running features, and the Double Deep Q-Network (DDQN) algorithm with a fully connected neural network is used as the policy net. The evaluation achieved an average training accuracy and EER (equal error rate) of 94.7% and 0.0126 and test accuracy and ERR of 81.06% and 0.0323 for all users when the number of encoder features was increased. Therefore, it is concluded that by continuously learning and adapting to changing behavior patterns, this approach can provide more secure and personalized authentication, lowering the possibility of unauthorized access and cyberattacks. Overall, the use of reinforcement learning and behavioral biometrics for continuous authentication has the potential to significantly enhance security in the digital age and are effective in identifying each user.

Machine learning applications often need large amounts of training data to perform well. Whereas unlabeled data can be easily gathered, the labeling process is difficult, time-consuming, or expensive in most applications. Active learning can help solve this problem by querying labels for those data points that will improve the performance the most. Thereby, the goal is that the learning algorithm performs sufficiently well with fewer labels. We provide a library called scikit-activeml that covers the most relevant query strategies and implements tools to work with partially labeled data. It is programmed in Python and builds on top of scikit-learn.

Machine learning has become an increasingly ubiquitous technology, as big data continues to inform and influence everyday life and decision-making. Currently in healthcare, as well as in most other industries, the two most prevalent machine learning paradigms are supervised learning and transfer learning. Both practices rely on large-scale, manually annotated datasets to train increasingly complex models. However, the requirement of data to be manually labeled leaves an excess of unused, unlabeled data available in both public and private data repositories. Self-supervised learning (SSL) is a growing area of machine learning that has the ability to take advantage of unlabeled data. Contrary to other machine learning paradigms, SSL algorithms create artificial supervisory signals from unlabeled data and pretrain algorithms on these signals. The aim of this review is two-fold: firstly, we provide a formal definition of SSL, divide SSL algorithms into their four unique subsets, and review the state-of-the-art published in each of those subsets between the years of 2014-2020. Second, this work surveys recent SSL algorithms published in healthcare, in order to provide medical experts with a clearer picture of how they can integrate SSL into their research, with the objective of leveraging unlabeled data.

The dawn of Artificial Intelligence (AI) in healthcare stands as a milestone in medical innovation. A plethora of different medical fields are heavily involved and pediatric emergency medicine is no exception. These new tools do not merely provide more advanced and efficient systems for patients’ diagnosis, management and treatment. They rather concern a strict shift from traditional methods based upon broad categories towards a more personalized healthcare. AI offers many promises in pediatric emergency medicine with a wide range of applications involving clinical decision making, patients’ flows management and prioritization. Main barriers to a widespread diffusion involve technological challenges but also ethical issues and the paucity of extensive datasets in pediatric contexts. We conducted a narrative review structured in two parts. The first part explores the theoretical principles of AI, providing all the necessary background to feel confident with these new state-of-the-art tools. The second part presents an informative analysis of AI models in pediatric emergencies, pointing out the actual applications and challenges until future feasible research perspectives.

This article investigates the prospect of implementing multidisciplinary and multicultural student teamwork (MMT) involving Case-based Learning (CBL) and Problem-based Learning (PBL) as a sustainable teaching practice. Based on a mixed methods approach, which includes direct observation (both physical and virtual), questionnaire distribution and focus-group interviews the study reveals that MMT through CBL and PBL can both facilitate and hinder sustainable learning. Our findings show that while MMT enhances knowledge sharing, it also poses a wide range of challenges, raising questions about its social significance as a sustainable teaching practice. The study suggests the implementation of certain mechanisms, such as ‘Teamwork Training’ and ‘Pedagogical Mentors’, aiming to strengthen the sustainable orientation of MMT through CBL and PBL.

Anomaly occurrences in hydraulic machinery may lead to massive systems shut down, jeopardizing the safety of the machinery and its surrounding human operator(s) and environment, and the severe economic implications succeeding the faults and their associated damage. Hydraulics are mostly placed in ruthless environments, where they are consistently vulnerable to many faults. Hence, not only the machines and their components are prone to anomalies, but also the sensors attached to them, which monitor and report their health and behavioral changes. In this work, a comprehensive applicational analysis of anomalies in hydraulic systems extracted from a hydraulic test rig is thoroughly achieved. Firstly, we provided a combination of a new architecture of LSTM autoencoders and supervised machine and deep learning methodologies to perform two separate stages of fault detection and diagnosis. The two phases are condensed by: the detection phase using the LSTM autoencoder. Followed by the fault diagnosis phase represented by the classification schema. The previously mentioned framework is applied to both component and sensor faults in hydraulic systems, deployed in the form of two in-depth applicational experiments. Moreover, a thorough literature review of the past decade related work for the two stages separately is successfully conducted in this paper.

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.

Online education allows learners to develop knowledge and skills flexibly and conveniently—such observation among students whose characteristics involve student engagement, self-regulation, and self-efficacy. However, studies to characterize Filipino online learners seem lacking. Thus, this study aimed to characterize science education tertiary students in the Philippines concerning their online student engagement (OSE), self-regulated learning (SRL), and online learning self-efficacy (OLSE). The unprecedented events brought by COVID-19 pandemic also urged the implementation of online modalities while there is no available information on students’ online learning profiles. Hence, researchers used a survey research employed through ex post facto approach to determine the effects of the demographic profile on OSE, SRL, and OLSE. The survey was participated by N=373 respondents who answered the questionnaire with informed consent administered via Google Forms. Results revealed that OSE indicators moderately characterized students, while SRL and OLSE indicators are true of them, substantiated by the overall mean M=3.85(SD=0.90), M=3.86(SD=0.92), and M=3.14(SD=0.73), respectively. Also, multivariate tests showed no significant effect among the independent groups (p&gt;0.05), except for gender and OLSE interaction (p&lt;0.05) so, only in OLSE was a significant difference found in gender. In conclusion, Filipino online learners are moderate across aspects of student engagement, self-regulation, and self-efficacy.

Generation Y is known as job jumpers because of the desire to earn a higher salary, career opportunities and opportunities to develop themselves. One type of work is a teacher because by becoming a teacher they get a better life, one of which is by getting extra income from the government for those who already have teacher certification, this is what encourages Generation Y to choose this profession. this study uses crosstabs for the data analysis process because the data comes from the LMS database owned by XYZ Campus. Millennial educators who have the highest performance value of PJJ Information Systems for Information System Engineering Cluster with a value of 5.19. The impact of transactions on millennial lecturers on students is able to fulfill any given workload. Appreciate millennial lecturers in tangible and intangible forms.

Federated learning techniques aim to train and build machine learning models based on distributed datasets across multiple devices, avoiding data leakage. The main idea is to perform training on remote devices or isolated data centers without transferring data to centralized repositories, thus mitigating privacy risks. Data analytics in education, in particular learning analytics, is a promising scenario to apply this approach to address the legal and ethical issues related to processing sensitive data. Indeed, given the nature of the data to be studied (personal data, educational outcomes, data concerning minors), it is essential to ensure that the conduct of these studies and the publication of the results provide the necessary guarantees to protect the privacy of the individuals involved and the protection of their data. In addition, the application of quantitative techniques based on the exploitation of data on the use of educational platforms, student performance, use of devices, etc., can account for educational problems such as the determination of user profiles, personalized learning trajectories, or early dropout indicators and alerts, among others. This paper presents the application of federated learning techniques to two learning analytics problems: dropout prediction and unsupervised student classification. The experiments allow us to conclude that the proposed solutions achieve comparable results from the performance point of view with the centralized versions, avoiding centralizing the data for training the models.

As a combination of both concepts of innovation and environmental development, green innovation is of great significance to the sustainable development of the country and industry. Previous literatures have found the separate roles of green entrepreneurial orientation and inter-organizational learning in understanding green innovation issues. However, few studies have done a comprehensive analysis of integrating three streams of research: green entrepreneurial orientation, green innovation and supply chain learning capability. Based on the resource-based view and dynamic capability theory, we examine the direct of green entrepreneurial orientation on green innovation as well as indirect effect through the mediation of supply chain learning capability. Meanwhile, an empirical data set of 228 manufacturing companies in China (Shaanxi, Guangdong, Hebei, Jiangsu, and Shandong) was used to test our hypotheses. Findings from our empirical study suggest that supply chain learning capability partially mediates the positive relationships between green entrepreneurial orientation and its two consequences—green incremental innovation and green radical innovation. In addition, this research implies that when enterprise has a strong green entrepreneurial orientation, the enterprise should make an effort to enhance the level of supply chain learning capability so as to fully develop their green innovation.

Skin cancer is an uncommon but serious malignancy. Dermoscopic images examination and biopsy are required for cancer detection. Deep learning (DL) is extremely effective in learning characteristics and predicting malignancies. However, DL requires a large number of images to train. Image augmentation and transferring learning were employed to overcome the lack of images issue. In this study we divided images into two categories: benign and malignant. To train and test our models, we used the public ISIC 2020 database. Melanoma is classified as malignant in the ISIC 2020 dataset. Along with categorization, the dataset was studied to demonstrate variation. The performance of three top pretrained models was then benchmarked in terms of training and validation accuracy. Three optimizers were employed to optimize the loss: RMSProp, SGD, and ADAM. Using ResNet, VGG16, and MobileNetV2, we obtained training accuracy of 98.73%, 99.12%, and 99.76%, respectively. Using these three pretrained models, we attained a validation accuracy of 98.39%.

This article presents a review of current advances and future prospects in the field of forecasting renewable energy generation using machine learning (ML) and deep learning (DL) techniques. With the increasing penetration of renewable energy sources (RES) into the electricity grid, accurate forecasting of their generation becomes crucial for efficient grid operation and energy management. Traditional forecasting methods have limitations, and thus ML and DL algorithms have gained popularity due to their ability to learn complex relationships from data and provide accurate predictions. This paper reviews the different approaches and models that have been used for renewable energy forecasting and discusses their strengths and limitations. It also highlights the challenges and future research directions in the field, such as dealing with uncertainty and variability in renewable energy generation, data availability, and model interpretability. Finally, this paper emphasizes the importance of developing robust and accurate renewable energy forecasting models to enable the integration of RES into the electricity grid and facilitate the transition towards a sustainable energy future.

SLI and SLD constitute two autonomous neurodevelopmental disorders, which frequently cause challenges in the diagnosis process, especially due to their nature, which has caused disagreement among clinicians regarding their recognition as separate or overlapping disorders. The objective of the study was to enlighten the path of valid diagnosis and intervention during adolescence when the two disorders change their manifestation and overlap. 200 Greek adolescents (140 boys and 60 girls), 124 already diagnosed with SLD and 76 diagnosed with SLI, 12-16 years old, participated in the study. All participants were assessed in reading, oral and written language and mathematics (mathematical operations and mathematical reasoning) along with IQ testing. In order to determine statistically significant differences, the chi-square test, independent samples t-test, odd ratios and their 95 per cent confidence intervals were implemented. The results revealed that the SLI group presented significantly greater difficulties than SLD in their overall cognitive-mental profile and in most language and mathematical measurements (number concept, executive-procedural part of solving operations and mathematical reasoning). The similarity of the two groups was mainly detected in their deficient metacognitive, metalinguistic and metamnemonic strategies. The research concludes that SLD adolescents managed to overcome their difficulties to a significant degree, while adolescents with SLI still struggle with many learning areas.

Introduction: Prostate cancer (PCa) is one of the deadliest and most common causes of malignancy and death in men worldwide, more specifically with higher prevalence and mortality in developing countries. Factors such as age, family history, race and certain genetic mutations are some of the factors contributing to the occurrence of PCa in men. The recent advances in technology and algorithms gave rise to the computer-aided diagnosis (CAD) of PCa. With the availability of medical image datasets and emerging trends in state-of-the-art machine and deep learning techniques, there is a growth in recent related publications. Materials and Methods: In this study, we present a systematic review of PCa diagnosis with medical images using machine learning and deep learning techniques. We conducted a thorough review through relevant studies indexed in four databases (IEEE, PubMed, Springer and ScienceDirect) using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. With well-defined search terms, a total of 608 articles were identified and 77 met the final inclusion criteria. Key elements in the included papers were presented and conclusions were drawn from them. Results: Findings showed that the United States has the most research in PCa diagnosis with machine learning, Magnetic Resonance Images are the most used datasets and transfer learning is the most used method of diagnosing PCa in recent times. In addition, some available PCa datasets and some key considerations for choice of loss function in the deep learning models were presented. The limitations and lessons learnt were discussed and some key recommendations were made. Conclusion: The discoveries and the conclusions of this work have been organized so as to enable researchers in the same domain to use this work and make crucial implementation decisions.

The unique characteristics of frescoes on overseas Chinese buildings can attest to the integration and historical background of Chinese and Western cultures. Reasonable analysis and preservation of overseas Chinese frescoes can provide sustainable development for culture and history. This research adopts the image analysis technology based on artificial intelligence, and proposes a ResNet-34 model and method integrating transfer learning. This deep learning model can identify and classify the source of the frescoes of the emigrants, and can effectively deal with the problems such as the small number of fresco images on the emigrants' buildings, poor quality, difficulty in feature extraction, and similar pattern text and style. The experimental results show that the training process of the model proposed in this article is stable. On the constructed Jiangmen and Haikou fresco JHD datasets, the final accuracy is 98.41%, and the recall rate is 98.53%. The above evaluation indicators are superior to classic models such as AlexNet, GoogLeNet, and VGGNet. It can be seen that the model in this article has strong generalization ability and is not prone to overfitting. It can effectively identify and classify the cultural connotations and regions of frescoes.

In recent years, online learning has become one of the most popular methods of educational delivery due to advances in technology, which has been made even more evident in the COVID-19 lockdown period. Online education has evolved into a distinct field of study within the educational system over the last few years. It is also important to note that parallel with the growth in this field, there has also been an increase in the number of scholarly journals that regularly publish research in this field, reflecting the importance of this field in the modern day. In spite of the fact that online learning offers a wide range of educational options, from short courses to full-time degrees, as well as being accessible, flexible, environmentally friendly, and affordable, there are also certain challenges associated with this educational approach. These challenges include the lack of social interaction, technical errors, a lack of hands-on training, and difficulties in assessing students. It is, therefore, imperative to ask the crucial question of whether online learning can replace traditional classroom learning or whether it can supplement it in hybrid models with it, as well as what factors and conditions are likely to determine this in the short- and long-term, as well as how it will be blended together in the future. The purpose of this commentary is to provide a brief summary of the current status of both learning models, as well as their pros and cons, in order to answer the question that was posed above.

There has been a growing interest in incorporating psychological and neuroscientific knowledge about the development of cognitive functions in educational policies and academic practices. In this paper, we argue that the current knowledge about the interactions between these functions and their neurodevelopmental characteristics should also be considered in order to develop practices that could be better suited to pupils depending on their age. To facilitate this, we review current neuroscientific knowledge on the competitive interactions between two neural circuitry underlying distinct learning functions, their developmental trajectories and how they are linked to other functions such as cognitive control. The incorporation of this knowledge in education could help improve academic outcomes.

The editorial explores the realm of emerging translational research in neurology and psychiatry, providing insights into various subjects, including neurodegenerative disorders and innovative therapeutic methods. The main objective is to study neurological and psychiatric disorders, such as Alzheimer's disease, with a particular emphasis on techniques for neural regeneration and repair. The editorial highlights the importance of taking a comprehensive approach to studying and treating complex neurological and psychiatric conditions. This involves thoroughly reviewing research papers on the causes, development, biomarkers, and treatment options for these conditions. The editorial emphasizes the necessity of using multidisciplinary and integrative methods to gain a deeper understanding and improve the treatment of these conditions. The development of an animal model for recurrent primary dysmenorrhea showcases the innovative techniques employed in the investigation of gynecological disorders, illustrating the capacity of translational research to connect fundamental scientific knowledge with practical medical applications. The cooperative endeavors of researchers and clinicians emphasized in this editorial lay the foundation for revolutionary advancements that hold the potential to enhance outcomes for individuals afflicted with neurological and psychiatric disorders globally.

If students do not fully apply themselves, then they may be considered responsible for the result of being inadequately prepared. +- Nevertheless, student outcomes are more likely to reflect a combination of both effort and systematic problems with overall course architecture. Deficiencies in course design result in inadequate preparation that adversely and directly impacts students’ productivity upon entering the workforce. Such an impact negatively influences students' ability to maintain gainful employment and provide for their families, which inevitably contributes to the development of issues concerning their psychological well-being. It is well-documented that incorporating active learning strategies in course design and delivery can enhance student learning outcomes. Despite the benefit of implementing active learning techniques, rarely in the real world will it be possible for techniques to be used in isolation of one another. Therefore, the purpose of this proposed study is to determine the interactive effects of two active learning strategies because, at a minimum, technique-pairs more accurately represent the application of active learning in the natural educational setting. There is a paucity of evidence in the literature directed toward investigating the interactive effects of multiple active learning techniques that this study is aimed at filling. The significance of this research is that, by determining the interactive effects of paired active learning strategies, other research studies on the beneficial effects of using particular active learning technique-pairs will be documented contributing to the literature so that ultimately classroom instruction may be customized according to the determination of optimal sequencing of strategy-pairs for particular courses, subjects, and desired outcomes that maximize student learning.

In this review we summarize existing trends of flow control used to improve the aerodynamic efficiency of wings. We first discuss active methods to control turbulence, starting with flat-plate geometries and building towards the more complicated flow around wings. Then, we discuss active approaches to control separation, a crucial aspect towards achieving high aerodynamic efficiency. Furthermore, we highlight methods relying on turbulence simulation, and discuss various levels of modelling. Finally, we thoroughly revise data-driven methods, their application to flow control, and focus on deep reinforcement learning (DRL). We conclude that this methodology has the potential to discover novel control strategies in complex turbulent flows of aerodynamic relevance.

Shallow landslides pose serious threats to human existence and economic development, especially in the Himalayan areas. Landslide susceptibility mapping (LSM) is a proven way for minimizing the hazard and risk of landslides. Modeling as an essential step, various algorithms have been applied to LSM. In this study, information value (IV) and logistic regression (LR) were selected as representatives of the conventional algorithms, categorical boosting (CatBoost) and conventional neural networks (CNN) as the advanced algorithms, for LSM in Yadong county, and their performance was compared. To begin with, 496 historical landslide events were compiled into a landslide inventory map, followed by a list of 11 conditioning factors, forming a data set. Secondly, the data set was randomly divided into two parts, 80% of which was used for modeling and 20% for validation. Finally, the area under the curve (AUC) and statistical metrics were applied to validate and compare the performance of the models. The results showed that the CNN model performed the best (AUC 0.974 and accuracy=93.3%), while the LR model performed the worst (AUC 0.974 and accuracy=93.3%) and CatBoost model performed better (AUC 0.974 and accuracy=93.3%). Besides, the LSM constructed by the CNN model did a more reasonable prediction of the distribution of susceptible areas. As for feature selection, did a more detailed analysis of conditioning factors but the results were uncertain. The result analyzed by GI may be more reliable but fluctuates with the amount of data. The conclusion reveals that the accuracy of LSM can be further improved with the advancement of algorithms, by determining more representative features, which serve as a more effective guide for land use planning in the study area or other highlands where landslides are frequent.

Predicting Length of Stay (LoS) and understanding its underlying factors is essential to minimize the risk of hospital-acquired conditions, improve financial, operational, and clinical outcomes, and to better manage future pandemics. The purpose of this study is to forecast patients’ LoS using a deep learning model and analyze cohorts of risk factors minimizing or maximizing LoS. We employed various pre-processing techniques, SMOTE-N to balance data, and Tab-Transformer model to forecast LoS. Finally, Apriori algorithm was applied to analyze cohorts of risk factors influencing LoS at hospital. The Tab-Transformer outperformed the base Machine Learning models with an F1-score (.92), precision (.83), recall (.93), and accuracy (.73) for discharge dataset, and F1-score (.84), precision (.75), recall (.98), and accuracy (.77) for deceased dataset. The association mining algorithm was able to identify significant risk factors/indicators belonging to lab, X-Ray, and clinical data such as elevated LDH, and D-Dimer, lymphocytes count, and comorbidities such as hypertension and diabetes responsible for extending patients LoS. It also reveals what treatments has reduced the symptoms of COVID-19 patients leading to reduction in LoS particularly when no vaccines or medication such as Paxlovid were available.

A key component for the proper functioning, availability, and reliability of power grids is the power trans- former. Although these are very reliable assets, the early detection of incipient degradation mechanisms are very important to prevent failures that may shorten their lifetime. In this work a review and comparative analysis, of classical Machine Learning algorithms (such as single and ensemble classification algorithms) and two automatic machine learning classifiers, are presented for the fault diagnosis of power transformers. The goal is to determine whether fully automated ML approaches are worse or better than traditional ML frameworks that require a human in the loop (such as a data scientist) to identify transformer faults from oil-dissolved gases data. The methodology uses a DB compiled from published and proprietary transformer fault samples. Faults data is obtained from the literature, the Duval pentagon method, and user-expert knowledge. The parameters from, either single or ensemble classifiers, were optimized through standard machine learning procedures. The results showed that the best performing algorithm is a robust automatic machine learning classifiers model followed by classical algorithms such as neural networks and stacking ensembles. These results highlight the ability of a robust automatic machine learning model for, handling imbalanced power transformers faults datasets with high accuracy, using the minimum tuning effort by the electrical experts. By providing the most probable transformer fault condition will reduce the time required to find and solve the fault.

Deep learning has taken over - both in problems beyond the realm of traditional, hand-crafted machine learning paradigms as well as in capturing the imagination of the practitioner sitting on top of petabytes of data. While the public perception about the efficacy of deep neural architectures in complex pattern recognition tasks grows, sequentially up-to-date primers on the current state of affairs must follow. In this review, we seek to present a refresher of the many different stacked, connectionist networks that make up the deep learning architectures followed by automatic architecture optimization protocols using multi-agent approaches. Further, since guaranteeing system uptime is fast becoming an indispensable asset across multiple industrial modalities, we include an investigative section on testing neural networks for fault detection and subsequent mitigation. This is followed by an exploratory survey of several application areas where deep learning has emerged as a game-changing technology - be it anomalous behavior detection in financial applications or financial time-series forecasting, predictive and prescriptive analytics, medical imaging, natural language processing or power systems research. The thrust of this review is on outlining emerging areas of application-oriented research within the deep learning community as well as to provide a handy reference to researchers seeking to embrace deep learning in their work for what it is: statistical pattern recognizers with unparalleled hierarchical structure learning capacity with the ability to scale with information.

The new Post Accelerating Data and Knowledge Online Society, or ‘Padkos’ requires a new model of decision making. This introductory paper proposes a model where decision making and learning are a single symbiotic process, incorporating man and machine, as well as the AADD (ánthrōpos, apparatus, decider, doctrina) diamond model of individual and organizational decision-making and learning processes. The learning is incorporated by using a newly proposed quadruple loop learning model. This model allows for controlled changes of identity, the process of creating and the sense making of new mental models and assumption, and reflections. The model also incorporates the recently proposed model of quantum decision-making, where time collapse of the opted past and the anticipated future (explicitly including its time horizon) into the present play a key role in the process, leveraging decision-making and learning by human as well as Artificial Intelligence (AI) and Machine Learning (ML) algorithms. The paper closes with conclusions.

The concept of ‘big data’ has been widely discussed, and its value has been illuminated throughout a variety of domains. To quickly mine potential values and alleviate the ever-increasing volume of information, machine learning is playing an increasingly important role and faces more challenges than ever. Because few studies exist regarding how to modify machine learning techniques to accommodate big data environments, we provide a comprehensive overview of the history of the evolution of big data, the foundations of machine learning, and the bottlenecks and trends of machine learning in the big data era. More specifically, based on learning principals, we discuss regularization to enhance generalization. The challenges of quality in big data are reduced to the curse of dimensionality, class imbalances, concept drift and label noise, and the underlying reasons and mainstream methodologies to address these challenges are introduced. Learning model development has been driven by domain specifics, dataset complexities, and the presence or absence of human involvement. In this paper, we propose a robust learning paradigm by aggregating the aforementioned factors. Over the next few decades, we believe that these perspectives will lead to novel ideas and encourage more studies aimed at incorporating knowledge and establishing data-driven learning systems that involve both data quality considerations and human interactions.

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.

Causality is the most important topic in the history of Western Science, and since the beginning of the statistical paradigm, it meaning has been reconceptualized many times. Causality entered into the realm of multi-causal and statistical scenarios some centuries ago. Despite of widespread critics, today Deep Learning and Machine Learning advances are not weakening causality but are creating a new way of finding indirect factors correlations. This process makes possible us to talk about approximate causality, as well as about a situated causality.

Machine learning has become widely adopted as a strategy for dealing with a variety of cybersecurity issues, ranging from insider threat detection to intrusion and malware detection. However, by their very nature, machine learning systems can introduce vulnerabilities to a security defence whereby a learnt model is unaware of so-called adversarial examples that may intentionally result in mis-classification and therefore bypass a system. Adversarial machine learning has been a research topic for over a decade and is now an accepted but open problem. Much of the early research on adversarial examples has addressed issues related to computer vision, yet as machine learning continues to be adopted in other domains, then likewise it is important to assess the potential vulnerabilities that may occur. A key part of transferring to new domains relates to functionality-preservation, such that any crafted attack can still execute the original intended functionality when inspected by a human and/or a machine. In this literature survey, our main objective is to address the domain of adversarial machine learning attacks and examine the robustness of machine learning models in the cybersecurity and intrusion detection domains. We identify the key trends in current work observed in the literature, and explore how these relate to the research challenges that remain open for future works. Inclusion criteria were: articles related to functionality-preservation in adversarial machine learning for cybersecurity or intrusion detection with insight into robust classification. Generally, we excluded works that are not yet peer-reviewed; however, the authors include some significant papers that make a clear contribution to the domain. There is a risk of subjective bias in the selection of non-peer reviewed articles; however, this is mitigated by co-author review. We selected the following databases with a sizeable computer science element to search and retrieve literature: IEEE Xplore, ACM Digital Library, ScienceDirect, Scopus, SpringerLink,Google Scholar. The literature search was conducted upto January 2022. We have striven to ensure a comprehensive coverage of the domain to the best of our knowledge. We have performed systematic searches of the literature, noting our search terms and results, and following up on all materials that appear relevant and fit within the topic domains of this review. This research was funded by the Partnership PhD scheme at the University of the West of England in collaboration with Techmodal Ltd.

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.

The contemporary landscape of education is witnessing a paradigm shift towards innovative instructional methods, with the flipped learning approach gaining considerable attention. The purpose of this research was to investigate the impact of the flipped learning approach on students' perception and acceptance throughout an entire semester in the 'Introduction to Programming with Java' course, using a research design with a quantitative approach, ultimately aiming to inform educational practice and advance our knowledge of innovative teaching methods in higher education. This study was conducted at a university with 174 students involved, divided into two groups, 87 students in the experimental group and 87 students in the control group. The data collected through the scales were analyzed by using descriptive and inferential statistical analysis techniques in statistical software. At the end of the measurements, the technology acceptance level and self-directed learning perceptions of engineering students who received education with flipped learning were high. The results suggest that educators should consider students' readiness for self-directed learning when implementing the flipped learning approach and focus on creating an environment that supports their autonomy and engagement. This research offers valuable guidance for instructors, curriculum designers, and educational policymakers seeking to enhance the effectiveness of flipped learning in higher education courses.

It is a challenging problem that air-written Arabic letters has received a lot of attention in the past decades when compared to commonly spoken languages like English languages. To fill this gap, we propose a strong model that brings together machine learning (ML) and optical character recognition (OCR) methods. The model applied several ML methods, (i.e., Neural Networks (NN), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), with deep convolutional neural networks (CNNs) such as VGG16, VGG19, and SqueezeNet for effective feature extraction. Our study utilizes the AHAWP dataset, which consists of varied writing styles and variations in hand signs, to train and evaluate the model. Preprocessing systems are applied to improve data quality by reduction bias. Besides, OCR methods are combined into our model to sequestrate individual letters from continuous air-written gestures and refine recognition results. Results of this study show that the proposed model has achieved the extreme accuracy of 88.8% using NN with VGG16. This study presents an inclusive approach that combines ML, deep CNNs, and OCR methods to address the issue of Arabic in air writing recognition research.

Continual learning, a crucial facet of machine learning, involves the perpetual acquisition of valuable insights from incoming data, sans the necessity for full dataset access. Esteemed as a fundamental goal in artificial intelligence, continual learning grapples with an enduring challenge—catastrophic forgetting. A proficient model must exhibit adaptability for new data assimilation and robustness to retain existing knowledge. Class-incremental learning (CIL) aids the gradual integration of knowledge from newly introduced classes, forming a universal classifier. However, directly training the model with fresh class instances triggers a problem—forgetting distinguishing features of prior classes, causing a performance decline. Addressing such issues in machine learning, this survey aims to delineate significant challenges, outcomes, and recent advancements, including our contributions to CIL techniques, especially in image classification.

There is only a very short reaction time for people to find the best way out of a building in a fire outbreak. Software applications can be used to assist the rapid evacuation of victims; however, this is an arduous task, which requires an understanding of advanced technologies. Since well-known pathway algorithms (such as, Dijkstra, Bellman-Ford and A*) can lead to serious problems over performance, we decided to make use of deep reinforcement learning techniques. A wide range of strategies including a random initialization of replay buffer and transfer learning were assessed in three projects involving schools of different sizes. The results showed the proposal was viable and that in most cases the performance of transfer learning was superior. In addition, the study raised challenges that had to be faced in the future.

Sparse reward long horizon task is a major challenge for deep reinforcement learning algorithm. One of the key barriers is data-inefficiency. Even in the simulation environment, it usually takes weeks to training the agent. In this study, a data-efficiency training framework is proposed, where a curriculum learning is design for the agent in the simulation scenario. Different distributions of the initial state are set for the agent to get more informative reward during the whole training process. A fine-tuning of the parameters in the output layer of the neural network for value function is conduct to bridge the gap between sim-to-real. An experiment of UAV maneuver control is conducted in the proposed training framework to verify the method more efficient. We demonstrate that data-efficiency is different for the same data in different training stages.

Scene classification plays an important role in the intelligent processing of high-resolution satellite (HRS) remotely sensed image. In HRS image classification, multiple features, e.g. shape, color, and texture features, are employed to represent scenes from different perspectives. Accordingly, effective integration of multiple features always results in better performance compared to methods based on a single feature in the interpretation of HRS image. In this paper, we introduce a multi-task joint sparse and low-rank representation model to combine the strength of multiple features for HRS image interpretation. Specifically, a multi-task learning formulation is applied to simultaneously consider sparse and low-rank structure across multiple tasks. The proposed model is optimized as a non-smooth convex optimization problem using an accelerated proximal gradient method. Experiments on two public scene classification datasets demonstrate that the proposed method achieves remarkable performance and improves upon the state-of-art methods in respective applications.

This study explores the Edaravone solubility space encompassing both neat and binary dissolution media. The efforts were made toward revealing the concentration limits inherently associated with common pure and mixed solvents. For this purpose, the published solubility data of the title drug were scrupulously inspected and cured. This enabled to make the dataset consistent and coherent. However, the lack of some important types of solvents in the collection called for an extension of the available pool of Edaravone solubility data. Hence, new measurements were performed for collecting Edaravone solubility in polar non-protic and polar di-protic media. Such an extended set of data was used for tuning the parameters of regressors models and formulating the ensemble for predicting new data. In both phases, namely the model training and ensemble formulation, close attention was paid not only to minimizing the deviation of computed values from the experimental ones but also to ensuring the high predictive power and computing solubility for new systems. Furthermore, the environmental friendliness characteristics determined based on the common green solvent selection criteria was included in the analysis. Our applied protocol led to the conclusion that the solubility space defined by ordinary solvents is limited, and it is unlikely to find solvents that are more suited for Edaravone dissolution than those depicted in this manuscript. The theoretical framework presented in this study provides a precise guide for conducting experiments, saving time and resources in the pursuit of new findings.

Laboratory medicine has evolved from a mainly manual profession, providing few selected test results to a highly automated and standardized medical discipline, generating millions of test results per year. As next inevitable evolutional step, artificial intelligence (AI) algorithms will need to assist us in structuring and making sense of the masses of diagnostic data collected today. Such systems will be able to connect clinical and diagnostic data and to provide valuable suggestions in diagnosis, prognosis or therapeutic options. They will merge the often so separated worlds of the laboratory and the clinics. When used correctly, it will be a tool, capable of freeing the physicians time so that he/she can refocus on the patient. In this narrative review I therefore aim to provide an overview of what AI is, what applications currently are available in healthcare and in laboratory medicine in particular. I will discuss the challenges and pitfalls of applying AI algorithms and I will elaborate on the question if healthcare workers will be replaced by such systems in the near future.

This paper provides the state of the art of data science in economics. Through a novel taxonomy of applications and methods advances in data science are investigated. The data science advances are investigated in three individual classes of deep learning models, ensemble models, and hybrid models. Application domains include stock market, marketing, E-commerce, corporate banking, and cryptocurrency. Prisma method, a systematic literature review methodology is used to ensure the quality of the survey. The findings revealed that the trends are on advancement of hybrid models as more than 51% of the reviewed articles applied hybrid model. On the other hand, it is found that based on the RMSE accuracy metric, hybrid models had higher prediction accuracy than other algorithms. While it is expected the trends go toward the advancements of deep learning models.

Accurate transfer learning of clinical outcomes, e.g., of the effects and side effects of drugs or other interventions, from one cellular context to another (in-vitro versus ex-vivo versus in-vivo, or across tissues), between cell-types, developmental stages, omics modalities or species, is considered tremendously useful. Ultimately, it may avoid most drug development failing in translation, despite large investments in the preclinical stages, which includes animal experiments requiring careful justification. Thus, when transferring a prediction task from a source (model) domain to a target domain, what counts is the high quality of the predictions in the target domain, requiring molecular states or processes common to both source and target that can be learned by the predictor, reflected by latent variables. These latent variables may form a compendium of knowledge that is learned in the source, to enable predictions in the target; usually, there are few, if any, labeled target training samples to learn from. Transductive learning then refers to the learning of the predictor in the source domain, transferring its outcome label calculations to the target domain, considering the same task. Inductive learning considers cases where the target predictor is performing a different yet related task as compared to the source predictor, making some labeled target data necessary. Often, there is also a need to first map the variables in the input/feature spaces (e.g. of gene names to orthologs) and/or the variables in the output/outcome spaces (e.g. by matching of labels). Transfer across omics modalities also requires that the molecular information flow connecting these modalities is sufficiently conserved. Only one of the methods for transfer learning we reviewed offers an assessment of input data, suggesting that transfer learning is unreliable in certain cases. Moreover, source domains feature their very own particularities, and transfer learning should consider these, e.g., as differences in pharmacokinetics, drug clearance or the microenvironment. In light of these general considerations, we here discuss and juxtapose various recent transfer learning approaches, specifically designed (or at least adaptable) to predict clinical (human in-vivo) outcomes based on molecular data, towards finding the right tool for a given task, and paving the way for a comprehensive and systematic comparison of the suitability and accuracy of transfer learning of clinical outcomes.

This paper compares a series of traditional and deep learning methodologies for the segmentation of textures. Six well-known texture composites first published by Randen and Hus{\o}y were used to compare traditional segmentation techniques (co-occurrence, filtering, local binary patterns, watershed, multiresolution sub-band filtering) against a deep-learning approach based on the U-Net architecture. For the latter, the effects of depth of the network, number of epochs and different optimisation algorithms were investigated. Overall, the best results were provided by the deep-learning approach. However, the best results were distributed within the parameters, and many configurations provided results well below the traditional techniques.

The need to alleviate the difficulties of abstraction and improve students’ achievement in Physical Geography informed this research. This study investigated the Effects of Mastery Learning Strategy and Learning Retention on Senior Secondary School Students’ Achievement in Physical Geography. The study adopted the quasi experimental non-equivalent pre-test, post-test control group design. The Multi-stage sampling technique at four levels was used to select four co-educational secondary schools in Ganye Educational Zone in Nigeria. The sample for the study was 218 Senior Secondary School two (SS II) students offering Geography from four intact classes in the four selected secondary schools. The instrument used for data collection was “Physical Geography Achievement and Retention Test” (PGART). The reliability of the instrument was established using Kendall tau b statistic. This gave a reliability index of 0.74. Data collected were analyzed using Mann-Whitney U and t-Test. The results showed that Mastery Learning Strategy has the potentials to improve students’ learning outcomes, retention and achievement in all spheres of cognitive domain in Physical Geography better than the Conventional Method. Hence the need to incorporate this teaching strategy during instruction so that learners would be guided to learn meaningfully and be assisted to retain content learnt in Geography.