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Recommender systems have gained significant attention as powerful tools for supporting decision-making processes in various domains. However, the understanding of their impact and application in the field of academic choices in higher education remains limited. This systematic review aims to provide a comprehensive summary of the current knowledge regarding recommender systems utilized in the context of academic choices and advising in higher education. The study is based on the systematic analysis of a set of primary studies (N = 56 out of 1578, published between 2011 and 2023) included according to defined criteria. The articles were categorized based on specific criteria, and their findings were analyzed and synthesized. Results show that the hybrid strategy has been the most effective method for producing recommendations. Evaluation measures such as offline experiments and case-study validation were prominently observed in the empirical studies, providing insights into the effectiveness of recommender systems. The findings reveal that the design of recommender systems in higher education is context-specific, with researchers considering various parameters to tailor recommendations to individual needs. However, most of the selected articles relied on lab-based studies rather than real-world applications, indicating a need for further research in practical settings. This systematic review also identifies future research directions, including the incorporation of deep learning technologies and the analysis of personality traits. By providing a comprehensive overview of the current state of recommender systems for academic choices in higher education, this review offers valuable insights for researchers and practitioners, guiding the development of more effective and personalized recommendation systems to unlock the full potential of individuals in their academic journey.

Currently, developing countries are experiencing a massive shift toward industrialization. Developing countries lack the technical sophistication and infrastructure to encourage low-carbon and sustainable economic growth because of weak public awareness, regulations, and technology. Developing countries must plan the industrialization process for maximum energy efficiency of production, thereby reducing their CO textsubscript 2 emissions significantly by increasing energy efficiency. This paper presents a systematic survey on the current pragmatic methods for forecasting the future load demands from minutes to years ahead in developing countries, following the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMA-P). The primary focus of this systematic survey paper is to provide an optimal forecasting model selection strategy for potential researchers and forecasters. Based on the strengths and weaknesses of the different models, we will discuss the most suitable methods to tailor them to multiple applications and scenarios of load forecasting. The comparison elements are Forecast horizons, Spatio-temporal resolutions, factors affecting the load, different dimensional reduction techniques, model complexity analysis, and the MAPE for error analysis. From the results, We have found ANN hybridized with meta-heuristic techniques to be superior in most of the analysis cases. ANN’s ability to handle non-linear data, flexibility, and robustness is why. Consumption data aggregated at the national level can capture trends efficiently. Meteorological and calendar features influence short-term forecasting extensively, whereas economic factors influence long-term load patterns. Finally, we have identified the trends and research gaps from the existing literature, presenting relevant technical recommendations for improvement.

Depression is the most common mental illness, which has become the major cause of fear and suicidal mortality or tendencies. Currently, about 10% of the world population has been suffering from depression. The classical approach for detecting depression relies on the clinical questionnaire, which depends on the patients’ responses as well as observing their behavioral activities. However, there is no established method to detect depression from EEG biomarkers. Therefore, exploration of EEG biomarkers for depression assessments is vital and has a great potential to improve our understanding and clinical interventions. In this study, we have conducted a systematic review of 52 research articles using the PRISMA-P systematic review protocol, where we analyzed their research methodologies and outcomes. We categorized the experimentations in these articles according to their physical and psychological aspects scaled by the commonly used clinical questionnaire-based assessments. This study finds that the negative stimuli are the better identification strategies for evaluating depression through EEG signals. From this exploration, researchers observed that the Neural Connectivity Analysis and Brain Topological Mapping have huge potentials for finding depression biomarkers, and it is evident that the right-side hemisphere and frontal and parietal-occipital cortex are distinct regions to detect depression using EEG signals. For this mechanism, researchers are using many signal processing and machine learning approaches. In the case of filtering, Independent Component Analysis (ICA) is commonly used to eliminate physiological and non-physiological artifacts. Among machine learning approaches, Convolutional Neural Network (CNN) and Support Vector Machine (SVM) showed better performance for classifying healthy and depressed brains. The authors hope, this study will create an opportunity to explore more in the future for EEG as diagnostic tool by analyzing brain functional connectivity for focusing on clinical interventions.