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Enhancing Scalability and Privacy in 5G-Enabled IoT Networks through Block chain Integration and AI Solutions - Indexed in Google Scholar : IJISEA

The proliferation of Internet of Things (IoT) devices in the digital landscape has ushered in a new era of connectivity, empowering billions of devices to communicate over the internet. However, the reliance on centralized protocols for data transfer poses significant security challenges. The emergence of 5G technology promises high-speed data transfer, yet it also underscores the need for robust security measures. Integrating Artificial Intelligence (AI) with 5G networks offers solutions to various challenges, including security concerns and the demand for autonomous systems like self-driving vehicles and virtual reality applications. Blockchain technology, known for its decentralized ledger system, presents an opportunity to address security and trust issues in IoT environments. However, integrating blockchain with IoT networks presents its own set of challenges, particularly in terms of throughput limitations. This paper addresses these challenges by proposing a solution that combines a Blockchain Distributed Network with the Raft consensus algorithm to enhance network scalability and throughput. Additionally, privacy concerns inherent in blockchain ledgers are addressed using zkLedger, a zero-knowledge based cryptographic solution. Through these innovations, this research contributes to the development of secure, scalable, and privacy-preserving 5G-enabled IoT networks.

 Key words: Blockchain, Artificial Intelligence, Internet of Things, 5G Network, Scalability, Privacy. Abbreviations: Blockchain (BC), Artificial Intelligence (AI), Internet of Things (IoT), 5G Network, Scalability (SC), Privacy (PR).

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Journal of Electrical System : SCI-mago Published

Abstract: - Unmanned Aerial Vehicles (UAVs) have grown into a more powerful type of data transmission due to this rapid progress of evolution of wireless communication technology. In addition, UAVs have been proven to be effective in a variety of applications, including intelligent transport, disaster risk management, surveillance, and environmental monitoring. When UAVs are deployed randomly, however, they can effectively accomplish challenging tasks because of the UAVs’ has low battery capacity, quick mobility, and dynamic in nature orientation. Due to this reason, a new technique must be designed for an optimal energy efficient UAV clustering as well as data routing protocols. In this work proposes a new hybrid model of Emperor penguin-based Generalized Approximate Reasoning Based Intelligent Control (EP-GARIC) cluster-based network topology. Furthermore, the optimal routing function is achieved by the proposed Artificial Jellyfish Optimization (AJO). The implementation of this research is carried out using Network Simulator (NS2). The simulation results displays the effective performance of the suggested approach in terms of reduced energy consumption, improved packet delivery ratio, reduced loss, and so on over compared to the conventional approaches. 

 Keywords: Clustering, Neural Network, Fuzzy method, Energy Efficiency, Parameter Tuning.


Recurrent neural network with emperor penguin-based Salp swarm (RNN- EPS2) algorithm for emoji based sentiment analysis

The opinions of the users are analyzed by the sentiment analysis process. Sometimes, the users reviewed their opinions using emojis and it is necessary to analyze them to find the classes (positive, negative, or neutral). However, the existing works lack the ability of emoji analysis in large databases, which induces computational complexity and reduces performance. To tackle those issues, we propose a novel Recurrent Neural network (RNN) with an Emperor Penguin-based Salp Swarm algorithm (EPS2) approach. The optimization algorithm can be used to choose the parameters of the RNN and provide better results. The experiments are conducted by taking the data from four social media platforms known as Reddit, Twitter, IMDB movie review, and Yelp dataset. The performance is analyzed by different metrics and compared the outcomes with other state-of-art works. From the results, it is found that our proposed approach effectively analyses the emojis from the social media platform and provides better results.

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Published SCOPUS indexed Journal

Abstract—Unmanned Aerial Vehicles (UAVs) have evolved into a potent form of data transmission, benefiting from the rapid advancements in wireless communication technology. Furthermore, UAVs have demonstrated their effectiveness across diverse applications, such as intelligent transportation, disaster risk management, surveillance, and environmental monitoring. When UAVs are deployed randomly, however, they can effectively accomplish challenging tasks because of the UAVs’ has low battery capacity, quick mobility, and dynamic in nature orientation. Due to this reason, a new technique must be designed for an optimal energy efficient UAV clustering as well as data routing protocols. In this work proposes a new hybrid model of Emperor penguin-based Generalized Approximate Reasoning Based Intelligent Control (EP-GARIC) cluster-based network topology. Moreover, the proposed model achieves the most efficient routing function through the utilization of the novel Artificial Jellyfish Optimization (AJO) technique. The execution of this study is conducted within the Network Simulator (NS2) environment. The outcomes of the simulations distinctly demonstrate the notable effectiveness of the suggested methodology. This is evidenced by a marked decrease in energy consumption, a substantial improvement in packet delivery ratio, a noteworthy reduction in losses, and other comparable metrics when contrasted with established conventional methods. Keywords—Clustering, Neural Network, Fuzzy method, Energy Efficiency, Parameter Tuning.

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Reducing Plagiarism from 67% to 3% - Engineering Journal

Plagiarism means using someone else’s work without giving them proper credit. In academic writing, plagiarizing involves using words, ideas, or information from a source without citing it correctly. In practice, this can mean a few different things.

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Tropical cyclone detection in South Pacific and Atlantic coastal area using optical flow estimation and RESNET deep learning model

Tropical cyclones (TC) are among the worst natural disasters, that cause massive damage to property and lives. The meteorologists track these natural phenomena using Satellite imagery. The spiral rain bands appear in a cyclic pattern with an eye as a center in the satellite image. Automatic identification of the cyclic pattern is a challenging task due to the clouds present around the structure. Conventional approaches use only image data to detect the cyclic structure using deep learning algorithms. The training and testing data consist of positive and negative samples of TC. But the cyclic structure's texture pattern makes it difficult for the deep learning algorithms to extract useful features. This paper presents an automatic TC detection algorithm using optical flow estimation and deep learning algorithms to overcome this draw-back. The optical flow vectors are estimated using the Horn-Schunck estimator, the Liu-Shen estimator, and the Lagrange multiplier. The deep learning algorithms take the optical flow vectors as input during the training stage and extract the features to identify the cyclone's circular pattern. The software used for experimental analysis is MATLAB 2021a. The proposed method increases the accuracy of detecting the cyclone pattern through optical flow vectors compared to using the pixel intensity values. By using proposed method 98% of accuracy will be achieved when compared with the existing methods.

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Published in SCI - Cybernetics and Systems


One of the most significant subfields in “Synthetic Aperture Radar (SAR)” research is considered to be target detection. Numerous studies have been conducted on target identification, with the majority of them favoring filter-oriented methods. The fundamental goal of radar systems is to “detect moving targets on the ground.” Decomposing a complex matrix into a structured sparse matrix and a low-rank matrix is a fundamental mathematics issue. Surveillance and reconnaissance rely heavily on “Ground Moving Target Indication (GMTI),” but it's not a simple task. The SAR ATI was first developed for calculating the radial velocity of ground-moving objects. Yet, overlapping stationary clutter can corrupt the recorded differential phase, resulting in mistakes in position and velocity calculations. The main concept of this paper is to propose a novel “Adaptive Simplified Fractional Fourier Transform (A-SFrFT)” using the intelligent meta-heuristic improvement. This adaptive SFrFT efficiently estimates the “Doppler parameters of the moving targets.” The improved “Harris Hawks Optimization (HHO)” termed Trio Updating HHO (TU-HHO) is used as the meta-heuristic algorithm that enhances the performance of the SFrFT-based target estimation. The mathematical analysis and simulation findings show that the suggested methods recommended strategy is successful.

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Book Publication : Data Analytics

Data analytics involves examining raw data to extract insights and make informed decisions. It includes various techniques and processes to clean, transform, and model data to discover patterns, trends, correlations, and other meaningful information. It's used across industries for purposes like optimizing business operations, understanding customer behavior, improving healthcare outcomes, and more. Tools like statistical analysis, machine learning, data mining, and visualization aid in this process, allowing analysts to derive actionable insights from large and complex datasets.

Data Analytics  (GCS PUBLISHERS, Dr.M.Dhasaratham,Dr Sreenivasulu Gogula ,J RAJYALAKSHMI ,Dr C THIRUMALAI SELVAN,,Dr.Siva Shankar S)

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published Book - IoT and Its Applications

The Internet of Things (IoT) is a revolutionary technological concept that has transformed the way we interact with the world around us. It refers to a network of interconnected devices, objects, and systems that have the ability to collect, exchange, and process data over the internet without human intervention. These devices, often embedded with sensors and connectivity features, can range from everyday objects like smartphones and wearable devices to specialized equipment in industries, such as sensors in manufacturing plants or smart meters in energy grids. The power of IoT lies in its ability to enable seamless communication and collaboration between these devices, leading to unprecedented levels of automation, efficiency, and convenience in various aspects of our lives. Through IoT, devices can communicate with each other, share information, and perform tasks autonomously, creating a more connected and intelligent environment. This interconnectedness has given rise to a wide array of applications across numerous industries. In healthcare, IoT enables remote patient monitoring and personalized healthcare solutions. In agriculture, it facilitates precision farming techniques. Smart homes and cities utilize IoT to enhance energy efficiency, security, and overall quality of life. Industries like manufacturing and transportation benefit from IoT through predictive maintenance and optimized

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