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VOLUME 13, ISSUE 7, JULY 2026
Publication is in progress..
Multi-Cloud Cybersecurity for LLMs in Banking: Governance and Threat Surfaces Across Financial AI Systems
Mubashir Ali Ahmed
A Review of Deep Learning Approaches for Image-Based Waste Classification and Segregation
Anita Markam, Kavita Verma, Anurag Shrivastava
DETECTION OF BLOOD GROUP FROM FINGERPRINT USING CNN
Dr. T. Suryakanthi, P. Divya
AUTISM SPECTRUM DISORDER PREDICTION IN KIDS USING DEEP LEARNING
D. Banu Kranthi, Aekula Advitiya
Innovative Rearing and Management Practices for Improving the Health, Survival, and Commercial Performance of the Mulberry Silkworm (Bombyx mori L.)
Shaik. Halima, Abbola. Thirumala, Alji. Kalpana, Vempapti.Uday Kiran, Paluru.Subramanyam
“Experimental Investigation On Strength Characteristics Of Concrete With GGBS And Metakaolin”
Darshana Chavan, Dr. Ansari Fatima Uz Zehra
SEISMIC RESPONSE COMPARISON OF DIFFERENT TALL BUILDING STRUCTURAL SYSTEMS
Anshul Jangra, Anju
ESG-ORIENTED ALTERNATIVE FUELS RESEARCH FACILITY (AFRF) METHODOLOGY FOR SUSTAINABLE WASTE MANAGEMENT IN PHARMACEUTICAL MANUFACTURING
Sowmiya S*, V. M. Madhavan, Senthilkumar D, Muralidharan K
An Edge-AI Powered Micro-Seismic Digital Twin for Predicting Dynamic Rockburst Risks in Deep Hard-Rock Excavations
Ashok Prajapat, Kratika Verma
Designing Multi-Layered Waste-Derived Technosols for Long-Term In-Situ Passive Treatment of Acid Mine Drainage
Ashok Prajapat, Kratika Verma
Triboelectric Fluidized-Bed Separation of Ultra-Fine Critical Minerals in Desert Climates
Ashok Prajapat, Kratika Verma
Multi-Agent Reinforcement Learning (MARL) for "Open Autonomy" in Mixed-Fleet Underground Mining
Ashok Prajapat, Kratika Verma
Waste Treatment Plants and Remediation Strategies in Indian Mining Areas: A Technical Review of Effluent Management, Acid Mine Drainage, Tailings Stacking, and Circular-Economy Recovery
Ashok Prajapat, Puneet Sharma, Kratika Verma
Evaluating Farmer Income Enhancement Through the Godhan Nyay Yojana: A Village-Level Study from Sarkhor, Baloda Bazar District, Chhattisgarh
Rakesh Kumar Ghritlahare*, Devendra Singh Porte
Right to light and air: Applicability with respect to prevailing Building Rules in India
Madhumita Roy
A Multi-Dataset Stacked Ensemble Framework for Multi-Class Lung Cancer Classification
Dhaval J. Rana, Keyur Rana
FROM CONTENT TO CART: THE INFLUENCE OF INSTAGRAM AND YOUTUBE SHOPPING FEATURES ON CONSUMER PURCHASE DECISIONS
Dr.S. MOHAMED IMRAN SHARIF, Dr. M. BALASUBRAMANIAN
BREAST CANCER RISK ASSESSMENT USING RANDOM FOREST CLASSIFICATION AND TENSORFLOW LITE BASED ANDROID DEPLOYMENT
THATHA.USHA, T. PAVAN KUMAR, V. NARENDRA, DR K. SREENIVASA REDDY, G.SWATHI
Deep Learning and Hybrid Time-Series Forecasting for Intelligent Traffic Management and Sustainable Urban Mobility
Shruti Vohra*, Smita Verma
INFLUENCE OF DIGITAL MEDIA ON SUSTAINABLE CONSUMER BEHAVIOUR
Dr. S. MOHAMED IMRAN SHARIF, DR. RAJA MOHAMED. M.A, Mr. A. SYED MYDEEN BUHARI
“Analysis on Bangalore Traffic”
Dr Mohana Kumari, Agraharam Sri vidya, Terla Laxmi Narasimha Charan, Srujan P Rao, Akula Sri Surya Durga Manisha, Pardha sai Puripanda, Neeraj Jayan Moolekkattil
STUDY ON CONSUMER’S PERCEPTION TOWARDS PURCHASING GROCERIES THROUGH ONLINE
Dr. MOHANA KUMARI, ABDUL ASEEM K, BHARATHVAJH V G, GOWTHAM ELANGOVAN, HARI VISHNU S S, THIRUMALAIVASAN R, YUVARAJ KRISHNA R
AUTOMATIC HEADLIGHT INTENSITY CONTROL FOR VEHICLE TO AVOID ACCIDENTS
Dr. Vemuri Sai Srikanth, B Ajay Kumar
Qualitative Phytochemical Analysis on Cordia Obliqua Willd.
R. Raja Jency Esther*, D. Ahino Mary, S. Juliet Santha Jothi
Language And Background to Language Learning and Teaching
J. Benita Selvakumari
Finite Element Modelling of Reinforced Concrete and Advanced-Material Beam–Column Joints: A Review with a Gradient-Regularised Microplane Damage–Plasticity Case Study
Ankita Dhananjay Kulkarni, Dr. G.R. Gandhe
Education Policies and Systemic Reforms for the 21st Century: Leading Schools into the Future
Dr. Layal Abou Mrad and Fatima Itani
Quantum Computing and IOT Integration For Next-Generation Smart Systems
Dr. T. Vamshi Mohana, P. Sampurna, Salma Begum
Abstract
Multi-Cloud Cybersecurity for LLMs in Banking: Governance and Threat Surfaces Across Financial AI Systems
Mubashir Ali Ahmed
DOI: 10.17148/IARJSET.2026.13701
Abstract: Large-scale neural networks referred to as LLMs are quickly transforming the world of banks with functionalities such as intelligent fraud detection, customer services, credit scoring, compliance management, AML, and financial forecasting among others. But then, given the increasing deployment of these artificial intelligence technologies in cloud computing ecosystems including Amazon Web Services, Microsoft Azure and Google Cloud Platform the cybersecurity risks are becoming very complex and difficult to manage. Multi-cloud computing offers many advantages regarding scalability, resilience, partial regulatory flexibility and business continuity among others; but at the same time increases the attack vectors and surfaces for cyber-attacks spread across distributed architectures, APIs, identity systems, vector databases, prompt pipelines and model registry infrastructure. Financial data is the type of data that any form of attack, whether it be a prompt injection, model poisoning and several others will be after illegally or improperly. This paper focuses on discussing some of the cybersecurity threats associated with multi-cloud deployments of LLMs within banking organizations together with the challenges surrounding their governance, followed by proposing a security framework that is governance-based.
Keywords: Multi-cloud security, large language models, banking cybersecurity, AI in finance systems, governance of AI, prompt injection, securing model regulators.
Keywords: Multi-cloud security, large language models, banking cybersecurity, AI in finance systems, governance of AI, prompt injection, securing model regulators.
Abstract
A Review of Deep Learning Approaches for Image-Based Waste Classification and Segregation
Anita Markam, Kavita Verma, Anurag Shrivastava
DOI: 10.17148/IARJSET.2026.13702
Abstract: The rapid increase in municipal solid waste has created significant environmental and public health challenges, making efficient waste segregation essential for recycling and sustainable waste management. Traditional manual classification methods are often labor-intensive, time-consuming, and prone to errors. This review examines recent advancements in image-based waste classification using deep learning techniques, particularly Convolutional Neural Network (CNN) architectures such as VGG16, VGG19, MobileNetV2, DenseNet121, EfficientNetB0, and Deep Convolutional Neural Networks (DCNNs). The reviewed studies demonstrate that deep learning models can accurately classify waste and improve segregation efficiency. Special focus is given to an improved multi-layered DCNN model that achieved 93.28% accuracy on a dataset of 25,077 waste images, outperforming several transfer learning models. The review also highlights commonly used datasets, evaluation metrics, current challenges, and future research directions, concluding that deep learning-based waste classification systems offer a promising solution for intelligent and sustainable waste management.
Keywords: Deep Learning, Waste Classification, Convolutional Neural Network (CNN), Smart Waste Management
Keywords: Deep Learning, Waste Classification, Convolutional Neural Network (CNN), Smart Waste Management
Abstract
DETECTION OF BLOOD GROUP FROM FINGERPRINT USING CNN
Dr. T. Suryakanthi, P. Divya
DOI: 10.17148/IARJSET.2026.13703
Abstract: The prediction of a person's blood group based on their fingerprints represents an innovative approach that merges biometric identification with medical data. This project explores the use of Convolution Neural Networks (CNNs), a powerful class of deep learning algorithms, to predict an individual’s blood type from fingerprint images. The CNN is trained on a large dataset containing fingerprint images and their corresponding blood group labels. The model learns to recognize unique patterns, such as ridge and valley configurations, that may have subtle correlations with the individual’s blood type. According to preliminary findings, the CNN-based technology can attain encouraging accuracy levels, indicating a potential substitute for conventional blood group identification techniques. Enhancing model accuracy, growing the dataset, and resolving possible ethical and privacy issues with the use of biometric data will be the main goals of future study. While the primary goal of this research is to explore the feasibility of blood group prediction using fingerprints, the findings could have broader implications in fields like security, healthcare, and forensic investigations.
By learning visual patterns and agglutination characteristics present in the samples, the CNN model can classify blood types with high accuracy. This approach significantly reduces diagnostic time and human dependency, offering a scalable and efficient alternative to conventional methods. The results demonstrate the potential of AI-powered diagnostic tools in transforming healthcare delivery, particularly in remote or resource-limited settings. Future work will focus on expanding the dataset, refining the model, and validating the system in real- world clinical environments.
Keywords: Blood Group Prediction, Fingerprint Recognition, Convolution Neural Network (CNN), Deep Learning, Biometric Identification, Medical Image Classification, Pattern Recognition.
By learning visual patterns and agglutination characteristics present in the samples, the CNN model can classify blood types with high accuracy. This approach significantly reduces diagnostic time and human dependency, offering a scalable and efficient alternative to conventional methods. The results demonstrate the potential of AI-powered diagnostic tools in transforming healthcare delivery, particularly in remote or resource-limited settings. Future work will focus on expanding the dataset, refining the model, and validating the system in real- world clinical environments.
Keywords: Blood Group Prediction, Fingerprint Recognition, Convolution Neural Network (CNN), Deep Learning, Biometric Identification, Medical Image Classification, Pattern Recognition.
Abstract
AUTISM SPECTRUM DISORDER PREDICTION IN KIDS USING DEEP LEARNING
D. Banu Kranthi, Aekula Advitiya
DOI: 10.17148/IARJSET.2026.13704
Abstract: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by challenges in social interaction, communication, and behavior. Early and accurate diagnosis is crucial for effective intervention, yet traditional diagnostic methods can be time-consuming and require specialized expertise. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), offer promising avenues for automated and efficient ASD detection.
This study explores the application of CNNs for early ASD detection in children by analyzing data modalities like facial images. For instance, leveraging facial image datasets, models like DenseNet-121 have achieved high accuracy rates, indicating distinctive facial features associated with ASD. Moreover, CNNs applied to neuroimaging data, such as resting-state functional MRI, have demonstrated the ability to identify functional connectivity patterns indicative of ASD. The integration of CNN-based approaches across these diverse data sources underscores the potential of deep learning in facilitating early and accurate ASD diagnosis. By automating the detection process, these methods can augment clinical assessments, leading to timely interventions and improved outcomes for children with ASD.
Keywords: Autism Spectrum Disorder (ASD), Deep Learning, Convolutional Neural Networks (CNNs), Early ASD Detection, Facial Image Analysis, Neuroimaging, DenseNet-121, Medical Image Classification.
This study explores the application of CNNs for early ASD detection in children by analyzing data modalities like facial images. For instance, leveraging facial image datasets, models like DenseNet-121 have achieved high accuracy rates, indicating distinctive facial features associated with ASD. Moreover, CNNs applied to neuroimaging data, such as resting-state functional MRI, have demonstrated the ability to identify functional connectivity patterns indicative of ASD. The integration of CNN-based approaches across these diverse data sources underscores the potential of deep learning in facilitating early and accurate ASD diagnosis. By automating the detection process, these methods can augment clinical assessments, leading to timely interventions and improved outcomes for children with ASD.
Keywords: Autism Spectrum Disorder (ASD), Deep Learning, Convolutional Neural Networks (CNNs), Early ASD Detection, Facial Image Analysis, Neuroimaging, DenseNet-121, Medical Image Classification.
Abstract
Innovative Rearing and Management Practices for Improving the Health, Survival, and Commercial Performance of the Mulberry Silkworm (Bombyx mori L.)
Shaik. Halima, Abbola. Thirumala, Alji. Kalpana, Vempapti.Uday Kiran, Paluru.Subramanyam
DOI: 10.17148/IARJSET.2026.13705
Abstract: Sericulture benefits the local economy by creating jobs and producing high-quality natural silk. Mulberry sericulture's productivity and profitability are heavily dependent on the mulberry silkworm's health, survival, and performance. Innovative raising and management strategies, such as improved rearing house sanitation, environmental have considerably increased silkworm development and cocoon production. Recent developments in raising efficiency, including as automated monitoring systems, eco-friendly disinfectants, precision feeding, and integrated pest and disease management, have reduced output losses. Sustainable management strategies also help to conserve the environment and ensure economic viability by reducing chemical use and maximizing resource utilization. This data emphasizes the importance of implementing modern rearing technologies and evidence-based management strategies to improve larval health, survival rate, cocoon quality, and overall commercial performance, thereby ensuring the sericulture industry's long-term sustainability and competitiveness.
Keywords: Sericulture; Bombyx mori L.; Mulberry silkworm; Rearing management; Cocoon quality; Disease management; Sustainable sericulture; Precision farming; Silkworm health; Commercial silk production.
Keywords: Sericulture; Bombyx mori L.; Mulberry silkworm; Rearing management; Cocoon quality; Disease management; Sustainable sericulture; Precision farming; Silkworm health; Commercial silk production.
Abstract
“Experimental Investigation On Strength Characteristics Of Concrete With GGBS And Metakaolin”
Darshana Chavan, Dr. Ansari Fatima Uz Zehra
DOI: 10.17148/IARJSET.2026.13706
Abstract: This study looks at how to make M30 grade concrete in a sustainable way by using less Ordinary Portland Cement and adding Ground Granulated Blast-Furnace Slag and Metakaolin. The goal is to reduce the impact that making traditional cement has on the environment without making the concrete weaker.The researchers are comparing a mix that is 100 percent Ordinary Portland Cement to three other mixes. One mix has 25 percent less Ordinary Portland Cement. Is replaced with 20 percent Ground Granulated Blast-Furnace Slag and 5 percent Metakaolin. Another mix has 35 percent less Ordinary Portland Cement. Is replaced with 25 percent Ground Granulated Blast-Furnace Slag and 10 percent Metakaolin. The last mix has 45 percent less Ordinary Portland Cement. Is replaced with 35 percent Ground Granulated Blast-Furnace Slag and 15 percent Metakaolin. They are testing the strength of these M30 grade concrete mixes at 7 days, 14 days and 28 days to see how strong they are. They are also testing how well the concrete can bend and stretch. The study wants to find the mix of Ground Granulated Blast-Furnace Slag and Metakaolin that is good, for the environment and still makes strong M30 grade concrete that can be used to build things. This will help us build in a sustainable way.
Keywords: IS 456-2000, IS 10262-2019, OPC53, M30 Grade, Metakaolin, Ground Granulated Blast Furnace, High Strength, Mechanical and Durability Properties.
Keywords: IS 456-2000, IS 10262-2019, OPC53, M30 Grade, Metakaolin, Ground Granulated Blast Furnace, High Strength, Mechanical and Durability Properties.
Abstract
SEISMIC RESPONSE COMPARISON OF DIFFERENT TALL BUILDING STRUCTURAL SYSTEMS
Anshul Jangra, Anju
DOI: 10.17148/IARJSET.2026.13707
Abstract: In order to guarantee structural safety and functionality during earthquakes, tall buildings' seismic performance is crucial. Moment-Resisting Frames (MRF), Frame-Shear Wall Systems (perimeter and corner placements), Braced Frames, Outrigger Systems, and Tube-in-Tube Systems are the six structural configurations whose seismic response is examined in this study. Comparing their efficacy in terms of stiffness, base shear, lateral displacement, storey drift, and reinforcement need is the goal.
To ensure fair comparison, three-dimensional building models were created in ETABS utilizing consistent plan dimensions, member sizes, and material attributes. The Response Spectrum Method was used to apply seismic inputs in accordance with IS 1893 (Part 1): 2016. To assess seismic performance across systems, both linear static and dynamic evaluations were performed. Findings indicate that shear wall-based and core-reinforced systems such as Tube-in-Tube and Outrigger exhibit superior stiffness and reduced displacement. Braced frames provide moderate drift control, while moment-resisting frames are the most flexible, resulting in higher displacements. Column reinforcement demand varies significantly across systems, influenced by stiffness and force redistribution.
This study offers useful insights into the structural efficiency and seismic behavior of different systems, supporting informed decision-making in the design of tall buildings for earthquake-prone regions.
Keywords: Seismic performance, tall buildings, structural systems, Response Spectrum Method, lateral displacement, storey drift, reinforcement demand shear
To ensure fair comparison, three-dimensional building models were created in ETABS utilizing consistent plan dimensions, member sizes, and material attributes. The Response Spectrum Method was used to apply seismic inputs in accordance with IS 1893 (Part 1): 2016. To assess seismic performance across systems, both linear static and dynamic evaluations were performed. Findings indicate that shear wall-based and core-reinforced systems such as Tube-in-Tube and Outrigger exhibit superior stiffness and reduced displacement. Braced frames provide moderate drift control, while moment-resisting frames are the most flexible, resulting in higher displacements. Column reinforcement demand varies significantly across systems, influenced by stiffness and force redistribution.
This study offers useful insights into the structural efficiency and seismic behavior of different systems, supporting informed decision-making in the design of tall buildings for earthquake-prone regions.
Keywords: Seismic performance, tall buildings, structural systems, Response Spectrum Method, lateral displacement, storey drift, reinforcement demand shear
Abstract
ESG-ORIENTED ALTERNATIVE FUELS RESEARCH FACILITY (AFRF) METHODOLOGY FOR SUSTAINABLE WASTE MANAGEMENT IN PHARMACEUTICAL MANUFACTURING
Sowmiya S*, V. M. Madhavan, Senthilkumar D, Muralidharan K
DOI: 10.17148/IARJSET.2026.13708
Abstract: Pharmaceutical manufacturing generates complex hazardous waste streams, including expired drugs, effluent treatment plant (ETP) sludge, spent solvents, used oils, and off-specification products, posing significant environmental, social, and governance (ESG) challenges. Conventional disposal methods such as incineration and landfilling result in emissions, leachate generation, and resource loss. This study proposes an Alternative Fuels Research Facility (AFRF) methodology that integrates waste segregation, pre-treatment, thermal conversion (pyrolysis, gasification, distillation, and controlled incineration), and digital traceability using IoT and blockchain. HAZOP and FMEA were conducted for process safety. Results demonstrate 72% overall energy recovery efficiency, 90% waste volume reduction, 78% solvent recovery, 61% reduction in greenhouse gas emissions, and 88% reduction in landfill dependency. ESG performance improved across all pillars (Environmental +86%, Social +61%, Governance +87%). The AFRF offers a scalable, economically viable pathway (3.5-year payback) toward circular economy practices in the pharmaceutical industry.
Keywords: Alternative Fuels Research Facility, pharmaceutical waste, circular economy, ESG, thermal conversion, digital traceability
Highlights:
• Proposes holistic AFRF methodology for multi-stream pharmaceutical waste valorization.
Keywords: Alternative Fuels Research Facility, pharmaceutical waste, circular economy, ESG, thermal conversion, digital traceability
Highlights:
• Proposes holistic AFRF methodology for multi-stream pharmaceutical waste valorization.
Abstract
An Edge-AI Powered Micro-Seismic Digital Twin for Predicting Dynamic Rockburst Risks in Deep Hard-Rock Excavations
Ashok Prajapat, Kratika Verma
DOI: 10.17148/IARJSET.2026.13709
Abstract: As mining operations aggressively push past depths of 2 km to 3 km, underground excavations encounter extreme, non-linear geo-stresses. At these depths, traditional microseismic (MS) systems fail to predict catastrophic, sudden rockburst events due to data transmission latency, manual waveform processing bottlenecks, and static geological models. This paper reviews the emerging paradigm of shifting from centralized cloud processing to decentralized, real- time edge computing integrated into high-fidelity Digital Twins (DTs). By processing massive, high-frequency waveform data directly at the underground sensor level (Edge-AI) and feeding these localized dynamic metrics into a continuously updating 3D physical-virtual model, modern deep mining can achieve automated, low-latency, and predictive hazard zoning. This review contextualizes the transition from legacy empirical seismic monitoring to dynamic, event-driven Edge-AI systems, identifying critical bottlenecks in micro-seismic wave classification, edge-hardware survival, and real- time bidirectional data synchronization.
Abstract
Designing Multi-Layered Waste-Derived Technosols for Long-Term In-Situ Passive Treatment of Acid Mine Drainage
Ashok Prajapat, Kratika Verma
DOI: 10.17148/IARJSET.2026.13710
Abstract: Acid Mine Drainage (ARD/AMD) remains one of the most severe environmental liabilities in the mining sector, characterized by extreme acidity (𝑝𝐻 < 3) and toxic heavy metal loads. Active chemical neutralization is economically unsustainable post-mine closure. This review synthesizes recent advances in constructing multi-layered, waste-derived Technosols—engineered soils constructed from industrial and municipal byproducts—designed for long- term, passive, in-situ treatment of AMD. We evaluate the synergistic mechanisms of combining alkaline wastes (e.g., steel slag, fly ash, bauxite residue) with organic wastes (e.g., spent mushroom compost, anaerobic digestate) to promote concurrent chemical neutralization, sorption, and biological sulfate reduction. This paper outlines the structural layering dynamics, biogeochemical pathways, and critical engineering bottlenecks governing system longevity, hydraulic conductivity, and substrate passivation.
Abstract
Triboelectric Fluidized-Bed Separation of Ultra-Fine Critical Minerals in Desert Climates
Ashok Prajapat, Kratika Verma
DOI: 10.17148/IARJSET.2026.13711
Abstract: The green energy transition has triggered an unprecedented global demand for critical minerals, including copper (𝐶𝑢), lithium (𝐿𝑖), rare earth elements (𝑅𝐸𝐸𝑠), and cobalt (𝐶𝑜). However, a massive portion of remaining reserves are located in hyper-arid desert regions where conventional wet froth flotation is severely constrained by extreme water scarcity and environmental regulations against tailing dams. This paper reviews the state-of-the-art in Triboelectric Fluidized-Bed Separation, a completely dry beneficiation paradigm tailored for ultra-fine particle sizes (< 45 𝜇m). We analyze the micro-mechanics of gas-solid fluidization for differential surface charge accumulation (contact electrification) and trace how mineral-specific work functions dictate particle trajectories through high-intensity electric fields. Finally, we evaluate the distinct environmental challenges of desert climates—specifically ambient humidity fluctuations and thermal stresses—on separation efficiency and fine-particle agglomeration.
Abstract
Multi-Agent Reinforcement Learning (MARL) for "Open Autonomy" in Mixed-Fleet Underground Mining
Ashok Prajapat, Kratika Verma
DOI: 10.17148/IARJSET.2026.13712
Abstract: Modern underground mining operations are progressively integrating autonomous haulage systems (AHS) and automated Load-Haul-Dump (LHD) vehicles. However, current commercial autonomy architectures are "closed systems" that rely on rigid, centralized dispatch servers and strict zone isolation, requiring human-driven utility vehicles to completely clear an area before automated fleets can operate. This paper reviews the emerging frontier of Multi-Agent Reinforcement Learning (MARL) designed to achieve "Open Autonomy"—a decentralized paradigm where heterogeneous, mixed fleets of autonomous and human-operated vehicles dynamically co-exist and cooperate. We evaluate MARL framework configurations (e.g., Centralized Training with Decentralized Execution [CTDE]), multi- agent communication topologies under intermittent subsurface wireless connectivity, and the integration of game- theoretic collision avoidance algorithms within tight, single-lane subterranean environments.
Abstract
Waste Treatment Plants and Remediation Strategies in Indian Mining Areas: A Technical Review of Effluent Management, Acid Mine Drainage, Tailings Stacking, and Circular-Economy Recovery
Ashok Prajapat, Puneet Sharma, Kratika Verma
DOI: 10.17148/IARJSET.2026.13713
Abstract: India's mining sector, a cornerstone of national industrial growth, generates vast volumes of solid, liquid, and gaseous waste at a time when per-capita water availability has fallen from roughly 5,000 cubic metres in 1950 to about 1,500 cubic metres today. This review synthesises current practice in mine-water treatment, acid mine drainage (AMD) bioremediation, tailings management, sector-specific hazardous waste handling, and the emerging circular-economy recovery of critical minerals from legacy waste. Evidence is drawn from operating case studies including Coal India Limited's community water-utilisation programmes, IIT Guwahati's constructed-wetland research, Hindustan Zinc's Dry Tailing Plant at Zawar, Tata Steel's hexavalent-chromium reduction facility at Sukinda, and the Uranium Corporation of India's radiological effluent controls at Jaduguda. The review finds that the sector is transitioning decisively from passive containment toward active resource recovery, Zero Liquid Discharge (ZLD) systems, and stacked dry tailings, while simultaneously identifying legacy dumps as a viable secondary source of nickel, cobalt, gallium, vanadium, and rare- earth elements.
Keywords: acid mine drainage, dry tailings stacking, hexavalent chromium, constructed wetlands, Zero Liquid Discharge, critical mineral recovery, red mud, jarosite.
Keywords: acid mine drainage, dry tailings stacking, hexavalent chromium, constructed wetlands, Zero Liquid Discharge, critical mineral recovery, red mud, jarosite.
Abstract
Evaluating Farmer Income Enhancement Through the Godhan Nyay Yojana: A Village-Level Study from Sarkhor, Baloda Bazar District, Chhattisgarh
Rakesh Kumar Ghritlahare*, Devendra Singh Porte
DOI: 10.17148/IARJSET.2026.13714
Abstract: Rural communities in Chhattisgarh, India, have historically depended on agriculture and allied livestock activities for sustenance, yet persistent income insufficiency, inadequate infrastructure, and structural unemployment have kept millions below the poverty threshold. Against this backdrop, the Chhattisgarh state government launched the Godhan Nyay Yojana (GNY) on 20 July 2020 — a pioneering welfare programme that procures bovine dung from cattle keepers at a government-fixed price of ₹2 per kilogram and converts it into vermicompost through Women Self-Help Groups (WSHGs). The present study examines the tangible income benefits accruing to a selected cohort of 91 registered farming beneficiaries in Sarkhor village, Baloda Bazar–Bhatapara district. Using structured field interviews and primary data collected from Gothan records, the research documents a cumulative cow-dung procurement volume of 92,849.76 kg and a total disbursement of ₹1,85,699.52 across 32 active cow-dung vendors. The findings confirm that the programme has (a) generated a meaningful supplementary income stream for smallholder and marginal farmers, (b) curtailed open grazing and associated road hazards, (c) empowered rural women through participatory production roles, and (d) fostered a shift from synthetic to organic fertilisation. The study concludes that the GNY model offers a replicable framework for holistic rural livelihood enhancement with strong scalability potential.
Keywords: Godhan Nyay Yojana, bovine-dung procurement, vermicompost, Women Self-Help Groups, rural income, Chhattisgarh.
Keywords: Godhan Nyay Yojana, bovine-dung procurement, vermicompost, Women Self-Help Groups, rural income, Chhattisgarh.
Abstract
Right to light and air: Applicability with respect to prevailing Building Rules in India
Madhumita Roy
DOI: 10.17148/IARJSET.2026.13715
Abstract: The 'Right to Light and Air' doctrine is based on English common law called a right to ancient lights, which was one of the earliest legal recognitions of an environmental amenity as a property right. India has instituted this right through various legislative, judicial and regulatory through, Inter alia, the Transfer of Property Act (1882), the Easements Act (1882), National Building Code (NBC) of India (2005, revised 2016) and building bylaws of various States. This research paper investigates the legal basis, technical standards, and regulatory lack of a right to light and air in India. It looks at how the rights guaranteed under the Development Control Regulations (DCR) are frequently denied due to the failure of setback rules, Floor Area Ratio (FAR/FSI), height restrictions, and ventilation standards amid rapid urbanisation. The study highlights landmark judicial pronouncements, international best practices and germane solar access rights in the wake of India’s net-zero energy aspirations. Moreover, the study identifies architectural practices that ultimately affect nearby premises and residents based on experience. Based on an eminent Indian city, the paper illustrates specific facets where imprinted standards markedly depart from reality. The paper sets forth a framework by which solar easement law and day-lighting-based urban design in India can be effectively strengthened.
Keywords: Ancient Light, Easement Act 1882, NBC 2016, FAR, Setback Regulations, Urban Heat Island, RERA, Building Byelaws, Solar Access Rights
Keywords: Ancient Light, Easement Act 1882, NBC 2016, FAR, Setback Regulations, Urban Heat Island, RERA, Building Byelaws, Solar Access Rights
Abstract
A Multi-Dataset Stacked Ensemble Framework for Multi-Class Lung Cancer Classification
Dhaval J. Rana, Keyur Rana
DOI: 10.17148/IARJSET.2026.13716
Abstract: Lung cancer is a major contributor to cancer-related deaths worldwide, emphasizing the need for accurate and early diagnostic systems. Although deep learning–based approaches have shown promising performance in classifying lung cancer from computed tomography (CT) images, many existing methods are limited by reliance on single datasets and restricted class categorization. To overcome these challenges, this paper introduces SEMLCC-5X, a stacked ensemble framework for multi-class lung cancer classification. The proposed approach integrates multi-source data by combining two publicly available CT datasets, improving data diversity and generalization. The classification task is extended to a fine-grained five-class problem, including normal, benign, adenocarcinoma, large-cell carcinoma, and squamous-cell carcinoma. The framework employs transfer learning with fine-tuning to train four deep learning base models by integrating custom CNN classification layers with the pre-trained Xception, VGG19, EfficientNetB7, and InceptionV3 architectures. These models are combined using a stacking strategy with a logistic regression meta-learner for optimized prediction aggregation. The SEMLCC-5X model is evaluated using comprehensive metrics, including accuracy, precision, recall, F1-score, and AUC. Experimental results demonstrate superior performance, achieving an accuracy of 98.06% and a macro AUC of 99.58%, outperforming individual models and conventional ensemble methods. In conclusion, SEMLCC-5X provides a robust and accurate framework for multi-class lung cancer classification, with strong potential for integration into computer-aided diagnostic systems and clinical workflows.
Keywords: Medical Image Analysis, Computed Tomography (CT), Stacked Ensemble Learning, Deep Learning, Multi- Class Lung Cancer Classification.
Keywords: Medical Image Analysis, Computed Tomography (CT), Stacked Ensemble Learning, Deep Learning, Multi- Class Lung Cancer Classification.
Abstract
FROM CONTENT TO CART: THE INFLUENCE OF INSTAGRAM AND YOUTUBE SHOPPING FEATURES ON CONSUMER PURCHASE DECISIONS
Dr.S. MOHAMED IMRAN SHARIF, Dr. M. BALASUBRAMANIAN
DOI: 10.17148/IARJSET.2026.13717
Abstract: The rapid evolution of social commerce has transformed Instagram and YouTube from content-sharing platforms into integrated digital marketplaces that influence consumer purchasing behaviour. This study examines how shopping features embedded within these platforms affect consumer purchase decisions by analyzing the roles of consumer trust, consumer engagement, and perceived convenience. The study is grounded in the Stimulus–Organism– Response (S–O–R) Theory, which explains how external digital stimuli shape consumers’ psychological responses and purchasing behaviour. A quantitative research design was adopted, and primary data were collected from 100 respondents using a structured questionnaire. Descriptive statistics, correlation analysis, and multiple regression analysis were employed to examine the relationships among the study variables. The findings reveal that social media shopping features significantly influence consumer purchase decisions, with integrated shopping functionalities emerging as the strongest predictor. Consumer trust, engagement, and perceived convenience also demonstrated significant positive effects on purchasing behaviour, indicating that consumers are more likely to complete purchases when shopping experiences are credible, interactive, and convenient. The regression model explained a substantial proportion of the variation in consumer purchase decisions, supporting the proposed conceptual framework and validating the study hypotheses. The findings contribute to the growing literature on social commerce by providing an integrated analysis of Instagram and YouTube shopping features within a single empirical framework. The study also offers practical insights for businesses, digital marketers, content creators, and platform developers seeking to design effective social commerce strategies that convert digital engagement into successful consumer purchases.
Keywords: Social Commerce, Instagram Shopping, YouTube Shopping, Consumer Purchase Decision, Consumer Trust, Digital Marketing.
Keywords: Social Commerce, Instagram Shopping, YouTube Shopping, Consumer Purchase Decision, Consumer Trust, Digital Marketing.
Abstract
BREAST CANCER RISK ASSESSMENT USING RANDOM FOREST CLASSIFICATION AND TENSORFLOW LITE BASED ANDROID DEPLOYMENT
THATHA.USHA, T. PAVAN KUMAR, V. NARENDRA, DR K. SREENIVASA REDDY, G.SWATHI
DOI: 10.17148/IARJSET.2026.13718
Abstract: Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide, making early detection and accurate diagnosis essential for improving patient survival rates. Traditional diagnostic methods often require specialised medical expertise, advanced healthcare facilities, and considerable processing time, which may lead to delays in diagnosis and treatment. To address these challenges, this study proposes an AI-enabled Android application for breast cancer prediction using a Random Forest machine learning classifier. The proposed system utilises a breast cancer diagnostic dataset that undergoes preprocessing, feature selection, and normalisation to enhance data quality and model performance. The dataset is divided into training and testing subsets, and a Random Forest classifier is trained to accurately classify tumours as benign or malignant. The trained model is evaluated using performance metrics such as accuracy, precision, recall, F1-score, and confusion matrix to ensure reliable prediction capability. To enable real-time mobile deployment, the optimised model is converted into TensorFlow Lite format and integrated into an Android application. The mobile application allows users to enter diagnostic parameters and instantly receive prediction results without requiring complex computational resources. Experimental results demonstrate that the proposed model achieves high classification accuracy and effectively distinguishes between benign and malignant cases with minimal prediction errors. The integration of machine learning and mobile healthcare technology provides a cost-effective, accessible, and user-friendly solution for breast cancer risk assessment. The proposed system has the potential to support healthcare professionals in clinical decision-making, facilitate early disease detection, and improve overall healthcare outcomes through intelligent and portable diagnostic assistance.
Keywords: Breast Cancer Prediction, Random Forest Classifier, Machine Learning, Android Application, TensorFlow Lite, Artificial Intelligence, Healthcare Analytics, Early Disease Detection.
Keywords: Breast Cancer Prediction, Random Forest Classifier, Machine Learning, Android Application, TensorFlow Lite, Artificial Intelligence, Healthcare Analytics, Early Disease Detection.
Abstract
Deep Learning and Hybrid Time-Series Forecasting for Intelligent Traffic Management and Sustainable Urban Mobility
Shruti Vohra*, Smita Verma
DOI: 10.17148/IARJSET.2026.13719
Abstract: Urban traffic congestion is a major barrier to sustainable mobility because it increases travel delay, fuel consumption, emissions, crash exposure, and uncertainty in daily travel. Recent advances in connected vehicles, Internet of Things (IoT) infrastructure, urban sensing, navigation platforms, and deep learning have created new opportunities for intelligent traffic management systems that can forecast congestion before it spreads and respond through signal control, route guidance, and demand management. This paper presents a research-oriented review and conceptual framework for deep learning and hybrid time-series forecasting in intelligent traffic management and sustainable urban mobility. It uses the supplied reference papers on urban traffic management, connected and automated vehicle signal control, smart-city traffic optimization, anomaly-aware deep learning, hybrid CNN-LSTM, CNN-LSTM-GRU, GSA-LSTM, SDLSTM-ARIMA, attention-based GRU-LSTM, and CPO-CNN-LSTM-Attention models. The paper argues that future traffic systems should combine temporal learning, spatial network representation, anomaly detection, contextual data such as weather and holidays, optimization algorithms, and sustainability evaluation. For better presentation, the paper also includes comparative tables and conceptual figures that summarize model categories, architecture flow, and sustainability links.
Keywords: Deep Learning; Hybrid Forecasting; Time-Series Prediction; Intelligent Traffic Management; Sustainable Urban Mobility.
Keywords: Deep Learning; Hybrid Forecasting; Time-Series Prediction; Intelligent Traffic Management; Sustainable Urban Mobility.
Abstract
INFLUENCE OF DIGITAL MEDIA ON SUSTAINABLE CONSUMER BEHAVIOUR
Dr. S. MOHAMED IMRAN SHARIF, DR. RAJA MOHAMED. M.A, Mr. A. SYED MYDEEN BUHARI
DOI: 10.17148/IARJSET.2026.13720
Abstract: Digital media has emerged as a powerful tool in shaping consumer awareness and attitudes toward sustainability. With increasing environmental concerns, understanding how digital platforms influence sustainable consumer behavior has become an important area of inquiry. This study examines the influence of digital media on sustainable consumer behavior through an exploratory empirical approach. Primary data were collected from a mixed group of students and working professionals using a structured questionnaire. The study employs descriptive statistics, correlation analysis, and one-way analysis of variance to examine the relationship between digital media influence and sustainable consumer behavior, as well as differences across demographic groups. The findings indicate a positive association between exposure to sustainability-related digital content and consumers’ environmentally responsible purchasing behavior. Significant variations in sustainable consumer behavior were also observed across age groups and levels of digital media usage. The study highlights the growing role of digital communication in promoting sustainable consumption and offers insights for policymakers, marketers, and sustainability advocates to design more effective digital engagement strategies. As an exploratory study, the findings provide a foundation for future research using broader samples and advanced analytical techniques.
Keywords: Digital Media, Sustainable Consumer Behavior, Green Consumption, Consumer Awareness, Environmental Sustainability, Exploratory Study.
Keywords: Digital Media, Sustainable Consumer Behavior, Green Consumption, Consumer Awareness, Environmental Sustainability, Exploratory Study.
Abstract
“Analysis on Bangalore Traffic”
Dr Mohana Kumari, Agraharam Sri vidya, Terla Laxmi Narasimha Charan, Srujan P Rao, Akula Sri Surya Durga Manisha, Pardha sai Puripanda, Neeraj Jayan Moolekkattil
DOI: 10.17148/IARJSET.2026.13721
Abstract: Bengaluru faces severe traffic congestion due to rapid urban growth and increasing vehicle numbers. This study analyzes public views on key causes of traffic issues, focusing on factors such as abrupt parking of vehicles, road encroachment, poorly planned roads, peak hours, and mixed-speed vehicle movement and congestion levels. Data was collected from 150 respondents using a Google Form and analyzed through SPSS using Descriptive Statistics, T-test, ANOVA, Chi-square, and Correlation. Our analysis is largely based on descriptive statistical tests. Results showed a high mean score (M = 3.82) for abrupt parking of vehicles as a cause of traffic problems, indicating strong agreement among respondents.Tests also showed no significant difference across demographic groups, but a significant positive relationship was found between road encroachment and lateness (p = 0.035). The study concludes that infrastructure and behavior-based issues, rather than demographics, are the major contributors to Bengaluru traffic problems and require better parking control and road management.
Keywords: Bangalore Traffic, Congestion, Travel time, Abrupt parking, Road Encroachment, Traffic Management, SPSS Analysis.
Keywords: Bangalore Traffic, Congestion, Travel time, Abrupt parking, Road Encroachment, Traffic Management, SPSS Analysis.
Abstract
STUDY ON CONSUMER’S PERCEPTION TOWARDS PURCHASING GROCERIES THROUGH ONLINE
Dr. MOHANA KUMARI, ABDUL ASEEM K, BHARATHVAJH V G, GOWTHAM ELANGOVAN, HARI VISHNU S S, THIRUMALAIVASAN R, YUVARAJ KRISHNA R
DOI: 10.17148/IARJSET.2026.13722
Abstract: This study explores how people feel about buying groceries online in India’s growing e-commerce market. It looks at important factors that influence customers such as convenience, saving time, product freshness, variety, pricing, value for money, delivery reliability, and return policies. Using quantitative methods like Independent Sample T-Tests and One-way ANOVA, the research found that gender, age, income, and family size do not create major differences in how consumers view online grocery shopping. This means people across all groups share similar positive perceptions, showing that online grocery shopping is becoming widely accepted. The study highlights the need for companies to offer reliable service, easy technology, and trustworthy systems to keep customers satisfied and encourage long-term use of online grocery platforms.
Keywords: Consumer perception, Online grocery purchasing behaviour, Convenience, Time saving, Effort saving, Product freshness, Product availability, Price perception, Value for money, Delivery speed, Packaging trust, Return policies, Digital shopping experience, Customer satisfaction, Service reliability and Quality assurance.
Keywords: Consumer perception, Online grocery purchasing behaviour, Convenience, Time saving, Effort saving, Product freshness, Product availability, Price perception, Value for money, Delivery speed, Packaging trust, Return policies, Digital shopping experience, Customer satisfaction, Service reliability and Quality assurance.
Abstract
AUTOMATIC HEADLIGHT INTENSITY CONTROL FOR VEHICLE TO AVOID ACCIDENTS
Dr. Vemuri Sai Srikanth, B Ajay Kumar
DOI: 10.17148/IARJSET.2026.13723
Abstract: This project focuses on reducing night-time road accidents caused by glare from high-beam headlights of oncoming vehicles. The proposed system uses light sensors to detect the intensity of incoming light and automatically switches the vehicle’s headlights from high beam to low beam when excessive glare is detected. Once the intensity of light decreases, the system restores the headlights back to high beam for better visibility. A microcontroller is used to control the entire process, ensuring quick and accurate response without driver intervention. This automatic headlight control system enhances driving safety by minimizing temporary blindness caused by glare, while also improving overall comfort and visibility for drivers. The design is simple, cost effective, and easy to implement in modern vehicles. By reducing human effort and reaction time, this system provides an efficient solution for safer night driving conditions and helps in preventing accidents on highways and busy roads.
Keywords: Automatic Headlight Control, High Beam/Low Beam Switching, Light Sensor, Microcontroller, Glare Reduction, Night Driving Safety, Vehicle Safety System, Intelligent Lighting System, Accident Prevention.
Keywords: Automatic Headlight Control, High Beam/Low Beam Switching, Light Sensor, Microcontroller, Glare Reduction, Night Driving Safety, Vehicle Safety System, Intelligent Lighting System, Accident Prevention.
Abstract
Qualitative Phytochemical Analysis on Cordia Obliqua Willd.
R. Raja Jency Esther*, D. Ahino Mary, S. Juliet Santha Jothi
DOI: 10.17148/IARJSET.2026.13724
Abstract: Cordia obliqua is a member of the Codiaceae family.It is a small, fast-growing, perennial tree. This plant is used as an antimicrobial, hypotensive, respiratory stimulant, and anti-inflammatory medication. It also possesses antibacterial and antifungal properties. Fresh parts of the Cordia obliqua plant, including the stem, petiole, leaf, and bark, were gathered separately in large quantities. These parts were then dried in the shade and ground into a coarse powder.The powdered plant material, totaling 75 grams, was extracted using ethanol for 48 hours through a process known as cold maceration. These extracts were then used for preliminary phytochemical analysis. The ethanolic extracts of Cordia obliqua showed positive results in several phytochemical tests. The compounds identified in the extract include alkaloids, phenols, terpenes, saponins, tannins, glycosides, flavonoids, and steroids. From this study, it can be concluded that these compounds are important secondary metabolites and are likely responsible for the medicinal properties of the plant.
Keywords: Cordia obliqua, Codiaceae, phytochemical, secondary metabolites.
Keywords: Cordia obliqua, Codiaceae, phytochemical, secondary metabolites.
Abstract
संत रैदास के जीवन मूल्यों की प्रासंगिकता
कृति कुमारी
DOI: 10.17148/IARJSET.2026.13725
सारांश: भारत
के इतिहास में मध्यकालीन काल धार्मिक संघर्षों का युग था, और सांस्कृतिक संकटों से गुजर रहे खंडित समाज का पुनर्गठन
या सुधार केवल धर्म की भाषा के माध्यम से ही किया जा सकता था। इसलिए सभी सामाजिक
सुधारकों या महान व्यक्तित्वों को समाज द्वारा राजनीतिक विचारकों के रूप में नहीं, बल्कि केवल धार्मिक भक्तों, धार्मिक शिक्षकों या संतों के रूप में ही स्वीकार किया जा
सकता था। संत शिरोमणि गुरु रविदास भी उनमें से एक थे। प्रारंभ में वे क्षत्रिय
चंवर वंश से जुड़े चमार समुदाय के संत थे, और
यहां तक कि ब्राह्मण भी उनके घर से भोजन और दान स्वीकार करते थे, लेकिन जब सिकंदर लोदी ने इस्लाम स्वीकार करने से इनकार करने
पर उनका अपमान किया और उन्हें “चमार” कहकर चमड़े का काम करने के लिए मजबूर किया, तब लोगों ने उन्हें “अछूत” कहना शुरू कर दिया। इसी प्रकार
के उत्पीड़न, प्रताड़ना, शोषण और विनाश की घटनाएं कई अन्य स्वाभिमानी समुदायों के
साथ भी हुईं, और वे सभी समुदाय जिन्हें विदेशी मुस्लिम
शासकों द्वारा जबरन कुचल दिया गया, “दलित”
कहलाने लगे।
मुख्य शब्द: अछूत, उत्पीड़न, प्रताड़ना, शोषण और विनाश आदि।
Abstract
संत रैदास का साहित्यिक अवदान
कृति कुमारी
DOI: 10.17148/IARJSET.2026.13726
संत’
शब्द संस्कृत भाषा से व्युत्पन्न है, यद्यपि संस्कृत में इसका प्रयोग बहुत कम मिलता है।
आधुनिक इंडो-आर्य भाषाओं में, विशेषकर सगुण और निर्गुण भक्तिकालीन साहित्य में, ’संत’ शब्द का व्यापक रूप से प्रयोग हुआ है। इस प्रकार, ’संत काव्य’ से आशय उन कवियों और चिंतकों की
काव्य-रचनाओं से है जिन्हें आचार्य रामचंद्र शुक्ल ने निर्गुण परंपरा की
’ज्ञानाश्रयी’ शाखा के अंतर्गत रखा है। प्राचीन काल से ही भारत की भूमि संतों, ऋषियों और आध्यात्मिक साधकों की पवित्र भूमि रही है।
समस्त मानवता के कल्याण के
Abstract
Language And Background to Language Learning and Teaching
J. Benita Selvakumari
DOI: 10.17148/IARJSET.2026.13727
Abstract: People use spoken and written language to communicate with each other especially students and teachers. In addition to that students learn language students learn to write, to read and to communicate. Now-a-days children learn both spoken and written language. They develop the use of grammatical structures and vocabulary. Students use language to communicate and to learn, to strive to make sense in this modern world. Informally children develop language. Students enhance their language learning and they know more new & context which leads to increase sophistication. Students are given training to develop their language fluency and proficiency. Always students who have rich learning experience leave school have good desire to continue their knowledge skills with good interests.
Learning, Teaching are interrelated with one another. Students use language to make sense in order to bring the entire world with prior knowledge, experiences and beliefs. To play an active role in various communities of learners within and beyond the classrooms. Language enables the students to develop and to reflect their thinking and learning processes. The students develop their language skills. Each student understands and appreciate language. They use it very confidently and use all the opportunities to listen, speak, write, read and represent.
Keywords: Communicate, Proficiency, Learning, Experience.
Learning, Teaching are interrelated with one another. Students use language to make sense in order to bring the entire world with prior knowledge, experiences and beliefs. To play an active role in various communities of learners within and beyond the classrooms. Language enables the students to develop and to reflect their thinking and learning processes. The students develop their language skills. Each student understands and appreciate language. They use it very confidently and use all the opportunities to listen, speak, write, read and represent.
Keywords: Communicate, Proficiency, Learning, Experience.
Abstract
Finite Element Modelling of Reinforced Concrete and Advanced-Material Beam–Column Joints: A Review with a Gradient-Regularised Microplane Damage–Plasticity Case Study
Ankita Dhananjay Kulkarni, Dr. G.R. Gandhe
DOI: 10.17148/IARJSET.2026.13728
Abstract: Reinforced concrete (RC) beam–column joints remain one of the most extensively investigated yet persistently challenging regions of framed structures, since they must transfer large, reversing shear forces through a compact, congested volume of concrete. This paper reviews the evolution of finite element (FE) approaches used to simulate beam– column joint behaviour, tracing the progression from classical pressure-sensitive plasticity models through continuum damage mechanics to coupled plasticity–damage and microplane formulations, culminating in the Gradient-Enhanced Plasticity-Damage Microplane (GPDM) model now implemented in ANSYS as the CPT215/CPT216 element families. Building on this theoretical foundation, the review synthesises recent experimental and numerical studies across three connection families: conventional reinforced concrete joints, joints constructed with Ultra-High-Performance Concrete (UHPC) and steel-fibre-reinforced concrete (SFRC), and steel or steel–concrete composite beam-to-column connections. To ground the review in a concrete demonstration, the paper presents a case study in which the GPDM model was applied to a benchmark exterior T-shaped RC joint (specimen JA-0, after Chalioris et al., 2008) under monotonic displacement- controlled loading. The FE model predicted a peak reaction force of 32.9 kN at 33.8 mm beam-tip displacement, within 4.4% of the experimentally reported peak, and reproduced the joint’s characteristic elastic, hardening and post-peak softening phases, together with a shear-dominated failure mechanism consistent with the reported damage pattern. The synthesis identifies three converging research gaps — limited systematic mesh-sensitivity studies of gradient-regularised models for RC joints, an immature numerical toolkit for UHPC and SFRC connections, and under-exploited cross- fertilisation between steel/composite and RC joint research — and outlines directions for future work, including cyclic loading, bond-slip modelling and full-frame extensions.
Keywords: Beam–column joint; finite element analysis; ANSYS; microplane theory; damage plasticity; Ultra-High- Performance Concrete; seismic performance; mesh sensitivity.
Keywords: Beam–column joint; finite element analysis; ANSYS; microplane theory; damage plasticity; Ultra-High- Performance Concrete; seismic performance; mesh sensitivity.
Abstract
Education Policies and Systemic Reforms for the 21st Century: Leading Schools into the Future
Dr. Layal Abou Mrad and Fatima Itani
DOI: 10.17148/IARJSET.2026.13729
Abstract: In an age marked by epistemic flux and ontological recalibration, the role of educational leadership stands at an inflection point. This chapter interrogates the paradigmatic limitations of current school leadership policies and reimagines them as dynamic instruments of systemic transformation. By unshackling leadership preparation, selection, and function from technocratic orthodoxy, it proposes a radical reorientation: one where school leaders emerge not as institutional custodians but as architects of ecological, responsive, and ethically situated learning environments. The discourse expands to middle leaders, conceptualized not as transactional conduits but as mitochondria—intracellular engines of intellectual energy—tasked with cultivating distributed leadership and seeding teacher agency at scale. Drawing on global literature, critical policy frameworks, and post humanist sensibilities, the chapter advances a nuanced vision for leadership in an era increasingly shaped by artificial intelligence, planetary interdependence, and deepening educational inequities. In doing so, it calls for policy architectures that transcend managerialism and instead privilege pedagogical imagination, systemic empathy, and the emergence of schools as civic and cognitive commons. The result is an invitation to re-story leadership not as a function, but as an ethos—an ontological commitment to justice, possibility, and collective futurity.
Keywords: educational policy innovation, transformational leadership, middle leadership, teacher leadership, student leadership
Keywords: educational policy innovation, transformational leadership, middle leadership, teacher leadership, student leadership
Abstract
Quantum Computing and IOT Integration For Next-Generation Smart Systems
Dr. T. Vamshi Mohana, P. Sampurna, Salma Begum
DOI: 10.17148/IARJSET.2026.13730
Abstract: The rapid expansion of the Internet of Things (IoT) has transformed the way physical devices communicate, process information, and provide intelligent services. With billions of interconnected devices generating enormous volumes of heterogeneous data, conventional computing architectures increasingly face limitations in computational efficiency, security, optimisation, and scalability. Quantum computing has emerged as a disruptive paradigm capable of solving computationally intensive problems exponentially faster than classical computers for selected problem classes. Integrating quantum computing with IoT offers unprecedented opportunities for intelligent decision-making, real-time optimisation, secure communication, and enhanced machine learning.
Quantum-enhanced IoT (QIoT) combines quantum algorithms, quantum machine learning, quantum communication, and post-quantum cryptography to create highly efficient and secure smart systems. Applications span healthcare, industrial automation, smart cities, intelligent transportation, agriculture, finance, energy management, and autonomous systems. Despite these promising opportunities, practical deployment faces several challenges, including hardware limitations, quantum decoherence, interoperability, high implementation costs, algorithmic complexity, and lack of standardised architectures. Recent research has increasingly shifted from theoretical discussions toward practical hybrid quantum- classical architectures and post-quantum security mechanisms suitable for resource-constrained IoT environments.
This review presents a comprehensive analysis of the integration of quantum computing with IoT systems, discussing enabling technologies, architectures, applications, challenges, research trends, and future directions, and synthesising findings from leading journal publications between 2010 and 2025.
Keywords: Quantum Computing, Internet of Things, Quantum Internet of Things (QIoT), Quantum Machine Learning, Post-Quantum Cryptography, Smart Cities, Smart Healthcare, Industrial IoT, Edge Computing, Artificial Intelligence.
Quantum-enhanced IoT (QIoT) combines quantum algorithms, quantum machine learning, quantum communication, and post-quantum cryptography to create highly efficient and secure smart systems. Applications span healthcare, industrial automation, smart cities, intelligent transportation, agriculture, finance, energy management, and autonomous systems. Despite these promising opportunities, practical deployment faces several challenges, including hardware limitations, quantum decoherence, interoperability, high implementation costs, algorithmic complexity, and lack of standardised architectures. Recent research has increasingly shifted from theoretical discussions toward practical hybrid quantum- classical architectures and post-quantum security mechanisms suitable for resource-constrained IoT environments.
This review presents a comprehensive analysis of the integration of quantum computing with IoT systems, discussing enabling technologies, architectures, applications, challenges, research trends, and future directions, and synthesising findings from leading journal publications between 2010 and 2025.
Keywords: Quantum Computing, Internet of Things, Quantum Internet of Things (QIoT), Quantum Machine Learning, Post-Quantum Cryptography, Smart Cities, Smart Healthcare, Industrial IoT, Edge Computing, Artificial Intelligence.