Volume 13 | Issue 4 |

 Abstract

The exponential growth of e-commerce has revolutionized global commerce, offering convenience and accessibility to consumers worldwide. However, this digital transformation has been accompanied by a surge in online payment fraud, posing a significant threat to businesses and consumers alike. Traditional rule-based fraud detection systems are increasingly inadequate against sophisticated and evolving fraudulent techniques. Machine learning (ML) has emerged as a powerful paradigm shift in fraud detection, offering the ability to learn complex patterns, adapt to dynamic fraud landscapes, and proactively identify fraudulent transactions in real-time. This paper explores the critical role of machine learning in online payment fraud detection. It delves into the various machine learning techniques employed, including supervised, unsupervised, and deep learning approaches, highlighting their strengths and limitations. The paper further examines the essential data pre-processing steps, feature engineering strategies, and evaluation metrics crucial for building robust and effective fraud detection systems. Moreover, it discusses the challenges and future directions in this dynamic field, emphasizing the ongoing need for innovation to stay ahead of increasingly sophisticated fraudsters in the evolving digital payment ecosystem. Ultimately, this paper underscores the transformative potential of machine learning in safeguarding online transactions and fostering a more secure and trustworthy e-commerce environment.

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 Keywords

Payment Fraud, Fraud Detection, Machine Learning, Supervised Learning, Unsupervised Learning, Deep Learning, Feature Engineering, Data Imbalance, Real-time Detection, Cybersecurity.

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 Abstract

Financial distress is a situation where a company is not able to meet or face difficulty to pay off its financial obligations. According to RBI's definition negative working capital, cash loss and negative networth are the factors influencing Distresses. There are lots of causes of corporate failure which includes Profitability, Liquidity and solvency complications. Bankruptcy prediction models are among the techniques and tools for predicting future status of companies which can estimate the bankruptcy probability by compounding a set of financial ratios. This research paper has attempted to device models for predicting probability of financial distresses among the PSUs working under the Engineering sector in Kerala. In order to evaluate the ratios that can influence group status and quantify their connection, Multiple Logistic Regression analysis tool is used. The main uses of logistic regression are that prediction of group membership and provide knowledge of the relationships and strength among the variables.

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 Keywords

Financial Distress, Logistic Regression, Liquidity Tribulations, Full-blown distress, logistic regression, cash profit, sick, RBI

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The coir industry plays a significant role in the rural economy of Tamil Nadu, particularly in Pollachi Taluk of Coimbatore District, which is a prominent hub for coir-based activities. Despite its traditional relevance and employment potential, the sector continues to face a multitude of production-related challenges that hinder its growth and sustainability. This study aims to identify and analyze the key production problems encountered by coir units in Pollachi Taluk. Primary data was collected through structured questionnaires and interviews with coir unit owners, workers, and local industry stakeholders. The findings reveal that the sector suffers from issues such as irregular raw material supply, out-dated machinery, shortage of skilled labor, high cost of production, limited access to finance, and environmental constraints. Additionally, lack of technological innovation and inadequate government support further exacerbate the operational difficulties. The study emphasizes the need for targeted policy interventions, improved infrastructure, and capacity-building initiatives to revitalize the coir industry in the region. Addressing these production problems is crucial for enhancing productivity, generating employment, and ensuring the long-term sustainability of this traditional rural industry.

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 Keywords

Coir production, Production problems, Fibre.

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Hydrological modelling plays a crucial role in understanding the predicting water cycle dynamics, which is essential for effective water resource management. This review paper provides a comprehensive overview of the current state of hydrological modelling, with a focus on geosciences-based approaches, remote sensing application, and GIS integration. We discuss the benefits and challenges of using remote sensing data in hydrological modelling, including the potential for improved parameter estimation and model calibration. The paper also explores the role of GIS in water resource management, highlighting its capabilities in spatial analysis, data integration and visualization. Furthermore, we examine the classification of hydrological models and their applications in various context. Finally, we identify the challenges for hydrological modelling, emphasizing the need for increased integration of geosciences, remote sensing, and GIS to improve model accuracy and predictability.

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 Keywords

Hydrological modelling, geosciences, remote sensing, GIS, water resource management

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A current sociological perspective examines the health status of migrant workers. Migration is defined as the movement from one place to another to meet the necessities of life. Migration is a phenomenon that occurs in situations such as war, invasion, famine, etc. This study highlights how the socio-economic factors, conditions and access to health care of migrant workers shape their overall well-being. Migrant workers, who are often engaged in demanding and low-wage jobs such as agricultural, construction and manufacturing, face increased vulnerability to health risks, including respiratory problems, occupational accidents and infectious diseases. Many migrants are left without adequate medical care or health insurance. Migrant workers face isolation, discrimination and unstable living conditions, which contribute to physical and mental health challenges. This study highlights the need for a comprehensive health policy framework. It addresses social protection for these vulnerable workers across India. Such measures are needed to improve the physical and mental health of the migrant population in a rapidly urbanizing country.

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 Keywords

Migrants, Socio-Economics, Employment, Health Status.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The shift toward Hyperlocal delivery operations created revolutionary changes in end-point distribution of services that deliver exceptional convenience but struggle to fulfil sustainability goals. This research analyses sustainability across multiple dimensions in Hyperlocal delivery services by studying economic aspects together with environmental and social variables. Beyond enhancing operational performance and protecting the environment this research considers customer retention and satisfaction as essential growth factors for Hyperlocal businesses. The research examines how delivery reliability and personalization alongside service quality build customer loyalty through approaches which minimize carbon emissions while maximizing resource efficiency. The study analyses industry practices alongside customer behaviour and feedback mechanisms to establish new approaches which unite sustainability goals with customer-driven operations. Stakeholders can employ this complete framework to develop Hyperlocal delivery systems which are both efficient and resilient and focused on customers so they succeed for the long-term in a competitive market.

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 Keywords

Hyperlocal Delivery, Micro-Fulfillment Centers (MFCs), Sustainability, Hyperlocal Logistics, End-Mile Connectivity, Proximity-Based Inventory Management

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With the proliferation of information technology, document forgery has become a growing concern across various sectors. Traditional document verification processes are time-consuming, error-prone, and unreliable. This paper introduces DOC-BLOCK, a decentralized web application based on the Ethereum blockchain to securely verify digital documents. By leveraging cryptographic hashing, peer-to-peer storage, and smart contracts, the system ensures data integrity, transparency, and authenticity. Our model minimizes human error, reduces verification time, and offers a tamper-proof alternative to conventional methods, ensuring a future-ready approach to document verification

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 Keywords

Blockchain, Hashing, Ethereum, Document Ver ification, Digital Signature, Cryptography

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Creative Commons Attribution 4.0 and The Open Definition

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One of the most prevalent and incapacitating neurological disorders is epilepsy. With the intention of offering herbal remedies for epilepsy, this article examines the clinical features of seizures and epilepsy. It defines seizures and epilepsy, discusses diagnostic techniques, classifies them, and discusses the usage of flavonoids. We have suggested using flavonoid-containing natural sources as neutraceuticals for epilepsy.

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 Keywords

Natural flavonoids , Epilepsy , Herbal sources , Neutraceuticals .

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The field of marketing is seeing a transformation due to the role of Artificial Intelligence (AI) using data-driven decision making strategies, personalized consumer relationship, and automation of redundant processes. This review paper studies various relevant previous research works to understand the incorporation of AI in marketing. The objective of the study is to determine AI's applications in customer segmentation, consumer-driven personalization, and predictive analytics. A shift in the dynamics of traditional marketing can be observed with the introduction of various AI technologies like, chatbots, sentiment analysis, target marketing, etc. Findings from the studies suggest that the marketing performance has observed an enhancement. This enhancement comes with the improvements in real-time decision-making, optimized advertising campaigns, and efficient customer engagement. Vast customer datasets are analyzed with the help of Machine Learning (ML) algorithms that are used to predict customer buying behaviour, and Natural language Processing (NLP) is utilized for effective methods of communication. Even with all the various advantages, AI's usage in marketing activities still faces certain challenges. The use of AI brings about multiple issues namely data privacy concerns, ethical dilemmas, high implementation costs, and expertise. Due to resource constraints, smaller firms face these challenges with higher severity. Larger corporations are able to leverage AI technology to build and maintain competitive advantage. The review paper concludes that with advancements in deep learning, content creation, and personalization, AI's role in marketing will continue to expand. This paper would assist future research in exploring the long-term impact of AI on brand loyalty, ethics in AI usage for marketing, and the evolving role of AI in building consumer relationship.

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 Keywords

Artificial Intelligence, Deep Learning, Marketing, Customer Relationship, Consumer Behaviour

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 Abstract

Background: Anubhuta Yogas are empirical formulations in Ayurveda validated through clinical experience but often lack structured scientific evaluation. Two proprietary yogas were designed: one for managing erectile dysfunction (ED), premature ejaculation (PME), loss of libido, and diabetic complications; and another for treating various types of coughs, throat infections. Objective: To evaluate the clinical efficacy and safety of two Anubhuta Yogas in the management of sexual dysfunction/metabolic disorders and respiratory ailments. Methods: A prospective observational case series was conducted over 12 weeks. Five patients with sexual dysfunctions and five with cough-related disorders were administered two different Ayurvedic formulations. Primary endpoints included improvements in IIEF-5 scores for sexual dysfunction and reductions in Cough Symptom Scores for respiratory illness. Safety was monitored throughout. Results: In the Rasayana Vajikarana group, IIEF-5 scores improved by 6-11 points, glycemic parameters improved in diabetic patients, and libido enhancement was noted. In the Kasa-Shwasa Hara Yoga group, there was a >70% reduction in cough severity, with significant improvements in quality of life parameters. No serious adverse events were reported. Conclusion: Both Anubhuta Yogas demonstrated substantial efficacy in their respective domains, showcasing Ayurveda's systems-based therapeutic potential. These formulations, grounded in classical Rasayana and Vyadhi Shamana principles, merit further validation through randomized controlled trials.

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 Keywords

Anubhuta Yoga, Rasayana, Vajikaran, Klaibya, Pramheha, Kasa, Shwasa

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Creative Commons Attribution 4.0 and The Open Definition