Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
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This Author published in this journals
All Journal Journal of Advanced Computer Knowledge and Algorithms Current Educational Review
Ahmad Farhad Rajab Zada
Samangan University
Computer Science & IT Education Languange, Linguistic, Communication & Media Mathematics

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Internet Consumption Patterns and Their Influence on Emotional and Cognitive Well-Being Among Young Individuals in University Faridoon Ayobi; Ahmad Farhad Rajab Zada; Sayed Ehsan Shamsi
Current Educational Review Vol. 2 No. 1 (2026)
Publisher : Balai Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56566/cer.v2i1.707

Abstract

The proliferation of internet connectivity has fundamentally transformed social, academic, and recreational behaviors among university students worldwide. This study investigates the nature of internet consumption patterns and their multifaceted influence on emotional and cognitive well-being among young university students. A cross-sectional survey was conducted with 646 undergraduate students (mean age = 20.6 years, SD = 1.82) recruited from four major Afghanistan universities. Validated instruments were administered, including the Warwick–Edinburgh Mental Well-Being Scale (WEMWBS), the Generalized Anxiety Disorder Scale (GAD-7), the Patient Health Questionnaire (PHQ-9), and a researcher-developed Internet Usage Pattern Inventory (IUPI). Data were analyzed using descriptive statistics, Pearson correlation, one-way ANOVA, and multiple linear regression. Results demonstrated that 61.1% of participants exceeded six hours of daily internet use, primarily for social media and entertainment. Significant negative correlations were found between daily usage duration and emotional well-being (r = −0.451, p < .001), cognitive concentration (r = −0.389, p < .001), and academic GPA (r = −0.389, p < .001). Conversely, purposeful educational use was positively associated with academic outcomes (β = +.214, p < .001). Nighttime screen exposure emerged as a critical mediator of sleep quality and subsequent mood disturbance. The regression model accounted for 46.7% of variance in well-being scores (R² = 0.467, F(5, 640) = 112.4, p < .001). These findings underscore the urgency of developing digital literacy interventions and university-level policies to cultivate healthier, more intentional internet consumption habits among young adults
A Comparative Analysis of Machine Learning Models for Climate Change Prediction and Climate Risk Assessment Ahmad Farhad Rajab Zada
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 2 (2026): Journal of Advanced Computer Knowledge and Algorithms - April 2026
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i2.26648

Abstract

Climate change represents one of the most pressing challenges facing humanity, and accurate prediction models are essential for effective risk assessment and policy formulation. This systematic literature review employs thematic analysis to examine and compare machine learning (ML) models applied to climate change prediction and climate risk assessment, synthesizing findings from 40 peer-reviewed studies published between 2015 and 2024. Five major thematic clusters were identified: (1) deep learning architectures for temperature and precipitation forecasting; (2) ensemble methods for extreme weather event prediction; (3) hybrid physics-informed neural networks; (4) spatiotemporal models for sea-level rise and glacier dynamics; and (5) ML-based climate risk assessment frameworks for socioeconomic impact modeling. Findings reveal that Long Short-Term Memory (LSTM) networks and Transformer-based architectures consistently outperform traditional statistical models for long-range climate forecasting, while gradient boosting methods excel in regional risk classification tasks. Physics-informed neural networks demonstrate superior interpretability and generalization in data-scarce environments. The review identifies significant research gaps including model interoperability, uncertainty quantification, and the integration of socioeconomic variables. Future research should focus on federated learning approaches and explainable AI frameworks to enhance transparency and stakeholder trust.
Co-Authors Faridoon Ayobi Sayed Ehsan Shamsi