iosr-JCE   Volume-27 ~ Issue-5 ~ Sep. - Oct. 2025

Abstract: This research concentrates on indexing the enormous legal documents to the aid of Tech-Savvy lawyers. Why corporate lawyers are ready for technological innovation? All the budding lawyers are moving towards technology or in other words Tech-Savvy. Undeniably, technology has revolutionized the business world, rapidly changing and expanding in every field imaginable. When it comes to the legal services industry, technological....

Key Words: SSARC, PDDP, SLIA, TFIDF, LSI, NLU, HTML

[1]. Koniaris, M., Anagnostopoulos, I., & Vassiliou, Y. (2016, November). Multi-Dimension Diversification In Legal Information
Retrieval. In International Conference On Web Information Systems Engineering (Pp. 174-189). Springer International Publishing.
[2]. Boley, D. (1998). Principal Direction Divisive Partitioning. Data Mining And Knowledge Discovery, 2(4), 325-344.
[3]. Krallinger, M. (2015). Development, Application And Evaluation Of Text-Mining Methods For Biomedical Literature Processing:
From Document Categorization To Gene Ranking.
[4]. Soderland, S., Fisher, D., Aseltine, J., & Lehnert, W. (1995). CRYSTAL: Inducing A Conceptual Dictionary. Arxiv Preprint Cmp-
Lg/9505020.
[5]. Riloff, E. (1996). An Empirical Study Of Automated Dictionary Construction For Information Extraction In Three
Domains. Artificial Intelligence, 85(1), 101-134

Abstract: AI has disrupted almost all businesses and introduced breakthroughs in such spheres as healthcare and finance, transport and entertainment. The foundation of AI is that a machine learning (ML) model can be trained on large quantities of data, and then learn patterns, make predictions and subsequently develop itself. However, with increasing levels of sophistication of AI model, the privacy issues have also increased. Conventional centralized machine learning techniques demand enormous quantities of data to be accumulated and handled in centralized servers and......

[1]. Mcmahan, B., Moore, E., Ramage, D., & Hampson, S. (2017). Communication-Efficient Learning Of Deep Networks From Decentralized Data. Proceedings Of The 20th International Conference On Artificial Intelligence And Statistics (AISTATS 2017), 1273-1282. Retrieved From Https://Arxiv.Org/Abs/1602.05629.
[2]. Abadi, M., Chu, A., & Goodfellow, I. (2016). Deep Learning With Differential Privacy. Proceedings Of The 2016 ACM SIGSAC Conference On Computer And Communications Security, 308-318. Https://Doi.Org/10.1145/2976749.2978318.
[3]. Bonawitz, K., Eichner, H., Gries, W., Hsu, D., Konečný, J., & Mcmahan, H. (2019). Towards Federated Learning At Scale: System Design. Proceedings Of The 2nd Sysml Conference (Pp. 1–14). Retrieved From Https://Arxiv.Org/Abs/1902.01046.
[4]. Smith, V., Chiang, M., & Shakkottai, S. (2017). Federated Learning For Mobile Keyboard Prediction. Proceedings Of The 27th ACM International Conference On Multimedia (Pp. 225-228). Https://Doi.Org/10.1145/3123266.3123280.
[5]. Geyer, R. C., Klein, T., & Nabi, M. (2017). Differentially Private Federated Learning: A Client-Level Perspective. Proceedings Of The 3rd International Conference On Learning Representations (ICLR 2017). Retrieved From
Https://Openreview.Net/Forum?Id=Sy2fJcsAW

Abstract: Deep learning-based face recognition has shown great accuracy but encounters big issues with fairness and bias between demographics. This survey systematically reviews the state-of-the-art bias mitigation methods in facial recognition systems based on fairness, showing 45 representative works in the years from 2015 to 2025. By conducting systematic quantitative and qualitative comparison among the state-of-the-art, we categorize these strategies into four major classes based on the type of the proposed mitigation strategy: data-level (15-25% reduction of bias), algorithm......

Keywords: Facial Recognition, Deep Learning, Bias Mitigation, Fairness, Artificial Intelligence, Algorithmic Ethics, Privacy-Preserving AI, Demographic Parity

[1]. Grand View Research. (2022). Facial Recognition Market Size, Share & Trends Analysis Report.
Https://Www.Grandviewresearch.Com/Industry-Analysis/Facial-Recognition-Market
[2]. Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities In Commercial Gender Classification. Proceedings Of The 1st Conference On Fairness, Accountability And Transparency, 77-91.
Https://Proceedings.Mlr.Press/V81/Buolamwini18a.Html
[3]. Grother, P., Ngan, M., & Hanaoka, K. (2019). Face Recognition Vendor Test (FRVT) Part 3: Demographic Effects. NIST Interagency Report 8280. Https://Doi.Org/10.6028/NIST.IR.8280
[4]. European Data Protection Board. (2025). Guidelines On Bias Evaluation In AI Systems. Https://Edpb.Europa.Eu/Our-Work-Tools/Documents/Public-Consultations/2025/Guidelines-Bias-Evaluation-Ai-Systems_En
[5]. U.S. Department Of Homeland Security. (2024). Facial Recognition Technology Update: Performance And Bias Assessment.
Https://Www.Dhs.Gov/Publication/Facial-Recognition-Technology-Update-2024.

Abstract: Digital technologies have presented not only unprecedented opportunities but also grave vulnerabilities to the education sector, but have also made education settings an ideal un-cyberattack target: strained rural school network resources, inadequate technical infrastructure, varying degrees of digital literacy, each of which compounds to form a trifecta of vulnerabilities. This paper discusses the critical research question of developing secure, trusted, and accessible digital ecosystems in rural education as a balance between the current tension of privacy, usability.....

[1]. Aichner, T. (2021). The Impact Of The Digital Divide On The Adoption Of Online Education. Journal Of Education And Information Technologies, 26(4), 4699-4713.
[2]. Akyildiz, I. F., & Wang, X. (2005). A Survey On Wireless Mesh Networks. IEEE Communications Magazine, 43(9), S23-S30.
[3]. Ali, W. (2020). Online And Remote Learning In Higher Education Institutes: A Necessity In Light Of COVID-19 Pandemic. Higher Education Studies, 10(3), 16-25.
[4]. Bailey, M., Et Al. (2019). The Challenges Of Cybersecurity Education In K-12. Proceedings Of The 50th ACM Technical Symposium On Computer Science Education.
[5]. CISA. (2022). K-12 School Security: A Guide For Preventing And Protecting Against Gun Violence. Cybersecurity And Infrastructure Security Agency.

Abstract: The fact that the world requires to acquire the skills of computational thinking at a young age has worsened the search on the pedagogical techniques that can be applied to engage novice students in programming sufficiently. Text based or traditional methods are steep in learning curve and therefore lead to high attrition rates and low self efficacy amongst the students. To eliminate these problems, gamification or the application of game-related ideas to non-games context, has emerged as a feasible paradigm and is examined in the paper to trace the impact of gamification...

[1]. Bandura, A. (1997). Self-Efficacy: The Exercise Of Control. W. H. Freeman.
[2]. Csikszentmihalyi, M. (1990). Flow: The Psychology Of Optimal Experience. Harper & Row.
[3]. Deci, E. L., Koestner, R., & Ryan, R. M. (1999). A Meta-Analytic Review Of Experiments Examining The Effects Of Extrinsic Rewards On Intrinsic Motivation. Psychological Bulletin, 125(6), 627–668.
[4]. Deci, E. L., & Ryan, R. M. (2000). The "What" And "Why" Of Goal Pursuits: Human Needs And The Self-Determination Of Behavior. Psychological Inquiry, 11(4), 227–268.
[5]. Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011). From Game Design Elements To Gamefulness: Defining "Gamification". In Proceedings Of The 15th International Academic Mindtrek Conference: Envisioning Future Media Environments (Pp. 9–15). ACM.