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enhanced Route Optimization for Wireless Networks Using meta-Heuristic engineering

computer Systems science & engineering 2022年第10期43卷 17-26页

作者： S.Navaneetha Krishnan P.Sundara Vadivel D.Yuvaraj T.Satyanarayana murthy Sree Jagadeesh malla S.Nachiyappan S.Shanmuga Priya Department of Electronics and Communication Engineering SACS MAVMM Engineering CollegeMaduraiIndia Department of Computer Science and Engineering SACS MAVMM Engineering CollegeMaduraiIndia Department of Computer Science Cihan University-DuhokKurdistan RegionIraq Department of Computer Science and Engineering Bapatla Engineering College BapatlaAndhra PradeshIndia Department of Computer Application National Institute of TechnologyThiruchirapalliIndia School of Computer Science and Engineering Vellore Institute of TechnologyChennaiIndia Department of Computer Science and Engineering M.I.E.T Engineering CollegeTrichyIndia

Wireless Sensor Networks(WSN)are commonly used to observe and monitor precise *** consist of a large num.er of inexpensive sensor nodes that have been separated and distributed in different *** base station received the amount of data collected by the numerous *** current developments designate that the attentFgion in applications of WSNs has been increased and extended to a very large *** Trust-Based Adaptive Acknowledgement(TRAACK)Intrusion-Detection System for Wireless Sensor Networks(WSN)is described based on the num.er of active positive deliveries and The Kalman filter used in modified Particle Swarm Optimization(mPSO)has been proposed to predict knot *** were run for non-malicious networks(0%malicious)and different percentages of malicious nodes were *** findings suggest that the proposed method TRAACK modified Particle Swarm Optimization(mPSO)packet delivery rate outperforms TRAACKPSO by 3.3%with 0%malicious ***,the packet delivery rate of TRAACKmPSO is 30%malicious,3.5%better than TRAACKPSO in WSN.

关键词： Trust-based adaptive acknowledgment(TRAACK) modified particle swarm optimization(mPSO) intrusion detection system(IDS) kalman filter

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meme expressive Classification in multimodal State with Feature extraction in Deep Learning 2

Meme Expressive Classification in Multimodal State with Feat...

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2nd International Conference on electrical, electronics, Information and Communication Technologies, ICeeICT 2023

作者： Barveen, A. Geetha, S. Faizal, m.K.mohamed M.I.E.T. Engineering College Department of Computer Science and Engineering Trichy India University College of Engineering Department of Computer Applications Tamilnadu Trichy India

ISBN: (纸本)9798350397635

memes are a socially interactive way to communicate online. memes are used by users to communicate with one another on social networking sites and other forums. memes essentially focus on speech recognition and image macros. While a meme is being created, it focuses on the semiotic type of resources that the internet community interprets with other resources, which facilitates the interaction among the internet and meme creators. memes recreate based on various approaches, which fall under various acts such as existing speech acts. Based on the expressive face with captioned short texts, even the short text is exaggerated. every year, meme mimicking applications are created that allow users to use the imitated meme expressions. memes represent the shared texts of the younger generations on various social platforms. The classifications of sentiment based on the various memetic expressions are the most efficient way to analyse those feelings and emotions. HOG feature extraction allows the images to be segmented into blocks of smaller size by using a single feature vector for dimension, which characterizes the local object appearances to characterize the meme classification. The existence of specific characteristics, including such edges, angles, or patterns, is then analyzed by combining HOG features using multi-feature analysis on patches. Based upon the classification methodology, it classifies the sentiments, which tend to improve the learning process in an efficient manner. By combining a deep learning approach with a recurrent neural network, the extended LSTm-RNN can identify subtle nuances in memes, allowing for more accurate and detailed meme classification. This proposed method effectively evaluates several classification techniques, including CNN and extended LSTm-RNN for meme image characterization. Through training and validation, extended LSTm-RNN achieved 0.98% accuracy with better performance than CNN. © 2023 Ieee.

关键词： Feature extraction

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Identification of Therapeutic Plants Using machine Learning

Identification of Therapeutic Plants Using Machine Learning

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emerging Trends in Information Technology and engineering (ic-eTITe), Conference on

作者： Rohini S Keerthana Shree m.U Jayalakshmi R malathi m L. Jabasheela M.E Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India B.E Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India Dept of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India

It takes a lot of work to develop a computerized method for classifying plants for medicinal purposes. There are countless kinds of plants found in India, and each one has its own unique set oftherapeutic advantages. Background information is necessary for manual identification and categorization because it is difficult for people to recall the proper titles of all plant species and their *** is crucial to preserve these medicinal plants since doing so will benefit several industries, including medicine, botanical research, and taxonomic studies, among others. Current technology cannot match thediversity of medicinal herb species that existin India. Utilizing textural traits that are crucial for recognizing and classifying leaves, the recom.ended technique makes itsimpler to categorize medicinal plants. The feature extraction, categorization, and picture enhancement stages are the three main components of the suggested method. The qualities that could be matched betweenthe leaf images were gathered using digital image processing methods after cell phones were used to snap the leaf photos. The CNN classifier is then used to create a machine learning classifier.

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Secure Key management Based mobile Authentication in Cloud

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computer Systems science & engineering 2022年第12期43卷 887-896页

作者： S.Shanmuga Priya D.Yuvaraj T.Satyanarayana murthy Varghese S.Chooralil S.Navaneetha Krishnan P.Banumathy P.SundaraVadivel Department of Computer Science and Engineering M.I.E.T Engineering CollegeTrichyTamilnaduIndia Department of Computer Science and Engineering Cihan UniversityDuhokKurdistan RegionIraq Department of Computer Science and Engineering Bapatla Engineering CollegeBapatlaAndhra PradeshIndia Department of Computer Science and Engineering Rajagiri School of Engineering and TechnologyKakkanadKeralaIndia Department Electronics and Communication Engineering SACS MAVMM Engineering CollegeMaduraiTamilnaduIndia Department of Information Technology Karaikal Polytechnic CollegeVarichikudy-KaraikalIndia Department of Computer Science and Engineering SACS MAVMM Engineering CollegeMaduraiIndia

Authentication is important to the security of user data in a mobilecloud environment. Because of the server’s user credentials, it is subject toattacks. To maintain data authentication, a novel authentication mechanism is proposed. It consists of three independent phases: Registration, login, and authentication and key agreement. The user registers with the Registration Center (RC)by producing a secret num.er that isn’t stored in the phone, which protects againstprivileged insider attacks. The user and server generate a nonce for dynamic useridentity and agree on a session secret key for safe communication. The passwordsare not stored on the computer or provided in plain text, they are resistant toreplay, guessing, and stolen verification attacks. The suggested protocol uses aone-way hash function and XOR operations, with the client having remote accessto a large num.er of servers over a secure communication channel. Concentrateson HmAC and SHA3 for Collision Free Hashing and to overcome length extension attacks. HmACs are substantially less affected by collisions than their underlying hashing algorithms alone. So adding an HmAC to an mD5 or SHA hashwould make it substantially more difficult to break via a rainbow table.

关键词： Registration center SIm ImeI

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enhancing wireless sensor network longevity and security: A quad-leach approach

Enhancing wireless sensor network longevity and security: A ...

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2024 Ieee International Conference on emerging Technologies in computer science for Interdisciplinary Applications, ICeTCS 2024

作者： Lohith, J.J. Shreya, S. Hamsa Priya, J.L. B.M.S. College of Engineering Dept. of Computer Science and Engineering Bangalore India D.S.C.E. Dept. of Information Science and Engineering Bangalore India B.M.S. College of Engineering Dept. of Electronics and Telecommunications Bangalore India

ISBN: (纸本)9798350372502

Researchers have increasingly focused on Wireless Sensor Networks (WSNs) in recent years, recognizing their versatile applications. The primary focus within WSNs is enhancing network longevity for efficient resource utilization, coupled with a strong emphasis on security and authentication. This paper specifically delves into the implementation of Quad LeACH for homogeneous networks, aiming to substantially improve network lifetime, stability, and throughput. The proposed protocol achieves this by employing a cluster head selection process and integrating security and authentication measures between cluster heads in the Quad-LeACH routing protocol. The research addresses the integration of a security agent within WSNs using the NS-2 tool, contributing to enhanced authentication. The implementation is carried out through the simulation software NS 2, where the security agent is seam.essly combined with the WSN infrastructure. The objective of this research is to incorporate a reliable authentication and security agent into NS-2 in conjunction with the LeACH protocol, which is the standard for wireless sensors networks (WSNs). The efficacy of the suggested methodology will be validated through simulated outcomes. © 2024 Ieee.

关键词： Wireless sensor networks

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Unlocking efficiency in Agricultural Supply Chains: A Secure and Transparent Approach through Blockchain Technology

Unlocking Efficiency in Agricultural Supply Chains: A Secure...

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2024 Ieee International Conference on emerging Technologies in computer science for Interdisciplinary Applications, ICeTCS 2024

作者： Lohith, J.J. Shreya, S. Hamsa Priya, J.L. B.M.S. College of Engineering Dept. of Computer Science and Engineering Bangalore India D.S.C.E Dept. of Information Science and Engineering Bangalore India B.M.S. College of Engineering Dept. of Electronics and Telecommunications Bangalore India

ISBN: (纸本)9798350372502

Supply Chain management (SCm) involves orchestrating various processes that extend from acquiring raw materials to delivering finished products to end consumers. The key objectives of a proposed supply chain in agriculture, emphasizes information-driven processes, cost reduction, and customer impact. It creates a versatile system capable of managing the supply chain for diverse commodities within a country, while also addressing the crucial need for increased transparency and traceability in data throughout the chain of activities. In our proposed approach to agricultural supply chain management (SCm), the focus is on creating a secure, transparent, and traceable SCm. To achieve this, we leverage cutting-edge technologies like the ethereum blockchain and the Interplanetary File System (IPFS). The resulting software is a Decentralized Application (DApp) that orchestrates a series of activities within the agricultural industry, using cryptocurrencies to establish a seam.ess and efficient supply chain management system. This software platform is fortified by the secure algorithms of blockchain, providing an added layer of reliability. Additionally, our application incorporates transparency and traceability, ensuring user-friendly functionality and making it more accessible for users. © 2024 Ieee.

关键词： Blockchain

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enhancement of PCA-Based Dimensionality Reduction Using BB-BC Optimization Algorithm

Enhancement of PCA-Based Dimensionality Reduction Using BB-B...

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2023 International Interdisciplinary PhD Workshop, IIPhDW 2023

作者： Kaur, Supreet Krishna, C. Rama Kumar, Shakti Singh, Jasm.et National Institute of Technical Teachers Training and Research Department of Computer Science and Engineering Chandigarh India Panipat Institute of Engineering & Technology Department of Computer Science and Engineering Panipat India Hochschule Wismar Department of Electrical Engineering and Computer Science Phillip-Müller-Straße 14 Wismar23966 Germany

ISBN: (纸本)9781665410847

Dimensionality reduction is an important step for various applications where usage of the internet and multimedia systems is involved which requires huge bandwidth and storage space. This paper presents Big Bang-Big Crunch (BB-BC) optimization algorithm based two new approaches to feature selection for dimensionality reduction. In first approach, PCA is used to extract the features (eigenvectors) and defines feature set based on computation of knee point and BB-BC optimization algorithm, in turn selects an optimal subset from the predefined feature set. In the second approach, PCA is used only for feature extraction and BB-BC optimization algorithm is used for optimal feature selection from all extracted features. Olivetti Research Laboratory (ORL) face database has been used for performing the experiment and recognition rate is the parameter to be optimized for face recognition as an application area. The experimentation proves BB-BC as a powerful soft computing technique to solve such NP hard problems. © 2023 Ieee.

关键词： Face recognition

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Data mining Techniques for ensuring Security in Large Datasets 3

Data Mining Techniques for Ensuring Security in Large Datase...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Singh Dari, Sukhvinder Hussein, Laith Kriplani, Khushbu Jeyaram, G. Kumar, S. Ramesh Pune India College of Technical Engineering The Islamic University Najaf Iraq Ies College of Technology Department of Computer Science & Engineering Bhopal India M.E.T Engineering College Department of Computer Science and Engineering Nagercoil India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science & Engineering India

ISBN: (纸本)9798350342338

In the contemporary digital landscape, the security of large datasets is paramount, given the increasing volume, variety, and velocity of data generation and collection. This paper provides an intricate examination of the application of various data mining techniques to bolster security measures in handling expansive datasets. Through a systematic exploration of methods including clustering, classification, and association, we aim to elucidate the efficacy and adaptability of these techniques in identifying and mitigating potential security threats. We critically analyze real-world scenarios and case studies, drawing comparative insights to underscore the nuanced applications and challenges inherent in diverse data environments. A detailed discussion is presented on the role of machine learning algorithms and artificial intelligence in augmenting data mining processes, enhancing predictive accuracy, and real-time responsiveness to security breaches. The findings indicate a significant positive correlation between advanced data mining applications and enhanced data security. However, they also highlight existing vulnerabilities and areas necessitating further research and innovation. We conclude with recom.endations for integrating adaptive, scalable, and robust data mining frameworks to ensure comprehensive security in managing large datasets, paving the way for future research intersections between data mining and cybersecurity. © 2023 Ieee.

关键词： Large Datasets Privacy Security Risks and Anonymization Techniques Sensitive Information Unauthorized Access Vulnerabilities

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Adaptive solar power generation forecasting using enhanced neural network with weather modulation

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10955-10968页

作者： Sujeeth, T. Ramesh, C. Palwe, Sushila Ramu, Gandikota Basha, Shaik Johny Upadhyay, Deepak Chanthirasekaran, K. Sivasankari, K. Rajaram, A. Department of CSE Siddartha Educational Academy Group of Institutions Andhra Pradesh Tirupati India Department of Mechanical Engineering M. Kumarasamy College of Engineering Karur India Department of Computer Engineering and Technology MIT-WPU Maharashtra India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Telangana Hyderabad India Andhra Pradesh Mylavaram India Department of Computer Science and Engineering Graphic Era hill univeristy Dehradun India Department of Electronics and Communication Engineering Saveetha Engineering College Chennai India Department of Electronics and Communication Engineering Akshaya College of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Nagapattinam India

Solar power generation forecasting plays a vital role in optimizing grid management and stability, particularly in renewable energy-integrated power systems. This research paper presents a comprehensive study on solar power generation forecasting, evaluating traditional and advanced machine learning methods, including ARImA, exponential Smoothing, Support Vector Regression, Random Forest, Gradient Boosting, and Physics-based models. moreover, we propose an innovative enhanced Artificial Neural Network (ANN) model, which incorporates Weather modulation and Leveraging Prior Forecasts to enhance prediction accuracy. The proposed model is evaluated using real-world solar power generation data, and the results demonstrate its superior performance compared to traditional methods and other machine learning approaches. The enhanced ANN model achieves an impressive Root mean Square error (Rm.e) of 0.116 and a mean Absolute Percentage error (mAPe) of 36.26%. The integration of Weather modulation allows the model to adapt to changing weather conditions, ensuring reliable forecasts even during adverse scenarios. Leveraging Prior Forecasts enables the model to capture short-term trends, reducing forecasting errors arising from abrupt weather changes. The proposed enhanced ANN model showcases its potential as a promising tool for precise and reliable solar power generation forecasting, contributing to the efficient integration of solar energy into the power grid and advancing sustainable energy practices. © 2024 – IOS Press.

关键词： Solar power generation

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multi-objective Deep Reinforcement Learning Approach for multiple -Input/multiple-output Routing in WSN

Multi-objective Deep Reinforcement Learning Approach for Mul...

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International Conference on 6G Communications Networking and Signal Processing, SGCNSP 2023

作者： Reddy, R. Archana Adnan, myasar mundher Paramesh, S.P. Petli, Vishwanath manohar, m. School of Computer Science and Artificial Intelligence SR University Telangana Warangal India The Islamic University Najaf Iraq Department of Computer Science and Engineering School of Engineering Central University of Karnataka Karnataka Kalaburagi India Electronics and Communication Engineering H.K.E. Society’s Sir M. Visvesvaraya College of Engineering Raichur India Bangalore India

ISBN: (纸本)9789819778751

The Wireless Sensor Network (WSN) is a network of numerous devices that are interconnected via the internet and significantly impact the network. However, despite their significant applications WSNs face challenges related to network security energy levels and information transmission delays. To address these challenges, a method utilizing multi-Objective Deep Reinforcement Learning (DRL) has been proposed. The proposed method aims to maximize energy utilization in the network by efficiently managing covered and uncovered cluster network routing. The performance of energy transmission is enhanced through the use of the markov Decision Process model based on multi-objective DRL combined with training the network using Deep Q Network (DQN) to reduce network energy consumption. Training the network with multiple objectives may pose challenges requiring more samples and leading to higher sample complexity, which can be a limiting factor in real-world applications. Despite this, the proposed multi-objective DRL method demonstrates high performance compared to existing methods such as Particle Swarm Optimization (PSO) and Convolutional Neural Network (CNN). Specifically, multi-Objective DRL method yields superior results, achieving an energy consumption of 42 J, Packet Delivery Ratio (PDR) of 90%, and an end-To-end Delay (eTeD) of 45 S. These outcomes outperform.existing methods in the context of WSNs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： markov processes

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