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Keywords: Contract Farming, Market Access, Farmers, Digital Agriculture, Smart Contracts, Supply Chain, Price Assurance.
VOLUME 13, ISSUE 9, SEPTEMBER 2026
Publication is in progress..
Church Leaders’ Role in Influencing Health Services: A Sub-Saharan African Perspective
Lucy R. Kimaro
MONKEYPOX TRANSMISSION DYNAMICS IN A POPULATION OF MEN WHO HAVE SEX WITH MEN: A MATHEMATICAL MODELLING APPROACH
Adepoju O. A., Ibrahim H. O*., Awojugbagbe J. I., Olanrewaju S. O., Salahu W. O., Adejumo A. E.
SecureCrowd: Fraud Detection and Trust Analysis in Crowdfunding
Atigadda Rama Devi, Panyashree K Murthy, P Dimple, Sonu C K, Dr. Ranjeet Kumar
An Empirical Assessment of Emotional Intelligence and Work–Life Balance among Women Academicians in Self-Financing Colleges in Chennai
I. LAKSHMI, Dr. P. MANJULA
Student Perceptions of Institutional Facilities and Services: An Exploratory Factor Analysis
Dr. Nitin Shrigiriwar, Prof. Charlie Fulzele
Machine Learning-Based Predictive Modeling for Early Detection of Subclinical Mastitis in Dairy Herds
Chethan M.S, Anand Inamdar, Chinmay P Gowda, Samarth Biradar, Prof. Dinesh Babu K B
From Skin to Self: A Neurocognitive–Psychological Taxonomy of Tattoo Motivation among Generation Z
Purvi Gupta, Prof. (Dr.) Bhupendra Kumar, Dr. Deepankar Singh, Dr. Anil Kumar
Self-Alienation and the Fragmented Self in Haruki Murakami's Kafka on the Shore and After Dark: A Psychoanalytic and Existential Reading
Ms. P.M. Manjusha, Dr.S. Henry Kishore
Synergistic Effect of Neem-Ginger and Garlic- Clove Extracts in Inhibiting Early Biofilms of Pseudomonas aeruginosa
Tadiboina Venkata Poojitha, Balla Sowmya, Yerra Sai Pravallika, Dharanikota Lakshmi Prasanna, Tillapudi Jaya Swaroopa, Sanchita Rani Roy
A Multi-agent Approach to Bioremediation of Organophosphate Pesticide Contaminated Soil: Bacteria, Fungi, Plant Extracts and Animal By- products.
Dharanikota Lakshmi Prasanna, Tillapudi Jaya Swaroopa, Sanchita Rani Roy, Tadiboina Venkata Poojitha, Balla Sowmya, Yerra Sai Pravallika
DIGITAL ADOPTION DRIVERS INFLUENCING OVERALL PERFORMANCE OF GROCERY RETAILERS SELLING ONLINE IN THE NORTHERN DISTRICTS OF KERALA
Dr. Arunpriya S, Hashik P P
Experimental Hardware Framework for Hybrid Cooling in EV Battery Thermal Safety
Leela Narasimha Viyyapu, Neelima Devi Chinta, Kilari Rakesh, Pradesh, India
Perceptional Analysis on the COVID-19 Digital Payment System: A Study
Syed Niyamath Pasha, Dr. Raji S.
An Approach to Designing a Novel Cognitive Transformation BI Framework for Realtime Intelligence with Adaptive Feedback
Rahul Chandrayan
Design And Implementation of an AI Powered Attendance System Using Natural Language Processing and Speech Recognition
Ms. Mayuri Muralidhar Rane, Prof. Pratiksha. R. Lad
Design And Implementation of an AI-Based Voice Chat Application Using Natural Language Processing and Speech Recognition
Ms. Pratiksha. A. Patil, Prof. Shubham. M. Lotwala
A STUDY ON EMPLOYEE PERCEPTION AND HR PERSONNEL ATTITUDES TOWARDS E-HRM PRACTICES IN DELHI NCR HOTELS
S. Yoganand, V. Jayaraman
Role of Azimuthal Attractive Fields in Shock Wave Formation in Non-Conducting Gase
Dr. Sarvesh Chandra Yadav, Divya Singh
SMART DEEPFAKE DETECTION SYSTEM USING CNN FOR DIGITAL IMAGE AUTHENTICITY
K. V. S. Radha Madhavi, Dr. D. Kishore Babu
Design and Evaluation of a Novel Model of IoT- Based Smart Grain Market
Parteek Singh and Dr.Harish Rohil
Data-Driven State-Wise Solar Energy Forecasting Using Advanced Machine Learning Models
Dr. P. Jayasaradadevi, Dr. M.V.S. Ramalakshmi, Dr. B. Ashok
Digital Selves and Embodied Realities: Cyborg Identity and Posthuman Subjectivity and Intertextuality in Patricia Lockwood’s No One Is Talking About This
Vasanthi A, Akshaya CU
Mechanical Analysis of Dodging and Direction-Change Skills (Ring Play and Chain Evasion) in Kho-Kho
Dr. Pathan Yusufkhan Hidayatkhan
AI-Based Prediction and Identification of Financial (Banking & Insurance) Needs Based on Demography and Economic / Farming Cycle
Miss. Devayani S. Shinde, Prof. Miss. Pratiksha R. Lad
Dream Weaver: Turning Words into Images with Artificial Intelligence Iterative Prompt Relabeling for Text-to-Image Diffusion Model Training
T. Supriya, Dr. Nazeer Shaik
The New Public Governance: Tracing the Shift from Public Administration and New Public Management
Dr. Gopesh Sharma
A MORPHOSTYLISTIC ANALYSIS OF MARK TWAIN’S “TRAIN TO DARJEELING”
Dr Prabhavati Arvind Patil
ASSURED CONTRACT FARMING SYSTEMS FOR STABLE MARKET ACCESS
Miss.Snehal Vinod Vyawahare, Prof.Miss Pratiksha Lad
Mobile App for Direct Market Access for Farmers
Miss. Harshali Varankar, Pratiksha Lad
FITNESS AND SPORTS: A TECHNOLOGY- ASSISTED APPROACH FOR ACTIVITY MONITORING AND MOTIVATION
Mr. Chetan D. Rajput, Prof. Miss. Pratiksha R.Lad
A WIKI-BASED DIGITAL KNOWLEDGE SHARING PLATFORM FOR WATER- EFFICIENT TECHNIQUES AND MINIMIZING WATER SCARCITY
Mr. Rohit Yashwant Sonawane, Pratiksha R. Lad
DEVELOPMENT OF AN EDUCATIONAL GAME
Ms. Samiksha. V. Patil, Prof. Shubham Lotwala
AI DRIVEN CROP DISEASE PREDICTION AND MANAGEMENT SYSTEM
Miss. Hemlata A. Patil, Prof. Mr. Shubham M. Lotwala
PARSING OF SOCIAL MEDIA FEEDS: AN ADAPTIVE FRAMEWORK
Miss. Kajal Hemant Patil, Asst. Prof. Shubham Lotwala
SUSTAINABLE FERTILIZER USAGE OPTIMIZER FOR HIGHER YIELD
Miss.Gayatri C. Shinde, Asst.Prof. Pratiksha Lad
Explanation versus Prediction in Macroeconomic Modelling: Reconciling Econometric and Machine-Learning Evidence on Nigerian FDI Ofierohor Ufuoma Earnest
Ofierohor Ufuoma Earnest
Abstract
PREPARATION AND EVALUATION OF HERBAL SHAMPOO
Sharvari Sachin Badade, Dr. Nitin M Gawai.
Abstract: The present work describes the preparation and evaluation of a herbal shampoo formulated from naturally derived ingredients such as shikakai, reetha, amla and selected essential oils. The herbal materials were processed and incorporated into a shampoo formulation. The finished preparation was assessed using quality parameters including physical appearance, pH, solid content, viscosity, dirt dispersion, wetting time, skin sensitization and foaming ability. The formulation showed a pH of 5.1, 4.76% solid content and a recorded viscosity of 2.6 cP. Light dirt dispersion, a wetting time of 48 seconds, good foaming ability and no skin irritation in the reported observation were recorded. The formulation also showed physical stability during the one-month observation period. The findings indicate acceptable physical and physicochemical characteristics under the reported evaluation conditions.
Keywords: Herbal shampoo; shikakai; reetha; amla; hibiscus; aloe vera; pH; formulation; evaluation
Keywords: Herbal shampoo; shikakai; reetha; amla; hibiscus; aloe vera; pH; formulation; evaluation
Abstract
Church Leaders’ Role in Influencing Health Services: A Sub-Saharan African Perspective
Lucy R. Kimaro
DOI: 10.17148/IARJSET.2026.13901
Abstract: In Sub-Saharan Africa of the twenty first century many nations are still challenged by the existence of poor health service systems where majority of the people cannot access preventive and curative health services. Church leaders have played a very crucial role in influencing the health services through the establishment of facilities that promote physical, emotional, spiritual health care and health of the soul. The paper highlights some initiatives taken by church leaders in providing health care and empowering communities to take responsibility for their own health. The paper further examines the role of the church leaders in providing spiritual and psychological healing of the Christian faithful. Church leaders have potentials to enhance public health services. The paper discussed some of the conflicts regarding the beliefs on health services as emphasized by various church leaders. Some of the conflicts are associated with globalization and world view regarding the human suffering in our time. The paper highpoints some issues of compassion, justice and ethical principles applied in medical field which strengthen or weaken the health services in contemporary sub-Saharan Africa. The question is how do church leaders handle some of the sociocultural matters that affect individual’s health today? Pope Francis says “health service is a right not a business”, also in pacem in terris health care is a right, so human dignity should be given priority. The paper will end up with recommendations and conclusion.
Keywords: Health services, Church Leaders, Human dignity, Compassion,socio-cultural practices, healing ministries.
Keywords: Health services, Church Leaders, Human dignity, Compassion,socio-cultural practices, healing ministries.
Abstract
MONKEYPOX TRANSMISSION DYNAMICS IN A POPULATION OF MEN WHO HAVE SEX WITH MEN: A MATHEMATICAL MODELLING APPROACH
Adepoju O. A., Ibrahim H. O*., Awojugbagbe J. I., Olanrewaju S. O., Salahu W. O., Adejumo A. E.
DOI: 10.17148/IARJSET.2026.13902
Abstract: The spread of monkeypox within a human population is investigated in this study using a mathematical model that incorporates the administration of two vaccine doses. The model’s fundamental properties including the non- negativity and boundedness of its solutions are established to ensure its biological and epidemiological significance. The effective reproduction number, an important threshold for assessing disease transmission, is derived using the next generation matrix operator and used to analyse the conditions for the existence of equilibrium states of the model. In addition, the normalized forward sensitivity is performed on the effective reproduction number to identify parameters that strongly influence the spread of the disease in the population. The results provides useful insights into effective control strategies and appropriate intervention measures in reducing monkeypox transmission within the population
Abstract
SecureCrowd: Fraud Detection and Trust Analysis in Crowdfunding
Atigadda Rama Devi, Panyashree K Murthy, P Dimple, Sonu C K, Dr. Ranjeet Kumar
DOI: 10.17148/IARJSET.2026.13903
Abstract: Crowdfunding has emerged as an accessible means of raising financial assistance for medical needs, education, disaster response, social welfare, and community initiatives. Despite its benefits, the open and decentralized nature of online fundraising can expose donors to fraudulent campaigns, inaccurate information, inadequate verification, and uncertainty regarding campaign credibility. This paper presents SecureCrowd, a web-based crowdfunding platform designed to support fraud identification and trust-oriented campaign verification through machine learning and administrative assessment. The proposed system enables campaign creators to submit fundraising requests together with relevant supporting documents, while administrators evaluate campaigns using machine- learning predictions and documentary evidence before approval. Logistic Regression, Decision Tree, and Random Forest classifiers are employed, and their individual predictions are combined through majority voting to obtain the final classification. Campaign characteristics such as fundraising category, intended purpose, required amount, beneficiary information, urgency, previous campaign activity, description-related attributes, contact information, and supporting-document details are considered during the analysis. SecureCrowd is developed using Python and Django, with SQLite for data storage and HTML, CSS, JavaScript, Scikit-learn, and Pandas for application and data-processing functions. The platform provides role-based access, campaign management, document handling, donation tracking, verification workflows, and fundraising-progress visualization. Functional testing indicates successful operation of the major authentication, campaign, verification, donation, database, and dashboard functions. The proposed platform demonstrates the potential of combining machine-learning-based analysis with human administrative verification to promote greater transparency and confidence in online crowdfunding.
Keywords: crowdfunding, fraud detection, trust analysis, machine learning, campaign verification, Django, donation management
Keywords: crowdfunding, fraud detection, trust analysis, machine learning, campaign verification, Django, donation management
Abstract
An Empirical Assessment of Emotional Intelligence and Work–Life Balance among Women Academicians in Self-Financing Colleges in Chennai
I. LAKSHMI, Dr. P. MANJULA
DOI: 10.17148/IARJSET.2026.13904
Abstract: Women academicians in higher education often face multiple demands arising from their professional responsibilities and personal lives, making work–life balance an important area of concern. This study empirically assesses work–life balance among women academicians working in self-financing colleges in Chennai, with particular attention to work–life imbalance, depression, and psychological stress. The study also examines the relationship among these variables and explores the role of emotional well-being in managing work and personal responsibilities. Primary data were collected through a structured questionnaire from women academicians working in selected self-financing colleges in Chennai. Descriptive statistics, factor analysis, ANOVA, and the Friedman test were employed to analyse the data. The findings provide insights into the factors associated with work–life balance and the extent to which imbalance is related to depression and psychological stress among women academicians. The study further establishes the interrelationship between work–life balance, work–life imbalance, depression, and psychological stress. The findings highlight the importance of supportive institutional practices, employee-friendly policies, and workplace well-being initiatives to help women academicians manage competing professional and personal responsibilities more effectively. The study contributes to the existing literature by providing empirical evidence on work–life balance and its association with psychological well-being among women academicians in the context of self-financing colleges in Chennai.
Keywords: Emotional Intelligence, Work–Life Balance, Women Academicians, Self-Financing Colleges, Higher Education, Chennai, Workplace Well-being.
Keywords: Emotional Intelligence, Work–Life Balance, Women Academicians, Self-Financing Colleges, Higher Education, Chennai, Workplace Well-being.
Abstract
Student Perceptions of Institutional Facilities and Services: An Exploratory Factor Analysis
Dr. Nitin Shrigiriwar, Prof. Charlie Fulzele
DOI: 10.17148/IARJSET.2026.13905
Abstract: Student perceptions of institutional facilities and services provide important evidence for evaluating institutional quality and identifying areas requiring improvement. The present study examines the underlying structure of students' perceptions of institutional facilities and services using Exploratory Factor Analysis (EFA). Primary data were collected from 91 students through a structured questionnaire consisting of 11 statements measured on a five-point Likert scale ranging from 1 = Strongly Disagree to 5 = Strongly Agree. The variables covered institutional premises, canteen, library, scholarship assistance, teaching, extracurricular activities, sports, annual social gathering, bus facility, placement initiatives, and mega placement drive. The data demonstrated strong suitability for factor analysis, with a Kaiser-Meyer-Olkin (KMO) value of 0.880 and a highly significant Bartlett's Test of Sphericity (χ² = 932.715, df = 55, p < 0.001). The overall internal consistency of the 11-item instrument was excellent (Cronbach's alpha = 0.946). The first eigenvalue was 7.289 and explained 66.26% of the total variance, while the second eigenvalue was 1.106 and explained 10.06%. Although the Kaiser criterion would indicate a two-factor solution, parallel analysis indicated that only the first empirical eigenvalue exceeded the corresponding random-data eigenvalue. A one-factor Principal Axis Factoring solution therefore provides the most parsimonious interpretation of the present data. Factor loadings were generally strong, ranging from 0.440 to 0.930, although the canteen item showed comparatively weaker commonality. The findings suggest that students tend to perceive institutional facilities, academic support and student-oriented services as interconnected components of a broader institutional experience rather than as completely independent dimensions. The study provides practical implications for institutional quality improvement and recommends further validation using a larger sample and Confirmatory Factor Analysis.
Keywords: Student perception, higher education, institutional facilities, student satisfaction, exploratory factor analysis, KMO, Bartlett's test, parallel analysis, service quality
Keywords: Student perception, higher education, institutional facilities, student satisfaction, exploratory factor analysis, KMO, Bartlett's test, parallel analysis, service quality
Abstract
Machine Learning-Based Predictive Modeling for Early Detection of Subclinical Mastitis in Dairy Herds
Chethan M.S, Anand Inamdar, Chinmay P Gowda, Samarth Biradar, Prof. Dinesh Babu K B
DOI: 10.17148/IARJSET.2026.13906
Abstract: Subclinical mastitis (SCM) is a symptomless yet economically damaging udder infection that silently reduces milk yield and quality in dairy cattle, making early identification difficult through conventional visual inspection. This paper presents a machine-learning based predictive framework for early SCM detection using three routinely measurable and low-cost milk parameters: Somatic Cell Count (SCC), pH, and Electrical Conductivity (EC). A dataset of 5,000 milk samples (14.6% mastitis-positive) was enhanced through feature engineering, including logarithmic transformation and SCC-to-EC ratio, and rolling statistical features that capture infection trends over time. Logistic Regression and LightGBM classifiers were trained and compared, and the tuning of LightGBM using Optuna-based automated hyperparameter tuning achieved a test accuracy of approximately 98.8%, with precision of 0.93, recall of 0.96, and an F1-score of 0.94, outperforming Logistic baseline. The SHAP (Shapley Additive Explanations) analysis confirmed SCC and EC as the dominant predictive features, consistent with established veterinary knowledge, and validated the biological relevance of the engineered features. The resulting framework provides an accurate, interpretable, and cost- effective decision-support tool suitable for real-world dairy farm deployment.
Keywords: subclinical mastitis, machine learning, LightGBM, somatic cell count, electrical conductivity, explainable AI, SHAP, precision dairy farming
Keywords: subclinical mastitis, machine learning, LightGBM, somatic cell count, electrical conductivity, explainable AI, SHAP, precision dairy farming
Abstract
From Skin to Self: A Neurocognitive–Psychological Taxonomy of Tattoo Motivation among Generation Z
Purvi Gupta, Prof. (Dr.) Bhupendra Kumar, Dr. Deepankar Singh, Dr. Anil Kumar
DOI: 10.17148/IARJSET.2026.13907
Abstract: Tattooing has progressively shifted from a socially marginal form of body modification toward a mainstream practice of symbolic self-expression, identity construction and social communication. National survey evidence now places tattoo prevalence among U.S. adults under 30 at roughly two in five, with women aged 18–29 substantially more likely to be tattooed than men in the same cohort (Pew Research Center, 2023). Existing scholarship has identified diverse motives for tattooing, including uniqueness, self-expression, appearance, affiliation, emotional expression, religious meaning and social influence. However, these motivations have generally been examined as separate psychological or sociological phenomena, with limited integration of developmental neuroscience, social cognition and identity-based decision-making. In particular, the literature does not yet provide a comprehensive, empirically testable taxonomy explaining why members of Generation Z choose to permanently inscribe particular meanings onto their bodies.
This article develops a neurocognitive and psychological framework for explaining tattoo motivation among Generation Z. Drawing on research in body modification, identity formation, social influence, reward processing, self-referential cognition, attachment, intrinsic motivation and lived religion, the study proposes four primary motivational profiles: Relational–Affiliative Inscription, Sacred–Transcendent Inscription, Identity–Autonomous Inscription, and Social–Normative/Status Inscription. A fifth mechanism, Experiential–Affective Inscription, is proposed as a cross-cutting pathway rather than a mutually exclusive category, because emotional regulation, sensation seeking and transformation may operate across the four principal profiles.
The proposed Neurocognitive Body-Inscription Model (NBIM) conceptualizes tattoo decision-making as a sequential process in which an internal or social cue is evaluated through personal meaning, identity relevance, anticipated emotional value, social valuation and perceived permanence. These processes contribute to subjective utility and behavioural intention, followed by reinforcement through memory, identity consolidation and social feedback. The framework does not claim that specific brain regions independently “cause” particular tattoo motivations; rather, it integrates established neural systems involved in self-referential processing, social cognition, reward valuation and affective salience.
The article proposes a mixed-method empirical design combining psychometric measurement, latent profile analysis, structural equation modelling and, in a subsequent subsample, neurophysiological validation. The resulting taxonomy could contribute to behavioural science, consumer psychology, body-image research, developmental neuroscience and the emerging literature on neuro consumer behaviour, while providing a methodological basis for distinguishing intrinsic identity-driven tattooing from socially induced or norm-sensitive tattoo adoption.
Keywords: Generation Z; tattoo motivation; body modification; identity; social influence; neurocognition; reward processing; self-reference; attachment; religiosity; latent profile analysis; behavioural neuroscience.
Abstract
Self-Alienation and the Fragmented Self in Haruki Murakami's Kafka on the Shore and After Dark: A Psychoanalytic and Existential Reading
Ms. P.M. Manjusha, Dr.S. Henry Kishore
DOI: 10.17148/IARJSET.2026.13908
Abstract: This paper investigates the theme of self-alienation in Haruki Murakami's Kafka on the Shore and After Dark. Murakami's fiction consistently portrays protagonists who struggle with fractured identities, emotional isolation, and existential uncertainty within postmodern society. While previous scholarship has extensively explored alienation in Norwegian Wood and Colorless Tsukuru Tazaki and His Years of Pilgrimage, comparatively less attention has been devoted to a comparative study of these two novels through the lens of self-alienation.
Using the theories of Erich Fromm, Karen Horney, and Jean-Paul Sartre, the study analyzes how Kafka Tamura, Nakata, Mari Asai, and Eri Asai negotiate fragmented identities, loneliness, memory, and interpersonal disconnection. The paper argues that Murakami presents self-alienation not merely as psychological suffering but as a transformative process through which characters reconstruct identity. Ultimately, the novels depict alienation as an unavoidable consequence of modern existence while simultaneously suggesting the possibility of healing through self-awareness and human connection.
Keywords: Self-alienation, identity, existentialism, psychoanalysis, postmodernism, Haruki Murakami
Using the theories of Erich Fromm, Karen Horney, and Jean-Paul Sartre, the study analyzes how Kafka Tamura, Nakata, Mari Asai, and Eri Asai negotiate fragmented identities, loneliness, memory, and interpersonal disconnection. The paper argues that Murakami presents self-alienation not merely as psychological suffering but as a transformative process through which characters reconstruct identity. Ultimately, the novels depict alienation as an unavoidable consequence of modern existence while simultaneously suggesting the possibility of healing through self-awareness and human connection.
Keywords: Self-alienation, identity, existentialism, psychoanalysis, postmodernism, Haruki Murakami
Abstract
Synergistic Effect of Neem-Ginger and Garlic- Clove Extracts in Inhibiting Early Biofilms of Pseudomonas aeruginosa
Tadiboina Venkata Poojitha, Balla Sowmya, Yerra Sai Pravallika, Dharanikota Lakshmi Prasanna, Tillapudi Jaya Swaroopa, Sanchita Rani Roy
DOI: 10.17148/IARJSET.2026.13909
Abstract: The rod-shaped, gram-negative bacterium Pseudomonas aeruginosa is extensively researched in biofilm studies. Because of its highly resistant biofilm-forming capabilities, this bacterium causes persistent infections in patients with cystic fibrosis, urinary infections, infections linked to medical devices, and chronic wounds that are challenging to treat. The current preliminary study uses natural extracts from the solvent extraction process, such as neem, ginger, garlic, and clove, in synergistic combinations to prevent biofilm formation in its early phases. This initial investigation analyses the synergistic effects of neem-ginger and garlic-clove combinations at five concentrations for each combination. The crystal violet assay is used to measure the extract's effectiveness in preventing early biofilm. This observation showed that each combination had a different range of inhibition. Depending on the concentration of the extracts applied, the garlic-clove combination produced consistent inhibition ranging from 32% to 57%, while the neem-ginger combination showed varying inhibition from 0% to 44%.
Keywords: Pseudomonas aeruginosa; Biofilm inhibition; Synergistic effect; Crystal violet assay.
Keywords: Pseudomonas aeruginosa; Biofilm inhibition; Synergistic effect; Crystal violet assay.
Abstract
A Multi-agent Approach to Bioremediation of Organophosphate Pesticide Contaminated Soil: Bacteria, Fungi, Plant Extracts and Animal By- products.
Dharanikota Lakshmi Prasanna, Tillapudi Jaya Swaroopa, Sanchita Rani Roy, Tadiboina Venkata Poojitha, Balla Sowmya, Yerra Sai Pravallika
DOI: 10.17148/IARJSET.2026.13910
Abstract: Pesticides are potentially hazardous chemicals that have a long half-life in the environment. They can be toxic to both aquatic and terrestrial organisms, endangering human and environmental health . The current study was conducted to evaluate the efficiency of the bioremediation process. Bacterial strains(Bacillus.coagulans,B.licheniformis,B.subtilis)from(B1-B7;B1-Bacilluscoagulans),(B2-B.licheniformis),(B3- B.subtilis),(B4-B.C+B.L), to (B5-B.L+B.S), (B6-B.S+B.C),(B7-B.C+B.L+B.S), fungal strains (Botrytis cinerea, Alternaria alternata, Saccharomyces cerevisiae, Rhizopus stolonifer, Penicillium chrysogenum) Obtained from Tomato,yeast and bread ( F1- Tomato cultures),(F2-Bread cultures),(F3-yeast),( F4- comb of Tomato+bread),(F5- bread+ yeast),(F6- Yeast+Tomato),(F7- Bread, Tomato,yeast), plant extracts (soap nuts, moringa seeds, fenugreek) from( P1-P7 P1- Soap Nut Extract; P2- Fenugreek Extract; P3- Moringa Extract; P4- Soap Nuts + Moringa; P5-Fenugreek Extract + Moringa Extract; P6- Soap Nuts Extract + Fenugreek Extract; and P7- Fenugreek + Moringa Extract + Soap Nut Extract), and animal-derived substrates (cow dung, fish scales, eggshells)from(A1-A7,A1-cowdung,A2-eggshells,A3- fishscales,A4-CD+ES,A5-ES+FS,A6-FS+CD,A7-CD+FS+ES).Along with these we also used combinations of two , in which first one is (plant + animal derivatives) and the second is(Bacterial+fungi specifies)were used in the experiment to treat the soil contaminated by 20% Emulsifiable Concentrate (EC) chlorpyrifos pesticide. The effectiveness of treatment was investigated using chemicals such as copper sulphate (CuSo4) and sodium hydroxide (NaOH), microbe growth measurements, organoleptic evaluation (including changes in the colour and smell of treated soil), and pH measurement methods. Certain combinations such as (B5,F3, F4, F5, F7,P6,A1,A2)of factors have been shown to degrade pesticides in soils by up to 90%.
Keywords: Bioremediation; Organophosphate Pesticide; Cow dung; Soil microbial degradation; Pesticides Contaminated Soil.
Keywords: Bioremediation; Organophosphate Pesticide; Cow dung; Soil microbial degradation; Pesticides Contaminated Soil.
Abstract
DIGITAL ADOPTION DRIVERS INFLUENCING OVERALL PERFORMANCE OF GROCERY RETAILERS SELLING ONLINE IN THE NORTHERN DISTRICTS OF KERALA
Dr. Arunpriya S, Hashik P P
DOI: 10.17148/IARJSET.2026.13911
Abstract: The rapid growth of digital platforms has considerably renovated the grocery retail promotions, specifically facilitating the grocery retailers to operate and reach beyond the boundaries of physical stores. This work explores the adoption of digital drivers influencing the overall strategic performance of grocery retail outlets selling online in the district of North Kerala. The objective is to study the digital adoption drivers influencing overall performance of grocery retailers selling online. The researcher obtained 480 grocery retailers from the study area. The grocery retailers selling online were selected by using convenient sampling method. Statistical Tools such as Percentage Analysis, Descriptive Statistics, Correlation and Regression are used for the study. Result shows digital adoption drivers fluctuate in their effects on performance outcomes. Significant association was found positive with digital readiness and perceived benefits demonstrating that adequate infrastructure and acceptance of digital world offers more vital support to strategic performance. In conclusion, the result highlights that a consistent digital initiative turns the online grocery retail business into strategic, sustainable and profitable.
Keywords: Grocery Retailers, Digital adoption, Online Selling, Strategy, Performance, etc.
Keywords: Grocery Retailers, Digital adoption, Online Selling, Strategy, Performance, etc.
Abstract
Experimental Hardware Framework for Hybrid Cooling in EV Battery Thermal Safety
Leela Narasimha Viyyapu, Neelima Devi Chinta, Kilari Rakesh, Pradesh, India
DOI: 10.17148/IARJSET.2026.13912
Abstract: Electric vehicles rely on lithium-ion batteries whose performance and safety are highly dependent on effective thermal regulation. Excessive heating can reduce capacity, accelerate aging, and trigger thermal runaway. This study focuses on the hardware design and implementation of a hybrid cooling framework that integrates passive and active elements for EV battery thermal management. The system employs a Phase Change Material (PCM) simulation to absorb excess heat and stabilize battery temperature, while an Arduino-based control unit coordinates sensors and actuators. Key hardware components include the Arduino Uno microcontroller, DHT11 temperature and humidity sensor, BLDC motor with driver, potentiometer for speed regulation, cooling fan, buzzer for alarm indication, and LCD display for real-time feedback. The hardware logic ensures that when the temperature exceeds 35°C, the system activates safety measures by stopping the motor, triggering the fan, and sounding an alarm. Once the temperature normalizes, the system resumes operation smoothly. Experimental validation demonstrates reliable monitoring and responsive control, confirming the effectiveness of the hardware framework in enhancing battery safety. This work provides a foundation for scalable EV battery cooling systems and highlights the importance of cost-effective hardware solutions in sustainable transportation.
Keywords: Electric Vehicles, Lithium-Ion Battery, Hybrid Cooling, Phase Change Material, Arduino Hardware, Thermal Management, BLDC motor & driver.
Keywords: Electric Vehicles, Lithium-Ion Battery, Hybrid Cooling, Phase Change Material, Arduino Hardware, Thermal Management, BLDC motor & driver.
Abstract
Perceptional Analysis on the COVID-19 Digital Payment System: A Study
Syed Niyamath Pasha, Dr. Raji S.
DOI: 10.17148/IARJSET.2026.13913
Abstract: The COVID-19 pandemic accelerated the shift from cash to contactless transactions in India, building on momentum already created by the Digital India campaign, demonetisation and inexpensive mobile data. This paper examines consumer perception of digital payment systems in the post-pandemic setting. Primary data were gathered from 100 respondents through a structured questionnaire and analysed using percentage analysis and mean scores on a five- point Likert scale, supported by secondary data from RBI and NPCI publications. The study finds that ATM/debit cards and Google Pay are the most widely used instruments, that round-the-clock availability, cashless convenience and time saving are the strongest perceived benefits, and that poor connectivity, technical errors and server failures are the most severe difficulties. Ninety-four per cent of respondents intend to continue using digital payments, indicating durable acceptance.
Keywords: Digital payment system, COVID-19, consumer perception, UPI, cashless transactions, e-wallets, demonetisation.
Keywords: Digital payment system, COVID-19, consumer perception, UPI, cashless transactions, e-wallets, demonetisation.
Abstract
An Approach to Designing a Novel Cognitive Transformation BI Framework for Realtime Intelligence with Adaptive Feedback
Rahul Chandrayan
DOI: 10.17148/IARJSET.2026.13915
Abstract: In today’s competitive business landscape, effective decision making become no more challenge and no need to relay on the experience and expertise if and only if you have the right data available at every stage of business cycle. BI framework proved to be an essential element for each and every decision-making in day-to-day business activities moreover BI framework enhance performance and provide quick, precise, and pertinent facts. Designing the BI framework become a significant challenge since it has a various objective to be fulfilled to make it sustain business dynamics and evolving markets and also due to large volumes of heterogeneous, dynamic, and distributed data Further the BI framework must integrate data from multiple sources, ensure data quality and security, support advanced analytics, and convert data into actionable insights for decision-making. Moreover, a BI framework needs to be flexible and scalable so that it can adapt to changing business requirements and technological environments.
Keywords: Business Intelligence, BI Framework, Decision Support System (DSS), Strategic Decision Making, Data Warehouse, Data Mart, OLAP, Data Governance, Explainable AI (XAI)
Keywords: Business Intelligence, BI Framework, Decision Support System (DSS), Strategic Decision Making, Data Warehouse, Data Mart, OLAP, Data Governance, Explainable AI (XAI)
Abstract
Design And Implementation of an AI Powered Attendance System Using Natural Language Processing and Speech Recognition
Ms. Mayuri Muralidhar Rane, Prof. Pratiksha. R. Lad
DOI: 10.17148/IARJSET.2026.13916
Abstract: With the growing need for automation and accurate record management, AI-based attendance systems are becoming useful in educational institutions, companies, and government organizations. This research presents an AI- powered attendance system that uses facial recognition to automatically identify individuals and record their attendance. The system uses face detection techniques such as Convolutional Neural Networks to detect and recognize faces. The attendance information is then stored and updated in a database in real time. The system is designed to reduce manual work, save time, and improve the accuracy of attendance records. It also considers common challenges such as fake identity attempts, changes in lighting, and managing a large number of users. Its modular design allows it to be used on different platforms and connected with existing academic or employee management systems. It can be useful in educational institutions, training centers, and organizations where regular attendance needs to be maintained. The expected outcome is an automated and user-friendly attendance system that demonstrates the practical application of AI, NLP, and Speech Recognition technologies.
Keywords: Artificial Intelligence, Attendance System, Natural Language Processing, Speech Recognition, Voice Recognition, Automated Attendance.
Keywords: Artificial Intelligence, Attendance System, Natural Language Processing, Speech Recognition, Voice Recognition, Automated Attendance.
Abstract
Design And Implementation of an AI-Based Voice Chat Application Using Natural Language Processing and Speech Recognition
Ms. Pratiksha. A. Patil, Prof. Shubham. M. Lotwala
DOI: 10.17148/IARJSET.2026.13917
Abstract: Voice-based interaction has become an important human–computer interaction paradigm because it allows users to communicate with software through natural spoken language rather than keyboard or touch input. This paper presents the design and implementation of an AI-based voice chat application that combines automatic speech recognition (ASR), natural language processing (NLP), conversational response generation, and text-to-speech (TTS) synthesis in a single interaction pipeline. The proposed application captures a user's spoken query through a microphone, converts the audio into text, processes the text to identify intent and conversational context, generates an appropriate response, and converts the response back into speech. The research follows an applied experimental design and focuses on system architecture, component integration, usability, response quality, latency, and recognition robustness. The implementation is organized as modular components so that speech recognition, language processing, response generation, and speech synthesis can be independently replaced or improved. The study also considers practical challenges such as background noise, accents, code-switching, ambiguous language, network dependence, privacy, and conversational context. The resulting design demonstrates how AI and speech technologies can be integrated into an accessible conversational interface for educational assistance, information retrieval, productivity support, and general-purpose virtual assistance. The paper concludes that an effective voice chat system requires not only accurate speech recognition but also contextual language understanding, efficient response generation, low interaction latency, and responsible handling of user audio and text data.
Keywords: Artificial Intelligence, Voice Chat, Natural Language Processing, Automatic Speech Recognition, Speech- to-Text, Text-to-Speech, Conversational AI, Human–Computer Interaction.
Keywords: Artificial Intelligence, Voice Chat, Natural Language Processing, Automatic Speech Recognition, Speech- to-Text, Text-to-Speech, Conversational AI, Human–Computer Interaction.
Abstract
The Anchor-and-Expand Principle: Timing Strategic Decoupling in Technology Ecosystems
Vivek Gujar
DOI: 10.17148/IARJSET.2026.13918
Abstract: Technology ecosystems often reward whichever firm first controls a tightly held physical or infrastructural asset — the “anchor” — because that control reduces uncertainty, sets de facto standards, and buys market access for a higher-margin software, service, or workflow layer — the “expansion.” Existing strategy research explains much of why this pattern works, drawing on transaction cost economics, complementary-assets theory, the resource-based view, platform strategy, and dominant-design theory. What it says less about is timing: exactly when an anchor's protective value starts to fade, and when a firm should decouple from it.
This paper develops the Anchor-and-Expand (A-E) Principle and introduces Anchor Half-Life (t½) as a way of describing how long an anchor provides real strategic cover before imitation, standardization, or commoditization erodes it. The framework rests on four linked elements — Anchor Asset (A), Expansion Layer (E), Diffusion Threshold (D) and Anchor Half-Life (t½) — and three propositions concerning anchor value under architectural uncertainty, the “coupling trap” created by staying attached to an anchor too long and the compression of anchor advantage as technology cycles accelerate. Six historical cases — mainframes, personal computing, mobile platforms, cloud infrastructure, software- defined computing and edge AI — illustrate the framework and its limits.
The analysis suggests anchor ownership is most valuable during periods of high uncertainty, but that durable value increasingly comes from making the expansion layer portable across many anchors before diffusion and commoditization take hold. The paper is conceptual rather than empirical: its t½ estimates are directional and illustrative, and the IndoAI case is offered as an application of the framework rather than as independent empirical validation. It closes with a research agenda for measuring t½, gauging diffusion thresholds, and testing the framework across industries.
Keywords: Appization, AI Advisor, AI agents, CCTV Implementation, Edgebox, Edge Computing, Indoai
This paper develops the Anchor-and-Expand (A-E) Principle and introduces Anchor Half-Life (t½) as a way of describing how long an anchor provides real strategic cover before imitation, standardization, or commoditization erodes it. The framework rests on four linked elements — Anchor Asset (A), Expansion Layer (E), Diffusion Threshold (D) and Anchor Half-Life (t½) — and three propositions concerning anchor value under architectural uncertainty, the “coupling trap” created by staying attached to an anchor too long and the compression of anchor advantage as technology cycles accelerate. Six historical cases — mainframes, personal computing, mobile platforms, cloud infrastructure, software- defined computing and edge AI — illustrate the framework and its limits.
The analysis suggests anchor ownership is most valuable during periods of high uncertainty, but that durable value increasingly comes from making the expansion layer portable across many anchors before diffusion and commoditization take hold. The paper is conceptual rather than empirical: its t½ estimates are directional and illustrative, and the IndoAI case is offered as an application of the framework rather than as independent empirical validation. It closes with a research agenda for measuring t½, gauging diffusion thresholds, and testing the framework across industries.
Keywords: Appization, AI Advisor, AI agents, CCTV Implementation, Edgebox, Edge Computing, Indoai
Abstract
A STUDY ON EMPLOYEE PERCEPTION AND HR PERSONNEL ATTITUDES TOWARDS E-HRM PRACTICES IN DELHI NCR HOTELS
S. Yoganand, V. Jayaraman
DOI: 10.17148/IARJSET.2026.13919
Abstract: The research investigates the diverse elements that play a role in advancing human resource development by improving the effects of electronic management on technological performance in Delhi's hotels. The study examines employee perceptions and HR personnel attitudes towards different aspects of Electronic Human Resource Management (E-HRM) in the National Capital Region of Delhi. Two surveys, one for employees and another for Human Resource staff, were conducted at hotels in India's National Capital Region, covering Delhi, Ghaziabad, Faridabad, Noida, and Gurugram. A total of 30 employees and 20 Human Resource staff members were included in the sample. Responses were gathered through a carefully prepared and pre-tested questionnaire. Non-probability convenience sampling was employed for the exploratory study. The findings indicate differing levels of ease in using E-HRM for multiple processes, with HR staff reporting greater comfort in utilizing most processes. Participants shared their views on talent retention, competitive standing, continuous innovation, profitability, and process efficiency associated with E-HRM.
Keywords: electronic human resource management, employee perception, hotel industry, human resource management, Delhi National Capital Region
Keywords: electronic human resource management, employee perception, hotel industry, human resource management, Delhi National Capital Region
Abstract
Role of Azimuthal Attractive Fields in Shock Wave Formation in Non-Conducting Gase
Dr. Sarvesh Chandra Yadav, Divya Singh
DOI: 10.17148/IARJSET.2026.13920
Abstract: This paper examines the development of shock waves in a nonconductive gas with a circumferentially directed attractive force using both analytical and mathematical methods. Shock waves develop in accordance with the principles of classical shock theory when there exists a high degree of one dimensional planarity (such as in a nozzle) governed by the law of conservation of mass, momentum and energy. Nonlinear steepening of compressional waves leads to shock waves. The introduction of a circumferential attractive force modifies the flow significantly as it causes rotationally convergent flow resulting in the redistribution of circumferential momentum. The governing equations for the modified problem which includes the effect of the circumferential attractive force are derived. The impact of the attractive force upon the characteristics of shock waves such as shock structure, shock propagation velocity, and shock stability is evaluated. It is demonstrated that the attractive force accelerates nonlinear steepening thereby increasing shock wave speed, decreasing shock wave thickness and increasing the compression ratio at the shock front. In addition, the presence of the attractive force changes the planar nature of shock fronts to curved or spiral shocks depending upon whether the flow exhibits multidimensional characteristics. Parametric studies were conducted utilizing nondimensional parameters to evaluate the effect of field strength upon various properties associated with shock waves. Numerical simulations were also utilized to illustrate these concepts. The results demonstrate that a circumferentially attractive force can act as a second source of concentrated energy. Thus, the attractive force has significant effects upon the shock dynamics associated with compressible flows. This study establishes a basis for studying field influenced shock phenomena in compressible flows and suggests several avenues for future experimental and numerical research.
Keywords: Shock wave, non-conducting gas, azimuthal attractive field, compressible flow, shock curvature, Mach number, dimensionless analysis
Keywords: Shock wave, non-conducting gas, azimuthal attractive field, compressible flow, shock curvature, Mach number, dimensionless analysis
Abstract
SMART DEEPFAKE DETECTION SYSTEM USING CNN FOR DIGITAL IMAGE AUTHENTICITY
K. V. S. Radha Madhavi, Dr. D. Kishore Babu
DOI: 10.17148/IARJSET.2026.13921
Abstract: The rapid development of generative artificial intelligence has increased the availability of realistic synthetic and manipulated facial images. This paper presents a Smart Deepfake Detection System Using Convolutional Neural Network (CNN) for automated binary classification of facial images into real and fake classes. The work extends the supplied project manuscript by providing a fuller IEEE-style treatment of the problem, preprocessing pipeline, CNN architecture, web deployment, evaluation methodology, security considerations, and reproducibility requirements. The implemented system resizes and normalizes an uploaded image, performs CNN inference, and displays the predicted class and confidence through a Flask-based interface. The supplied project evidence demonstrates two functional cases: a representative genuine face is returned as “Real Face Detected,” while a representative manipulated/synthetic face is returned as “Fake Face Detected.” Because the source material does not provide dataset counts or statistical metrics, this paper deliberately does not fabricate accuracy, precision, recall, F1-score, ROC-AUC, or confusion-matrix values. Instead, it specifies a publication-ready evaluation protocol that can be populated from the actual experimental run.
Keywords: Deepfake detection, convolutional neural network, facial image forensics, synthetic media, image classification, Flask, digital security, deep learning.
Keywords: Deepfake detection, convolutional neural network, facial image forensics, synthetic media, image classification, Flask, digital security, deep learning.
Abstract
Design and Evaluation of a Novel Model of IoT- Based Smart Grain Market
Parteek Singh and Dr.Harish Rohil
DOI: 10.17148/IARJSET.2026.13922
Abstract: Digital agricultural marketplaces can improve price discovery while leaving the physical grain-market workflow largely manual, fragmented and difficult to audit. Here we present an end-to-end Internet of Things (IoT) architecture for a smart grain market that links digital identity, gross and tare weighing, queue allocation, conveyor- assisted unloading, moisture sensing and conditional drying, automated sampling, quality records, e-NAM trading, payment tracking and market-exit control through a persistent lot identifier. The design also integrates low-power monitoring of lighting, water, waste, air quality and transport. Requirements were derived from a baseline survey of 290 grain-market stakeholders in Haryana and Punjab, India. The proposed system was subsequently explained and demonstrated to 702 stakeholders and evaluated using eight feature-level items. Across 5,609 valid item responses, 4,970 indicated approval (88.6%; Wilson 95% confidence interval, 87.7–89.4%). Approval was highest for anticipated time saving (95.0%) and labour reduction (91.6%), and remained above 83% for all evaluated features. These findings establish stakeholder acceptability and a traceable design rationale; they do not establish operational effectiveness because the complete architecture has not yet been tested in a live market. We therefore specify a staged pilot and falsifiable performance measures—including turnaround time, weighing disagreement, queue duration, energy and water use, system uptime and exception resolution—to convert the concept into field evidence. The study reframes the smart mandi as cyber-physical public infrastructure that complements, rather than replaces, the e-NAM transaction layer.
Keywords: Internet of Things; Smart Grain Market; e-NAM; LoRaWAN; Agricultural Marketing; Smart Mandi; Automation; Stakeholder Evaluation
Keywords: Internet of Things; Smart Grain Market; e-NAM; LoRaWAN; Agricultural Marketing; Smart Mandi; Automation; Stakeholder Evaluation
Abstract
Data-Driven State-Wise Solar Energy Forecasting Using Advanced Machine Learning Models
Dr. P. Jayasaradadevi, Dr. M.V.S. Ramalakshmi, Dr. B. Ashok
DOI: 10.17148/IARJSET.2026.13923
Abstract: In this paper, we present a machine learning framework for forecasting state-wise solar power generation in India, leveraging historical solar output and meteorological data. The models employed Random Forest, Gradient Boosting, and XGBoost are designed to capture the complex, nonlinear relationships inherent in weather-influenced energy generation. To enhance predictive performance and model robustness, hyperparameter tuning techniques are applied. Among the approaches, XGBoost outperforms others, demonstrating superior accuracy and adaptability. These findings emphasize the potential of machine learning in advancing solar energy forecasting and enabling data-driven, sustainable energy planning.
Keywords: Solar forecasting, XGBoost, Hyperparameter tuning, Machine learning, Renewable energy
Keywords: Solar forecasting, XGBoost, Hyperparameter tuning, Machine learning, Renewable energy
Abstract
Digital Selves and Embodied Realities: Cyborg Identity and Posthuman Subjectivity and Intertextuality in Patricia Lockwood’s No One Is Talking About This
Vasanthi A, Akshaya CU
DOI: 10.17148/IARJSET.2026.13924
Abstract: The paper draws on the ideas from Haraway (1985) and Hayles (1999) to investigate how a digital techno sphere transforms our selfhoods and relays qualities of circulation and visibility as it pertains to Patricia Lockwood's No one is talking about this. The narrator exists online in what Hayles calls a posthuman identity defined through life in the techno sphere, although Lockwood's narrator as Haraway's cyborg has no name or corporeal singularity ascribed to her. Lockwood’s self is built almost entirely in “the portal” generics; her vocabulary drifts closer to the internet than that of any single assured narrator, and her habits of thought, her jokes, or even her sense of emotion exist to be circulated and exchanged rather than to be contemplated. Julia Kristeva's theory of intertextuality provides a guiding precursor to understand this problem because Lockwood's narrator treats her interior sense of humour as simply an amalgamation of screenshots and memes. The second half of Lockwood's novel depicts the birth of her sister's baby who's hard to see and suffers from Proteus syndrome. These characters are confronted with caregiving, and illness, and loss. In the face of this exposure of vulnerability, Lockwood's narrator starts to find her ironic distance and vocabulary unsuited to suffering, love and the kind of labour with a body that refuses to work for her name. What is poignant about Care work is that while the first half of the book is light-hearted in tone. Since most of the critical readings on Lockwood's novel have tended toward Comics studies, literary experimentation or Cultural Studies of the internet itself, few formal readings engage with Lockwood's use of a textual mythos of the cyborg, or borrowing from Hayles' formal texts.
Keywords: Cyborg, Identity, Intertextuality, Portal, Posthuman, Suffering.
Keywords: Cyborg, Identity, Intertextuality, Portal, Posthuman, Suffering.
Abstract
Mechanical Analysis of Dodging and Direction-Change Skills (Ring Play and Chain Evasion) in Kho-Kho
Dr. Pathan Yusufkhan Hidayatkhan
DOI: 10.17148/IARJSET.2026.13925
Abstract: The runner's ability to evade a pursuing chaser in kho-kho depends on rapid, repeated changes of direction executed within the narrow rectangular lanes of the field -- a skill set locally termed “ring play” and “chain” evasion. Although these evasive manoeuvres are among the most visually distinctive and tactically decisive actions in the sport, they have not previously been analysed using the mechanical frameworks developed for cutting and agility tasks in other team sports. This paper applies principles of rotational kinetics, ground reaction force (GRF) analysis, and centre-of- mass (COM) control to the dodging and direction-change skills of kho-kho, treating each evasive cut as a discrete mechanical event bounded by a braking phase, a reorientation phase, and a re-acceleration phase. Eighteen state-level kho-kho runners (male and female, 17-23 years) were recorded using synchronised high-speed video and a force platform embedded at a marked cutting zone while executing 45°, 90°, and near-135° evasive cuts against a live chaser. Joint angles, COM height, foot-plant distance, and GRF components were extracted and compared between evasions that successfully avoided the tap and those that did not. Successful evaders demonstrated a significantly lower COM height at foot plant, greater knee flexion at initial contact, a shorter penultimate step, and a higher ratio of braking-to-propulsive GRF than unsuccessful evaders. Cut angle was inversely related to completion speed but positively related to evasion success, indicating a speed-accuracy trade-off consistent with agility literature from other sports. These findings characterise kho-kho evasion as a specialised agility skill governed by torque, angular momentum conservation, and frictional limits at the foot-ground interface, and they carry direct implications for both performance coaching and injury- risk awareness at the knee.
Abstract
AI-Based Prediction and Identification of Financial (Banking & Insurance) Needs Based on Demography and Economic / Farming Cycle
Miss. Devayani S. Shinde, Prof. Miss. Pratiksha R. Lad
DOI: 10.17148/IARJSET.2026.13926
Abstract: Banking and insurance needs are rarely static. They shift with age, occupation, household size, income, savings and debt, and, for farming households, with the stage of the agricultural cycle. Most outreach, however, is still built around a fixed product calendar rather than what a household is actually experiencing, so these shifts often get noticed only after they matter. This study proposes an AI-based classification framework that brings demographic details, economic indicators and farming-cycle stage together to flag a customer's likely banking or insurance need. Five supervised learning algorithms - Logistic Regression, Decision Tree, Random Forest, Support Vector Machine and XGBoost - are compared through a structured workflow covering data preprocessing, feature engineering, model training and evaluation. Since no real customer dataset was available for this project, the reported results are illustrative rather than empirical; even so, the ensemble methods, particularly XGBoost and Random Forest, come out as the strongest candidates for capturing the nonlinear interactions among these variables. The framework is meant only as decision support for timing outreach, not as an automated approval or pricing tool.
Keywords: Artificial Intelligence, Machine Learning, Financial Needs, Banking, Insurance, Demography, Economic Cycle, Farming Cycle, Classification, XGBoost
Keywords: Artificial Intelligence, Machine Learning, Financial Needs, Banking, Insurance, Demography, Economic Cycle, Farming Cycle, Classification, XGBoost
Abstract
Dream Weaver: Turning Words into Images with Artificial Intelligence Iterative Prompt Relabeling for Text-to-Image Diffusion Model Training
T. Supriya, Dr. Nazeer Shaik
DOI: 10.17148/IARJSET.2026.13927
Abstract: Diffusion models have shown impressive performance in many domains. However, the model’s capability to follow natural language instructions (e.g., spatial relationships between objects, generating complex scenes) is still unsatisfactory. In this work, we propose Iterative Prompt Relabeling (IPR), a novel algorithm that aligns images to text through iterative image sampling and prompt relabeling with feedback. IPR first samples a batch of images conditioned on the text, then relabels the text prompts of unmatched text-image pairs with classifier feedback. We conduct thorough experiments on SDv2 and SDXL, testing their capability to follow instructions on spatial relations. With IPR, we improved up to 15.22% (absolute improvement) on the challenging spatial relation VISOR benchmark, demonstrating superior performance compared to previous RL methods.
Keywords: Diffusion Models, Text-to-Image Generation, Iterative Prompt Relabeling, Vision–Language Alignment, Spatial Relationship Understanding, Instruction Following, Self-Supervised Learning, Classifier Feedback, Reinforcement Learning, VISOR Benchmark
Keywords: Diffusion Models, Text-to-Image Generation, Iterative Prompt Relabeling, Vision–Language Alignment, Spatial Relationship Understanding, Instruction Following, Self-Supervised Learning, Classifier Feedback, Reinforcement Learning, VISOR Benchmark
Abstract
The New Public Governance: Tracing the Shift from Public Administration and New Public Management
Dr. Gopesh Sharma
DOI: 10.17148/IARJSET.2026.13928
Abstract: Over the last hundred years there has been a transition in the ways in which people think about the operation of government. There have been three major transitions. The first was an emphasis on hierarchy, written rules and a politically neutral civil service. This was associated with Classical Public Administration. The second emphasized efficiency using techniques from the private sector and principles of markets. This was known as New Public Management (NPM). The third is currently being referred to as New Public Governance (NPG). NPG starts from the premise that the production of public services occurs through multiple relationships between organizations and citizens rather than by one single state machine. This study reviews the history of this shift both intellectually and practically. It looks at the underlying assumptions of each paradigm, examines why NPM failed to sustain itself and shifted toward NPG. This paper also outlines the characteristics of NPG, and evaluates some of the criticisms made against it such as lack of accountability and ambiguity. In addition to reviewing these issues, a brief section also discusses how the three logics operate simultaneously in India’s administrative system. Finally, the study concludes that it would be more accurate to view these paradigms as overlapping layers rather than as successive replacements, and that NPG has made a unique contribution to redirecting focus towards networking, legitimacy and participative orientation in public service.
Keywords: new public governance; public administration; new public management; networks; participation; neutrality.
Keywords: new public governance; public administration; new public management; networks; participation; neutrality.
Abstract
A MORPHOSTYLISTIC ANALYSIS OF MARK TWAIN’S “TRAIN TO DARJEELING”
Dr Prabhavati Arvind Patil
DOI: 10.17148/IARJSET.2026.13929
Abstract: Mark Twain was one of the most influential American writers and humourists of the nineteenth century. His “Train to Darjeeling” is a travel narrative that describes a journey through the mountainous region leading to Darjeeling. The text presents the author’s observations of the landscape, people, and travel experiences encountered during the trip. This paper examines the Mark Twain’s use of lexical categories, pronouns, nouns, adjectives, verbs, adverbs, and word- formation processes to reveal how linguistic choices contribute to meaning and artistic effect. Using a qualitative descriptive approach, the study analyses selected passages from the text to identify prominent morphostylistic features and their functions. The findings indicate that Twain’s extensive use of descriptive adjectives, dynamic verbs, first-person pronouns, and concrete nouns enhances vivid imagery, personal engagement, and narrative realism. Additionally, repetition, comparative structures, and figurative expressions contribute to the text’s humour, expressiveness, and travel- writing style.
The analysis demonstrates that morphological elements are not merely grammatical devices but significant stylistic resources that shape the narrative voice and reader response. This research contributes to the field of stylistics by illustrating how morphostylistic analysis can provide deeper insights into literary texts and authorial style.
Keywords: Morphostylistics, Travel Writing, Morphology, Literary Linguistics.
The analysis demonstrates that morphological elements are not merely grammatical devices but significant stylistic resources that shape the narrative voice and reader response. This research contributes to the field of stylistics by illustrating how morphostylistic analysis can provide deeper insights into literary texts and authorial style.
Keywords: Morphostylistics, Travel Writing, Morphology, Literary Linguistics.
Abstract
ASSURED CONTRACT FARMING SYSTEMS FOR STABLE MARKET ACCESS
Miss.Snehal Vinod Vyawahare, Prof.Miss Pratiksha Lad
DOI: 10.17148/IARJSET.2026.13930
Abstarct: Agriculture
faces many problems such as changing market prices, uncertain demand, low
bargaining power of farmers, and difficulty in finding reliable buyers. Because
of these problems, farmers may not get a fair or stable price for their crops.
An assured
contract farming system can help reduce these problems. In this system, farmers
and buyers make an agreement before cultivation. They can decide in advance
which crop will be grown, the required quality, price, quantity, delivery date,
and payment conditions.
This
research proposes a digital contract farming system that brings farmers,
buyers, agricultural experts, and administrators together on one platform. The
system stores important information such as crop details, contracts, expected
production, agreed prices, quality requirements, delivery schedules, and
payment records.
The system
also provides notifications and status updates to improve communication between
farmers and buyers. Keeping clear digital records can help reduce
misunderstandings and disputes and can increase trust between both parties.
The main
purpose of this system is to provide farmers with better and more stable market
access, reduce uncertainty after harvesting, and help them plan their farming
activities. It can also help buyers receive the required crops with the agreed
quality and quantity. The research also focuses on the feasibility,
requirements, risks, system design, and future development of the proposed
system.
Keywords: Contract Farming, Market Access, Farmers, Digital Agriculture, Smart Contracts, Supply Chain, Price Assurance.
Abstract
Mobile App for Direct Market Access for Farmers
Miss. Harshali Varankar, Pratiksha Lad
DOI: 10.17148/IARJSET.2026.13931
Abstract: Agriculture is an important source of livelihood for many people, but farmers often receive low prices for their produce due to the involvement of multiple middlemen. This research proposes a mobile application that connects farmers directly with buyers such as consumers, retailers, restaurants, and wholesalers. The application allows farmers to register, create profiles, list their products, check current market prices, negotiate prices, and manage orders. Secure digital payments and order tracking are also included to improve trust and transparency. The main aim of the system is to reduce intermediaries, provide better market access, improve price transparency, and increase farmers' income. The proposed application demonstrates how mobile technology can make agricultural selling more efficient, simple, and beneficial for farmers.
Abstract
FITNESS AND SPORTS: A TECHNOLOGY- ASSISTED APPROACH FOR ACTIVITY MONITORING AND MOTIVATION
Mr. Chetan D. Rajput, Prof. Miss. Pratiksha R.Lad
DOI: 10.17148/IARJSET.2026.13932
Abstract: Fitness and regular physical activity are important for maintaining a healthy and active lifestyle. However, busy schedules, lack of motivation, limited guidance, and difficulty in tracking progress can make it difficult for people to maintain a consistent routine. The Fitness and Sports problem encourages student innovations that use technology to increase physical activity and maintain fitness. This paper proposes a technology-assisted fitness and sports monitoring approach that can collect movement, duration, repetitions, and activity-level information using smartphones, sensors, or wearable devices. The system can organize this information, provide progress feedback, support goals and reminders, and use achievement-oriented features to encourage continued participation. The approach is intended for home exercise, outdoor activity, and sports training. It is a fitness-support concept rather than a replacement for professional medical or specialized sports guidance.
Keywords: Fitness, Sports, Physical Activity, Wearable Sensors, Activity Monitoring, Mobile Application, Data Analysis, Motivation
Keywords: Fitness, Sports, Physical Activity, Wearable Sensors, Activity Monitoring, Mobile Application, Data Analysis, Motivation
Abstract
A WIKI-BASED DIGITAL KNOWLEDGE SHARING PLATFORM FOR WATER- EFFICIENT TECHNIQUES AND MINIMIZING WATER SCARCITY
Mr. Rohit Yashwant Sonawane, Pratiksha R. Lad
DOI: 10.17148/IARJSET.2026.13933
Abstract: Water scarcity is not caused by a single factor. Increasing demand, irregular rainfall, climate variability, groundwater depletion and avoidable losses can place pressure on already limited water resources. At the same time, many practical water-saving methods are known locally but remain scattered across reports, websites, institutional documents and personal experience. This paper proposes a wiki-based digital knowledge sharing platform that brings such information into one searchable and continuously editable knowledge space. The proposed platform allows farmers, households, community members, water management experts and moderators to create, improve and review pages describing practices such as rainwater harvesting, drip irrigation, sprinkler irrigation, soil-moisture conservation, wastewater reuse, irrigation scheduling and leak reduction. Each knowledge page is designed to record the method, suitable conditions, benefits, practical limitations, images or supporting material, and revision history. Search, categories and tags are included so that users can locate practices according to purpose and local conditions. Because water management advice can be sensitive to context, the system also includes a moderation and expert verification layer. The study follows a proposed-system research approach based on secondary literature and requirements analysis rather than claiming field results that have not yet been collected. The paper defines the platform architecture, information flow, user roles, quality controls and an evaluation framework for future implementation. The central contribution is a practical digital knowledge-sharing model intended to make water-efficiency knowledge easier to find, understand, compare and reuse, thereby supporting informed adoption of suitable water-saving practices and reducing avoidable water wastage.
Keywords: Water scarcity, Water-use efficiency, Wiki, Knowledge sharing, Rainwater harvesting, Irrigation, Community knowledge, Digital platform
Keywords: Water scarcity, Water-use efficiency, Wiki, Knowledge sharing, Rainwater harvesting, Irrigation, Community knowledge, Digital platform
Abstract
DEVELOPMENT OF AN EDUCATIONAL GAME
Ms. Samiksha. V. Patil, Prof. Shubham Lotwala
DOI: 10.17148/IARJSET.2026.13934
Abstract: Education is increasingly influenced by digital technologies, creating opportunities to develop new approaches that make learning more interactive and engaging. Traditional teaching methods can sometimes make it difficult for students to maintain attention and motivation, particularly when learning involves repetitive practice or abstract concepts. Game-based learning provides an alternative approach by combining educational content with interactive challenges, feedback, goals, and rewards. (UNESCO, 2023)
This research project focuses on the development of an educational game for enhancing student learning and engagement. The purpose of the study is to examine how educational game elements can be incorporated into a digital learning environment and how such an approach may support students' motivation, participation, and learning outcomes. The proposed study involves reviewing existing research on game-based learning and educational gamification, identifying suitable game-design elements, designing an educational game, and evaluating its potential educational usefulness. (Clark et al., 2016; Wang et al., 2022)
The literature indicates that digital games can produce positive learning effects, although outcomes depend on factors such as game design, learning objectives, duration, learner characteristics, and educational context. Therefore, the proposed educational game will focus not only on entertainment but also on alignment between gameplay and learning objectives. The research is expected to contribute to the development of engaging and learner-centred digital educational resources (Clark et al., 2016; Wang et al., 2022)
This research project focuses on the development of an educational game for enhancing student learning and engagement. The purpose of the study is to examine how educational game elements can be incorporated into a digital learning environment and how such an approach may support students' motivation, participation, and learning outcomes. The proposed study involves reviewing existing research on game-based learning and educational gamification, identifying suitable game-design elements, designing an educational game, and evaluating its potential educational usefulness. (Clark et al., 2016; Wang et al., 2022)
The literature indicates that digital games can produce positive learning effects, although outcomes depend on factors such as game design, learning objectives, duration, learner characteristics, and educational context. Therefore, the proposed educational game will focus not only on entertainment but also on alignment between gameplay and learning objectives. The research is expected to contribute to the development of engaging and learner-centred digital educational resources (Clark et al., 2016; Wang et al., 2022)
Abstract
AI DRIVEN CROP DISEASE PREDICTION AND MANAGEMENT SYSTEM
Miss. Hemlata A. Patil, Prof. Mr. Shubham M. Lotwala
DOI: 10.17148/IARJSET.2026.13935
Abstract: Agriculture is an important part of our life and provides food and income to many people. Crop diseases are a major problem for farmers because they can damage plants, reduce crop quality, and decrease production. Early identification of crop diseases is important to prevent further damage. This project presents an AI-Driven Crop Disease Prediction and Management System that identifies possible crop diseases using crop leaf images. In this system, leaf images are collected and processed to extract important features such as colour, texture, and shape. The K-Nearest Neighbors (KNN) algorithm is used to predict the disease by comparing the features of a new leaf image with images available in the dataset. The system also provides basic information about the predicted disease and simple management suggestions. Its performance can be evaluated using accuracy, precision, recall, and F1-score. The proposed system provides a simple and quick method for initial crop disease identification. It can help farmers save time, take early action, and reduce crop damage. Overall, the project demonstrates the usefulness of Artificial Intelligence and Machine Learning in agriculture for better crop disease management.
Keywords: Artificial Intelligence, Machine Learning, Crop Disease Prediction, KNN, Leaf Image Analysis.
Keywords: Artificial Intelligence, Machine Learning, Crop Disease Prediction, KNN, Leaf Image Analysis.
Abstract
PARSING OF SOCIAL MEDIA FEEDS: AN ADAPTIVE FRAMEWORK
Miss. Kajal Hemant Patil, Asst. Prof. Shubham Lotwala
DOI: 10.17148/IARJSET.2026.13936
Abstract: Machine learning classification and natural language processing play a significant role in extracting insights from high-velocity social media datasets. Social media platforms generate vast quantities of dynamic, unstructured content that can inform trend tracking, sentiment analysis, and social research. However, systematically ingesting and parsing these streams presents substantial challenges due to restrictive platform interfaces, structural schema drift, and high lexical noise in colloquial microblog text. This paper presents an adaptive architectural framework designed to integrate data collection, multi-stage text preprocessing, named entity extraction, and schema normalization into a unified, modular pipeline. By coupling asynchronous queue buffering with staged text cleaning and domain-adapted language representations, the proposed design aims to balance processing efficiency with semantic fidelity across heterogeneous platforms. The architecture distinguishes conceptual system design from empirical validation, establishing a principled foundation and evaluation methodology for future experimental benchmarking in streaming social intelligence systems.
Keywords: Social Media Parsing, Natural Language Processing, API Ingestion, Text Classification, Data Normalization.
Keywords: Social Media Parsing, Natural Language Processing, API Ingestion, Text Classification, Data Normalization.
Abstract
SUSTAINABLE FERTILIZER USAGE OPTIMIZER FOR HIGHER YIELD
Miss.Gayatri C. Shinde, Asst.Prof. Pratiksha Lad
DOI: 10.17148/IARJSET.2026.13937
Abstract: Agriculture is an important part of our daily life, but farmers face many problems such as increasing fertilizer costs, poor soil quality, changing weather conditions, and improper use of fertilizers. Using too much fertilizer can damage the soil and cause water pollution, while using less fertilizer than required can reduce crop growth and yield. Therefore, there is a need for a smart system that can help farmers use fertilizers in the correct amount.
This research proposes a Sustainable Fertilizer Usage Optimizer for Higher Yield using Data Analytics and Machine Learning. The system uses information such as soil nutrients (Nitrogen, Phosphorus and Potassium), soil pH, crop type, weather conditions, previous crop yield, and fertilizer usage. The collected data is cleaned and analyzed before applying suitable machine learning algorithms such as Random Forest and regression models. The system then recommends a suitable fertilizer and quantity according to the crop and soil conditions. The expected outcome is to reduce unnecessary fertilizer usage, decrease farming costs, maintain soil health, and improve crop yield. Overall, the proposed system aims to support smart, economical, and environmentally friendly farming.
Keywords: Sustainable Agriculture, Fertilizer Optimization, Machine Learning, Data Analytics, Random Forest, Crop Yield Prediction, Soil Nutrients, NPK, Precision Agriculture, Fertilizer Recommendation.
This research proposes a Sustainable Fertilizer Usage Optimizer for Higher Yield using Data Analytics and Machine Learning. The system uses information such as soil nutrients (Nitrogen, Phosphorus and Potassium), soil pH, crop type, weather conditions, previous crop yield, and fertilizer usage. The collected data is cleaned and analyzed before applying suitable machine learning algorithms such as Random Forest and regression models. The system then recommends a suitable fertilizer and quantity according to the crop and soil conditions. The expected outcome is to reduce unnecessary fertilizer usage, decrease farming costs, maintain soil health, and improve crop yield. Overall, the proposed system aims to support smart, economical, and environmentally friendly farming.
Keywords: Sustainable Agriculture, Fertilizer Optimization, Machine Learning, Data Analytics, Random Forest, Crop Yield Prediction, Soil Nutrients, NPK, Precision Agriculture, Fertilizer Recommendation.
Abstract
Explanation versus Prediction in Macroeconomic Modelling: Reconciling Econometric and Machine-Learning Evidence on Nigerian FDI Ofierohor Ufuoma Earnest
Ofierohor Ufuoma Earnest
DOI: 10.17148/IARJSET.2026.13938
Abstract: A variable can be statistically significant in a structural model and carry no weight in a predictive one, or the reverse. This paper works through that problem using two companion analyses of the same Nigerian quarterly dataset, 2008Q2 to 2024Q4: an ARDL bounds-testing model of FDI and a leakage-free machine-learning forecasting pipeline. The interest rate makes the point cleanly. In the ARDL model it is significant and negative under both classical and HAC standard errors (coefficient -57.24, HAC p = 0.005). In the forecasting model its permutation importance has a bootstrap 95% confidence interval of [-29.34, 42.94], which spans zero, so its predictive contribution is not established at all. Exchange-rate volatility fails on both axes: insignificant in the ARDL model (p = 0.70) and with a bootstrap interval of [-12.35, 44.06]. Neither pattern is a contradiction. The two frameworks answer different questions, one about a stable structural association over the full sample and one about whether a variable improves forecasts on held-out data, and neither licenses a causal claim. We set out a three-way distinction between association, predictive usefulness and causation, and draw practical guidance for reading the two kinds of output side by side.
Keywords: Explanation versus Prediction; ARDL; Machine Learning; Causality; Nigeria.
Keywords: Explanation versus Prediction; ARDL; Machine Learning; Causality; Nigeria.