Abstract-A focus on synthesizing different types of two-dimensional Carbon nanostructures using Methane and Argon without catalyst has been conducted in Radio Frequency Plasma Enhanced Chemical Vapor Deposition. This study reports the variation in growth density, size and morphological characteristics of Carbon nanostructures by varying the gas compositions. Field Emission Scanning Electron Microcopy (FE-SEM) and Atomic Force Microscopy (AFM) studies shows the high percentage of Methane gas in the composition is directly proportional to the density and inversely proportional to the size of the nanostructure. We report that the concentration of Methane usually offers more carbon species or driving force for the growth of the two-dimensional carbon nanostructures. This process enables to increase the density and decreases the size of the nanostructures. The results of Raman spectroscopy show the typical carbon features at 1321,1571 and 2639cm-1 respectively. The intensity ratio of these two peaks ID/IG increases with increase in the Methane gas percentage in the composition indicates the nanocrystalline nature of two-dimensional carbon nanostructures with a large number of defects.

Abstract-Network connection for systems with purpose of exchanging with collection of Mobile communication system with ground operating surface for allowing mobile devices with telecommunication network for transmitting data with use of underground devices While the research paper focuses on Self – evolving method for featuring Time – series analysis with use of magnetic field of lines for self-adaptive signaling recombining and readvancing patterns for distribution and maintaining automated Rekeying Technology for Wireless Communication system . Intelligent Ecosystem Networking with the use of Cloud or Hybrid Cloud environments with the future of wireless communication network involves solutions for users, applications and devices involving identity management with securing adaptive access, identifying governance and user experience with use of self – evolving patterns for allowing mobile communication while transmitting network through all medium. The Main objective of the paper is Readvancing patterns for self-adaptive signaling following approach for distribution patterns.

Abstract-In this project, a hand-operated hydraulic Metal Sheet cutter machine has been fabricated. A hydraulic jack is used as the hydraulic component here. The project was started to minimize the effort required in shearing metal sheets of various thicknesses compared to that required when using a simple hand-operated mechanical sheet cutter. The cutting of metal sheets is an essential process in various industries, but conventional cutting machines are often expensive, energy-intensive, and cumbersome to operate. This thesis presents the design and fabrication of a cost-effective, hand-operated hydraulic metal sheet cutter aimed at providing a simple yet efficient solution for small-scale industries and workshops. The device operates using a hydraulic mechanism, eliminating the need for electrical power, and can be manually operated with minimal physical effort. The cutter is designed to handle a range of metal sheet thicknesses, offering versatility while maintaining precision and durability. The design focused on optimizing the cutting force and mechanism to achieve high cutting efficiency with reduced human exertion. The project encompasses the entire development process, including the design calculations, material selection, and fabrication techniques. Performance tests were conducted to assess the functionality and efficiency of the cutter under various conditions. The results demonstrate that the hand-operated hydraulic cutter can effectively cut metal sheets with minimal deformation and high accuracy, making it a practical tool for small workshops or environments with limited resources. This study concludes that the developed system is not only economical and environmentally friendly but also provides an innovative alternative to conventional electrically powered cutting machines. Further optimization could potentially enhance its applications across various industries.

Abstract-Authenticity plays a crucial role in shaping consumer purchase decisions, influencing brand perception, trust, and overall satisfaction. In an era where consumers have access to vast information and numerous product choices, authenticity has emerged as a key differentiator for brands. This study explores the impact of authenticity on consumer behaviour, examining factors such as brand transparency, product originality, ethical practices, and emotional connection. By analysing consumer preferences and decision-making patterns, the research highlights how perceived authenticity fosters brand loyalty and drives purchasing intent. The findings suggest that businesses that prioritize authenticity in their branding, communication, and product offerings gain a competitive edge in the market. This study provides valuable insights for marketers and brand strategists aiming to build long-term consumer relationships based on trust and credibility.

Abstract-This research paper conducts a comparative analysis of the old and new tax regimes for the financial year 2023-2024 in order to evaluate their impact on individual taxpayers, businesses, and government revenue. The study compares the main differences in tax slabs, deductions, and overall tax burden at different income levels. Further, it covers the compliance burden and administrative efficiency of both regimes, analysing how they affect taxpayer behaviour and economic decision making. This research will apply a combination of both qualitative and quantitative methodologies. The financial impact of each regime for different taxpayer groups is analysed by comparing tax liabilities under different income brackets, showing which regime provides more benefits for each group of taxpayers. Interviews and surveys with tax professionals and salaried people reveal information related to preferences, challenges, and practical implications associated with each regime. The study further analyses broader macroeconomic indicators, such as revenue generation, disposable income, and investment trends, in order to find out the broader economic implications of the tax reforms. The research results find that the old tax regime remains beneficial for those with significant investments that result in savings under the deduction sections: 80C, 80D, and HRA. The old regime is likable by high-income earners and those with complicated financial structures because it saves on taxes. On the other hand, middle-income earners and those without substantial investments prefer the new tax regime since it reduces complexity in tax filing and compliance. The new regime may also involve an increase in disposable income, which may fire up consumer spending, although it is less clear what the effect will be on long-term savings and investment patterns. This, therefore, implies that both regimes have their respective advantages and limitations, and the optimal choice would depend on an individual’s financial situation and tax saving strategy. Policymakers must continue to refine tax structures for better revenue generation and taxpayer convenience, ensuring economic stability. This detailed comparative assessment will help taxpayers make informed financial decisions and contribute to the ongoing discourse on tax policy improvements in India.

Abstract-This paper introduces Vishwanath’s Law of Dynamic Mass-Energy Redistribution, which proposes a novel framework to understand the adaptive behavior of mass in non-inertial reference frames. Traditional mass-energy equivalence fails to incorporate mass fluctuations due to high internal energy shifts and entropy variations. Using advanced tensor calculus and Lagrangian mechanics, we derive a modified mass-energy relationship. Applications in missile propulsion, quantum mechanics, and astrophysical anomalies are explored, providing new insights into mass-energy interactions.

Abstract-This paper examines the pivotal role of artificial intelligence (AI) in transforming business practices, tracing its evolution from foundational research and innovation to practical market deployment. As AI technologies rapidly advance, they are reshaping industries by enhancing productivity, enabling data-driven decision-making, and fostering the development of intelligent products and services. The study highlights the dual nature of AI’s impact, addressing both the opportunities it presents for economic growth and innovation, as well as the challenges and ethical considerations it raises for various stakeholders, including businesses, consumers, and policymakers. Through an analysis of key research breakthroughs and their implications for entrepreneurial activities, the paper identifies trends in AI start-ups and their contributions to the market. Ultimately, this research aims to provide a comprehensive understanding of how AI is not only revolutionizing business operations but also influencing the broader economic landscape, thereby offering valuable insights for practitioners and researchers alike. In recent years, the emergence of a multitude of intelligent products and services has sparked widespread interest in artificial intelligence (AI) and its commercial viability, raising critical questions about whether this trend represents genuine transformation or mere hype. This paper investigates the extensive implications of AI, exploring both its positive and negative impacts on governments, communities, companies, and individuals. By examining the journey of AI from research and innovation to market deployment, the study highlights significant academic achievements and innovations in the field, as well as their influence on entrepreneurial activities and the global market landscape. Additionally, the paper identifies key factors driving the advancement of AI technologies. To further explore entrepreneurial engagement with AI, two lists of the top 100 AI start-ups are analyzed. The findings aim to enhance understanding of AI innovations and their broader impact on businesses and society, ultimately providing insights into how AI can transform business operations and contribute to the global economy.

Abstract-The design and development of a drone for spraying pesticides on agricultural lands have been described in this paper. The drone developed is a quadcopter integrated with a spraying mechanism. A quadcopter can be described as a mechanical device that can hover using propellers fitted into it is four arms. Hovering is achieved using one set of clockwise spinning propellers and another set of counter- clockwise spinning propellers that generate the thrust required to facilitate the taking off and hovering process. The agricultural industry contributes heavily to India’s GDP, thus making it one of the chief sources of revenue. It is the foundation of India’s economy and contributes to approximately one-fourth of its gross domestic product. It is inevitable that fertilizers and pesticides will be used to increase crop yields. However, few health-related problems can arise due to prolonged exposure to such chemicals during manual spraying. A few examples include mild skin irritation to congenital disabilities, changes in genetics, falling into a coma, or even death in severe cases. Drones have been used extensively in agriculture over the past few years. This paper describes the components required for the successful design and development of a quadcopter that can be utilized for spraying fertilizer on agricultural lands. The quadcopter is equipped with a container carrying a Direct Current water pump fitted with a pipe and nozzle arrangement. The liquid passes and is controlled using the instructions that the user provides the controller.

Abstract-Ransomware attacks have escalated, posing significant threats to organizations by encrypting critical data and demanding ransoms. Traditional security measures often fall short against sophisticated ransomware variants. This paper explores the deployment of hardware-based honeypot files utilizing Server Message Block (SMB) traps as a proactive defense mechanism. By integrating deceptive SMB shares at the hardware level, organizations can detect, analyze, and mitigate ransomware activities more effectively.

Abstract-In the era of advanced artificial intelligence and machine learning, collaborative deep learning has emerged as a powerful approach to leverage distributed data and computational resources. However, a significant challenge that persists is ensuring the generalizability of models developed in collaborative environments. This project addresses the generalizability challenge in collaborative deep learning by proposing a novel framework that integrates advanced techniques in model training and validation. Deep learning models typically require data to be collected at a centralized location to learn effective representations, which introduces several issues such as communication costs and risks to data privacy. These issues are particularly critical in the case of clinical data, where patient privacy is paramount. In such contexts, distributed machine learning offers a viable solution where various data-holding sites can locally train a mutually agreed-upon model and share their knowledge. Federated learning (FL) facilitates this process using a client-server framework. Clients in the FL environment are independent small edge devices that retain their data locally, while the server acts as a central site that aggregates and distributes the knowledge learned by each client to others. The server receives locally trained weights from all participating clients, aggregates them, and then transfers the aggregated weights back to all clients before the next training round begins. This iterative process continues until the server achieves the desired accuracy. FL thus enables multiple clients to collaboratively train a shared global model without sharing their local data, preserving data privacy and addressing issues of limited data availability. However, FL faces challenges such as high communication costs for transferring weights, statistical data heterogeneity among clients, and the single point of failure of the server. Client heterogeneity arises mainly due to differences in data distribution among clients and their respective computational power. This project targets statistical data heterogeneity in the FL environment and proposes a simple yet effective attention-based approach to address this issue. Specifically, in the proposed setting, each client sends a mean representation to the centralized server along with the trained model’s weights. A similarity matrix is computed based on the similarity score of each client’s mean representation from every other participating client. This similarity matrix determines the weightage of each client’s model in the aggregated model. The centralized server computes the attention vector for each client using this similarity matrix and then broadcasts this attention vector to all clients. This attention mechanism is implemented both on the centralized server and the participating clients. We consider FedAvg, FedProx, and FedMomentum as baselines for comparison, and our proposed approach outperforms all of them. For statistical heterogeneity, we perform extensive experiments on FOOD101 and CIFAR10, demonstrating that our approachperforms well even with highly skewed data. To address the single point of failure issue in FL, we propose an efficient version of swarm learning. We demonstrate the effectiveness of context- aware swarm learning through experiments on the HAM10000 and ISIC Skin Lesion 2019 datasets. Additionally, to mitigate the high communication costs in FL, we propose BAFL (Federated Learning for Base Ablation), which introduces a fine-tuning approach to leverage the feature extraction ability of layers at different depths of deep neural networks. We evaluate the proposed approach using VGG-16 and ResNet-50 models on datasets including WBC, FOOD-101, and CIFAR-10, achieving up to two orders of magnitude reduction in total communication cost compared to conventional federated learning.