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Bi-Monthly Journal (2 monthly)

📊 ISSN NO: 3108-1312

Subject: Multidisciplinary

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✍️ Editorial Message

Welcome to the International Journal of Convergent Technologies and Future Systems (IJCTFS). Our journal aims to provide a platform for innovative research in AI, Machine Learning, Cloud, Cybersecurity, IoT, and other emerging technologies. We encourage interdisciplinary collaboration and the sharing of ideas that can shape the future of technology. IJCTFS is committed to publishing high-quality research that bridges theory with practical impact. We thank our contributors, reviewers, and readers for supporting this journey of innovation and knowledge dissemination.
With best regards,
Dr. D. M. Marathe
Founder & Editor-in-Chief
International Journal of Convergent Technologies and Future Systems (IJCTFS)
MauliKavyanshu Foundation

International Journal of Convergent Technologies and Future Systems (IJCTFS)

Vol. 2, Issue 4 • July - August 2026 • ISSN: ISSN NO: 3108-1312 • Frequency: Bi-Monthly
Published by: MauliKavyanshu Foundation

Papers (July - August 2026)

📚 Submitted Research Papers

1. DIGITAL MINDS, TROUBLED HEARTS A SURVEY ON TECHNOLOGY AND MENTAL HEALTH AMONG YOUNG ADULTS

Author: Faizan Khatib , Kasim Shah , Ammar Shaikh

Email: kasimshah998@gmail.com

Abstract: Today, smartphones, social media, and artificial intelligence are not just tools — they are a core part of how young people live, learn, socialise, and see themselves. For the 15–26 age group, being online is as natural as breathing. But this constant digital immersion comes at a cost that we are only beginning to understand: a quiet, creeping crisis in mental health. This research paper — Digital Minds, Troubled Hearts — investigates how technology use affects the psychological well-being of young adults in India, with a focus on undergraduate students. Using a mixed-methods design (a structured survey of 11 students + extensive review of academic literature), the study examines six mental health dimensions: anxiety, sleep disruption, concentration difficulty, social comparison, loneliness, and AI-induced career anxiety. The findings are striking and consistent. On a 1–5 scale, respondents scored 4.00 for concentration difficulty (High Risk), 3.82 for sleep disruption (Moderate-High), 3.55 for social comparison (Moderate-High), 3.36 each for anxiety and AI career fear (Moderate), and 3.00 for loneliness (Moderate). Over 54% reported they would feel panicked or highly stressed if they lost internet access for just one day. A full 36% said they desperately want to take breaks from social media — but find themselves unable to do so. Crucially, the paper does not simply condemn technology. It recognises that digital platforms have democratised education, expanded career pathways, and even helped young people find mental health support. The challenge is not to eliminate technology — that would be neither possible nor desirable — but to use it with awareness, intention, and self-compassion. Drawing on Social Comparison Theory (Festinger, 1954), Uses and Gratifications Theory, and the Attention Economy framework, this paper situates its findings in solid academic theory. It closes with a comprehensive, evidence-based set of recommendations for students, educational institutions, parents, and policymakers.

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Submitted on: 2026-07-30 13:14:13

2. Deepfake Detection using Multimodal Forensics: A Unified Vision-Audio Framework with Cross-Modal Attention

Author: Mr. Ketan Vilas Patil

Email: ket.patil77@gmail.com

Abstract: The rapid growth of generative adversarial networks, diffusion models, neural rendering systems and high-fidelity voice cloning has made synthetic video and audio increasingly difficult to distinguish from authentic media. Conventional deepfake detectors usually operate on a single modality: either they analyze facial artifacts in frames or they inspect acoustic traces in speech. Such unimodal approaches are fragile because modern forgeries can suppress artifacts in one channel while leaving inconsistencies in the other. This paper presents MultiForensic-Net, a unified multimodal forensics framework that jointly analyzes facial video and speech audio for robust deepfake detection. The framework uses a dual-stream encoder composed of a Vision Transformer branch for spatial-temporal facial representation and a Conformer branch for log-mel audio representation. A Cross-Modal Attention Fusion module aligns visual and audio token streams, while a Contrastive Multimodal Pair Loss encourages coherent genuine pairs to be close in embedding space and manipulated pairs to be separable. The system is evaluated on FaceForensics++, DFDC, KoDF and a controlled AVSpeech-DF stress-test subset. MultiForensic-Net achieves 97.84 percent accuracy on FaceForensics++, 96.21 percent on DFDC, 95.60 percent on KoDF and 93.47 percent on AVSpeech-DF, with strong ROC-AUC scores across datasets. Ablation analysis confirms that bidirectional cross-attention and contrastive pair training provide consistent gains over simple concatenation and unimodal baselines. The final system also includes an interpretable forensic pipeline with attention visualizations, confusion matrix analysis, training convergence plots and deployment considerations for practical media verification workflows.

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Submitted on: 2026-08-22 06:45:50

3. Wireless Power Transfer Efficacy: A Study on Efficiency, Applications, and Future Scope

Author: RISHIKESH VIRENDRA PATIL

Email: rishikeshpatil626@gmail.com

Abstract: Wireless Power Transfer (WPT) is a modern technology that transfers electrical energy from one device to another without using physical wires. This technology is becoming popular in mobile charging, electric vehicles, medical devices, and smart electronics. The main purpose of this research paper is to study the efficacy and efficiency of wireless power transfer systems. The paper explains the working principle, advantages, limitations, and real-life applications of WPT. It also discusses different methods such as inductive coupling and resonant coupling. The study concludes that wireless power transfer can improve convenience and reduce cable dependency, but efficiency and distance limitations still need improvement. Future developments may help WPT become more reliable and widely used in daily life.

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Submitted on: 2026-08-24 14:23:01

4. Student Mental Wellness Prediction Using Machine Learning

Author: Tejas Jijabrao Patil

Email: tejasjijabraopatil@gmail.com

Abstract: Mental wellness is an essential factor influencing students' academic achievement,personal growth,and overall quality of life. Increasing academic demands, prolonged screen time,social expectations,financial concerns, and lifestyle changes have contributed to higher levels of stress,anxiety,and emotional fatigue among students. Early identification of these concerns allows educational institutions to provide timely guidance and support before problems become more severe. This study proposes a machine learning-based approach for predicting student mental wellness by analyzing academic,behavioral, and lifestyle characteristics. The proposed framework considers factors such as attendance,study duration,sleep patterns,physical activity,academic performance, social interaction,and self reported stress levels.

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Submitted on: 2026-08-26 12:45:50

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