Category Archives: Uncategorized

Authors: Abubakar Sadiq Yusha’u, Aminu Aliyu Abdullahi

Abstract: Artificial Intelligence (AI) systems are increasingly embedded in public governance for decision-making in areas such as welfare distribution, predictive policing, taxation, immigration, and electoral administration. While these systems promise efficiency and scalability, they also introduce significant risks of algorithmic bias with direct implications for equity, accountability, and democratic legitimacy. This study presents a systematic literature review (SLR) on metrics for identifying algorithmic bias in AI-driven governance models, with a particular emphasis on novel and governance-aware measurement approaches. The review follows PRISMA guidelines and analyzes peer-reviewed journal articles, conference proceedings, and high-impact policy reports published between 2014 and 2025. Literature was sourced from Scopus, Web of Science, IEEE Xplore, ACM Digital Library, and SpringerLink using structured search strings related to algorithmic bias, fairness metrics, and AI governance. After a multi-stage screening and eligibility process, the selected studies were subjected to qualitative thematic synthesis and comparative analysis. The results reveal that traditional statistical fairness metrics such as demographic parity, equalized odds, and predictive parity are widely used but insufficient for governance contexts due to their lack of contextual, temporal, and institutional sensitivity. The review identifies and classifies emerging bias metrics into five major categories: causal metrics, intersectional metrics, temporal and dynamic metrics, structural–institutional metrics, and explainability-driven indicators. These novel metrics demonstrate stronger alignment with governance principles, particularly in addressing power asymmetries, historical discrimination, and policy constraints. The study contributes a consolidated taxonomy of bias metrics and proposes an integrated, multi-dimensional framework for evaluating algorithmic bias in AI-driven governance systems. The findings offer practical guidance for policymakers, regulators, and system designers, while highlighting critical research gaps related to standardization, empirical validation, and Global South governance contexts.

DOI: http://doi.org/10.5281/zenodo.18312609

Published by: vikaspatanker

Authors: Vansh Shisodia, Saibee Alam, Anish Kushwaha, Aarchi Goyal

Abstract: This research constructs a hybrid system for one- step-ahead (H=1) stock forecasting, addressing the non-linear and non-stationary nature of financial time-series. The objective is twofold: a regression task for the Adjusted Close Price and a binary classification task for directional movement. The pro- posed ensemble design combines three model families: classical econometrics (ARIMA), deep learning (LSTM) for long-term dependencies, and ensemble tree methods (XGBoost, RF) for non- linear feature interactions. The methodology emphasizes rigorous feature engineering, including technical indicators and GARCH- derived volatility features, and robust validation using Time Series Cross-Validation (TSCV) and Nested Cross-Validation (nCV). The system culminates in a stacked ensemble (blending layer) and utilizes advanced loss functions like Huber Loss to manage heavy-tailed return distributions Evaluation is based on both statistical fit and financial utility metrics, such as directional accuracy and the Sharpe Ratio.

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Authors: Sandhyarani Balasaheb Kunjir, Suraj Rajiv Jaybhaye, Pradeep Sanjay Kapse, Abhishek Sunil Adagale, Mrs. Adagle P. M

Abstract: Tunnel electrification is essential for road safety and visibility inside tunnels. Conventional tunnel lighting consumes high power because lights remain ON continuously irrespective of traffic density. This research paper presents an ESP32 based smart lighting and safety system for road tunnels. The system uses sensors such as IR/Ultrasonic sensor for vehicle detection and LDR sensor for ambient light detection. Based on real-time sensor input, the ESP32 controller activates tunnel lighting section-wise, which reduces unnecessary electricity consumption. The system is further expandable with IoT monitoring through Wi-Fi for remote supervision and fault indication. The proposed smart tunnel electrification system improves energy efficiency, enhances safety and provides an economical solution for smart city infrastructure.

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Authors: Gauri Bhambere, Roshani Gaikwad, laxmi Ingole, Sanjivini Gore, prof. Prachi Walunj

Abstract: Urban drainage and sewer manholes often pose serious safety risks due to the accumulation of toxic gases and abnormal temperature rise, which can lead to explosions, health hazards, and infrastructure damage. This project presents an IoT-based Manhole Monitoring System designed to continuously monitor hazardous gas levels and temperature inside manholes in real time. The system employs gas sensors to detect harmful gases such as methane, hydrogen sulfide, and carbon monoxide, along with temperature sensors to identify overheating or fire risks. Sensor data is collected by a microcontroller and transmitted wirelessly to a cloud platform using IoT communication technologies. When abnormal conditions are detected, instant alerts are sent to authorities via a web or mobile application, enabling quick preventive action. The proposed system improves worker safety, enhances public safety, reduces manual inspection efforts, and supports smart city infrastructure by providing reliable, real-time monitoring of underground manhole conditions.

DOI: http://doi.org/

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Authors: Prof. Abhijit Chakraborty, Vinayak Ramakrishna Bhat, Samartha Vinayak Hegde, Prasanna Vighneshwara Hegde

Abstract: The rapid expansion of sustainable investment has created an urgent demand for reliable, comprehensive, and verifiable ESG information. Yet, despite its growing importance, the field continues to struggle with fragmented reporting standards, selective disclosures, and the increasing prevalence of greenwashing, all of which undermine the integrity of sustainability-focused financial decisions. This study provides an in-depth qualitative exploration of how Artificial Intelligence (AI) and Big Data are beginning to address these persistent issues and reshape the foundations of sustainable investment strategies. Drawing on a wide range of scholarly publications, institutional reports, and contemporary discussions from 2020 to 2025, the research examines how emerging digital tools are being used to interpret and validate sustainability performance. The literature shows that AI techniques such as natural language processing, intelligent screening algorithms, and pattern recognition models are improving the credibility of ESG evaluations by identifying inconsistencies, revealing undisclosed risks. Big Data strengthens this process by incorporating diverse and externally sourced evidence, including satellite-based environmental monitoring, supply-chain traceability systems, climate-risk datasets, and real-time operational. These combined capabilities not only enhance transparency but also reduce information asymmetry, allowing investors to form a more accurate understanding of a firm’s sustainability behavior and long-term risk exposure. The study finds that the use of AI and Big Data is gradually shifting sustainable investing from a disclosure-driven model dependent on voluntary corporate reporting to a more evidence-based and analytically rigorous approach. This transition supports stronger investor confidence, encourages more accountable corporate behavior, and aligns investment decisions more closely with global sustainability objectives. While challenges remain, including data standardization and ethical concerns in AI use, the overall trajectory suggests that AI and Big Data are poised to become essential pillars of future sustainable finance. This study examines how advanced analytical technologies contribute to improving the reliability, transparency, and investment relevance of ESG assessments. Using secondary data from a sample of forty publicly listed companies across multiple industry sectors, the study employs descriptive statistics, sector-wise analysis, regression modelling, and one-way ANOVA to examine patterns in ESG performance and sustainability risk. The analysis reveals significant sectoral differences in ESG scores, with technology and healthcare firms demonstrating relatively higher sustainability performance compared to energy and manufacturing sectors. Regression results indicate a strong negative relationship between ESG scores and sustainability risk, suggesting that higher sustainability performance is associated with improved risk management outcomes. The findings also show that firms adopting AI-driven analytics and structured sustainability reporting practices tend to achieve higher ESG scores and lower risk exposure.

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Authors: Nagender Yamsani

Abstract: Enterprise-wide reference data has emerged as a foundational element for ensuring consistency, control, and operational integrity within complex institutional environments where fragmented data ownership and system proliferation create structural risk. Persistent inconsistencies in shared reference domains often undermine governance objectives, increase reconciliation effort, and propagate errors across dependent processes, highlighting a gap between enterprise data strategy and practical implementation models. The purpose of this research is to establish a structured architectural and operating framework for centralized reference data foundations that aligns stewardship accountability, governance controls, and technical design into a cohesive institutional capability. A mixed-methods approach is adopted, integrating qualitative analysis of enterprise operating models and governance mechanisms with comparative evidence mapping drawn from large-scale institutional reference data implementations. The findings demonstrate that effective centralization depends not on tooling alone, but on the coordinated design of stewardship roles, control workflows, integration patterns, and distribution services that collectively enforce data integrity at scale. The research contributes to a practical, implementation-oriented framework that clarifies how reference data hubs can be institutionalized as shared infrastructure rather than treated as isolated data initiatives. The implications extend to both academic inquiry and professional practice by providing a replicable foundation for reducing operational risk, strengthening governance assurance, and enabling dependable downstream consumption in environments characterized by high system interdependence and regulatory sensitivity.

DOI: http://doi.org/10.5281/zenodo.18296676

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Authors: Nagender Yamsani

Abstract: High-performance analytics and reporting within large enterprises depend on data integration pipelines that can operate reliably across fragmented operational systems, governance boundaries, and performance constraints. As organizations expand their digital footprints, analytical workloads increasingly rely on structured data access mechanisms that balance scalability, control, and responsiveness. This study examines the design and implementation of enterprise data integration pipelines that support analytics and reporting in complex operational environments. It focuses on the interaction between API-mediated data access, SQL-based service layers, and transformation workflows that mediate between transactional systems and analytical consumers. The paper argues that sustainable analytics capability emerges from architectural coherence rather than isolated tooling choices. Evidence from large-scale enterprise environments suggests that pipelines emphasizing modular integration layers, performance-aware data transformations, and governed access models achieve higher analytical reliability and operational resilience. Empirical patterns indicate that separating data exposure concerns from transformation logic improves system adaptability while reducing downstream reporting volatility. The study introduces a conceptual framework that aligns integration architecture, operational performance controls, and governance enforcement into a unified model for enterprise analytics enablement. By articulating practical design trade-offs and architectural patterns grounded in real operational constraints, this work contributes a structured perspective that supports both applied implementation and future academic inquiry. The findings provide a foundation for understanding how disciplined integration engineering can enhance analytical trust, scalability, and long-term maintainability in enterprise reporting systems.

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Authors: Swetha Pradeep, Shreedharini Y

Abstract: Cloud computing has emerged in recent times as a disruptive technology that has favourably influenced the functioning of many businesses, organizations, and institutions. The utilisation and prevalence of cloud computing arise from an on- demand model that provides computing services via the internet. Several academic institutions have incorporated cloud computing into the educational process to enhance pedagogical outcomes. The review aims to examine cloud computing in education and the need for educational institutions to comprehend its primary advantages. In this review, we discussed the architectural integrations of cloud computing services in education, encompassing Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS) models. The outcome of this study includes a visual representation of the educational trends in cloud computing, the impact of cloud educational technologies, and the major challenges facing its adoption. This review will augment literature on cloud computing, its application in educational institutions, and anticipated challenges.

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Authors: Wofu, N. B, Nwauzoma, A. B, Chuku, E. C, Nmom F. W.

Abstract: Plantain (Musa paradisiaca L.), Nigeria’s third most important starchy staple, depends on rhizosphere bacteria for nutrient acquisition and stress tolerance, yet its microbial profile remains underexplored. This study applied 16S rRNA amplicon sequencing to characterize bacterial diversity and GC content in plantain rhizosphere from Rivers State, Nigeria. Diseased plantain roots were collected from the Rivers State Institute of Agriculture Research and Teaching (RIART) Farm, Port Harcourt, Nigeria. Genomic DNA was extracted from plantain roots and amplicons sequenced following Laragen’s validated proprietary. The metagenomic data were analyzed using Laragen’s proprietary in-house pipeline based on BLAST searches for taxonomic classification. The results revealed that Proteobacteria dominated (54.81%), followed by Verrucomicrobia (16.48%), Bacteroidetes (12.28%), Actinobacteria (8.10%), and Planctomycetes (3.16%). Alphaproteobacteria (29.1%), Gammaproteobacteria (21.4%), and Rhizobiales (23.1%) were prevalent at class and order levels. Dominant genera included Luteolibacter sp. (14.5%), Pseudoxanthomonas sp. (14.5%), and Devosia sp. (13.8%), with unclassified taxa reaching 38.4% at genus/species levels. GC content varied widely (<30% to ~70%), highest in Gordonia sp. and Paracoccus sp., lowest in Paludibacter sp. and Pseudoxanthomonas sp. The study revealed marked genomic diversity in the rhizosphere of plantain. Future studies should use shotgun metagenomics, isolate key taxa, and develop targeted bioinoculants to improve plantain productivity and sustainability in Nigerian agroecosystems.

DOI: http://doi.org/10.5281/zenodo.18279647

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Authors: Pankaj Kumar Rai, Dr. P. N. Ahirwar

Abstract: Additive manufacturing (AM) processes qualifies in producing high-performance, complex design component with an efficient use of material. However, processing of fusion based additive manufacturing processes such as Laser Powder Bed Fusion Processes (LPBF) generates thermal stresses due to rapid heating and cooling cycles. The accumulation of these residual stresses in the printed component is undesirable and may result in dimensional distortion, anisotropy, and premature failure of components during service. Aluminium alloys such as AlSi10Mg are processed through LPBF route of AM due its excellent printability and its application in aerospace applications due to its superior fly to weight ratio. However, the printed AlSi10Mg faces challenges due to its high thermal conductivity and residual stresses. These stresses hinder dimensional tolerances and worsen mechanical performance. This review provides the overview of additive manufacturing processes with the physics of residual stress development and residual stresses in AlSi10Mg. A detailed discussion on residual stress generation, measurement and management are presented. The residual measurement strategies involving destructive, semi-destructive, and non-destructive and state-of-the-art numerical modeling approaches, including finite element–based and data-driven methods. This review aims to provide a comprehensive insight of the residual stress in additively manufactured AlSi10Mg to help in designing of component for practical application.

DOI: http://doi.org/10.5281/zenodo.18279481

Published by: vikaspatanker