Category Archives: Uncategorized

Authors: Assistant Professor Vinay.M, Assistant Professor Raghuvarancheerla

Abstract: The blockchain technology has emerged as one such revolutionary solution to solving some major problems in the field of supply chain management that include information asymmetry, non-traceability, frauds and inefficiencies. In this paper, a novel methodology for managing the supply chain through the use of blockchain technology and improved consensus mechanism is proposed. The proposed methodology incorporates the use of permissioned blockchain along with an improved consensus algorithm known as the Delegated Proof of Stake (DPOS) which uses the concept of reputation-based voting and incentives. The performance improvements of the proposed system have been validated through the agent-based simulations of a four-tier supply chain model and are observed to be as high as 30.8% in service level improvement, 23.7% in cost saving and 30.8% in inventory reduction.

DOI: https://doi.org/10.5281/zenodo.22012991

Published by: vikaspatanker

Authors: Assistant Professor Subhadip Sarkar, Assistant Professor Ms. Preeta Rajiv Sivaraman

Abstract: Employee turnover is a challenging situation for any organization because of high associated costs related to recruitment and training of new employees as well as losing organizational knowledge. This research aims to examine how machine learning can be used in predictive analytics in managing employee turnover through the use of the IBM HR Analytics data set. Five models including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and Gradient Boosting were tested systematically. It was revealed that ensemble models such as Random Forest have the highest predictive accuracy and AUC equal to 87.3% and 0.9348, respectively. Overtime, job satisfaction, monthly income, and tenure are found to be important factors affecting employee turnover. These results show that machine learning may change HR practices from reactive to proactive retention strategies based on data analysis.

DOI: https://doi.org/10.5281/zenodo.22012826

Published by: vikaspatanker

Authors: Assistant Professor M Sravan Kumar Babu, Assistant Professor Devanshi Hemal Shah

Abstract: Robotic Process Automation (RPA) in combination with Artificial Intelligence (AI) has become a revolutionizing paradigm for enterprise automation that allows for surpassing the boundaries of traditional automation based on rules. In this paper, an advanced framework for Intelligent Process Automation (IPA) is proposed, combining deterministic nature of RPA with the cognitive capabilities of AI, which makes it possible to automate not only structured but unstructured business processes as well. Hybrid approach is designed to use natural language processing, optical character recognition, and machine learning models to deal with difficult decisions. Empirical study shows impressive increases in efficiency, as the time of task completion is reduced by 33%, and errors occur 65% less often. Comparative analysis with conventional RPA shows IPA to be a better solution in many aspects of performance. The results add to the literature on intelligent automation and have practical implications for organizations.

DOI: https://doi.org/10.5281/zenodo.22012612

Published by: vikaspatanker

Authors: Bilal Ibrahim Maijamaa, Salim Ahmad, Zaharaddeen Salele Iro, Aminu Aliyu Abdullahi

Abstract: Missing values are a common challenge in real-world datasets, often reducing the accuracy and reliability of machine learning models. Although numerous machine learning and ensemble-based imputation techniques have been proposed, many rely on single-level stacking architectures and do not explicitly model both linear and nonlinear relationships during prediction. This study proposes a Particle Swarm Optimization (PSO)-optimized two-level stacking ensemble for numerical data imputation. The framework employs PSO for base-learner selection and hyperparameter optimization; the selected base learners are Random Forest and XGBoost. A two-metalearner architecture is then used in which Linear Regression captures linear dependencies and Random Forest models nonlinear interactions to generate the final imputed values. The proposed framework was evaluated on the Breast Cancer Wisconsin and Wine Quality datasets under Missing Completely at Random (MCAR) mechanisms at 30%, 20%, and 10% missingness. Performance was assessed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R²), and processing time. Experimental results demonstrate that the proposed model consistently outperformed the standalone Random Forest and XGBoost models across all missingness levels. On the Breast Cancer dataset, the proposed model achieved RMSE values of 0.0747, 0.0627, and 0.0529 with corresponding R² values of 67.66%, 70.51%, and 74.63% at 30%, 20%, and 10% MCAR, respectively. Similarly, on the Wine Quality dataset, it recorded RMSE values of 0.1873, 0.1751, and 0.1681 with corresponding R² values of 49.54%, 52.15%, and 54.64%. Furthermore, the proposed approach outperformed a recently reported hybrid imputation method, achieving substantially lower prediction errors while maintaining acceptable computational cost. These findings demonstrate that integrating PSO optimization with a two-level stacking ensemble provides an accurate, robust, and scalable framework for numerical missing value imputation across datasets with varying degrees of missingness.

DOI: https://doi.org/10.5281/zenodo.22009422

Published by: vikaspatanker

Authors: Paul ‘Seun Sosina, Odebiyi Olusola James

Abstract: Artificial intelligence is transforming national security governance by strengthening intelligence gathering, threat detection, border surveillance, cybersecurity, crime prevention and conflict early warning. However, its deployment across Africa raises concerns about privacy, algorithmic bias, accountability, misinformation, institutional readiness and technological dependence. This study examined artificial intelligence and national security governance in Africa through a comparative analysis of Nigeria, South Africa, Kenya, Rwanda and Egypt. Anchored in Technology Domestication Theory, the study adopted a qualitative multiple-case design. Data were obtained through documentary analysis of peer-reviewed studies, national policies, legislative frameworks, security strategies and African Union policy documents. The data were analysed using thematic synthesis and comparative policy analysis. Findings revealed that AI improves data processing, threat identification, surveillance capability and proactive security decision-making, although the scale of adoption varies across the five countries. Egypt and Rwanda demonstrate centralized policy arrangements, while Kenya and South Africa possess digital ecosystems but face implementation and regulatory challenges. Nigeria’s adoption is constrained by infrastructure deficiencies, institutional responsibilities and technical capacity. Shared challenges include inadequate oversight, poor data quality, skills shortages, algorithmic bias, privacy risks and dependence on foreign technologies. The study concludes that effective AI-supported security requires context-sensitive governance grounded in human rights, democratic accountability, strategic autonomy and human oversight. It recommends risk-based regulation, oversight, impact assessments, indigenous capacity development, stronger data protection and harmonized standards.

DOI: https://doi.org/10.5281/zenodo.21993615

Published by: vikaspatanker

Authors: Ritik Sharma, Balvir Singh Thakur

Abstract: General-purpose large language models (LLMs) are increasingly deployed as autonomous research agents: given a web-search tool and a task-specific instruction, they are expected to compile structured, factually complete documents from public sources with little human oversight. We report an empirical case study, spanning three successive system architectures of a deployed research-automation module, evaluating whether a GPT-5-class model can reproduce the completeness and accuracy of a curated reference document. We show that a completeness gap recurs at every layer of the pipeline, not just at the LLM layer intuition would blame first. At the LLM layer, a self-directed search-stopping criterion does not scale with true task size, producing near-complete coverage on a low-activity subject but only 9 of 16 known events (56%) on a high-activity subject under comparable search effort. On a large structured disclosure table, the model correctly reports the verified aggregate figure but declines to enumerate individual line items it cannot verify from search snippets, capturing 0% of line items in the case measured. Replacing the model's self-directed search with a deterministic, non-LLM pre-fetch of an authoritative source, combined with a mandatory reconciliation checklist, more than triples measured coverage (9 to 28 events) — but building that deterministic stage itself surfaced two further completeness bugs, both caught and fixed before production through direct re-verification against live source data: a naive entity-name query missing 4 of 5 known related records until query construction was corrected, and a silent 1,000-record pagination cap that would have discarded 69% of one subject's true history had it gone undetected. We then report the most severe instance of the same underlying pattern: in a substantially more deterministic third-generation architecture, purpose-built to eliminate LLM-driven table extraction entirely, a "most recently filed wins" version-selection heuristic silently selected an incomplete partial-amendment filing over a complete original filing for the same reporting period, capturing only 1 of 10 real holdings (3.8% of true portfolio value) — a bug caught only via comparison against a second, independent reference for a different subject, not by the team's own prior testing, and which in turn silently disabled a downstream data-quality safety check that depended on the same broken data. We report these as an empirical case study rather than a statistically powered benchmark, and specify the exact follow-up measurement needed to convert the paper's central open finding into a confirmed before/after result.

DOI: https://doi.org/10.5281/zenodo.21992676

Published by: vikaspatanker

Authors: Yathish Kumar S

Abstract: This paper proposes a theoretical synthesis between the predictive processing framework in computational neuroscience most rigorously formalized as Karl Friston's free energy principle and the literary phenomenon of narrative suspense. We argue that suspense is not merely a felt quality of certain texts but a measurable consequence of how narrative structures modulate prediction error across a reader's generative model of unfolding events. Drawing on empirical work in neuroaesthetics, psycholinguistics, and narratology, we develop a model in which classical plot devices foreshadowing, withheld information, dramatic irony, the ticking clock, and the twist function as deliberate engineering of precision-weighted prediction error. We review existing fMRI and behavioral evidence on narrative transportation and the Default Mode Network, and we present, as a Stage 1 Registered Report, a fully specified 2×2 within-subjects experimental design with power analysis, a pre-registered statistical analysis plan using linear mixed-effects modeling, and directional hypotheses derived independently from competing theoretical accounts. We close by addressing the principal objection to this research program that formalizing suspense in information-theoretic terms risks reducing literary value to a computable quantity and argue that this concern rests on a confusion between mechanism and meaning.

DOI: https://doi.org/10.5281/zenodo.21990599

Published by: vikaspatanker

Dr. Priyanka Dubey
Affiliation	Assistant Professor,De[artment of Computer science,Amity University,Manesar, Gurugram
Email-Id:	priyankathapak07@gmail.com
Publication: Patents: AI-Enhanced ECG monitoring device for workforce and student performance assessment Application No. 6486798. Autonomous machine learningh-enabled ronot for inspection campus monitoriang and educational resource management Application No. 6513703.  Publications: A Systematic Literature Review of Semantic Validation, Consequence Prediction, and Adversarial Robustness in ICS Security Gateways: Indian Journal of Computer Science and Technology@2026. A Multilingual Customer Support Assistant Using Machine Learning and Streamlit for Real-Time Global Communication: International Journal for Multidisciplinary Research@2/4/2025. Bridging the perception Gap A YOLO V8 Power Object Detection System for Enhanced Mobility of Impaired individuals,First International Conference on Technological Innovations and Advance Computing . Optimizing Real-Time Object Detection- A Comparison of YOLO Models: International Journal of Innovative Research in Computer Science and Technology (IJIRCST) ISSN(Online): 2347-5552, Volume-12, Issue-3, May 2024. Wireless Sensor Network Energy Optimization by Frog Leaping Genetic Algorithm, International Conference on Universal Threats in Expert Applications and Solutions.

Published by: vikaspatanker

Authors: Ar. Arjun Sharma, Zuneid Khan

Abstract: This study aimed to assess green building principles, sustainable construction attributes, industry practices, and barriers to sustainable construction implementation as a basis for developing a Sustainable Construction Framework for Nueva Vizcaya, Philippines. The study employed a descriptive mixed-methods research design using an explanatory sequential approach. The quantitative phase involved 20 construction professionals and stakeholders, while the qualitative phase involved 10 purposively selected key informants with relevant professional experience in construction, sustainability, and infrastructure development. Data were collected through a structured questionnaire and semi-structured interviews. Quantitative data were analyzed using frequency and percentage distributions, weighted mean, and standard deviation, while qualitative data were analyzed using thematic analysis. The findings revealed a generally favorable assessment of sustainable construction among the respondents. Green building principles were generally recognized and practiced, with construction waste management and recycling obtaining the highest weighted mean of 4.80. Sustainable construction attributes were also rated favorably, with promoting worker and community well-being receiving the highest weighted mean of 4.90. Among industry practices, worker training and regular monitoring of project performance for sustainability compliance obtained the highest weighted mean of 4.80. However, significant barriers to sustainable construction implementation were identified. The lack of government incentives, limited availability of sustainable materials, and weak enforcement of environmental policies each obtained the highest weighted mean of 5.00. High initial construction costs, resistance to organizational change, and limited technical knowledge were also identified as significant barriers. Overall, the findings indicate that construction stakeholders recognize the importance and benefits of sustainable construction but continue to encounter financial, technical, institutional, and organizational constraints. The study therefore recommends the development of a localized Sustainable Construction Framework for Nueva Vizcaya that incorporates stronger policy enforcement, capacity-building programs, financial incentives, improved access to sustainable materials and technologies, stakeholder collaboration, and sustainability monitoring mechanisms. Such a framework may contribute to environmentally responsible, economically viable, and socially responsive construction practices in the province.

Published by: vikaspatanker

Authors: Khan Junaid Ahmed, Md Qasimumar Noorulamin, Khan Miran Altaf, Mohammed Juned Qazi

Abstract: Concrete requires adequate internal moisture during cement hydration to achieve specified compressive strength and long-term durability. Conventional curing methods (ponding, spraying, and wet coverings) require significant volumes of potable water and continuous site labor—resources that are severely restricted in water-stressed regions. Self-curing concrete provides an effective alternative by retaining internal moisture through chemical admixtures. This experimental investigation evaluates the fresh and hardened properties of M30 grade concrete incorporating Polyethylene Glycol-400 (PEG-400) as a self-curing agent at replacement levels of 0%, 0.5%, 1.0%, 1.5%, and 2.0% by weight of cement. Workability was measured via slump tests, while mechanical performance was evaluated through compressive strength (7, 14, and 28 days), split tensile strength, and flexural strength. The experimental results indicate that workability increases linearly with PEG-400 dosage. An optimum dosage of 1.5% PEG-400 yielded the highest mechanical performance, achieving a 28-day compressive strength of 39.1 MPa (14.3% higher than the control mix). Beyond 1.5%, a marginal decrease in mechanical properties was observed.

DOI: http://doi.org/10.5281/zenodo.21930217

Published by: vikaspatanker