Preliminaries Convolutional Neural Network (CNN) applications have recently emerged in Structural Health Monitoring (SHM) systems focusing mostly on vibration analysis. However, the SHM literature shows clearly that there is a lack of application regarding the combination of PZT (Lead Zirconate Titanate) based method and CNN. Likewise, applications using CNN along with the Electromechanical Impedance (EMI) technique applied to SHM systems are rare. To encourage this combination, an innovative SHM solution through the combination of the EMI-PZT and CNN is presented here. To accomplish this, the EMI signature is split into several parts followed by computing the Euclidean distances among them to form a RGB (red, green and blue) frame. As a result, we introduce a dataset formed from the EMI-PZT signals of 720 frames, encompassing a total of 4 types of structural conditions for each PZT. In a case study, the CNN-based method was experimentally evaluated using three PZTs glued onto an aluminum plate. The results reveal an effective pattern classification; yielding a 100% hit rate which outperforms other SHM approaches. Furthermore, the method needs only a small dataset for training the CNN, providing several advantages for industrial applications.

This paper presents a brief account and rationale for implementation of competency-based learning in any form of management and leadership development programmes in South Africa. The fact that competency-based learning (CBL), also known as outcomes-based learning has been scrapped from the schooling system in South Africa is unfortunate as this method enhances critical thinking skills and practical problem-solving skills. This paper presents CBL as a model of meta-cognitive approach to learning that integrates both theory and practice into experiential learning through six dimensions, namely, active learning, constructive learning, cumulative learning, goal-oriented learning, learner-centred and curriculum design strategies. In the advent of the fast-paced global economy, managers and leaders need to seek those business schools that value real-time practical approach to curriculum for relevance and to maximize shareholder value through human resource development. This brief presentation ends by recommending an approach through CBL that fosters three critical pillars of leadership development, namely: intellectual, behavioural and emotional agenda.

This paper explores the ever-changing topic of cybersecurity and provides a summary of the most recent developments. In a time of unparalleled digital revolution and changing threat environments, being aware of new trends is essential to managing risks and protecting important assets. To highlight significant advancements in fields like artificial intelligence and machine learning for threat detection, the proliferation of IoT devices and related security challenges, the emergence of quantum-resistant cryptography, the development of zero trust architectures, and the growing significance of cybersecurity awareness and education, the report synthesizes insights from industry experts, academic research, and real-world case studies. The goal of the report's analysis of these patterns is to provide stakeholders with useful information so they may modify their cybersecurity plans and strengthen defenses against new threats. Cybersecurity plays a significant role in the IT industry.

The present paper explored the possibility of discriminating between different malignant degrees of Chondrosarcoma (CS), a diffused bone tumor, and Enchondroma (EC), its benign version, through an approach based on the coupling between Confocal Raman Microscopy (CRM) and Machine Learning (ML) techniques. Recently, Raman Spectroscopy has proven to be a powerful tool for a complete grading of CS by distinguishing between grade 1, 2 and 3, and to distinguish CS from EC. In this paper, we tested some models, belonging either to ML or to the sub-type called Deep Learning (DL), showing excellent classification performances, especially the DL algorithms, with classification accuracy approaching to 100%, making the models promising for future implementations of Raman spectroscopy when applied to oncology diagnosis. In turn, we highlighted how some proper ML models result in worse classification performances but better resolution of specific chemical target compounds, possible candidate to become malignant markers, with relevant implication for a correct diagnosis.

In an era driven by technology and connectivity, where the sphere of learning goes beyond conventional classrooms, e-Service-Learning (e-SL) merges the possibilities of technology with the principles of experiential education in a powerful blend [1]. Service-learning (SL) is an educational proposal that has been cited by UNESCO [2] as an innovative educational tool through which to address the complex challenges of our society. The Sustainable Development Goals [3] provide a framework around which to structure the SL projects and thus develop the skills needed to act on the full range of goals. Embedding digital means open a debate on the intricate relationship between technology, education, and societal impact [4]. By analyzing two focus groups involving faculty, students and community partners, this contribution aims to explore the potential of combining SL and digital empowerment (DE). The focus group participants discussed the opportunities for innovation, the expected impact on them and the society at large, their needs and the pre-conditions needed to make SL and DE successful, and limitations. The results are presented according to the six principles of sustainability education in the university environment, highlighting the transformative potential of combining these principles within SL and DE.

This study examined the effect of an educational hybrid physical education (PE) intervention on cognitive performance and academic achievement in adolescents. A 9-month group-randomized controlled trial was conducted in 150 participants (age: 14.63 ± 1.38) allocated into control group (CG, n = 37) and experimental group (EG, n = 113). Inhibition, verbal fluency, planning and academic achievement were assessed. Significant differences were observed in the post-test for cognitive inhibition, verbal fluency in animals, and the average from verbal fluency in favour of the EG. With regard to the intervention, verbal fluency in animals, verbal fluency in vegetables, the average of verbal fluency, cognitive inhibition, language, the average of all subjects, the average of all subjects except PE, and the average from the core subjects) increased significantly in the EG. The last five variables (the academic ones and cognitive inhibition) also increased in the CG, in addition to mathematics. This study contributes to the knowledge by suggesting that both methodologies produced improvements in the measured variables, but the use of a hybrid program based on TPSR and gamification strategies produce improvements in cognitive performance, specifically through the cognitive inhibition and verbal fluency.

An accurate, economical, and reliable device for detecting falls in persons ambulating with the assistance of an orthopedic walker is crucially important for the elderly and patients with limited mobility. Existing wearable devices such as wristbands are not designed for walker users, and patients may not wear them at all times. This research proposes a novel idea of attaching an internet-of-things (IoT) device with an inertial measurement unit (IMU) sensor directly to an orthopedic walker to perform real-time fall detection as well as activity logging. A dataset is collected and labeled for walker users in four activities, including idle, motion, step, and fall. Classic machine learning algorithms are evaluated using the dataset by comparing their classification performance. Deep learning with convolutional neural network (CNN) is also explored. Furthermore, the hardware prototype is designed by integrating a low-power microcontroller for onboard machine learning, an IMU sensor, a rechargeable battery, and Bluetooth wireless connectivity. The research results show the promise of improved safety and well-being of walker users.

The number of connected IoT devices is increasing significantly due to their many benefits, including automation, improved efficiency and quality of life, and waste reducing. However, these devices have several vulnerabilities that have led to the rapid growth in the number of attacks. Therefore, several machine learning-based Intrusion Detection Systems (IDS) tools have been developed to detect intrusions and suspicious activity to and from a host (HIDS - Host IDS) or, in general, within the traffic of a network (NIDS - Network IDS). The proposed work performs a comparative analysis and an ablative study among recent machine learning-based NIDS to develop a benchmark of the different proposed strategies. The proposed work compares both shallow learning algorithms, such as Decision Trees, Random Forest, Naïve Bayes, Logistic Regression, XGBoost, and Support Vector Machine, and deep learning algorithms, such as DNN, CNN, and LSTM, whose approach is relatively new in the literature. The algorithms are evaluated on the KDD-99, NSL-KDD, UNSW-NB15, and IoT-23 datasets. The results show that the NIDS tools based on deep learning approaches achieve better performance in detecting network anomalies than shallow learning approaches.

The makeup of human microbiota has been linked to a number of autoimmune disorders. Recent developments in whole metagenome sequencing and 16S rRNA sequencing technology have considerably aided research into the microbiome and its relationship to disease. Due to the inherent high dimensionality and complexity of data generated by high-throughput platforms, conventional bioinformatics techniques could only provide an inadequate explanation for the most relevant changes and seldom provide correct predictions. Machine learning, on the other hand, is a subset of artificial intelligence applications that enable the untangling of high-dimensional systems and intricate knots in correlation by learning complex patterns and improving automatically from training data without being explicitly programmed. Machine learning is increasingly being utilized to research the influence of microbes on the onset of illness and other clinical features since computer power has increased dramatically in the last few decades. In this review paper, we focused on emerging methodological approaches of supervised machine learning algorithms for identification of autoimmune disorders utilizing metagenomics data, as well as the potential benefits and limitations of machine learning models in clinical applications.

Regardless of country and age, the importance of history education is always being emphasized. Although the importance of history education is being emphasized in Korea, there are many difficulties in getting students to understand history properly through school classes alone, and it is also difficult to attract students to participate in classes. The effectiveness of education using games has been proven 20 years ago, and the demand for game-based education is gradually increasing in the current education world, which is becoming more open. In this paper, based on the effects proven through research on the existing game-based education, the improvement of historical thinking ability, experiential history learning, and the problems of game-based education introduced in the ESN report and the discomfort of teachers who participated in the education were improved. A plan was suggested to select and use games suitable for basic education. In this thesis, we selected a history game with a clear historical and periodic background and without distortion of history, and experimented with teaching using games focusing on historical thinking and empirical history learning. The learning achievement of textbook-based education was compared.

Regardless of country and age, the importance of history education is always being emphasized. Although the importance of history education is being emphasized in Korea, there are many difficulties in getting students to understand history properly through school classes alone, and it is also difficult to attract students to participate in classes. The effectiveness of education using games has been proven 20 years ago, and the demand for game-based education is gradually increasing in the current education world, which is becoming more open. In this paper, based on the effects proven through research on the existing game-based education, the improvement of historical thinking ability, experiential history learning, and the problems of game-based education introduced in the ESN report and the discomfort of teachers who participated in the education were improved. A plan was suggested to select and use games suitable for basic education. In this thesis, we selected a history game with a clear historical and periodic background and without distortion of history, and experimented with teaching using games focusing on historical thinking and empirical history learning. The learning achievement of textbook-based education was compared.

Thanks to the rapid advancements in Connected and Automated Vehicles (CAVs) and vehicular communication, the concept of the Internet of Vehicles (IoV) combined with Artificial Intelligence (AI) and big data technologies promotes the vision of an Intelligent Transportation System (ITS). By enabling a comprehensive data exchange platform, ITS is critical in enhancing road safety, traffic efficiency, and the overall driving experience. However, the open and dynamic nature of IoV networks brings significant performance and security challenges to IoV data acquisition, storage, and usage. To comprehensively tackle these challenges, this paper proposes a Decentralized Digital Watermarking framework for smart Vehicular networks (D2WaVe). Specifically, D2WaVe consists of two core components: FIAE-GAN, a novel feature-integrated and attention-enhanced robust image watermarking model based on a Generative Adversarial Network (GAN), and BloVA, a Blockchain-based Video frames Authentication scheme. By leveraging an encoder-noise-decoder framework, trained FIAE-GAN watermarking models can achieve the invisibility and robustness of watermarks that can be embedded in video frames to verify the authenticity of video data. Then, BloVA ensures the integrity and auditability of IoV data in the storing and sharing stages. Experimental results based on a proof-of-concept prototype implementation validate the feasibility and effectiveness of our D2WaVe scheme for securing and auditing video data exchange in smart vehicular networks.

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.

Artificial intelligence methods and application have recently shown great contribution in modeling and prediction of the hydrological processes, climate change, and earth systems. Among them, deep learning and machine learning methods mainly have reported being essential for achieving higher accuracy, robustness, efficiency, computation cost, and overall model performance. This paper presents the state of the art of machine learning and deep learning methods and applications in this realm and the current state, and future trends are discussed. The survey of the advances in machine learning and deep learning are presented through a novel classification of methods. The paper concludes that deep learning is still in the first stages of development, and the research is still progressing. On the other hand, machine learning methods are already established in the fields, and novel methods with higher performance are emerging through ensemble techniques and hybridization.

Generic text embeddings have demonstrated considerable success across a multitude of applications. However, these embeddings are typically derived by modeling the co-occurrence patterns within text-only corpora, which can limit their ability to generalize effectively across diverse contexts. In this study, we investigate methodologies that incorporate visual information into textual representations to overcome these limitations. Through extensive ablation studies, we introduce a novel and straightforward architecture named VisualText Fusion Network (VTFN). This architecture not only surpasses existing multimodal approaches on a range of well-established benchmark datasets but also achieves state-of-the-art performance on image-related textual datasets while utilizing significantly less training data. Our findings underscore the potential of integrating visual modalities to substantially enhance the robustness and applicability of text embeddings, paving the way for more nuanced and contextually rich semantic representations.

We study how abstract representations emerge in a Deep Belief Network (DBN) trained on benchmark datasets. Our analysis targets the principles of learning in the early stages of information processing, starting from the “primordial soup" of the under-sampling regime. As the data is processed by deeper and deeper layers, features are detected and removed, transferring more and more “context-invariant” information to deeper layers. We show that the representation approaches an universal model – the Hierarchical Feature Model (HFM) – determined by the principle of maximal relevance. Relevance quantifies the uncertainty on the model of the data, thus suggesting that “meaning" – i.e. syntactic information – is that part of the data which is not yet captured by a model. Our analysis shows that shallow layers are well described by pairwise Ising models, which provide a representation of the data in terms of generic, low order features. We also show that plasticity increases with depth, in a similar way as it does in the brain. These findings suggest that DBNs are capable of extracting a hierarchy of features from the data which is consistent with the principle of maximal relevance.

Fruit quality is a critical factor in the produce industry, affecting producers, distributors, consumers, and the economy. High-quality fruits are more appealing, nutritious, and safe, boosting consumer satisfaction and revenue for producers. Artificial Intelligence can aid in assessing the quality of the fruit using images. This paper presents a general machine-learning model for assessing fruit quality using deep image features. The model leverages the learning capabilities of the recent successful networks for image classification called Vision Transformers (ViT). The ViT model is built and trained with a combination of various fruit datasets and learned to distinguish between good and rotten fruit images. The general model demonstrated impressive results in accurately identifying the quality of various fruits such as Apples (with a 99.50% accuracy), Cucumbers (99%), Grapes (100%), Kakis (99.50%), Oranges (99.50%), Papayas (98%), Peaches (98%), Tomatoes (99.50%), and Watermelons (98%). However, it showed slightly lower performance in identifying Guavas (97%), Lemons (97%), Limes (97.50%), mangoes (97.50%), Pears (97%), and Pomegranates (97%).

Tremendous progress has been witnessed in artificial intelligence within the domain of neural network backed deep learning systems and its applications. As we approach the post Moore’s Law era, the limit of semiconductor fabrication technology along with a rapid increase in data generation rates have lead to an impending growing challenge of tackling newer and more modern machine learning problems. In parallel, quantum computing has exhibited rapid development in recent years. Due to the potential of a quantum speedup, quantum based learning applications have become an area of significant interest, in hopes that we can leverage quantum systems to solve classical problems. In this work, we propose a quantum deep learning architecture; we demonstrate our quantum neural network architecture on tasks ranging from binary and multi-class classification to generative modelling. Powered by a modified quantum differentiation function along with a hybrid quantum-classic design, our architecture encodes the data with a reduced number of qubits and generates a quantum circuit, loading it onto a quantum platform where the model learns the optimal states iteratively. We conduct intensive experiments on both the local computing environment and IBM-Q quantum platform. The evaluation results demonstrate that our architecture is able to outperform Tensorflow-Quantum by up to 12.51% and 11.71% for a comparable classic deep neural network on the task of classification trained with the same network settings. Furthermore, our GAN architecture runs the discriminator and the generator purely on quantum hardware and utilizes the swap test on qubits to calculate the values of loss functions. In comparing our quantum GAN, we note our architecture is able to achieve similar performance with 98.5% reduction on the parameter set when compared to classical GANs. With the same number of parameters, additionally, QuGAN outperforms other quantum based GANs in the literature for up to 125.0% in terms of similarity between generated distributions and original data sets.

This paper presents a new method : HIVEC to learn latent vector representations of graphs in a manner that captures the semantic dependencies of sub-structures. The representations can then be used in machine learning algorithms for tasks such as graph classification, clustering etcetera. The method proposed is unsupervised and uses the information of co-occurrence of sub-structures. It introduces a notion of hierarchical embeddings that allows us to avoid repetitive learning of sub-structures for every new graph. As an alternative to deep learning methods, the edit distance similarity between sub-structures is also used to learn vector representations. We compare the performance of these methods against previous work.

Recent studies have shown the potential of Transformer-based neural networks in increasing prediction capacity. However, classical transformers present several problems such as computational time complexity and high memory requirements, which make Long Sequence Time-Series Forecasting (LSTF) challenging. The contribution to the prediction of time series of flood events using deep learning techniques is examined, with a particular focus on evaluating the performance of the Informer model (a particular implementation of transformer architecture), which attempts to address the previous issues. This study explores the predictive capabilities of the Informer model compared to statistical methods, stochastic models and traditional deep neural networks. The accuracy, efficiency as well as the limits of the approaches are demonstrated via numerical benchmarks relating to real river streamflow applications. Using daily flow data from the River Test in England as the main case study, we conduct a rigorous evaluation of the Informer efficacy in capturing the complex temporal dependencies inherent in flood time series. Among other things, the present work extends its analysis to encompass diverse time series datasets from various locations (>100) in the United Kingdom, providing insights into the generalizability of the Informer. The results highlight the significant superiority of the Informer model over established forecasting methods, especially regarding the LSTF problem. Furthermore, it is observed that the structure of time series, as expressed by climacogram, affects the performance of the Informer network.

Undergraduate learning is a multifaceted concept that many researchers have investigated to promote student engagement, academic performance, etc. Exploring factors promoting learning among undergraduates in FYW programs, which are required for undergraduates might potentially contribute to sustainable, cross-disciplinary undergraduate education. This study used the 10-year longitudinal grade and survey data at San José State University (SJSU) and the data was collected from Academic Years (AYs) 2015 to 2020, and five student surveys constituted the sample size of 18,101 students. In 2016, SJSU changed the remedial writing course to college writing courses. Since then, students need to complete an online module course before placing themselves into a one-semester or two-semester writing course. The previous study showed that students with higher writing comfort and confidence tend to place themselves into a semester-long class. Regression analysis was applied to examine the relationships among factors promoting learning among those students. University admitted students from various diversity and backgrounds, the results of this study suggested that what students chose and persisted in were more important in advancing their academic success rather than prior learning experience or psychological factors before the class.

A common challenge in real-world classification scenarios with sequentially appending target domain data is insufficient training datasets during the training phase. Therefore, conventional deep learning and transfer learning classifiers are not applicable especially when individual classes are not represented or are severely underrepresented at the outset. Domain Generalization approaches reach their limits when domain shifts become too large, making them occasionally unsuitable as well. In many (technical) domains, however, it is only the defect/ worn/ reject classes that are insufficiently represented, while the non-defect class is often available from the beginning. The proposed classification approach addresses such conditions and is based on a CNN encoder. Following a contrastive learning approach, it is trained with a modified triplet loss function using two datasets: Besides the non-defective target domain class (= 1st dataset), a state-of-the-art labeled source domain dataset that contains highly related classes (e.g., a related manufacturing error or wear defect) but originates from a (highly) different domain (e.g., different product, material, or appearance) (= 2nd dataset) is utilized. The approach learns the classification features from the source domain dataset while at the same time learning the differences between the source and the target domain in a single training step, aiming to transfer the relevant features to the target domain. The classifier becomes sensitive to the classification features and – by architecture – robust against the highly domain-specific context. The approach is benchmarked in a technical and a non-technical domain and shows convincing classification results. In particular, it is shown that the domain generalization capabilities and classification results are improved by the proposed architecture, allowing for larger domain shifts between source and target domains.

Deep learning has achieved lots of successes in many fields, but when trainable sample are extremely limited, deep learning often under or overfitting to few samples. Meta-learning was proposed to solve difficulties in few-shot learning and fast adaptive areas. Meta-learner learns to remember some common knowledge by training on large scale tasks sampled from a certain data distribution to equip generalization when facing unseen new tasks. Due to the limitation of samples, most approaches only use shallow neural network to avoid overfitting and reduce the difficulty of training process, that causes the waste of many extra information when adapting to unseen tasks. Euclidean space-based gradient descent also make meta-learner's update inaccurate. These issues cause many meta-learning model hard to extract feature from samples and update network parameters. In this paper, we propose a novel method by using multi-stage joint training approach to post the bottleneck during adapting process. To accelerate adapt procedure, we also constraint network to Stiefel manifold, thus meta-learner could perform more stable gradient descent in limited steps. Experiment on mini-ImageNet shows that our method reaches better accuracy under 5-way 1-shot and 5-way 5-shot conditions.

3D reality capturing has demonstrated increased efficiency and consistently accurate outcomes in architectural digitisation. Nevertheless, despite advancements in data collecting, 3D reality capturing still lacks full automation, especially in the post-processing and modelling phase. Artificial intelligence (AI) has been a significant focus, especially in computer vision, and tasks such as image classification and object recognition might be beneficial to the digitisation process and its subsequent utilisation. This article aims to examine the potential outcomes of integrating AI technology into the field of 3D reality-capturing, with a particular focus on its use in architectural scenarios. The main methods used for data collection are laser scanning (static or mobile) and photogrammetry. As a result, image data including RGB-D data (files containing both RGB colours and Depth information) and point clouds have become the most common raw datasets available for object mapping. The study comprehensively analyses the current use of 2D and 3D deep learning techniques in documentation tasks, particularly downstream applications. It also highlights the ongoing research efforts in developing real-time applications with the ultimate objective of achieving generalisation and improved accuracy.

This paper provides a thorough examination of important themes critical to understanding and tackling modern cyber threats. It explores the revolutionary significance of AI and machine learning in cybersecurity, emphasizing their dynamic influence on defense systems. It also addresses how zero-trust designs have caused a paradigm change in network security, highlighting the significance of trust verification in current cybersecurity frameworks. Furthermore, the article investigates the integration of cybersecurity with corporate strategy, arguing for a collaborative approach to efficiently mitigating risks while minimizing operational impact. Furthermore, it investigates the idea of cyber resilience, highlighting an organization's ability to recover from assaults and adapt to changing threats. Furthermore, the paper discusses the issues faced by deepfakes in the synthetic media revolution and emphasizes the rising worry about data privacy in the digital era. Finally, it looks into the introduction of behavioral biometrics as a possible way to improve authentication and security methods. This paper contributes to the discussion of effective tactics for mitigating cyber threats in today's digital ecosystem by providing a thorough overview of these subjects.

Accurate and timely prediction of Napa cabbage (Brissica rapa subsp. Perkinensis) fresh weight is crucial for optimizing harvest timing, crop management, and supply chain logistics, contributing to food security and price stabilization. Traditional manual sampling methods are labor-intensive and imprecise. This study addresses this challenge by developing a comprehensive (artificial intelligence) AI-powered model for predicting Napa cabbage fresh weight using unmanned aerial vehicle (UAV)-based multi-sensor data. High-resolution RGB, multispectral, and thermal infrared (TIR) imagery were collected over a Napa cabbage field throughout the 2020 growing season. Various vegetation indices, crop features (vegetation fraction, crop height model), and water stress indi-cators (CWSI) were extracted from the imagery. Three AI algorithms—deep neural network (DNN), support vector machine (SVM), and random forest (RF)—were trained and evaluated, with the DNN model consistently outperforming the others. The DNN model achieved the highest accuracy (R² = 0.86 for training, 0.82 for testing; root mean square error (RMSE) = 0.432 kg for training, 0.465 kg for testing) during the mid-to-late rosette growth stage (DAP 35-42), highlighting this period as crucial for fresh weight estimation due to stable leaf area and well-developed canopy structure. The model tended to underestimate the weight of Napa cabbages exceeding 5 kg, potentially due to limited samples and saturation effects of vegetation indices. However, the overall error rate was less than 5%, demonstrating the feasibility and effectiveness of this approach. Spatial analysis revealed that the model accurately captured the variability in Napa cabbage growth across different soil types and irrigation conditions, particularly reflecting the positive impact of drip irrigation on the sandy loam plot. Bias analysis indicated the DNN model's tendency to overestimate smaller Napa cabbages (2 kg), suggesting areas for future refinement. This study demonstrates the potential of UAV-based multi-sensor data and AI algorithms for accurate and non-invasive prediction of Napa cabbage fresh weight. The developed DNN model offers a promising tool for optimizing harvest timing, improving crop management practices, and en-hancing supply chain efficiency. Future research should focus on refining the model for specific weight ranges and diverse environmental conditions, as well as extending its application to other crops, to further advance precision agriculture and contribute to sustainable food production.

Exploring new approaches to innovating higher vocational education is a worldwide pursuit driven by the increasing demand for graduates equipped with competencies. This study investigated how Outcomes-Based Education (OBE) influences the attainment of graduate competence (AGC) in China’s higher vocational education, using the theory of Constructive Alignment (CA) as its foundation. The OBE framework incorporates intended learning outcomes (ILOs), project-based learning (PjBL), assessment strategies (ASs), and AGC. The study tested the model by assessing the impact of ILOs, PjBL, and ASs on AGC, respectively and sequentially. Surveys were administered to 320 CBEC learners in April 2024, resulting in 301 usable responses. Data were analyzed using structural equation modeling (SEM) with SPSS 23.0 and AMOS 24.0. Results indicated that PjBL and ASs were directly and positively related to graduate competency attainment, while ILOs were indirectly associated with graduate competence through the mediating roles of PjBL and ASs. The most substantial mediating effect between ILOs and graduate competence was ASs, followed by PjBL, and then, the chain mediating effect of PjBL and ASs. These findings enhance the theoretical understanding of OBE and provide detailed procedures for tracking and enhancing sustainability in higher vocational education. Beneficiaries include educators, policymakers, and accreditation bodies.

We introduce a modelling framework for the investigation of on-line machine learning processes in non-stationary environments. We exemplify the approach in terms of two specific model situations: In the first, we consider the learning of a classification scheme from clustered data by means of prototype-based Learning Vector Quantization (LVQ). In the second, we study the training of layered neural networks with sigmoidal activations for the purpose of regression. In both cases, the target, i.e. the classification or regression scheme, is considered to change continuously while the system is trained from a stream of labeled data. We extend and apply methods borrowed from statistical physics which have been used frequently for the exact description of training dynamics in stationary environments. Extensions of the approach allow for the computation of typical learning curves in the presence of concept drift in a variety of model situations. First results are presented and discussed for stochastic drift processes in classification and regression problems. They indicate that LVQ is capable of tracking a classification scheme under drift to a non-trivial extent. Furthermore, we show that concept drift can cause the persistence of sub-optimal plateau states in gradient based training of layered neural networks for regression.

This study is a synthesis of 159 articles that were selected for their relevance to comprehend key aspects of the Massive Open Online Courses (MOOCs) phenomenon, from a discourse analysis perspective. Since 2011, MOOCs are expanding worldwide so that the number of subscribers outpointed 101 million at the end of 2018. This paper explores the question whether the MOOCs are the embodiment of the global one-world classroom or whether, instead, they represent a low-cost alternative tailored to a segment that doesn´t have enough time or resources to attend a brick-and-mortar college. In addition, the review tackles the link between motivation and low completion rates. Finally, we discuss the need to devise better methods to assess the pedagogical value of MOOCs.

The importance of sleep for healthy brain function is widely acknowledged. However, it remains unclear how the internal generation of dreams might facilitate cognitive processes. In this perspective we review a computational approach inspired by artificial intelligence that proposes a framework for how dreams occurring during rapid-eye-movement (REM) sleep can contribute to learning and creativity. In this framework, REM dreams are characterized by an adversarial process that, against the dream reality, tell a discriminator network to classify the internally created sensory activity as real. Such an adversarial dreaming process is shown to facilitate the emergence of real-world semantic representations in higher cortical areas. We further discuss the potential contributions of adversarial dreaming beyond learning, such as balancing fantastic and realistic dream elements, and facilitating the occurrence of creative insights. We characterize non-REM (NREM) dreams, where a single hippocampal memory is replayed at a time, as serving a complementary role of improving the robustness of cortical representations to environmental perturbations. We finally explain how subjects can become aware of the adversarial REM dreams, but less of the NREM dreams, and how the awareness phenomenon of wake, dream and lucid dreaming may appear.

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.

Background: Studies have tried to identify predictors of functional markers of keratoconus progression, whereas structural outcomes of collagen cross-linking have not been properly investigated. Objective: find and set up a combination of diagnostic modalities and measurements to reliably reflect CXL efficiency. Material and methods: We retrospectively reviewed the medical histories of 107 KC patients (131 eyes) who underwent CXL in a medical center from January 2018 to December 2022. The dataset included preoperative examinations and follow-up results, which provided a total of 796 observations. The cases of other corneal diseases, repeated CXL, pregnancy, and observations without follow-up examinations were excluded. Results: The study findings revealed a significant drop in the central and corneal thickness during 14.01±9.98 months of observations. Linear and polynomial equations suggested different trends in pachymetry change after CXL. The linear model showed corneal thinning, and according to the polynomials, pachymetry findings return to baseline values two years after CXL. Conclusion: The most reliable prognosis of postoperative corneal thickness is achieved with the models trained on keratometry readings and topography indices. BAD indices are also reliable predictors of the corneal thickness after CXL. A combination of the aforementioned structural parameters and their derivatives can reliably predict CXL efficiency.

Due to the digitization and Internet of Things revolutions, the present electronic world has a wealth of cybersecurity data. Efficiently resolving cyber anomalies and attacks is becoming a growing concern in today's cyber security industry all over the world. Traditional security solutions are insufficient to address contemporary security issues due to the rapid proliferation of many sorts of cyber-attacks and threats. Utilizing artificial intelligence knowledge, especially machine learning technology, is essential to providing a dynamically enhanced, automated, and up-to-date security system through analyzing security data. In this paper, we provide an extensive view of machine learning algorithms, emphasizing how they can be employed for intelligent data analysis and automation in cybersecurity through their potential to extract valuable insights from cyber data. We also explore a number of potential real-world use cases where data-driven intelligence, automation, and decision-making enable next-generation cyber protection that is more proactive than traditional approaches. The future prospects of machine learning in cybersecurity are eventually emphasized based on our study, along with relevant research directions. Overall, our goal is to explore not only the current state of machine learning and relevant methodologies but also their applicability for future cybersecurity breakthroughs.

This research work describes an architecture for building a system that guide a user from a forecast generated by a machine learning model through a sequence of decision-making steps. The system is demonstrated in manufacturing demand forecasting use case and can be extended to other domains. In addition, the system provides means for knowledge acquisition by gathering data from users. Finally, it implements an active learning component and compares multiple strategies to recommend media news to the user. Such media news aims to provide additional context to demand forecasts and enhance judgment on decision-making.

In this paper major machine learning (ML) tools and the most important applications developed elsewhere for numerical weather and climate modeling systems (NWCMS) are reviewed. NWCMSs are briefly introduced. The most important papers published in this field in recent years are reviewed. The advantages and limitations of the ML approach in applications to NWCMS are briefly discussed. Currently, this field is experiencing explosive growth. Several important papers are published every week. Thus, this paper should be considered a simple introduction to the problem.

The rapid advancement of artificial intelligence (AI) brought about significant opportunities and challenges in educational environment. It is disputable whether the application of AI to education is beneficial or not regarding human learning and teaching. This study aims at exploring the implications of the evolving educational landscape by comparing and discussing human and AI learning processes. Even though AI is not a conscient agent, it does not spell out any words randomly but provide meaningful texts with any reasoning. It helps us to understand human learning better by comparing learning in biological and artificial intelligences. Moreover, the new concepts in learning in AI come about the change of learning paradigm such as connectionism/connectivism. After all, AI will empower the abilities of learning and teaching for students as well as teachers. This study will give some implications for the future education with AI.

This study presents a novel approach for automating the body condition scoring of dairy cows, leveraging advancements in 3D imaging technology and deep neural networks. The primary objective was to design and implement a system capable of accurately and efficiently assessing the body condition of dairy cows, a critical metric in livestock management for optimizing health and productivity. To achieve this, a 3D camera was employed to capture detailed point cloud data, reconstructing the three-dimensional morphology of individual cows. The obtained data were then fed into a deep neural network, specifically tailored for the task of ranking body condition. The neural network was trained on a diverse dataset of annotated body condition score representing varying degrees of body condition, ensuring robust performance across different physiological states. The results demonstrate the efficacy of the proposed system in automatically and objectively scoring the body condition of dairy cows. The automated process not only expedites the assessment but also reduces the subjectivity associated with manual scoring methods. This innovative approach holds promise for improving the efficiency of dairy farm management by providing timely and accurate body condition assessments. The integration of 3D imaging and deep learning techniques paves the way for future advancements in precision livestock farming, contributing to enhanced animal welfare, optimized production, and sustainable agriculture practices.

The study is an attempt to enquire into the preference of undergraduate students, after a considerable and over a year-long experience with Online Teaching and Learning (OTL), under the emergency preventive measure of switching from the traditional face-to-face classes to online. The study followed an exploratory approach, with a quantitative survey followed by a qualitative one, and a convenient sampling method to collect responses from a substantial sample size. The study is positioned after more than a year of remote classes by the undergraduate student, and hence represents highly experienced reflections and preferences from these students, as compared to other studies conducted last year. The study has profound implications in considering, and questioning, the importance of on-campus classes, and significance of the physical presence of a tutor in the class, and its effect on the learning experience of undergraduate students.

The aim of this article is to discuss the transformative amalgamation of Internet of Things and Machine Learning technologies within the domain of vegetable hydroponic systems for nutrition management. Hydroponics, being an efficient cultivation method, benefits a great deal from the precision and adaptability that machine learning algorithms can offer, along with the real-time monitoring facilitated by the devices using the Internet of Things. This study summarizes the latest research and underlines how ML and IoT may work together, focusing on nutrient optimization, plant development, and resource efficiency. The use of ML algorithms, the function of IoT devices for real-time monitoring, communication protocols, scalability issues, and implementation are among some of the key subjects of the discussion. The research indicates benefits to crop output, efficiency in using resources, and sustainability based on the case studies and the analysis of results. However, ethical problems and some complications concerning data privacy do call for responsible adoptions. The conclusion of this paper provides directions for further research and calls for more investigation into state-of-the-art machine learning approaches and scalable solutions for the re-silient and sustainable future of hydroponic agriculture. The machine learning and deep learning models introduced in this research were evaluated against contemporary studies, revealing an accuracy enhancement ranging from 1.17% to 5.25%, depending on the dataset and algorithm employed. The present study conducts a comparative analysis involving machine learning algo-rithms, indicating that among all the models, the Decision Tree and Gradient Boosting Classifier achieved an accuracy of 99.42% in the dataset by making stage-wise decisions.

In 2020, the early Covid-19 lockdown and social distancing induced the abrupt migration of traditionally presential learning activities to online domains. This “new normal” environment affected management not only of training courses, but also educational events whose main added value consisted in providing students with an interactive experience onsite, as it is the case of inter-institutional summer schools. The following research corresponds to a case study in which our organization, a French university, had to reformulate, in less than three months, one of its traditional summer schools while trying to keep the original goals. We aimed at identifying the impact of four managerial decisions by analyzing, through qualitative and quantitative surveys, students’ perception of gained or lost value regarding four topics: a) online teaching, b) pre-recorded busines cases providers, c) online social events, and d) technical solutions. With an emphasis in both didactics (i.e., knowledge) and pedagogics (i.e., learning experience), the analysis of perceived value allowed as to learn about students’ assessment position, which revealed two main issues at stake: belonging, in relation to students’ identity and academic background, and performing, related to students’ expectations.

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.

In the age of digital revolution technology is being used more and more in education around the world to improve student learning. By enabling ongoing skill development and knowledge acquisition through digital platforms e-learning has become a crucial tool. Through E-Learning online learning is becoming increasingly popular and effective in addition to traditional methods of instruction as highlighted by the latest statistics from Badan Pusat Statistik Indonesia (BPS). In order to address the issues of scalability, interactivity and security in e-learning platforms. This study introduces a web-based learning environment that is focused on teaching programming. Leveraging the MERN stack, while also integrating TypeScript for improved code reliability, Next.js for accelerated React.js functionality, Tailwind CSS for responsive design. Backend features include media management with Cloudinary, secure video streaming with VdoCipher, data storage with MongoDB, and caching with Redis. Iterative development will be following agile methodology. In order to create a vibrant and encouraging learning environment the platform includes interactive course pages multimedia content real-time feedback mechanisms and community-building tools. Despite some early difficulties the implementation phase effectively achieved development goals. To further support international online learning initiatives further efforts will be directed toward improving platform security and functionality.

Ensuring precise segmentation of colorectal polyps holds critical importance in the early diagnosis and treatment of colorectal cancer. Nevertheless, existing deep learning-based segmentation methods are fully-supervised, requiring extensive precise manual pixel-level annotation data, which leads to high annotation costs. Additionally, it remains challenging to train large-scale segmentation models when confronted with limited colonoscopy data. To address these issues, we introduce the general segmentation foundation model—Segment Anything Model (SAM) into the field of medical image segmentation. Fine-tuning the foundation model is an effective approach to tackle sample scarcity. However, current SAM fine-tuning techniques still rely on precise annotations. To overcome this limitation, we propose WSPoly-SAM, a novel weakly-supervised approach for colonoscopy polyp segmentation. WSPoly-SAM utilizes weak annotations to guide SAM in generating segmentation masks, which are then treated as pseudo-labels to guide the fine-tuning of SAM, thereby reducing the dependence on precise annotations data. To improve the reliability and accuracy of pseudo-labels, we have designed a series of enhancement strategies to improve the quality of pseudo-labels and mitigate the negative impact of low-quality pseudo-labels. Experimental results on five medical image datasets demonstrate that WSPoly-SAM outperforms current fully-supervised mainstream polyp segmentation networks on the Kvasir-SEG, ColonDB, CVC-300, and ETIS datasets. Furthermore, by using different amounts of training data in weakly-supervised and fully-supervised experiments, it is found that weakly-supervised fine-tuning can save 70% to 73% of annotation time costs compared to fully-supervised fine-tuning. This study provides a new perspective on the combination of weakly-supervised learning and SAM models, significantly reducing annotation time and offering insights for further development in the field of colonoscopy polyp segmentation.

Radiator reliability is crucial in environments characterized by high temperatures and friction, where prompt interventions are often required to prevent system failures. This study introduces a proactive approach to radiator fault diagnosis, leveraging the integration of Gaussian Mixture Model (GMM) and Long-Short Term Memory (LSTM) autoencoders. Vibration signals from radiators were systematically collected through randomized durability vibration bench tests, resulting in four operating states—two normal, one unknown, and one faulty. Time-domain statistical features of these signals were extracted and subjected to Principal Component Analysis (PCA) to facilitate efficient data interpretation. Subsequently, this study discusses the comparative effectiveness of GMM and LSTM in fault detection. GMMs are deployed for initial fault classification, leveraging their clustering capabilities, while LSTM autoencoders excel in capturing time-dependent sequences, facilitating advanced anomaly detection for previously unencountered faults. This alignment offers a potent and adaptable solution for radiator fault diagnosis, particularly in challenging high-temperature or high-friction environments. Consequently, the proposed methodology not only provides a robust framework for early-stage fault diagnosis but also effectively balances diagnostic capabilities during operation. Additionally, this study presents the foundation for advancing reliability life assessment in accelerated life testing, achieved through dynamic threshold adjustments using both the absolute log-likelihood distribution of the GMM and the reconstruction error distribution of the LSTM autoencoder model.

Radiological imaging is an essential component of a swallowing assessment. Artificial intelligence (AI), and especially deep learning (DL) models, have enhanced the efficiency and efficacy through which imaging is interpreted, and subsequently have important implications for swallow diagnostics and intervention planning. However, the application of AI for the interpretation of videofluoroscopic swallowing studies (VFSS) is still emerging. This review showcases recent literature in the use of AI to interpret VFSS and highlights clinical implications for speech pathologists (SPs). With a surge in AI research, there have been advances made in dysphagia assessment. Several studies have demonstrated successful implementation of DL algorithms to analyze VFSS. Notably, convolutional neural networks (CNNs) have been used to detect pertinent aspects of the swallowing process with high levels of precision. DL algorithms have the potential to streamline VFSS interpretation, improve efficiency and accuracy, and enable precise interpretation of instrumental dysphagia evaluation, which is especially advantageous when access to skilled clinicians is not ubiquitous. By enhancing precision, speed, and depth of VFSS interpretation, SPs can obtain a more comprehensive understanding of swallow physiology and deliver targeted and timely intervention that is tailored towards the individual. This has practical applications for both clinical practice and dysphagia research. As this research area grows and AI technologies progress, the application of DL in the field of VFSS interpretation is clinically beneficial and has the potential to transform dysphagia assessment and management. With broader validation and inter-disciplinary collaborations, AI-augmented VFSS interpretation is likely to transform swallow evaluation and ultimately improve outcomes for individuals with dysphagia.

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.

Digital recruitment systems have revolutionized the hiring paradigm, imparting exceptional efficiencies and extending the reach for both employers and job seekers. This investigation scrutinizes the efficacy of classical machine learning methodologies alongside advanced large language models (LLMs) in aligning resumes with job categories. Traditional matching techniques, such as Logistic Regression, Decision Trees, Naïve Bayes, and Support Vector Machines, are constrained by the necessity of manual feature extraction, limited feature representation, and performance degradation, particularly as dataset size escalates, rendering them less suitable for large-scale applications. Conversely, LLMs like GPT-4, GPT-3, and LLAMA, adeptly process unstructured textual content, capturing nuanced language and context with greater precision. We evaluate these methodologies utilizing two datasets comprising resumes and job descriptions to ascertain accuracy, efficiency, and scalability. Our results reveal that while conventional models excel with structured data, LLMs significantly enhance the interpretation and matching of intricate textual information. This study highlights the transformative potential of LLMs in recruitment, offering insights into their application and future research avenues.

The potential of generalized machine learning models developed for crop water estimation was examined in the current study. Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), and Random Forest (RF) are three ensembled machine learning models that were developed using all of the data from a single location from 1976 to 2017 and then immediately applied at eleven different locations without the need for any local calibration. For the test period of January 2018 to June 2020, the model's capacity to estimate the numerical values of crop water requirement (Pen-man-Monteith (PM) ETo values) was assessed. In comparison to the GBM and RF models, the XGBoost model outperformed them both marginally and significantly. The estimate's weighted standard error was smaller than 0.85 mm/day, and the model's effectiveness varied from 96% to 99% across various locations. The model's strong performance was indicated by the decreased noise-to-signal ratio. A real-time water management system at the regional level can be seamlessly linked with this type of model due to its accuracy in estimating crop water requirements and its capacity to generalize.

A topic of growing interest in urban remote sensing is the automated extraction of geometrical building information for 3D city modeling. Roof geometry information is useful for applications such as urban planning, solar potential estimation and telecommunication installation planning, and wind flow simulations for pollutant diffusion analysis. Recent research has proven that the advance in remote sensing technologies and deep learning methods offer the prospects of deriving the roof structure information accurately and efficiently. In this study, we propose a Vectorized Roof Extractor- method based on Fully Convolutional Networks (FCNs) and advanced polygonization method to extract roof structure from aerial imagery and a normalized Digital Surface Models (nDSM) in a regularized vector format. The roof structure consists of building outlines, external edges of the building roof, inner rooflines, internal intersections of the main roof planes. The methodology is comprised of segmentation, vectorization and post-processing for outer rooflines, external edges of the building roof, and inner rooflines, and internal intersections of the main roof planes. For the comparison, we adapt the Frame field Learning (FFL) method originally designed to extract building polygons [1]. Our experiments are conducted on a custom data set derived for the city of Enschede, The Netherlands, using aerial imagery, nDSM and manually digitized training polygons. The results show that the proposed Vectorized Roof Extractor outperformed adapted FFL on PoLiS distance with values of 3.5 m and 1.2 m for outlines and inner rooflines, respectively. Furthermore, the model surpassed the adapted FFL on PoLiS-thresholded F-score for outlines and inner rooflines, with 0.31 and 0.57, respectively. The Vectorized Roof Extractor produced adequate visual results, with straighter walls and fewer missed inner roofline detections. It can predict buildings with common walls thanks to skeleton graph computation. To summarize, the proposed method is suitable for urban applications and has the potential to be improved further.

This paper explores the application of machine learning (ML) in predicting functional recovery in patients with ischemic stroke. As technology advances, ML shows significant potential in the field of stroke medicine, especially in the areas of big data analytics and personalized medicine. Studies have shown that ML algorithms can improve the accuracy of stroke image analysis, subtype classification, risk assessment, treatment guidance, and prognosis prediction. However, the widespread use of ML still faces challenges such as data standardization, model validation, privacy, and bias. This paper reviews the current application status of ML in the field of stroke, discusses the challenges faced, and looks forward to the future development direction, aiming to promote the practical application of ML technology in the diagnosis and treatment of stroke to improve the prognosis and quality of life of patients.

As cardiovascular diseases continue to be a leading cause of mortality, recent-ly, wearable devices for monitoring cardiac activity have gained much interest among medical community. This paper introduces an innovative ECG monitoring system based on a single – lead ECG machine enhanced with machine learning methods. The system only processes and analyses the ECG data, but also predict potential heart disease at an early stage. The wearable device was built on the ADS1298 and a microcontroller STM32L151xD. A server module based on REST API architecture style was designed to fa-cilitate interaction with web-based segment of the system. The module is responsible for receiving data in real time from microcontroller and their deliver to web – based segment. Algorithms for analyzing ECG signals have been developed, including band filter artifact removal, K-means clustering for signal segmentation, and PQRST analysis. Machine leaning methods as Isolation Forest have been employed for ECG anomaly detection. Moreover, a comparative analysis with various machine learning methods, including lo-gistic regression, random forest, SVM, XGBoost, decision forest and, CNNs was conducted to predict cardiovascular diseases. Convoluted Neural Networks (CNN) showed an accu-racy of 0.926, proving high effectiveness in ECG data process.

This is break direction on oxygen sources and conveyance methodologies for COVID-19 treatment. It has been adjusted from WHO and UNICEF's specialized details and direction for oxygen treatment gadgets, which is important for the WHO clinical gadget specialized arrangement, 1 and depends on current information on the circumstance in China and different nations where cases have been distinguished. This direction is proposed for wellbeing office chairmen, clinical leaders, acquisition officials, arranging officials, biomedical architects, foundation engineers and strategy producers. It portrays how to: measure oxygen interest, to distinguish oxygen sources that are accessible, and select suitable flood sources to best react to COVID-19 patients' requirements, particularly in low-and-center pay nations. WHO will refresh these suggestions as new data opens up. Coronavirus pandemic spurred fake interest for oxygen gas chambers for clinical use - both at emergency clinics and inquisitively, for home use by patients. A few patients and surprisingly sound people investigate the conceivable outcomes and likely benefits of utilizing oxygen from chambers for private utilization. Be that as it may, this isn't continuously protected, and sufficient safety measures are to be taken, bombing which there can be fatalities. This paper investigates the significance of keeping up satisfactory degrees of oxygen levels appropriate for human utilization. It advises the clinical use and the advantages and disadvantages of putting away oxygen chambers at home. The investigation likewise addresses lawful and administrative perspectives. The investigation's discoveries can help people settle on an educated choice on the protected use regarding oxygen gas. Further, it cautions on the expanded significance of guidelines and limiting access and use. This paper aims at designing an oxygen level monitoring technique in an oxygen cylinder. The amount of oxygen present inside the oxygen cylinder is very vital information when such cylinder is in use for supply of oxygen to a critical patient. The amount of oxygen present inside the cylinder is measured by the pressure at the outlet nozzle. The pressure is measured using a high precision MEMS Pressure Sensor. The output of the MEMS pressure sensor is voltage of the order milli. An amplifier is used to amplify this milli volt signal. A microcontroller is used in cascade to process the signal and display the pressure of oxygen cylinder.

With the advancement of technology, signal modulation types are becoming increasingly diverse and complex. The phenomenon of signal time-frequency overlap during transmission poses significant challenges for the classification and recognition of mixed signals, including poor recognition capabilities and low generality. This paper presents a recognition model for fine-grained analysis of mixed signal characteristics, proposing a Geometry Coordinate attention mechanism and introducing a low-rank bilinear pooling module to more effectively extract signal features for classification. The model employs a residual neural network as its backbone architecture and utilizes the Geometry Coordinate attention mechanism for time-frequency weighted analysis based on information geometry theory. This analysis targets multiple-scale features within the architecture, producing time-frequency weighted features of the signal. These weighted features are further analyzed through a low-rank bilinear pooling module, combined with the backbone features, to achieve fine-grained feature fusion. This results in a fused feature vector for mixed signal classification. Experiments were conducted on a simulated dataset comprising 39,600 mixed signal time-frequency plots. The model was benchmarked against a baseline using a residual neural network. The experimental outcomes demonstrated an improvement of 9% in the exact match ratio and 5% in the Hamming score. These results indicate that the proposed model significantly enhances the recognition capability and generalizability of mixed signal classification.

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.

Landslides, resulting from disturbances in slope equilibrium, pose a significant threat to landscapes, infrastructure, and human life. Triggered by factors such as intense precipitation, seismic activities, or volcanic eruptions, these events can cause extensive damage and endanger nearby communities. A comprehensive understanding of landslide characteristics, including spatio-temporal patterns, dimensions, and morphology, is vital for effective landslide-disaster management. Existing Remote Sensing approaches mostly use either optical or Synthetic Aperture Radar (SAR) sensors. Integrating information from both these types of sensors promises greater accuracy for identifying and locating landslides. This study proposes a novel approach, the ML-LaDeCORsat (Machine Learning-based coseismic Landslide Detection using Combined Optical and Radar Satellite Imagery), that integrates freely available Sentinel-1, Palsar-2, and Sentinel-2 imagery data, along with relevant spectral indices and suitable bands using machine-learning-based classification for coseismic landslide detection implemented in Google Earth Engine (GEE). The approach includes a robust and reproducible training and validation strategy. Using landslides from four different earthquake case studies, we demonstrate the superiority of our approach over existing solutions in coseismic landslide iden-tification and localization, providing a detection accuracy of 87-92%. ML-LaDeCORsat can be adapted to other landslide events (GEE script is provided). Transfer learning experiments proofed that our model can be applied to other coseismic landslide events without the need for additional training data. Our novel approach therefore facilitates quick and reliable identification of co-seismic landslides, highlighting its potential to contribute towards more effective disaster management.

Generating executable code from natural language instructions using Large Language Models (LLMs) presents challenges such as semantic understanding and handling ambiguous input. This study focuses on the SantaCoder model and explores the impact of in-context learning on code generation using the MBPP and HumanEval datasets for evaluation. Our results demonstrate significant improvements in three key metrics (defined in the paper): correctness@k, similarity@k and pass@k. To address the problem of selecting optimal demonstrations to maximize correctness and pass rates, we investigate two methods: latent concept selection and random selection in this paper. These findings highlight the effectiveness of in-context learning and the critical role of demonstration selection in enhancing the accuracy, efficiency, and versatility of the SantaCoder model in code generation.

Dementia education and training for workforce development is becoming increasingly important in bridging knowledge gaps among health and social care practitioners in the UK and internationally. Dementia Education for Workforce Excellence (DEWE) was developed during the COVID-19 pandemic blending both synchronous and asynchronous instruction and delivered across three different contexts: care homes, home care, and nurse education within the UK and India. This study aimed to evaluate DEWE using mixed methods with online survey data analyzed descriptively and interview data analyzed thematically. Integration of survey and interview data aimed toward a comprehensive evaluation of this novel approach for dementia workforce development. Thirty-four social care practitioners and nurse educators completed the online survey demonstrating high-level learner satisfaction; learning gains; behavioral change, and motivation to share new knowledge. Four key themes developed from the analysis of interviews (n=9) around participants’ pursuit of new knowledge; delivery modes in DEWE; learning gains and impact of DEWE; and adaptations for future program implementation. Findings suggest DEWE is an innovative resource that promotes person- and relationship-centered dementia care across all stages of one’s dementia journey. Cultural adaptations are recommended for international delivery to ensure contextual alignment and maximum impact.

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

At the beginning of 2020, with the onset of the pandemic, the traditional learning environment for learners drastically changed globally. Since then, most students/teachers have started and practiced distance and virtual learning/teaching. Thus, a technological breakthrough in virtual learning has followed. In connection with this, many countries worldwide have commenced allocating additional financing and funds for educational institutions' technological improvement and development. The long-term stay in distance learning has revealed and highlighted new problems students face: their knowledge level has decreased, they lack socialization skills, and they face psychological and physical health problems. Due to this negative impact on students, a need to research and evaluate how much the EU countries allocated to solve the distance learning-caused problems and what programs or models they prepared has emerged and encouraged further studies. The research has found that many countries increased their allocations very minimally, e.g., 0.0.1%, but some increased their available budgets to 32%. Notably, most countries did not separate distance learning exit funding from distance learning preparation funding. Based on the problems the countries saw, only a few states identified withdrawal from distance learning as a problem. Considering this, we set ourselves the goal to evaluate exit models from distance learning and allocated funding amounts. The following objectives were planned to achieve the goal: · to evaluate the global practice of exit from distance learning; · to determine the scope of funding for pandemic management; · to evaluate the amounts of funding allocated to manage pandemic-caused consequences and the GDP ratio. Research methods: mathematical-statistical analysis, empirical analysis, and analysis of scientific literature.

This review paper delves into using micro-credentials in higher education ecosystems as a digital enablers. Micro-credentials, which are digital credentials that attest to a learner’s mastery of a specific skill or knowledge area, are becoming more popular in higher education. The paper examines the successful implementation of micro-credential frameworks in higher education, using case studies to demonstrate the advantages of micro-credentials. The review emphasizes the agility and flexibility of microcredentials, which enable learners to acquire new skills quickly and respond to changes in the job market. In addition, the paper discusses the digital nature of micro-credentials and how they allow institutionsto provide targeted, skills-based training that isrelevant to employers. It also explores how micro-credentials are delivered through online platforms, making them convenient and easily accessible for learners. The review underscores the significance of digital infrastructure, connectivity, and public utility for promoting micro-credentials. The paper argues that micro-credentials function as a digital enabler for higher edu- cation ecosystems, allowing learners to acquire targeted training and enabling institutions to expand their offerings and reach more students. The paper concludes by highlighting the potential for micro-credentials to help bridge the skills gap and equip learners with the skills necessary to succeed in today’s digital economy.

Tuberculosis (TB) disease still remain a major global threat due to the growing number of drug-resistant species and global warming. Despite the fact that there are new molecular diagnostic approaches, however, majority of developing countries and remote clinics depends on conventional approaches such as Tuberculin test, microscopic examinations and radiographic imaging (Chest X-ray). These techniques are hindered by several challenges which can lead to miss-diagnosis especially when interpreting large number of sample cases. Thus, in order to reduce workload and prevent miss-diagnosis, scientists incorporated computer-aided technology for detection of medical images known as Computer aided Detection (CADe) or Diagnosis (CADx). The use of AI-powered techniques has shown to improve accuracy, sensitivity, specificity. In this review, we discussed about the epidemiology, pathology, diagnosis and treatment of tuberculosis. The review also provides background information on Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Transfer Learning (TL) and their applications in detection of tuberculosis from both microscopic slide images and X-ray images. The review also proposed an IoT/AI powered system which allows transfer of results obtained from DL models with end users through internet networks. The concept of futuristic diagnosis, limitations of current techniques and open research issues are also discussed.

Major depressive disorder (MDD) and generalized anxiety disorder (GAD) exert significant burdens on individuals and society, underscoring the importance of accurate predictions using advanced machine learning (ML) algorithms. Leveraging electronic health records (EHRs) and survey data, these algorithms offer potential in forecasting such mental health conditions. Yet, the precision of these predictions can be compromised by biases or inaccuracies inherent in subjective survey responses. In this research, we assess the reliability of four prominent ML algorithms—Random Forest, XGBoost, Logistic Regression, and Naïve Bayesian—in predicting MDD and GAD. Our dataset encompasses a rich array of information, from biomedical metrics and demographic details to self-reported survey insights. A focal point of our investigation is the algorithms' performance under scenarios with varying degrees of subjective response inaccuracies, such as memory recall biases or subjective interpretation. Our findings reveal that while all algorithms exhibit commendable accuracy with pristine survey data, their performance diverges when faced with erroneous or biased responses. Notably, XGBoost remains stable and excels in identifying true positive cases, even in the presence of such noise. These observations underscore the criticality of algorithmic resilience in mental health prediction, especially when relying on subjective data. The robustness of certain algorithms to noisy inputs positions them as more reliable choices for predicting mental health conditions based on self-reported data, emphasizing the need for careful algorithm selection in such contexts.

(1) Background: In the year of 2020 Covid-19 was declared epidemic by WHO. From that time millions of people were affected and died by this disease. The main detection process for this epidemic is RT-PCR test or reverse polymerase transcription chain reaction test. One of the reason of spreading of this disease so much is lack of efficiency in RT-PCR test. Sampling error and low viral load were two main reasons for what the testing process faced such problems. Lung infection is a very common symptom for covid-19 patients, so, CT scan and chest X-ray imaging technique can be applied to detect patient at a very early stage of infection. Which will be very effective and also can be a better option to RT-PCR test; (2) Methods: We searched data in Scopus for articles published between 2020 and 2023. The initial set of articles was 189, from which 21 articles will eventually be selected by exclusion criteria; (3) Results: A total of thirteen (61.90%) articles were found working on detecting Covid-19 by extracting the X-ray and CT scan data individually. Three (14.28%) of those articles focused on a hybrid model of CT scan and X-ray Image Data. Another four articles made a comparison between Covid-19, pneumonia and a normal person to identify a Covid-19 patient. Where others have worked on unsupervised learning methods or SVM to detect Covid-19.; (4) Conclusions: We have conducted a systematic review of the studies that have been published up to this time, with the purpose to present a summary of the evidence about the detection of COVID-19. In this article, we have summarized and critically reviewed literatures which worked on development or application or both of different AI or ML technique from chest CT and X-ray images to find a solution to detect covid-19.

Although commercially-available low-cost air quality sensors have low accuracy, the sensor system are being used to collect the data for the regulation of PM2.5 emission caused by industrial activities or to estimate the personal exposure for PM2.5. In this work, to solve the accuracy problem of low-cost PM sensor, we developed a new PM2.5 calibration model by combining the deep neural network (DNN) optimized in calibration problem and a LSTM optimized in time-dependent characteristics. First, two datasets were generated to test the accuracy performance and generalization performance of the PM2.5 calibration machine learning (ML) model. The PM2.5 concentrations, temperature and humidity by low-cost sensor and gravimetric-based PM2.5 measuring instrument were sampled for a sufficiently long time. The proposed model was compared with benchmark (multiple linear regression model) and low-cost sensor results. For root mean square error (RMSE) for PM2.5 concentrations, the proposed model reduced 41-60% of error compared to the raw data of low-cost sensor, and reduced 30-51% of error compared to the benchmark model. R2 of ML model, MLR and raw data were 93, 80 and 59 %. Also, the developed model still showed consistent calibration performance when calibrated with new sensors in different locations. Low-cost sensors combined with ML model not only can improve the calibration performance of benchmark, but also can be applied to the sensor monitoring systems for various epidemiologic investigations and regulatory decisions.

Data Extraction is a technique is called as clustering which is used to retrieve data either from the files or data bases or both. This paper focuses on the performance evaluation parameters of the clustering algorithms based on different parameters or conditions or constraints and parameters which are used to perform the clustering process to get the clusters on the data sets. Therefore best clusters are retrieved when best parameters or conditions or constraints or preferences which are applied on the data sources for the clustering process. These parameters or conditions or constraints are opted by the user called as user preferences.

This paper presents an Application Based Active Learning (ABAL) methodology on Power Electronics (PE) and Electric Machines (EM) as a hybrid laboratory course for the undergraduate students to design and implement the real-world engineering problems. The ABAL is a type of active learning which is a branch of Learner-centered teaching (LCT). The DC/DC converter along with the speed control of DC separately excites the motor. In addition, a DC/AC converter is designed to control the speed of an induction motor. The results are then investigated on a hardware platform under the ABAL experimental methodology. This paper also discusses the problem identification selection of the equipment, circuit design, hardware mounting and critical analysis of the results acquired from the hybrid laboratory. The ABAL methodology was evaluated based on student satisfaction, feedback, grades and interest to solve the real-world problem rather than cramming the engineering concepts and fulfill so-called lab routine and tasks

Oral drug absorption is the primary route for drug administration. However, this process hinges on multiple factors, including the drug’s physicochemical properties, formulation characteristics, and gastrointestinal physiology. Given its intricacy and the exorbitant costs associated with experimentation, the trial-and-error method proves prohibitively expensive. Theoretical models have emerged as a cost-effective alternative by assimilating data from diverse experiments and theoretical considerations. These models fall into three categories: data-driven models, encompassing classical pharmacokinetics, quantitative-structure models (QSAR), and machine/deep learning. Mechanism-based models include quasi-equilibrium, steady-state, and physiologically based pharmacokinetics, alongside first principles such as molecular dynamics and continuum models. This review provides an overview of recent modeling endeavors across these categories, evaluating their respective advantages and limitations. Additionally, a primer on partial differential equations and their numerical solutions is included in the supplementary materials, recognizing their utility in modeling physiological systems despite their mathematical complexity limiting their widespread application in this field.

Chronic kidney disease (CKD) is a growing public health issue with significant morbidity and mortality rates. Traditional diagnostics, with about 70% accuracy, often delay CKD detection, highlighting the need for more efficient methods. Recent studies indicate a potential link between CKD and auditory health, yet this relationship remains underexplored due to methodological challenges and the complexity of establishing causality. Our research utilizes data spanning from 2000 to 2020 from the National Health and Nutrition Examination Survey (NHANES), covering 12,392 participants, including 2,060 diagnosed with CKD. Through meticulous analysis employing logistic regression and Mendelian randomization, we have unearthed novel insights into the bi-directional associations between hearing impairment and CKD. Furthermore, we developed and validated a machine learning model that surpasses traditional diagnostic approaches in terms of accuracy and predictive power. These findings highlight the innovative integration of auditory examinations with demographic data to enhance CKD detection. Our approach demonstrates the potential of machine learning in transforming diagnostic methodologies, thus offering a significant advancement in the field of nephrology and public health.

Skin image analysis using artificial intelligence (AI) has recently attracted significant research interest, particularly for analyzing skin images captured by mobile devices. Acne is one of the most common skin conditions with profound effects in severe cases. In this study, we developed an AI system called AcneDet for automatic acne object detection and acne severity grading using facial images captured by smartphones. AcneDet includes two models for conducting two tasks: (1) a Faster R-CNN-based deep learning model for the detection of acne lesion objects of four types including blackheads/whiteheads, papules/pustules, nodules/cysts, and acne scars; and (2) a LightGBM machine learning model for grading acne severity using the Investigator’s Global Assessment (IGA) scale. The output of the Faster R-CNN model, i.e., the counts of each acne type, were used as input for the LightGBM model for acne severity grading. A dataset consisting of 1,572 labeled facial images captured by both iOS and Android smartphones was used for training. The results show that the Faster R-CNN model achieves a mAP of 0.54 for acne object detection. The mean accuracy of acne severity grading by the LightGBM model is 0.85. With this study, we hope to contribute to the development of artificial intelligent systems that are able to help acne patients understand more about their conditions and support doctors in acne diagnosis.

Childhood is an obvious period for motor learning since children’s musculoskeletal and nervous systems are still in development. Adults adapt movements based on reward feedback about success and failure but it is less established whether school-age children also exhibit such reward-based motor learning. We designed a new ‘circle-drawing’ task suitable for assessing reward-based motor learning in both children and adults. Participants drew circles with their unseen hand on a tablet. They received binary reward feedback after each attempt based on the proximity of the average radius of their drawing to a target radius set as double the radius of their baseline drawings. We rewarded about 50% of the trials based on a performance-dependent reward criterion. Both children (7-17 years old) and adults increased the radius of their drawings in the direction of the target radius. We observed no difference in learning between children and adults. Moreover, both groups changed the radius less following reward than following reward absence, which is a sign of reward-based motor learning. We conclude that school-age children, like adults, exhibit reward-based motor learning.

Data sets for medical images are generally imbalanced and limited in sample size because of high data collection costs, time-consuming annotations, and patient privacy concerns. The training of deep neural network classification models on these data sets to improve the generalization ability does not produce the desired results for classifying the medical condition accurately and often overfit the data on the majority of class samples. To address the issue, we propose a framework for improving the classification performance metrics of deep neural network classification models using transfer learning: pre-trained models, such as Xception, InceptionResNet, DenseNet along with the Generative Adversarial Network (GAN) – based data augmentation. Then, we trained the network by combining traditional data augmentation techniques, such as randomly flipping the image left to right and GAN-based data augmentation, and then fine-tuned the hyper-parameters of the transfer learning models, such as the learning rate, batch size, and the number of epochs. With these configurations, the Xception model outperformed all other pre-trained models achieving a test accuracy of 98.7%, the precision of 99%, recall of 99.3%, f1-score of 99.1%, receiver operating characteristic (ROC) - area under the curve (AUC) of 98.2%.

Video self-supervised learning (VideoSSL) offers significant potential for reducing annotation costs and enhancing a wide range of downstream tasks in video understanding. The ultimate goal of VideoSSL is to achieve human-level video intelligence across a spectrum of tasks, from low-level tasks such as pixel temporal correspondence to high-level complex spatio-temporal tasks like action recognition. However, most existing VideoSSL methods focus on isolated aspects of this spectrum and fail to integrate different levels of task complexity. Our study presents the first comprehensive survey that connects all families of VideoSSL methods. We provide a detailed review of the full spectrum of VideoSSL, from low to high levels, by conceptually linking their self-supervised learning objectives and including a comprehensive categorization. Our extensive evaluation highlights the strengths and limitations of each SSL objective across various downstream task families. We also detail the challenges in current VideoSSL research such as data curation, interpretability, deployment, and privacy concerns, an area that previous surveys have not thoroughly explored. In addressing these challenges, we recognize the strengths of existing methods in addressing these challenges and outline future directions for research.

Artificial intelligence (AI) refers to computer algorithms that replicate the cognitive function of humans. Machine learning is widely applicable using structured and unstructured data, while deep learning is derived from the neural networks of the human brain that process and interpret information. During the last decades, AI has been introduced in several aspects of healthcare. In this review, we aim to present the current application of AI in the neonatal intensive care unit. AI-based models have been applied to neurocritical care, including automated seizure detection algorithms and electroencephalogram-based hypoxic-ischemic encephalopathy severity grading systems. Moreover, AI models evaluating magnetic resonance imaging contributed to the progress of the evaluation of the neonatal developing brain and the understanding of how prenatal events affect both structural and functional network topologies. Furthermore, AI algorithms have been applied to predict the development of bronchopulmonary dysplasia and assess the extubation readiness of preterm neonates. Automated models have been also used for the detection of retinopathy of prematurity and the need for treatment. Among others, AI algorithms have been utilized for the detection of sepsis, the need for patent ductus arteriosus treatmnet, the evaluation of jaundice, and the detection of gastrointestinal morbidities. Finally, AI prediction models have been constructed for the evaluation of the neurodevelopmental outcome and the overall mortality of neonates. Although the application of AI in neonatology is encouraging, further research in AI models is warranted in the future including retraining clinical trials, validating the outcomes, and addressing serious ethics issues.

Distance Learning has become the new standard, especially after the pandemic and due to the technological advances, that are incorporated into the teaching procedure. At the same time, the augmented use of the internet has blurred the borders between distance and conventional learning. Students interact mainly through LMSs, leaving their digital traces that can be leveraged to improve the educational process. This work aims to propose a model that can capture the students' behaviors based on the clickstream data associated with the discussion forum and additionally to suggest interpretable patterns that will support education administrators and tutors in the decision-making process. To achieve our goal, we use Social Network Analysis (SNA) as networks represent complex interactions in a meaningful and easily interpretable way. Moreover, simple or complex network metrics are becoming available to provide valuable insights into the students’ social interaction. This study concludes that by leveraging the imprint of these actions in an LMS and using metrics of SNA, differences can be spotted in the communicational patterns that go beyond simple participation recording. Although HITS and PageRank algorithms were created with completely different targeting, it is shown that they can also reveal methodological features in students’ communicational approach.

Documenting cultural heritage by using artificial intelligence (AI) is crucial for preserving the memory of the past and a key point for future knowledge. However, modern AI technologies make use of statistical learners that lead to self-empiricist logic, which, unlike human minds, use learned non-symbolic representations. Nevertheless, it seems that it is not the right way to progress in AI. If we want to rely on AI for these tasks, it is essential to understand what lies behind these models. Among the ways to discover AI there are the senses and the intellect. We could consider AI as an intelligence. Intelligence has an essence, but we do not know whether it can be considered “something” or “someone”. Important issues in the analysis of AI concern the structure of symbols -operations with which the intellectual solution is carried out- and the search for strategic reference points, aspiring to create models with human-like intelligence. For many years, humans, seeing language as innate, have carried out symbolic theories. Everything seems to have skipped with the advent of Machine Learning. In this paper, after a long analysis of history, the rule-based and the learning-based vision, we propose KERMIT as a unit of investigation for a possible meeting point between the different learning theories. Finally, we propose a new vision of knowledge in AI models based on a combination of rules, learning and human knowledge.

While the use of crime data has been widely advocated in the literature, its availability is often limited to large urban cities and isolated databases tend not to allow for spatial comparisons. This paper presents an efficient machine learning framework capable of predicting spatial crime occurrences, without using past crime as a predictor, and at a relatively high resolution: the U.S. Census Block Group level. The proposed framework is based on an in-depth multidisciplinary literature review allowing the selection of 188 best-fit crime predictors from socio-economic, demographic, spatial, and environmental data. Such data are published periodically for the entire United States. The selection of the appropriate predictive model was made through a comparative study of different machine learning families of algorithms, including generalized linear models, deep learning, and ensemble learning. The gradient boosting model was found to yield the most accurate predictions for violent crimes, property crimes, motor vehicle thefts, vandalism, and the total count of crimes. Extensive experiments on real-world datasets of crimes reported in 11 U.S. cities demonstrated that the proposed framework achieves an accuracy of 73 and 77% when predicting property crimes and violent crimes, respectively.

The world has been evolving with new technologies and advances day-by-day. With the advent of various learning technologies in every field, the research community is able to provide solution in every aspect of life with the applications of Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, etc. However, with such high achievements, it is found to lag behind the ability to provide explanation against its prediction. The current situation is such that these modern technologies are able to predict and decide upon various cases more accurately and speedily than a human, but failed to provide an answer when the question of why to trust its prediction is put forward. In order to attain a deeper understanding into this rising trend, we explore a very recent and talked-about novel contribution which provides rich insight on a prediction being made -- ``Explainability.'' The main premise of this survey is to provide an overview for researches explored in the domain and obtain an idea of the current scenario along with the advancements published to-date in this field. This survey is intended to provide a comprehensive background of the broad spectrum of Explainability.

Machine learning (ML) has emerged as a critical tool for making sense of the growing amount of genetic and genomic data available because of its ability to find complex patterns in high dimensional and heterogeneous data. While the complexity of ML models is what makes them powerful, it also makes them difficult to interpret. Fortunately, recent efforts to develop approaches that make the inner workings of ML models understandable to humans have improved our ability to make novel biological insights using ML. Here we discuss the importance of interpretable ML, different strategies for interpreting ML models, and examples of how these strategies have been applied. Finally, we identify challenges and promising future directions for interpretable ML in genetics and genomics.

The increasing trend of studying the innate softness of robotic structures and amalgamating it with the benefits of the extensive developments in the field of embodied intelligence has led to sprouting of a relatively new yet extremely rewarding sphere of technology. The fusion of current deep reinforcement algorithms with physical advantages of a soft bio-inspired structure certainly directs us to a fruitful prospect of designing completely self-sufficient agents that are capable of learning from observations collected from their environment to achieve a task they have been assigned. For soft robotics structure possessing countless degrees of freedom, it is often not easy (something not even possible) to formulate mathematical constraints necessary for training a deep reinforcement learning (DRL) agent for the task in hand, hence, we resolve to imitation learning techniques due to ease of manually performing such tasks like manipulation that could be comfortably mimicked by our agent. Deploying current imitation learning algorithms on soft robotic systems have been observed to provide satisfactory results but there are still challenges in doing so. This review article thus posits an overview of various such algorithms along with instances of them being applied to real world scenarios and yielding state-of-the-art results followed by brief descriptions on various pristine branches of DRL research that may be centers of future research in this field of interest.

Crowdsourcing is one of the new themes that has appeared in the last decade. Considering its potential, more and more organisations reach for it. It is perceived as an innovative method that can be used for problem solving, improving business processes, creating open innovations, building a competitive advantage, and increasing transparency and openness of the organisation. Crowdsourcing is also conceptualised as a source of a knowledge-based organisation. The importance of crowdsourcing for organisational learning is seen as one of the key themes in the latest literature in the field of crowdsourcing. Since 2008, there has been an increase in the interest of public organisations in crowdsourcing and including it in their activities. This article is a response to the recommendations in the subject literature, which states that crowdsourcing in public organisations is a new and exciting research area. The aim of the article is to present a new paradigm that combines crowdsourcing levels with the levels of learning. The research methodology is based on an analysis of the subject literature and exemplifications of organisations which introduce crowdsourcing. This article presents a cross-sectional study of four Polish municipal offices that use four types of crowdsourcing, according to the division by J. Howe: collective intelligence, crowd creation, crowd voting, and crowdfunding. Semi-structured interviews were conducted with the management personnel of those municipal offices. The research results show that knowledge acquired from the virtual communities allows the public organisation to anticipate changes, expectations, and needs of citizens and to adapt to them. It can therefore be considered that crowdsourcing is a new and rapidly developing organisational learning paradigm.

Droughts have a substantial effect on water resources, agriculture, and ecosystems on a worldwide scale. In the Mekong Delta of Vietnam, droughts exacerbated by climate change are significantly endangering the region's agricultural sustainability and output. Conventional forecasting techniques frequently do not capture the intricate dynamics of meteorological phenomena associated to drought effectively, prompting the exploration of more advanced methodologies. This work utilises artificial intelligence, par-ticularly machine learning methods like Gradient Boosting and Extreme Gradient Boosting (XGBoost), to enhance drought pre-diction in the Mekong Delta. The study utilises data from 11 meteorological stations spanning from 1990 to 2022 to create and evaluate Machine Learning models based on several climatic factors. We utilise Gradient Boosting and XGBoost algorithms to estimate the Standardised Precipitation-Evapotranspiration Index (SPEI) and evaluate their effectiveness in comparison to con-ventional forecasting techniques. The results show that Machine Learning, particularly XGBoost, surpasses traditional approaches in predicting SPEI accuracy at various time scales. XGBoost demonstrates skill in understanding the complex relationships between climatic factors, with R² values falling between 0.90 and 0.94 for 1-month forecasts. The progress highlights the potential of Machine Learning in improving drought management and adaptation tactics, proposing the incorporation of Machine Learning forecasting models into decision-making processes to enhance drought resistance in susceptible areas.

Plant diseases not only cause economic losses, but they also pose a health risk. In this regard, aflatoxins are linked to the development of some type of liver cancer, especially the hepatocellular carcinoma. Aflatoxins are usually produced by the fungi Aspergillus flavus and Parsiticus in figs, and their identification is usually carried out using invasive and complex methods or by visual inspection at an advanced stage. Given that Spain is one of the main producers of figs and Extremadura is the leading region in the country, it is necessary to explore alternative ways of detecting the presence of aflatoxins in figs and preventing them from entering the food chain. The aim of this research is to address the early detection of Aspergillus flavus fungus using non-invasive techniques with the aid of hyperspectral imaging and the application of artificial intelligence techniques, in particular deep learning. The images were taken after inoculation of the microtoxin using 3 different concentrations, related to three different classes and healthy figs (healthy controls). The analysis of the hyperspectral images was performed at the pixel level. Firstly, a full connected neural network was used to analyse the spectral signature associated with each pixel; second, the wavelet transform was applied to each spectral signature. The resulting images are fed to a convolutional neural network. The hyperparameters of the proposed models were adjusted based on the parameter tuning process also performed. The results are promising, with 83% accuracy, 82.75% recall and 83.25% F1-measure for the single neural network, and 77.25% accuracy, recall and F1 for the convolution neural network.

Continual learning (CL), a.k.a lifelong learning, is an emerging research topic that has been attracting increasing interest in the field of machine learning. With human activity recognition (HAR) playing a key role in enabling numerous real-world applications, an essential step towards the long-term deployment of such systems is to extend the activity model to dynamically adapt to changes in people’s everyday behavior. Current research in CL applied to HAR domain is still under-explored with researchers exploring existing methods developed for computer vision in HAR. Moreover, analysis has so far focused on task-incremental or class-incremental learning paradigms where task boundaries are known. This impedes the applicability of such methods for real-world systems. To push this field forward, we build on recent advances in the area of continual learning and design a lifelong adaptive learning framework using Prototypical Networks, LAPNet-HAR, that processes sensor-based data streams in a task-free data-incremental fashion and mitigates catastrophic forgetting using experience replay and continual prototype adaptation. Online learning is further facilitated using contrastive loss to enforce inter-class separation. LAPNet-HAR is evaluated on 5 publicly available activity datasets in terms of its ability to acquire new information while preserving previous knowledge. Our extensive empirical results demonstrate the effectiveness of LAPNet-HAR in task-free CL and uncover useful insights for future challenges.

The rise in the volume, variety and complexity of data in healthcare has made it as a fertile-bed for Artificial intelligence (AI) and Machine Learning (ML). Several types of AI are already being employed by healthcare providers and life sciences companies. The review summarises a classical machine learning cycle, different machine learning algorithms; different data analytical approaches and successful implementation in haematology. Although there are many instances where AI has been found to be great tool that can augment the clinician’s ability to provide better health outcomes, implementation factors need to be put in place to ascertain large-scale acceptance and popularity.

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.

The digital world has a wealth of data, such as Internet of Things (IoT) data, business data, health data, mobile data, urban data, security data, and many more, in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting knowledge or useful insights from these data can be used for smart decision-making in various applications domains. In the area of data science, advanced analytics methods including machine learning modeling can provide actionable insights or deeper knowledge about data, which makes the computing process automatic and smart. In this paper, we present a comprehensive view on "Data Science'' including various types of advanced analytics methods that can be applied to enhance the intelligence and capabilities of an application through smart decision-making in different scenarios. We also discuss and summarize ten potential real-world application domains including business, healthcare, cybersecurity, urban and rural data science, and so on by taking into account data-driven smart computing and decision making. Based on this, we finally highlight the challenges and potential research directions within the scope of our study. Overall, this paper aims to serve as a reference point on data science and advanced analytics to the researchers and decision-makers as well as application developers, particularly from the data-driven solution point of view for real-world problems.

Genome sequencing is enabling precision medicine—tailoring treatment to the unique constellation of variants in an individual’s genome. The impact of recurrent pathogenic variants is often understood, leaving a long tail of rare genetic variants that are uncharacterized. The problem of uncharacterized rare variation is especially acute when it occurs in genes of known clinical importance with functionally consequent frequent variants and associated mechanisms. Variants of unknown significance (VUS) in these genes are discovered at a rate that outpaces current ability to classify them using databases of previous cases, experimental evaluation, and computational predictors. Clinicians are thus left without guidance about the significance of variants that may have actionable consequences. Computational prediction of the impact of rare genetic variation is increasingly becoming an important capability. In this paper, we review the technical and ethical challenges of interpreting the function of rare variants in two settings: inborn errors of metabolism in newborns, and pharmacogenomics. We propose a framework for a genomic learning healthcare system with an initial focus on early-onset treatable disease in newborns and actionable pharmacogenomics. We argue that (1) a genomic learning healthcare system must allow for continuous collection and assessment of rare variants, (2) emerging machine learning methods will enable algorithms to predict the clinical impact of rare variants on protein function, and (3) ethical considerations must inform the construction and deployment of all rare-variation triage strategies, particularly with respect to health disparities arising from unbalanced ancestry representation.

Uncontrolled and fast cell proliferation is the cause of brain tumors. Early cancer detection is vitally important in order to save many lives. Brain tumors can be divided into several categories depending on the kind, place of origin, pace of development, and stage of progression; as a result, tumor classification is crucial for targeted therapy. The aim of brain tumor segmentation is to accurately delineate the areas of the brain. A specialist with a thorough understanding of brain illnesses must manually identify the proper type of brain tumor. Additionally, processing a lot of images takes time and is tiresome. Therefore, automatic segmentation and classification techniques are required to speed up and enhance the diagnosis of brain tumors. Tumors can be quickly and safely detected by brain scans using imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and others. Machine learning (ML) and artificial intelligence (AI) have shown promise in the development of algorithms that aid in automatic classification and segmentation utilizing various imaging modalities. This review discussed various types of brain tumors, publicly accessible datasets, enhancement methods, segmentation, feature extraction, classification, machine learning techniques, and deep learning, learning through transfer, for the study of brain tumors.

This study explored the incidence of metaphorical virtual classrooms and interactive learning objects in the interaction of students in online mode. The main objective was to analyze how these digital tools, driven by a set of strategies to promote their use, affect the interaction of students in the virtual classroom system and their derived effects. To this end, the latest version of Moodle was implemented in conjunction with gamification plugins and interactive tools in the higher education institution used as a case study. The methodology consisted of data collection through ordinal instruments applied to the teachers and student performance metrics gathered using a plugin developed to extract accurate metrics of each student's usage and performance through direct queries to the Moodle database and its processing through a neural network. This facilitated the collection of standardized data on the actual metrics of each virtual classroom at the end of the teaching of each subject from both the previous LMS and the newly implemented one. This data was then analyzed using advanced statistical techniques, including Mahalanobis distances, confirmatory factor analysis, and the Wilcoxon signed-rank test. These methods provided a compelling comparison between the old and new systems, revealing significant improvements in the metrics and factors evaluated. The results showed a significant improvement in teachers' perceptions of the usability of the virtual classroom system and an increase in students' academic performance, interaction, progress, and time spent learning in virtual contexts. These results provide solid empirical evidence of the added value of these educational tools as effective strategies for improving student interaction, performance, and motivation in online education.

Experiential learning, defined as the result, source, or context of knowledge obtained from direct experience, is increasingly bolstered by technology and human-computer interaction (HCI) principles and protocols. This article will describe how technology-based human-computer interaction (HCI) has been used in experiments to enhance direct knowledge experience. In this paper, immersive technologies (such as Virtual Reality, Augmented Reality, and haptics) will be described based on cognitive learning theory, empirical research and practical experience gained from implementing cutting-edge technology and maximizing HCI principles in constructive learning. Additionally, this article will describe how AI can be used to create individualized learning environments according to a student's needs in the future. While cost, technical issues and specialised trainings are the obstacle to deliver personalized learning experiences especially to economically disadvantaged students, technological change opportunities are projected, and these technologies will take educational outcomes to the next level.

This study assessed the potential of back extensor strength as an alternative marker of frailty. A total of 560 farmers were included. Computed tomography scans measured fat and muscle mass volumes at the mid-L4 vertebral level. Back extensor strength was measured in a seated posture. Multivariate linear regression analyzed the associations between back extensor strength and trunk muscle/fat compositions. The participants were divided into two groups based on back extensor strength. Propensity score matching, multivariate logistic regression, and Extreme Gradient Boosting (XGBoost) were employed to evaluate the relationship between Fried's frailty criteria and back extensor strength. Back extensor strength exhibited positive associations with abdominal muscle volume (r = 1.12) as well as back muscle volume (r = 0.89) (p < 0.05). Lower back extensor strength was linked to more frail status, such as reduced grip strength, walking speed, and fre-quent self-reported exhaustion. Multivariate logistic regression indicated a significant association between lower back extensor strength and higher Fried frailty score (OR, 0.990; p<0.05). XGBoost analysis identified back extensor strength as the most important predictor (gain = 0.502) for frailty, surpassing the significance of age (gain = 0.325). These findings suggested the potential of back extensor strength as an alternative frailty marker.

A remarkable strengths of deep neural networks lies in their ability to discover patterns and representations from complex data. Deep learning models can learn and refine features at different levels of abstraction, enabling them to handle complex tasks in computer vision recognition, natural language processing, and speech recognition. The ability to generalize from examples, coupled with the capacity to process vast amounts of data, empowers deep learning to achieve state-of-the-art results across a wide spectrum of applications. An argument for this ability is based on similarities between deep neural networks and the nervous system. In this report, we argue that despite the remarkable performance of deep learning, there are still gaps between deep learning and the nervous system that needs to be closed to enable deep learning doing tasks that currently only the nervous system can perform

Edible mushrooms have been recognized as highly nutritional food for a long time, due to their specific flavor, texture and also for therapeutic effects. This study proposes a new simple approach, based on FT-IR analysis, followed by statistical methods, in order to differentiate three wild mushrooms species from Romanian spontaneous flora, namely Armillaria mellea, Boletus edulis and Cantharellus cibarius. The preliminary data treatment consisted of data set reduction with principal component analysis (PCA), which provided scores for the next methods. Linear discriminant analysis (LDA) manage to 100% classify the three species and the cross validation step of the method returned 97.4% of correctly classified samples. Only one A. mellea sample overlapped on B. edulis group. When kNN was used in the same manner as LDA, the overall percent of correctly classified samples from the training step was 86.21%, while for holdout set the percent raised at 94.74%. The lowered values obtained for the training set was due to one C. cibarius sample, two B. edulis and five A. mellea, which were placed to other species. Anyway, for holdout sample set, only one sample from B. edulis was misclassified. The fuzzy c-means clustering (FCM) analysis successfully classified investigated mushroom samples according to their species, meaning that in every partition the predominant specie had the biggest DOMs, while samples belonging to other specie had lower DOMs.

The learning system is primarily based on teaching, and teaching through digital resources is referred to as e-learning. As the world progresses towards success day by day, the use of technology, digital devices, and modern techniques is becoming increasingly prevalent. The global shift from traditional paperwork to digital learning is one of the most significant changes in the education sector. However, many factors have created numerous hurdles during the online setup. The Covid-19 pandemic has led to serious problems all over the world, with the education sector being one of the most affected. One of the greatest challenges students face during online education is the lack of digital infrastructure, especially in remote areas of Pakistan. The biggest challenge is connecting students with teachers due to limited internet access. Without proper internet connectivity, both students and teachers cannot access educational materials. In this context, the government plays a crucial role in providing adequate facilities to students, particularly in primary schools. If proper infrastructure is not made available during this pandemic, students will be deprived of a good quality education. Therefore, good infrastructure is essential for effective online education. With the proper resources in place, students will be able to receive a quality education.

This study introduces the Dynamic Feedback-Driven Learning Optimization Framework (DFDLOF), an innovative machine learning-based approach to personalize educational pathways. DFDLOF leverages advanced algorithms for in-depth analysis of learner data, enabling the dynamic customization of educational content and methodologies. Its application in diverse online platforms, including Khan Academy and Coursera, underscores its adaptability and scalability. DFDLOF enhances learner engagement and outcomes by providing tailored learning experiences and real-time feedback. Key insights reveal its strengths in personalized education, particularly in dynamically adapting learning paths and the importance of real-time feedback. Challenges identified include integration complexities and data privacy concerns. Future research directions emphasize the need for longitudinal impact studies and broader demographic applications. This study contributes significantly to the evolving domain of adaptive learning technologies, demonstrating the transformative potential of machine learning in personalizing education.

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

Few-shot fine-grained image classification (FSFGIC) methods refer to machine learning methods which aim to classify images (e.g., bird species, flowers, and airplanes) belonging to subordinate object categories of the same entry-level category with only a few samples. It is worth to note that feature representation learning is used not only to represent training samples, but also to construct classifiers for performing various FSFGIC tasks. In this paper, starting from the definition of FSFGIC, a taxonomy of feature representation learning for FSFGIC is proposed. According to this taxonomy, we discuss key issues on FSFGIC (including data augmentation, local or/and global deep feature representation learning, class representation learning, and task specific feature representation learning). The existing popular datasets and evaluation standards are introduced. Furthermore, a novel classification performance evaluation mechanism is designed with a 0.95 confidence interval for judging whether the classification accuracy obtained by a certain specified method is good or bad. Moreover, current challenges and future trends of feature representation learning on FSFGIC are elaborated.

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

This study investigates how digital alchemy can enhance literature and language teaching through a combination of online and face-to-face learning. It uses constructivist learning theory and the SAMR model to examine how digital tools and resources can transform learner engagement, critical thinking, and creativity. The study gathered data from interviews, focus groups, and document analysis with 30 participants from private HEI schools. The results show that digital alchemy through blended learning allows learners to personalize and collaborate in their exploration of various literary texts. However, the study also identifies some challenges related to resource availability and educator training. The study emphasizes the importance of using digital alchemy in literature and language teaching and suggests some ways for educators, administrators, and policymakers to effectively use digital tools and resources for student-centered teaching methods in the digital era.

The use of artificial intelligence (AI) applications in education has the potential to revolutionize the learning experience for international students, who face unique challenges when studying in a foreign country. This paper explores various examples of AI applications in education and their potential impact on international students. AI applications such as personalized learning experiences, adaptive testing, predictive analytics, and chatbots for learning and research are examined for their potential to improve learning efficiency and provide customized education support. It also explores the significant risks and limitations associated with AI technologies, such as privacy, cultural differences, language proficiency, and ethical implications. To maximize the potential benefits of AI applications in higher education, it is crucial to implement appropriate safeguards and regulations. This paper provides a starting point for research on the potential impact of artificial intelligence on international students’ educational experiences and how AI may be integrated into educational administration and learning processes.