Predicting Student Performance And Its Impact On Mental Health Using Machine Learning
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Predicting Student Performance and Its Impact on Mental Health Using Machine Learning
Today, the main aim of educational institutes is to provide a high level of education to the students. Career selection is one of the most important and quite difficult decisions for learners. It is very important to examine student's capabilities and interests. As stresses of tests, peer and parental pressure on marks scored and job opportunities are some of the factors that lead to mental illness for university students. Determining the factors underlying mental illness from academic success to maintain the proper balance of life is becoming increasingly necessary. This kind of novel machine learning prediction system would help students studying in engineering institutes to address these key challenges So that they will focus on their targeted carrier. In this study, both classification and clustering techniques have been tested on the student academic and family datasets of various engineering students in Delhi, India. Although all the classifier models show comparably high classification performances, the Hybrid neural network is the best-concerning accuracy and precision. In addition, the analysis shows that mental health based on the performance of the students depends on various factors. The findings of this paper indicate the effectiveness and expressiveness of data mining models in performance evaluation. The result proves that the hybrid algorithm combining clustering and classification approaches yields results that are far superior in terms of achieving accuracy in the prediction of academic performance as well as mental wellnesses of the students.
Advances in Artificial Intelligence and Machine Learning
This book presents a collection of peer-reviewed articles from the International Conference on Advances and Applications of Artificial Intelligence and Machine Learning—ICAAAIML 2023. The book covers research in the areas of artificial intelligence, machine learning, and deep learning applications in health care, agriculture, business, and security. This book contains research papers from academicians, researchers as well as students. There are also papers on core concepts of computer networks, intelligent system design and deployment, real-time systems, wireless sensor networks, sensors and sensor nodes, software engineering, and image processing. This book is a valuable resource for students, academics, and practitioners in the industry working on AI applications.
Using Machine Learning to Detect Emotions and Predict Human Psychology
In the realm of analyzing human emotions through Artificial Intelligence (AI), a myriad of challenges persist. From the intricate nuances of emotional subtleties to the broader concerns of ethical considerations, privacy implications, and the ongoing battle against bias, AI faces a complex landscape when venturing into the understanding of human emotions. These challenges underscore the intricate balance required to navigate the human psyche with accuracy. The book, Using Machine Learning to Detect Emotions and Predict Human Psychology, serves as a guide for innovative solutions in the field of emotion detection through AI. It explores facial expression analysis, where AI decodes real-time emotions through subtle cues such as eyebrow movements and micro-expressions. In speech and voice analysis, the book unveils how AI processes vocal nuances to discern emotions, considering elements like tone, pitch, and language intricacies. Additionally, the power of text analysis is of great importance, revealing how AI extracts emotional tones from diverse textual communications. By weaving these systems together, the book offers a holistic solution to the challenges faced by AI in understanding the complex landscape of human emotions.