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Leveraging Usage of AI in education: Knowledge, Attitude and Behavioral Analysis on Students
The paper explores the possible advantages and drawbacks of artificial intelligence (AI) on sustainability, with an emphasis on using AI to positively achieve SDGs. The study finds a significant vacuum in the literature on the association between knowledge, attitudes, and behaviors towards the use of AI tools and techniques in education and demographic characteristics (sex, age, education level, area of study, and city of origin). The purpose of this research is to close this knowledge gap and advance our understanding of how these demographic factors affect the integration of AI in educational environments. The study specifically aims to comprehend how students awareness, beliefs, and actions towards AI in educational situations are influenced by demographic characteristics. This research attempts to offer insights into practical methods for utilizing AI in education while addressing potential obstacles and minimizing negative effects through a thorough analysis of data gathered from students across a range of demographic backgrounds. 2025, Binghamton University Libraries. All rights reserved. -
Enhancing Online Education Through Sentiment Analysis and Complex Systems Modelling
This research explores the application of sentiment analysis through the lens of complex systems modelling to enhance the quality of online certification courses, with a particular focus on global platforms such as Coursera. The COVID-19 pandemic catalyzed significant growth in online learning, creating an urgent need for adaptive and student-centric approaches to ensure relevance and effectiveness. Leveraging unstructured textual data from student reviews of courses, this study integrates methodologies from systems science, computer science, and education to address real-world challenges in online education. By employing both lexiconbased (SentiWordNet and VADER) and supervised machine learning techniques (Multinomial Naive Bayes, Support Vector Machine, and Stochastic Gradient Descent), the research conducts a detailed sentiment analysis to identify patterns, emergent behaviours, and feedback loops inherent in course design and delivery. Findings reveal that Support Vector Machine achieves the highest accuracy at 97.3%, offering insights that guide iterative improvements in course content and pedagogical strategies. The study demonstrates how interdisciplinary approaches to sentiment analysis can inform responsive education environments, aligning with broader societal goals of accessibility, inclusivity, and quality in online learning ecosystems. 2025, Binghamton University Libraries. All rights reserved. -
Orchestrating Complexity: The Art of Virtual Leadership in System Modelling
This paper explores the dynamics of virtual leadership within global remote work environments, focusing on the application of complex system modelling to understand and enhance leadership efficacy. The application of computational modelling has been a regular feature in economics, science and technology fields, however its application in virtual leadership with linkage to sport leadership appears to be a novel concept. Adopting a multidisciplinary approach, this paper incorporates Game Theory as a conceptual framework to make the leadership model more relevant and applicable that can offer simpler understanding of complex play of leadership drivers. The model incorporates five key leadership dimensional drivers such as communication, culture, motivation, trust, and technology as agents that influence change in other agents, specifically team members. The research attempts to discover approach for understanding leadership and team behavior through Relational Leadership dynamic in business as well as in sport environment. 2025, Binghamton University Libraries. All rights reserved. -
Exploring the Interplay Between Economic Growth and Sustainable Development: A Complex Systems Approach to GSDP and SDGs in Indian States
Pursuing Sustainable Development Goals (SDGs) necessitates aligning business and management practices on a global scale. This paper delves into the intricate dynamics between Gross State Domestic Product (GSDP) and SDGs across diverse states in India, offering nuanced insights to policymakers, businesses, and stakeholders. This paper explores the dynamic relationship between Gross State Domestic Product (GSDP) and the Sustainable Development Goals (SDGs) in the context of India's diverse states by applying modern machine learning techniques such as XG boost, Decision trees, and K mean clustering. The study delves into how economic growth influences the progress towards SDGs. The research integrates complex systems methodologies, combining exploratory data analysis, correlation analysis, and clustering to offer actionable insights for policymakers and businesses. The paper emphasizes the need for tailored strategies that consider the economic development stages of states to achieve sustainable development goals more effectively. Through this multidimensional approach, the study provides a comprehensive understanding of how GSDP can guide the pursuit of SDGs and proposes innovative, data-driven solutions for fostering sustainable growth across India. 2025, Binghamton University Libraries. All rights reserved. -
Modelling Complex Psychological and Behavioral Dynamics: Analyzing Perception and Psychological Ownership in Gen Z's Re-subscription Intentions towards OTT Platforms
This study explores the complex dynamics between perception, psychological ownership, and re-subscription intentions among Gen Z users of OTT platforms, specifically examining how perceived benefits and perceived drawbacks shape user behavior and investigating the moderating role of psychological ownership in this context. The research focuses on a sample of Gen Z users from India who actively engage with OTT platforms, and data were collected through a structured questionnaire comprising three sections; a structural equation modeling (SEM) technique was applied to analyze the data obtained from 304 valid responses. The analysis reveals that perceived benefits significantly enhance Gen Z's resubscription intentions, while perceived drawbacks have a negative impact; moreover, the study highlights that psychological ownership moderates the influence of perceived drawbacks, mitigating their adverse effect on resubscription intentions. Although the study is limited to Gen Z users in India and focuses on a specific set of independent constructs, future research could expand this scope by incorporating other generational cohorts and a broader range of influencing factors to deepen the understanding of user behavior in diverse contexts. This research contributes to the broader literature on consumer behavior in the digital landscape by modeling the interaction between psychological and perceptual factors within a complex system, providing empirical evidence on the moderating role of psychological ownership and emphasizing the importance of these dynamics in designing effective engagement strategies for OTT platforms. Insights from this study underscore the significance of enhancing user perception factors to boost re-subscription rates, and industry practitioners are encouraged to focus on delivering personalized and memorable digital experiences to strengthen psychological ownership and minimize perceived drawbacks. The study also highlights practical strategies for OTT platforms, such as developing high-quality content, intuitive interfaces, and fostering a sense of community and ownership among users, with a focus on addressing perceived drawbacks and enhancing the social value of these platforms as crucial measures for retaining Gen Z users. As one of the first studies to employ complex systems modeling techniques to understand the interplay between perception factors and psychological ownership in influencing re-subscription intentions among Gen Z OTT users, the findings offer valuable insights for the online service industry to refine their service delivery and user engagement strategies. 2025, Binghamton University Libraries. All rights reserved. -
Regional Drought Modulation by ENSO and IOD as Indicated by the Standardized Precipitation Index
Understanding the modulation of drought by large-scale oceanatmosphere teleconnections is crucial for strengthening drought prediction and resilience in India. This study investigates the influence of the El NiSouthern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) on meteorological drought characteristics across India from 1950 to 2024 using the Standardized Precipitation Index (SPI) at a 12-month timescale. Drought events were quantified in terms of frequency, duration, severity, and intensity and linked to ENSOIOD variability through composite, correlation, and mediation analyses. Results reveal that El Ni events consistently correspond to widespread and severe droughts, particularly over central and southern India, with drought frequency exceeding 30% and SPI < ?1.5. Conversely, La Ni phases enhance monsoon rainfall and alleviate drought conditions across much of the subcontinent. Spatial correlations demonstrate that ENSO exerts a stronger, more coherent influence on both rainfall and SPI than the IOD, while positive IOD phases can partly offset El Ni-driven drought in limited regions. Mediation and wavelet coherence analyses confirm ENSOs dominant control at interannual (48 year) timescales and reveal secondary, episodic modulation by the IOD. These findings highlight the complex, evolving dynamics between Pacific and Indian Ocean drivers in shaping Indias hydroclimate variability. The study underscores the need for integrated ENSOIOD monitoring and inclusion of multi-ocean indicators in Indias drought early warning and seasonal forecast frameworks. 2026 Binghamton University Libraries. All rights reserved. -
A Multi-Layer Complex Adaptive System Framework for AI-Driven Robo-Advisory Services
The rapid integration of Artificial Intelligence (AI) into investment advisory services has changed financial decision-making, giving rise to adaptive robo-advisory systems capable of real-time analysis, personal recommendations, and autonomous portfolio optimization. Existing research evaluates these systems primarily through technological performance or investor adoption, overlooking the complex feedback-driven interactions that emerge when AI analytics, data environments, and human behavior operate together. This study addresses this gap by conceptualizing AI-enabled robo-advisors as a multi-layered Complex Adaptive System comprising historical data, real-time data, AI analytics, investor perception, and decision-making layers. A simulation model grounded in machine learning dynamics, behavioral finance, and complexity theory is developed to capture nonlinear interactions, adaptive learning, and emergent investor responses. Results show that historical data acts as a stabilizing memory, real-time data amplifies short-term volatility, AI analytics self-organize toward performance equilibrium, and investor perception evolves through nonlinear trust thresholds that ultimately drive decision lock-in. Complexity measures reveal that adaptive intelligence is concentrated in the historical and perception layers, while the decision layer becomes increasingly deterministic as feedback loops strengthen. The findings provide a unified system-level understanding of robo-advisory ecosystems and highlight the need for governance structures that incorporate transparency, behavioral dynamics, and adaptive model monitoring. This framework offers a foundation for designing more resilient, trustworthy, and sustainable AI-driven financial advisory systems. 2026 Binghamton University Libraries. All rights reserved. -
Behavioral Biases as Drivers of Complexity in Stock Markets: An Agent-Based Modeling Approach
By modeling financial systems as Complex Adaptive Systems, this study investigates how behavioral biases influence emergent complexity in stock markets. The study integrates heterogeneous agents, such as rational traders, herding agents, overconfident traders, and anchoring/disposition-driven investors, within a Limit Order Book framework calibrated to both U.S. and Indian market conditions using an Agent-Based Modeling (ABM) approach implemented through the high-fidelity ABIDES simulation environment. Price dynamics, volatility patterns, and liquidity structures were analyzed by Monte Carlo simulation experiments with different behavioral compositions. The results show that behavioral biases cause nonlinear price reactions, produce heavy-tailed return distributions that distort order-book complexity, and greatly increase volatility. The market shifts from a stable, rational regime to a highly volatile, complex regime characterized by contagion and fragile liquidity as the proportion of biased actors rises. Overall, the findings show that the complexity and systemic instability of emerging markets are primarily driven by behavioral heterogeneity. 2026 Binghamton University Libraries. All rights reserved. -
Complex Systems Mapping of Fiscal Growth Dynamics at Strategic Maritime Chokepoints Using Time-Series Slopes
This study examines how maritime and trading states allocate public resources between defence, health, and economic growth around three strategic chokepoints the Strait of Malacca, the Strait of Hormuz, and the Suez Canal. The analysis extends the classic guns versus butter framing by treating defence and health spending as co-evolving components of an interconnected fiscal-growth system. Using World Development Indicators data (1999-2024), trend slopes are estimated for military spending (% of GDP), healthcare spending (% of GDP), and GDP growth (annual %). Two derived indicators are computed, a defence-to-health slope ratio (military slope/health slope) and a fiscal-balance proxy (health slope - military slope). Augmented Dickey-Fuller tests are used to assess stationarity (unit-root behaviour), and Granger causality tests to examine whether GDP growth temporally precedes changes in spending shares. Hormuz chokepoint states show non-negative health slopes (e.g., UAE +0.1199) alongside negative GDP growth slopes in some cases (e.g., Qatar -0.4754). Suez chokepoint states exhibit negative defence slopes (e.g., Egypt -0.0899) with comparatively small or negative health slopes (e.g., Egypt -0.0211). The United States is included as an external benchmark because it is the largest trading nation by monetary trade volume and is directly or indirectly coupled to chokepoint flows; it shows health +0.1758 and military -0.0116 (ratio -0.0657). These quantified configurations support chokepoint-specific fiscal regimes and provide a compact visual map of security, health, growth dynamics in a small integrated complex systems. @ Binghamton (The ORB), 2026. -
Reducing Systemic Bias in Behavioral Targeting Using Explainable AI: The HARMONIA Complex Systems Approach
Behavioral targeting is a key part of the modern advertising web's algorithmic engine. However, it is unclear whether optimization processes worsen bias, promote unchecked spread in filter bubbles or lower overall users' trust levels. This paper introduces HARMONIA (Holistic Adaptive Regulatory Model for Optimizing Non-transparent Intelligent Advertising), a comprehensive, data-driven Explainable Artificial Intelligence (XAI) framework aimed at transforming behavioral targeting via transparency, interpretability, and adaptive ethical regulation. This paper conducted a comprehensive Explorative Data Analysis (EDA) on the public Criteo Display Advertising Dataset, which contains over 45 million records, to identify patterns in high-dimensional user-ad interaction space. This analysis uncovered latent behavioral signals that affect the relevance of ads based on users' online behavior. The analysis identified four interrelated behavioral dynamics: ad fatigue attenuation, diurnal engagement oscillations, device-driven preference divergence, and category-affinity dominance. These dynamics served as the foundational architectural principles for HARMONIA's design. The method uses gradient boosted prediction models and a multilayer explainability stack that includes SHAP for global interpretability, LIME for local surrogate approximation, and counterfactual reasoning for causal transparency. Quantitative evaluation indicates that HARMONIA maintains relevance accuracy (approximately 1.2% CTR), achieves a 31% enhancement in transparency metrics, and a 27% improvement in user-trust indices, while concurrently reducing systemic entropy by nearly one-third. This research redefines personalization to be self-explanatory and ethically sound AI by incorporating explainability as a regulatory mechanism in the adaptive ecosystem of complex digital advertising. This system takes explainable computational marketing from an idea to a full-scale implementation. 2026 Binghamton University Libraries. All rights reserved. -
Modeling Flood-Induced Cascading Disruptions in the Indian Electronics Supply Chain Using Influence Network Analysis
This study investigates flood induced disruptions in the Indian electronics supply chain using influence network analysis. Monsoon floods are recurring hazards that significantly impact economic activities, logistics, and industrial productivity. This study integrates district-level rainfall data (2020 to 2025) with supply chain network models to quantify cascading failures. The methodology applies rainfall thresholds (? 300 mm/month) to identify flood-prone districts and constructs a stochastic influence matrix representing inter-firm dependencies. Flood propagation dynamics are modeled iteratively with a propagation coefficient (? = 0.6) and convergence threshold (? = 10-4). The resulting disruption profiles are mapped onto company-level revenues calibrated to India-specific scales, adjusted for disruption durations (two months per year). This approach produces district and company-level economic loss estimates consistent with observed flood impacts (e.g., Chennai 2015 flood losses of USD 3 to 5 billion). Key contributions include linking meteorological hazards to systemic supply chain failures, demonstrating economic vulnerabilities at district and sectoral scales, and providing a framework for resilience planning. 2026 Binghamton University Libraries. All rights reserved. -
AI-Powered Transformation in Home Textiles: Efficiency, Sustainability, and Consumer Experience
Background The home textile sector, including bed linens, towels, and curtains, is under pressure from rising consumer expectations, stricter sustainability standards, and supply chain uncertainties. Artificial intelligence (AI) is emerging as a strategic enabler, offering innovative solutions across design, production, quality control, logistics, and customer interaction. Methods The scope includes a scoping review (20152025) of peer-reviewed literature and reputable industry reports, supplemented by documented corporate cases in home textiles. Inclusion required explicit metrics (e.g., yield %, energy or water usage, forecast error) or reproducible descriptions of AI workflows. Results Analysis shows that AI improves efficiency and competitiveness through multiple pathways: (i) trend forecasting and generative design tools; (ii) optimized color matching and dyeing via machine learning and spectral systems; (iii) automated defect detection and predictive maintenance using computer vision and IoT; (iv) cutting-room efficiency through AI nesting algorithms; (v) supply chain resilience with demand sensing and drone-assisted inventory checks; and (vi) blockchain-based platforms that ensure cotton traceability. On the consumer side, AI enhances personalization and supports the growth of smart bedding products. These applications reduce waste, improve product quality, and reinforce sustainability initiatives. Conclusion AI complements rather than replaces human creativity and craftsmanship. Organizations in the home textile industry that embrace AI strategically across design studios, mill operations, and retail channels can achieve measurable improvements in productivity, sustainability, and consumer trust, positioning themselves for long-term competitive advantage. 2025, Textile Association (India). All rights reserved. -
QSPR ANALYSIS OF ANTI-ASTHMATIC DRUGS USING REDEFINED FIRST ZAGREB POWER INDEX
In this paper, we introduce a novel degree-based topological in-dex, the Redefined First Zagreb Power Index (ReZG1P I(G)). Explicit formu-lae for ReZG1P I(G) are derived for several standard graphs. Furthermore, we investigate the quantitative structureproperty relationship (QSPR) of anti-asthmatic drugs. The study reveals a strong correlation between the physicochemical properties of these drugs and their corresponding ReZG1P I(G), reflecting the structural representation of molecules as graphs. Finally, we establish linear and quadratic regression models between the proposed molec-ular descriptor and the physicochemical properties of anti-asthmatic drugs. 2026, MUK Publications and Distribution. All rights reserved. -
AI-driven surveillance in India: Reconciling privacy, national security and legal oversight
Artificial intelligence (AI) is having a significant impact on how the surveillance apparatus in India operates. Along with the numerous possibilities, the indoctrination of AI in surveillance mechanisms poses serious privacy concerns. The conflict between state surveillance and the fundamental right of privacy is apparent even at the conceptual level. On the one hand, the rise of advanced surveillance mechanisms has been an abetting factor in this conflict, while on the other hand, many theorists have been at work to find a harmonisation between them. Throughout Indian history, surveillance apparatus has helped thwart threats to national security and maintain the nations integrity. The apparent disadvantage of surveillance can be its intrusion into citizens right to privacy, which poses several legal challenges. This paper explores how incorporating AI in surveillance mechanisms enhances Indias surveillance apparatus and influences the conflict between national security and privacy rights. The paper examines how revolutionary AI technologies such as predictive policing, facial recognition (FRT) and AI-enhanced monitoring systems aggravate the apparent conflict between national security interests and the fundamental right to privacy, as adjudged in the Puttaswamy judgment. The paper critically analyses the existing legal architecture, which consists of the Telecommunications Act and the IT Act, and highlights its shortcomings. Further, the paper traverses how legal frameworks of other jurisdictions such as the European Union (EU) AI Act, the Canadian AI and Data Act (AIDA) and the US regulatory guidelines could guide India in determining a well-rounded regulatory approach. Additionally, the paper proposes adopting a context-based or risk-based approach to AI regulation and the practical challenges therewith in an attempt to harmonise the state security imperative with citizens privacy rights without obstructing technological advancement. The comparative analysis of different regulatory guidelines and legislations and the potential regulations would provide practical insights for the legislature, law enforcement and other stakeholders. The paper ultimately argues that there is an exigence for a comprehensive regulatory framework to conciliate national security and privacy rights in the AI-powered digital landscape. This article is also included in The Business and Management Collection which can be accessed at https://hstalks.com/business/. Henry Stewart Publications. -
Harnessing mobile multimedia for entrepreneurial innovation and sustainable business growth
This research investigates the role of mobile multimedia platforms and artificial intelligence (AI) in driving innovation and ensuring the sustainability of entrepreneurial businesses, focusing particularly on technology acquisition, integration, and infrastructure. For data collection, the study employed a quantitative research design and surveyed 150 Indian technology firms that had adopted mobile multimedia applications. Structural equation modeling was used to analyze the data, supported by descriptive statistics, correlation, regression analysis, and mixed methods to understand the adoption and use of digital technologies for innovation activities. The results show that AI-driven applications, when combined with multimedia content and real-time analytics, significantly enhance entrepreneurial innovation by improving operational efficiency, increasing customer engagement, and facilitating expansion into new international markets. Companies utilizing mobile multimedia platforms gain a competitive advantage, translating into long-term business growth and sustainability. This research contributes to the literature on AI and entrepreneurship in the context of digital transformation, highlighting the need for startups to invest in AI-enabled mobile technologies. It equally serves policymakers by informing the regulation of an environment that promotes innovation and business sustainability through digital initiatives. This research addresses a significant gap in the literature by providing evidence on how AI acts as a driver of change and provides insight into the adoption of new technologies in the context of entrepreneurship, an area that remains largely underexplored. 2025, Society of Sytematic Innovation. All rights reserved. -
RP-HPLC METHOD FOR QUANTITATIVE ESTIMATION OF NAFTIFINE HYDROCHLORIDE IN FORMULATED PRODUCTS: DEVELOPMENT AND VALIDATION
Background: Naftifine hydrochloride is an allylamine antifungal agent commonly used to treat dermatophyte infections. It inhibits squalene epoxidase, a key enzyme in ergosterol biosynthesis, thereby disrupting the integrity of the fungal cell membrane. It exhibits broad-spectrum activity against dermatophytes, yeasts, and molds, and is typically formulated as a 1% topical cream or gel. Methodology: A rapid and robust reverse-phase high-performance liquid chromatography (RP-HPLC) method was developed and validated for the estimation of naftifine hydrochloride in a topical cream formulation (2% Naftifast, Zydus), in accordance with ICH and FDA guidelines. Chromatographic separation was achieved on an Inertsil ODS column using an isocratic mobile phase consisting of 35% acetonitrile, 40% methanol, 25% water, and 0.8% triethylamine (pH adjusted to 5.5 with acetic acid) at a flow rate of 1.4 mL/min. Detection was performed at 265 nm. Results and Discussion: Naftifine hydrochloride showed a retention time of approximately 4.0 minutes with a total run time of 6.0 minutes. The method displayed excellent linearity over a concentration range of 20120 g/mL (R > 0.999). Recovery studies indicated a mean recovery of 100.4%. Precision was confirmed by relative standard deviation (RSD) values of less than 2%, demonstrating the methods reproducibility. Conclusion: The proposed RP-HPLC method is simple, precise, and time-efficient. It is suitable for routine quality control of naftifine hydrochloride in pharmaceutical dosage forms due to its short analysis time and strong validation performance. 2025 The authors. -
DIGITAL TWINBASED INTELLIGENT MONITORING OF INDUSTRIAL SYSTEMS USING EXPLAINABLE AI
Industrial systems increasingly rely on Industrial Internet of Things (IIoT) sensors for real-time monitoring and predictive maintenance. However, most existing digital twinbased monitoring solutions depend on static or black-box machine learning models, limiting interpretability, operator trust, and safe deployment in safety-critical environments. In response to these challenges, the author develops the Adaptive Hybrid Digital Twin with Causality-Aware Explainable Artificial Intelligence (HADT-C-XAI) framework to offer transparency and intelligence in industrial monitoring. The framework describes three integrated layers: (i) acquisition of real-time sensors, (ii) continually synchronized hybrid digital twin modeling, which is the integration of physics and data hybrid modeling and (iii) an intelligent analysis layer where LSTM-based anomaly detection is ungraded with explainable feature attribution. A closed-loop learning mechanism updates the model dynamically to adapt to operational drift while generating interpretable fault causes for operator decision support. Experiments were conducted on a multi-sensor industrial testbed containing 120 hours of vibration, temperature, acoustic, and rotational data. The implemented system shows a 94.8% detection accuracy, 95.4% recall, and a 4.1% low false alarm rate, which surpasses standard LSTM (88.5%) and threshold-based monitoring (82.9%). With edge-level inference, detection latency has been reduced to 26-30 ms, which allows for real-time deployment. Results demonstrate that integrating adaptive digital twins with explainable AI improves reliability, transparency, and fault diagnosis while maintaining computational efficiency. The proposed framework provides a scalable and trustworthy solution for predictive maintenance, Industry 4.0 applications, and cyberphysical system monitoring. 2025, Technical institute of Bijeljina. All rights reserved. -
TECHNOLOGY-ENABLED SOLUTIONS FOR INCLUSIVE WORKPLACE DESIGN TO SUPPORT TRANSGENDER EMPLOYMENT RIGHTS
The study fills the gap in the current understanding of available legal safeguards and the actual inclusion of transgender workers that, despite constitutional and legislative requirements in India, there is still no equal access to work-related facilities, computerized systems, and company policies. In order to fill this gap, the research will use a User-Centered Design (UCD) methodology, which involves the active involvement of transgender employees in all the phases of requirement collection, co-design, prototyping, implementation, and testing. The model includes adaptive digital platforms, workspace redesign, smart engine, other policy-promoting, and promotes inclusivity with the help of data analytics and feedback loops. Pilot testing and simulated data evaluated parameters, including participation by the user, ergonomic inclusivity, policy-response intelligent, job satisfaction, mental wellbeing, retention, and team functioning. Findings show significant gains compared to conventional HR models with a 95% increment in user participation, 4% increment of ergonomic inclusiveness, 65% increment of policy-response acumen, 4.5-point increment of job satisfaction, 8 % increment of mental wellbeing, 41-point increment of team collaboration and improvement in retention by 20% in three-years. Overall, these results indicate that UCD-based technology-enhanced systems have the potential to decrease the level of exclusion, improve psychological safety, and performance of organizations. This paper finds that sustainable transgender workplace equity must incorporate participatory feedback, inclusive design and technology-based solutions into the system of the organizations, developing scalable and evidence-based programs to enhance inclusion, wellbeing, satisfaction, and retention and creating a supportive and equitable workplace environment. 2025, Technical institute of Bijeljina. All rights reserved. -
IoT Behavioural Analytics for Retail Engagement
The modern-day retailing world is struggling to provide real-time and hyper-personalised customer interaction in the context of fragmented behavioural data, sluggish analytics, and in-store interventions that are generic. Current Internet of Things (IoT) retail systems are mainly focused on inventory and transactional insights and do not capture more in-depth behavioural and emotional indicators that affect purchase intent and satisfaction. In this context, this paper will suggest an Internet of Things (IoT)-Based Behavioural Analytics Platform to Hyper-Personalised Consumer Engagement in Retail Management (IBAPS-RM). The framework incorporates multimodal Internet of Things (IoT) sensing, edge computing, and cloud intelligence in creating Multimedia Behavioural Digital Twins (Behavioural Digital Twin (BDT) that dynamically change in response to contextual, environmental, and Interaction-driven information. One of the most notable novelties is the Behavioural Fusion Neural Unit (BFNU) (Behavioural Fusion Neural Unit (BFNU)), that conducts real-time sensor fusion between gaze movement, dwell time, gestures, proximity, and purchase latency to determine behavioural intent and launch micro-personalised interventions in the form of adaptive light, context sensitive offers and personalised digital content. Reinforcement learning also enhances engagement policies through continuous optimisation based on feedback. Experimental analysis shows that IBAPS-RM has better engagement intelligence, with over 93% of personalisation accuracy, 73% shorter decision latency, and 64% higher conversion rate than traditional Internet of Things (IoT) retail systems. The suggested solution improves responsiveness, consumer experience, and operational effectiveness, and promotes privacy-conscious behavioural modelling. In general, IBAPS-RM creates a dynamic, proactive retail intelligence paradigm that dedicates behavioural inference to real-time engagement delivery. 2025, International Academic Institute for Science and Technology. All rights reserved. -
HumanWildlife Conflict in Kerala Conservation Policies and the Elusive Ethics of Peaceful Coexistence
The troubled humanwildlife relationship in the highlands of Kerala is a matter of growing concern due to the constant disruption of the lives and livelihoods of the people who share space with wildlife. Debates surrounding the complexities of humanwildlife conflict often persist, largely due to the divide between the environmentalist perception of conservation and the experiences of farmers confronting wildlife-related threats. This study demonstrates that the precarious social and economic circumstances of the farmers and local communities directly affected by the inter-species conflict undermine the skewed discourse promoting coexistence between humans and wildlife. 2025, Economic and Political Weekly. All rights reserved.