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Progression in Information and Communications Technology (ICT) has changed individuals in innumerable ways. Utilization of innovation can upgrade work environment proficiency, usefulness, and adaptability. However, innovative tools can adversely affect an individual's physiological, Psychological, as well as organizational satisfaction and have likewise prompted higher technostress among technology users. The study aims at examining the association of techno-stressors with the Psychological well-being of postgraduate learners. Using a quantitative research approach, data were collected through an adapted survey questionnaire tool, from 564 postgraduate learners enrolled in MS (Computer Science, Mathematics) and M.Phil. (Education) programs at a private sector University. To analyze the data, a correlation test was run using SPSS 27.0 version. Moreover, the findings revealed that there is a weak association between techno-stressors and the Psychological well-being of learners. Among the five intertwined techno-stressors, techno-insecurity has a moderate effect on the Psychological well-being of learners. Whereas, both male and female respondents were aware of the characteristics of technology and errands. Finally, in instructive settings, the most apparent well-being research initiatives have been taken by the Organization of Economic Cooperation and Development (OCED). However, even in these programs the examination has been done in tertiary settings and is restricted to 15years learners. This study contributes to bridging this gap. Alongside this, the current assemblage of writing adds to the field of literature in the context of Sindh, Pakistan.

Machine-based classification usually involves some computer programs, known as algorithms, developed using several mathematical formulations to accelerate the automated classification process. Along with the exponential increase in the size and computational complexity of the data today, such optimized, robust, agile and reliable computational algorithms are required which can efficiently carry out these conforming classification tasks. In this review paper, deterministic optimization techniques have been analysed that are efficiently employed for machine learning applications. In this review, systematic literature review approach has been adopted in which 200 research articles were downloaded from which 100 latest articles has been selected based on the most commonly employed neural networks’ techniques. Moreover, the reported neural networks techniques based on Back Propagation Neural Network (BPNN), Recurrent Neural Networks (RNNs) Algorithm and Levenberg-Marquardt (LM) with several hybridized classification algorithms based on optimization techniques have been indicated that are commonly used to optimize and benefit the classification process.