Volume 14 | Issue 3

 Abstract

By enabling brands to offer highly personalised experiences that increase engagement and loyalty, AI-driven personalisation is changing consumer-centric marketing. Artificial intelligence (AI) has evolved into an essential resource for marketing strategies as consumer demand for tailored interactions expands and data-driven insights help to build more important brand-customer links. Emphasising its relevance for contemporary brand engagement, the paper investigates how artificial intelligence is transforming personalisation. Emphasising its impact on consumer engagement, this research aims to provide a thorough review of the present literature on AI-driven personalisation. The paper compiles data from several sources to examine how AI technologies such as machine learning, predictive analytics, and natural language processing improve marketing strategies by means of tailored advertising, personalised recommendations, and dynamic content distribution. This study uses journal studies and scholarly research articles for a meticulous examination of the literature. To ensure an in-depth examination of the topic, sources were selected for their relevance to AI-driven personalisation and brand interaction. Studies show that by developing deeper connections, increasing brand loyalty, and improving customer experiences, AI-driven personalisation greatly improves consumer-brand interactions. Still, major determinants of customer trust are data privacy concerns, questions of ethics, and algorithmic biases. This paper synthesises critical insights to improve comprehension of AI's function towards sustainable consumer-centric marketing and offers brands solutions to personalisation concerns. Future research should look at consumer opinions on trustworthy AI frameworks, consumer personalisation of AI, and the combination of AI with subsequent technologies to improve marketing methods

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 Keywords

Personalization, Artificial Intelligence, Ethics, Brand Engagement, Sustainability, Consumer-Centric Marketing, Consumer-Brand interactions

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Extraction of valuable bioactive compounds from plant matrices and food by-products is essential for developing value-added products in nutraceuticals, pharmaceuticals, cosmetics, and functional foods. This study represents the various extraction techniques helps to extract the compound, majorly it classified into conventional (traditional solvent-based) and non-conventional (modern, often green) techniques. The traditional technique like maceration, percolation, soxhlet extraction are widely used, they often require longer extraction times, higher solvent volumes, and greater energy input. In contrast, non-conventional techniques including ultrasound-assisted extraction (UAE), microwave-assisted extraction (MAE), supercritical fluid extraction (SFE), natural deep eutectic solvents (NADES), are presented as advanced, sustainable alternatives. These offer superior efficiency, reduced solvent and energy use, shorter times, better preservation of thermolabile compounds, and environmental benefits, though challenges like equipment costs and scale-up persist. The study also compares the sustainability, scalability, economic feasibility, and application potential of each method. Overall, the transition from conventional to innovative green extraction technologies reflects the growing demand for environmentally friendly, high-efficiency, and industrially viable processes for bioactive compound recovery.

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Bioactive compound, Extraction, Green Technology, Eco-friendly

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The term civil service in India first emerged during the administration of the East India Company. The Civil Servants were initially engaged in trade; however, as the Company commenced its territorial acquisitions, it progressively evolved from a commercial entity into an administrative government, thereby transforming its 'civil servants' from mere traders into competent administrators. This paper examines the establishment of Fort William College in 1800 and its role in transforming the East India Company's civil servants from commercial agents into administrators of a territorial empire. Founded under Richard Wellesley, the College reflected the Company's transition from trade to governance, as its officials assumed judicial, revenue, and diplomatic responsibilities across diverse Indian societies. Wellesley envisioned a structured program of education that would equip civil servants with linguistic competence, legal knowledge, and moral discipline suited to their expanded authority.

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East India Company; British civil servants; Fort William College; Colonial Civil Servant; language studies; colonial administration;

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In the contemporary health care system, to effectively manage the patients, it is imperative to make right forecasts of the diseases like heart disease and diabetes, among others. This article provides a Hybrid Quantum-Classical System which makes use of Quantum Variational Classifier (QVC) to predict illnesses in medicine. The suggested system takes advantage of the capabilities of quantum computing to investigate high-dimensional spaces that are generally difficult to find by classical machine learning models. Combination of quantum variational circuits and classical machine learning methods allows the effective classification of the clinical data, including age, glucose levels, and BMI. This type of system is tested by using medical datasets, and the outcomes reveal that this system is more robust, especially in the presence of noise or in case of smaller training sets. The QVC demonstrates the improvements in prediction accuracy that are promising when compared to the traditional models such as Support Vector Machines (SVM), Logistic Regression, and Neural Networks. Such a framework preconditions the next-generation quantum-powered medical diagnostics, which will be more computationally efficient and future predictions, despite limited or noisy data.

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Hybrid Quantum-Classical System, Quantum Variational Classifier, Medical Disease Prediction, Heart Disease, Diabetes, Quantum Computing, Clinical Data Classification, Noisy Data, Quantum Variational Circuits, Computational Efficiency

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Rapid access to patient medical information is critical in emergency situations where timely clinical decisions can greatly affect the outcome of treatment. However, there is still a large portion of the population without readily accessible digital health records, and many of the solutions are internet-dependent and battery-powered, which limits their reliability in a critical situation. In this paper, a secure battery-free wearable and cloud-based framework for on-the-spot emergency medical information retrieval is presented. The proposed system combines a battery-free wearable device that is embedded with Near Field Communication (NFC) and Quick Response (QR) technologies to achieve fast and contactless access to encrypted patient information. A cross-platform mobile application written in the Dart language (Flutter) and a cloud backend (Firebase) for secure storage, authentication and real-time synchronization of medical records. The framework has the provision for both online and offline access to ensure the availability of crucial medical data even in low-connectivity environments. Encryption mechanisms are implemented to ensure the security of sensitive patient information and to ensure controlled access to that information. Experimental evaluation shows system is able to access critical medical data in seconds and helps to improve emergency response efficiency and digital healthcare accessibility in resource constrained environments.

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Emergency medical information retrieval, Battery-free wearable devices, NFC-based healthcare, Cloud-based medical records, Secure health data systems, Offline-first access.

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Self-efficacy plays a vital role in achievement in mathematics. This study is an attempt to study the relationship between self-efficacy and achievement in mathematics. The sample was comprised of 200 secondary students studying in Government Senior Secondary School, Bajakhana, and Hans Raj Memorial Senior Secondary School District Faridkot, Punjab. For the data collection, Self-efficacy Scale by Mathur and Bhatnagar (2012) and the marks obtained by student in Xth (P.S.E.B.) considered as their achievement scores in mathematics were used by investigator. T-test was used to ascertain the difference of means between variables. The analysis revealed that school students studying in Private schools had greater self-efficacy mathematics as compared to school students studying in Government schools. It was also found that urban students had higher self-efficacy as compared to rural students. There was a positive and significant relationship between self-efficacy and achievement in mathematics.

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Self-efficacy, achievement in mathematics, rural and urban, government and private, secondary school students.

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The rapid digitization of modern infrastructures has significantly expanded the scale and complexity of cyber threats. Traditional security mechanisms, primarily dependent on rule-based detection and centralized monitoring, are increasingly inadequate against sophisticated attacks such as advanced persistent threats (APTs), zero-day exploits, ransomware variants, and AI-driven intrusions. In response to these evolving challenges, this study proposes an intelligent cybersecurity framework that integrates Artificial Intelligence (AI) and Blockchain technology to create a decentralized, adaptive, and resilient security architecture. Artificial Intelligence enhances cybersecurity by enabling real-time anomaly detection, predictive threat intelligence, and automated incident response through machine learning and deep learning models. However, AI-based systems often rely on centralized data repositories, making them vulnerable to manipulation and single points of failure. Blockchain technology addresses these limitations by offering decentralized trust management, tamper-proof logging, immutable audit trails, and smart contract- based policy enforcement. The proposed hybrid framework combines AI's analytical capabilities with blockchain's distributed integrity mechanisms. The architecture includes layered components comprising secure data acquisition, AI-driven intelligence processing, decentralized consensus validation, and administrative control interfaces. The system is evaluated using simulated cyberattack scenarios and benchmark intrusion datasets to assess detection accuracy, response latency, system reliability, and resistance to data tampering. Results indicate that the integrated AI-Blockchain model improves detection precision, reduces false positives, enhances log integrity, and minimizes centralized vulnerabilities compared to standalone AI or blockchain solutions. The research concludes that hybrid intelligent architectures represent a promising direction for next-generation cybersecurity systems capable of protecting critical digital ecosystems.

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Artificial Intelligence, Blockchain Technology, Cybersecurity Framework, Intrusion Detection, Decentralized Security, Smart Contracts, Anomaly Detection, Distributed Ledger, Threat Intelligence, Secure Logging

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In the field of education, Cognitive Style plays a pivotal role in shaping the teaching and learning process of future educators. The present study, titled " A study on Locality-based Variation in Cognitive Style among Secondary Student Teachers," was undertaken to investigate the cognitive patterns of those preparing for the secondary teaching profession. To achieve this, the researcher adopted the Survey Method. A total sample of 150 secondary student teachers (64 Rural and 86 Urban) was selected using the Simple Random Sampling Technique. Data were collected using a validated Self-made Cognitive Style Inventory which measures Cognitive Style in five dimensions namely Knowing, Planning, Creative, understanding and Thinking style. The statistical analysis revealed that there is no significant difference in the overall cognitive style of secondary student teachers based on their locality.

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Cognitive Style, Secondary Student Teachers, Locality Variation, Cognitive Style Dimensions.

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In today's world, digital technology has become an integral part of people's daily lives.With the increasing dependance on the internet, smartphone, social media and online services personal information has now become an important asset. In this situation the question of privacy has emerged as not only a moral or social issue but also a method of constitutional right. Article 21 of the Constitution of India which guarantees the right to life and personal liberty has been expanded over time to judicial interpretation. The landmark judgement of the supreme court in the case of Justice KS Pattaswami vs. Union of India in 2017 is an important milestone in this evolution. A nine judge bench unanimously declared that privacy is a fundamental right to shrink in article 21 of the constitution. This judgement let the Constitutional foundation for the protection of citizens data in the digital era This paper analyzed the historical evolution of article 21, the need for privacy in the digital era, State surveillance, cyber security and the events of the digital personal data protection Act 2023. The conflict between personal freedom and national security is also discussed. The study seeks to show that privacy is an essential constitutional value for protecting human dignity in the digital era.

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Article 21, Right to Privacy, Indian constitution, Digital era, Fundamental Rights, Data Protection

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The rapid advancement of Artificial Intelligence (AI) has significantly transformed governance mechanisms and policy-making processes across the world. Governments are increasingly integrating AI-driven technologies to enhance efficiency, transparency, and participatory governance. In democratic societies, the effectiveness of public policy depends not only on institutional capacity but also on citizen participation and collaborative governance mechanisms. Artificial Intelligence offers new possibilities for evidence-based policy formulation, efficient implementation, continuous monitoring, and systematic impact assessment. In India, the expansion of digital infrastructure has created an enabling environment for the application of AI in governance. One of the most significant welfare initiatives in the health sector is the Ayushman Bharat Yojana, which aims to provide financial protection and healthcare access to economically vulnerable populations. Managing such a large-scale healthcare program requires sophisticated systems capable of handling vast amounts of data related to beneficiaries, healthcare providers, treatment outcomes, and financial transactions. Artificial Intelligence has the potential to strengthen policy processes in such programs by enabling predictive analytics, fraud detection, automated verification, and real-time monitoring of healthcare delivery systems. Additionally, AI-enabled digital platforms can facilitate citizen engagement through grievance redressal mechanisms, feedback systems, and participatory decision-making processes. This research paper examines the role of Artificial Intelligence as a tool for constructive public policy that promotes participative governance. Using Ayushman Bharat Yojana as a case study, the study analyzes how AI can enhance policy formulation, policy implementation, policy monitoring and evaluation, impact assessment, and citizen participation in governance processes. The study also highlights the ethical challenges and governance concerns associated with AI-driven policy systems. The findings suggest that AI can significantly improve policy effectiveness, transparency, and responsiveness while strengthening collaborative governance structures. However, the successful integration of AI in public policy requires robust institutional frameworks, data protection measures, and accountability mechanisms to ensure democratic and ethical governance.

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Artificial Intelligence, Public Policy, Participative Governance, Digital Governance, Health Policy, Welfare Schemes, Data Governance

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Career guidance and job recommendations have become increasingly complex due to the rapid evolution of industry skill requirements and the diversity of individual user profiles. Accurate identification of skill gaps and alignment between candidate capabilities and job market expectations is essential for improving employability outcomes. This paper presents PathFinder AI, an intelligent career guidance system that leverages Retrieval-Augmented Generation (RAG) to analyze resumes, recommend suitable job roles, and generate personalized learning paths. The system processes unstructured resume data to extract relevant skills and experience, which are then semantically compared with industry job descriptions using vector embeddings. A knowledge base containing curated job requirements and learning resources is employed to retrieve contextually relevant information through similarity-based search. The retrieved knowledge is integrated with a lightweight language model to generate explainable job recommendations and structured learning guidance. By combining semantic retrieval with controlled language generation, PathFinder AI improves the accuracy, relevance, and interpretability of career recommendations compared to traditional keyword-based systems.

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Retrieval-Augmented Generation (RAG), Career Guidance System, Resume Analysis, Skill Gap Analysis, Job Recommendation, Personalized Learning Path, Semantic Search, Vector Embeddings, Artificial Intelligence, Full-Stack Web Application.

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Regional histories play a crucial role in decentralizing dominant historiographical narratives and highlighting the dynamic interplay between ecology, society, and political transformation. Purulia District, situated at the western frontier of West Bengal and historically associated with the Manbhum region, represents a distinctive cultural landscape shaped by environmental constraints, tribal resilience, religious plurality, colonial restructuring, and post-independence integration. Despite its historical significance, Purulia has often remained peripheral within mainstream Bengal historiography, which tends to privilege deltaic and urban-centered developments. This study critically examines the cultural heritage and historical evolution of Purulia from early historical periods to the contemporary era. Employing a qualitative historical-analytical methodology based on archival records, archaeological findings, anthropological accounts, and policy documents, the research situates Purulia within broader debates on regional historiography, subaltern agency, and intangible cultural heritage. The findings suggest that Purulia's socio-cultural identity evolved through processes of negotiation rather than passive assimilation. The district's tangible heritage--including temple architecture, sculptural remains, and settlement patterns--reflects trans-regional connectivity, while intangible traditions such as Chhau dance, Tusu, Bhadu, and Karam festivals demonstrate continuity within transformation. Colonial administrative interventions restructured agrarian relations and forest governance, yet indigenous frameworks of identity persisted. Post-independence linguistic reorganization and development policies further reshaped the district's political and cultural orientation. The study argues that Purulia should be understood as an active historical agent whose ecological setting and tribal foundations shaped a distinctive regional modernity. Recognizing its layered heritage contributes to broader discourses on cultural sustainability and decentralized historiography in India.

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Purulia; Regional Historiography; Tribal Society; Cultural Heritage; Chhau Dance; Colonial Transformation; Indigenous Identity; Eastern India.

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In modern sports, data and artificial intelligence are widely used to improve player performance, predict injuries, and support strategic planning. However, many AI models are difficult to interpret because they do not clearly explain how their predictions are generated, which reduces trust among coaches and analysts. This research proposes an Explainable Artificial Intelligence (XAI)-based sports analytics system that integrates player performance analysis, injury risk prediction, and match outcome prediction within a unified framework. The system analyzes sports data, including player statistics, match history, and injury records, using machine learning techniques, while XAI methods are applied to clearly explain the reasoning behind each prediction. The results demonstrate that the proposed system provides accurate predictions along with meaningful explanations, thereby enhancing decision-making and increasing confidence in AI-based systems. Overall, the study highlights the importance of XAI in making sports analytics more transparent, reliable, and suitable for real-world applications.

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Explainable AI, Sports Analytics, Machine Learning, Injury Risk Prediction, Player Performance Analysis, Match Outcome, SHAP, Data Science

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n the modern era, digitalization is transforming traditional sectors, including agriculture. Despite being the backbone of India's economy, agriculture still faces challenges such as limited access to quality resources, middlemen exploitation, and lack of real-time weather information. AgroPrime is a full-stack web application developed using the MERN (MongoDB, Express.js, React.js, Node.js) architecture to provide a smart digital solution for farmers. The platform offers an integrated e-commerce system for purchasing seeds, fertilizers, and agricultural equipment, along with a real-time weather forecasting module and an informational section covering pest control and government schemes. The system is designed with a responsive interface and multi-language support (English, Marathi, Hindi) to ensure accessibility for rural users. AgroPrime aims to bridge the gap between traditional farming practices and modern digital technology, improving efficiency, transparency, and productivity in agriculture

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MERN Stack, Smart Agriculture, E-Commerce Platform, Real-Time Weather Forecasting, Digital Farming, MongoDB, React.js, Agricultural Technology

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The Gaze-Based Mouse Control System with Voice Assistant is designed to provide a hands-free and user-friendly way to interact with a computer without using a traditional mouse or keyboard. The system uses a standard webcam to track the user's eye movements and control the mouse cursor based on gaze direction, while eye blinks are used to perform mouse click actions. In addition to eye tracking, a voice assistant is integrated to recognize spoken commands for tasks such as opening applications, scrolling, typing, and executing system operations. Computer vision techniques are used for real-time face and eye detection, and speech recognition is employed to convert voice commands into actions. This approach is especially beneficial for physically challenged users and also supports touch-free computing for general users. The proposed system is low-cost, easy to use, and improves accessibility by offering a natural and intuitive human-computer interaction experience.

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Gaze-Based Mouse Control, Eye Tracking, Voice Assistant, Computer Vision, Speech Recognition, Human-Computer Interaction, Assistive Technology, Hands-Free Computing, Accessibility, Webcam-Based Interaction.

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the observational study to assess Sthula Purusha Lakshana in overweight and obese individuals, has shown the classical features of Sthula Purusha Lakshana, as described in Ayurvedic texts, are observed with varying severity among overweight and obese individuals, as measured by the Body Adiposity Index (BAI).

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sthoulya, body adiposity index, overweight , obese

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Artificial General Intelligence (AGI) represents the next stage in artificial intelligence, aiming to develop systems with human-like cognitive abilities such as reasoning, learning, adaptability, and autonomous decision making across multiple domains, in contrast to narrow AI designed for specific tasks. The rapid growth of the Internet of Things (IoT), with its vast network of interconnected sensors, devices, and cyber-physical systems, offers a rich environment for AGI by continuously generating large volumes of heterogeneous, real-time data that enhance contextual awareness and intelligent behavior. The integration of AGI into IoT infrastructures holds transformative potential for applications including smart cities, healthcare monitoring, industrial automation, autonomous transportation, and intelligent energy management, enabling systems to evolve from rule-based automation to self learning, predictive, and context-aware intelligence capable of operating in complex and dynamic environments. However, this convergence also introduces significant challenges such as computational scalability, energy efficiency, privacy and security risks, ethical concerns, and the difficulty of deploying robust, generalizable learning models on decentralized and resource-constrained IoT devices. Addressing these challenges through approaches like edge intelligence, federated learning, and hybrid cloud-edge architectures is essential to realizing the full potential of AGI-enabled IoT systems, which could ultimately redefine intelligent digital ecosystems by enabling autonomous, resilient, and human-like decision-making.

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Artificial General Intelligence (AGI), Internet of Things (IoT), Cognition-Driven Connectivity, Intelligent IoT Systems, AIoT, Edge Intelligence, Distributed Intelligence, Context-Aware Computing, Autonomous Decision-Making

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In Tamil Nadu, 55.4% of urban women workers are employed in the formal sector in regular wage or salaried jobs; around 32% of women employed in Chennai are often considered a proxy for formal sector employment; however, their participation in stock market investments continues to be significantly low. This study examines the level of financial autonomy, risk appetite, and investment portfolio of urban working women in Chennai. Using primary data of 100 respondents collected through a structured questionnaire, the study analyses the influence of demographic and socio-economic factors such as education, income, risk appetite and investment experience on women's investment decisions. Descriptive statistics, chi-square analysis, and graphical representations are employed to assess decision-making autonomy and risk behaviour. The findings indicate that higher levels of education and income enhance women's financial autonomy and confidence in investment decision-making. Nevertheless, most respondents prefer low-risk and secure investment instruments such as mutual funds, gold bonds, and government securities, while direct participation in equities and other high-risk assets remains limited. Persistent risk aversion, gaps in financial literacy, and social influences continue to constrain women's active involvement in the stock market. The research highlights the need for targeted financial literacy initiatives, women-centric investment advisory services, and supportive institutional frameworks to promote informed risk-taking and greater stock market participation among women. Key Words: In Tamil Nadu, 55.4% of urban women workers are employed in the formal sector in regular wage or salaried jobs; around 32% of women employed in Chennai are often considered a proxy for formal sector employment; however, their participation in stock market investments continues to be significantly low. This study examines the level of financial autonomy, risk appetite, and investment portfolio of urban working women in Chennai. Using primary data of 100 respondents collected through a structured questionnaire, the study analyses the influence of demographic and socio-economic factors such as education, income, risk appetite and investment experience on women's investment decisions. Descriptive statistics, chi-square analysis, and graphical representations are employed to assess decision-making autonomy and risk behaviour. The findings indicate that higher levels of education and income enhance women's financial autonomy and confidence in investment decision-making. Nevertheless, most respondents prefer low-risk and secure investment instruments such as mutual funds, gold bonds, and government securities, while direct participation in equities and other high-risk assets remains limited. Persistent risk aversion, gaps in financial literacy, and social influences continue to constrain women's active involvement in the stock market. The research highlights the need for targeted financial literacy initiatives, women-centric investment advisory services, and supportive institutional frameworks to promote informed risk-taking and greater stock market participation among women.

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Financial Literacy, Autonomy, Investment Portfolio, Risk Appetite

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This paper examines evolving trends in household finance in Paschim Bardhaman district of West Bengal, focusing on income levels, savings behaviour, and expenditure patterns. Drawing on primary survey data and broader state-level socioeconomic statistics, the study documents the financial behaviour of households within the district and situates local patterns within broader Indian household finance trends. The analysis highlights the linkages between income levels, savings choices, and consumption behaviour, offering policy implications for financial inclusion and socioeconomic welfare.

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Household finance, savings, expenditure, income distribution, Paschim Bardhaman, financial behaviour.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

?????????? ??? ??????? ????? ???? ????? ?????????? ?????????? ??? ????????? ????? ????. ?????, ?????, ???????, ????? ?????????? ???????????? ?????????? ????? ????. ?????????? ????? ????? ??? ??????????? ???? ???????????? ?????? ???? ???, ???? ?????????? ????? ??? ???????? ?? ???? ?????????? ????. ????? ???????????? ???????? ??????? ????????, ?????????? ????, ??????, ??.??.?????, ???? ??????, ?????????? ?????????? ?????? ??????? ??????. ??????????? ?????????????? ????? ???? ????, ????? ???????? ?????, ????? ?????, ?????? ?????? ????? ???? ???? ????. ?? ???????? ????? ???? ???? ????? ????? ?????? ????. ??????? ?????????? ?????????? ??????? ?????? ???? ??????????, ??? ??? ??????? ?? ??????? ??? ???????? ???????? ????. ????????? '? ???????' (2009) ?? ???????? ?????????? ??????????, ??????? ????????? ??? ????????? ???????? ???????? ???????? ?????????? ?????? ???. "???? ??????", "???? ??", ????????? ???????", "?? ???? ?????" ?? ????? ?????????? ???????????? ??????, ???????? ??? ?????????? ????? ????? ????? ??? ?????. ???? ????? ??????? ?????? ??????? ???????, ??????????, ??, ???, ????????? ? ????????????? ?????? ?????? ?????? ???????? ??????. ??????? ????? ???, ??????? ??? ???????? ???? ????? ????????? ?? ???????? ??? ??????? ?????? ???.

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