The rapid integration of digital technology into everyday life has significantly reshaped the developmental environment of adolescents. This paper investigates the psychological effects of digital overexposure on emotional development, drawing from a synthesis of secondary data and empirical research. Focusing on adolescents aged 14 to 18, the study analyzes how excessive and emotionally immersive use of digital platforms, particularly social media, influences self-esteem, depressive symptoms, emotional regulation, and gender-based responses.

The research reveals that emotional outcomes are not solely determined by the amount of screen time, but by the type of engagement and the user’s emotional investment. Girls, in particular, demonstrate heightened vulnerability to emotional distress linked to digital behaviors, especially during periods of societal disruption like the COVID-19 pandemic. This study also integrates theoretical frameworks such as Social Cognitive Theory and the Socio-Technical Interaction Networks model to explain behavioral patterns and digital norms. Visual representations of data further illustrate key patterns in screen time and mental health, gender disparities, and pandemic-specific outcomes. The paper concludes with recommendations for educators, policymakers, and families to support healthy digital habits and outlines critical directions for future interdisciplinary and inclusive research. Ultimately, the goal is to inform the development of responsive strategies that foster emotional well-being in the digital age.

Feature Selection, High-Dimensional Biometric Data, Iris Recognition, Variable Length Optimization, Meta-Heuristic

Y. Yin, S. He, R. Zhang, H. Chang, and J. Zhang, “Deep learning for iris recognition: a review,” Neural Comput. Appl., pp. 1–49, 2025.

M. Trokielewicz, A. Czajka, and P. Maciejewicz, “Iris recognition after death,” IEEE Trans. Inf. Forensics Secur., vol. 14, no. 6, pp. 1501–1514, 2018.

M. Wang and W. Deng, “Deep face recognition: A survey,” Neurocomputing, vol. 429, pp. 215–244, 2021.

Z. Zhao and A. Kumar, “Towards more accurate iris recognition using deeply learned spatially corresponding features,” in Proceedings of the IEEE international conference on computer vision, 2017, pp. 3809–3818.

R. Gad et al., “Iris recognition using multi-algorithmic approaches for cognitive internet of things (CIoT) framework,” Futur. Gener. Comput. Syst., vol. 89, pp. 178–191, 2018.

J. Miao and L. Niu, “A survey on feature selection,” Procedia Comput. Sci., vol. 91, pp. 919–926, 2016.

M. Abdel-Basset, R. Mohamed, O. M. Elkomy, and M. Abouhawwash, “Recent metaheuristic algorithms with genetic operators for high-dimensional knapsack instances: A comparative study,” Comput. Ind. Eng., vol. 166, p. 107974, 2022.

A. Kumar, M. Nadeem, and H. Banka, “Nature inspired optimization algorithms: a comprehensive overview,” Evol. Syst., vol. 14, no. 1, pp. 141–156, 2023.

T. O. Q. Saraf, N. Fuad, and N. S. A. M. Taujuddin, “Framework of meta-heuristic variable length searching for feature selection in high-dimensional data,” Computers, vol. 12, no. 1, p. 7, 2022.

T. Butler-Yeoman, B. Xue, and M. Zhang, “Particle swarm optimisation for feature selection: A hybrid filter-wrapper approach,” in 2015 IEEE congress on evolutionary computation (CEC), IEEE, 2015, pp. 2428–2435.

J. M. Saarwade et al., “A novel approach for iris recognition system using genetic algorithm,” J. Artif. Intell. Technol., vol. 4, no. 1, pp. 9–17, 2024.

T. O. Q. Saraf, N. Fuad, and N. S. A. M. Taujuddin, “Feature encoding and selection for Iris recognition based on variable length black hole optimization,” Computers, vol. 11, no. 9, p. 140, 2022.

Z. Beheshti, “A fuzzy transfer function based on the behavior of meta-heuristic algorithm and its application for high-dimensional feature selection problems,” Knowledge-Based Syst., vol. 284, p. 111191, 2024.

E. Ekinci, Z. Garip, and K. Serbest, “Meta-heuristic optimization algorithms based feature selection for joint moment prediction of sit-to-stand movement using machine learning algorithms,” Comput. Biol. Med., vol. 178, p. 108812, 2024.

Asraa, Safaa Ahmed, et al. "An Accurate Model for Text Document Classification Using Machine Learning Techniques." Ingenierie des Systemes d'Information 30.4 (2025): 913.‏

S. Jayachitra, M. Balasubramani, A. M. Kaleem, J. Karthik, G. Keerthiga, and R. Mythili, “An efficient ranking based binary salp swarm optimization for feature selection in high dimensional datasets,” Meas. Sensors, vol. 35, p. 101291, 2024.

J. Daugman, “How iris recognition works,” in The essential guide to image processing, Elsevier, 2009, pp. 715–739.

A. M. Nor, S. Halunga, and O. Fratu, “Neural network based IRSs-UEs association and IRSs optimal placement in multi IRSs aided wireless system,” Sensors, vol. 22, no. 14, p. 5216, 2022.

Z. S. Naser, H. N. Khalid, A. S. Ahmed, M. S. Taha, and M. M. Hashim, “Artificial Neural Network-Based Fingerprint Classification and Recognition.,” Rev. d’Intelligence Artif., vol. 37, no. 1, 2023.

N. K. Jabbar, M. Naderan, and M. S. Taha, “HybridIoMT: A Dual-Phase Machine Learning Framework for Robust Cybersecurity in Internet of Medical Things.,” Int. J. Intell. Eng. Syst., vol. 18, no. 4, 2025.

N. S. Ali, A. F. Alsafo, H. D. Ali, and M. S. Taha, “An Effective Face Detection and Recognition Model Based on Improved YOLO v3 and VGG 16 Networks,” J. homepage http//iieta. org/journals/ijcmem, vol. 12, no. 2, pp. 107–119, 2024.