Enhancing News Verification System Using Blockchain and Text Analysis

Authors: Durga Prasad, Dr. Avadhesh Kumar Dixit

Abstract: The spread of fake news and misinformation on the internet has becoming the very serious problem, which making it difficult for the people to know what to trust. The current methods for verifying news often have the limitations: they can be too slow, lack transparency, or fail to confirm that the original source of story. This paper is proposing a new system that is combining two powerful technologies to addressing these challenges. Where first, we use the text analysis techniques from the field of Natural Language Processing (NLP) to scan news contents and identifying linguistic patterns often associated with the false and misleading information. Second, we leveraging the blockchain technology which is used to create a tamper-proof record of a news article’s origin and any changes made to it over time. By storing a digital footprint of the verified content on the blockchain, our system which allowing the readers to check if the news they are viewing matches the original version published by a trusted source. This dual-prolonged approach not only helps flag the potentially deceptive content through analysis but also builds a trustworthy chain of provenance. In the proposed system offers a more reliable and transparent way to verify digital news, empowering users to make informed judgments about the information they will consume.

DOI: http://doi.org/10.5281/zenodo.21333813

Learning System for Tree Traversal Algorithms: An Enhanced Algorithm Visualization Tool

Authors: Samir Yau Nuhu, Jamilu Awwalu, Zaharaddeen Salele Iro, Zaharaddeen Sufyanu

Abstract: Tree traversal algorithms form a major part of undergraduate computer science education because they teach important concepts about how data is organized and processed in trees. However, students find these algorithms difficult to understand since they are abstract and involve following specific rules to visit each node in a particular sequence. Existing visualization tools fail to help students fully because they do not clearly separate or explain the differences between the three main Depth-First Search variants, and they do not show the step-by-step order of visited nodes in a simple and structured format. To solve these problems, this thesis presents a new interactive learning tool that runs directly in a web browser and is built using HTML, CSS, JavaScript and SVG. The tool allows students to create their own binary trees by adding nodes, gives them full control to move through each step of any traversal method, and includes a table-generation feature that automatically builds and displays a clear list showing exactly which nodes are visited and in what order. We carried out an analysis of the system’s algorithms and showed that it remains efficient and scalable even when working with larger tree. In addition, we tested the tool in a real classroom setting with 100 undergraduate computer science students. Before using the tool, their average score on a test about tree traversals was only 40%. After use of the tool their average score increased to 68%. These results clearly demonstrate that the new system overcomes the main weaknesses of earlier visualization tools and helps students gain a better understanding of tree traversal algorithms.

DOI: http://doi.org/10.5281/zenodo.21334804

State-of-the-Art Machine Learning Paradigms And Explainable Artificial Intelligence (XAI) Frameworks For Intelligent Network Intrusion Detection: A Comprehensive Literature Survey

Authors: Aravind Chagantipati

Abstract: The deployment of high-throughput deep neural networks within modern enterprise multi-cloud backbones has significantly advanced the accuracy of automated anomaly tracking. However, their highly complex, multi-layered topologies operate as opaque black boxes, creating substantial validation and trust barriers for security operations teams. This paper provides a comprehensive literature survey analyzing the structural shift from traditional shallow machine learning classifiers to deep temporal topologies using benchmark corpuses (NSL-KDD, CICIDS, and UNSW-NB15). Furthermore, it reviews contemporary post-hoc Explainable AI (XAI) integration paradigms, focusing on SHAP and LIME architectures designed to manage the performance-trust trade-off across production boundaries. We provide a rigorous analysis of classification metrics, mathematical foundations of feature attribution, and practical implications for next-generation security operations centers.

DOI: http://doi.org/10.5281/zenodo.21336418

An Explainable Transformer Based Framework For Detecting Misinformation In Social Media

Authors: Dr. Prakash Kammam, Mukkapati Venu, K. Ashwini

Abstract: The fast spread of misinformation on social media platforms is causing serious issues with regards to public trust, democracy, and making sound decisions. The following paper provides a comprehensive overview of transformer-based systems for explainable misinformation detection, considering the latest research in the field of multimodal fusion, large language models implementation, and explainable AI. As can be seen from the systematic analysis, transformers surpass traditional methods in performance, with the multimodal system providing an accuracy of up to 94.5% and 81.1% on benchmark datasets. Moreover, large language models are very useful when generating background knowledge and enriching context, whereas explainability methods such as SHAP and LIME offer human-interpretable rationales for decisions made by the model. It was found that hierarchical progressive transformers successfully incorporate multimodality, combining different types of data such as text, images, background knowledge, and user comments, resulting in better performance than current methods.

DOI: http://doi.org/10.5281/zenodo.21338113

Explainable Artificial Intelligence For Early Disease Prediction: A Multi-Modal Healthcare Analytics Framework For Precision Medicine

Authors: N Jeevana Jyothi, M. Priyatharshini

Abstract: Combination of multimodal healthcare data such as genomic profiles, EHRs, medical imaging, and wearable sensor data brings in new opportunities for early disease prediction and precision medicine. Nevertheless, the complexity and black-box nature of state-of-the-art machine learning models bring many challenges towards their clinical deployment. This paper introduces a novel explainable artificial intelligence (XAI) architecture for early disease prediction using multi-modal healthcare analytics. The proposed framework combines various data streams using cross-modal generative transformers, interprets results by means of feature attribution and attention heatmaps computed using SHAP values, and utilizes federated learning techniques to preserve the privacy of the participating institutions. Evaluation using real-world multimodal datasets confirms high effectiveness of our solution with accuracy of 97% and AUC of 0.971, which is much better compared to unimodal and black box models.

DOI: http://doi.org/10.5281/zenodo.21338356

Strategic Decision-Making Framework Using Business Analytics for Sustainable Organizational Growth

Authors: Assistant Professor Dr.S.Sujatha, Assistant Professor Dr Sayantani Chakraborty

Abstract: In times when organizations face a high level of challenges, they need sophisticated models that help them to reconcile conflicting goals and maintain sustainable development. In this paper, we propose an analytical strategic decision-making model combining the use of business analytics with multi-objective optimization and sustainability principles. We base our research on Multi-Objective Optimization (MOO) concept, Pareto frontier theory, and Triple Bottom Line (TBL) framework. As a result, we have developed a model that allows organizations to deal with conflicting goals concerning profitability and risk level and maintain sustainable development at the same time. The analysis of quantitative data from different industries shows that application of the proposed model significantly increases the efficiency of decision making and strategic planning in organizations.

DOI: https://doi.org/10.5281/zenodo.21350647

Real-Time IoT Environmental Monitoring: Unmasking Diurnal Thermodynamic Transitions, Inverse Humidity Relations, and Atmospheric Scrubbing Effects

Authors: Prince Pawar, Sujal Sisodiya, Associate Professor Pradeep Patel

Abstract: Rapid microclimatic fluctuations often evade detection by sparse, conventional meteorological networks, necessitating hyper-local, real-time monitoring solutions. This paper presents an analysis of an 8-hour diurnal environmental dataset (10:00 AM to 5:00 PM) captured via an Internet of Things (IoT)-based monitoring node. The system integrates low-cost sensors to continuously log ambient temperature, relative humidity, air quality (particulate/gas concentrations in ppm), light intensity, and precipitation. The empirical data reveals distinct thermodynamic transitions and strong inter-parameter correlations. Specifically, the dataset captures a textbook meteorological shift: midday solar heating—evidenced by a peak temperature of 34∘C, peak light intensity (100%), and a concurrent relative humidity drop to 45%—followed by a sudden convective afternoon rain shower. The onset of precipitation at 3:00 PM triggered an immediate environmental inversion, characterized by a 3∘C drop in temperature, a sharp moisture surge to 68% relative humidity by 5:00 PM, and a significant reduction in airborne pollutants (from a peak of 180 ppm down to 125 ppm) due to the atmospheric scrubbing effect of the rain. These findings demonstrate that high-frequency IoT sensor networks provide highly reliable, granular data essential for unmasking the velocity and impact of localized weather fronts. The proposed approach offers scalable, actionable insights applicable to urban climate mapping, smart agriculture, and industrial environmental compliance.

DOI: https://doi.org/10.5281/zenodo.21353849

Hybrid Deep Learning Approach for Enhancing Security and Disease Detection in Healthcare Systems

Authors: Ms. Nibha kumari, Associate Professor Dr. Pramod K

Abstract: The rapid growth of digital technologies in healthcare has led to the generation and storage of vast amounts of sensitive medical data, making security and efficient data processing critical concerns. Deep learning, a powerful subset of artificial intelligence, has shown significant potential in addressing these challenges by enabling accurate analysis of complex healthcare data and enhancing system security. This study examines the application of deep learning models in healthcare systems with a focus on improving security, disease detection, and data reliability. The research reviews existing studies related to artificial intelligence and deep learning techniques used in medical image analysis, disease prediction, and healthcare data protection.

DOI: https://doi.org/10.5281/zenodo.21355298

Ethical Artificial Intelligence: Bridging Innovation and Social Responsibility

Authors: Ms. Neha Yadav, Dr. Nitin Kumar

Abstract: Smartphone addiction has become a growing concern due to excessive dependence on mobile devices for communication, entertainment, and social interaction. This research focuses on a data-centric machine learning framework for detecting smartphone addiction by analyzing user behavioral patterns such as screen time, unlock frequency, app usage, and night-time activity. Unlike traditional model-focused approaches, the proposed framework emphasizes data quality, preprocessing, feature engineering, and reliable labeling to improve prediction performance. The study aims to support early identification of addiction risk and contribute to the development of intelligent digital well-being systems for healthier smartphone usage habits.

DOI: https://doi.org/10.5281/zenodo.21355847

Dual-Transporter Targeted Lectin-Omega-3 Nanoparticles For Enhanced Neuronal Resilience In Neurodegenerative Disease Models

Authors: Hammed, Hammidat D, Enoma, Samuel, Donkoh, Christian J. K, Kikeh, Emric N, Agboola, Anthonia O, Benin, Sandra

Abstract: Neurodegenerative illnesses pose a significant problem, partly due to the challenge of getting therapeutic drugs across the blood-brain barrier (BBB), as well as the complex interactions between neuroinflammation and metabolic dysregulation. As a way to address these issues, we have created a lipid nanoparticle (LNP) system. It combines two different types of transporters with the intended goal of delivering omega-3 fatty acids through the use of plant lectins to help facilitate the movement of DHA and EPA across the BBB. As a special feature of this created LNP system, when utilizing the GLUT1 and LAT1 transporters located on the endothelium of the BBB in order to move the LNPs across the BBB from the circulation to the brain, both GLUT1 and LAT1 are used simultaneously, allowing a more efficient means of delivering the LNP system across the BBB without being limited by saturation kinetics when both GLUT1 and LAT1 are engaged. Within the LNP, both DHA and EPA are contained in an optimized ratio for both optimal delivery and maximal effect, supporting the activation of neuroprotective pathways (NF-κB suppression) and the promotion of mitochondrial biogenesis. The use of lectin (a binding agent derived from plant sources) as a means by which to reduce inflammation and provide a pathway to help the LNP system penetrate the BBB and provide an inflammatory reduction via helping to change microglial polarity towards an anti-inflammatory phenotype was also demonstrated. The experimental validations done with this LNP system, using human induced pluripotent stem cell (iPSC) derived human BBB models, clearly showed significantly greater levels of transcytosis flow than what is typically expected. As well, in transgenic Alzheimer’s mouse models, the oxidative stress levels were significantly decreased and the synaptic structure was maintained. The novel nature of this work is due to the ability of each of the transporters, GLUT1 and LAT1, to target neurodegenerative disorders while also utilizing immunomodulatory and metabolic pathways in tandem. The strategy applied here provides an innovative and effective platform to enhance neuronal resiliency through the combination of neuroprotection and directional/neural specific drug delivery with applicability across a broad range of neurodegenerative diseases.

DOI: http://doi.org/10.5281/zenodo.21357715

A Review On Solar Energy Based Electricity Production

Authors: Dr Hari Gangadhar Kale

Abstract: The solar energy is generated by the Sunlight is a sustainable renewable energy source that doesn’t harm the environment. The earth receives enough solar energy per hour to cover all of the world’s energy needs for a full year. In the modern era we require electricity on a daily basis. This solar energy is produced for commercial, residential and industrial uses. It can readily absorb energy from direct sunshine. As a result it is highly effective and pollution free. We have analyzed solar energy from sunlight and spoken about its future developments and characteristics in this essay. Additionally, the page attempts to clarify how different types of solar panels operate and highlights the numerous uses and strategies for promoting the advantages of solar energy.

A Versatile Approach to Design Thinking Applied in Educational Contexts

Authors: Jereen Susan John

Abstract: Design thinking has emerged as an innovative, human-centered approach to addressing complex challenges in education by fostering creativity, collaboration, critical thinking, and problem-solving. This paper explores the versatility of design thinking and its application across diverse educational contexts, including school, higher, and professional education. It examines the core stages of the design thinking process—empathize, define, ideate, prototype, and test—and their role in promoting learner-centered, experiential, and inquiry-based learning. The study highlights how design thinking enables educators to develop inclusive teaching strategies, redesign curricula, enhance student engagement, and encourage interdisciplinary collaboration. Furthermore, it discusses the integration of digital technologies and real-world problem-solving activities that prepare learners with essential 21st-century skills. The paper argues that adopting design thinking as both a pedagogical framework and an institutional innovation strategy can transform teaching and learning environments by encouraging adaptability, empathy, and continuous improvement. It concludes that the widespread implementation of design thinking can contribute significantly to educational quality, innovation, and lifelong learning.

Magnetohydrodynamic Blood-Based Nanofluid Transport In Cardiovascular Prosthetics: A Numerical Study Of Coupled Heat And Mass Transfer With Nanoparticle Dynamics

Authors: Sheid, Avidime Momohjimoh, Sheidu Omeiza Momoh, Oyem Onyekachi Anslem, Shuaibu, Muhib Amoto

Abstract: This study presents a comprehensive numerical investigation of magnetohydrody-namic (MHD) blood-based nanofluid flow with coupled heat and mass transfer for cardiovascular prosthetic applications. The research addresses critical gaps in thermal regulation and targeted drug delivery modeling by developing a physiolog-ically realistic mathematical framework that incorporates electromagnetic effects, nanoparticle dynamics, and the non-Newtonian rheology of blood. The govern-ing conservation equations for mass, momentum, energy, and species concentration are formulated within a boundary layer framework, incorporating Lorentz forces, variable thermal conductivity, viscous dissipation, Joule heating, Brownian motion, thermophoresis, and chemical reactions. Through similarity transformations, the nonlinear partial differential equations are reduced to a system of coupled ordinary differential equations, which are solved numerically using a collocation method im-plemented in Python (scipy.integrate.solve_bvp). A comprehensive parametric analysis reveals that thermal and solutal buoyancy significantly enhance momentum transport, while the Prandtl number governs thermal boundary layer characteristics. Nanoparticle transport is predominantly controlled by thermophoresis and Brown-ian motion, with thermophoresis promoting nanoparticle accumulation and Brown-ian motion enhancing diffusion. The Schmidt number suppresses species diffusion, while the Casson parameter exhibits minimal influence on velocity but significantly affects thermal distribution. The magnetic parameter introduces resistive Lorentz forces that modify both momentum and thermal fields. These findings provide valu-able insights for the design of cardiovascular prosthetics, thermal therapy systems, and targeted drug delivery platforms.

DOI: http://doi.org/10.5281/zenodo.21407708

Wireless Signal Interference Detection Using Machine Learning

Authors: Nehneen Ali, Neenansha Jain Assistant Professor , Dr.Divya Jain Associate Professor

Abstract: Ensuring reliable spectrum efficiency in modern wireless communication networks requires robust and automated signal interference management. However, the dynamic and non-uniform nature of wireless environments introduces complex overlapping signals, complicating traditional energy-detection methods. This study evaluates the performance of advanced machine learning and deep learning models for detecting and classifying co-channel and adjacent-channel wireless interference. Through comprehensive experimental testing and simulation, an optimized neural network architecture is identified. Subsequently, the capability of the detection system is assessed under varying signal-to-noise ratios (SNR). The results indicate that while traditional threshold-based methods fail under fluctuating noise floor conditions, the proposed model maintains a detection accuracy above 98% even at low SNR levels down. As a typical example of intelligent spectrum management, this study provides a crucial reference for the optimization of next-generation cognitive radio and 5G/6G wireless network.

DOI: https://doi.org/10.5281/zenodo.21426161

Corporate Ethics Policies: A Quantitative Framework for Evaluating AI Governance in Major Technology Firms

Authors: Mariyam Malik, Professor Dr. B Sasi Kumar

Abstract: Artificial Intelligence (AI) is now widely used in decision-making systems developed by technology giants to drive decisions, triggering concerns related to fairness, transparency, and accountability. In response, organizations such as IBM, Microsoft, and Google have published internal AI ethics policies aligned with international standards. Crucially, these policies prove descriptive in nature and lack measurable methods for evaluation. The work envisioned in this paper, purposefully, a quantitative framework to assess the alignment between corporate AI ethics policies and deployed machine learning systems. A supervised learning model is implemented as a case study using a public income prediction dataset containing sensitive demographic attributes. Fairness is evaluated using demographic parity, while model transparency is examined through SHAP-based feature attribution techniques. We apply additional plausible constraints to address privacy and accountability concerns by limiting sensitive identifiers and enforcing a modular, reproducible pipeline. An aggregated Ethics Compliance Score combines multiple ethical dimensions into a single evaluation measure, showing that ethical risks may persist even in accurate models. Unlike prior work that focuses 27 separately on principles or tools, the proposed framework links corporate ethics commitments directly to quantitative system-level indicators, providing a practical basis for internal AI governance.

ORM Forge: A Natural Language To Django ORM Query Generator Using Large Language Models

Authors: Rajesh Chauhan, Akshay Bhardwaj, Vinay Kumar

Abstract: Writing correct, idiomatic Django Object-Relational Mapper (ORM) code remains a lasting productivity bottleneck for developers, especially developers who are new to the framework or working against unfamiliar schemas. In this paper, we present ORM Forge, a Django web application that utilizes the Anthropic Claude large language model (LLM) to translate natural language descriptions into production-ready Django ORM queries, and describe an empirical evaluation of the underlying natural language to query translation task, based on a publicly available benchmark, distributed as a structured tabular dataset. Instead of reiterating the system overview, we reframe ORM Forge as a research problem in natural-language-to-structured-query (NL2Query) translation, contextualize it within the wider text-to-SQL literature, and perform a quantitative analysis of translation difficulty based on a stratified sample of 120 question-query pairs from a cross-domain text-to-SQL benchmark.Queries were categorized by structural complexity (single-table, join, aggregation, nested/subquery) and manually mapped to their idiomatic Django ORM equivalents to evaluate how readily each SQL construct translates into ORM syntax such as Q objects, F expressions, annotate(), select_related(), and prefetch_related(). The results show that single-table filter and simple join queries translate almost directly (over 90% direct mapping) while nested subqueries and multi-level aggregations require non-trivial restructuring. This confirms that the query complexity is the dominant factor of translation difficulty. These observations motivate the design choices made by ORM Forge: structured JSON output, schema-grounded prompting, and bounded multi-turn refinement, and set the stage for discussing the limitations and future extensions of LLM-assisted ORM tooling.

DOI: http://doi.org/10.5281/zenodo.21427283

Using A Polynomial Regression Machine Learning Model To Predict Depression Severity Among People Living With HIV

Authors: Geofrey Nyabuto, Peters Anselemo Ikoha, Samuel Mungai Mbuguah

Abstract: Background: Binary depression screening does not distinguish patients with mild symptoms from those with clinically urgent symptom burden. Among people living with HIV (PLHIV), depressive-symptom severity may reflect nonlinear interactions among anxiety, immune status, treatment adherence, stigma, behavioural exposures, and demographic characteristics. Routine electronic medical records (EMRs) provide an opportunity to model these relationships using computationally reproducible methods. Objective: To develop and internally validate a polynomial regression machine learning model for predicting continuous PHQ-9 depressive-symptom severity among PLHIV using routine HIV-care EMR data. Methods: A cross-sectional patient-level analytical dataset was constructed from 54,301 de-identified EMR records from Bungoma and Busia counties, Kenya. Fifteen clinical, treatment, psychosocial, behavioural, and demographic predictors were processed using training-derived imputation, encoding, log transformation, and standardisation. Stratified training (n=38,010), validation (n=8,145), and held-out test (n=8,146) partitions were used. Ordinary least-squares regression with degree-1, degree-2, and degree-3 polynomial feature expansions was compared using R², adjusted R², mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). Results: The degree-3 model comprised 816 fitted parameters and achieved the strongest held-out performance: R² 0.753, adjusted R² 0.730, MAE 1.808, MSE 6.359, and RMSE 2.522 PHQ-9 points. The linear model achieved R² 0.729, MAE 1.939, and RMSE 2.641. Mean and median cubic-model residuals were 0.040 and 0.005, respectively, although the maximum positive residual was 15.186, indicating important underprediction in a small number of high-severity cases. The largest reported terms were anxiety × CD4 (β=−0.444) and anxiety² (β=0.413). Conclusions: Degree-3 polynomial regression modestly improved prediction of PHQ-9 depressive-symptom severity over linear and quadratic alternatives. Its average error may support broad risk stratification, but threshold crossing, model complexity, coefficient instability, and severe case underprediction preclude autonomous clinical use.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue4.198

Deep Learning-Based Kidney Disease Classification Using Transfer Learning Models And Flask-Based Web Deployment

Authors: Bhavana N, Kumar Siddamallappa. U, Neelamma. G, Anusha Jajur. J

Abstract: Kidney disease is a significant health concern worldwide, and identifying renal abnormalities at an early stage is essential for preventing disease progression and improving treatment outcomes. Manual interpretation of medical images can be time-consuming and may vary depending on clinical expertise. To address this challenge, this study presents a convolutional neural network (CNN)-based kidney disease classification system that incorporates transfer learning with ResNet101 and VGG16 architectures. The proposed model is trained and evaluated using an augmented dataset of renal ultrasound and CT images categorized into four classes: normal, cyst, stone, and tumor. Image preprocessing and data augmentation techniques are applied to improve image quality, increase dataset diversity, and enhance the model’s generalization capability. By utilizing pre-trained deep learning models, the system effectively extracts meaningful image features while reducing training time and computational complexity. Experimental evaluation shows that ResNet101 achieves a classification accuracy of 97.4% while VGG16 achieves 95.8 %. Performance assessment using precision, recall, and F1-score further confirms the reliability of the proposed approach for multi-class kidney disease classification. The developed framework demonstrates the effectiveness of transfer learning for medical image analysis, particularly when labeled datasets are limited. In addition, the system has the potential to support radiologists by providing faster and more consistent diagnostic assistance, leading to improved clinical decision-making. Overall, the proposed approach ResNet101 offers an efficient and accurate solution for automated kidney disease detection and classification using deep learning techniques.

DOI: http://doi.org/10.5281/zenodo.21453407

A Review On Alternative Fuels in Internal Combustion Engine and Its Characterization & Performance Analysis

Authors: Mr. Santhosh Kumar .S, Dr.Anix joel singh

Abstract: The increasing demand for sustainable energy resources, along with growing environmental concerns related to fossil fuel consumption, has accelerated research into alternative fuels for internal combustion engines. This study focuses on the experimental investigation and performance analysis of an internal combustion engine operated with alternative fuel as a potential replacement for conventional diesel fuel. The experimental investigation was conducted to evaluate the effects of alternative fuel usage on engine performance and exhaust emission characteristics under different operating conditions. The performance evaluation was carried out based on key parameters, including brake power, torque, brake thermal efficiency (BTE), and brake-specific fuel consumption (BSFC). In addition, the emission characteristics were analyzed by measuring exhaust gases such as carbon monoxide (CO), unburned hydrocarbons (HC), oxides of nitrogen (NOx), carbon dioxide (CO₂), and smoke opacity. The results demonstrated that alternative fuel operation produced comparable engine performance with conventional diesel fuel. Variations in brake power, torque, thermal efficiency, and fuel consumption were observed due to differences in fuel properties, combustion behavior, and energy content. The alternative fuel showed a slight increase in fuel consumption due to its lower calorific value; however, it provided improved emission characteristics by reducing CO, HC, and smoke opacity emissions. A minor change in NOx emissions was observed, which was mainly associated with variations in combustion temperature and oxygen availability during the combustion process. Overall, the experimental findings indicate that alternative fuels can be effectively utilized in internal combustion engines while maintaining satisfactory performance and reducing harmful exhaust emissions. The study highlights the potential of alternative fuels as sustainable energy solutions for cleaner and more efficient engine operation.

DOI: http://doi.org/10.5281/zenodo.21468929

Artificial Intelligence at Home: Evolution, Benefits, Challenges, and Its Impact on Everyday Life

Authors: Sukeshni Moon

Abstract: Artificial Intelligence (AI) has become an important part of modern households, transforming the way people communicate, work, learn, and manage daily activities. Once considered a technology limited to research laboratories and large organizations, AI is now available to common people through smartphones, smart home devices, virtual assistants, recommendation systems, healthcare applications, and educational tools. This paper examines the evolution of artificial intelligence in homes, its advantages and disadvantages, and how it has improved the lives of ordinary individuals. It also discusses concerns related to privacy, security, dependence on technology, and ethical challenges. The study highlights that AI has the potential to make life more convenient and efficient, but responsible use and awareness are necessary to maximize its benefits.

DOI: https://doi.org/10.5281/zenodo.21469831

AI-Driven Decision Support Systems for Strategic Business Management

Authors: Manjusha Chennareddy, Assistant Professor Ch. Lavanya

Abstract: The integration of Artificial Intelligence in Decision Support Systems has revolutionized the process of business strategic management through making data-driven, predictive, and adaptive decisions possible. The following paper provides an extensive analysis of AI-Driven Decision Support Systems (AI-DSS) for strategic business management, with the focus on the system architecture, implementation strategies, and results of the performance. The paper proposes a hybrid approach to implementing AI-DSS, using such methods as machine learning predictive analytics, Explainable AI, and multi-criteria decision-making. The empirical analysis shows that the implementation of AI-DSS results in improving the decision accuracy by 16.4%, decreasing decision time by 35.6%, and increasing user satisfaction by 22.8%. The comparative analysis proves that the implementation of AI-DSS outperforms the traditional approach of using spreadsheets on all measures.

DOI: https://doi.org/10.5281/zenodo.21473763

Metal Matrix Composites Innovative Materials For Molding The Future

Authors: Maaz Bahauddin Naveed

Abstract: Research on composite materials has gradually replaced traditional materials and alloys in an effort to create effective and cost-effective solutions for a range of applications. Metal Matrix Composites (MMCs) offer several benefits over traditional composites, including a lower thermal expansion coefficient, improved resistance to wear and abrasion, a higher strength-to-weight ratio, and reduced density. This review focuses on MMCs based on aluminium, magnesium, copper, titanium, and zinc as well as their alloys. It examines their physical and mechanical properties, manufacturing techniques (such as in-situ, liquid-phase, and solid-phase manufacture), and recent advancements. The properties of MMCs have been enhanced. As a result, they are suitable for critical applications in electronics, autos, and aerospace. Significant findings show how they can overcome the shortcomings of conventional materials by improving a range of mechanical properties. Their tensile characteristics, density, thermal expansion coefficient, elasticity modulus, hardness and fracture resistance, fatigue resistance, creep stiffness, and electrical conductivity can all be enhanced by using reinforcing techniques. The text also emphasises how important it is to select the right production processes to obtain the necessary characteristics while lowering costs and defects. This evaluation is a comprehensive resource for researchers and business professionals. It highlights current advancements, challenges, and the vast potential of MMCs as future resources.

DOI: http://doi.org/10.5281/zenodo.21473843

Student Performance Prediction Using Learning Analytics

Authors: Mr. Nikhil Barapatre, Ms. Ruchika Kadu, Mr. Uday Mahure, Ms. Vaishnavi Nawale

Abstract: The rapid advancement of digital technologies has significantly transformed the education sector by introducing intelligent learning environments, online learning platforms, Learning Management Systems (LMS), Massive Open Online Courses (MOOCs), and virtual classrooms. These technological developments generate enormous volumes of educational data that capture students' learning activities, academic performance, attendance, assignment submissions, examination scores, participation in online discussions, and interaction with digital learning resources. The availability of such educational data has created new opportunities for educational institutions to improve teaching strategies, personalize learning experiences, and enhance academic outcomes through data-driven decision-making. Learning Analytics has emerged as a multidisciplinary research field that combines Artificial Intelligence (AI), Educational Data Mining (EDM), Machine Learning (ML), Data Analytics, and statistical techniques to analyze educational data and predict student performance with greater accuracy. By identifying learning patterns and behavioral characteristics, learning analytics enables educators to provide timely interventions that support student success and reduce academic failure. Student performance prediction has become one of the most important applications of learning analytics because academic achievement directly influences educational quality, institutional effectiveness, and workforce development. Traditional evaluation methods primarily depend on periodic examinations and manual assessment, which often identify learning difficulties only after students experience poor academic performance. Such delayed identification limits the opportunity for educators to provide timely academic support. In contrast, learning analytics continuously monitors students' learning behavior throughout the educational process, enabling early detection of students who may be at risk of poor performance, course failure, or dropout. This proactive approach allows instructors to implement personalized learning strategies and academic interventions before significant learning problems occur. This research investigates the application of learning analytics for predicting student academic performance using multiple educational indicators collected from both traditional and digital learning environments. The proposed study considers a comprehensive set of predictive variables, including attendance records, assignment completion rates, quiz performance, laboratory activities, examination marks, participation in online learning platforms, classroom engagement, discussion forum activities, learning duration, assessment consistency, and demographic information.

DOI: http://doi.org/10.5281/zenodo.21486978

Statistical Analysis of Fat-Tailed Inflation Using Trimmed Mean and Maximum Likelihood Estimation

Authors: Research Scholar Krishnapriya T S, Associate Professor Dr.P Arumugam

Abstract: Inflation is one of the most significant macroeconomic indicators influencing the stability and sustainable growth of an economy. During periods of economic uncertainty, particularly the COVID-19 pandemic, inflation exhibited substantial fluctuations characterized by extreme observations and increased volatility, indicating the presence of fat-tailed behaviour. Such characteristics often reduce the effectiveness of conventional statistical measures, making robust estimation techniques essential for reliable analysis. This study investigates the behaviour of inflation in India and examines its relationship with the Reserve Bank of India's (RBI) monetary policy, represented by the repo rate, over the period from 2014 to 2022. Monthly data obtained from the Reserve Bank of India (RBI) Database on Indian Economy (DBIE) are used for the empirical analysis. To obtain a robust measure of central tendency in the presence of extreme observations, a 25% trimmed mean is employed and compared with the conventional arithmetic mean. Furthermore, a simple linear regression model is developed to examine the relationship between inflation and the repo rate, and the model parameters are estimated using the Maximum Likelihood Estimation (MLE) method. The empirical findings indicate that inflation exhibited noticeable fat-tailed characteristics during the pandemic period, while the trimmed mean provided a more stable and representative estimate than the conventional mean. The regression analysis further demonstrates a significant association between inflation and the repo rate, highlighting the effectiveness of monetary policy interventions in maintaining price stability during periods of economic uncertainty. The study concludes that robust statistical techniques, such as trimmed mean estimation combined with maximum likelihood methods, provide valuable tools for analysing inflation dynamics and supporting evidence-based monetary policy decisions.

AI Protocols For Job Readiness Assessment: Design And Implementation Of DIJOB In Côte D’Ivoire

Authors: Dr. Bayomock Linwa André Claude, Mrs. Diagone Grace

Abstract: In modern days, getting his first job is a very hard process for newly graduated students. Several factors contribute to this issue: no experience, poor resume qualities, few knowledge of job market and required skills for a given job title, no real exposure to job interview (phone, virtual, physical). In the past 30 to 50 years, structured human resources agencies played great role helping young graduates to be well prepared for job readiness. Techniques as how to write a good resume, determining job title and job profile in a particular domain, how to handle a phone, virtual and physical interview were taught to a candidate. Traditional advisory systems are often insufficient in addressing these structural issues at scale. This paper presents an AI-powered web-based career development platform designed to enhance employability among students and job seekers in Côte d'Ivoire. The built application is called DIJOB (Digitalized Job), DIJOB integrates Groq’s LLaMA 3.3-70B large language model to provide four core services: intelligent job search with AI-based CV matching, automated résumé quality assessment, skill compatibility scoring against job descriptions, and a personalized eleven-module career coaching system. The coaching module includes salary estimation in FCFA, LinkedIn profile optimization, and interview preparation guidance. The system is implemented using Jakarta EE 10, GlassFish 7, MySQL 8, and a responsive XHTML frontend, following a three-tier architecture with a RESTful API layer. Results indicate that the platform provides real-time, actionable career insights, including résumé scoring, skill gap analysis, and personalized career recommendations. DIJOB shows strong potential in improving alignment between job seekers and employer expectations, thereby contributing to reduced skills mismatch and improved employability outcomes.

DOI: http://doi.org/10.5281/zenodo.21505674

Role Of Artificial Intelligence In Improving Student Engagement And Classroom Interaction: A Systematic Literature Review Following PRISMA 2020 Guidelines

Authors: Dr Abhijit Das, Dr Namrata Yadav Das

Abstract: Background: Artificial intelligence (AI) technologies are increasingly integrated into educational settings to enhance student engagement and classroom interaction. However, the evidence base regarding their effectiveness remains fragmented. This systematic review synthesizes empirical evidence on the role of AI in improving student engagement and classroom interaction in K-12 and higher education contexts. Methods: Following PRISMA 2020 guidelines, we conducted a comprehensive literature search across multiple databases (SciSpace, Google Scholar, PubMed) from January 2016 to March 2026. Studies were included if they reported empirical evidence on AI interventions targeting student engagement or classroom interaction outcomes in educational settings. Two independent reviewers screened 267 unique records, assessed 204 full-text articles, and included 142 studies in the final synthesis. Risk of bias was assessed using the ROBINS-I tool for six representative studies. Results: From 267 unique records identified, 142 studies met inclusion criteria after title/abstract screening and full-text assessment. Six representative studies (N=50–20,000+ participants) demonstrated that AI interventions—including personalized recommendation systems, intelligent tutoring systems, conversational agents, and adaptive learning platforms—consistently improved student engagement metrics. Two randomized controlled trials showed low risk of bias, while four quasi-experimental studies showed moderate risk. AI-driven interfaces increased engagement by up to 25.13% in large-scale field tests. Personalized AI recommendations significantly improved learning performance and engagement, particularly for students with moderate motivation levels. AI tutors enabled students to learn more than twice as much in less time compared to traditional active learning approaches. Conclusions: The evidence demonstrates that AI technologies can effectively enhance student engagement and classroom interaction across diverse educational contexts. Randomized controlled trials provide the strongest evidence, while quasi-experimental studies show consistent positive effects despite moderate methodological limitations. Future research should prioritize rigorous experimental designs with preregistration, comprehensive reporting of missing data, and investigation of long-term effects and equity considerations.

DOI: http://doi.org/10.5281/zenodo.21509136

Next-Generation Explainable Artificial Intelligence Framework For Transparent And Reliable Autonomous Decision-Making In Critical

Authors: Vaibhav Singh Chouhan, Rupali Chaure

Abstract: Image caption: Word Cloud for Explainable A Explainable Artificial Intelligence (XAI) is an important subject area… This extensive review paper provides a meta-analysis of the most prominent explainable artificial intelligence (XAI) frameworks, methodologies and techniques that have been developed in the last decade. We conduct a systematic analysis of > 150 peer-reviewed publications to integrate novel transparent techniques (attention mechanisms, LIME, SHAP, prototype-based methods, counterfactual explanations) and assess their usefulness for promoting interpretability and user trust. Our meta-analysis exposes severe shortcomings in interpretability standardization, validation metrics and real-world applicability. We introduce a universal taxonomy to classify XAI methods based on their explanation scopes, computational complexity and applicability for different application domains. In addition we discuss the accountability-interpretability tradeoff, scalability issues and the need for domain specific explanation frameworks as key challenges still facing this field. Our paper contributes to the field by offering a holistic roadmap that will guide researchers and practitioners to select, implement and evaluate an XAI solution, trace future research paths which are required in order to endow autonomous decision-making systems with trustworthiness when applied on critical infrastructure.

DOI: http://doi.org/10.5281/zenodo.21545756

Combustion and Emission Characteristics of Biomass- Derived 2-Methyltetrahydrofuran Compared with 2- Methylfuran, Ethanol and Gasoline in a Direct- Injection Spark Ignition Engine

Authors: Rafiu Kayode.Olalere, Hongming Xu, Sheriff Lamidi, Y.O Bankole

Abstract: The growing demand for carbon-neutral transportation has intensified research into renewable oxygenated fuels capable of improving engine efficiency while reducing exhaust emissions. Among emerging biofuels, biomass-derived 2-methyltetrahydrofuran (MTHF) has attracted considerable attention because of its favourable physicochemical properties. Nevertheless, comprehensive experimental comparisons of neat MTHF with gasoline, 2-methylfuran (MF), and ethanol under identical direct-injection spark-ignition (DISI) engine operating conditions remain limited. This study experimentally investigates the combustion characteristics, engine performance, gaseous emissions, and particulate emissions of neat MTHF relative to commercial gasoline (ULG95), MF, and ethanol using a single-cylinder DISI engine. Experiments were conducted at a constant engine speed of 1500 rpm, stoichiometric operation (λ = 1), and engine loads ranging from 3.5 to 8.5 bar indicated mean effective pressure (IMEP). Ignition timing for each fuel was optimized using the maximum brake torque (MBT) criterion or knock-limited spark advance (KLSA). At 5.5 bar IMEP, MTHF generated peak in-cylinder pressures of about 10%, 15%, and 25% higher than MF, ethanol, and gasoline, respectively. Compared with MF and ethanol, MTHF reduced indicated specific fuel consumption by approximately 8% and 33%, while maintaining thermal efficiency comparable to gasoline. Furthermore, MTHF produced lower total hydrocarbon and particulate emissions than gasoline and MF, together with lower NOₓ emissions than MF. These findings demonstrate that MTHF is a promising renewable gasoline substitute capable of simultaneously enhancing combustion performance and reducing exhaust emissions in modern DISI engines.

DOI: http://doi.org/10.5281/zenodo.21637881

An Agricultural Soil Carbon Credit Framework for Indian Smallholder Farmers: Integrating Low-Cost Measurement Technologies and Farmer-Centric Incentive Mechanisms

Authors: Sweta Kumari

Abstract: Indian agriculture faces the dual challenge of enhancing soil health while mitigating climate change. This research paper presents a comprehensive agricultural soil carbon credit framework specifically designed for Indian smallholder farmers operating under semi-arid conditions. The framework integrates six core research objectives: (1) developing a reliable measurement and verification system suited to Indian farming conditions, (2) creating income opportunities through carbon credit programs, (3) establishing baseline data on soil organic carbon and health indicators, (4) implementing and evaluating soil management practices, (5) measuring seasonal and long-term changes in soil organic carbon, and (6) developing practical monitoring guidelines. The framework was developed using a doctrinal and conceptual research methodology based on a critical review and synthesis of existing literature, policy documents, carbon market mechanisms, and emerging soil carbon measurement technologies relevant to Indian agriculture. We propose innovative solutions, including affordable low-cost measurement technologies (portable NIR spectroscopy, handheld sensors, smartphone-based tools), a Small Local Carbon Service Organisation (SLCSO) model for accessible carbon measurement services, green incentives with fast remuneration mechanisms featuring tiered payment systems, and a "Carbon Check First" mechanism providing free or subsidised initial baseline assessments. This framework addresses critical barriers preventing smallholder farmer participation in carbon markets, including high measurement costs, complex procedures, and delayed payments. By combining technological innovation with farmer-centric service delivery models, this research aims to transform Indian smallholder agriculture into a viable carbon-sequestration sector while improving farmers' livelihoods and soil health.

DOI: https://doi.org/10.5281/zenodo.21639077

Artificial Intelligence for Predicting Currency Fluctuation and Investment Risk: A Secondary-Data Synthesis of LSTM, Random Forest, and Hybrid GARCH Approaches

Authors: Assistant Professor Dr. Ranjeet Kumar Ambast, Aditya Vikram

Abstract: The foreign exchange (Forex) market is characterised by high liquidity, pronounced volatility, and non-linear dynamics. This makes accurate prediction of currency fluctuation and investment risk exceptionally difficult for traditional econometric models (Bollerslev, 1986; Cont, 2001). This paper synthesises secondary evidence on the comparative predictive performance of two Artificial Intelligence (AI) approaches Long Short-Term Memory (LSTM) deep learning networks (Hochreiter & Schmidhuber, 1997) and Random Forest (RF) ensemble learning (Breiman, 2001) against the traditional Generalised Autoregressive Conditional Heteroskedasticity (GARCH) family of models (Bollerslev, 1986), drawing on published studies that use historical USD, EUR, GBP, JPY, BRL, and ZAR exchange-rate data spanning approximately 2012–2025. The review evaluates model performance across stable and volatile market regimes for directional accuracy through Value-at-Risk (VaR) estimation, and risk-adjusted investment signals. Findings from the reviewed literature indicate that AI-based LSTM models achieve superior performance for short-horizon volatility forecasts. It’s particularly in capturing sudden shifts in implied volatility (Kraus & Feuerriegel, 2024). Random Forest models tend to deliver the highest directional accuracy and the lowest point-prediction error across several currency pairs and cryptocurrency markets (Milionis & Konstantinou, 2024; Ndlovu, 2025). Hybrid GARCH-LSTM architectures further improve predictive accuracy, with an APARCH-LSTM specification reported to achieve a coefficient of determination (R²) of 95.53% for USD/BRL volatility forecasting (Hottz, 2025), while hybrid models improve Value-at-Risk estimation accuracy by up to 10% during periods of elevated volatility relative to standalone GARCH or LSTM specifications (Nsengiyumva et al., 2025). The synthesis further shows that risk-adjusted performance measured through the Sharpe ratio, Calmar ratio, and maximum drawdown favours hybrid deep-learning architectures for multi-asset portfolios and tree-based ensembles such as XGBoost for equity-index applications (Saly-Kaufmann et al., 2026; Singh & Praveen, 2025). The paper concludes that no single AI technique dominates across all currency pairs, forecast horizons, and objectives, and that model choice should be aligned with the specific trading, risk-management, or regulatory objective at hand.

DOI: https://doi.org/10.5281/zenodo.21641515

Syntheses, Spectroscopic Characterization and Antimicrobial Activities of Novel Transition Metal Complexes of 2-fluorobenzylidene)-2-(2-(hydroxyimino)-1,2-diphenylethylidene)hydrazine-1-carbothiohydrazide

Authors: Tanhaji Walunj, Madhukar Badgujar

Abstract: A new series of transition metal complexes derived from 2-fluorobenzylidene)-2-(2-(hydroxyimino)-1,2-diphenylethylidene)hydrazine-1-carbothiohydrazide (FBHT) was successfully synthesized and systematically characterized using a range of spectroscopic and analytical techniques. The complexes were obtained through the reaction of the FBHT ligand with copper(II), zinc(II), and nickel(II) salts in a 1:2 metal-to-ligand molar ratio. Comprehensive characterization was carried out by elemental analysis, UV–Visible spectroscopy, Fourier-transform infrared (FT-IR) spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, and mass spectrometry to confirm the structures and coordination behavior of the synthesized compounds. The electronic absorption spectra of the metal complexes exhibited noticeable shifts relative to the free ligand, providing clear evidence of successful metal coordination. Infrared spectral analysis further supported complex formation by displaying new absorption bands in the low-frequency region, which were assigned to metal–ligand vibrations involving the hydroxyimino and thiohydrazide donor sites. Moreover, the 1H and 13C NMR spectra revealed significant changes in the chemical shifts of the ligand signals following complexation, indicating alterations in the electronic environment caused by coordination with the metal ions. The antimicrobial potential of the synthesized complexes was assessed against representative Gram-positive and Gram-negative bacterial strains, namely Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa, as well as the fungal pathogen Candida albicans, using the disk diffusion assay. All of the metal complexes demonstrated enhanced antimicrobial activity compared with the uncoordinated ligand. Among them, the copper(II) complex exhibited the strongest inhibitory effect, particularly against S. aureus and P. aeruginosa. This enhanced performance was further confirmed by lower minimum inhibitory concentration (MIC) values compared with the corresponding zinc(II) and nickel(II) complexes. In contrast, the free FBHT ligand displayed only weak antimicrobial activity, highlighting the beneficial role of metal complexation in improving biological efficacy. Overall, these findings suggest that the synthesized FBHT transition metal complexes, especially the copper(II) derivative, represent promising candidates for the development of novel antimicrobial agents.

DOI:

Assistify: Customer Service Chatbot

Authors: Utkarsh Singh

Abstract: Despite the widespread adoption of chatbots for customer service, small businesses continue to lack the resources required to develop and customize such solutions effectively. In this work, we propose an approach that enables small businesses to construct tailored customer service chatbots by fine-tuning an open-weight large language model (LLM) using QLoRA [1] on a domain-specific customer service dataset. The proposed method leverages parameter-efficient transfer learning to adapt a pre-trained LLM to the customer service domain, preserving its general natural language understanding and generation capabilities while specializing its behaviour. To extend the chatbot’s functional scope beyond the fine-tuning corpus, we integrate a Retrieval-Augmented Generation (RAG) [4] module that retrieves relevant product information, warranty details, and installation guides from PDF documentation at inference time. This allows the chatbot to ground its responses in accurate, business-specific information rather than relying solely on parametric knowledge. In addition, we log chatbot conversations and apply sentiment analysis to the resulting interaction data, forming a feedback loop that surfaces unsatisfactory exchanges for human follow-up and informs iterative improvement of the system. Taken together, these components constitute a cost-effective pipeline through which small businesses can deploy personalized customer service chatbots without incurring the cost of large-scale data collection or bespoke system engineering.

Comparative Effectiveness of Low-Intensity versus High-Intensity Aerobic Exercise on Blood Pressure and Functional Capacity in Patients with Essential Hypertension: A Prospective Comparative Pre–Post Study

Authors: Professor Dr. B.R. Shaalini, Raja Senthil

Abstract: Background- Essential hypertension is a major modifiable risk factor for cardiovascular disease and remains a leading cause of morbidity and mortality worldwide. Aerobic exercise is recommended as a first-line non-pharmacological intervention for blood pressure management. However, evidence comparing the effectiveness of low-intensity and high-intensity aerobic exercise on cardiovascular and functional outcomes remains limited, particularly in the Indian population. Objective- To compare the effectiveness of low-intensity and high-intensity aerobic exercise on blood pressure, functional capacity, cardiorespiratory fitness, resting heart rate, body mass index (BMI), and health-related quality of life in adults with essential hypertension. Methods- A prospective comparative pre-post study was conducted among 60 adults aged 40–65 years with essential hypertension. Participants were allocated into two groups (n = 30 each). Group A performed supervised low-intensity aerobic exercise (40–55% heart rate reserve), whereas Group B performed supervised high-intensity aerobic exercise (70–85% heart rate reserve) for 12 weeks. Outcome measures included systolic blood pressure (SBP), diastolic blood pressure (DBP), Six-Minute Walk Test (6MWT), estimated VO₂max, resting heart rate (RHR), BMI, and Short Form-36 (SF-36) quality-of-life scores. Assessments were performed at baseline and after completion of the intervention. Data were analysed using paired and independent Student's t-tests, with statistical significance set at p < 0.05. Results- Both exercise programmes produced significant improvements in all measured outcomes (p < 0.001). In the low-intensity group, mean SBP decreased from 146.8 ± 8.7 mmHg to 136.4 ± 8.1 mmHg, whereas the high-intensity group demonstrated a greater reduction from 147.3 ± 9.1 mmHg to 128.1 ± 8.8 mmHg. Functional capacity improved significantly, with the 6MWT increasing from 418.6 ± 52.3 m to 456.7 ± 52.1 m in Group A and from 421.7 ± 50.8 m to 490.8 ± 48.6 m in Group B. Significant improvements were also observed in VO₂max, resting heart rate, BMI, and all SF-36 domains, with superior outcomes in the high-intensity exercise group. Conclusion- Both low-intensity and high-intensity aerobic exercise are effective physiotherapy interventions for improving blood pressure control, functional capacity, cardiorespiratory fitness, body composition, and health-related quality of life in adults with essential hypertension. High-intensity aerobic exercise demonstrated significantly greater improvements across all outcome measures, supporting its use in appropriately screened and supervised patients.

Performance Enhancement Of Internal Combustion Engines Using Alternative Biofuels

Authors: Dr. R. Bhaskar Reddy, Dr. A. Kumaraswamy

Abstract: Due to the increasing demands for energy in the world coupled with increased environmental regulations, it is now necessary to use sustainable energy sources instead of the traditional fossil fuels for internal combustion engines. In this paper, a study on performance improvement in compression ignition engines through the use of alternative biofuels has been presented. The study focuses on hydrotreated vegetable oil (HVO), biodiesel blends and the use of dual fuel where the two fuels used include biogas and hydrogen. The results of experimental and computer simulations show that the use of HVO leads to reduction of NOx and CO by 9.0% and 38.7%, respectively, compared to fossil diesel. Turbocharged B20 biodiesel blends lead to fuel consumption reduction of 20% and brake torque increase of 75%. The canola biodiesel gives brake thermal efficiency of 31.2% and brake-specific fuel consumption of 0.26 kg/kWh.

DOI: http://doi.org/10.5281/zenodo.21675388

Decentralized Identity Management System Using Blockchain For Privacy-Preserving Digital Authentication

Authors: Mrs.Aruna C, Rajeswary Nair

Abstract: With the advent of digital services, various vulnerabilities in centralized identity management systems such as Single Point of Failure (SPOF), data leakage, and user privacy invasion are evident. In this paper, an architecture framework for designing Decentralized Identity Management System (DIDMS) by leveraging blockchain technologies is proposed for privacy preserving authentication in digital age. The DIDMS framework makes use of Decentralized Identifier (DID) with Verifiable Credentials (VC) and Zero Knowledge Proof (ZKP) techniques to implement the concept of self-sovereign identity. Four phases of framework including registration, credential issuance, authentication and storage is designed and tested. The performance results obtained from the experimentation indicate that the proposed framework gives an authentication latency of 320 ms, storage overhead reduction of 42%, and verification accuracy of 98.6% with 31% gas optimization over conventional smart contract approaches. The comparative evaluation with existing frameworks indicates improvement in the areas of privacy protection, scalability and user control.

DOI: http://doi.org/10.5281/zenodo.21676274

Identifying Decayed Fruits And Vegetables From Large Food Retailers Utilizing Machine Learning And Deep Learning, And Transforming Them Into Biogas

Authors: Dr.Sumbul Alam

Abstract: Food waste management is a contemporary global issue. According to UNDP, food waste constitutes 40-51% of the waste in the Kingdom, followed by paper, cardboard, plastics, and other materials. Consequently, an effective waste segregation system is essential to address this problem. This research proposal aims to develop a mechanism for the segregation of decayed fruits and vegetables from the shelves, storage, and inventories of retailers such as Hyper Panda, Carrefour , and Lulu hypermarkets, utilising a hybrid machine learning algorithm comprising Linear SVM and PCA, alongside YOLO for real-time detection. Following segregation, this organic waste may be transported to a biorefinery where, using anaerobic digestion technology, it can be turned into methane gas. Currently, the door-to-door accessibility of processed foods, consumer products, and groceries is in great demand. Consequently, this methane gas may be utilised by their own delivery trucks and vehicles, so minimising food waste reduction in retail, promoting energy sustainability, and decreasing pollution.

DOI: http://doi.org/10.5281/zenodo.21676317

Heat Transfer Enhancement In Microchannel Flows Using Hybrid Nanofluids

Authors: Dr. G. Sugendran, Surinder Kumar

Abstract: The continuing shrinking trend of electronics along with growing power densities has resulted in many heat dissipation difficulties. Microchannel heat sinks are promising and compact heat sinks but limited by thermal performance due to thermal capabilities of traditional coolant fluids. This work examines the prospect of using hybrid nanofluids, i.e., a mixture of several different nanoparticles, to improve the convective heat transfer coefficients of microchannel flow. A detailed numerical analysis based on the two-phase Eulerian-Eulerian approach is performed to assess the thermohydrodynamic properties of aluminum oxide-copper/water hybrid nanofluid through rectangular microchannels under Reynolds numbers between 200 and 1000 and nanoparticle volume fractions between 0.5% and 2.0%. The simulations reveal a considerable heat transfer enhancement where the Nusselt number rises up to 38.4% at 1.0% volume fraction while reducing the thermal resistance by 29.6% and achieving the highest thermal efficiency ratio of 1.31. Although there is an increased pressure drop (18.7%), the thermal-hydraulic performance proves the feasibility of hybrid nanofluids as a new coolant.

DOI: http://doi.org/10.5281/zenodo.21697150

Recent Advances in Turbulent Flow Past Square Cylinders: A Critical Review of Experimental and Numerical Investigations

Authors: Avinash M, Manjunath S.H, Vikram C.K

Abstract: The turbulent flow past square cylinders is a fundamental problem in fluid mechanics because it governs aerodynamic loading, vortex-induced vibration, drag, and heat transfer in engineering systems such as high-rise buildings, bridge piers, offshore structures, heat exchangers, and electronic cooling devices. Despite extensive research, accurately predicting flow separation, wake development, and thermal transport under varying operating conditions remains challenging. This review critically synthesizes recent advances in both experimental and numerical investigations of turbulent flow past square cylinders. Experimental techniques, including Particle Image Velocimetry, Laser Doppler Velocimetry, hot-wire anemometry, pressure measurements, and flow visualization, are assessed alongside Computational Fluid Dynamics approaches based on Reynolds-Averaged Navier–Stokes, Large Eddy Simulation, Detached Eddy Simulation, Scale-Adaptive Simulation, and Direct Numerical Simulation. The influence of Reynolds number, blockage ratio, corner modifications, thermal boundary conditions, and multiple-cylinder arrangements on wake dynamics and heat transfer is systematically evaluated. The review shows that Large Eddy Simulation provides the most accurate prediction of vortex shedding, wake structures, and aerodynamic forces, whereas Reynolds-Averaged Navier–Stokes models remain computationally efficient for engineering applications. Corner rounding, chamfering, and passive flow-control techniques consistently reduce drag and suppress wake instability while improving thermal performance. Remaining challenges include high-Reynolds-number simulations, coupled thermo-fluid analysis, turbulence model accuracy, and systematic experimental validation. The review identifies current research gaps and highlights future opportunities in advanced turbulence modelling, high-performance computing, and data-driven approaches to support the design of safer, more efficient, and thermally optimized bluff-body engineering systems.

DOI: https://doi.org/10.5281/zenodo.21702349

Experimental Investigation Of Combustion, Performance, And Emissions Characteristics Of Gasoline And Ethanol In A Spray-Guided Gasoline Direct-Injection Engine

Authors: Rafiu K. Olalere, Y.O Bankole, Hongming Xu, Animashahun L. A, Sheriff Lamidi

Abstract: The increasing demand for cleaner and more sustainable transportation fuels has intensified interest in renewable oxygenated fuels capable of improving the combustion and emission characteristics of gasoline direct-injection (GDI) spark-ignition engines. This study presents a comparative experimental investigation of the combustion behaviour, engine performance, and gaseous emissions of commercial unleaded gasoline (ULG95) and absolute ethanol in a spray-guided GDI engine. Experiments were conducted under stoichiometric operating conditions (λ = 1) at an engine speed of 1500 rpm over an indicated mean effective pressure (IMEP) range of 3.5–8.5 bar using optimized spark timing. Combustion characteristics were evaluated through in-cylinder pressure analysis, coefficient of variation of IMEP (COVIMEP), combustion phasing (MFB50), combustion duration, combustion efficiency, indicated thermal efficiency, and indicated specific fuel consumption. Regulated gaseous emissions comprising carbon monoxide (CO), unburned hydrocarbons (HC), and nitrogen oxides (NOₓ) were also measured and compared. The results demonstrate that ethanol produced higher peak cylinder pressures, shorter combustion durations, improved combustion stability, and higher combustion and indicated thermal efficiencies than gasoline throughout the investigated load range. The superior combustion characteristics of ethanol were attributed to its inherent oxygen content, higher octane rating, and greater resistance to knock, which enabled optimum combustion phasing without knock-limited spark retard. Ethanol also produced substantially lower CO and HC emissions than gasoline, although differences in NOₓ emissions reflected variations in combustion temperature and ignition characteristics. Overall, the study demonstrates that ethanol offers significant advantages in combustion quality and exhaust emission reduction, highlighting its potential as a sustainable fuel for future high-efficiency GDI spark-ignition engines.

DOI: http://doi.org/10.5281/zenodo.21714967

Artificial Intelligence for Cybersecurity: Threats, Defenses, and Emerging Challenges in the Era of Generative AI

Authors: Sonal Sinha

Abstract: Artificial Intelligence (AI) has a dual relationship with cybersecurity: it is both a powerful defensive technology and, increasingly, a high-value attack target in its own right. Machine learning, deep learning, foundation models, and large language models (LLMs) now underpin intrusion detection, malware analysis, phishing identification, and autonomous incident response. At the same time, adversaries exploit AI for automated reconnaissance, AI-generated phishing, and attacks aimed directly at AI models — adversarial evasion, data poisoning, model extraction, prompt injection, jailbreaking, and AI supply-chain compromise. This paper condenses a comprehensive survey into a conference-length treatment: it traces the evolution of AI in cybersecurity, presents a unified taxonomy of attacks across the AI lifecycle, reviews key defense mechanisms (explainable AI, federated learning, differential privacy, guardrails, zero trust), summarizes major governance frameworks (NIST AI RMF, ISO/IEC 42001, EU AI Act, OWASP LLM Top 10), and outlines open research challenges for building trustworthy, resilient AI-driven security systems.

DOI: https://doi.org/10.5281/zenodo.21715925

Online Health Information Behaviour

Authors: Research Scholar Merin Titty D Cunha, Assistant Professor Dr. Jisha S Kumar

Abstract: In today’s digital age, the internet serves as a fast, cost-effective, and accessible medium for accessing health-related information. The availability of audiovisual content and the ability to obtain direct insights from medical experts have further enhanced the appeal of online health information among users. This study primarily aims to explore the connection between individuals’ online health information seeking patterns and the outcomes that follow after their search. To analyse these relationships, Smart PLS-SEM (Partial Least Squares Structural Equation Modelling) is employed to examine the interplay among the key variables. Findings reveal that an individual’s motivation to seek health information online significantly influences post-search actions, such as modifying health practices, consulting a healthcare provider, or disseminating the acquired information to others. The study underscores the importance for healthcare professionals to recognize the underlying motivations and behavioural intentions driving consumers’ engagement with online health resources.

Environmental Impact Assessment of Mobile Campaign Apps: Insights from the Red Rose One App in Nigeria

Authors: Muhammad Yakubu Yakubu

Abstract: The proliferation of mobile applications in Information and Communication Technology for Development (ICT4D) has delivered critical social and health benefits across low-resource settings. However, these digital solutions often impose hidden environmental costs including high energy consumption, accelerated device obsolescence, and electronic waste that undermine their long-term sustainability and equity. This paper introduces and empirically validates the Environmental Mobile App Impact Assessment (EMAIA), a novel framework designed to measure and mitigate the sustainability impacts of ICT4D (Information and Communication Technology for Development) applications. Through a comprehensive case study of Nigeria's RedRose One app used by 128 frontline health workers in Seasonal Malaria Chemoprevention campaigns we demonstrate EMAIA’s practical utility across five key dimensions: Energy Consumption, Device Lifecycle, Network Utilization, E-Waste Contribution, and Carbon Emissions. Survey results reveal a significant environmental and user burden: 77.3% of workers report excessive battery drain, 60.2% were compelled to upgrade devices specifically for app compatibility, 70.3% face high data costs, and 49.2% have disposed of devices due to compatibility issues. These indicators culminate in a poor overall EMAIA score of 1.90/5.00, highlighting a critical disconnect between the app’s public health objective and its unsustainable operational footprint. Qualitative analysis of open-ended responses further underscores user priorities, with 62% of suggestions advocating for robust offline functionality and 41% requesting broader compatibility with older and low-specification devices. The study establishes that environmental sustainability in ICT4D is not a peripheral concern but is intrinsically linked to equity, accessibility, and long-term viability. By making environmental impacts measurable and actionable, the EMAIA framework provides developers, practitioners, and policymakers with an essential tool to align digital innovation with the Sustainable Development Goals. We argue for the mandatory integration of such assessments into the ICT4D project lifecycle to ensure that technological progress delivers social benefits without imposing ecological costs or financial burdens on the communities it aims to serve.

DOI: https://doi.org/10.5281/zenodo.21769173

The Strategic Wolverine Theory (TSWT): A Philosophical Theory of Strategic Planning and Determination as the Fundamental Pillars of Self-Reliance and Dependable Achievement

Authors: Jackson Matsanga

Abstract: The development and achievement of sustainable success among individuals, organisations, and institutions increasingly depends on the integration of strategic planning and sustained determination. Although these concepts have been extensively examined within strategic management, psychology, leadership, and organisational studies, existing scholarship has largely considered them independently or within discipline-specific frameworks. Consequently, limited philosophical attention has been devoted to explaining how strategic planning and determination function together to promote self-reliance and dependable achievement. This study developed the Strategic Wolverine Theory (TSWT), an original philosophical framework inspired by the documented behavioural characteristics of the wolverine (Gulo gulo). The study adopted a philosophical research design and employed conceptual analysis, literature synthesis, observational behavioural analysis, theory development procedures, and inductive reasoning to formulate the theory. The findings revealed that the wolverine consistently demonstrates strategic food caching, prudent resource management, determination, resilience, adaptability, self-reliance, and dependable survival under harsh environmental conditions. These behavioural characteristics were systematically interpreted and synthesised into a coherent philosophical framework. The study established that strategic planning and determination are the fundamental pillars of self-reliance and dependable achievement. Strategic planning provides purposeful direction through anticipation, preparation, and prudent resource utilisation, while determination sustains disciplined implementation despite adversity. Their interaction strengthens self-reliance, resulting in consistent and sustainable achievement across diverse contexts. The theory further identifies leadership effectiveness and resource availability as moderating factors that may influence these relationships. The Strategic Wolverine Theory contributes to contemporary scholarship by integrating strategic planning, determination, self-reliance, and dependable achievement into a unified philosophical framework grounded in documented behavioural evidence. The theory provides a conceptual foundation for future empirical research and offers practical guidance for leadership, education, organisational management, entrepreneurship, governance, public administration, and community development. It therefore represents an original contribution to nature-inspired philosophical theory development and the broader understanding of sustainable success.

DOI: https://doi.org/10.5281/zenodo.21802083

Design And Implementation Of A Child Tracking System

Authors: Ms. Achal A. Koyale, Ms. Shravani S. Golegaonkar, Ms. Maithili V. Mangalagiri

Abstract: In recent years, the increasing number of incidents related to missing children and child safety has created a strong need for reliable and intelligent monitoring systems. This paper proposes a smart child tracking and safety system that uses Internet of Things (IoT) technology integrated with Global Positioning System (GPS) and Global System for Mobile Communication (GSM) for real-time location monitoring and emergency communication. The proposed system enables parents or guardians to continuously track the child’s location through a mobile application and receive instant notifications whenever the child moves outside a predefined safe zone. The system also includes an emergency alert feature that allows immediate communication during critical situations. The proposed model is designed with a focus on accuracy, low power consumption, affordability, and ease of use, making it suitable for practical day-to-day applications. The integration of geofencing and real-time data transmission improves the efficiency and reliability of child monitoring in crowded places such as schools, malls, parks, and public transport areas. Experimental analysis shows that the system provides fast response time, reliable location tracking, and improved child security compared to conventional monitoring methods.This research contributes toward the development of a cost-effective and user-friendly child safety solution capable of reducing risks associated with child loss, kidnapping, and unauthorized movement. The proposed system demonstrates how modern wireless communication and IoT technologies can be effectively utilized to enhance child protection and parental confidence in real-world environments

DOI: http://doi.org/10.5281/zenodo.21802462

Robotic Sortation in Ecommerce Fulfillment: A Case Study of AI-Driven Pick-and-Sort Automation Across a 3PL Network

Authors: Ashvin Kulkarni

Abstract: Parcel sortation to postal sacks is one of the most labor-intensive, error-prone, and operationally costly processes in ecommerce fulfillment. This paper documents the multi-site rollout of an AI-driven robotic pick-and-sort system across a major 3PL logistics network. Deployed under a Robotics-as-a-Service (RaaS) model, the technology replaced manual sortation operations at three facilities and achieved a 75% increase in parcels per hour relative to fully manual operations, while reducing required headcount per sortation pod by 75%. Over a 4-year contract term, the projected total savings across the network reach approximately million. Beyond the headline economics, this case surfaces a subtler argument: in a labor market where wages are volatile and worker turnover in sortation operations is chronic, the fixed monthly cost of robotics is not just cheaper—it is structurally more predictable. That predictability turns out to matter as much as the savings themselves.

DOI: https://doi.org/10.5281/zenodo.21802715

Beyond Carbon Accounting: A Unified Digital ESG Framework for Manufacturing Industries Integrating CSRD, ESRS, the GHG Protocol, and the United Nations Sustainable Development Goals

Authors: Arpit Darbari

Abstract: The accelerating impacts of climate change, resource depletion, and increasing stakeholder expectations have fundamentally transformed the way organizations approach sustainability. Manufacturing industries, which account for a significant proportion of global greenhouse gas emissions, water consumption, and resource utilization, are under growing pressure to move beyond voluntary sustainability initiatives toward structured, measurable, and transparent Environmental, Social, and Governance (ESG) management. Regulatory developments such as the European Union's Corporate Sustainability Reporting Directive (CSRD), the European Sustainability Reporting Standards (ESRS), and internationally accepted frameworks including the Greenhouse Gas (GHG) Protocol and the United Nations Sustainable Development Goals (UN SDGs) have collectively established a comprehensive sustainability reporting landscape. However, organizations continue to face considerable challenges in translating these independent frameworks into a cohesive operational strategy. This paper proposes a Unified Digital ESG Framework (UDEF) designed specifically for manufacturing industries. Unlike existing studies that examine ESG reporting, carbon accounting, or sustainability regulations independently, the proposed framework integrates CSRD compliance, ESRS disclosure requirements, GHG Protocol-based emissions accounting, and UN SDG alignment into a single implementation architecture supported by digital technologies. The framework incorporates smart metering, Industrial Internet of Things (IIoT) devices, cloud-based Energy Management Systems (EMS), real-time KPI monitoring, and artificial intelligence-assisted decision support to facilitate continuous ESG performance measurement. The proposed framework establishes a systematic methodology for identifying material sustainability issues, collecting operational data, quantifying environmental impacts, mapping ESG indicators to regulatory disclosure requirements, and supporting strategic decision-making through digital dashboards. A manufacturing-oriented implementation model demonstrates how energy, water, fuel, waste, emissions, and governance indicators can be consolidated into an integrated ESG management ecosystem. The paper further introduces a Digital ESG Maturity Model to evaluate organizational readiness and monitor continuous improvement. The study contributes to existing sustainability literature by presenting an implementation-oriented framework that bridges the gap between regulatory compliance and operational sustainability management. It offers practical guidance for manufacturing organizations seeking to improve ESG performance, strengthen corporate governance, enhance investor confidence, and accelerate progress toward net-zero and circular economy objectives. The framework also provides a foundation for future research on digital sustainability, AI-enabled ESG reporting, and Industry 5.0-driven environmental governance.

Steel Vision Net A Hybrid Deep Learning Framework For Intelligent Strip Steel Surface Defect Detection

Authors: Miss Anukula Roja, Athili Venkat Raju

Abstract: Surface defect detection in strip steel is a critical quality assurance task in modern steel manufacturing, as defects such as scratches, inclusions, patches, and rolled-in scales can significantly degrade product quality, mechanical performance, and production efficiency. Conventional manual inspection methods are labour-intensive, subjective, and incapable of satisfying the speed and accuracy requirements of automated manufacturing environments. This paper presents an intelligent hybrid framework for automated strip steel surface defect detection by integrating traditional machine learning and deep learning techniques. The proposed approach incorporates mean filtering for noise reduction and adaptive threshold-based segmentation to accurately extract defect regions from strip steel images. To improve classification performance, an ensemble model combining Random Forest (RF) and ResNet50 is developed, where ResNet50 extracts rich hierarchical visual features and Random Forest effectively classifies discriminative statistical features. The proposed framework is evaluated using a multi-class strip steel surface defect dataset comprising various defect categories. Experimental results demonstrate that the hybrid RF–ResNet50 model outperforms individual machine learning and deep learning models in terms of classification accuracy, robustness, and generalization capability. The complementary learning characteristics of both models enable effective representation of low-level texture information and high-level semantic features, resulting in reliable defect identification under diverse surface conditions. Furthermore, the proposed framework is computationally efficient and scalable for real-time industrial deployment, reducing reliance on manual inspection while enhancing product quality and manufacturing productivity. These findings highlight the potential of hybrid artificial intelligence techniques as an effective solution for next-generation intelligent surface inspection and quality control systems in smart manufacturing.

DOI: http://doi.org/http://doi.org/10.61137/ijsret.vol.12.issue4.146

Hydrochemical Characterization and Sodium Adsorption Ratio (Sar) of Groundwater in the Central Kyzylkum Desert Ecosystem, Uzbekistan

Authors: Adilov Sobit Uktamovich, Gulomov Gofir Komil o‘g‘li, Umirzoqov Jamshid Mirzayevich, Umirov Ismoil Eshonqulovich

Abstract: This study presents a hydrochemical characterization of groundwater from four wells (Chanishbay yakka quduq, Kupet turar joy, Nissan uy oldi, and Beknazar quduq) within the Central Kyzylkum desert ecosystem, Uzbekistan, focusing on major cations (Ca²⁺, Mg²⁺, Na⁺, K⁺), a set of regulated trace elements (Cu, Cr, Fe, Cd), and the Sodium Adsorption Ratio (SAR). Samples were collected on 30 April 2026 and analysed by Flame Atomic Absorption Spectrometry (FAAS) at an accredited laboratory (Workshop No. 081, order No. 23764). Results were compared against World Health Organization (WHO) drinking-water guidelines and the United States Salinity Laboratory (USSL) SAR classification. The hydrochemical facies at all sites were sodium-dominant (Na⁺ > Ca²⁺ > Mg²⁺ > K⁺), and sodium concentrations exceeded the WHO palatability threshold (200 mg/L) at every site. Calculated SAR values ranged from 4.9 (low hazard, S1) to 55.8 (very high hazard, S4), indicating a substantial sodium/salinity hazard should these waters be used for irrigation. Concentrations of the regulated heavy metals (Cu, Cr, Cd) were below the detection limit at all sites. The findings underline the need for expanded hydrochemical monitoring of groundwater in arid desert ecosystems such as Central Kyzylkum.

DOI: https://doi.org/10.5281/zenodo.21805869

Explainable Reinforcement Learning for Real-Time Credit Risk Decisioning in Wholesale Banking Ecosystems

Authors: Sakthi Sankara Balaji Sathyamurthy

Abstract: Wholesale banking credit decisions involve large and interconnected exposures, evolving borrower conditions, collateral movements, covenant compliance, and changing market environments. Conventional credit-risk models are commonly designed for one-time prediction tasks and may not adequately support sequential decisions such as adjusting exposure limits, revising pricing, requesting additional collateral, or escalating cases for review. This study proposes an explainable reinforcement learning framework for real-time credit-risk decisioning in wholesale banking ecosystems. The framework represents credit management as a sequential decision problem in which an agent observes dynamic borrower, facility, portfolio, and market conditions before recommending appropriate risk actions. It integrates offline reinforcement learning with policy constraints to reduce the risk of unsafe or non-compliant recommendations in regulated banking environments. The proposed model incorporates an explainability layer that provides feature-based explanations, reward decomposition, policy rationale, and counterfactual action analysis. These outputs are intended to clarify why a specific credit action is recommended, which risk factors influenced the decision, how the recommendation aligns with risk appetite, and what changes in borrower conditions could lead to an alternative outcome. The study adopts a design science and empirical evaluation approach using historical wholesale credit data, with performance assessed against conventional statistical models, machine- learning models, rule-based decision engines, and non-explainable reinforcement-learning approaches. Evaluation criteria include credit-risk performance, expected-loss control, risk-adjusted return, decision latency, policy compliance, robustness, and explanation quality. The study contributes a structured framework for applying explainable reinforcement learning to high-impact credit decisions. It also provides guidance for controlled deployment through offline training, human oversight, model validation, audit logging, and continuous monitoring. The proposed approach may strengthen the timeliness, consistency, transparency, and governance of credit-risk decisioning across wholesale banking operations.

DOI: https://doi.org/10.5281/zenodo.21819118

Explainable Deep Learning for Automated Image-Based Disease Classification

Authors: Rabins Porwal

Abstract: — Deep convolutional networks now match or exceed specialist performance on several image-based diagnostic tasks, yet their adoption in clinical practice remains limited by a problem that accuracy alone cannot solve: a clinician asked to act on a prediction cannot see why it was made. Post-hoc saliency methods offer a partial answer, but different methods applied to the same network routinely disagree, and a map that looks convincing is not necessarily one that reflects the computation the model actually performed. This paper proposes an Explainable Deep Learning (XDL) framework in which interpretability is a training objective rather than an afterthought. An attention-guided refinement module reweights backbone feature maps so that spatial evidence is concentrated before classification. Three complementary attribution methods – Grad-CAM++, integrated gradients and GradientSHAP – are then fused into a single saliency map, and the disagreement among them is quantified as an explanation-consistency score. Finally, a deletion-based faithfulness penalty is added to the loss, so that the network is optimised not only to classify correctly but to concentrate its evidence on regions whose removal genuinely changes the prediction. Evaluation across five public datasets spanning radiography, dermoscopy, fundus photography, magnetic resonance imaging and histopathology gave a mean accuracy of 93.7 per cent, 2.1 percentage points above the strongest baseline. More importantly, faithfulness improved substantially: deletion AUC fell from 0.157 to 0.128 and agreement with expert-annotated lesion masks rose from 0.452 to 0.518 in intersection over union. A blinded review by three clinicians rated the fused maps 4.2 out of 5 for plausibility against 3.6 for the best competing method.

DOI: https://doi.org/10.5281/zenodo.21819333

FlightSense AI: Real-Time Flight Price Intelligence Platform

Authors: Reddi Sri Venkata Lakshmi Lahari, K Ravi Kumar

Abstract: The increasing volatility of airline ticket pricing, driven by dynamic market demand, seasonal variations, operational costs, and competitive pricing strategies, has made accurate flight fare prediction a challenging task. Conventional forecasting approaches often struggle to model the nonlinear and time-dependent relationships that influence airfare fluctuations. This paper presents an intelligent web-based flight price prediction framework that integrates advanced machine learning and deep learning techniques to deliver accurate and real-time airfare forecasts. The proposed system employs comprehensive data preprocessing, feature engineering, and sequential learning to capture complex pricing patterns from historical flight data. A Long Short-Term Memory (LSTM) network is utilized to model temporal dependencies, while adaptive learning mechanisms enable the framework to remain responsive to continuously changing market conditions. The developed web application provides users with an interactive platform for obtaining instant fare predictions, thereby supporting informed travel planning and strategic pricing decisions. Experimental evaluation demonstrates that the proposed framework achieves reliable predictive performance and effectively models dynamic airfare trends. The proposed solution offers a scalable, data-driven decision support system for airlines, travel agencies, and passengers, contributing to the advancement of intelligent transportation analytics and real-time predictive systems.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue4.151

An Intelligent Framework For Real-Time Disaster Detection Using Geo-Spatial Social Media Analytics

Authors: Thogaru Aahalya, P.S.V Krishna

Abstract: The widespread adoption of social media platforms has transformed the way disaster-related information is generated and disseminated, providing valuable real-time insights during emergency situations. However, extracting reliable and actionable information from the enormous volume of unstructured social media content remains a significant challenge due to data noise, misinformation, and incomplete contextual information. This paper presents an intelligent disaster monitoring framework that integrates artificial intelligence, natural language processing, sentiment analysis, and location intelligence to enable real-time detection of disaster events from social media streams. The proposed framework systematically collects and preprocesses user-generated content, identifies disaster-related posts through machine learning techniques, extracts geographic information to determine affected regions, and evaluates public sentiment to assess the severity and urgency of ongoing incidents. By combining textual analysis with geospatial intelligence, the system enhances situational awareness and provides timely decision support for emergency response agencies. Experimental evaluation demonstrates that the proposed framework achieves reliable disaster detection performance while improving the accuracy of event localization and public sentiment assessment. The proposed approach offers a scalable and intelligent solution for disaster management, facilitating faster emergency response, effective resource allocation, and improved public safety during crisis situations.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue4.152

Artificial Intelligence for Gaming Accessibility: A Comparative Analysis of Current Advances, User Perspectives, and Future Directions

Authors: Balvir Singh Thakur, Chakshu Bhardwaj

Abstract: Artificial intelligence (AI) is frequently suggested as a means to reduce the participation thresholds that presently inhibit gamers with disabilities from playing digital games. However, evidence to support this assertion has been scattered across academic literature, open datasets, industry standards, and practitioner discourse and seldom consolidated. This review brings together and compares the four kinds of evidence, not to develop new theoretical concepts or to present new experimental data, but to emphasize the areas of agreement and disagreement in the literature. Following the PRISMA 2020 methodology, a repeatable search and filtering pipeline was developed, covering IEEE Xplore, the ACM Digital Library, Scopus, Web of Science, and Google Scholar, for the period 2018-February 2026, further enriched by a qualitative evidence synthesis of open accessibility resources: large-scale Steam review datasets, the Game Accessibility Guidelines (GaG), the IncluSet repository, and AbleGamers Accessible Player Experiences (APX), and a comparative analysis of academic research results against player signals and practitioner recommendations. Analysis of this corpus reveals recurrent themes, with advanced AI applications being largely limited to speech-to-text captioning, text-to-speech audio, computer vision for navigation and object recognition, and reinforcement learning for adaptive difficulty. Cutting-edge yet less-explored solutions involve large language models and other forms of generative AI. There is considerable agreement between literature, expressed user interest, and practitioner recommendations regarding captioning, data privacy in adaptive accessibility systems, and multiplayer game balance. Still more research on making games accessible to players with disabilities is focused on visual and hearing impairments than on motor or cognitive disabilities. Together, these point to opportunities for an integrated agenda that prioritizes disability-balanced training data, ecologically valid evaluation of gaming accessibility, and participatory AI design approaches centered on user privacy.

DOI: https://doi.org/10.5281/zenodo.21822669

AI-CloudAssist: An Intelligent Cloud-Based File Organization And Categorization Framework Leveraging Random Forest And SHAP-Based

Authors: Moaiz Kazi, Sufiyan Ansari, Arhaan Shaikh, Usaid Khairdi

Abstract: The modern digital world is generating unprece-dented amounts of unstructured data, which has given rise to “digital hoarding.” Cloud users often feel overwhelmed by massive file repositories, experience slow searches, and see their organized systems break down. To tackle this, we introduce XAI-CloudAssist, a robust, cloud-native assistant designed to automatically classify files using an ensemble of Random Forest models. Unlike traditional automated systems that operate as black boxes, our approach puts transparency first by integrating Explainable AI. We use SHAP values to reveal the reasoning behind each classification, assigning quantitative importance scores to metadata features. In experiments, the system achieved 98 Percentage classification accuracy, showing that metadata-driven cues like storage footprint and access frequency are highly predictive. Moreover, XAI-CloudAssist provides localized expla-nations for every categorization, helping users understand the de-cisions and fostering trust in cloud automation. Keywords: Cloud Computing, File Organization, Machine Learning, Explainable AI, SHAP, Random Forest, Automation, Data Governance.

AI-LoanAI-LoanApproveX: An Intelligent Machine Learning-Based Loan Approval Prediction

Authors: Moaiz Kazi, Sufiyan Ansari, Arhaan Shaikh, Madhvi Saxena, Usaid Khairdi

Abstract: The digital world we live in today is creating an amount of unorganized data. This has led to something called hoarding. People who use cloud storage often feel overwhelmed by the number of files they have. They have a time searching for things and their organized systems start to fall apart. To solve this problem we are introducing XAI-CloudAssist. XAICloudAssist is a tool that is designed to work in the cloud. It automatically sorts files into categories using a group of Random Forest models. Unlike automated systems that are hard to understand our approach is transparent. We use something called Explainable AI to make sure people can see how it works. We use SHAP values to show why each file is sorted into a category. We give a score to each piece of information about the file to show how important it is. When we tested XAI-CloudAssist it was able to sort files 98 percent of the time. This shows that things like how space a file takes up and how often it is used are very good, at predicting what category it belongs in. XAICloudAssist also explains why each file is sorted into a category. This helps people understand the decisions it makes and trust that it is working correctly. XAI-CloudAssist is related to Cloud Computing and File Organization and Machine Learning and Explainable AI and SHAP and Random Forest and Automation and Data Governance.

Fuzzy Logic-Based Smart Parking Congestion Detection: A Lightweight Real-Time System Using Vehicle Count And Slot Availability

Authors: Arhaan Shaikh

Abstract: Parking congestion has become one of the major challenges in modern urban areas because of rapid population growth, expansion of cities, and the continuously increasing number of private vehicles. In many commercial zones, res-idential complexes, shopping malls, railway stations, airports, and educational campuses, drivers often face difficulty in finding available parking spaces. This leads to unnecessary delays, traffic buildup, fuel wastage, driver frustration, and increased air pollution. Traditional parking management systems generally depend on fixed thresholds or simple binary decision-making methods, where congestion is classified only as full or empty. Such systems are not flexible enough to handle real-time changes in parking demand and uncertain traffic situations. This paper presents a Mamdani fuzzy logic-based smart parking congestion detection system that can intelligently es-timate parking congestion levels using two important input parameters: vehicle count and free slot availability. Instead of using rigid boundaries, fuzzy logic uses linguistic terms such as Low, Medium, and High to represent real-world conditions more naturally. The proposed model uses triangular membership functions for fuzzification, a nine-rule inference engine for decision-making, and centroid defuzzification to generate a final congestion output. The system provides smoother transitions between congestion states, better handling of boundary values, and more realistic results compared to conventional methods. Due to its low computational complexity, the proposed system is highly suitable for real-time embedded devices, IoT-based smart city applications, and automated parking guidance systems.

Artificial Intelligence and Computer-Aided Retrosynthesis in the Present Scenario: Transforming Modern Organic Synthesis – A Review

Authors: Sateesh Kumar Beepala

Abstract: Artificial Intelligence (AI) is transforming synthetic Organic Chemistry by enabling rapid and efficient planning of synthetic routes through Computer-Aided Retrosynthesis (CASP). Traditional Retrosynthetic analysis, based on expert knowledge, has evolved into AI-driven systems capable of learning from millions of published chemical reactions. Modern approaches, including machine learning (ML), deep learning (DL), graph neural networks (GNNs), and transformer models, have significantly improved reaction prediction, retrosynthetic route generation, and reaction optimization. AI-powered platforms such as IBM RXN, ASKCOS, AiZynthFinder, and SYNTHIA have become valuable tools in pharmaceutical research, natural product synthesis, and sustainable chemistry. This review summarizes recent advances in AI-assisted retrosynthesis, highlights current applications, discusses existing challenges, and outlines future prospects for intelligent and autonomous chemical synthesis.

DOI: https://doi.org/10.5281/zenodo.21834182

An Intelligent Explainable Diagnostic Framework for Clinical Evaluation of Metabolic Syndrome

Authors: Sayyed Yasmin Sultana Begum, Associate Professor M.Jyothi

Abstract: Accurate and transparent clinical evaluation of Metabolic Syndrome using radiographic imaging remains a significant challenge due to the subjective nature of manual interpretation and the limited explainability of conventional deep learning models. Although recent advances in artificial intelligence have substantially improved automated disease classification, the lack of model interpretability restricts their adoption in real-world clinical environments where transparency and trust are essential. This paper proposes an Explainable and Interpretable Smart Diagnostic Framework that combines deep convolutional feature learning with Explainable Artificial Intelligence (XAI) techniques to provide both accurate predictions and clinically meaningful explanations. The framework incorporates comprehensive image preprocessing, hierarchical feature extraction, multi-scale severity classification, and explanation-driven decision support through visualization and feature attribution mechanisms. Attention-based localization and feature contribution analysis enable clinicians to identify the anatomical regions and predictive factors influencing the diagnostic outcome, thereby enhancing model transparency and clinical confidence. Furthermore, training on heterogeneous radiographic datasets improves robustness, generalization, and adaptability across diverse patient populations. Experimental evaluation demonstrates that the proposed framework achieves high diagnostic performance while providing reliable interpretability, making it an effective decision-support system for intelligent clinical assessment and early disease severity evaluation.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue4.158

Mechanistic–Empirical Design Of Perpetual Pavements Using Recycled And Modified Asphalt Materials

Authors: Gaurav kumar, Jitendra Chauhan, Umesh Rathod

Abstract: The increasing demand for durable, sustainable, and cost-effective roadway infrastructure has led to significant advancements in pavement engineering, particularly in the development of perpetual pavements. This research presents a comprehensive analysis of the Mechanistic–Empirical (M–E) design approach for perpetual pavements incorporating recycled and modified asphalt materials. The M–E design methodology integrates mechanistic analysis of pavement responses (stress, strain, and deflection) with empirical performance models to predict long-term distress such as fatigue cracking, rutting, and thermal cracking. The study critically evaluates the performance of recycled materials including Reclaimed Asphalt Pavement (RAP), Recycled Asphalt Shingles (RAS), and Warm Mix Asphalt (WMA), along with modified binders such as polymer-modified asphalt, crumb rubber-modified bitumen, and nano-modified binders.

Artificial Intelligence-Based Prediction Of Asphalt Binder Aging And Pavement Distress

Authors: Nishant Kumar, Jitendra Chauhan

Abstract: Asphalt binder aging significantly affects the long-term performance and durability of flexible pavements, leading to various pavement distresses such as cracking, rutting, and raveling. Accurate prediction of asphalt binder aging and associated pavement deterioration is essential for effective pavement design, maintenance planning, and lifecycle cost management. In recent years, Artificial Intelligence (AI) techniques have emerged as powerful tools for modeling complex, nonlinear relationships among environmental factors, material properties, traffic loading, and aging characteristics of asphalt binders. This work explores the application of Artificial Intelligence–based methods for predicting asphalt binder aging and pavement distress. Various AI techniques such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), Machine Learning (ML), Deep Learning (DL), and hybrid optimization models are examined in terms of their ability to analyze large datasets and provide accurate predictions. The study existing literature on AI-driven predictive models that incorporate factors such as temperature variation, oxidation processes, traffic load, binder composition, and environmental conditions. Furthermore, the paper highlights the advantages of AI models over traditional empirical and mechanistic methods, including improved prediction accuracy, adaptive learning capability, and efficient data-driven decision-making. The work also identifies current research gaps, challenges in data availability, and opportunities for integrating AI with mechanistic–empirical pavement design approaches. Overall, the study provides a comprehensive overview of recent advancements in AI-based prediction of asphalt binder aging and pavement distress, emphasizing its potential to enhance pavement performance evaluation, optimize maintenance strategies, and support sustainable infrastructure development.

QFusionNet: A Quantum-Enhanced Hybrid Learning Framework for Intelligent Early Cancer Detection

Authors: Nagabathula Poorna Praveen, Assistant Professor K V V Ramana

Abstract: Early and accurate cancer detection is essential for improving patient survival rates and enabling timely clinical intervention. However, conventional diagnostic approaches and classical machine learning techniques often encounter challenges in processing high-dimensional biomedical datasets due to feature redundancy, computational complexity, and limited predictive performance. To address these limitations, this paper proposes a quantum-assisted intelligent framework for early cancer detection by integrating advanced quantum-inspired preprocessing, feature optimization, and hybrid predictive modeling techniques. The proposed methodology employs Quantum-Normalized Adaptive Refinement (Q-NAR) to enhance data quality through effective preprocessing, Wrapper Component Attribute Analysis (WCAA) to rank significant biomedical features, and Swing L-Bee Mustard Optimization (SLBMO) to identify the optimal feature subset while reducing dimensionality. Finally, a Quantum Boosted Vector Fusion Network (QBVFN) is developed to perform accurate cancer classification and treatment outcome prediction. The proposed framework is validated using biomedical data from The Cancer Genome Atlas (TCGA) within a Python-based implementation environment. Experimental evaluation demonstrates that the proposed approach achieves superior prediction accuracy, improved feature optimization, and enhanced computational efficiency compared with conventional machine learning models. The obtained results highlight the effectiveness of integrating quantum computing principles with machine learning techniques to support next-generation intelligent cancer diagnosis and precision healthcare systems.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue4.161

Recycling of Waste Clothing: Opportunities for Resource Conservation and Value Creation

Authors: Associate Professor M MGanganallimath, Associate Professor S J Sanjay, Abhishekh M Hiremath, Prajwal Mane, Raghavendra I Yaragudri, Siddalingesh M Matoli

Abstract: The fast expansion of the textile and garment sector has greatly increased waste clothing creation, causing major environmental and resource management concerns. Most wasted clothing are disposed of by landfilling or incineration, leading to resource depletion, pollution, and greenhouse gas emissions. Waste clothes recycling has emerged as an efficient approach for encouraging resource conservation and fostering a circular economy. This study discusses important recycling technologies, including mechanical, chemical, thermal, and biological processes, and their potential for transforming textile waste into value-added goods such as regenerated fibers, composites, insulating materials, geotextiles, and construction items.The study also addresses how new technologies, like as artificial intelligence and the Internet of Things, can enhance the effectiveness of textile collection, sorting, and recycling. Additionally, the economic and environmental advantages of recycling are emphasized, including less carbon emissions, decreased landfill waste, resource conservation, and job creation. There is also discussion of current issues such contamination, insufficient infrastructure, mixed-fiber separation, and policy constraints. The analysis comes to the conclusion that effective waste clothing recycling presents important chances for value development, sustainable resource management, and the shift to a circular textile economy.

DOI: https://doi.org/10.5281/zenodo.21838501

Role of Neuro-marketing Factors in Influencing Consumer Buying Behaviour

Authors: Research Scholar Reeta, Assistant Professor Dr Meentu Grover

Abstract: Neuro-marketing has emerged as an interdisciplinary field that integrates marketing, psychology, and neuroscience to understand the cognitive and emotional processes underlying consumer decision-making. The present study empirically examines the influence of key neuro-marketing factors consumer attention, emotional response, memory, sensory appeal, and subconscious influence on consumer behaviour. The study adopts a quantitative research design and considers a sample of 200 respondents. Data are measured using a five-point Likert scale and analyzed through descriptive statistics, Pearson correlation, and multiple regression analysis. The empirical analysis indicates that attention, emotional response, memory, and subconscious influence have positive and significant relationships with consumer behaviour, whereas sensory appeal demonstrates a positive but comparatively weaker influence when other factors are considered simultaneously. The regression model indicates that the selected neuro-marketing factors collectively explain a substantial proportion of the variation in consumer behaviour. Among the factors examined, emotional response emerges as one of the most influential predictors, highlighting the importance of affective engagement in shaping consumer responses. The findings suggest that marketers can improve consumer engagement by designing marketing stimuli that effectively capture attention, generate positive emotions, strengthen brand memory, and influence automatic decision processes. The study contributes to the growing empirical literature on neuro-marketing by demonstrating a structured framework for examining the relationship between psychological marketing stimuli and consumer behaviour. However, the findings should be interpreted in light of the study's methodological limitations and should be validated through larger samples and actual consumer data in future research.

Structural, Optical, and Morphological Characterization of PbS Thin Films Prepared by the SILAR Technique

Authors: Ramina. K

Abstract: Lead sulphide (PbS) thin films have attracted significant research interest because of their narrow band gap, excellent infrared sensitivity, and promising applications in optoelectronic devices, infrared detectors, solar cells, and photoconductive systems. The present study investigates the synthesis and characterization of PbS thin films deposited on glass substrates using the Successive Ionic Layer Adsorption and Reaction (SILAR) technique. The films were prepared under controlled deposition conditions and subsequently subjected to different cooling durations to evaluate their influence on structural and optical characteristics. Structural analysis was performed using X-ray Diffraction (XRD), while surface morphology and optical properties were examined using Scanning Electron Microscopy (SEM) and UV–Visible spectroscopy. XRD analysis confirmed the formation of polycrystalline PbS thin films with a face-centred cubic crystal structure. Variations in grain size, strain, and dislocation density were observed with different cooling durations, indicating that post-deposition treatment significantly affects crystal growth. SEM images revealed uniform surface morphology with densely packed grains, whereas UV–Visible analysis demonstrated favourable optical absorption characteristics suitable for photovoltaic and infrared sensing applications. The findings indicate that the SILAR method is an economical, simple, and effective technique for producing high-quality PbS thin films with potential applications in advanced optoelectronic devices.

DOI: https://doi.org/10.5281/zenodo.21847782

Teacher Knowledge And Teacher Efficacy As Predictors Of Students’ Learning Outcomes In Secondary School Mathematics: Evidence From Selected Secondary Schools In Bududa District, Uganda:

Authors: Jackson Matsanga

Abstract: Teacher knowledge and teacher efficacy have long been recognised as important determinants of effective teaching and learning. This study examined the influence of teacher knowledge and teacher efficacy on students’ learning outcomes in Mathematics in selected secondary schools in Bududa District, Uganda. A cross-sectional survey design incorporating quantitative and qualitative approaches was used. Data were collected using questionnaires, interviews, classroom observations and Mathematics achievement measures. Quantitative data were analysed using descriptive statistics and Spearman’s rank correlation, while qualitative evidence was organised thematically and used to enrich interpretation. The findings indicated positive relationships among teacher knowledge, teacher efficacy and students’ learning outcomes. Teachers who demonstrated stronger subject-matter understanding, pedagogical competence and confidence in instructional practice were better positioned to explain mathematical concepts, manage learning activities, motivate learners and respond to learning difficulties. The study further indicated that teacher knowledge and efficacy were mutually reinforcing: professional competence strengthened instructional confidence, while successful teaching experiences reinforced teachers’ beliefs in their capabilities. The study concludes that improving Mathematics achievement requires simultaneous attention to teachers’ content knowledge, pedagogical competence and professional efficacy. Continuous professional development, supportive instructional supervision, adequate teaching resources and strengthened teacher preparation are recommended.

DOI: http://doi.org/10.5281/zenodo.21868584

Adaptive AI-Assisted Doppler Compensation and Predictive Handover Optimization for LEO Satellite Communication in 6G Non-Terrestrial Networks

Authors: Prateek Anand, Assistant Professor Rishi Sharma, Assistant Professor Gaurav Morghare

Abstract: Low Earth Orbit (LEO) satellite communication has emerged as a key enabler of sixth-generation (6G) Non-Terrestrial Networks (NTNs), offering global coverage, low propagation delay, and high-capacity broadband connectivity. However, the high orbital velocity of LEO satellites introduces significant challenges, including severe Doppler frequency shifts, rapidly varying channel conditions, and frequent handovers, which adversely affect communication reliability, throughput, and Quality of Service (QoS). Existing Doppler compensation and handover mechanisms are generally treated as independent processes and often rely on static threshold-based strategies or computationally intensive artificial intelligence (AI) models, limiting their adaptability in highly dynamic satellite communication environments. This paper proposes an Adaptive AI-Assisted Doppler Compensation and Predictive Handover Optimization (AIDCPHO) framework for LEO satellite communication in 6G Non-Terrestrial Networks. The proposed framework integrates real-time Doppler estimation, AI-assisted predictive handover decision-making, adaptive beam selection, and dynamic link quality assessment into a unified optimization model. A predictive mobility module estimates future satellite-user link conditions using orbital dynamics and user mobility information, while an adaptive Doppler compensation module minimizes frequency estimation errors before communication degradation occurs. Furthermore, a multi-parameter handover decision algorithm utilizes Signal-to-Noise Ratio (SNR), Doppler shift, elevation angle, received signal strength, and predicted link quality to proactively initiate seamless handovers, thereby reducing service interruption and packet loss. The proposed framework is implemented and evaluated using MATLAB-based simulations that model realistic LEO satellite orbital movement, time-varying communication channels, and user mobility scenarios.

Performance Evaluation of Reinforced Flexible Pavements Using Geotextiles and Geogrids Over Clay Subgrades

Authors: Ramanuj Singh, Assistant Professor Hariram Sahu

Abstract: Flexible pavements constructed over clay subgrades are highly susceptible to premature distress on account of the low bearing capacity and pronounced moisture sensitivity of clay soils. This paper presents an experimental investigation into the effectiveness of geosynthetic reinforcement in improving the California Bearing Ratio (CBR) of a high-plasticity clay (CH) subgrade collected from Damoh, Madhya Pradesh, India. Two commercially available geosynthetics, a Mirafi HP370 woven polyester geotextile and a Tensar BX1200 biaxial polypropylene geogrid, were evaluated at three reinforcement depths within the CBR mould, namely H/3, H/2, and 2H/3, under both soaked and unsoaked conditions. The soil was classified as CH under IS 1498, with a liquid limit of 55.36%, plasticity index of 30.03%, optimum moisture content of 19.5%, and maximum dry density of 1.69 g/cm³. The unreinforced clay exhibited unsoaked and soaked CBR values of 4.8% and 1.9%, respectively, both indicative of a very weak subgrade. Reinforcement with Tensar BX1200 at H/2 produced the greatest improvement, raising the CBR to 9.6% (unsoaked) and 4.9% (soaked), corresponding to gains of 100.0% and 157.9% over the unreinforced control. The Mirafi HP370 geotextile performed best at H/3, with improvements of 85.4% and 121.1%. The results confirm that both reinforcement type and placement depth significantly influence subgrade performance, and that biaxial geogrid reinforcement at mid-depth offers the greatest structural benefit for the clay investigated. The experimentally derived CBR values are discussed in relation to their implications for flexible pavement thickness design over weak clay subgrades.

DOI: https://doi.org/10.5281/zenodo.21887185

Comparative Structural Performance of Cable-Stayed Bridges with Concrete, Steel, and Prestressed Concrete Deck Systems: A Review

Authors: Hari Om Dhar Badgaiyan, Assistant Professor Hariram Sahu

Abstract: The deck girder is one of the most influential components governing the structural response, stiffness, weight, durability, and overall economy of a cable-stayed bridge, and the choice between reinforced concrete (RC), structural steel, and prestressed concrete (PSC) remains a central design decision. This paper presents a structured literature review of published research relevant to this choice, covering material-specific behaviour of RC, steel, and PSC deck girders; design practice and analytical studies on tower geometry, dynamic/seismic response, and finite element modelling; direct comparative studies between deck materials and hybrid/composite systems; economic and life-cycle cost studies; construction-stage and composite-action behaviour; and studies conducted under Indian IRC loading provisions. The review finds general agreement that RC decks are economical and durable under normal service but suffer from higher self-weight and tensile cracking over longer spans; steel decks offer a superior strength-to-weight ratio and rapid erection but require sustained corrosion protection and careful fatigue design; and PSC decks combine improved stiffness and crack control with the durability of concrete, at the cost of higher initial complexity. The review further finds that although each material has been studied extensively in isolation or in pairwise comparison, very few investigations evaluate reinforced concrete, structural steel, and prestressed concrete decks together under identical bridge geometry, loading, and analytical procedure, and fewer still combine such a structural comparison with a consistent life-cycle economic evaluation. This gap is identified as the principal motivation for a unified, controlled-variable comparative investigation of the three deck systems.

DOI: https://doi.org/10.5281/zenodo.21888527

Effectiveness of Mindfulness Meditation in Controlling Test Anxiety among Ninth Grade Students in Different Levels of Achievement

Authors: Muraleedharan, T, Arjunan, N. K., Deepthi, T.

Abstract: The present study investigated the effectiveness of Mindfulness Meditation in reducing test anxiety among secondary school students and examined whether its effectiveness varied according to students’ levels of academic achievement. A quasi-experimental pretest–post-test control group design was employed. The participants comprised 166 Standard IX students from four intact divisions of a Government Higher Secondary School in Kerala. One division served as the Control Group, while the remaining three constituted the Treatment Group. Test anxiety was assessed using the Test Anxiety Scale for Secondary School Students (Arjunan & Bindu, 2021). The Treatment Group underwent a modified Mindfulness-Based Stress Reduction programme consisting of eight structured sessions supplemented by regular mindfulness practice. Data were analysed using descriptive statistics, t-test, ANCOVA, and one-way ANOVA. The findings revealed no significant initial difference in test anxiety among students belonging to different achievement levels. After controlling for pre-test scores, Mindfulness Meditation produced a significant reduction in test anxiety, with a large treatment effect (F = 444.276, p < .001, partial η² = .730). The effectiveness of the intervention also differed significantly according to academic achievement, with average- and high-achieving students showing greater reductions than low-achieving students. The findings support Mindfulness Meditation as an effective psycho-pedagogical strategy for managing test anxiety among secondary school students.

DOI: https://doi.org/10.5281/zenodo.21899194

A Finite Element Study on Structural Performance of Reinforced Concrete, Steel, and Prestressed Concrete Deck Girders in Cable-Stayed Bridges

Authors: Hari Om Dhar Badgaiyan, Assistant Professor Hariram Sahu

Abstract: Cable-stayed bridges are widely adopted for medium- to long-span crossings owing to their structural efficiency and aesthetic appeal, and the deck girder material strongly influences their stiffness, weight and overall performance. This paper presents a comparative finite element evaluation of a representative 180 m (30 m + 120 m + 30 m) cable-stayed bridge with three deck girder alternatives — reinforced concrete (RC), structural steel, and prestressed concrete (PSC) — modelled in SAP2000 under identical geometry, cable arrangement, support conditions and IRC Class AA loading. Linear static analysis was carried out with the deck material as the sole variable across the three models. Structural response was compared in terms of joint reactions, joint displacements and rotations, internal frame forces, and area-shell stresses. Results show that the steel deck consistently produced the lowest reactions, displacements, internal forces and stresses owing to its high strength-to-weight ratio; the RC deck exhibited intermediate global response but the highest tensile stress; and the PSC deck attracted the largest internal forces and vertical reactions yet achieved the lowest tensile stress because of prestressing. An indicative life-cycle cost comparison further shows that RC offers the lowest first cost, steel the highest, and PSC the most favourable long-term economy. The findings provide practical, results-based guidance for deck-material selection in cable-stayed bridge design.

DOI: https://doi.org/10.5281/zenodo.21903156

A Comprehensive Review On Different Types of Fuel Cells and Their Applications

Authors: Adeleye, S. A., Adewumi, A. A, Falana, A. B

Abstract: The increasing demand for clean, efficient, and sustainable energy systems has created the need for alternatives to conventional fossil fuel-based power generation technologies, which are associated with environmental pollution, greenhouse gas emissions, and gradual resource depletion. Among the emerging energy technologies, fuel cells have attracted significant attention because they convert chemical energy directly into electrical energy through electrochemical reactions without combustion, resulting in higher efficiency and lower environmental impact. This study aims to review different types of fuel cells, their operating principles, materials, performance characteristics, applications, and associated challenges. The study also evaluates the advantages and limitations of fuel cell systems in relation to modern energy demands and sustainable development goals. A systematic literature review approach was adopted for the study. Relevant information was obtained from peer-reviewed journal articles, textbooks, conference proceedings, technical reports, and publications from recognized scientific databases and energy organizations. The reviewed literature focused on major fuel cell technologies such as Proton Exchange Membrane Fuel Cells (PEMFCs), Solid Oxide Fuel Cells (SOFCs), Alkaline Fuel Cells (AFCs), Molten Carbonate Fuel Cells (MCFCs), and Phosphoric Acid Fuel Cells (PAFCs). Findings from the review revealed that fuel cells possess high energy conversion efficiency, low emissions, quiet operation, and wide application potential in transportation, stationary power generation, portable devices, and industrial systems. The study also showed that PEMFCs are more suitable for transportation applications due to their low operating temperature and fast response time, while SOFCs and MCFCs are more effective for stationary power systems because of their high efficiency and fuel flexibility. Despite these advantages, several limitations were identified, including high production cost, dependence on expensive catalyst materials, hydrogen storage and distribution challenges, durability issues, and inadequate infrastructure for large-scale commercialization. The study concludes that fuel cell technology remains one of the most promising clean energy solutions for future energy systems. However, wider adoption will depend on continuous technological improvement, cost reduction, development of hydrogen infrastructure, and supportive government policies aimed at promoting sustainable energy development.

DOI: http://doi.org/10.5281/zenodo.21913138

Evaluation of Polycyclic Aromatic Hydrocarbon and Heavy Metals Contamination in Stockfish Species Available in Onitsha Market: Implication for Human Health Risk

Authors: Ikechukwu S. Chikwe, Erienu Obruche Kennedy, Ofogharenor Tanro Maris, Naku Julius Uko, Abubakar Bilyamini Mu’azu, Anyanwu Chidimma Gogo, Omorodion J.I, Michael Asukwo Nseabasi

Abstract: This study evaluated the concentrations of polycyclic aromatic hydrocarbons (PAHs) and selected heavy metals in two commercially important stockfish species, Apama (Sepia species) and Haddock (Melanogrammus aeglefinus), obtained from Onitsha Market, Nigeria, to determine their potential human health implications. Six representative samples collected from different market locations were analyzed using Gas Chromatography–Mass Spectrometry (GC–MS) for PAHs and Atomic Absorption Spectrophotometry (AAS) for heavy metals. Iron was the predominant metal, ranging from 40.28–83.88 mg/kg, followed by zinc (13.00–21.00 mg/kg), copper (0.80–1.90 mg/kg), manganese (3.00–5.99 mg/kg), chromium (≤0.006 mg/kg), lead (ND–0.002 mg/kg), and cadmium (0.00001–0.00020 mg/kg), while nickel, arsenic, and mercury were not detected. The detected concentrations of lead, cadmium, mercury, and arsenic were below the maximum limits recommended by WHO/FAO for fish and fishery products, indicating minimal heavy metal contamination. Sixteen priority PAHs were identified, with phenanthrene, fluoranthene, and pyrene being the most frequently detected compounds. Total PAH concentrations ranged from 0.01034 to 0.23780 µg/kg, whereas benzo(a)pyrene concentrations (ND–0.00020 µg/kg) were far below the European Union maximum limit of 2 µg/kg for smoked fish products. Overall, the analyzed stockfish samples were considered safe for human consumption; however, routine monitoring remains essential to detect future contamination and ensure continued consumer protection.

DOI: https://doi.org/10.5281/zenodo.21913433

Steel Bracing Systems for Seismic Performance Enhancement of Reinforced Concrete Frames: A Review

Authors: Harish Mishra, Dr. Hariram Sahu

Abstract: Reinforced concrete (RC) moment-resisting frames are widely used in multi-storey construction but are inherently flexible under lateral seismic loading, which can lead to excessive storey drift, non-structural damage and, in severe cases, structural collapse. Supplementing an RC frame with steel bracing is one of the most widely researched and applied means of improving its seismic performance, and a large and growing body of literature has examined the many forms this strategy can take. This paper presents a review of published research on steel-braced RC frames, covering the classification of bracing systems (concentric diagonal, X, V, inverted-V/chevron and K arrangements; eccentric and off-diagonal bracing; buckling-restrained and self-centering braces), their reported effects on lateral stiffness, storey displacement, inter-storey drift, ductility and energy dissipation, the influence of building height and structural irregularity on bracing effectiveness, retrofit and brace-to-frame connection strategies, and the analytical, numerical and experimental methods used to evaluate them. The review finds broad agreement that steel bracing substantially improves lateral stiffness and reduces storey drift relative to a bare RC frame, that X-type and inverted-V (chevron) configurations are the most frequently reported as most effective for stiffness and drift control though the relative ranking varies between studies, and that eccentric and buckling-restrained systems offer superior ductility and energy dissipation at some cost in stiffness. The review also identifies persistent gaps in the literature, including a shortage of controlled, zone-wise or intensity-wise comparisons of a single bracing configuration across the full range of seismic demand, limited experimental validation of brace-to-RC-frame connection detailing at full scale, and a continuing reliance on linear or pushover-level analysis rather than nonlinear time-history assessment for tall buildings. These gaps are identified as priorities for future research.

DOI: https://doi.org/10.5281/zenodo.21914752

Comparative Seismic Performance Evaluation of Peripheral X-Type Steel Bracing in a Twenty-Storey Reinforced Concrete Building

Authors: M.Tech. Scholor Harish Mishra, Assistant Professor Dr. Hariram Sahu

Abstract: Reinforced concrete (RC) multi-storey buildings are highly susceptible to lateral forces induced by earthquakes, which can cause excessive storey displacement, inter-storey drift and structural damage. Steel bracing is a widely used and economical means of improving the lateral stiffness and seismic performance of RC frames, but its effectiveness depends strongly on configuration. This paper presents a comparative seismic evaluation of a three-dimensional twenty-storey (G+20) RC building modelled with and without peripheral X-type steel bracing, analysed under Seismic Zones II, III, IV and V using the Equivalent Static Method in STAAD.Pro CONNECT Edition, in accordance with IS 456:2000, IS 875 (Parts 1 and 2):1987 and IS 1893 (Part 1):2016. Eight structural cases were compared in terms of maximum bending moment, axial force, shear force, support reaction, overall roof displacement, storey displacement and inter-storey drift. The results show that peripheral X-type bracing reduced every response parameter examined in all four seismic zones. The largest and most consistent benefit was in lateral deformation control, with roof displacement reduced by 28.0-33.0% and maximum inter-storey drift reduced by 29.4-32.5%, both widening as seismic zone increased. Reductions in axial force (15.9-34.3%) and support reaction (15.4-34.3%) grew markedly with seismic zone, while the benefit on bending moment fell from 23.4% in Zone II to about 1.9% in Zones IV-V, and the shear-force benefit stayed nearly constant near 25%. The study concludes that peripheral X-type bracing is most reliably effective as a deformation-control measure across all seismic zones, and recommends its use as a supplementary lateral load-resisting system in multi-storey RC construction.

DOI: https://doi.org/10.5281/zenodo.21915048

Next-Generation Advanced Concrete Technology: Engineering High-Performance, Intelligent, and Sustainable Cementitious Composites for Resilient Infrastructure

Authors: Assistant Professor Aluvala Sindhuja

Abstract: The construction industry is undergoing a major transformation driven by the need for high-performance, durable, environmentally responsible, and intelligent construction materials. Conventional concrete remains the most widely used construction material; however, its high consumption of Portland cement, associated carbon emissions, brittle behavior, durability limitations, and increasing demand for natural resources have created a strong need for advanced alternatives. Advanced Concrete Technology (ACT) integrates material engineering, supplementary cementitious materials, industrial by-products, fibers, nanomaterials, chemical admixtures, smart sensing mechanisms, and optimized mixture-design techniques to produce concrete with superior mechanical and functional characteristics. This study presents a comprehensive investigation into next-generation advanced concrete incorporating supplementary cementitious materials and fiber reinforcement for sustainable and resilient infrastructure. An experimental framework is proposed in which conventional concrete is compared with modified concrete mixtures containing fly ash, ground granulated blast-furnace slag (GGBS), silica fume, and optimized fiber reinforcement. Fresh properties, compressive strength, split tensile strength, flexural strength, water absorption, sorptivity, and durability-related characteristics are evaluated at different curing ages. The study further discusses developments in high-performance concrete, self-compacting concrete, engineered cementitious composites, geopolymer concrete, self-healing concrete, recycled aggregate concrete, ultra-high-performance concrete, nano-modified concrete, and smart concrete. The proposed advanced concrete system is expected to demonstrate improved strength, crack resistance, durability, service life, and resource efficiency compared with conventional concrete. The integration of low-carbon binders and advanced reinforcement mechanisms provides an effective pathway for reducing the environmental footprint of infrastructure without compromising structural performance. The study concludes that future concrete technology should progress from conventional strength-oriented design toward performance-based, multifunctional, intelligent, and life-cycle-oriented material engineering.

Neural Network Based Medical Data Classification By Moth Flame Feature Optimization

Authors: Nand Kishor, Prof. Akrati Shrivastava

Abstract: Medical practioner have huge load with growing population after corona in 2020, most of researcher work in this medical field. This paper has proposed a model to classify the diabetic patient retinopathy disease image class detection. This work has processed the image input image quality and train the model. Proposed (Medical Data Classification by Moth Flame Optimization) MDCMFO optimized the input image by moth flame algorithm that cluster input image. Clustered image was used for the feature extraction spatial and frequency domain. Extracted features were used for the training of the neural network. Experiment was done on real image dataset and result shows that proposed work has improved the work efficiency of correct class detection.

Breathability Characteristics of Graphene-Coated Sheep Wool, Goat Wool and Their Composite Fabrics

Authors: Elif Altürk, Mert Parlak, Nazlı Tatar

Abstract: The development of breathable and thermal comfortable textile materials is of increasing significance for personal thermal comfort products. In this study, graphene-coated sheep wool, goat wool, and sheep wool/goat wool composite fabrics were developed and their moisture management properties were evaluated according to ISO 11092. Water vapor permeability (PWVP) and water vapor resistance (Ret) were measured to assess the breathability performance of the fabrics. The results revealed that the graphene-coated sheep wool fabric exhibited the highest water vapor permeability (36.60%) and the lowest water vapor resistance (5.96 Pa·m²/W), indicating superior moisture transport capability. The graphene-coated goat hair fabric showed the lowest permeability (28.43%) and the highest Ret value (9.80 Pa·m²/W), while still remaining within the "good breathability" category. The graphene-coated sheep wool/goat hair composite fabric demonstrated intermediate behavior with a PWVP of 35.10% and a Ret value of 7.80 Pa·m²/W. According to ISO 11092 comfort classification, all investigated fabrics can be considered breathable materials.

DOI: https://doi.org/10.5281/zenodo.21917421