Volume 13 | Issue 4 |

 Abstract

The rapid expansion of the Internet of Things (IoT) has enabled transformative applications across various sectors such as healthcare, smart cities, and industrial automation. However, this surge in connectivity has simultaneously exposed IoT networks to heightened cybersecurity threats, particularly Distributed Denial of Service (DDoS) attacks. Due to limited processing and security capabilities, IoT devices are easily compromised, making traditional Intrusion Detection Systems (IDS) inadequate in such environments. This study introduces an adaptive and lightweight IDS framework that utilizes a hybrid ensemble of bio-inspired optimization techniques--Spotted Hyena Optimizer (SHO), Parrot Optimizer (PO), and Grey Wolf Optimizer (GWO)--for feature selection. By embedding a self-attention mechanism, the system dynamically identifies key features that enhance detection accuracy while minimizing computational costs. The selected features are used to train machine learning classifiers including Decision Tree, SVM, Random Forest, XGBoost, and shallow Neural Networks. Evaluated on the UNSW-NB15 dataset, the proposed model demonstrates high performance with reduced false positives and latency, offering a scalable and real-time solution for DDoS mitigation in IoT ecosystems.

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 Keywords

Internet of Things (IoT), Distributed Denial of Service (DDoS), Intrusion Detection System (IDS), Bio-Inspired Optimization, Feature Selection, Machine Learning.

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 Abstract

Abstract: With the rapid advancement of wireless communication technologies, 5G mm Wave patch antennas have become increasingly important due to their capability to operate at high frequencies, their small size, and their support for high-speed data transmission. However, the development and analysis of these antennas often involve time-consuming full-wave electromagnetic simulations, which can slow down the design cycle. To overcome this limitation, Artificial Neural Networks (ANNs) offer a promising alternative by enabling quick prediction of essential antenna characteristics. In this work, a compact patch antenna operating in two bands 38.93-39.63GHz and 41.79 GHz to 42.92 GHz band is introduced, making it well-suited for 5G applications. The trained ANN model provides fast and accurate S11 predictions, effectively minimizing the need for lengthy simulation processes.

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 Keywords

Artificial neural network, 5G antenna, Machine Learning

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

dakshin-purv asia mein bharat ki pahuch:act east niti ki uplabdhiyan aur chunautiyan

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Creative Commons Attribution 4.0 and The Open Definition

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dakshin-purv asia mein bharat ki pahuch:act east niti ki uplabdhiyan aur chunautiyan

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Creative Commons Attribution 4.0 and The Open Definition

 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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 Abstract

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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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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

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Creative Commons Attribution 4.0 and The Open Definition

 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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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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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 Keywords

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

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Creative Commons Attribution 4.0 and The Open Definition