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EDRLNet: privacy preservation in online social networks via deep reinforcement learning and graph convolutional networks

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International Journal of Information Technology (Singapore) 2025年 1-10页

作者： Gowda, Nithish Ranjan Venkatesh Venugopal, K.R. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bengaluru India Associate Professor Department Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bangalore India University Visvesvaraya College of Engineering Bangalore University Bangalore India

Online social networks (OSN) have become extremely popular in the past few decades. Prominent OSN companies, such as Facebook and Twitter, control a huge amount of information on its users as well as interactions that may cause people to have major privacy concerns. The OSN information is combined with other online sources to reveal sensitive information. As a result, there is a great need to anonymize the OSN data before providing it to a third party. Thus, in this study, we propose a Deep Reinforcement Learning (DRL)-based rewiring operation that entails changing the relationships (edges) between nodes (users) without changing their degree to protect users’ privacy. To accomplish this, we introduce a novel technique for Enhanced DRL Networks called “EDRLNet” which combines an Optimized Policy Network (OPN) and Graph Convolutional Network (GCN) that preserves the essential characteristics of OSN data while anonymizing it to prevent privacy breaches. This approach employs a GCN to extract embeddings (features) from the edges and nodes. Furthermore, OPN applies the graph feature representation learned by the GCN model to policy learning. Additionally, the hyperparameters of OPN are optimized by using the Addax optimization algorithm (AOA). The suggested scheme’s performance is assessed using PageRank with mean-squared error (MSE), Root mean-squared error (RMSE), mean absolute error (MAE), Privacy score, Utility Score, Clustering coefficient, privacy and information loss. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Addax optimization algorithm Anonymization Deep reinforcement learning Graph convolutional network Online social network Privacy preservation

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Intelligent Irrigation Mechanism to Enhance The Sensor Network Topology by Using Machine Learning And Artificial Intelligence 2

Intelligent Irrigation Mechanism to Enhance The Sensor Netwo...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Sriram, K.P. Anbalagan, E. Sasikumar, S. Kumar, M. Guru Vimal Paramesh, J. Sujatha, P. Kola Assistant Professor Information technology St.Joseph's Institute of Technology OMR Chennai – 600119 Professor Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Professor Department of Computer Science and Engineering Saveetha Engineering College Chennai Associate Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Professor Department of Computer Science and Engineering Mohamed Sathak A.J. College of Engineering Ekattur OMR Siruseri Chennai Associate Professor Department of Information Technology MIT Campus Anna University Chennai Tamil Nadu India – 600044

ISBN: (纸本)9798350383867

This research paper is about the effectiveness of a fusion of machine learning and artificial intelligence into smart irrigation systems in terms of both safety and benefit of the environment and the economy in farming. We've experienced a intensive process of experiment and application of this succeeded method in a real world scenario and we've also detailed the performance of the dynamic irrigation scheduling and the adapter sensor network optimization. Results indicate a significant improvement in key metrics: scheduling was done dynamically which on average was a 10% rise in crop yield and 15% water saving, on the other hand, sensor network optimization with adaptive technology resulted into a 15% increase in cover area and 20% improvement achieved in data transmission reliability. Furthermore, the introduction of the smart irrigation system got impressive results signified by a 10 kg more production per hectare, irrigation efficiency that rose from 70% to 85%, and saving 20% of the water consumed. This calls to mind the capability of cutting-edge technologies to contribute to innovative water management paradigms, improved resource use, and security in food production in face of unpredictable climatic variability. ©2024 IEEE.

关键词： Adversarial machine learning

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Design of Multiple Share Creation with Optimal Signcryption based Secure Biometric Authentication System for Cloud Environment

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International Journal of computers and Applications 2022年第11期44卷 1047-1055页

作者： Prabhu, D. Vijay Bhanu, S. Suthir, S. Department of Computer Science and Engineering Annamalai University Chidambaram India Research Supervisor & Associate Professor Department of Computer Science and Engineering Annamalai University Chidambaram India Department of Computer Science and Engineering Annamalai University Chidambaram India

Biometric authentication plays a vital role in the cloud computing (CC) environment to accomplish security and privacy. Several kinds of biometrics are used for the authentication process, particularly, the fingerprint is a commonly used biometric, which accomplishes maximum security. But it can be easily stolen, unintentionally revealed, or difficult to remember. Therefore, biometric authentication techniques are needed and an encryption process is included to improve the security of the authentication process. In this aspect, this paper proposes a multiple share creation with an optimal signcryption-based biometric authentication system (MSCOS-BAS- FLNN- PeSeOA) for the cloud environment. The proposed model enables the creation of multiple shares of the input biometric image (fingerprint) and encrypts them for a secure authentication system. In addition, the encryption of individual shares takes place with an optimal signcryption system where the key generation process is carried out using the penguin search optimization algorithm (PeSeOA). Finally, the functional link neural network (FLNN) model is applied for the biometric authentication verification process. A wide range of simulation analyses are performed to highlight the supreme efficacy of the MSCOS-BAS- FLNN- PeSeOA technique. The experimental outcomes pointed out the hopeful performance of the MSCOS-BAS- FLNN- PeSeOA technique over the recent techniques. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Authentication

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Twitter sentiment analysis using conditional generative adversarial network

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 161-169页

作者： Mahalakshmi, V. Shenbagavalli, P. Raguvaran, S. Rajakumareswaran, V. Sivaraman, E. Department of Information Technology Sengunthar College of Engineering Tiruchengode Tiruchengode637205 India Assistant Professor Karunya Institute of Technology and Sciences Coimbatore India Assistant Professor Department of Computational Intelligence School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Kanchipuram Chennai Tamil Nadu 603203 India Assistant Professor Department of Computer Science and Design Erode Sengunthar Engineering College Tamil Nadu Thuduppathi India Department of Electrical and Computer Engineering Curtin University Miri Malaysia

Sentiment analysis, which aims to extract information from textual data indicating people's ideas or attitudes about a particular problem, has developed into one of the most exciting study issues in natural language processing (NLP) with the development of social media. Twitter is a social network with an extensive audience that expresses their thoughts and opinions clearly and readily. Due to the prevalence of slang phrases and incorrect spellings in short phrase styles, Twitter data analysis is more challenging than data analysis from other social networks. Automated feature selection still has several limitations, such as higher computing costs that rise with the number of characteristics. Deep learning, which is self-learned and more accurate at processing vast amounts of data, is utilized to overcome these challenges. This paper introduces a conditional generative adversarial network (GAN) for Twitter sentiment analysis, whereas a convolutional neural network (CNN) has been used to extract traits from Twitter data. Compared to existing works, the proposed work has outperformed in accuracy, recall, precision, and F1 score. The suggested method is the most accurate, with a classification accuracy of 93.33 %. © 2024

关键词： Sentiment analysis

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A Study of Deep Learning Techniques for Predicting Breast Cancer Types

A Study of Deep Learning Techniques for Predicting Breast Ca...

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International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2022

作者： Ashwini, P. Suguna, N. Vadivelan, N. Research Scholar Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Chidambaram India Associate Professor Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Chidambaram India Professor Department of Computer Science and Engineering Teegala Krishna Reddy Engineering College Hyderabad India

ISBN: (纸本)9789819947164

Breast cancer is today’s deadly health issue which causes high mortality in woman worldwide. The preliminary detection and classification may help for proper treatment of the same. Understanding the causes of this cancer using traditional machine learning techniques misread feature extraction are leading to a complication. Traditional machine learning models are best suited for feature-based raw data. The novel deep learning techniques have been introduced for diagnosis of breast abnormalities using various imaging modalities like mammogram, magnetic resonance imaging (MRI) and ultrasound with high accuracy. This survey focuses on challenges of classical machine learning models and supervene efficient prediction models using novel deep learning techniques. This review presents comparison analysis of traditional machine learning and deep learning models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Magnetic resonance imaging

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Stacked Generative Adversarial Networks (StackGAN) Text-to-Image Generator 3

Stacked Generative Adversarial Networks (StackGAN) Text-to-I...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Arya, Rahul Joshi, Dikshant Sharma, Khushi Bhakuni, Vihan Singh Vats, Satvik Sharma, Vikrant Graphic Era Hill University Computer Science and Engineering Dehradun India Graphic Era Hill University Adjunct Professor Graphic Era Deemed to Be University Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350384598

Generating high quality images from textual description is an ongoing challenge which can have a wide range of practical uses. Some of such fields are computer vision, creation industry, e-commerce, education and many more. Currently existing text to image synthesizing techniques, while offering valuable insights still struggles to capture fine details and vivid object components that are crucial for real world application. We use an approach that uses Stacked Generative Adversarial Networks (StackGAN) to solve the problem of text-to-image generation. This includes using a two-stage process. In the first stage (Stage - 1 GAN), we aim to develop a system that is capable of generating the fundamental shape and color attributes of the object according to the text. In this stage, a low-resolution image is generation that is used as a foundation for the subsequent image refinement process. In the second stage (Stage - 2 GAN), we focus on enhancing the quality of image by incorporating the text descriptions and generating high-resolution image. This project aims to bridge the gap and advance the text-to-image generation to create images to create images that are textually accurate. By doing so, the project seeks to unlock the potential of this technology across various fields and bridge the gap between the power of language and art, design and content creation. © 2024 IEEE.

关键词： Generative adversarial networks

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Crop Yield Prediction Based on Weather and Soil Parameters Using Regression Tree Model 1

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4th International Conference on Communication, Devices and Computing (ICCDC 2023)

作者： Narasimha Reddy, Kandi Venkata Brahma Reddy, Evuri Assistant Professor Department of Computer Science and Engineering Narasaraopeta Engineering College Narasaraopeta Guntur India Department of Computer Science and Engineering Narasaraopeta Engineering College Narasaraopeta Guntur India

ISBN: (数字)9789819927104

ISBN: (纸本)9789819927098

The development of artificial intelligence and advances in computer science have provided a significant impetus for progress in the field of machine learning. In recent years, machine learning algorithms have been used effectively in the sector of agriculture with the purpose of accurately estimating the amount of crop output. The process of estimating the yield of crops using accessible historical data, such as characteristics relating to the weather and soil, as well as previous crop yields, is known as crop yield prediction. A function of machine learning known as regression makes predictions about a number. The use of regression methods may be highly beneficial when attempting to forecast the yield of crops. The development of regression methods applicable to the agricultural industry is the primary emphasis of this research. When attempting to estimate the amount of wheat, maize, and cotton that will be harvested, many types of regression analysis, including quadratic, pure-quadratic, interactions, and polynomial, are used. Finally, a regression tree model has been suggested that is capable of reliably predicting the yields of cotton, maize, and wheat. The metrics of Root Mean Squared Error (RMSE), R’, and Mean Percentage Prediction Error (MPPE) are used in the decision-making process to predict the yield in an effective way. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Crops

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Levy Flight Adopted Particle Swarm Optimization-based Resource Allocation Strategy in Fog Computing

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Chinese Journal of Electronics 2023年

作者： Sharmila Patil(Karpe) Brahmananda S H Research Scholar Department of Computer Science and Engineering GITAM School of Technology GITAM Deemed University Professor Department of Computer Science and Engineering GITAM Bengaluru

The prevalence of the Internet of Things(Io T) is unsteady in the context of cloud computing, it is difficult to identify fog and cloud resource scheduling policies that will satisfy users’ Qo S need. As a result, it increases the efficiency of resource usage and boosts user and resource supplier profit. This research intends to introduce a novel strategy for computing fog via emergencyoriented resource allotment, which aims and determines the effective process under different parameters. The modeling of a non-linear functionality that is subjected to an objective function and incorporates needs or factors like Service response rate, Execution efficiency, and Reboot rate allows for the resource allocation of cloud to fog computing in this work. Apart from this, the proposed system considers the resource allocation in emergency priority situations that must cope-up with the immediate resource allocation as well. Security in resource allocation is also taken into consideration with this strategy. Thus the multi-objective function considers 3 objectives such as Service response rate, Execution efficiency, and Reboot rate. All these strategies in resource allocation are fulfilled by Levy Flight adopted Particle Swarm Optimization(LF-PSO). The evaluation is performed to determine whether the developed strategy is superior to numerous traditional schemes. The cost function attained by the adopted technique is 120, which is 19.17%, 5%, and 2.5%greater than the conventional schemes like GWSO, EHO,and PSO, when the number of iterations is 50.

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Analysis of Congestion Control Techniques for Improving QoS using Machine Learning Approach in MANET 3

Analysis of Congestion Control Techniques for Improving QoS ...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Bhargava, Medhavi Chaturvedi, Anoop School of Computer Science & Engineering School of Computer Science and Technology Lnct University Bhopal India Professor School of Computer Science and Technology Lnct University Bhopal India

ISBN: (纸本)9798350343274

Congestion is the main reason why connections fail when there are too many people on the network. This leads to the loss of nodes and modify the way setup of Mobile Ad-hoc Network (MANET). As we know, MANET is a network with no permanent infrastructure. It is made up of a temporary network that doesn't need a central manager or the usual support equipment found in traditional networks. To assure the longevity of MANET, networks can be constructed anywhere. Buffer overflow and congested links are caused by severe network demand, resulting in significant packet loss. Proper routing results in packet delays and has an effect on the MANET protocol's packet delivery rate. Machine learning (ML) is a part of Artificial Intelligence (AI) which is able to improve the QoS of routing in networks. In this paper, we discuss the latest research work to learn some innovative concepts and focus on doing something new in the field of Machine Learning in MANET. A well-balanced strategy is a method for avoiding network congestion. Load balancing allows network consumption to be optimized, packet latency to be reduced, and packet distribution ratio to be enhanced. Shifting loads from congested to less congested paths enhance the network's overall efficiency. MANET routing protocols prefer paths with less hops and exclude paths with more hops, but in extreme load conditions, they prefer paths with less hops. Furthermore, we investigate congestion management routing options in order to suggest a new approach in the future for identifying and preventing congestion risk in MANET. © 2023 IEEE.

关键词： Mobile ad hoc networks

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A Hybrid Harmony Search Algorithm based on Firefly Algorithm and Boltzmann Machine

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2022年第1期49卷 241-251页

作者： Yang, J. Zhang, Xiaoxia Fu, S. Hu, Y.Y. Master Student of School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China Professor of School of computer Science and Software Engineering University of Science and Technology LiaoNing Anshan11041 China

Harmony Search Algorithm (HS) is a new meta-heuristic algorithm based on music creation process. The algorithm has been widely used in many fields because of its simple and flexible structure and fewer controlled parameters. However, HS is easy to be affected by the initial harmony memory and falls into local optimization. In addition, there are also problems, such as low searching accuracy and slow convergence speed of the optimal solution. Therefore, this paper proposes a hybrid harmony search algorithm based on firefly algorithm and boltzmann machine (HSFA-BM). First, in order to avoid the possibility of harmony search algorithm falling into local optimum, boltzmann machine (BM) mechanism is used to update the harmony memory in this paper. Second, in order to enhance the ability of the algorithm to exploit optimal solutions, this paper uses the movement mode of fireflies to generate new solutions. The improved algorithm enhances the ability of global optimization and search precision, and speeds up the convergence speed. In this paper, six classical test functions are used to test the effectiveness of the algorithm, and a very obvious optimization result is obtained. © 2022. All Rights Reserved.

关键词： Flexible structures

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