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

Published by: vikaspatanker

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

Published by: vikaspatanker

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.

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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.

Published by: vikaspatanker

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

Published by: vikaspatanker

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

Published by: vikaspatanker

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.

Published by: vikaspatanker

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.

Published by: vikaspatanker

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.

Published by: vikaspatanker

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

Published by: vikaspatanker