Machine Learning-Based Intervention Recommendation System for Student Stress Management

The coronavirus disease 2019 (COVID-19) global pandemic poses the threat of overwhelming healthcare systems with unprecedented demands for intensive care resources. Managing these demands cannot be effectively conducted without a nationwide collective effort that relies on data to forecast hospital demands on the national, regional, hospital and individual levels. To this end, we developed the COVID-19 Capacity Planning and Analysis System (CPAS)—a machine learning-based system for hospital resource planning that we have successfully deployed at individual hospitals and across regions in the UK in coordination with NHS Digital. In this paper, we discuss the main challenges of deploying a machine learning-based decision support system at national scale, and explain how CPAS addresses these challenges by (1) defining the appropriate learning problem, (2) combining bottom-up and top-down analytical approaches, (3) using state-of-the-art machine learning algorithms, (4) integrating heterogeneous data sources, and (5) presenting the result with an interactive and transparent interface. CPAS is one of the first machine learning-based systems to be deployed in hospitals on a national scale to address the COVID-19 pandemic—we conclude the paper with a summary of the lessons learned from this experience.

Introduction: This research examines the potential of cloud-based healthcare systems, enhanced by artificial intelligence (AI) and predictive analytics, to improve patient care. With increasing demand for more efficient healthcare solutions, AI-driven systems offer promising advancements in real-time health monitoring, risk assessments, and personalized treatment recommendations. By integrating machine learning algorithms like random forests, support vector machines (SVMs), and gradient boosting, these systems predict patient health trends and identify high-risk individuals. This research aims to demonstrate how AI-powered frameworks can optimize resource allocation, improve health outcomes, reduce costs, and enhance overall patient satisfaction. Top of Form Bottom of Form Objectives: This research are to develop AI-driven cloud-based healthcare systems for real-time patient monitoring, risk assessment, personalized treatment recommendations, and improved resource allocation to enhance patient care. Methods: This research employs a multi-stage methodology to evaluate the potential of AI-driven cloud-based healthcare systems. First, a comprehensive literature review is conducted to explore current advancements in healthcare technologies, AI algorithms, and cloud infrastructures. Next, various machine learning algorithms, including random forests, support vector machines (SVMs), and gradient boosting, are developed and integrated into predictive analytics frameworks. These algorithms process large-scale healthcare datasets to predict patient health, risk factors, and disease progression. The developed systems are tested in cloud environments to assess their effectiveness in real-time health monitoring, risk assessment, and targeted treatment suggestions. Top of Form Bottom of Form Results: AI-driven cloud-based healthcare systems significantly improve patient care. Predictive analytics frameworks, utilizing random forests, SVMs, and gradient-boosting algorithms, accurately forecast patient outcomes, identify high-risk individuals, and predict disease progression. Real-time health monitoring and risk assessment enabled by these systems allow healthcare providers to deliver more targeted treatment recommendations and optimize resource allocation. The research shows potential for enhanced patient outcomes, reduced healthcare costs, and improved patient satisfaction. These findings confirm that combining AI and cloud-based systems can transform the healthcare industry by offering data-driven, personalized care solutions. Conclusions: The cloud-based healthcare systems powered by AI and predictive analytics can significantly enhance patient care. By enabling real-time monitoring, accurate risk assessment, and personalized treatments, these systems improve health outcomes, reduce costs, and increase patient satisfaction, offering a transformative approach to modern healthcare management.  

Cervical cancer is a leading cause of mortality among women worldwide, particularly in developing countries where access to regular screening and timely diagnosis is limited. Early detection is critical to improve patient outcomes, yet conventional diagnostic methods such as Pap smears and colposcopy are time-consuming, costly, and prone to human error. This study proposes a machine learning-based predictive system for early detection of cervical cancer using patient demographic data, medical history, and laboratory test results. The system integrates data preprocessing, feature extraction, and selection techniques to handle missing values, noise, and class imbalance. Multiple supervised learning algorithms—including Logistic Regression, Decision Tree, K-Nearest Neighbors, Support Vector Machine, Random Forest, Gradient Boosting, AdaBoost, and XGBoost—are developed and evaluated using metrics such as accuracy, precision, recall, and F1-score. The platform is implemented in Python, providing a user-friendly decision support interface for healthcare professionals. By automating the prediction process and improving diagnostic accuracy, this system aims to facilitate early intervention, reduce clinical workload, and contribute to lowering the global burden of cervical cancer, particularly in resource-constrained healthcare settings.

A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness

Predicting students’ academic performance at an early stage of a semester is one of the most crucial research topics in the field of Educational Data Mining (EDM). Students are facing various difficulties in courses like “Programming” and “Data Structures” through undergraduate programs, which is why failure and dropout rates in these courses are high. Therefore, EDM is used to analyze students’ data gathered from various educational settings to predict students’ academic performance, which would help them to achieve better results in their future courses. The main goal of this paper is to explore the efficiency of deep learning in the field of EDM, especially in predicting students’ academic performance, to identify students at risk of failure. A dataset collected from a public 4-year university was used in this study to develop predictive models to predict students’ academic performance of upcoming courses given their grades in the previous courses of the first academic year using a deep neural network (DNN), decision tree, random forest, gradient boosting, logistic regression, support vector classifier, and K-nearest neighbor. In addition, we made a comparison between various resampling methods to solve the imbalanced dataset problem, such as SMOTE, ADASYN, ROS, and SMOTE-ENN. From the experimental results, it is observed that the proposed DNN model can predict students’ performance in a data structure course and can also identify students at risk of failure at an early stage of a semester with an accuracy of 89%, which is higher than models like decision tree, logistic regression, support vector classifier, and K-nearest neighbor.

With the emerging intelligent technology and larger talent gaps training and development are the need of the hour. Organizations use competencies to remain adaptable and competitive in their respective fields. In this paper, we propose a technique to manage competency across a business unit to keep up with the want of the customers and ever-changing technology. The solution is powered by Artificial Intelligence and E-learning. We propose a technique to customize a learning roadmap for an individual by using a machine learning-based hybrid recommendation system. By using the technique managers can identify and optimize the skills required to deliver on an organization’s business strategy. The last section of the paper discusses a technique to auto-rank the algorithms used within a hybrid recommendation system based accuracy of predictions that help reduce the training time of the machine learning model itself. The solution indirectly boosts innovation, job satisfaction, and morale among employees resulting in higher levels of engagement, thus enabling a better productive workforce.

Over the last decade, there has been a considerable increase in the popularity of online education. As a result, the online learning or e-learning industry has flourished, providing benefits to students, learners, educators, and education experts. Despite the advantages of e-learning, it also has its drawbacks. While e-learning enables students to access the learning materials at their convenience from any location, one of the significant challenges is the lack of monitoring of their level of attention during e-learning sessions. It is challenging to ascertain whether a student is actively engaged in the learning process. To address this issue, we have proposed a decision support system (DSS) based on wearable physiological sensor signals (i.e., Electroencephalogram (EEG) signals) that can inform the instructor whether a student is attentive. For developing DSS, we recorded an EEG-based dataset using a neurosky device, and 100 individuals participated in the study. The learning state is divided into two categories: attentive and inattentive. In this paper, machine learning techniques are employed to integrate the proposed DSS, which can predict, analyse, and validate the student’s level of attention or inattention throughout the e-learning session. The findings show that the Support Vector Machine (SVM) approach is the most efficient method for attention prediction, achieving an accuracy of 91.68% compared to logistic regression and ridge regression. Additionally, we examined the frequency bands that were most significant in predicting the learning state, with beta and alpha waves being identified as the key contributors in predicting attention. To further evaluate the data, we use K-means and Hierarchical algorithms to cluster beta and alpha data points. K-means effectively identifies an ideal representative of an attentive or inattentive state. Thus, EEG waves can effectively reveal whether a student is attentive during real-time e-learning sessions, providing a promising approach for providing a valuable tool for decision support in the E-Learning Environment.

Predictive analytics in customer behavior: Anticipating trends and preferences

Purpose This study aims to systematically review the methods, predictive factors and evaluation metrics used to forecast university students’ academic performance. By synthesizing recent literature, it addresses the lack of an integrated overview of state-of-the-art approaches and key determinants, and provides evidence-based insights to inform personalized teaching strategies and early intervention practices in higher education. Design/methodology/approach A systematic literature review (SLR) was conducted in accordance with the PRISMA framework. Articles published between 2015 and 2024 were retrieved from the Web of Science database using predefined keywords and Boolean search strategies. Following a structured screening process based on explicit inclusion and exclusion criteria, 31 studies were selected for analysis. These studies were systematically examined to synthesise predictive methods, key influencing factors and commonly used evaluation metrics in forecasting university students’ academic performance. Findings Academic performance is influenced by personal factors (e.g. entry scores, prior Grade Point Average [GPA], gender, age, emotional responses, self-esteem) and external factors (e.g. family income, parental education, accommodation, learning environment). Main prediction approaches include statistical regression models, machine learning algorithms (e.g. random forest, support vector machine (SVM), gradient boosting) and deep learning techniques. Machine learning shows strong performance with complex, non-linear data, while deep learning achieves high accuracy but requires large datasets. Common evaluation metrics include accuracy, precision, recall, F1 score and mean squared error (MSE). Originality/value This study provides a systematic and integrative synthesis of predictive methods, influencing factors and evaluation metrics related to university students’ academic performance. By consolidating evidence that has previously been scattered across methodological and thematic strands, the review offers a structured reference for researchers and actionable insights for educators and policymakers to support early identification of at-risk students and inform data-driven educational decision-making.

Worldwide, 30%-45% of adults have sleep disorders, which are linked to major health issues such as diabetes and cardiovascular disease. Long-term monitoring with traditional in-lab testing is impractical due to high costs. Wearable artificial intelligence (AI)-powered solutions offer accessible, scalable, and continuous monitoring, improving the identification and treatment of sleep problems. This scoping review aims to provide an overview of AI-powered wearable devices used for sleep disorders, focusing on study characteristics, wearable technology features, and AI methodologies for detection and analysis. Seven electronic databases (MEDLINE, PsycINFO, Embase, IEEE Xplore, ACM Digital Library, Google Scholar, and Scopus) were searched for peer-reviewed literature published before March 2024. Keywords were selected based on 3 domains: sleep disorders, AI, and wearable devices. The primary selection criterion was the inclusion of studies that utilized AI algorithms to detect or predict various sleep disorders using data from wearable devices. Study selection was conducted in 2 steps: first, by reviewing titles and abstracts, followed by full-text screening. Two reviewers independently conducted study selection and data extraction, resolving discrepancies by consensus. The extracted data were synthesized using a narrative approach. The initial search yielded 615 articles, of which 46 met the eligibility criteria and were included in the final analysis. The majority of studies focused on sleep apnea. Wearable AI was widely deployed for diagnosing and screening disorders; however, none of the studies used it for treatment. Commercial devices were the most commonly used type of wearable technology, appearing in 30 out of 46 (65%) studies. Among these, various brands were utilized rather than a single large, well-known brand; 19 (41%) studies used wrist-worn devices. Respiratory data were used by 25 of 46 (54%) studies as the primary data for model development, followed by heart rate (22/46, 48%) and body movement (17/46, 37%). The most popular algorithm was the convolutional neural network, adopted by 17 of 46 (37%) studies, followed by random forest (14/46, 30%) and support vector machines (12/46, 26%). Wearable AI technology offers promising solutions for sleep disorders. These devices can be used for screening and diagnosis; however, research on wearable technology for sleep disorders other than sleep apnea remains limited. To statistically synthesize performance and efficacy results, more reviews are needed. Technology companies should prioritize advancements such as deep learning algorithms and invest in wearable AI for treating sleep disorders, given its potential. Further research is necessary to validate machine learning techniques using clinical data from wearable devices and to develop useful analytics for data collection, monitoring, prediction, classification, and recommendation in the context of sleep disorders.

The exponential growth of recommender systems research has drawn the attention of the scientific community recently. These systems are very useful in reducing information overload and providing users with the items of their need. The major areas where recommender systems have contributed significantly include e-commerce, online auction, and books and conference recommendation for academia and industrialists. Book recommender systems suggest books of interest to users according to their preferences and requirements. In this article, we have surveyed machine learning techniques which have been used in book recommender systems. Moreover, evaluation metrics applied to evaluate recommendation techniques is also studied. Six categories for book recommendation techniques have been identified and discussed which would enable the scientific community to lay a foundation of research in the concerned field. We have also proposed future perspectives to improve recommender system. We hope that researchers exploring recommendation technology in general and book recommendation in particular will be finding this work highly beneficial.

Stress impairs human cognition and over long periods of time can cause severe negative health effects for older adults. Real-time stress detection can be a useful tool in stress mitigation strategies, but stress detection is often implemented with machine learning models too large to run on an edge device. This paper presents a stress detection framework for older adults which is designed to be executed with low latency by a wearable device or other embedded system. We use Galvanic Skin Response, Blood Volume Pulse, Interbeat Interval, and skin temperature data with cortisol as a stress biomarker to evaluate the performance of several machine learning models. The best performing model was a radial basis function-based SVC (Support Vector Machine). The model achieved a test data accuracy of 82% and a test data F1-Score of 0.70. Performance data indicates that the framework performs well and could be improved for use on a wearable device.

BackgroundDelivery of acute stroke endovascular intervention can be challenging because it requires complex coordination of patient and staff across many different locations. In this proof-of-concept paper we (a) examine whether...

Agriculture fundamentally depends on comprehending the complex interplay between crops and their environmental conditions, especially soil ecology. As in India, nearly 60% of people are dependent on agriculture in rural areas, and it also supports related industries like textiles, food processing, and ag machinery by providing raw materials. As in the past, machine learning methodologies are used for various soil ecologies to deliver accurate crop suggestions. Our methodology determines the optimal crops for certain places by evaluating essential soil properties, including pH, nitrogen (N), phosphorus (P), potassium (K) levels, and organic carbon content, in conjunction with weather conditions such as temperature, rainfall, and humidity. The suggested method assesses various machine learning algorithms, including decision trees, random forests, support vector machines, and ensembles, to choose the most effective models for precise predictions. We achieved a top accuracy of 98.4% by random forest and also 99.4% accuracy by decision tree, but with a risk of overfitting. Showing that integrating soil ecology leads to more precise and sustainable crop recommendations.

The condition of road surface has a significant role in land transportation. Due to poor road conditions, the logistics and supply chain industry face a drastic loss in their business. Unmaintained roads can cause damage to goods and accidents. The existing routing techniques do not consider factors like shock, temperature and tilt of goods etc. but these factors have to be considered for the logistics and supply chain industry. This paper proposes a recommender system which target management of goods in logistics. A 3 axis accelerometer is used to measure the road surface conditions. The pothole location is obtained using Global Positioning System (GPS). Using these details a hybrid recommender system is built. Hybrid recommender system combines multiple recommendation techniques to develop an effective recommender system. Here content-based and collaborative-based techniques is combined to build a hybrid recommender system. One of the popular Multiple Criteria Decision Making (MCDM) method, The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used for content based filtering and normalised Euclidean distance and KNN algorithm is used for collaborative filtering. The best route recommended by the system will be displayed to the user using a map application.