Technology and innovation empower higher educational institutions (HEI) to use different types of learning systems—video learning is one such system. Analyzing the footprints left behind from these online interactions is useful for understanding the effectiveness of this kind of learning. Video-based learning with flipped teaching can help improve student’s academic performance. This study was carried out with 772 examples of students registered in e-commerce and e-commerce technologies modules at an HEI. The study aimed to predict student’s overall performance at the end of the semester using video learning analytics and data mining techniques. Data from the student information system, learning management system and mobile applications were analyzed using eight different classification algorithms. Furthermore, data transformation and preprocessing techniques were carried out to reduce the features. Moreover, genetic search and principle component analysis were carried out to further reduce the features. Additionally, the CN2 Rule Inducer and multivariate projection can be used to assist faculty in interpreting the rules to gain insights into student interactions. The results showed that Random Forest accurately predicted successful students at the end of the class with an accuracy of 88.3% with an equal width and information gain ratio.

Business organizations experience cut-throat competition in the e-commerce era, where a smart organization needs to come up with faster innovative ideas to enjoy competitive advantages. A smart user decides from the review information of an online product. Data-driven smart machine learning applications use real data to support immediate decision making. Web scraping technologies support supplying sufficient relevant and up-to-date well-structured data from unstructured data sources like websites. Machine learning applications generate models for in-depth data analysis and decision making. The Internet Movie Database (IMDB) is one of the largest movie databases on the internet. IMDB movie information is applied for statistical analysis, sentiment classification, genre-based clustering, and rating-based clustering with respect to movie release year, budget, etc., for repository dataset. This paper presents a novel clustering model with respect to two different rating systems of IMDB movie data. This work contributes to the three areas: (i) the “grey area” of web scraping to extract data for research purposes; (ii) statistical analysis to correlate required data fields and understanding purposes of implementation machine learning, (iii) k-means clustering is applied for movie critics rank (Metascore) and users’ star rank (Rating). Different python libraries are used for web data scraping, data analysis, data visualization, and k-means clustering application. Only 42.4% of records were accepted from the extracted dataset for research purposes after cleaning. Statistical analysis showed that votes, ratings, Metascore have a linear relationship, while random characteristics are observed for income of the movie. On the other hand, experts’ feedback (Metascore) and customers’ feedback (Rating) are negatively correlated (−0.0384) due to the biasness of additional features like genre, actors, budget, etc. Both rankings have a nonlinear relationship with the income of the movies. Six optimal clusters were selected by elbow technique and the calculated silhouette score is 0.4926 for the proposed k-means clustering model and we found that only one cluster is in the logical relationship of two rankings systems.

Higher educational institutes (HEI) are adopting ubiquitous and smart equipment such as mobile devices or digital gadgets to deliver educational content in a more effective manner than the traditional approaches. In present works, a lot of smart classroom approaches have been developed, however, the student learning experience is not yet fully explored. Moreover, module historical data over time is not considered which could provide insight into the possible outcomes in the future, leading new improvements and working as an early detection method for the future results within the module. This paper proposes a framework by taking into account module historical data in order to predict module performance, particularly the module result before the commencement of classes with the goal of improving module pass percentage. Furthermore, a video streaming server along with blended learning are sequentially integrated with the designed framework to ensure correctness of teaching and learning pedagogy. Simulation results demonstrate that by considering module historical data using time series forecasting helps in improving module performance in terms of module delivery and result outcome in terms of pass percentage. Furthermore, the proposed framework provides a mechanism for faculties to adjust their teaching style according to student performance level to minimize the student failure rate.

The data presented in this article comprise an educational dataset collected from the student information system (SIS), the learning management system (LMS) called Moodle, and video interactions from the mobile application called “eDify.” The dataset, from the higher educational institution (HEI) in Sultanate of Oman, comprises five modules of data from Spring 2017 to Spring 2021. The dataset consists of 326 student records with 40 features in total, including the students’ academic information from SIS (which has 24 features), the students’ activities performed on Moodle within and outside the campus (comprising 10 features), and the students’ video interactions collected from eDify (consisting of six features). The dataset is useful for researchers who want to explore students’ academic performance in online learning environments, and will help them to model their educational datamining models. Moreover, it can serve as an input for predicting students’ academic performance within the module for educational datamining and learning analytics. Furthermore, researchers are highly recommended to refer to the original papers for more details.

Operational technology, industrial automation, advanced healthcare systems, and smart city infrastructures are common forms of IoT integrated distributed networks. Numerous IoT components require vast amounts of power for sensing, data extraction, processing, and sharing over the internet. Moreover, IoT devices are in operation with a lack of standards that poses a severe security threat. Intrusion detection systems and cryptographic algorithms are two important components in the security posture of a cyberspace. IoT applications provide real-time services, so security measures are active 24/7/365. Hence, these algorithms are also related to energy consumption. It is a study of security algorithms based on their hardware and software implementations. This review systematically searches for and selects the latest research documents, conducting an analysis of sustainability and security. It depicts the lightweight features of cryptographic algorithms; the relationship of gate density, chip area and power consumption in CMOS technology; and the importance of Machine Learning (MA) applications for IDS and cryptographic solutions in Next Generation IoT (NGIoT).

Background: Cardiovascular disease (CVD) remains a leading cause of global mortality, with many risk factors being potentially modifiable, highlighting the importance of early risk assessment. Machine learning (ML) can support accurate CVD risk prediction and provide explanations for individual predictions. However, the practical use of explainable AI (XAI) remains limited by unstable feature rankings across training samples, limited distinction between modifiable and non-modifiable factors, and insufficient verification of whether recommended changes reduce model-estimated risk. Objective: To bridge these gaps, this study introduces pCARE-CV, a unified XAI framework that integrates predictive modeling, bootstrap-based SHapley Additive exPlanations (SHAP) stability analysis, a SHAP-guided recommendation module (SGRM), and multi-method counterfactual (CF) analysis within a single, deployable, and clinically interpretable cardiovascular risk assessment system. Thus, our work extends the methodological foundation of interpretable ML for applications in biomedical. Methods: Seven ML classifiers were evaluated on two synthetic cohorts, EarlyMed (n = 70,000) and HDD-25 (n = 3,069), and one real-world cohort, UCI-HDD (n = 920), using multiple predictive performance metrics with bootstrap confidence intervals. SHAP ranking stability was assessed through 200 bootstrap refits using Spearman correlation, Kendall’s tau, and top-k overlap. Multi-Objective Counterfactuals (MOC), Nearest Instance Counterfactual Explanations (NICE), and What-If CF methods were compared on EarlyMed and UCI-HDD using validity, sparsity, proximity, plausibility, and predicted probability reduction. The proposed framework is demonstrated through a Streamlit-based web application that enables real-time translation and provides an applicability and feasibility recommendation. Results: CatBoost achieved accuracies of 99.36%, 63.03%, and 85.87% on EarlyMed, HDD-25, and UCI-HDD, respectively. Mean bootstrap Spearman correlations of SHAP rankings were 0.886, 0.732, and 0.837, indicating differences in attribution stability across datasets. On EarlyMed, MOC achieved the highest validity (1.000) and mean probability reduction (0.7075), while What-If achieved the highest plausibility (1.3255). NICE generally required fewer feature modifications but showed slightly lower validity. On UCI-HDD, the relative performance of the CF methods varied across evaluation criteria, underscoring the potential applicability of CFs in clinical settings. Conclusion: pCARE-CV integrates prediction, explanation stability, actionable recommendations, and CF analysis within a single deployable framework. The results highlight the importance of evaluating predictive performance and explanation stability across datasets, as well as CF explanations in medical domains using multiple complementary criteria. Together, these evaluations can support more nuanced and effective cardiovascular patient care strategies.