Volume 13 | Issue 4

 Abstract

In the rapidly evolving field of cloud computing, proper expedient management to accommodate different workloads is essential for both cost-effectiveness and peak performance. This study presents a hybrid workload forecasting model designed for cloud systems that blends statistical and machine learning techniques to increase prediction accuracy. By using operational parameters, historical workload data, and environmental conditions, the model generates projections that adapt to the shifting demands on cloud resources. By combining time series analysis with advanced machine learning algorithms like gradient boosting machines (GBMs) and recurrent neural networks (RNNs), the model finds both linear and non-linear patterns in the data., which improves resource allocation decision-making. We show that our hybrid methodology can predict workload changes more precisely than conventional forecasting techniques by validating its efficacy through comprehensive simulations and real-world experiments across several cloud platforms. A more resilient cloud architecture is eventually the consequence of the results, which show notable gains in resource utilization, lower over-provisioning costs, and improved service quality. In addition, the model has an adaptive learning mechanism that enables it to adjust its predictions over time in response to shifting trends and workload patterns. This hybrid forecasting methodology helps cloud service providers and businesses alike by tackling the crucial issue of workload fluctuation in cloud settings. This promotes better informed strategic planning and improved operational efficiency.

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 Keywords

Soft Skills, Artificial Intelligence, Speech Recognition, Virtual Simulations, Skill Enhancement

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The digital revolution has significantly altered the consumer landscape in India, with online marketplaces and e-commerce platforms becoming integral to modern commerce. This shift, while enhancing convenience and accessibility, has also exposed consumers to novel forms of exploitation, including data breaches, misleading advertisements, unfair trade practices, and limited access to redressal mechanisms. In response, the Consumer Protection Act, 2019 (CPA), supported by the Consumer Protection (E-Commerce) Rules, 2020, seeks to provide a robust regulatory framework for ensuring consumer rights in the digital domain. This dissertation critically examines the role of the CPA in regulating digital platforms, focusing on the effectiveness of key statutory provisions such as Section 2(7) (definition of consumer), Section 18 (powers of the CCPA), and Section 94 (measures for digital consumer protection). It also evaluates the duties imposed on e-commerce entities under Rules 4, 5, and 6 of the E-Commerce Rules, particularly in relation to grievance redressal, transparency obligations, and the prohibition of unfair trade practices. The study explores the practical challenges faced by consumers, including lack of awareness, jurisdictional hurdles in cross-border transactions, and inefficiencies in online dispute resolution (ODR) systems. A comparative lens is applied to evaluate India's regulatory framework alongside international standards, such as the European Union's General Data Protection Regulation (GDPR) and the United States' Federal Trade Commission (FTC) guidelines, identifying best practices and potential reforms. Using doctrinal research and supplemented by case law and empirical insights, the dissertation concludes that while the CPA has made notable strides in addressing digital consumer issues, there remain critical gaps in enforcement, platform accountability, and technological adaptation. It recommends a multi-pronged approach--combining legislative reform, regulatory clarity, enhanced consumer education, and cross-border legal collaboration--to fortify consumer trust and legal efficacy in India's rapidly evolving digital marketplace.

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 Keywords

Consumer Protection Act 2019, E-Commerce Rules 2020, digital platforms, online marketplaces, consumer rights, unfair trade practices, grievance redressal, data protection, online dispute resolution (ODR), Central Consumer Protection Authority (CCPA), cross-border transactions, GDPR, FTC, platform accountability, consumer awareness, digital commerce regulation, India, comparative legal analysis, legislative reform, consumer trust.

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 Abstract

Communication barriers between the hearing-impaired and non-sign language users pose significant challenges in everyday interactions. This paper presents a real-time system for converting live sign language into Tamil speech, enabling seamless communication for the deaf and hard-of-hearing community. The system utilizes computer vision and deep learning techniques to recognize hand gestures and facial expressions, which are then mapped to corresponding Tamil words and sentences. A text-to-speech (TTS) module further converts the recognized text into natural Tamil speech, ensuring an intuitive and accessible experience. The proposed solution aims to bridge the gap between sign language users and the general public, fostering inclusivity and accessibility.

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 Keywords

Sign Language Recognition, Tamil Speech Synthesis, Computer Vision, Deep Learning, Real-Time Gesture Recognition

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 Abstract

The Web3 Complaint Portal is a decentralized application that revolutionizes the traditional complaint management system by leveraging blockchain technology. Built using Next.js, TypeScript, and Tailwind CSS for the frontend, and Solidity smart contracts with Ethers.js for blockchain integration, this platform enables citizens to submit complaints that are permanently recorded on the Ethereum blockchain through the Sepolia testnet. The system implements a three-tier role-based access control (User, Officer, and Admin) with MetaMask wallet authentication, ensuring secure and transparent complaint handling. The portal features real-time complaint tracking, automated officer assignment, and immutable record-keeping, making it a robust solution for transparent and efficient complaint management. The integration of ThirdWeb and Hardhat development environment facilitates seamless blockchain interactions while maintaining a user-friendly interface for all stakeholders.

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Blockchain Technology

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Current mirrors are fundamental circuit components in analog and mixed-signal circuits, playing a crucial role in biasing, signal amplification, and active loads. The efficiency of a current mirror is determined by parameters such as output resistance, compliance voltage, power dissipation, and current accuracy. This paper presents a comprehensive study on the design, simulation, and comparative analysis of different current mirror configurations, including simple, Wilson, cascode, and regulated cascode current mirrors. The study evaluates the impact of various semiconductor technologies (180nm, 45nm, and beyond) on these topologies. Simulations are conducted using industry-standard tools like Cadence Virtuoso and HSPICE to compare each design's efficiency, reliability, and power performance. The findings provide in-depth insights into selecting the most suitable current mirror topology for VLSI, RF, and low-power analog design applications.

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 Keywords

Current Mirror, Wilson Current Mirror, Cascode, Analog Design, VLSI, Simulation, CMOS Technology.

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In recent years, the increase in counterfeit products has caused serious problems for consumers and businesses, and has led to the loss of trust in e-commerce and physical stores. This study presents a new method for artifact detection using convolutional neural networks (CNN), a deep learning technique suitable for image recognition. We have created a comprehensive database containing images of structures and artifacts from multiple categories, which provides greater representation of features. We increase the robustness of the data and reduce the effects of overfitting by using advanced data enhancement techniques. Our CNN models are carefully designed to include multiple convolutional processes, followed by joint and fully connected processes, and optimized using methods such as version and batch normalization. The performance of the model was evaluated using metrics such as accuracy, precision, recall, and F1 score, resulting in significant improvements in the detection process. Our results show that the CNN-based method can identify counterfeit products with high accuracy and provides a reliable tool for consumers and retailers. This research not only contributes to the field of product recognition, but also lays the foundation for the future development of the use of deep learning in counterfeit product protection.

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 Keywords

CNN, SVM, ReLU, RFID, densenet

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 Abstract

The formulation and evaluation of natural lip balm with pineapple juice with the main ingredients and with other ingredients are glycerins, vit E oil, almond oil, bees wax, and petroleum jelly. Formulated the natural ingredients with the blends of herbs. Lip balm is the one of the use cosmetics. Herbals lip balm provides a holistic approach to the Lip care. Lip balm is the waxy substance that is applied to the lips for moisturizing and softening of the lips. The product help to hydrates and shield the lips from the environmental stressors, while its soothing properties calm and comfort the lips from the dryness and irritation. It also reduce the pain occurs with chapped and heal lips. Subsequently, the physical, chemical, and sensory aspects of the lip balm formulation are evaluated. The overall quality and consumer attractiveness of the product are assessed by analyzing various parameters, including color, pH, texture, fragrance, and moisturizing efficacy. Ultimately, the research seeks to offer a viable alternative to conventional lip balms, promoting both health-conscious and eco-friendly beauty solutions.

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Pineapple, Herbal Lip Balm, Natural Ingredients, Antioxidants, Hydrated, Cosmetics, Nourishment, Moisturizing.

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Nostalgia-driven food-related companies in South India are reviving forgotten childhood favorites, bringing back traditional sweets and snacks that once held a particular place in people's recollections. In addition to classic candies like Mango Bite, treats like Javvu Mittai (pulling toffee), Thaen Mittai (sugar syrup candy), Kadalai Mittai (peanut chikki), Nela Kadalai Urundai (groundnut balls), Kamarakattu (jaggery and coconut candy), Pottukadalai Urundai (roasted gramme balls), and Ellu Urundai (sesame seed balls) are making a big a comeback. Consumers, particularly millennials and Gen Z, are drawn to these products not only for their nostalgic value but also for their natural ingredients and traditional flavors. To preserve South India's rich culinary legacy and bring these traditional treats back to a younger audience, startups are utilizing digital marketing, e-commerce platforms, and sustainable packaging. This study looks at how these companies are influencing consumer preferences in the changing food industry, the effects of nostalgia marketing, and the difficulties of bringing discontinued products back to life.

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 Keywords

Nostalgia marketing, South Indian snacks, traditional candies, food startups.

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 Abstract

Managing multiple insurance policies from various providers often leads to missed renewals and policy lapses due to the absence of a centralized tracking system. To address system, propose the Policy Renewal Reminder System (PRRS)--a user-centric platform that consolidates all insurance policies in one secure location. PRRS streamlines policy management by providing automated, time-based renewal alerts and integrated payment options, ensuring timely renewals and continuous coverage. Unlike fragmented insurer-specific reminders, PRRS reduces administrative burden and minimizes lapse risks. It enhances user convenience and supports insurers by improving customer retention. Future upgrades may include AI-driven predictive reminders and broader insurer integrations for improved personalization and interoperability. By simplifying insurance tracking and automating key processes, PRRS offers a comprehensive solution for seamless, reliable policy management.

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renewal system, automated notifications, tracking tools, payments, admin dashboard

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Abstract: Herbal syrup is prepared by adding concentrated decoction of herbs with either honey or sugar and we also use alcohol. The herbal syrup is made by decoction process. Mixing a decoction of herbs with sugar it helps to the formulation for thicken and preserve the formulation1. Most of herbal syrup was originally derived from plant herbal medicine refers to use of extract of leaves, fruit for medicinal purpose. The anti-pyretic syrup is used for fever management, offering relief from high temperatures while addressing inflammation and oxidative damage. The extraction of Ivy Gourd, Ginger, Tulsi is used for medicinal purpose. In this Ivy Gourd used as anti-pyretic, Ginger as anti-inflammatory and Tulsi as anti-oxidant while Fennel used as flavoring agent and black pepper used as preservative. Three formulation viz. F1, F2 and F3 were prepared with variation in quantity of ingredient like sugar, black pepper and final volume of syrup. All prepared formulation was by parameters like density, specific gravity, organoleptic characteristics. The results shown that herbal syrup formulation number F3 is more stable and elegant as compared to other formulation.

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 Keywords

Herbal syrup, Ivy Gourd, Ginger, Tulsi, Fennel, Evaluation

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 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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 Abstract

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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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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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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Migrants, Socio-Economics, Employment, Health Status.

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

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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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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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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