Water quality monitoring plays a significant part in the transition towards intelligent and smart agriculture and provides an easy transition to automated monitoring of crucial components of human daily needs as new technologies are continuously developed and adopted in agricultural and human daily life (water). For the monitoring and management of water quality, this effort, however, requires reliable models with accurate and thorough datasets. Analyzing water quality monitoring models by utilizing sensors that gather water properties during live experiments is possible due to the necessity for precision in modeling. To convey numerous conclusions regarding the concerns, issues, difficulties, and research gaps that have existed throughout the past five years (2018–2022), this review article thoroughly examines the water quality literature. To find trustworthy peer-reviewed publications, several digital databases were searched and examined, including IEEE Xplore^®, ScienceDirect, Scopus, and Web of Science. Only 50 articles out of the 946 papers obtained, were used in the study of the water quality monitoring research area. There are more rules for article inclusion in the second stage of the filtration process. Utilizing a real-time data acquisition system, the criteria for inclusion for the second phase of filtration looked at the implementation of water quality monitoring and characterization procedures. Reviews and experimental studies comprised most of the articles, which were divided into three categories. To organize the literature into articles with similar types of experimental conditions, a taxonomy of the three literature was created. Topics for recommendations are also provided to facilitate and speed up the pace of advancement in this field of study. By conducting a thorough analysis of the earlier suggested methodologies, research gaps are made clear. The investigation largely pointed out the problems in the accuracy of the models, the development of data-gathering systems, and the types of data used in the proposed frameworks. Finally, by examining critical topics required for the development of this research area, research directions toward smart water quality are presented.

The increasing reliance on Massive Open Online Courses (MOOCs) has transformed the landscape of education, particularly during the COVID-19 pandemic, where e-learning became essential. However, the effectiveness of MOOCs in enhancing student academic performance and engagement remains a key challenge, compounded by high dropout rates and low retention. This study presents a systematic literature review (SLR) conducted over a five-year period (2019–2024) to identify factors affecting student academic performance and engagement prediction in MOOCs, utilizing Deep Learning (DL) methods. The review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, systematically analyzing articles from five major academic databases: ScienceDirect, SpringerLink, Scopus, Taylor & Francis, and Wiley Online. A total of 70 articles were selected for in-depth analysis, focusing on key predictors of student performance and engagement, including demographic data, behavioral patterns, learning activities, and clickstream data. The review highlights the capabilities of DL techniques in predicting student outcomes, such as retention, dropout, and engagement, offering valuable insights for educators and policymakers aiming to improve MOOC-based learning environments. By conducting SLR using PRISMA model, we identified research findings and gaps by proposing a conceptual framework for developing future personalized and adaptive e-learning environment for the inclusive MOOC based hard of hearing and low vision learners. This paper concludes by discussing implications for future personalized and adaptive e-learning environments and the necessity of comprehensive teacher training programs to navigate these evolving educational technologies.

A review is conducted to deeply analyse and map the research landscape of current technologies in finger vein (FV) biometric authentication in medical systems into a coherent taxonomy. This research focuses on articles related to the keywords ‘biometrics’, ‘finger veins’ and ‘verification’ and their variations in three major databases, namely, Web of Science, ScienceDirect and IEEE Xplore. The final set of collected articles related to FV biometric authentication systems is divided into software- and hardware-based systems. In the first category, software development attempts are described. The experiment results, frameworks, algorithms and methods that perform satisfactorily are presented. Moreover, the experiences obtained from conducting these studies are discussed. In the second category, hardware development attempts are described. The final articles are discussed from three aspects, namely, (1) number of publications, (2) problem type, proposed solutions, best results and evaluation methods in the included studies and (3) available databases containing different scientific work collected from volunteers, such as staff and students. The basic characteristics of this emerging field are identified from the following aspects: motivations of using FV biometric technology in authentication systems, open challenges that impede the technology’s utility, authors’ recommendations and future research prospects. A new solution is proposed to address several issues, such as leakage of biometrics that leads to serious risks due to the use of stolen FV templates and various spoofing and brute-force attacks in decentralised network architectures in medical systems, including access points and various database nodes without a central point. This work contributes to literature by providing a detailed review of feasible alternatives and research gaps, thereby enabling researchers and developers to develop FV biometric authentication medical systems further. Insights into the importance of such a technology and its integration into different medical applications and fields are also provided.