Category Archives: Uncategorized

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

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

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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

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Dr. K. Murali Mohan Achari
Affiliation	Assistant Professor,Department of Chemistry, Sri Venkateswara College, University of Delhi, New Delhi.
Email-Id:	mmakamsali@svc.ac.in
Publication: . Books: Kamsali, M. M. A., & Thoti, V. (2025). Biomedical applications of functionalized magnetic nanomaterials (FMNs). In Nanostructure Science and Technology (pp. 39–58). doi:10.1007/978-3-031-97199-0_2. Publications: Seema, V., Kamsali, M. M. A., Alakonda, L. M., Varala, R., Hussein, M., & Alam, M. M. (2026). Mini-update on the applications of Hypophosphites in organic synthesis with a special focus on sodium hypophosphite (NaH2PO2). Tetrahedron Letters, 174(155875), 155875. doi:10.1016/j.tetlet.2025.155875. Majumder, A. B., Kamsali, M. M. A., Varala, R., & Ansari, S. A. (2026). Biocatalysis in bioorthogonal reactions: Use of hydrolases and transferases for selective modifications. Mini-Reviews in Organic Chemistry, 23(1), 10–19. doi:10.2174/0118756298358967250117114300. Alakonda, L. M., Kamsali, M. M. A., Alam, M. M., & Varala, R. (2026). Recent advances in ammonium iodide (NH 4 I)‐mediated transformations: Toward greener and metal‐free organic synthesis. European Journal of Organic Chemistry, (e202501115). doi:10.1002/ejoc.202501115. Varala, R., Seema, V., Kamsali, M. M. A., Kousar, N., Hussein, M., & Alam, M. M. (2026). Visible light-driven innovative approaches for the synthesis of quinoline and isoquinoline based heterocycles. Tetrahedron Letters, 175(155922), 155922. doi:10.1016/j.tetlet.2025.155922. Varala, R., Kamsali, M. M. A., Hussein, M., & Alam, M. M. (2025). Applications of 1,8-diazabicyclo[5.4.0]undec-7-ene (DBU) in heterocyclic ring formations. Organic & Biomolecular Chemistry, 23(41), 9285–9329. doi:10.1039/d5ob01209k.

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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

Published by: vikaspatanker

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

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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

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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

Published by: vikaspatanker

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.

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