Volume 13 | Issue 4 |

 Abstract

The impact of coalition governments on the Indian political system, focusing on their evolution since the 1990s. The rise of coalition politics marked a significant shift from single-party dominance to a more fragmented and pluralistic governance model. The study examines how coalitions have influenced policy-making, political stability, federal relations, and democratic representation. While coalition governments have enhanced inclusivity and regional representation, they have also led to compromises in governance and frequent political realignments. Overall, the analysis highlights the complex role coalition politics play in shaping India's democratic and administrative landscape.

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 Keywords

Coalition Government, India, 1990s, political stability, policy-making, federalism, regional representation, inclusivity, governance challenges, party alliances, democratic evolution.

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Attention Deficit Hyperactivity Disorder (ADHD) is the most common neurological disorder of childhood. In this growing era of competition children suffering with ADHD find it difficult to cope up and even for parents and teachers to exactly determine the distinguishing factor for the children. Ayurveda is found to be the world's oldest and most complete holistic healing modality. So, this article deals with the general knowledge about ADHD and an Ayurveda approach towards its causes, pathophysiology and treatment which include the basic treatment of Manas Roga Chikitsa i.e Daiva-Vyapashraya Chikitsa, Satvajay Chiktisa and Aushada Chikitsa

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Manas Roga Chikitsa, Daiva-Vyapashraya Chikitsa, Satvajay Chiktisa. behavioral, neurological hyperactive disorders

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Credit card fraud has become a significant threat in the financial sector, with substantial economic implications. The increasing reliance on digital payment systems necessitates the development of advanced techniques for detecting fraudulent activities. Machine learning (ML) has emerged as a powerful tool for identifying and preventing fraudulent transactions by learning from historical data and recognizing suspicious patterns. This paper explores the application of various ML algorithms for credit card fraud detection. We evaluate techniques such as Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, and Neural Networks using a public dataset. Our study emphasizes the importance of data preprocessing, handling imbalanced datasets, and selecting appropriate evaluation metrics. The results demonstrate that ensemble methods, particularly Random Forest, provide high accuracy and robustness in detecting fraud. Future research directions are also suggested, focusing on real-time fraud detection and adaptive learning systems.

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Credit Card Fraud, Machine Learning, Classification, Imbalanced Data, Random Forest

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The 2019-2020 Hong Kong protests marked a pivotal moment in the city's political landscape, originating from opposition to a proposed extradition bill and evolving into a broader pro-democracy movement. This essay critically examines the role of media in shaping narratives around the protests, focusing particularly on how coverage influenced public perception and international discourse. Through a metaphor analysis of selected media reports, the paper explores the rhetorical strategies and linguistic choices employed by various outlets across different regions. By drawing on media theory and political communication frameworks, the study highlights the media's capacity to both amplify and polarize social movements. It also considers how the portrayal of protestors and law enforcement impacted the dynamics of the conflict. Ultimately, the essay aims to unpack the intersection between media, language, and power during one of Hong Kong's most turbulent periods in recent history.

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Hong Kong Protests, Media Representation, Metaphor Analysis, Political Communication, Police Brutality

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Introduction: Hypothyroidism is a prevalent condition affecting individuals of both sexes mostly females , Significantly impacting quality of life. Homoeopathy based on the principle of individualization, offers a unique therapeutic approach to managing this condition. Objectives: This study aims to evaluate the effect of homoeopathic medicine in management of hypothyroidism . Methods: A total 30 patients diagnosed with hypothyroidism to evaluate the effect of homoeopathic medicine in management of hypothyroidism as an add therapy in adult population a randomised control trial are treated with individualized homoeopathic remedies based on their presenting symptoms, mental general, and physical generals. The selection of remedies given according to detailed case - taking, repertorization, and systemic examination. The outcome were assessed based on symptomatic relief and overall improvement in well- being. Result: Out of 30 patients, Out of 30 patients, 23 showed significant improvement in their hypothyroid complaints. . The positive response in the majority of cases highlights the potential effectiveness of individualized homoeopathic treatment in Hypothyroidism management. Conclusion: This study suggest that individualized homoeopathic treatment plays a beneficial role in managing Hypothyroidism. However, further large - scale studies with randomized control trail are recommended to establish the efficacy and reproducibility of these findings.

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Hypothyroidism, Homoeopathic Individualized remedies, Standardized medicines, Randomized Control Trial

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In India, cultural norms often place a significant financial burden on families with daughters, particularly those living in poverty, where marriage expenses become a major obstacle. This economic pressure can unfortunately lead to child marriages. To address this issue, the Telangana government has initiated the Kalyana Laxmi scheme. The core objective of this scheme is to relieve poor families from the overwhelming financial strain of marrying off their daughters, thereby providing a safety net and reducing the incidence of child marriages. This research examines the Kalyana Lakshmi scheme, a Telangana government initiative providing financial assistance to brides from marginalized communities, focusing on its implementation and impact within Kamareddy district. The study aims to know the extent of awareness, evaluate the scheme's effectiveness in curbing child marriage, and addressing associated challenges. Findings indicate a high level of awareness regarding the scheme's eligibility criteria, with a significant impact on the postponement of marriages until legal age. The financial assistance primarily supports wedding expenses, demonstrating its immediate utility. However, the study identifies certain implementation challenges, related to documentation, which affected a substantial majority of respondents.

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Key words: Child marriage, Gender inequality, Legal age of marriage, financial assistance.

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This study examines the employee welfare measures and their impact on employee satisfaction and productivity at Ident Automation and Info Technologies .Employee welfare entails everything from services , facilities and benefits that are provided or done by the employer for the advantage or comfort of an employee. The Study aims to identify the existing welfare schemes, assess employee perceptions, and analyze the relationship between welfare measures and employee outcomes . We collected data from 120 respondents through structured questionnaires, utilizing a descriptive research methodology. The analysis employs tools such as chi-square, and one way anova to interpret employee welfare and the challenges they face. The Findings provide valuable recommendations for enhancing employee welfare and improving organizational performance.

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Employee welfare ,Employee Satisfaction, Productivity.

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Accurate and robust lane detection is a critical component of safe, stable autonomous driving systems that navigate vehicles efficiently. This work proposes an improved lane detection method by advanced image preprocessing and hybrid machine learning pipeline. The frame combines traditional computer vision approaches with deep literacy models to overcome issues related lighting conditions, occlusions, and road Inconsistencies. A large dataset has been used for training and testing the model to achieve better precision in accuracy and near- real-time performance. The experiments carried out demonstrate that our technique supersedes the baseline techniques in terms of accuracy, recall, and robustness. Some takeaways have been shared toward building scalable, adaptive lane detection systems in real-world driving scenarios.

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Autonomous Driving, Lane Detection, Prepro- cessing, Machine Learning, Deep Learning, Computer Vision, Real-time Systems

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Emojis have become an essential part of digital communication, influencing how sentiment is conveyed in online interactions. However, traditional sentiment analysis models often struggle to interpret emojis accurately due to their context dependent nature and cultural variations. This study explores the application of machine learning techniques to classify emoji sentiment, integrating deep learning models such as Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and transformer-based architectures. Additionally, a multimodal approach is proposed, combining textual and visual features to enhance sentiment classification accuracy. The research evaluates model performance using precision, recall, F1-score, and accuracy metrics. Results indicate that transformer-based models and multimodal learning outperform conventional methods, offering a more nuanced understanding of emoji sentiment. By addressing contextual dependencies and cultural influences, this study contributes to the development of more advanced sentiment analysis systems, improving their effectiveness in social media monitoring, customer feedback analysis, and human-computer interaction.

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Emoji Sentiment Analysis, Machine Learning, Deep Learning, Sentiment Classification, Multimodal Learning, Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), Sentiment Classification.

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In today's digital world, the Internet plays a vital role in transforming shopping habits.Zepto is one of the leading Q-commerce platforms in India and it is also known for 10-minute delivery service in big cities. With the rise of online services, people now prefer purchasing groceries through digital platforms.The objective of this study is to identify the customer awareness and satisfaction levels of Zepto (Q-Commerce) in Coimbatore city. It highlights customer preferences, factors influencing usage, and suggests improvements to enhance service quality in emerging markets.

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 Keywords

Zepto, Online Shopping, Q-Commerce, Fast Delivery.

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Creative Commons Attribution 4.0 and The Open Definition