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2023 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2023

作者： Vaidya, Chandu Takalkar, Kunal Ghosekar, Anurag Nimgade, Susmit Ghode, Vivek Rajiv Gandhi College of Engineering and Research Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350398748

The popularity of peer-to-peer architecture has expanded during some recent years. This is due to the fact in comparison to conventional systems, Peer-to-peer systems offer better performance, scalability, availability, security, and anonymity. Most applications and services rely on central authorities for transfer of files. This introduces a single point of failure to the system. The relevant authority should be depended on to have facts saved via way of means of the utility to be had at any given time. More importantly, the privacy of the consumer relies upon the provider company's functionality to hold the facts safe. A decentralized system could be the technique to remove the dependency on a central authority. Moreover, because of the speedy boom of cellular tool utilization, the provision of decentralization should now no longer be confined best to desktop computers. This survey research examines the architecture of such systems using various peer-to-peer architectures. In this work, we aim at studying a decentralized file-sharing system to make it more secure than in previous works. This can be achieved by implementing a P2P protocol with authentication. We validate the outcomes via way of means of deploying and measuring the accuracy and security of file transfer in multiple devices. © 2023 IEEE.

关键词： Authentication

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Dear: vehicle mobility prediction using diffusion-expanded attention network based on IoV trajectory data

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Neural Computing and Applications 2024年 1-15页

作者： Yang, Jiali Yang, Kehua Zeng, Fanzi Cheng, Qixuan Xiao, Zhu Jiang, Hongbo College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Chongqing Research Institute of Hunan University Chongqing404100 China

In recent years, the rapid development of vehicle networking technology makes the acquisition of Internet of vehicles (IoV) trajectory data easier and more extensive. Mobility prediction based on these IoV trajectory data has become one of the research hotspots in the field of transportation. However, due to the randomness and nonlinear characteristics of IoV trajectory data, vehicle mobility prediction is a challenging task. In addition to traditional statistical methods, neural networks are promising methods for predicting vehicle mobility. Therefore, we design a deep neural network model and introduce the attention mechanism to achieve accurate prediction of vehicle mobility by learning and modeling the timing characteristics of IoV trajectory data. Diffusion-Expanded Attention Recurrent Neural Network (DEAR), a position prediction model for preference perception, is proposed. The introduction of diffusion model can better consider the dynamics of vehicle movement, adapt to the ever-changing traffic environment, and improve the accuracy of trajectory prediction. The experimental results show that our method can accurately predict the moving behavior of vehicles such as position, speed, and acceleration, which provides important support for real-time traffic flow optimization and driving safety, and has a wide application prospect. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Prediction models

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Med Assistant Android Application

Med Assistant Android Application

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2023 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2023

作者： Turkar, Hemant Tiwari, Shubham Mate, Aman Naranje, Atish Jaulkar, Jay Hadke, Kunal Rajiv Gandhi College of Engineering and Research Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350398748

Fitness apps that can help people improve their health and stamina by increasing incentive for physical activity utilising gamification principles and wearables have been made possible because to the rise in 'smartphone ownership.' However, compared to other app kinds, these apps have a low retention rate. In this research, the most popular fitness mobile apps were reviewed, and various gamification strategies were compared. The research revealed that most fitness applications are difjicult to use, have an excessive number of features, and don't motivate users enough. A straightforward fitness application that links to a watch and employs levels and avatars to boost motivation was created to address some of the aforementioned drawbacks. A team of volunteers evaluated the application, and the findings were generally favourable. The outcomes demonstrated that there is room for future advancement in application development. © 2023 IEEE.

关键词： Android (operating system)

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Prediction of Bitcoin Price using Optimized Genetic ARIMA Model and Analysis in Post and Pre Covid Eras* 3

Prediction of Bitcoin Price using Optimized Genetic ARIMA Mo...

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3rd International Conference on Smart Data Intelligence, ICSMDI 2023

作者： Srivastava, Vibha Kumar Dwivedi, Vijay Kumar Singh, Ashutosh United College of Engineering & Research Department of Computer Science and Engineering Prayagraj India

ISBN: (纸本)9781665464871

Predicting Bitcoin price is a universal research area as it attains significance in predicting the market way of its rate so that, investors could procure profits. Concurrently, with the evolution of Machine Learning (ML), researchers attempted to use ML based algorithms for forecasting the Bitcoin price. However, these researches have resulted in inefficient prediction due to error rate. For alleviating such pitfalls, this study intends to forecast the Bitcoin price by comparing its deviations pre and post Covid using suitable ML algorithms. To achieve this, the study proposes Auto Regressive Integrated Moving Average (ARIMA) with Optimized Genetic Algorithm (OGA). In this case, ARIMA model is considered as it possess the innate ability in capturing standard temporal reliances which is distinct to time-series data. Further, hyperparameters are selected by GA based on the fitness function. Based on this, hyperparameter tuning is performed which assist to improvise the model performance. For determining if there exists any deviations in Bitcoin price (pre and post Covid), Augmented Dickey Fuller (ADF) test is considered. Further, comparative analysis is regarded in accordance with performance metrics to validate the performance of the proposed system which proves its effectiveness in predicting Bitcoin price. © 2023 IEEE.

关键词： Genetic algorithms

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Resource Allocation in Edge Computing Environment Using Deterministic Policy Gradient Algorithm 10

Resource Allocation in Edge Computing Environment Using Dete...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Shabariram, C.P. Shanthi, N. Ponnuswamy, P. Priya Subramaniyaswamy, V. Sathana, V. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Erode India School of Computing Sastra University Department of Computer Science and Engineering Thanjavur India

ISBN: (纸本)9798350384369

In recent years, the edge computing paradigm enables the movement of processing units and storage nearer to the data available locations. The mechanism completes the computation in a short span of time in minimum bandwidth. Edge ecosystem is a type of distributed computing that is sensitive to topology and geography;the Internet of Things is a prime instance of this. Rather than referring to a single technology, Edge computing refers to an architecture. This paper proposes a resource allocation methodology that will enliven the situation between users and edge servers. By creating continuous control at the edge servers to determine resource allocation, edge computing improves reaction time, provides high security with decreased risk, scalability, lowers transmission costs, and versatility (offload targets, migration bandwidth and computing resources). The Deterministic Policy Gradient, Deep learning and Quality Network concepts are combined in the proposed system. The continuous action space is achieved by a deterministic policy gradient. The experience relay includes a quality network. In the proposed system, the actor-critic network produces a single continuous action instead of resulting probability based actions. The critic-part uses Q-value from a quality network based on current status and activity. The goal of the proposed system is to develop a Deep Deterministic Policy Gradient methodology to allocate servers for mobile users with the help of the Edge computing while taking computation resources, offloading goals and migration bandwidth into consideration. The simulation result indicates that deterministic policy gradients integrated deep learning models improve the system performance compared with Game theory. © 2024 IEEE.

关键词： Mobile edge computing

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An Approach Towards Decentralized E-Voting 11

An Approach Towards Decentralized E-Voting

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11th International Conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET - SIP 2023

作者： Vaidya, Chandu Kirnapure, Chinmay Rithe, Jitesh Sonkusare, Devashish Khade, Prathmesh Kharche, Kartik Rajiv Gandhi College of Engineering and Research Computer Science Engineering Nagpur India

ISBN: (纸本)9798350348422

Voting is an essential component of democratic regimes because it allows every member of a community to express their opinions. Voter turnout has decreased recently, while worries about the reliability, security, and accessibility of the current voting technologies have grown. Voting was introduced to address those concerns, however, it still requires full supervision by a central authority. The blockchain is an emerging, decentralized, and distributed technology. A shared, distributed record of all finished transactions or digital activities between all parties involved. Each vote is viewed as a separate transaction in this instance. We store voting transactions on a private blockchain that is built using a peer-to-peer network. Voting specifics are abstracted from the user by programming of this application. While system is running, users will have adequate time to cast their votes. This study is about (decentralized voting system) that protects voters privacy while maintaining transparency and security. The system has three implementation layers: a user interface for voters to register and vote a smart contract and MetaMask wallet for automatic vote transactions on the Ethereum blockchain, and an administrator function for managing the election. The smart contract is programmed to check for registration and validity, and once deployed on the blockchain, it cannot be tampered with. The administrator adds candidate information and manages the election. © 2023 IEEE.

关键词： Blockchain

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Sarcasm Detection Analysis - Comparative Analysis

Sarcasm Detection Analysis - Comparative Analysis

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2023 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2023

作者： Vaidya, Chandu Gupta, Ajit Shastrakar, Aditya Kathane, Anil Atmande, Kunal Iyer, Krishnan Rajiv Gandhi College of Engineering and Research Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350398748

Sarcasm is a unique class of figurative language, that is in particular utilized in on-line social media. Such messages are used for sarcastic feedback the usage of contemptuous, taunt, and mock-associated phrases or phrases. Normally, the term 'rule-primarily based totally method is implemented to structures concerning human-crafted or curated rule sets. There are many algorithms for figuring out whether or not a sentence is sarcastic or now no longer. This paper complies with the Rule-primarily based totally method this is evolved to address the trouble of sarcasm detection and additionally describes numerous datasets. However we can in particular cognizance on Random Forest, Decision Tree, Support Vector Machine. In our project we are going to compare the accuracies from the model used and find the most accurate model giving high accuracy. © 2023 IEEE.

关键词： Sentiment analysis

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Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

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computers, Materials & Continua 2023年第7期76卷 993-1007页

作者： A.Manju R.Kaladevi Shanmugasundaram Hariharan Shih-Yu Chen Vinay Kukreja Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang Department of Computer Science and Engineering SRM Institute of Science and TechnologyRamapuramChennaiIndia Department of Computer Science and Engineering Saveetha Engineering CollegeChennaiIndia Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabadIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin 64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin 64002Taiwan Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Computing Science University of AberdeenAberdeenUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh 12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh 11437Saudi Arabia School of Computer&Communication Engineering Changsha University of Science&TechnologyChangshaChina

The medical community has more concern on lung cancer *** experts’physical segmentation of lung cancers is time-consuming and needs to be *** research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning ***-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor *** application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation *** research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold *** Honey hook process categorizes lung cancer based on characteristics retrieved using several *** this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using *** proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)*** study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal *** contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the *** is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.

关键词： Lung tumor deep wave auto encoder decision tree classifier deep neural networks extraction techniques

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Detection of Perfect Stealthy Attacks on Cyber-Physical Systems Subject to Measurement Quantizations: A Watermark-Based Strategy

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 114-125页

作者： Yu-Ang Wang Zidong Wang Lei Zou Bo Shen Hongli Dong the College of Information Science and Technology Donghua University the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education IEEE the College of Electrical Engineering and Automation Shandong University of Science and Technology the Department of Computer Science Brunel University London Uxbridge the Sanya Offshore Oil and Gas Research Institute Northeast Petroleum University the Artificial Intelligence Energy Research Institute Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University

In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.

关键词： Attack detection cyber-physical systems (CPSs) perfect stealthy attacks watermark-based strategy

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Design of Neural Network Based Wind Speed Prediction Model Using GWO

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computer Systems science & engineering 2022年第2期40卷 593-606页

作者： R.Kingsy Grace R.Manimegalai Department of Computer Science and Engineering Sri Ramakrishna Engineering CollegeCoimbatore641022India Department of Computer Science and Engineering PSG Institute of Technology and Applied ResearchCoimbatore641062India

The prediction of wind speed is imperative nowadays due to the increased and effective generation of wind *** power is the clean,free and conservative renewable *** is necessary to predict the wind speed,to implement wind power *** paper proposes a new model,named WT-GWO-BPNN,by integrating Wavelet Transform(WT),Back Propagation Neural Network(BPNN)and GreyWolf Optimization(GWO).The wavelet transform is adopted to decompose the original time series data(wind speed)into approximation and detailed ***-BPNN is applied to predict the wind *** is used to optimize the parameters of back propagation neural network and to improve the convergence *** work uses wind power data of six months with 25,086 data points to test and verify the performance of the proposed *** proposed work,WT-GWO-BPNN,predicts the wind speed using a three-step procedure and provides better *** Absolute Error(MAE),Mean Squared Error(MSE),Mean absolute percentage error(MAPE)and Root mean squared error(RMSE)are calculated to validate the performance of the proposed *** results demonstrate that the proposed model has better performance when compared to other methods in the literature.

关键词： Wind speed wavelet transform back propagation neural network grey wolf optimization time series

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