Volume 14 | Issue 5 |

 Abstract

Abstract Tamil television serials play a significant role in shaping societal perceptions of gender roles, women's empowerment, and decision-making. This study examines how these serials influence women's autonomy in family matters, social attitudes, and career aspirations. Using a mixed-methods approach, including surveys and statistical analysis, the research explores the extent to which Tamil serials reinforce or challenge traditional norms. Findings indicate that Tamil television serials have a notable impact on women's decision-making within households, influencing their authority in financial, familial, and social matters. The portrayal of women in these serials also shapes viewers' perceptions of gender roles, often reinforcing stereotypes but occasionally challenging patriarchal norms. While some serials depict empowered women in leadership and career-oriented roles, the overall narrative still largely revolves around domestic responsibilities and emotional struggles. The study also finds that occupational status plays a role in how women perceive these serials. Working women tend to be less influenced by the regressive aspects of these narratives, whereas homemakers are more likely to internalize traditional portrayals. Additionally, Tamil serials influence women's aspirations but to a lesser extent than their impact on family-related decision-making. The study highlights the powerful role of television in shaping women's empowerment and underscores the need for more progressive and diverse representations of women in Tamil serials. By promoting stronger, career-driven female characters, television content can contribute to broader societal change, encouraging women to pursue professional growth and greater autonomy in personal and public life.

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 Keywords

Keywords: Tamil television serials, women's empowerment, decision-making, societal perceptions, career aspirations, media influence, cultural norms

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study investigates the implementation and efficiency of stateful, cyclical multi-agent architectures in autonomous educational technology environments. To address the limitations of linear Large Language Model (LLM) pipelines, a novel framework, the Personalized Curriculum Builder, was proposed and developed. The system orchestrates five specialized autonomous AI agents--a Learner Profiler, a Curriculum Planner, a Content Sourcing Agent, a Quiz Generator, and an Adaptive Feedback Agent--within a stateful directed graph architecture. The architectural framework relies on Python, LangChain, LangGraph, FastAPI, ChromaDB and Redis. The core technical contribution of this study is the engineering and evaluation of a closed adaptive feedback loop, where student performance metrics trigger conditional back-routing execution policies to dynamically inject remedial learning content. The analytical framework evaluates system latency, state persistence overhead, and learning gain efficacy through a comparative empirical user study of static linear baseline pipelines.

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 Keywords

Autonomous Agents, Closed-Loop Feedback, Educational Technology, LangGraph, Multi-Agent Systems, Stateful Graphs.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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India ranked 131st out of 148 countries participating in the 2025 Global Gender Parity Index. The enormous disparity in opportunity between women and men is highlighted by this ranking, which raises serious concerns. We observe so many types of discrimination against women in society, which leads to inequality. Female enrollments in basic education are good, but inequalities are found at various level of higher education enrollments. Recent data on all India GER is 29.5% for the ( 18-23 age population). India aims to achieve a GER of 50% by 2035. Now it is essential to study challenges for higher education enrolments and consequences that follow. Socail, cultural and traditional factors affect women's higher education drastically, how should women overcome all the barriers in higher education and solution part plays significant role in this regard and if female higher education enrollments gets decreased then directly it will impact on work force participation.in this regard gender issues in accessing higher education to be addressed on a first priority bases.

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 Keywords

Gender inequality, Causes, Consequences, Higher education, GER.

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This study examines the financial performance of Tata Consultancy Services over a period of five years from 2020-2021 to 2024-2025 using ratio analysis. The study focuses on key areas such as profitability, liquidity, and solvency based on secondary data collected from annual reports and financial statements. The findings indicate that the company maintains strong profitability, satisfactory liquidity, and a stable solvency position with minimal dependence on debt. Overall, the study concludes that TCS has a sound financial performance with efficient management practices.

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Financial Performance ,Profitability , Liquidity , Solvency ,Ratio Analysis

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Creative Commons Attribution 4.0 and The Open Definition

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The present research work was carried out to formulate and evaluate a natural herbal moisturizer prepared using Mangifera indica leaf extract. Mango leaves contain various bioactive phytoconstituents such as flavonoids, tannins, mangiferin, glycosides, and phenolic compounds which possess antioxidant, antimicrobial, anti-inflammatory, and skin-protective properties beneficial for skincare applications. The mango leaves were collected, shade dried, powdered, and extracted using the Soxhlet extraction method with methanol as solvent. The obtained extract was subjected to preliminary phytochemical screening which confirmed the presence of important active constituents. Three batches of herbal moisturizer formulations were prepared using different concentrations of mango leaf extract along with suitable excipients by using the oil-in-water emulsion method. The prepared formulations were evaluated for various physicochemical parameters including organoleptic properties, pH, spreadability, viscosity, homogeneity, centrifugation test, dye test, irritancy test, mechanical vibration test, and microbial stability. The evaluation results revealed that all formulations showed good appearance, smooth texture, suitable pH, satisfactory spreadability, acceptable stability, and no signs of irritation. Among all formulations, batch F2 showed superior consistency, optimum spreadability, desirable viscosity, enhanced stability, and overall better performance compared to other batches. The study concluded that Mangifera indica leaf extract can be effectively utilized in the preparation of herbal moisturizers and may serve as a safe and natural alternative to synthetic skincare products, with F2 identified as the optimized formulation due to its superior physicochemical characteristics and stability profile.

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Mangifera indica Leaf Extract, Herbal Moisturizer, Phytocosmeceuticals, Skin Hydration, Antioxidant Activity

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Creative Commons Attribution 4.0 and The Open Definition

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Organic farming has emerged as an important and sustainable agricultural practice in recent years due to increasing concerns over environmental degradation, soil fertility, and human health. Unlike conventional farming, organic farming avoids the use of synthetic fertilizers, pesticides, and chemicals, and instead depends on natural inputs such as compost, green manure, and biological pest control methods. This study focuses on analysing the perception, awareness, and marketing strategies of organic farmers in Pollachi Taluk.

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 Keywords

Organic Farming - Organic Farmers - Farmers' Perception -Marketing Strategies

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This project presents a supervised machine learning approach for predicting customer purchase behavior and integrating the trained model into a Blender-based simulation environment. The dataset includes age and estimated salary as input variables and purchase decision as the target variable. Three classification models - Logistic Regression, K-Nearest Neighbors (KNN), and Random Forest - were implemented and evaluated. Performance was measured using accuracy, precision, recall, F1-score, and confusion matrix analysis. The KNN model achieved the highest accuracy and was exported for integration into Blender simulation.

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Integrating Supervised Machine Learning with Real-Time 3D Simulation for Customer Purchase Behavior Modeling

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Creative Commons Attribution 4.0 and The Open Definition

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In recent years, the digital explosion has completely reshaped how brands and shoppers interact. Platforms like Instagram, Facebook, and YouTube are no longer just for chatting; they are now central to how we discover and buy products. This study dives into how social media marketing actually moves the needle for consumers in Dehradun, specifically looking at those from semi-urban backgrounds. To get the full picture, I surveyed 150 local participants using a structured questionnaire and backed up those insights with existing industry reports and academic journals. Using a descriptive research approach, I looked at how people spend their time online, what they prefer, and what ultimately makes them hit the "buy" button. The results show a clear trend: younger shoppers are highly active online and are frequently swayed by digital ads, limited-time discounts, honest reviews, and influencer content. Instagram and Facebook come out on top as the most influential platforms, with a significant number of people admitting they've made a purchase simply because they saw it on their feed. While most are happy with what they buy, there's still a lingering hesitation regarding online trust and "clickbait" advertising. Ultimately, this research confirms that while social media is a powerhouse for modern business, success depends on being transparent and building genuine trust with the audience.

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 Keywords

Social Media Marketing (SMM), Consumer Buying Behaviour, Digital Marketing, Influencer Marketing, Online Consumer Behaviour, Electronic Word-of-Mouth (eWOM), Brand Loyalty, Social Media Advertising, Consumer Trust, Dehradun, Semi-Urban Consumers, Purchase Decision

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Customer churn prediction has emerged as a crucial challenge in the telecommunications industry due to its significant impact on revenue generation, customer retention, and overall business growth. This project presents the development of a machine learning-based churn prediction system designed to identify telecom subscribers who are likely to discontinue their services. The system utilizes historical customer data, including demographic information, service usage behavior, billing details, and customer interaction records, to build predictive models capable of detecting churn tendencies at an early stage. Comprehensive data preprocessing and feature selection techniques are applied to improve data quality and enhance predictive performance. Multiple supervised machine learning classification algorithms are implemented and comparatively evaluated to ensure reliable and accurate predictions. The system is developed using Python with the Django web framework to provide scalability, maintainability, and efficient deployment. An interactive dashboard is integrated into the system to visualize churn risks, highlight key factors influencing customer attrition, and display predictive performance metrics for business stakeholders. The proposed solution enables telecom operators to make proactive decisions, implement targeted customer retention strategies, and reduce financial losses associated with customer churn.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Customer Churn Prediction, Telecommunications, Machine Learning, Predictive Analytics, Classification Algorithms, Django Framework, Customer Retention, Data Visualization.

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Creative Commons Attribution 4.0 and The Open Definition