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VOLUME 13, ISSUE 8, AUGUST 2026
Publication is in progress..
Preparation and Evaluation of Herbal Elixir For Haemoglobin Enhancements
Sakshi Ladkat, Misses Pooja Gaikwad
Chandra Namaskar as a Mind–Body Practice for Improving Youth Lifestyle and Well-Being
Dr. Hemant Pandya, Garvit Choudhary
Ethics, Efficiency and Equity: The Triple Challenge of AI in Sustainable Management
Prof.Gangadhar Hugar, Dr.Rajshekhar Basavapattan, Prof.Sharanabasavareddy Patil
Comparative Assessment of Corporate Social Responsibility Initiatives and Government Welfare Schemes emphasising Orunodoi on Women’s Livelihoods and Empowerment in Assam – Study based on Barak Valley
Subrajyoti Dey, Dr. M. Gangabhushan Molankal
A Comparative Study of TabTransformer and Temporal Fusion Transformer for Loan Approval Prediction Using Static and Temporal Financial Features
Dr. Balaji K, K. Sridhar, Rajinish S
Performance Evaluation of M40 Grade Concrete Using Recycled Coarse Aggregate and Plastic Granules as Sustainable Aggregate Replacements
Sandhya Annasaheb Daud, Rahul S Patil
Comparative Study of Latency-Aware Serverless Function Orchestration using XGBoost and Random Forest Regression Models in Edge– Cloud Environments
Bhavana B R, Lekhana HN, Reshma G
Influence of AI-Powered Personalization on Consumer Purchase Intention: The Mediating Role of Consumer Trust
Dr. Prashantha Kumar O, Dr. Shashidhar S Mahantshetti, Ms. Priya.K, Ms. Anuradha H N
GIS-Based Landfill Site Suitability Analysis in Deogarh Municipal Area: A Geographical Study
Deepak Kumar Prajapat, Dr. Hemendra Singh Shaktawat
Experimental Investigation on Concrete with Partial Replacement of Aggregates by Marble Pieces and Plastic Granules
Jayesh Yashwant Gawade, Prof. R. S. Patil
Effect of Aggregate Size and Specimen Dimensions on Mechanical Performance of Concrete
Vishakha D. Ghuge, Dr.D.H. Tupe
INTELLIGENT MEDICAL AI CHATBOT
DR. BHARATHI M P, YOGESH K K, KRISHNE GOWDA G S
An Efficient Diffusion-Based Framework for Accelerated Image Generation Using Fast-DDPM
Y Vidya Indrasena, Dr C Prakasa Rao
Deep Learning and Kinematic Telematics Integration for Real-Time Pothole Detection and Dynamic Navigation Routing
Sahithyaa Krishna Kumar, Sanjay C, Mark Owen A, Yogeshwar P, R.T. Charulatha
Assessment of Construction Professionals’ Perceptions: A Questionnaire-Based Study
N.Sivapiran, DR.G.Murugesan
Intelligent Auto Scaling and Profit Optimization in Cloud Computing
Pooja M S, Jeevika N, Khushi K Y, Priyanka
Women Safety System Using AI Techniques
Mr. Santhosh Kumar T R, Kavana N, Kavana V M, M Kavana, Namratha S
FPGA Based Low-Power and Area-Efficient 4- bit Arithmetic Logic Unit
Uppula Shirisha, Dr A Mamatha
Fuzzy Logic-Based Clinical Decision Making for Early Detection and Management of Cardiovascular Diseases
Chandrashekhar Diwakar, Ram Kishor
DESIGN AND DEVELOPMENT OF AN OUTDOOR AIR PURIFIERFOR DOMESTIC AND TRAFFIC SIGNAL AREAS
V.Tirumalaiah, K Usha Rani
IoT-Based Saline Bottle Monitoring and Control System Using Wi-Fi and Android Application
Rashmi Tandi, Nidhi Sharma
Entrepreneurial Ecosystems for Sustainable MSME Development: Aligning SDG 8 and SDG 9 in the Context of North Karnataka
Dr. Pushpa Hongal
A Study to Design an Outcome Based Curriculum to Teach English as a Second Language
Dr.C.Ramakrishnan
Fake Profile Detection Using Machine Learning
Dhanush K, Saniya S J, Spoorthi D, Chinmayee K N, Geethanjali S G
PERCEPTION OF LABOUR CONTRACT SOCIETIES ON LABOUR PRODUCTIVITY IN KERALA
Dr.Suguna.S, Riji.T
Abstract
Preparation and Evaluation of Herbal Elixir For Haemoglobin Enhancements
Sakshi Ladkat, Misses Pooja Gaikwad
Abstract: Anemia is one of the most common nutritional disorders caused mainly by iron deficiency and low hemoglobin levels. Conventional iron supplements often produce gastrointestinal side effects such as constipation, nausea, and metallic taste, leading to poor patient compliance. Herbal formulations are considered safer and more acceptable alternatives due to their natural origin and additional nutritional benefits act. Oral dosage forms are most popular among other dosage forms. In terms of bioavailability liquid dosage form is better than that of solid dosage form. Herbal preparation is mainly preferred due to their safety, cost effectiveness and fewer side effects as compared to the synthetic iron supplements. The present study focuses on the preparation and evaluation of herbal elixir for hemoglobin enhancement which containing the extract of pomegranate, beetroot, papaya, amla for increasing the level of hemoglobin or iron in to the blood.It serve as the natural hematinic preparation for hemoglobin enhancement. The prepare formulation evaluated by the organoleptic properties, viscosity, pH, stability and microbial contamination.
Keywords: Hemoglobin enhancement, papaya, pomegranate, amla, beetroot etc.
Keywords: Hemoglobin enhancement, papaya, pomegranate, amla, beetroot etc.
Abstract
Chandra Namaskar as a Mind–Body Practice for Improving Youth Lifestyle and Well-Being
Dr. Hemant Pandya, Garvit Choudhary
Abstract: In recent years, unhealthy lifestyles among youth—characterized by sedentary behavior, excessive screen time, poor sleep patterns, and increasing stress—have become a significant public health concern. Mind–body practices such as yoga have gained increasing attention as holistic interventions that integrate physical activity, breathing regulation, and mental awareness. Chandra Namaskar (Moon Salutation) is a sequence of yoga postures performed with coordinated breathing and mindful movement that promotes relaxation, emotional balance, and physiological harmony. Unlike more vigorous yoga sequences, Chandra Namaskar emphasizes calm and cooling movements that activate the parasympathetic nervous system and promote mental tranquility. Regular practice has been associated with improved flexibility, better sleep quality, enhanced emotional stability, and reduced stress levels. Studies on yoga-based interventions demonstrate positive effects on physical fitness, cognitive functioning, and psychological well-being among young individuals. Additionally, emerging research suggests that Chandra Namaskar may influence cardiovascular autonomic function and improve cognitive performance in young adults. Therefore, incorporating Chandra Namaskar into daily routines may serve as an effective, low-cost strategy for improving youth lifestyle habits and enhancing overall well-being. This paper explores the role of Chandra Namaskar as a mind–body practice and highlights its potential contribution to improving lifestyle behaviors and holistic health among youth. This study examines the impact of Chandra Namaskar, a yogic practice emphasizing lunar energy, on the lifestyle and well-being of youth. Using empirical data from 100 youth respondents aged 18–25, the research assesses physical, psychological, and lifestyle changes after a six-week Chandra Namaskar intervention. Findings indicate significant improvements in well-being, stress reduction, sleep quality, and lifestyle behaviors
Keywords: Chandra Namaskar, Yoga, Mind–Body Practice, Youth Lifestyle, Well-Being, Mental Health
Keywords: Chandra Namaskar, Yoga, Mind–Body Practice, Youth Lifestyle, Well-Being, Mental Health
Abstract
STUDIES ON VISCOSITY, DENSITY AND REFRACTIVE INDEX OF -Ethyl--methyl N-, [-(-{[(trans--methylcyclohexyl) carbamoyl], sulfamoyl}phenyl)ethyl]--oxo-, -dihydro-H-, pyrrole--carboxamide IN MIXED SOVENT at, .K, M. K. Mahajan
M. K. Mahajan
Abstract: Refractive index ,molar refractivities and molar polarizability constant of 3-ethyl-4-methyl-N-[2-[4-[(4- methylcyclohexyl)carbamoylsulfamoyl]ethyl]-5-ethyl-3-methyl-2-oxo-1H-pyrrol-1-yl]benzamide in 20% Methanol media at 303.15 K ± 0.10C temperature and different concentrations ( 0.625x10-3 to10.0x 10-3M ).The values of molar refraction (Rm) and molar polarizability (α) constant are found to be decreased with decreasing concentration of solute in solvent. Viscosity coefficient (A, B) evaluate by using john–dole equation. These parameters throw the light on the solute-solvent interaction and solute-solute interaction.
Keywords: molar polarizability constant, Molar refractivities and Viscosity coefficient.
Keywords: molar polarizability constant, Molar refractivities and Viscosity coefficient.
Abstract
Studies the acoustic properties of mixed ligand in DMF-water mixture at 303.15K
Y. C. Mavle
Abstract: Acoustical properties have been measured for ligand (Ondansetron) in DMF-water mixture at 303.15K. The measurement have been perform to evaluate acoustical parameter such as adiabatic compressibility (bs), Partial molal volume (fv), intermolecular free length (Lf), apparent molal compressibility (fk), specific acoustic impedance (Z), relative association (RA), salvation number (Sn).
Keywords: Ultrasonic velocity, Salvation number, specific acoustic impedance.
Keywords: Ultrasonic velocity, Salvation number, specific acoustic impedance.
Abstract
Prompt Security and Insider-Risk Defense in Banking AI Systems
Syed Sharik Ali
DOI: 10.17148/IARJSET.2026.13801
Abstract: Artificial Intelligence (AI) assistants are being utilized in several banking systems to improve their efficiency, customer service capability, fraud detection and prevention capability, compliance monitoring system, and decision- making process. However, with the increasing usage of LLMs and intelligent assistants comes the emergence of severe cybersecurity issues including the prompt injection attacks, insider threat issues, access issues, and sensitive data exposure. Prompt injection refers to taking control of the AI system by injecting prompts and hidden instructions that enable security threats to bypass existing measures and reveal sensitive financial information or make the AI system do what it is not supposed to be doing. In the case of banks where AI systems are linked to customer records, transactions, and authorization processes, prompt injection and insider threat issues will lead to financial fraud and compliance problems. This paper highlights and mitigates the two most serious types of security risk associated with prompts and insider issues to the bank AI systems by developing a layered security strategy for prompt isolation and insider threat mitigation through role-based access control, human validation criteria, and restricted scope of impact zones over the duration of alert monitoring period. Simulation studies prove that our methodology can reduce the success rate of attacks and provide financial institutions with safe AI governance.
Keywords: Prompt Injection, Prompt Security, AI Banking Solutions, Internal Threats Detection, Information Protection from Leakage, LLM, Banking AI Assistants, Financial Cybersecurity, RBAC, Human-in-the-Loop Security, AI Governance, AI-based Banking Automation, Compliance Monitoring, Prevention of Fraud, LLM Security
Keywords: Prompt Injection, Prompt Security, AI Banking Solutions, Internal Threats Detection, Information Protection from Leakage, LLM, Banking AI Assistants, Financial Cybersecurity, RBAC, Human-in-the-Loop Security, AI Governance, AI-based Banking Automation, Compliance Monitoring, Prevention of Fraud, LLM Security
Abstract
Ethics, Efficiency and Equity: The Triple Challenge of AI in Sustainable Management
Prof.Gangadhar Hugar, Dr.Rajshekhar Basavapattan, Prof.Sharanabasavareddy Patil
DOI: 10.17148/IARJSET.2026.13802
Abstract: Artificial Intelligence (AI) has emerged as a transformative force in business and management, promising unprecedented efficiency, innovation, and sustainability. Yet, its rapid integration into organizational practices raises critical questions about ethics, inclusivity, and long-term impact. This paper explores the triple challenge of AI in sustainable management balancing ethical responsibility, operational efficiency, and equitable access. Drawing on secondary data, industry cases, and policy frameworks, the study highlights how AI-driven solutions can optimize decision-making, streamline operations, and support environmental goals, while also exposing risks such as algorithmic bias, workforce displacement, and unequal access for small and rural enterprises. Special attention is given to the role of AI in empowering MSMEs and tourism based entrepreneurship in regions like Karnataka, where digital inclusion can bridge socio-economic divides. The paper argues that sustainable management in the AI era requires not only technological innovation but also strong ethical governance and equitable policies. By proposing actionable recommendations for academia, industry, and government, this research contributes to shaping a future where AI serves as a catalyst for inclusive growth and responsible development.
Keywords: Artificial Intelligence, Sustainable Management, Ethics, Efficiency, Equity, MSMEs, Rural Entrepreneurship, Digital Transformation, Inclusive Growth, Policy Frameworks
Keywords: Artificial Intelligence, Sustainable Management, Ethics, Efficiency, Equity, MSMEs, Rural Entrepreneurship, Digital Transformation, Inclusive Growth, Policy Frameworks
Abstract
Comparative Assessment of Corporate Social Responsibility Initiatives and Government Welfare Schemes emphasising Orunodoi on Women’s Livelihoods and Empowerment in Assam – Study based on Barak Valley
Subrajyoti Dey, Dr. M. Gangabhushan Molankal
DOI: 10.17148/IARJSET.2026.13803
Abstract: This study examines how women in Assam’s Barak Valley perceive and benefit from private-sector CSR initiatives versus a major government welfare scheme (Orunodoi). A survey of 60 rural women (30 CSR beneficiaries, 30 Orunodoi recipients) was done and conduction of focus-group discussions to compare program accessibility, effectiveness, satisfaction, and livelihood outcomes. Findings suggest Orunodoi is generally seen as more accessible and immediately beneficial for household needs, whereas CSR programs (e.g. skill‐training or microcredit by companies) are valued for longer-term income improvements. Both interventions increased women’s sense of financial security, but differences in targeting and bureaucratic ease emerged. For example, Orunodoi recipients reported higher ease of enrollment (mean score ~3.6/5 vs. 2.8/5 for CSR), while CSR trainees reported slightly higher income gains (Table 2). Policy recommendations include better coordination of CSR and government efforts: for instance, linking CSR skill training with Orunodoi cash support to maximize impact. Despite a small sample, the study highlights that each approach has merits, and that women’s preferences hinge on both economic needs and social factors.
Keywords: Women’s empowerment; CSR initiatives; Orunodoi scheme; Barak Valley; livelihood outcomes.
Keywords: Women’s empowerment; CSR initiatives; Orunodoi scheme; Barak Valley; livelihood outcomes.
Abstract
A Comparative Study of TabTransformer and Temporal Fusion Transformer for Loan Approval Prediction Using Static and Temporal Financial Features
Dr. Balaji K, K. Sridhar, Rajinish S
DOI: 10.17148/IARJSET.2026.13804
Abstract: In the banking and financial services industry, loan approval is an important process that helps minimize credit risk and increase lending efficiency. Traditional machine-learning models can struggle to represent complex interactions among customer financial features and changes in financial activity over time. This paper presents a comparative study of two transformer-based deep-learning models - TabTransformer and Temporal Fusion Transformer (TFT) - for intelligent loan approval prediction. TabTransformer learns meaningful representations from structured customer data, whereas TFT is trained using twelve months of sequential financial data to model long-term financial patterns. For a fair comparison, both models are trained and tested using the same enhanced loan dataset, preprocessing pipeline and experimental setting. Performance is assessed using accuracy, precision, recall, F1-score, ROC-AUC and confusion- matrix analysis. The results show that TFT achieves 94.75% accuracy compared with 93.13% for TabTransformer, indicating that temporal financial information provides additional predictive value for automated loan application decision-making.
Keywords: Loan Approval Prediction, Tab Transformer, Temporal Fusion Transformer, Deep Learning, Credit Risk Assessment, Financial Data Analytics, Transformer Networks, Sequential Learning, Artificial Intelligence.
Keywords: Loan Approval Prediction, Tab Transformer, Temporal Fusion Transformer, Deep Learning, Credit Risk Assessment, Financial Data Analytics, Transformer Networks, Sequential Learning, Artificial Intelligence.
Abstract
Performance Evaluation of M40 Grade Concrete Using Recycled Coarse Aggregate and Plastic Granules as Sustainable Aggregate Replacements
Sandhya Annasaheb Daud, Rahul S Patil
DOI: 10.17148/IARJSET.2026.13805
Abstract: The increasing demand for natural aggregates and the disposal of construction and demolition (C&D) waste and plastic waste have created significant environmental challenges. This study investigates the mechanical properties of M40 concrete by partially replacing natural coarse aggregate with recycled aggregate (RA) and fine aggregate with plastic granules (PG). Four concrete mixes were prepared: a control mix and three modified mixes containing 20% RA + 5% PG, 30% RA + 7% PG, and 40% RA + 9% PG. Fresh and hardened concrete properties were evaluated through slump, density, compressive strength, split tensile strength, and flexural strength tests. The results indicate that moderate replacement levels provide satisfactory strength while promoting sustainable construction and reducing environmental pollution.
Keywords: Recycled Aggregate, Plastic Granules, Sustainable Concrete, M40 Concrete, Mechanical Properties.
Keywords: Recycled Aggregate, Plastic Granules, Sustainable Concrete, M40 Concrete, Mechanical Properties.
Abstract
Comparative Study of Latency-Aware Serverless Function Orchestration using XGBoost and Random Forest Regression Models in Edge– Cloud Environments
Bhavana B R, Lekhana HN, Reshma G
DOI: 10.17148/IARJSET.2026.13806
Abstract: The number of Internet of Things (IoT) applications and edge–cloud computing environments has expanded, and efficient and low-latency execution of tasks is desired. Cloud native orchestration solutions are often not well suited to deal with real-time needs due to the frequent movement of cloud workloads, changes in network and resource variations. In this study, we are interested in using two ensemble ML algorithms (Random Forest and XGBoost) to predict the time required for the execution of the tasks in the vmCloud workload dataset in a serverless edge–cloud environment. Seven workload attributes—CPU utilisation, memory usage, network latency, task size, cold-start delay, number of instances, and bandwidth—were used to train the models. RMSE, MAE, MAPE, R2 score and 5-fold cross-validation were used for the performance evaluation. The results of the experiments indicate that the performance of XGBoost is better than that of the RF, with Test R2 of 0.9209, RMSE is 10.08 ms, and MAPE is 14.57%, while RF’s Test R2 is 0.8898, RMSE is 11.89 ms, and MAPE is 18.38%. The feature importance analysis shows that network latency and cold start delay are the most important features that affect the time to execute. The findings indicate that XGBoost is a proper algorithm for latency-aware serverless function orchestration and can be applied to intelligently schedule in the edge– cloud computing environment.
Keywords: Edge Computing, Cloud Computing, IoT, Serverless Computing, XGBoost, Random Forest, Function Orchestration, Latency Prediction.
Keywords: Edge Computing, Cloud Computing, IoT, Serverless Computing, XGBoost, Random Forest, Function Orchestration, Latency Prediction.
Abstract
Influence of AI-Powered Personalization on Consumer Purchase Intention: The Mediating Role of Consumer Trust
Dr. Prashantha Kumar O, Dr. Shashidhar S Mahantshetti, Ms. Priya.K, Ms. Anuradha H N
DOI: 10.17148/IARJSET.2026.13807
Abstract: Artificial Intelligence (AI)-powered personalization has become a cornerstone of modern e-commerce by enabling organizations to deliver customized product recommendations that enhance consumer experiences and influence purchasing decisions. However, the effectiveness of AI-driven personalization depends not only on the quality of recommendations but also on consumers' trust in AI systems. This study examines the influence of AI-powered personalization, perceived transparency, explainability of AI, and perceived privacy protection on purchase intention, with consumer trust serving as the mediating variable. A quantitative, cross-sectional research design was adopted, and data were collected from consumers who had prior experience with AI-enabled personalized recommendations on online shopping platforms. The proposed conceptual model was analyzed using Structural Equation Modelling (SEM) in Jamovi. The findings reveal that AI-powered personalization, perceived transparency, and explainability of AI significantly and positively influence consumer trust, whereas perceived privacy protection does not significantly affect trust. Consumer trust, in turn, exerts a significant positive influence on purchase intention. Furthermore, perceived privacy protection has a significant direct effect on purchase intention, while the direct effects of AI-powered personalization, perceived transparency, and explainability of AI on purchase intention are not statistically significant, indicating that their influence is primarily transmitted through consumer trust. The study highlights the pivotal role of trust as the underlying mechanism linking AI-enabled personalization characteristics to consumers' purchase intentions. The findings contribute to the growing literature on AI-driven consumer behaviour by extending the understanding of trust formation in AI-enabled retail environments and offer practical implications for e-commerce firms seeking to design transparent, explainable, and trustworthy AI recommendation systems that enhance consumer purchase intentions.
Keywords: AI-Powered Personalization; Consumer Trust; Purchase Intention; Explainability of Artificial Intelligence; Perceived Transparency
Keywords: AI-Powered Personalization; Consumer Trust; Purchase Intention; Explainability of Artificial Intelligence; Perceived Transparency
Abstract
GIS-Based Landfill Site Suitability Analysis in Deogarh Municipal Area: A Geographical Study
Deepak Kumar Prajapat, Dr. Hemendra Singh Shaktawat
DOI: 10.17148/IARJSET.2026.13808
Abstract: The rapid increase in municipal solid waste (MSW) resulting from population growth, urbanization, and changing consumption patterns has emerged as a major environmental challenge in developing countries. Scientific landfill site selection is essential to minimize adverse environmental and public health impacts while ensuring sustainable waste management. The present study aims to identify suitable landfill sites in the Deogarh Municipal Area, Rajsamand District, Rajasthan, using Geographic Information System (GIS) and Multi-Criteria Decision Analysis (MCDA). Spatial datasets, including land use/land cover, road networks, water bodies, built-up areas, agricultural land, railway lines, and slope, were acquired through field surveys and secondary data sources and processed using QGIS. Buffer analysis and weighted overlay techniques were applied in accordance with the Solid Waste Management Rules, 2016, and the guidelines of the Central Pollution Control Board (CPCB) to evaluate landfill site suitability. The integrated analysis classified the study area into different suitability zones ranging from unsuitable to highly suitable. The results indicate that the existing dumping site is environmentally less suitable because of its proximity to residential areas, agricultural land, and public institutions, thereby posing potential environmental and public health risks. In contrast, the proposed alternative sites are located on barren land with favourable environmental conditions, adequate road accessibility, and greater compliance with landfill site selection criteria, making them more suitable for future landfill development. The study demonstrates that the integration of GIS and MCDA provides a robust, reliable, and scientifically sound decision-support framework for landfill site selection. The proposed methodology can assist municipal authorities and planners in developing sustainable solid waste management strategies and may serve as a replicable model for other urban areas facing similar waste management challenges. The final suitability analysis showed that 12.4% of the study area falls under highly suitable class while 28.6% was moderately suitable.
Keywords: Municipal Solid Waste (MSW), Multi-criteria Decision Analysis (MCDA), Buffer Analysis, Landfill Site Selection, (GIS), QGIS, Spatial Analysis
Keywords: Municipal Solid Waste (MSW), Multi-criteria Decision Analysis (MCDA), Buffer Analysis, Landfill Site Selection, (GIS), QGIS, Spatial Analysis
Abstract
Experimental Investigation on Concrete with Partial Replacement of Aggregates by Marble Pieces and Plastic Granules
Jayesh Yashwant Gawade, Prof. R. S. Patil
DOI: 10.17148/IARJSET.2026.13809
Abstract: Concrete is the most widely used construction material because of its strength, durability, versatility, and availability. At the same time, the construction sector consumes large quantities of natural aggregates and generates or receives large quantities of waste materials. Marble processing produces solid waste in the form of pieces and fragments, while plastic waste creates long-term environmental concerns because of its persistence and difficult disposal. This study investigates the possibility of using these two waste materials in concrete as partial replacements for conventional aggregates.
The experimental study considers M30 grade concrete. Marble pieces are used as a partial replacement for natural coarse aggregate, while plastic granules are used as a partial replacement for natural fine aggregate. The project methodology includes material testing, concrete mix proportioning, casting and curing of specimens, and strength evaluation after 7, 14, and 28 days. The synopsis specifies compressive strength, split tensile strength, and flexural strength as strength characteristics to be evaluated.
The reported results show that concrete strength generally increases with curing age. Among the replacement levels considered, the mixes containing 30% marble pieces and 7% plastic granules produced the highest reported strength performance. The synopsis concludes that higher replacement levels of 40% marble pieces and 9% plastic granules resulted in a reduction in strength, attributed to weaker bonding and increased voids.
The study therefore identifies 30% marble pieces with 7% plastic granules as the optimum combination among the investigated mixes. The findings indicate that appropriately proportioned marble and plastic waste can be incorporated into concrete while reducing dependence on natural aggregates and supporting more sustainable construction practices.
Keywords: M30 concrete, marble waste, plastic granules, coarse aggregate, fine aggregate, compressive strength, split tensile strength, sustainable concrete.
The experimental study considers M30 grade concrete. Marble pieces are used as a partial replacement for natural coarse aggregate, while plastic granules are used as a partial replacement for natural fine aggregate. The project methodology includes material testing, concrete mix proportioning, casting and curing of specimens, and strength evaluation after 7, 14, and 28 days. The synopsis specifies compressive strength, split tensile strength, and flexural strength as strength characteristics to be evaluated.
The reported results show that concrete strength generally increases with curing age. Among the replacement levels considered, the mixes containing 30% marble pieces and 7% plastic granules produced the highest reported strength performance. The synopsis concludes that higher replacement levels of 40% marble pieces and 9% plastic granules resulted in a reduction in strength, attributed to weaker bonding and increased voids.
The study therefore identifies 30% marble pieces with 7% plastic granules as the optimum combination among the investigated mixes. The findings indicate that appropriately proportioned marble and plastic waste can be incorporated into concrete while reducing dependence on natural aggregates and supporting more sustainable construction practices.
Keywords: M30 concrete, marble waste, plastic granules, coarse aggregate, fine aggregate, compressive strength, split tensile strength, sustainable concrete.
Abstract
Effect of Aggregate Size and Specimen Dimensions on Mechanical Performance of Concrete
Vishakha D. Ghuge, Dr.D.H. Tupe
DOI: 10.17148/IARJSET.2026.13810
Abstract: Concrete is a heterogeneous material. Past research has established the effect of its primary constituents namely water, cement, aggregates on the behavior at the micro level as well as the macro level. In particular, several researchers have investigated the role of aggregates on the behavior of the concrete mix, the goal being to design aggregate gradations which results in higher concrete quality. The goal of this is to study the effect of specimen size on strength in unconfined concrete.
An experimental program to determine the compressive strength of a set of unconfined concrete cylinders of varying size (100mm, 150mm, 190mm diameter) and slenderness ratio of 2 is adopted. However, while studying the effect of meso-structure on strength and size effect, care has to be taken to ensure similar levels of workability and compaction in all specimens, since otherwise no meaningful conclusions can be drawn from the test results.
Average strength of specimen is taken and It is concluded from the experimental study that with increase in size of specimen its compressive strength is observed decreased.
Keywords: compressive strength Test, size effect, meso geometry
An experimental program to determine the compressive strength of a set of unconfined concrete cylinders of varying size (100mm, 150mm, 190mm diameter) and slenderness ratio of 2 is adopted. However, while studying the effect of meso-structure on strength and size effect, care has to be taken to ensure similar levels of workability and compaction in all specimens, since otherwise no meaningful conclusions can be drawn from the test results.
Average strength of specimen is taken and It is concluded from the experimental study that with increase in size of specimen its compressive strength is observed decreased.
Keywords: compressive strength Test, size effect, meso geometry
Abstract
Digital Influence on Economic and Lifestyle Dynamics of Working Women
Dr. P. Karthika
DOI: 10.17148/IARJSET.2026.13811
Abstract: Digitalization has increasingly transformed the economic, social and personal lives of working women by facilitating access to digital services, electronic payments, online information and technology-enabled decision-making. The present study examines the relationship between digitalization and lifestyle dynamics among working women, with particular emphasis on digital usage, digital services, digital payment practices and perceived digital influence on lifestyle. The study is based on primary data collected from 542 working women. The analysis incorporates digitalization variables such as devices used, frequency of digital usage, purpose of usage, digital services, device purpose, digital payment usage, security awareness, security level and perceived digital influence. Lifestyle dynamics are examined through living standard, consumption change, lifestyle decision-making, balance of life and quality of life. Descriptive statistics and Pearson's Chi-square test were employed to identify significant associations between digitalization and lifestyle-related variables. The findings reveal a significant association between digital influence and balance of life (χ² = 11.853, p = 0.003), usage purpose and consumption change (χ² = 8.005, p = 0.018), and usage frequency and balance of life (χ² = 7.365, p = 0.025). Digitalization was also significantly associated with selected economic outcomes, including salary satisfaction, income change and income control. The findings indicate that digitalization is becoming an important component of the contemporary lifestyle of working women, although its effects vary across different dimensions of economic and personal life. The study recommends strengthening digital literacy, secure digital payment practices and women-centred digital financial services to promote inclusive economic participation and improved quality of life.
Keywords: Digitalization, Working Women, Lifestyle Dynamics, Digital Payments, Economic Empowerment, Digital Financial Services, Work-Life Balance
Keywords: Digitalization, Working Women, Lifestyle Dynamics, Digital Payments, Economic Empowerment, Digital Financial Services, Work-Life Balance
Abstract
INTELLIGENT MEDICAL AI CHATBOT
DR. BHARATHI M P, YOGESH K K, KRISHNE GOWDA G S
DOI: 10.17148/IARJSET.2026.13812
Abstract: Artificial intelligence and natural language processing have opened up new possibilities for healthcare delivery, yet patients in rural and underserved regions still struggle to get timely medical consultations, which often means delayed diagnoses and complications that could have been avoided. This paper presents an Intelligent Medical Chatbot that pairs an interactive 3D human body visualization system with a custom deep learning model, aimed at giving users real-time symptom assessment and preliminary diagnosis support. The framework brings together a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model for intent classification and entity recognition, a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) hybrid for multi-label disease prediction, and a WebGL-rendered 3D anatomical interface where users can simply click on the body region that's bothering them. On a curated medical symptom-disease dataset, the system reaches high diagnostic accuracy while keeping response latency under a second. Our experiments show it outperforming existing rule-based and single-modal chatbots on prediction accuracy, user engagement, and clinical relevance. Overall, the work offers a scalable, accessible way to provide preliminary healthcare assistance across web and mobile platforms, with no specialized hardware required.
Keywords: Medical chatbot, deep learning, BERT, CNN-LSTM, 3D human visualization, symptom checker, healthcare AI, natural language processing, real-time diagnosis.
Keywords: Medical chatbot, deep learning, BERT, CNN-LSTM, 3D human visualization, symptom checker, healthcare AI, natural language processing, real-time diagnosis.
Abstract
Medication Management and Reminders
Dr.P.S. Vachaspati, M. Pallavi
DOI: 10.17148/IARJSET.2026.13813
Abstract: Medication adherence is an important challenge among patients, as missed or incorrect doses can lead to poor treatment outcomes and medication-related errors. This paper proposes a Smart Medication Reminder System enhanced with Machine Learning to help patients take their prescribed medicines correctly and on time. The proposed system provides timely medication reminders, maintains medication history, and analyzes patient medication patterns to predict the possibility of missed doses. It also enables family members and healthcare providers to monitor medication adherence and support patients when necessary. By combining automated reminders, medication tracking, and machine learning-based prediction, the system aims to improve medication adherence, reduce missed doses and medication errors, and provide a reliable solution for different groups of patients.
Abstract
An Efficient Diffusion-Based Framework for Accelerated Image Generation Using Fast-DDPM
Y Vidya Indrasena, Dr C Prakasa Rao
DOI: 10.17148/IARJSET.2026.13814
Abstract: Denoising Diffusion Probabilistic Models (DDPMs) have emerged as a leading class of generative models for image synthesis, yet their reliance on long Markov chains of up to one-thousand-time steps makes them impractical for many medical imaging applications, where training a single model can take days and generating one image volume can take minutes to hours. This paper presents a comprehensive analysis and implementation framework built around Fast- DDPM, an efficient diffusion-based approach that aligns the training and sampling procedures of DDPMs so that both stages operate over only ten time steps rather than the conventional one thousand. Two complementary noise-scheduling strategies, a uniform time-step scheduler and a non-uniform time-step scheduler, are described in detail, along with the modified forward and reverse processes that allow the denoising network to be trained efficiently without sacrificing sample fidelity. The framework is evaluated on three representative medical image-to-image generation tasks: multi- image super-resolution of prostate MRI, denoising of low-dose lung CT scans, and cross-modality translation of brain MRI. Across all three tasks, the accelerated framework matches or exceeds the image quality of the full DDPM baseline while reducing training time to roughly one-fifth and sampling time to roughly one-hundredth of the original cost. These results indicate that carefully aligning the number of training and sampling steps, rather than relying solely on post hoc fast samplers or expensive distillation procedures, is sufficient to close the efficiency gap that has limited the clinical adoption of diffusion models. We further discuss the implications of this efficiency gain for real-time clinical workflows, the trade-offs revealed by an ablation over the number of time steps, and directions for extending the framework to three- dimensional and four-dimensional medical imaging data.
Keywords: Diffusion models, Denoising Diffusion Probabilistic Models, medical image generation, image super- resolution, image denoising, image-to-image translation, fast sampling, computational efficiency.
Keywords: Diffusion models, Denoising Diffusion Probabilistic Models, medical image generation, image super- resolution, image denoising, image-to-image translation, fast sampling, computational efficiency.
Abstract
Deep Learning and Kinematic Telematics Integration for Real-Time Pothole Detection and Dynamic Navigation Routing
Sahithyaa Krishna Kumar, Sanjay C, Mark Owen A, Yogeshwar P, R.T. Charulatha
DOI: 10.17148/IARJSET.2026.13816
Abstract: Potholes represent a severe hazard to vehicular safety, traffic efficiency, and municipal road infrastructure sustainability. Traditional manual road inspection paradigms are inefficient, expensive, and structurally non-scalable. This paper presents a multimodal framework integrating vision-based edge deep neural networks and mobile kinematic sensor fusion for real-time pothole detection, localized classification, and dynamic navigation routing. The optical perception pipeline incorporates Contrast Stretching Adaptive Gaussian Star Filtering (CAGF) with POT-YOLOv8 and ResNet50-YOLOv8n architectures to isolate road anomalies under variable environmental conditions. Concurrently, dynamic vehicular impact is captured using three-axis accelerometer and gyroscope telemetry modeled via Quarter Vehicle Dynamics. Geotagged detection telemetry is transmitted via Application Programming Interfaces (APIs) to cloud routing servers, enabling dynamic path recalculations in Google Maps API platforms and generating automated notifications for public works departments. Empirical evaluations demonstrate benchmark object detection accuracies up to 𝟗𝟗. 𝟏𝟎 ± 𝟎. 𝟑𝟏% and real-time inference speeds of 18.4 ms/frame (≈ 𝟓𝟒. 𝟑 FPS) on edge hardware. Multimodal sensor fusion achieves a 77.5% reduction in false positives (lowering the false-positive rate to 3.2%) and field deployment accuracies between 𝟖𝟎% and 𝟖𝟕% (𝟖𝟑. 𝟓𝟎 ± 𝟑. 𝟐𝟎%), validating the framework for intelligent transportation systems.
Keywords: Pothole Detection, YOLOv8, Sensor Fusion, Dynamic Routing, Edge Computing, Geographic Information System
Keywords: Pothole Detection, YOLOv8, Sensor Fusion, Dynamic Routing, Edge Computing, Geographic Information System
Abstract
Assessment of Construction Professionals’ Perceptions: A Questionnaire-Based Study
N.Sivapiran, DR.G.Murugesan
DOI: 10.17148/IARJSET.2026.13817
Abstract: This article presents a questionnaire-based comparative assessment of five different construction professionals, analyzing their perceptions across five key dimensions relevant to the construction industry. A structured questionnaire survey was administered, responses were quantified using a Likert scale, and average scores were tabulated. The study compares each professional category individually and collectively, supported by tabulations, comparative charts, and a concise literature review. The outcome highlights perceptual similarities, divergences, and practical implications for project performance and policy formulation.
Keywords: Questionnaire survey, Construction professionals, Comparative analysis, Likert scale, Construction management.
Keywords: Questionnaire survey, Construction professionals, Comparative analysis, Likert scale, Construction management.
Abstract
Intelligent Auto Scaling and Profit Optimization in Cloud Computing
Pooja M S, Jeevika N, Khushi K Y, Priyanka
DOI: 10.17148/IARJSET.2026.13818
Abstract: Cloud computing offers resources as and when needed but poses a great challenge in terms of efficient management, especially when the resource demands are highly variable. Literature has explored various approaches in this regard, including resource allocation, auto-scaling, virtualization, workflow scheduling, cost optimization, and QoS management. Predictive, adaptive, and reinforcement-learning based auto-scaling techniques are some of the emerging trends in this area, which address the shortcomings of traditional CPU or memory-threshold-based auto-scaling methods. While traditional approaches are simple to implement, they are often associated with the risk of over-provisioning in case of low load and poor responsiveness to sudden traffic surges. Some recent works have focused on addressing these limitations, as well as exploring horizontal and vertical scaling, serverless platforms, ML-centric workloads, and hybrid and multi-cloud environments, among others. Taken together, these approaches aim to improve performance, utilization, latency, cost-effectiveness, scalability, and SLA compliance, among others. However, the issue of provider profit maximization still remains under-addressed in this context, with most existing contributions focusing on a limited set of optimization criteria. This work makes an attempt to address this challenge by considering the interplay between intelligent auto-scaling, efficient resource allocation, and profit maximization in cloud environments.
Keywords: Predictive Analysis, Dynamic resource Provisioning, Load Balancing, Cost Optimization, Workload Prediction, Resource Optimization.
Keywords: Predictive Analysis, Dynamic resource Provisioning, Load Balancing, Cost Optimization, Workload Prediction, Resource Optimization.
Abstract
Women Safety System Using AI Techniques
Mr. Santhosh Kumar T R, Kavana N, Kavana V M, M Kavana, Namratha S
DOI: 10.17148/IARJSET.2026.13820
Abstract: Women’s safety has become a significant social concern due to the increasing number of crimes and emergency situations affecting women in public and private environments. Recent advancements in Artificial Intelligence (AI), the Internet of Things (IoT), Edge AI, wearable devices, and mobile computing have enabled the development of intelligent safety systems capable of providing rapid detection, monitoring, and emergency assistance. This survey presents a comprehensive review of thirteen recent research works that focus on AI-driven women safety technologies, including smart wearable devices, audio-based distress detection, sound anomaly recognition, offline emergency response frameworks, intelligent mobile applications, secure communication systems, health monitoring devices, safe route navigation, and Edge AI-based surveillance systems.This survey analyzes the objectives, methodologies, system architectures, advantages, and limitations of the selected studies and provides a comparative understanding of current AI- based women safety solutions. The analysis identifies existing research gaps, including challenges related to detection accuracy, scalability, false alarm reduction, privacy protection, and integration of multiple safety features into a unified platform. The survey highlights recent technological advancements and offers valuable insights into the development of intelligent, reliable, and user-centric women safety systems using Artificial Intelligence.
Keywords: Women Safety, Artificial Intelligence (AI), Internet of Things (IoT), Edge AI, Smart Wearable Devices, Audio Recognition, Sound Anomaly Detection, Distress Detection, Emergency Response System, GPS Tracking, Health Monitoring, Safe Route Navigation, Machine Learning.
Keywords: Women Safety, Artificial Intelligence (AI), Internet of Things (IoT), Edge AI, Smart Wearable Devices, Audio Recognition, Sound Anomaly Detection, Distress Detection, Emergency Response System, GPS Tracking, Health Monitoring, Safe Route Navigation, Machine Learning.
Abstract
FPGA Based Low-Power and Area-Efficient 4- bit Arithmetic Logic Unit
Uppula Shirisha, Dr A Mamatha
DOI: 10.17148/IARJSET.2026.13821
Abstract: Arithmetic Logic Units (ALUs) are fundamental components of digital processors and embedded systems, responsible for executing arithmetic and logical operations with high speed and reliability. As modern FPGA-based systems increasingly target portable, real-time, and energy-constrained applications, designing ALUs with reduced power consumption and optimized hardware utilization has become a critical research objective. This paper presents an FPGA- Based Low-Power and Area-Efficient 4-bit Arithmetic Logic Unit (LPAE-4ALU) that achieves improved energy efficiency while maintaining high computational performance. The proposed architecture integrates optimized combinational logic with an efficient operation selection mechanism to minimize switching activity and reduce logic resource utilization. The ALU supports essential arithmetic operations, including addition, subtraction, increment, and decrement, along with logical operations such as AND, OR, XOR, NOT, NAND, NOR, and XNOR. The design is implemented using Verilog HDL and synthesized on a Xilinx FPGA platform to evaluate its performance in terms of lookup table (LUT) utilization, flip-flop usage, operating frequency, propagation delay, and power consumption. Experimental results demonstrate that the proposed architecture achieves lower dynamic power consumption and reduced hardware area compared with conventional 4-bit ALU implementations while maintaining accurate functionality and high-speed operation. The proposed LPAE-4ALU is therefore well suited for low-power embedded processors, Internet of Things (IoT) devices, digital signal processing systems, and FPGA-based System-on-Chip (SoC) applications.
Keywords: 4-bit Arithmetic Logic Unit (ALU), FPGA Implementation, Low-Power Design, Area Optimization, Verilog HDL.
Keywords: 4-bit Arithmetic Logic Unit (ALU), FPGA Implementation, Low-Power Design, Area Optimization, Verilog HDL.
Abstract
Fuzzy Logic-Based Clinical Decision Making for Early Detection and Management of Cardiovascular Diseases
Chandrashekhar Diwakar, Ram Kishor
DOI: 10.17148/IARJSET.2026.13822
Abstract: The use of Fuzzy Logic to create a Mamdani-based Clinical Decision-Making Model is useful in assessing the Risk of Cardiovascular Diseases based on several Key Clinical Parameters including Glycated Hemoglobin (HbA1c), Low-Density Lipoprotein Cholesterol (LDL-C), and Body Mass Index (BMI). A Three Dimensional Surface Analysis was conducted and demonstrated smooth transition between different Risk Levels. Also, Numerical Case Studies were conducted that showed clinically meaningful results. For example, an individual with (LDL-C = 150 mg/dL, HbA1c = 6.2%, BMI = 31 kg/m²) had a Defuzzified Risk Score of 81.55 which would indicate High Cardiac Risk. The proposed model is a simple, transparent and computationally inexpensive method for Personalized Assessment of the Risk of Cardiovascular Disease and Early Intervention.
Keywords: Cardiovascular disease, Fuzzy logic, Mamdani fuzzy inference system, Clinical decision support system, LDL cholesterol HbA1c
Keywords: Cardiovascular disease, Fuzzy logic, Mamdani fuzzy inference system, Clinical decision support system, LDL cholesterol HbA1c
Abstract
DESIGN AND DEVELOPMENT OF AN OUTDOOR AIR PURIFIERFOR DOMESTIC AND TRAFFIC SIGNAL AREAS
V.Tirumalaiah, K Usha Rani
DOI: 10.17148/IARJSET.2026.13823
Abstract: This project focuses on the design and development of an out door air purifier suitable for domestic surroundings and traffic signal areas, where air pollution levels are significantly high due to vehicular emissions and dust. The system integrates high efficiency particulate air (HEPA)filtration, activated carbon filters, and UV-C sterilization to remove particulate matter (PM2.5 and PM1O0), toxic gases, and microbialogical contaminants. A solar- powered system with a smart air quality monitoring unit (AQI sensor) ensures energy efficiency and real-time performance tracking. The compact and weather-resistant design enables easy installation and maintenance. This innovation aims to reduce localized air pollution, improve public health, and enhance air quality in urban environments. The designed outdoor air purifier successfully addresses the growing problem of air pollution in domestic and traffic signal areas by combining advanced filtration, sterilization, and smart monitoring technologies. Through the integration of HEPA and activated carbon filters along with UV-C sterilization, the system effectively removes harmful pollutants, toxic gases, and microorganisms from the surrounding air. The inclusion of solar power ensures sustainable operation, while the smart AQI monitoring unit provides real-time feedback for efficient performance management. Overall, this project contributes to improving air quality, promoting public health, and offering an eco-friendly, cost-effective solution to combat air pollution in high-risk environments.
Abstract
IoT-Based Saline Bottle Monitoring and Control System Using Wi-Fi and Android Application
Rashmi Tandi, Nidhi Sharma
DOI: 10.17148/IARJSET.2026.13825
Abstract: Continuous monitoring of intravenous (IV) saline bottles is important during fluid administration because the saline level needs to be observed until the required quantity has been delivered. In conventional practice, the bottle is generally checked manually by healthcare personnel. This can be difficult when several patients require attention at the same time. To address this problem, this paper presents an IoT-based saline bottle monitoring and control system using ESP32-C3, capacitive sensing, Wi-Fi, and an Android application.
The proposed system uses an XW02E-based capacitive sensing arrangement mounted externally on the saline bottle to detect changes in the saline level. The ESP32-C3 processes the sensor output and transmits the saline status through Wi- Fi to the Android application. LED and buzzer indicators are used for local status indication and alerts. When the saline level reaches the defined critical condition, a servo-operated flow-control mechanism is activated to stop the saline flow. The Android application provides the saline status and notification information to the user.
A working prototype was developed and tested under controlled conditions to verify the operation of the saline-level sensing, wireless communication, alert generation, and ON/OFF flow switching mechanism. The developed prototype demonstrates the practical implementation of combining saline-level monitoring with automatic ON/OFF flow switching in a compact IoT-based system. Further testing with different bottle types and real healthcare conditions is required to evaluate its performance for practical deployment.
Keywords: IoT, Saline Bottle Monitoring, Saline Flow Control Switch, ESP32-C3, Capacitive Sensing, Wi-Fi, Android Application, Smart Healthcare.
The proposed system uses an XW02E-based capacitive sensing arrangement mounted externally on the saline bottle to detect changes in the saline level. The ESP32-C3 processes the sensor output and transmits the saline status through Wi- Fi to the Android application. LED and buzzer indicators are used for local status indication and alerts. When the saline level reaches the defined critical condition, a servo-operated flow-control mechanism is activated to stop the saline flow. The Android application provides the saline status and notification information to the user.
A working prototype was developed and tested under controlled conditions to verify the operation of the saline-level sensing, wireless communication, alert generation, and ON/OFF flow switching mechanism. The developed prototype demonstrates the practical implementation of combining saline-level monitoring with automatic ON/OFF flow switching in a compact IoT-based system. Further testing with different bottle types and real healthcare conditions is required to evaluate its performance for practical deployment.
Keywords: IoT, Saline Bottle Monitoring, Saline Flow Control Switch, ESP32-C3, Capacitive Sensing, Wi-Fi, Android Application, Smart Healthcare.
Abstract
Entrepreneurial Ecosystems for Sustainable MSME Development: Aligning SDG 8 and SDG 9 in the Context of North Karnataka
Dr. Pushpa Hongal
DOI: 10.17148/IARJSET.2026.13826
Abstract: Micro, Small, and Medium Enterprises (MSMEs) play a significant role in employment generation, innovation, regional development, and sustainable economic growth; however, their long-term performance depends not only on firm-level capabilities but also on the quality of the entrepreneurial ecosystem in which they operate. This study develops a conceptual model explaining how entrepreneurial ecosystem dimensions contribute to MSME performance and sustainability in the industrial clusters of North Karnataka, while positioning these relationships within the United Nations Sustainable Development Goals (SDGs). The framework integrates Entrepreneurial Ecosystem Theory, Cluster Theory, the Resource-Based View (RBV), and Social Capital Theory to explain how entrepreneurial capabilities and external ecosystem conditions jointly support sustainable enterprise development.
The model incorporates entrepreneurial antecedents—educational qualification, industry experience, family support, and social network support—and MSME cluster characteristics, including geographical proximity, supporting industries, labour availability, and competitive intensity. At the centre of the framework are ten entrepreneurial ecosystem dimensions: Formal Institutions, Entrepreneurial Culture, Social Networks, Physical Infrastructure, Demand, Leadership, Talent, Finance, Knowledge, and Intermediate Business Support Services. These dimensions facilitate access to resources, finance, markets, skills, knowledge, infrastructure, institutional support, and collaborative networks, thereby strengthening firm performance and long-term MSME sustainability.
The framework has particular relevance to SDG 8 (Decent Work and Economic Growth) through entrepreneurship, MSME growth, productivity, and employment; SDG 9 (Industry, Innovation and Infrastructure) through infrastructure, industrial development, innovation, finance, and technological capability; and SDG 17 (Partnerships for the Goals) through stakeholder networks and institutional collaboration. It also provides a supporting linkage to SDG 4 (Quality Education) through entrepreneurial skills, talent development, and knowledge creation. The model therefore extends entrepreneurial ecosystem research by connecting regional MSME development with the broader sustainable development agenda and provides a conceptual basis for policymakers, financial institutions, universities, industry associations, and development agencies seeking to build resilient and sustainable MSME ecosystems.
Keywords: Entrepreneurial Ecosystem; MSME Sustainability; Firm Performance; Industrial Clusters; Sustainable Development Goals; SDG 8; SDG 9; North Karnataka
The model incorporates entrepreneurial antecedents—educational qualification, industry experience, family support, and social network support—and MSME cluster characteristics, including geographical proximity, supporting industries, labour availability, and competitive intensity. At the centre of the framework are ten entrepreneurial ecosystem dimensions: Formal Institutions, Entrepreneurial Culture, Social Networks, Physical Infrastructure, Demand, Leadership, Talent, Finance, Knowledge, and Intermediate Business Support Services. These dimensions facilitate access to resources, finance, markets, skills, knowledge, infrastructure, institutional support, and collaborative networks, thereby strengthening firm performance and long-term MSME sustainability.
The framework has particular relevance to SDG 8 (Decent Work and Economic Growth) through entrepreneurship, MSME growth, productivity, and employment; SDG 9 (Industry, Innovation and Infrastructure) through infrastructure, industrial development, innovation, finance, and technological capability; and SDG 17 (Partnerships for the Goals) through stakeholder networks and institutional collaboration. It also provides a supporting linkage to SDG 4 (Quality Education) through entrepreneurial skills, talent development, and knowledge creation. The model therefore extends entrepreneurial ecosystem research by connecting regional MSME development with the broader sustainable development agenda and provides a conceptual basis for policymakers, financial institutions, universities, industry associations, and development agencies seeking to build resilient and sustainable MSME ecosystems.
Keywords: Entrepreneurial Ecosystem; MSME Sustainability; Firm Performance; Industrial Clusters; Sustainable Development Goals; SDG 8; SDG 9; North Karnataka
Abstract
A Study to Design an Outcome Based Curriculum to Teach English as a Second Language
Dr.C.Ramakrishnan
DOI: 10.17148/IARJSET.2026.13827
Abstract: An Outcome-Based Education (OBE) framework for English prioritizes demonstrable student competencies over the mere coverage of content. Unlike traditional syllabi that list texts and topics, an OBE curriculum begins with the end in mind: defining exactly what students should be able to do with language upon completion. The design process starts by identifying clear, measurable exit outcomes. Where English is taught as second language, this might be: "Students will be able to craft a persuasive argument supported by textual evidence," or "Students will be able to analyse how an author’s use of literary devices shapes meaning." These overarching goals then cascade down to specific learning objectives for each unit. Curriculum is then built backwards from these outcomes. If the goal is persuasive writing, the unit isn't just about reading famous speeches; it’s structured around deconstructing rhetorical appeals, practicing thesis formulation, and peer-reviewing drafts. Texts become the vehicle for achieving the skill, not the destination itself. Generally, a novel is studied not just for its plot, but to enable students to analyse dystopian themes and evaluate arguments about power. Assessment in this model is authentic and continuous. Students demonstrate proficiency through performances of understanding—such as composing a literary analysis essay, delivering a speech, or creating a multi- modal narrative. Rubrics articulate the criteria for success, providing a transparent roadmap for both instruction and evaluation. Ultimately, an outcome-based English curriculum ensures students graduate not just reading books but possessing effective communication and critical thinking are essential for their future.
Keywords: Syllabi, outcomes, second language, curriculum
Keywords: Syllabi, outcomes, second language, curriculum
Abstract
Fake Profile Detection Using Machine Learning
Dhanush K, Saniya S J, Spoorthi D, Chinmayee K N, Geethanjali S G
DOI: 10.17148/IARJSET.2026.13828
Abstract: This paper proposes an With the rapid growth of social networking platforms, fake profiles have become a major concern as they are often used for spreading misinformation, scams, phishing, and other malicious activities. Detecting these fake profiles is essential to ensure user safety and platform integrity. This paper presents a machine learning–based approach to identify fake profiles by analysing behavioural, structural, and content-based features. The system collects user data such as friend count, posting frequency, profile completeness, message patterns, and interaction rates. These features are then preprocessed and fed into supervised learning algorithms such as logistic regression to classify profiles as genuine or fake. The model’s performance is evaluated using metrics like accuracy, precision, recall, and F1-score. Experimental results demonstrate that machine learning techniques can effectively distinguish fake profiles with high accuracy, thereby providing a reliable solution for social media security and trust enhancement.
This project aims to design and implement an effective fake profile detection system using various machine learning algorithms. The system utilises a Twitter profile dataset containing both genuine and fake user accounts. Genuine accounts are categorised as TFP and E13, while fake ones are classified as INT, TWT, and FSF. Several attributes— such as the number of followers, friend count, frequency of status updates, account creation date, and profile completeness—are extracted and analysed to distinguish between real and fake accounts.
This project aims to design and implement an effective fake profile detection system using various machine learning algorithms. The system utilises a Twitter profile dataset containing both genuine and fake user accounts. Genuine accounts are categorised as TFP and E13, while fake ones are classified as INT, TWT, and FSF. Several attributes— such as the number of followers, friend count, frequency of status updates, account creation date, and profile completeness—are extracted and analysed to distinguish between real and fake accounts.
Abstract
PERCEPTION OF LABOUR CONTRACT SOCIETIES ON LABOUR PRODUCTIVITY IN KERALA
Dr.Suguna.S, Riji.T
DOI: 10.17148/IARJSET.2026.13830
Abstract: Labour Contract Societies (LCS) perform a vital part in coordinating and balancing the participation of workforce in different sectors of Kerala. Considering the perception of members of LCS, the present work is a maiden attempt to evaluate the factors influencing productivity. The objective is to study the perception of labour contract societies on Labour Productivity in Kerala. A total of 500 valid responses were obtained and the data were analysed using percentage analysis, descriptive statistics, correlation analysis, and regression techniques. The statistically observed results clearly proved that the majority of explanatory constructs such as job involvement, organisational commitment,, training and skill competence finally, the team cohesiveness are significant predictors of the outcome construct (labour productivity). Overall, a fair tactics that are mingled with skill progression, participative management, supportive but non-intrusive guidance, and a secured organisational climate can explicitly elevate productivity among labour in the labour contract societies also there is a need among LCS to administer human centric practices to constantly achieve elevated productivity.
Keywords: Labour Contract, Society, Organisation, Job, Productivity, etc..
Keywords: Labour Contract, Society, Organisation, Job, Productivity, etc..