| Paper ID |
IJIFR/V14/E2/006
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| Author |
Dr.Sahana H M, Government First Grade College Sakaleshapura
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| Paper Title |
Household Nutrition and Child Health: A Comparative Study of Dietary Patterns, Food Security and Nutritional Status
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| Subject Category |
Sociology
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| Abstract |
Nutrition serves as a crucial factor in child development, health, and human progress, whereas household food security and the quality of the diet significantly affect nutritional results. This research explores the link among household food security, dietary variety, parents' nutritional knowledge, economic circumstances, and the nutritional status of children. A cross-sectional design that is both descriptive and analytical was utilized with an illustrative sample of 500 households, assessing one eligible child from each household. Data were analyzed through descriptive statistics and chi-square analyses. The results show that 68.0% of households had food security, whereas 32.0% faced moderate or severe food insecurity. A total of 43.6% of households reported high dietary diversity. In total, 61.6% of children exhibited satisfactory nutritional status, while undernutrition and overweight/obesity continued to be significant issues. Notable correlations were found between children's nutritional status and household food security, dietary diversity, parental awareness of nutrition, and socioeconomic factors. The research emphasizes the necessity for cohesive household interventions that tackle food accessibility, diet quality, nutrition education, and monitoring of child development.
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| Keyword |
Household nutrition; Child nutritional status; Food security; Dietary diversity; Nutrition awareness; Malnutrition; Socioeconomic status.
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| Paper ID |
IJIFR/V14/E2/005
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| Author |
Monika Mehra, Research Scholar, Punjab University, Chandigarh
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| Paper Title |
Representation of Lord Rama as a Cultural Revival in The Indian Express: A Cultural and Media Study
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| Subject Category |
Cultural and Media Study
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| Abstract |
After the Babari Masjid demolition, articles about Lord Rama as a Hindu deity surged. Although nationalist leaders like Mahatma Gandhi proclaimed Lord Rama as God of all, the name of God as Truth, and Swami Vivekananda described Rama as “the ancient idol of the heroic ages." Debate over Lord Rama’s ideological construction and representation has continued for centuries. This discursive construction of Lord Rama needs to be analyzed through the lens of culture and media studies, as culture and media are the soul of any society. Drawing on Michael Foucault’s concept of discourse, archive, episteme, and discontinuity, and Stuart Hall’s theory of representation, this paper examines the discursive representation of Lord Rama in selected articles published by The Indian Express around the consecration of the Ram Temple in Ayodhya in January and February 2024. The study shows that media representation, through its interpretive lens, participates in the cultural revival through headlines and opinion articles so that readers can understand the relationship among faith, memory, culture, national identity, and citizenship. Through a qualitative close reading of selected articles, this paper concludes that the media, through its recurring representation of Rama as a cultural and civilizational figure, aims to enhance cultural imagination among the masses.
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| Keyword |
Lord Rama, Discursive Construction, Michael Foucault, Stuart Hall, Media, and Cultural Revival
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| Paper ID |
IJIFR/V14/E2/004
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| Author |
Dr. Aarcha S. S., Assistant Professor, Department of Commerce, Sree Narayana College, Kollam, Kerala.
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| Paper Title |
Influence of Social Media Commerce on Consumer Buying Decisions: An Empirical Study of Instagram and Facebook Shopping
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| Subject Category |
Commerce
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| Abstract |
Social commerce has emerged as one of the important phenomena in today's digital marketplace where consumer buying behaviour is greatly influenced by social media sites such as Instagram and Facebook. This current research study focuses on examining the impact of social media commerce on consumer buying decisions focusing on Instagram and Facebook shopping. This study is empirical in nature and uses primary data collected from 250 samples via a structured questionnaire. Data analysis was done using statistical tools like percentage analysis, correlation analysis, t-test, ANOVA, chi-square test, and regression analysis. The results indicate that factors like influencer marketing, online reviews, personalized advertising, and social media activity greatly influence consumer buying decisions. Convenience and trust are found to be two main factors that influence consumer to buy goods online through social media commerce. But fake reviews and issues related to data privacy are found to be the major constraints. The study concludes that social commerce platforms play a significant role in shaping modern consumer buying behaviour and digital purchasing trends.
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| Keyword |
Social Commerce, Consumer Buying Behaviour, Instagram Shopping, Facebook Shopping, Digital Marketing
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| Paper ID |
IJIFR/V14/E2/003
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| Author |
J. M. Mahadevan
S M Luxmi
Preethika R K
V Rajendran
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| Paper Title |
Artificial Intelligence Adoption and Economic Transformation: An Empirical Analysis of Industrial Productivity and Growth
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| Subject Category |
Commerce
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| Abstract |
Artificial Intelligence (AI) has emerged as a transformative general-purpose technology with the potential to reshape industrial production, enhance operational efficiency, and contribute to long-term economic growth. Building on previous research on technological innovation, digital transformation, automation, and productivity, this study examines the impact of AI adoption on industrial productivity and its broader implications for economic growth. The study conceptualizes AI adoption through dimensions including the integration of AI-enabled technologies, intelligent automation, data-driven decision-making, process optimization, and organizational readiness, while industrial productivity is assessed in terms of operational efficiency, labour productivity, production performance, innovation capability, and resource utilization. Economic growth is considered through improvements in value creation, investment, employment-related outcomes, competitiveness, and aggregate economic performance. The proposed research develops an empirical framework to examine whether greater adoption of AI contributes significantly to productivity enhancement and whether improvements in industrial productivity subsequently translate into stronger economic growth. Drawing on quantitative evidence from industrial organizations and relevant secondary economic indicators, the study proposes the application of reliability and validity assessment, correlation analysis, regression/structural equation modelling, and mediation analysis to establish the direct and indirect relationships among the study variables. The framework also considers organizational and technological factors that may influence the effectiveness of AI implementation. The study is expected to contribute to the growing literature by linking firm-level AI adoption and productivity outcomes with broader economic-growth mechanisms rather than examining AI adoption solely as a technological phenomenon. The findings may provide useful evidence for industry leaders, policymakers, and researchers seeking to understand the productivity and economic implications of responsible and strategically aligned AI adoption.
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| Keyword |
Artificial Intelligence Adoption, Industrial Productivity, Economic Growth, AI-Enabled Technologies, Intelligent Automation, Digital Transformation, Technological Innovation, Labour Productivity, Operational Efficiency, Economic Performance
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| Paper ID |
IJIFR/V14/E2/002
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| Author |
K. Vanitha Bharathi
P. Raghunathan
Pranav T Murthy
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| Paper Title |
An Adaptive Explainable Machine Learning Framework for Early Ransomware Detection from System and Network Behaviors
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| Subject Category |
Computer Networks Engineering
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| Abstract |
The increasing sophistication of ransomware attacks presents a significant challenge to conventional signature-based and heuristic detection mechanisms, particularly against previously unseen and rapidly evolving attack variants. This study proposes an explainable artificial intelligence (XAI)-driven framework for early ransomware detection that integrates behavioral feature extraction, anomaly detection, and ensemble machine learning to distinguish malicious ransomware activities from legitimate system behavior. The proposed framework analyzes behavioral indicators derived from system logs, network traffic, process execution, and file metadata, with particular emphasis on abnormal file-access patterns, process behavior, network communication characteristics, and file modification activities. Isolation Forest is employed as an unsupervised anomaly-detection mechanism to identify deviations from normal behavioral patterns, while Random Forest subsequently classifies detected activities as benign or ransomware. The framework incorporates explainability mechanisms to facilitate interpretation of model decisions and identify the behavioral characteristics contributing to ransomware classification. Performance is evaluated using accuracy, precision, recall, F1-score, false-positive rate, detection rate, and five-fold cross-validation. Experimental results indicate that the Random Forest classifier achieved 97.8% accuracy, 96.5% precision, 98.2% recall, and a 97.3% F1-score, while the Isolation Forest component achieved a 95.0% detection rate with a 3.2% false-positive rate. Five-fold cross-validation produced an average training accuracy of 97.9%, validation accuracy of 96.9%, precision of 98.0%, and recall of 97.3%, indicating consistent predictive performance across validation folds. Comparative evaluation further demonstrates higher reported detection performance than the signature-based and heuristic baselines considered in the study. Overall, the findings indicate that combining behavioral anomaly detection, ensemble classification, and explainability can support earlier identification of ransomware activity while improving the interpretability and adaptive monitoring capabilities of cybersecurity detection systems.
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| Keyword |
Explainable Artificial Intelligence; Ransomware Detection; Behavioral Anomaly Detection; Isolation Forest; Random Forest; Ensemble Machine Learning; Cybersecurity; Anomaly Analysis; Early Threat Detection; XAI.
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| Paper ID |
IJIFR/V14/E2/001
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| Author |
R. Saravanan, Assistant Professor, Department of Commerce Applications Sri Krishna Knowledge Foundation Deemed to be University, Coimbatore, India
Dr. R. Abibu Rahman, Assistant Professor, School of Commerce, Sree Saraswathi Thyagaraja College, Palani Road, Tamil Nadu, India
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| Paper Title |
Startup Ecosystem in India: Evidence from Economic and Labour Market Indicators
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| Subject Category |
Commerce
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| Abstract |
The rapid expansion of India startup ecosystem has heightened its significance in entrepreneurship, innovation, job creation, and economic development. The present study examines the growth of startup value and DPIIT-recognised startups in India during 2017–2025 and analyses their relationships with selected macroeconomic indicators. The study adopts a quantitative, descriptive, and empirical research design and is based entirely on secondary data compiled on a calendar-year basis. Annual growth rates and the Compound Annual Growth Rate (CAGR) are used to examine growth trends, while Pearson correlation coefficient is used to assess the relationships between startup value and GDP, and between DPIIT-recognised startups and the unemployment rate. The findings indicate substantial growth in startup value and the number of DPIIT-recognised startups during the study period. Startup value recorded a CAGR of 37.03%, while DPIIT-recognised startups recorded a CAGR of 31.66%. The correlation analysis revealed a positive relationship between startup value and GDP (r = 0.674, p = 0.067), which was not statistically significant at the 5% level. In contrast, a very strong negative and statistically significant relationship was observed between DPIIT-recognised startups and the unemployment rate (r = -0.882, p = 0.004). The study provides empirical evidence of the association between startup expansion and selected economic and employment indicators and contributes to the understanding of India evolving startup ecosystem. However, the findings should be interpreted as associations rather than causal relationships.
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| Keyword |
Indian Startups; DPIIT-Recognised Startups; Startup Value; GDP; Unemployment Rate; Entrepreneurship; Economic Growth
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