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Archive Issue – Vol.5, Issue.4 (Oct-Dec 2025)

Archive Issue – Vol.6, Issue.1 (January-March 2026)


A Vision-Based AI Framework for Real-Time Fatigue and Workload Detection in IT Professionals Using MediaPipe and a Fusion Neural Network

Yasha Goyal, Yuvraj Singh Rathore, Srashti Kawde, Vishal Chourasiya, Imran Ali Khan

Research Paper | Journal Paper

Vol.6, Issue.1, pp.01-07, Jan-2026

DOI: 10.5281/zenodo.18149292

Abstract References Citation Download

Abstract

In the prevailing work setting of the modern technology sector, screen usage, static positions, and cognitive engagements of the brain contribute to physical and mental exhaustion. Existing solutions to fatigue monitoring and alerting are often computationally complex and wearable and invasive technology. This research work introduces the use of a vision-tracking AI model that is non-invasive and exclusive to the specific requirements of the technical professionals. The model considers the eye movements, body positions, and human interactions to provide an accurate level of physical and mental fatigue. Through the learning concept of fusion learning, the model differentiates between the drastic and short-lived work patterns and the continuous physical and mental states. The proposed model is validated to collectively work in a timely and expert manner with very low computational complexity, thereby imparting expert warning notifications related to physical and mental fatigue. The model adheres to the concepts and requirements of Industry 5.0.

Key-Words / Index Term: Computer Vision, Workload Detection, Mediapipe, Fusion Neural Network, Machine Learning, Artificial Intelligence, MentalFatigue

References

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      12. A. R. Gowda and S. Merikapudi M, "Keystroke Pattern Analysis for Cognitive Fatigue Prediction Using Machine Learning," Advanced International Journal for Research, vol. 6, no. 5, pp. 1–9, 2025.
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Citation

Yasha Goyal, Yuvraj Singh Rathore, Srashti Kawde, Vishal Chourasiya, Imran Ali Khan, "A Vision-Based AI Framework for Real-Time Fatigue and Workload Detection in IT Professionals Using MediaPipe and a Fusion Neural Network" International Journal of Scientific Research in Technology & Management, Vol.6, Issue.1, pp.01-07, 2026. DOI: 10.5281/zenodo.18149292

A Hybrid PQC + Multi-Source-Enhanced Entropy Key-Distribution and End-to-End Encrypted Email Client

Mansi Trivedi, Kashish Singh, Shivank Soni

Research Paper | Journal Paper

Vol.6, Issue.1, pp.08-14, Dec-2025

DOI: 10.5281/zenodo.18149482

Abstract References Citation Download

Abstract

The threat of quantum computing to classical cryptographic systems rises the necessity for development of quantum resistant security framework for digital communication. Current email systems depend completely on these centralized architectures which are vulnerable to server breaches, while their cryptographic foundation and currently used encryption standards and protocols (RSA and ECC), will face existential crisis and risk from quantum algorithms like Shor's algorithm. To address these challenges, this paper presents a unified and intelligent quantum-resistant email security framework that integrates post-quantum cryptography with multi-source entropy-driven key generation for protecting emails and attachments. The proposed system employs lattice-based cryptographic schemes combined with AI-assisted randomness generation to enhance key unpredictability and resilience. Performance evaluation demonstrates a system efficiency of 90.32% with an effective 135-bit quantum-safe security strength, achieving a practical balance between performance and security with the framework ensuring true end-to-end encryption, guaranteeing that only authorized clients can access sensitive data even in the event of server compromise. Furthermore, the proposed approach provides a scalable foundation for future expansion into a comprehensive quantum-safe digital workspace incorporating secure collaboration tools, enhanced usability, and regulatory compliance.

Key-Words / Index Term: Post-Quantum Cryptography, Quantum-Safe Email, Multi-Source-Enhanced Security, Cryptographic Efficiency, Kyber Algorithm, Quantum Key Distribution, Entropy Generation, Secure Communication, Lattice-Based Cryptography, Quantum Resistance.

References

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      5. L. Ducas, E. Kiltz, T. Lepoint, V. Lyubashevsky, P. Schwabe, G. Seiler, and D. Stehle, “CRYSTALS-Dilithium: A lattice-based digital signature scheme,” J. Cryptographic Eng., vol. 10, no. 1, pp. 57–68, 2020.
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      13. B. Ramsdell, “S/MIME version 3 message specification,” IETF RFC 2633, Jul. 1999.
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Citation

Mansi Trivedi, Kashish Singh, Shivank Soni, "A Hybrid PQC + Multi-Source-Enhanced Entropy Key-Distribution and End-to-End Encrypted Email Client" International Journal of Scientific Research in Technology & Management, Vol.6, Issue.1, pp.08-14, 2026. DOI: 10.5281/zenodo.18149482

AI-Driven Zero-Trust Blockchain Framework for Secure and Scalable IoT Data Sharing

Niharika Sarathe, Nikha Yadav, Monu Kumar, Imran Ali Khan

Research Paper | Journal Paper

Vol.6, Issue.1, pp.15-21, Jan-2026

DOI: 10.5281/zenodo.18193372

Abstract References Citation Download

Abstract

The rapid expansion of Internet of Things (IoT) networks has increased the demand for secure, transparent, and scalable data-sharing mechanisms, while conventional centralized IoT architectures remain vulnerable to data tampering, unauthorized access, and single-point failures. Additionally, the lack of adaptive trust management allows compromised devices to impact the entire network. Although blockchain-based solutions provide immutability, they offer limited dynamic trust evaluation, and Zero-Trust models focus mainly on authentication without tamper-proof logging. To overcome these limitations, this paper proposes an integrated Blockchain–Zero Trust IoT Security Framework that combines immutable recordkeeping with continuous trust assessment. Device fingerprints and user credentials are hashed using SHA-256, and all device activities are recorded on a blockchain employing a Merkle Forest structure for scalable verification. An AI-based reputation model evaluates device behavior in real time, allowing only trustworthy devices to execute operations. Experimental results show that the proposed framework achieves up to 92% malicious device detection accuracy, compared to 55–65% in traditional approaches, while maintaining an average latency of approximately 2.01 seconds per action. Furthermore, the Merkle Forest–based blockchain ensures near-linear scalability as the number of devices increases from 100 to 10,000, providing enhanced security and transparency with minimal performance overhead.

Key-Words / Index Term: Internet of Things, Blockchain, Zero Trust Architecture, Merkle Forest, AI-Based Reputation System, IoT Security.

References

      1. J. Kindervag, “Build Security Into Your Network’s DNA: The Zero Trust Network Architecture,” Forrester Research, 2010.
      2. S. Rose, O. Borchert, S. Mitchell, and S. Connelly, “Zero Trust Architecture,” NIST Special Publication 800-207, National Institute of Standards and Technology, 2020.
      3. A. Dorri, S. S. Kanhere, and R. Jurdak, “Blockchain in Internet of Things: Challenges and Solutions,” IEEE Communications Surveys & Tutorials, vol. 19, no. 3, pp. 1736–1762, 2017.
      4. M. Conoscenti, A. Vetrò, and J. C. De Martin, “Blockchain for the Internet of Things: A Systematic Literature Review,” IEEE/ACS International Conference on Computer Systems and Applications, 2016.
      5. K. Christidis and M. Devetsikiotis, “Blockchains and Smart Contracts for the Internet of Things,” IEEE Access, vol. 4, pp. 2292–2303, 2016.
      6. Y. Zhang, R. Yu, S. Xie, Y. Zhang, and M. Guizani, “Securing Internet of Things with Blockchain: Challenges and Opportunities,” IEEE Network, vol. 32, no. 1, pp. 40–46, 2018.
      7. L. Atzori, A. Iera, and G. Morabito, “The Internet of Things: A Survey,” Computer Networks, vol. 54, no. 15, pp. 2787–2805, 2010.
      8. F. Z. Yousaf, M. Bredel, S. Schmid, and M. Menth, “Network and Service Management for the Internet of Things,” IEEE Communications Magazine, vol. 55, no. 7, pp. 42–49, 2017.
      9. X. Wang, W. Cheng, P. Mohapatra, and T. Abdelzaher, “Enabling Secure and Efficient Trust Management in IoT Using Machine Learning,” IEEE Internet of Things Journal, vol. 6, no. 4, pp. 6503–6514, 2019.
      10. S. Sicari, A. Rizzardi, L. A. Grieco, and A. Coen-Porisini, “Security, Privacy and Trust in Internet of Things: The Road Ahead,” Computer Networks, vol. 76, pp. 146–164, 2015.
      11. P. Fremantle, B. Aziz, J. Kopecký, and P. Scott, “Federated Identity and Access Management for the Internet of Things,” International Workshop on Secure Internet of Things, IEEE, 2014.
      12. M. Abomhara and G. M. Køien, “Security and Privacy in the Internet of Things: Current Status and Open Issues,” International Conference on Privacy and Security in Mobile Systems, IEEE, 2014.
      13. H. Xu, W. Yu, D. Griffith, and N. Golmie, “A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective,” IEEE Access, vol. 6, pp. 78238–78259, 2018.
      14. R. Roman, J. Zhou, and J. Lopez, “On the Features and Challenges of Security and Privacy in Distributed Internet of Things,” Computer Networks, vol. 57, no. 10, pp. 2266–2279, 2013.
      15. A. Reyna, C. Martín, J. Chen, E. Soler, and M. Díaz, “On Blockchain and Its Integration with IoT: Challenges and Opportunities,” Future Generation Computer Systems, vol. 88, pp. 173–190, 2018.
      16. T. Alladi, V. Chamola, and N. Guizani, “Blockchain Applications for Industry 4.0 and Industrial IoT,” IEEE Communications Magazine, vol. 57, no. 8, pp. 86–92, 2019.
      17. Z. Mlika, A. Chehab, and H. Karam, “Trust Management in IoT Systems: A Survey,” IEEE Internet of Things Journal, vol. 7, no. 6, pp. 5130–5147, 2020.

Citation

Niharika Sarathe, Nikha Yadav, Monu Kumar, Imran Ali Khan, "AI-Driven Zero-Trust Blockchain Framework for Secure and Scalable IoT Data Sharing" International Journal of Scientific Research in Technology & Management, Vol.6, Issue.1, pp.15-21, 2026. DOI: 10.5281/zenodo.18193372

CampusSmart Scheduler: AI-Based Automated Timetable Management System

Jagrati Agrawal, Medha Agrawal, Narayani Puranik, Maneshwari Pawar

Research Paper | Journal Paper

Vol.6, Issue.1, pp.22-26, Jan-2026

DOI: 10.5281/zenodo.18193372

Abstract References Citation Download

Abstract

Designing an academic timetable is a complex and time-consuming task that requires balancing multiple constraints related to classrooms, faculty availability, student batches, and institutional policies. This challenge, formally known as the University Course Timetabling Problem (UCTP), belongs to the class of NPhard combinatorial optimization problems due to its vast search space and tightly coupled constraints. In most universities, timetable preparation is still carried out manually, often taking 12–15 days and resulting in poor utilization of physical resources, typically below 40%. This paper presents Campus Smart Scheduler, an intelligent and automated timetable management system developed using the OptaPlannerconstraint-solving framework. The proposed solution models the UCTP as a Weighted Constraint Satisfaction Problem (WCSP) and employs a hybrid approach that combines Constraint Satisfaction Programming for feasibility with Genetic Algorithms for optimization. A key contribution of this work is a mathematically defined softconstraint model that explicitly promotes smart space utilization by penalizing room underutilization and fragmented scheduling. Experimental results demonstrate significant improvements in scheduling speed, feasibility, and room utilization efficiency, making the system a practical and scalable solution for modern academic institutions.

Key-Words / Index Term: University Timetabling, Constraint Satisfaction Problem, Genetic Algorithm, OptaPlanner, Smart Space Utilisation, NP-Hard Problem.

References

      1. Ahmed, S., Burke, E. K., and Pham, N. (2022). A hybrid optimisation approach for university course timetabling problems. Journal of Scheduling, 25(2), 145–160.
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Citation

Jagrati Agrawal, Medha Agrawal, Narayani Puranik, Maneshwari Pawar, "CampusSmart Scheduler: AI-Based Automated Timetable Management System" International Journal of Scientific Research in Technology & Management, Vol.6, Issue.1, pp.22-26, 2026. DOI: 10.5281/zenodo.18244099

Reducing Decision Uncertainty in AI-Based Student Career Guidance Using a Hybrid Machine Learning and Large Language Model Framework

Shubham Mallick, Prakrati Mishra, Saksham Kumar, Sakshi Pawar

Research Paper | Journal Paper

Vol.6, Issue.1, pp.27-38, Jan-2026

DOI: 10.5281/zenodo.18271325

Abstract References Citation Download

Abstract

Educational decision-making, particularly the selection of academic streams and career pathways, involves high levels of uncertainty and long-term consequences for students. Although machine learning–based guidance systems have demonstrated strong predictive performance, many students remain hesitant or unconvinced by algorithmic recommendations due to limited interpretability and contextual understanding. This paper presents a hybrid Machine Learning–Large Language Model (ML–LLM) framework designed to reduce decision uncertainty rather than focusing solely on prediction accuracy. The proposed system integrates supervised machine learning models for academic stream prediction, psychometric assessment, and dropout-risk analysis with an LLM-based advisory module that provides natural-language explanations and confidence-aware guidance. To evaluate system effectiveness, uncertainty-oriented metrics such as Prediction Entropy, Decision Stability Score, and Risk Reduction Index are employed alongside traditional performance measures. Experimental results based on real student data demonstrate that the inclusion of LLM-driven explanations significantly improves decision confidence and stability compared to ML-only systems. The findings highlight the importance of uncertainty-aware evaluation in educational AI systems and support the role of explanation-driven hybrid frameworks in improving student-centered decision support.

Key-Words / Index Term: Decision Uncertainty; Educational Decision Support Systems; Machine Learning; Large Language Models; Career Guidance; Explainable Artificial Intelligence; Hybrid AI Framework.

References

      1. M. Alamri, M. Khan, and M. Rahman, “A machine learning framework for academic and career path prediction using student performance data,” IEEE Access, vol. 11, pp. 45566–45578, 2023. DOI: https://doi.org/10.1109/ACCESS.2023.10051343
      2. A. Kumar, A. Tripathi, and R. S. Anand, “Predicting suitable academic fields using ensemble learning techniques,” Education and Information Technologies, vol. 29, no. 4, pp. 4417–4432, 2024. DOI: https://doi.org/10.1007/s10639-023-12046-7
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      4. Y. Li and H. Chen, “A hybrid recommendation model for academic guidance,” IEEE Access, vol. 10, pp. 23011–23022, 2022. DOI: https://doi.org/10.1109/ACCESS.2022.3146109
      5. E. B. Costa, B. Fonseca, M. A. Santana, F. F. de Araújo, and J. L. Rego, “Educational dropout prediction using machine learning: A systematic review,” Computers & Education, vol. 176, p. 104354, 2022. DOI: https://doi.org/10.1016/j.compedu.2022.104354
      6. Z. Zhang, L. Hu, and T. Wang, “Explainable artificial intelligence for educational decision systems,” Computers & Education, vol. 196, p. 104635, 2023. DOI: https://doi.org/10.1016/j.compedu.2023.104635
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      8. N. Manouselis and C. Costopoulou, “Educational recommender systems: State of the art and future challenges,” User Modeling and User-Adapted Interaction, vol. 32, no. 2, pp. 389–416, 2022. DOI: https://doi.org/10.1007/s11257-021-09306-0
      9. W. Holmes, M. Bialik, and C. Fadel, “Artificial intelligence in education: Promises and implications for teaching and learning,” Nature Human Behaviour, vol. 7, no. 3, pp. 478–489, 2023. DOI: https://doi.org/10.1038/s41562-022-01427-9
      10. E. Kasneci et al., “ChatGPT for good? On opportunities and challenges of large language models for education,” Education and Information Technologies, vol. 28, no. 6, pp. 1–20, 2023. DOI: https://doi.org/10.1007/s10639-023-11706-y
      11. E. Molan, R. Gupta, and S. Lee, “Explainable AI for student counseling systems: A systematic review,” AI in Education Journal, vol. 6, no. 1, pp. 92–120, 2024.
      12. O. C. Santos, “AI-powered adaptive guidance in education: A comprehensive review,” Computers & Education, vol. 197, p. 104744, 2023. DOI: https://doi.org/10.1016/j.compedu.2023.104744
      13. R. S. Baker and G. Siemens, “Learning analytics and educational data mining: Towards communication and collaboration,” British Journal of Educational Technology, vol. 51, no. 4, pp. 1005–1015, 2020. DOI: https://doi.org/10.1111/bjet.12938
      14. R. Maurya and J. DeDiego, “Ethical integration of AI in human services: Transparency and bias mitigation in educational counseling,” AI and Ethics, vol. 4, no. 2, pp. 223–237, 2023. DOI: https://doi.org/10.1007/s43681-022-00211-7
      15. G. Bansal et al., “Does the whole exceed its parts? The effect of AI explanations on human decision-making,” Proceedings of the ACM CHI Conference on Human Factors in Computing Systems, pp. 1–16, 2021. DOI: https://doi.org/10.1145/3411764.3445268
      16. H. Lakkaraju, O. Bastani, and B. Kim, “Interpretable and explorable explanations for black-box models,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 04, pp. 4942–4949, 2020. DOI: https://doi.org/10.1609/aaai.v34i04.5979
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Citation

Shubham Mallick, Prakrati Mishra, Saksham Kumar, Sakshi Pawar, "Reducing Decision Uncertainty in AI-Based Student Career Guidance Using a Hybrid Machine Learning and Large Language Model Framework" International Journal of Scientific Research in Technology & Management, Vol.6, Issue.1, pp.27-38, Jan 2026. DOI: 10.5281/zenodo.18271325