| Paper ID |
IJIFR/V14/E2/015
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| Author |
Janisben Bino, Kerala University of Fisheries and Ocean Studies
Dr. Anoop K.K, Kerala University of Fisheries and Ocean Studies
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| Paper Title |
Digital Self-Efficacy: A Systematic Literature Review Using the TCCM and AMC Frameworks
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| Subject Category |
Management
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| Abstract |
Digital self-efficacy (DSE) refers to individuals’ perceived capability to perform, learn and adapt in digital environments. This systematic literature review examines the main theories, research contexts, characteristics, and methods used in DSE studies. It also organises the relationships found using an antecedent-mediating mechanism-consequence (AMC) framework. Following PRISMA 2020 guidelines, a Scopus search identified 405 documents. After applying filters for subject area, document type, and language, 291 articles were screened, and 29 studies were included in the review. Data from these studies were analysed using the Theory, Context, Characteristics, and Methodology (TCCM) framework. Most studies were based on Social Cognitive Theory and self-efficacy theory. The largest share of evidence came from educational settings, with additional research from workplaces and organisations. Some evidence also came from entrepreneurship, consumer services, adolescent online gaming, and adult assessment. DSE was commonly conceptualised as perceived digital capability, but measurement varied from broad tools to more specific scales for ICT, online learning, workplaces, banking, and augmented reality. The AMC analysis showed that factors such as digital engagement, technology access, training, leadership, support, and institutional conditions were examined as antecedent conditions associated with DSE. DSE was also examined as a mechanism associated with technology integration, digital literacy, task-technology fit, workforce agility, digital creativity, satisfaction, and entrepreneurship. Most studies used quantitative, cross-sectional, and self-report survey designs. The review clarifies the distinction between DSE and related constructs and identifies opportunities for context-sensitive measurement and longitudinal, experimental, qualitative and mixed-methods research.
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| Keyword |
digital self-efficacy; systematic literature review; TCCM framework; PRISMA; antecedent–mediator–consequence framework; digital technology
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| Paper ID |
IJIFR/V14/E2/014
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| Author |
Dr.P.Karthika, NGM College, Pollachi, Coimbatore
Dr.S.Subaithani, NGM College, Pollachi, Coimbatore
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| Paper Title |
Digital Financial Literacy and Safe UPI Usage among Adults with Limited Formal Education in Rural Tamil Nadu: An Empirical Study
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| Subject Category |
Commerce
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| Abstract |
The rapid expansion of digital payment systems has transformed the delivery of financial services in India. The Unified Payments Interface (UPI) enables individuals to transfer money and make payments conveniently through mobile applications. However, the effective and safe use of digital payment services requires financial knowledge, technological skills and awareness of security practices. Adults with limited formal education may experience difficulties in understanding transaction procedures, identifying fraudulent requests and resolving failed payments. This study proposes to examine digital financial literacy and safe UPI usage among adults with limited formal education in rural Tamil Nadu. The study focuses on awareness of UPI, independent transaction capability, security knowledge, perceived financial risks and barriers to digital payment usage. Primary data will be collected through a structured questionnaire administered in Tamil. Descriptive statistics, chi-square analysis, correlation and logistic regression are proposed to examine the relationships between respondents’ characteristics, digital financial literacy and safe UPI usage. The study is expected to provide evidence-based recommendations for strengthening digital financial education and inclusive payment practices among rural communities.
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| Keyword |
Digital Financial Literacy, UPI, Rural Communities, Limited Formal Education, Digital Payments, Financial Inclusion, Consumer Protection.
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| Paper ID |
IJIFR/V14/E2/013
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| Author |
Mr. KOMMU. NAGESWARA RAO, College of Teacher Education (T.W)
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| Paper Title |
NEP 2020 AND TRIBAL EDUCATION: CHALLENGES AND PATHWAYS FOR EMPOWERMENT OF TRIBAL GIRL STUDENTS IN TELANGANA
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| Subject Category |
EDUCATION
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| Abstract |
ABSTRACT
National Education Policy 2020 is a transformative framework for equitable and inclusive education, with special focus on Socio-Economically Disadvantaged Groups (SEDGs) including tribal girls. In Telangana, tribal population is 9.08% with major concentration in ITDA Bhadrachalam covering Bhadradri Kothagudem district. Despite constitutional guarantees and schemes like Ashram Schools, KGBVs, EMRS, and pre-metric scholarships, tribal girls face severe challenges leading to dropout and low empowerment. This empirical paper based on secondary data analysis and field insights from ITDA Bhadrachalam examines major educational challenges and proposes NEP 2020 aligned pathways for empowerment. UDISE 2023-24 reveals ST girls GER drops from 97.4% at elementary to 48.4% at higher secondary. Present study found 71% girls travel >3km, 68% face poverty pressure, 65% face Koya-Telugu language barrier, 60% parents favour early marriage before 18 years, 61% schools lack female teachers and 90% lack digital facilities. Overall empowerment mean is 3.30 (moderate) - high in literacy aspiration (4.2) but low in opposing early marriage (2.6). NEP 2020 Chapter 6 provides solutions through Special Education Zones (SEZs), Gender Inclusion Fund, Mother Tongue Based Education, Vocationalisation from class 6, and upgrading Ashram schools to model schools.
The paper proposes 4E model - Access, Equity, Quality and Opportunity, with convergence of ITDA, Education, Women & Child and Health departments. Findings align with recent studies of Punnaiah (2018), IJCRT (2025) and Frontiers in Education (2024). Study concludes NEP 2020 implementation with tribal-centric pedagogy and community participation is key to achieve SDG 4 and SDG 5 for tribal girls in Telangana.
Keywords: NEP 2020, Tribal Girls, ITDA Bhadrachalam, SEZs, Empowerment, Gender Inclusion Fund
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| Keyword |
NEP 2020, Tribal Girls, ITDA Bhadrachalam, SEZs, Empowerment, Gender Inclusion Fund
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| Paper ID |
IJIFR/V14/E2/012
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| Author |
Amrutha Anilkumar, Research Scholar, PG and Research Department of Commerce, Government College, Mahatma Gandhi University, Kottayam , Kerala, India
Dr. Rema Devi V N, Professor, PG and Research Department of Commerce, Government College, Mahatma Gandhi University, Kottayam, Kerala, India
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| Paper Title |
A STUDY ON THE INFLUENCE OF SOCIAL MEDIA INFLUENCERS ON LUXURY COSMETIC BRAND PURCHASE INTENTION AMONG URBAN WOMEN IN ERNAKULAM DISTRICT KERALA
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| Subject Category |
Commerce
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| Abstract |
The growing influence of social media has transformed consumer decision-making in the luxury cosmetics industry, making influencer credibility and attractiveness important considerations in digital marketing. Despite the increasing adoption of influencer marketing, limited empirical research has examined how these characteristics shape the purchase intentions of urban women in the Indian luxury cosmetics market, particularly in Ernakulam, Kerala. This study investigates the effects of social media influencers’ trustworthiness and attractiveness on consumer attitudes and purchase intentions toward luxury cosmetic brands, with particular emphasis on the mediating role of consumer attitude. Grounded in Social Influence Theory, the research adopts a quantitative approach using primary data collected through a structured questionnaire from 240 urban women aged 20–55 years in Ernakulam. Data were analysed using SPSS and Partial Least Squares Structural Equation Modelling (PLS-SEM) through SmartPLS 3. The measurement model demonstrated satisfactory internal consistency and convergent and discriminant validity, while multicollinearity diagnostics indicated no substantial concerns. The structural model results revealed that influencer attractiveness positively influenced consumer attitude (ß=0.059,p=0.042) and purchase intention (ß=0.234,p=0.009). Trustworthiness demonstrated positive associations with consumer attitude (ß=0.223,p=0.043) and purchase intention (ß=0.518,p=0.001), emerging as the stronger direct predictor of purchase intention. Consumer attitude also exhibited a positive relationship with purchase intention (ß=0.033,p=0.039). Furthermore, the mediation analysis indicated significant indirect relationships between influencer attractiveness and purchase intention (ß=0.002,p=0.045) and between trustworthiness and purchase intention (ß=0.007,p=0.039), through consumer attitude. These findings highlight the importance of credible, trustworthy, and visually appealing influencers in shaping consumer responses to luxury cosmetic brands. The study contributes to influencer marketing literature by examining consumer attitude as a mediating mechanism within an Indian regional context and offers practical insights for luxury cosmetics marketers seeking to develop authentic influencer partnerships and strengthen consumers’ purchase intentions.
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| Keyword |
Social Media Influe.ncers, Trustworthiness, Attractive.ness, Attitude, Purchase Inte.ntio.n, Luxury Cosmetic Bra.nds
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| Paper ID |
IJIFR/V14/E2/011
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| Author |
Dr. L. A. Razia Fathima, Kumaraguru College of Liberal Arts and Science
Sangamitra S, Kumaraguru College of Liberal Arts and Science, Coimbatore
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| Paper Title |
Price, Convenience and Persuasion: Determinants of Eco-Friendly Product Adoption and the Role of Media among Urban Households in Coimbatore City, India
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| Subject Category |
Commerce
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| Abstract |
Household Consumption is a vital and increasing source of greenhouse gas emissions, but the decision to adopt eco-friendly products remains low in rapidly growing Tier – 2 cities. This study examines (i) the factors influencing consumers’ adoption or avoidance of green products and (ii) the role of media in shaping sustainability practices among the households in Coimbatore city, Tamilnadu. Structured questionnaire is framed and 110 respondents were selected by convenience sampling method and the statistical tools like percentage analysis, weighted average ranking method and chi-square tests were used. Price, convenience of use and brand trust emerged as the major purchase determinants, while lack of information and limited availability of products were common barriers. Social media was the dominant source of awareness and advertisement was ranked least as a purchase drivers. Findings are discussed in comparison with the sustainability goals of 11, 12 and 13 and price-sensitive, information rich, digitally led strategies are recommended.
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| Keyword |
Green consumer behaviour, household carbon footprint, eco-friendly products, social media, SDG 12, SDG13.
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| Paper ID |
IJIFR/V14/E2/010
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| Author |
Simi S, Research Scholar, PG and Research Department of Commerce Government Arts College, Thiruvananthapuram Email- [email protected] Mobile: 7994421266
Dr. Manjula K G, Associate Professor& Research supervisor,PG department of commerce,VTM NSS college,Dhanuvachapuram,[email protected],9645001516
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| Paper Title |
SUSTAINING TRADITIONAL HANDICRAFTS THROUGH VILLAGE TOURISM: A SYSTEMATIC LITERATURE REVIEW
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| Subject Category |
commerce
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| Abstract |
Traditional handicrafts play an important role in the cultural identity and livelihoods of rural communities, but many craft traditions face challenges from urbanisation, youth migration, changing markets, and competition from industrial products. Village and rural tourism are increasingly considered potential mechanisms for supporting craft sustainability. This systematic literature review examines how village, rural, and community-based tourism contributes to the sustainability of traditional handicrafts and identifies the factors that influence this relationship. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines were followed in conducting the review. Data were extracted into an evidence matrix and analysed using descriptive statistics and thematic synthesis. The findings show that research on craft-based tourism is geographically concentrated in Asia and largely based on qualitative approaches. Six major themes emerged: economic livelihoods; cultural preservation and intergenerational transmission; experiential and creative tourism; participation and governance; infrastructure, space and environment; and product innovation and technology. Tourism can support craft livelihoods, cultural preservation, and visitor learning when supported by market access, community participation, effective governance, skills, production space, and raw materials. However, risks include commodification, loss of authenticity, unequal distribution of benefits, and limited youth participation. The review develops an integrative framework linking cultural capital, tourism practices, governance, and sustainability outcomes and proposes four propositions for future research. Key gaps include limited longitudinal evidence, craft-specific sustainability indicators, gender and youth perspectives, and empirical testing of the proposed relationships.
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| Keyword |
Traditional handicrafts, village tourism, rural tourism, community-based tourism, cultural heritage, sustainability, systematic literature review
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| Paper ID |
IJIFR/V14/E2/009
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| Author |
Dr. Sneha Patel, Department of Artificial Intelligence & ML, School of Engineering, Pune, India
Aditya Deshmukh, Department of Artificial Intelligence & ML, School of Engineering, Pune, India
Riya Chatterjee, Department of Artificial Intelligence & ML, School of Engineering, Pune, India
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| Paper Title |
Reducing Process Variability through Digital Twin–AI Integration: A Predictive Analytics Framework for Operational Excellence and Quality Improvement
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| Subject Category |
Computer Science
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| Abstract |
Process variability remains a significant challenge in manufacturing and industrial operations, contributing to inconsistent product quality, production delays, material wastage, and increased operating costs. The integration of Digital Twin technology and Artificial Intelligence (AI) offers a promising approach to addressing these challenges through real-time monitoring, predictive analytics, and intelligent process optimization. This study proposes an integrated Digital Twin–AI framework that combines sensor-generated operational data, virtual process simulation, machine learning algorithms, and feedback-based process control to identify abnormal operating conditions and predict deviations before they result in quality failures. The framework aims to assess the relationship between AI-enabled digital simulation and key performance indicators, including process variability, defect rates, cycle time, and equipment performance. By enabling continuous comparison between physical processes and their digital counterparts, the proposed approach supports early anomaly detection, scenario-based optimization, and data-driven decision-making. The study provides a conceptual and empirical foundation for improving process stability and operational excellence in data-intensive industrial environments. Its contribution lies in integrating predictive intelligence with dynamic process simulation to establish a proactive approach to variability reduction.
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| Keyword |
Digital Twins; Artificial Intelligence; Process Variability; Predictive Analytics; Machine Learning; Process Optimization; Quality Management; Industry 4.0; Smart Manufacturing; Operational Excellence
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| Paper ID |
IJIFR/V14/E2/008
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| Author |
Dr.Syed Hasan Qayed, Maulana Azad National Urdu University
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| Paper Title |
The Paradox of Demographic Dividend in India: Education, Unemployment, and Economic Vulnerability
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| Subject Category |
Economics
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| Abstract |
India’s massive youth population is frequently celebrated as a major demographic dividend, however, severe job scarcity among educated young individuals now poses a critical structural threat to this economic promise. While access to higher education has surged across the country, the creation of secure, formal employment has lagged far behind. Drawing on secondary data from the Periodic Labour Force Survey (PLFS) and the National Sample Survey Office (NSSO), this study examines the shifting trends, underlying drivers, and broader socio-economic fallout of educated youth unemployment. The findings point to deep institutional friction, driven by an acute mismatch between university curricula and industry needs, bureaucratic delays in public sector recruitment, and a rapid drift toward precarious, informal labor. Beyond the macroeconomic numbers, the paper unpacks the psychological strain this crisis inflicts on graduates, highlighting how chronic joblessness fuels frustration, anxiety, and a loss of personal agency. Ultimately, we argue that educated unemployment cannot be solved by isolated interventions; it requires a deeply integrated education-to-employment strategy that actively drives sustainable job creation.
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| Keyword |
Demographic dividend, Youth Unemployment, PLFS, Educational Institutions, Economic Vulnerability
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| Paper ID |
IJIFR/V14/E2/007
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| Author |
T V B Swami, Rajalakshmi Engineering College , Thandalam, Chennai, Tamil Nadu
R J. Lakshmi, Rajalakshmi Engineering College , Thandalam, Chennai, Tamil Nadu
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| Paper Title |
ACCELERATING AI ADOPTION ACROSS INDUSTRIES TO IMPROVE PRODUCTIVITY AND EFFICIENCY
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| Subject Category |
Computer Engineering
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| Abstract |
To assess AI’s potential for India, a detailed analysis was conducted on its ability to enhance productivity across industries. The study covered over 850 occupations across 16 sectors and examined more than 2,100 distinct work activities. The analysis indicates that AI adoption could contribute an additional $500–600B to India’s GDP by 2035, beyond the projected growth trajectory, driven by productivity improvements, operational efficiencies, and the reallocation of human effort to higher-value tasks.
AI-skilling ecosystem, AI Open University, national certification program. Specific adoption scenario models were considered (i.e., the pace at which industry adopts the technology at scale, resulting in impact on productivity)—early, midpoint, and late—to estimate when AI could effectively take on these activities based on currently demonstrated technologies and their expected development in the future and country-specific factors such as wage levels and occupational mix. The model incorporates software capabilities such as machine learning, data analytics and hardware-driven automation such as robotics.
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| Keyword |
AI-skilling ecosystem, AI Open University, national certification program
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| 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 |
Aim: This study examines the influence of consumer trust, online reviews, marketing and personalized advertising on consumer buying decisions in social commerce, focusing on Instagram and Facebook shopping platforms. It addresses the need to understand the relative contribution of these factors to consumer decision-making within social media-based purchasing environments.
Research Approach: An empirical, descriptive and analytical research design was adopted. Primary data were collected through a structured questionnaire administered to 250 respondents selected using convenience and purposive sampling techniques. The collected data were analysed using descriptive statistics, Pearson correlation, multiple regression analysis, independent-samples t-tests, one-way analysis of variance (ANOVA) and the chi-square test.
Analytical Outcomes: The empirical findings demonstrate substantial statistical relationships between social commerce determinants and consumer buying decisions. Pearson correlation analysis revealed that consumer trust had the strongest positive association with buying decisions (r=0.75), followed by online reviews (r=0.73), influencer marketing (r=0.70) and personalized advertising (r=0.68). Multiple regression analysis indicated that the four predictors jointly explained 66% of the variance in consumer buying decisions (R2=0.66; adjusted R2=0.65), with the overall model reported as statistically significant (F=118.34, p<0.001). Consumer trust emerged as the strongest reported predictor (ß=0.36), followed by online reviews (ß=0.30), influencer marketing (ß=0.24) and personalized advertising (ß=0.22); all four coefficients were reported as statistically significant (p<0.001). Additionally, buying decision scores differed significantly by gender (t=3.43, p<0.001) and age group (F=4.85, p=0.009), while preferred shopping platform was significantly associated with buying frequency (?2=11.265, p=0.024).
Research Implications: The findings emphasize the importance of trust-building mechanisms, credible online reviews, authentic influencer communication and relevant personalized advertising in shaping consumer responses to social commerce. The study provides empirical insights for developing consumer-oriented digital marketing strategies on social networking platforms.
Explicit Contribution: By combining regression analysis with demographic comparisons and platform-preference analysis, the study provides a multidimensional perspective on social commerce-driven consumer behaviour. These findings extend the practical understanding of how trust-related and information-based mechanisms operate alongside digital promotional strategies and offer actionable insights for developing more credible, transparent and consumer-oriented social commerce practices.
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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. P. 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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