Volume 14 | Issue 3

 Abstract

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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 Keywords

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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 Keywords

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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 Abstract

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

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???-????: ??????, ????????, ???? ?????, ????????????, ?????, ???????????, '? ???????'.

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Water Quality, Seasonal Variation, Physico-Chemical Parameters, TDS, Hardness, Dissolved Oxygen, Water Quality Index.

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Creative Commons Attribution 4.0 and The Open Definition

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The proposed project offers a web-based chess system with dual functionality: Play Mode for normal play and Learn Mode with AI support for move validation, hints, and optimal move suggestions. There is also a Puzzle Mode for tactical training with pre-set scenarios, and a Post-Game Review Module for analysis of accuracy, errors, and decision time. The complexity of the system is in combining deterministic rule validation, adaptive AI search depth based on difficulty levels, natural language processing interfaces for voice-controlled move execution, and statistical models for performance analysis. The system employs chess.js for rule validation, Stockfish.js for move calculation, Web Speech APIs for voice automation, and gamification through achievement patches. The combination of game logic, AI calculation, and speech functionality provides a seamless platform for both play and learning.

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Chess Learning System, Interactive Chess Training, Voice-Controlled Chess, Piece Movement Visualization, User Interface Design, Game Automation, Skill-Based Customization, Human-Computer Interaction, Web-Based Chess Application)

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This paper examines gendered trauma in Bapsi Sidhwa's An American Brat, focusing on Feroza Ginwalla and what she goes through as an expatriate. When Feroza leaves Pakistan for the United States, her inner struggles only get sharper--she faces not just culture shock but even tighter patriarchal pressure. Using trauma theory, especially Cathy Caruth's ideas around belatedness and fractured identity, alongside Homi K. Bhabha's "third space" and hybridity, the study shows how Feroza's trauma isn't just personal--it's shaped by her gender. Her journey through sexual awakening, racial bias, forbidden love, and being pushed away by her family reveals how women's bodies turn into battlegrounds for cultural fears in a global setting. In the end, the paper argues that trauma for expatriates in this novel doesn't come just from being uprooted. It grows out of the tangle of gender, religion, and life in the diaspora.

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Gendered trauma, expatriation, hybridity, patriarchy, diaspora, identity crisis, transnationalism.

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The present study examined the relationship between academic achievement and family environment among Pre-University College students in Kalaburagi city, Karnataka. The study also compared academic achievement and family environment based on gender, locality, type of institution, and type of college management. A descriptive survey method was adopted. A sample of 800 Pre-University students was selected using random sampling technique. Academic Achievement was measured using students' marks, and Family Environment was measured using a standardized Family Environment Scale. Pearson's Product Moment Correlation (r-test) was used to determine the relationship between academic achievement and family environment among different groups. The results revealed a significant positive relationship between academic achievement and family environment among male, female, urban, and rural students. The findings indicate that a favorable family environment contributes positively to students' academic achievement.

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Academic Achievement, Family Environment, Pre-University Students, Gender, Locality, Correlation

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Agriculture acts as an important factor in ensuring food supply and pillar for economic development, but current farming method faces many challenges such as unpredictable weather, crop diseases, and insufficient access to equipment, and fluctuating market prices. These issues majorly affect the small and marginal farmers, resulting in reduced crop production and income. To overcome these issues, the system "TechFarm: Your Personalized Farm Assistant" provides a smart and user-friendly web-based digital platform that includes weather forecasting with alert advisory through the use of API's, crop planning and scheduling for crop, disease detection by image processing, equipment sharing services, and real -time market price update. The system uses trustworthy third party API's to provide accurate information.

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TechFarm, Crop Disease Detection, Weather Forecasting, Equipment Sharing, Market Price Analysis, and Crop planning and scheduling.

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Mobile applications often request sensitive permissions such as camera, microphone, location, contacts, and storage, which can threaten user privacy and data security. The lack of centralized monitoring mechanisms makes it difficult for users to clearly understand how their personal data is accessed and utilized. This study proposes a Privacy Dashboard for Android Applications that collects and analyzes permission and usage data through Android system APIs. The system evaluates application risk levels using rule-based scoring techniques and generates privacy scores for better transparency. It provides real-time monitoring and interactive graphical visualizations to present privacy insights in a user-friendly manner. Experimental evaluation demonstrates that the system is responsive, accurate, and effective in enhancing user awareness. The framework ultimately supports informed decision-making and strengthens mobile privacy protection.

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Privacy Dashboard, Android Applications, Mobile Privacy, Permission Monitoring, Usage Statistics, Privacy Risk Assessment, Data Security, Android Permissions, Application Analytics, User Privacy Protection

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Creative Commons Attribution 4.0 and The Open Definition

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The global fashion industry is increasingly criticized for high carbon emissions, excessive water consumption, chemical pollution, and dependence on synthetic materials. In contrast, traditional textile techniques rooted in indigenous knowledge systems demonstrate environmentally responsive production models grounded in ecological balance and localized economies. This study critically evaluates selected Indian textile practices including natural dyeing, handloom weaving, and eco-friendly fibre processing as sustainable production systems. Drawing on peer-reviewed literature, government reports, and sustainability assessments, the paper analyzes environmental performance indicators such as carbon intensity, water usage, chemical load, and energy dependency alongside socio-economic factors including employment generation and decentralization of production. Comparative evaluation highlights reduced fossil fuel dependence in handloom weaving, biodegradability of natural fibres, and lower toxicity profiles in plant-based dyeing systems. The study further examines structural constraints including income instability, scalability challenges, and market integration barriers. By developing a conceptual sustainability framework, the paper argues that indigenous textile systems represent empirically grounded, low-carbon production models aligned with contemporary sustainable development objectives. Strategic policy support and hybrid innovation approaches are necessary to enhance their long-term viability within modern fashion economies.

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Indigenous Textiles; Sustainable Fashion; Handloom Weaving; Natural Dyeing; Craft Economy; Sustainable Production

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It is becoming more widely acknowledged that chronic diabetes mellitus (DM) increases the incidence of neurodegenerative illnesses, including Parkinson's disease (PD). Oxidative stress as well as neuroinflammation stand highlighted as important, interconnected drivers for dopaminergic neurodegeneration among the major mechanisms connecting these two disorders. Diabetes-related chronic hyper glycemia results in a compromised antioxidant defense system, increased generation in reactive oxygen molecules (ROS) and compromised mitochondrial activity. Due to their great susceptibility to oxidative insults, dopamine-producing neurons of the substantia nigra are especially affected by the prolonged oxidative stress caused by these alterations. Furthermore, neuroprotective pathways may be disrupted by poor insulin transmission in the brain, making neurons even more susceptible to oxidative & inflammatory assaults. Oxidative stress & neuroinflammation are key and perhaps changeable pathways in diabetes-induced neurodegeneration, even though other elements like mitochondrial malfunction and protein aggregation (like ?-synuclein) also contribute to Parkinson's disease pathogenesis. ?-synuclein aggregation & dopaminergic cell death are two classic PD diseases that are caused by the synergistic combination of oxidative stress and neuroinflammation, which upsets neuronal homeostasis. These results highlight how crucial it is to target oxidative & inflammatory pathways in diabetic populations help prevent or delay the emergence of Parkinson's disease. One promising approach to risk reduction and therapeutic intervention is to address these pathways.

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Diabetes, Oxidative stress Neuroinflammation, Parkinson

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Artificial Intelligence (AI) is influencing personal lifestyles and professional environments across the globe, with tech-forward cities like Hyderabad at the forefront. This study presents findings from a qualitative statistical survey that explores AI's impact on daily routines and workplace practices in Hyderabad. Drawing on responses from 250 participants from various sectors, the study reveals the dual nature of AI's role: enhancing convenience and productivity on one side while raising concerns around job security and privacy on the other the paper concludes with insights into how individuals and organizations can adapt to AI's continuing evolution.

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Artificial Intelligence, Daily Life, Workplace Automation, Hyderabad, Human-AI Interaction, Job Displacement, AI Ethics, Technology Adoption, Smart Devices, AI Integration.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Diabetes mellitus is a complex metabolic disease characterized by persistent hyperglycemia arising from defects in insulin secretion, insulin responsiveness, or both. Despite the availability of numerous therapeutic options, diabetes continues to impose a substantial clinical and socioeconomic burden due to its progressive nature and associated complications. Therapeutic paradigms have evolved significantly, shifting from conventional glucose-lowering approaches to multidimensional strategies that address cardiovascular, renal, and metabolic risks. This review critically evaluates established and emerging treatments for diabetes mellitus, including lifestyle-based interventions, traditional and novel pharmacological agents, advanced insulin formulations, and innovative therapeutic platforms. Particular emphasis is placed on mechanistic diversity, clinical outcomes, safety considerations, and future directions aimed at achieving durable glycemic control and ?-cell preservation.

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Diabetes mellitus, antidiabetic therapy, insulin analogues, incretin system, SGLT-2 inhibitors, emerging treatments.

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Creative Commons Attribution 4.0 and The Open Definition

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K R Meera's Yudasinte Suvishesham offers a radical reinterpretation of the biblical narrative of Judas Iscriot, portraying him not as a simple traitor but as a complex, morally ambiguous human being. This paper examines K R Mera's Yudasinte Suvishesham through the twin lenses of existentialism and psychoanalysis to explore the interwoven themes of betrayal, guilt, and the human condition. Through an existential lens, Judas's internal conflicts and choices are analysed as reflections of freedom, ethical dilemmas, and the burden of personal responsibility. Simultaneously, a psychoanalytic approach highlights the psychological dimensions of guilt, repression, and unconscious drives that influence his behaviour and interactions with others. By combining these perspectives, the study analyses how Meera challenges traditional moral binaries, situating betrayal as both a deeply human condition and a site of ethical and psychological complexity. The paper ultimately argues that Yudasinte Suvishesham not only reconstructs biblical narratives but also offers profound insights into human fallibility, conscience, and the existential struggles inherent in moral decision-making.

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Judas Iscariot; Existentialism; Psychoanalysis; Betrayal; Guilt; Human Condition; Malayalam Literature; Biblical Rewriting

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Agricultural marketing systems often involve intermediaries that significantly reduce farmers profit margins and limit their direct access to consumers. The lack of an integrated, real-time digital marketplace frequently causes pricing inefficiencies, delayed transactions, and restricted visibility of agricultural products. In order to collect, manage, and present farm product data from multiple sellers through a unified platform, this study proposes a Mobile App for Direct Market Access for Farmers. The system uses cloud-based database technologies and secure authentication mechanisms to manage product listings, inventory updates, and order processing efficiently. The proposed application provides category-based browsing, real-time synchronization, and seamless cart management to assist both farmers and buyers. The technology enhances pricing transparency, improves direct communication between stakeholders, and accelerates decision-making in agricultural trade.

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Direct Market Access, Mobile Marketplace, Cloud Computing, Smart Farming, E-Commerce in Agriculture, Real-Time Database, Flutter, Firebase

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Creative Commons Attribution 4.0 and The Open Definition

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Bipolar disorder is a chronic and debilitating psychiatric illness characterized by recurrent episodes of mania and depression, significantly affecting an individual's emotional stability, cognitive functioning, and quality of life. Accurate diagnosis remains challenging due to symptom overlap with other mood disorders and reliance on subjective clinical assessments. This study presents a clinically driven deep learning framework for the automated prediction of bipolar disorder using a structured, symptom-based dataset reflecting common psychiatric indicators. Three deep learning models--Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM)--were designed and evaluated to analyze behavioral and clinical symptom patterns. Standard preprocessing techniques, including normalization and train-test splitting, were applied to ensure reliable model performance. The predictive capability of each model was assessed using classification accuracy and confusion matrix analysis. Results demonstrate that deep learning approaches effectively model complex clinical relationships within psychiatric data. The ANN model achieved the highest predictive accuracy, indicating its suitability for tabular clinical datasets. This work emphasizes the potential of deep learning as a supportive clinical decision-making tool for early bipolar disorder screening and improved mental health assessment.

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Artificial Intelligence, Deep learning, Psychiatric Illness, Bipolar Disorder, Mental health.

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Creative Commons Attribution 4.0 and The Open Definition

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S??khya's Philosophy, Yogic Path

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This paper is about cross-domain sentiment analysis for product reviews. People write reviews for products like electronics, books, and clothes on websites like Amazon. Sentiment analysis helps to find whether these reviews are positive, negative, or neutral. But sometimes, there are not enough labelled reviews in one domain to train the system. So, cross-domain sentiment analysis uses data from one domain to help another domain. In this paper, a web based system is implemented that collects reviews, processes text using NLP techniques, and classifies the sentiment using machine learning models. This helps to understand customer opinions and improve decision-making for products across different domains. Additionally, the system integrates an AI-powered chatbot that interacts with users in real time, answers queries about products, provides sentiment summaries, and guides customers in exploring product feedback efficiently.

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Creative Commons Attribution 4.0 and The Open Definition

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AI Chatbot interaction, Cross-domain sentiment analysis, Machine learning models, Natural language processing

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Creative Commons Attribution 4.0 and The Open Definition

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Cinnarizine, Dimenhydrinate, Orodispersible, SSG, Crospovidone

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Creative Commons Attribution 4.0 and The Open Definition