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Towards Developing Privacy-Preserved Data Security Approach(PP-DSA)in Cloud computing Environment

Computer Systems science & engineering 2023年第3期44卷 1881-1895页

作者： S.Stewart Kirubakaran V.P.Arunachalam S.Karthik S.K annan Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia School of Computing Science and Engineering Vellore Institute of TechnologyBhopalMadhya PradheshIndia

In the present scenario of rapid growth in cloud computing models,several companies and users started to share their data on cloud ***,when the model is not completely trusted,the data owners face several security-related problems,such as user privacy breaches,data disclosure,data corruption,and so on,during the process of data *** addressing and handling the security-related issues on Cloud,several models were *** that concern,this paper develops a Privacy-Preserved Data Security Approach(PP-DSA)to provide the data security and data integrity for the out-sourcing data in Cloud *** preservation is ensured in this work with the Efficient Authentication Technique(EAT)using the Group Signature method that is applied with Third-Party Auditor(TPA).The role of the auditor is to secure the data and guarantee shared data ***,the Cloud Service Provider(CSP)and Data User(DU)can also be the attackers that are to be handled with the ***,the major objective of the work is to enhance cloud security and thereby,increase Quality of Service(QoS).The results are evaluated based on the model effectiveness,security,and reliability and show that the proposed model provides better results than existing works.

关键词： Third-party auditor(TPA) efficient auditing technique(EAT) cloud service provider(CSP) data user(DU) data security privacy-preserving cloud computing cloud security

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A Novel Proactive AI-Based Agents Framework for an IoE-Based Smart Things Monitoring System with Applications for Smart Vehicles

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Computers, Materials & Continua 2025年第2期82卷 1839-1855页

作者： Meng-Hua Yen Nilamadhab Mishra Win-Jet Luo Chu-En Lin Department of Electronic Engineering National Chin-Yi University of TechnologyTaichung411030Taiwan School of Computing Science&Engineering VIT Bhopal UniversityBhopal466114India Department of Refrigeration Air-Conditioning and Energy EngineeringNational Chin-Yi University of TechnologyTaichung411030Taiwan

The Internet of Everything(IoE)coupled with Proactive Artificial Intelligence(AI)-Based Learning Agents(PLAs)through a cloud processing system is an idea that connects all computing resources to the Internet,making it possible for these devices to communicate with one *** featured in the IoE include embedding,networking,and sensing *** achieve the intended results of the IoE and ease life for everyone involved,sensing devices and monitoring systems are linked *** IoE is used in several contexts,including intelligent cars’protection,navigation,security,and fuel *** Smart Things Monitoring System(STMS)framework,which has been proposed for early occurrence identification and theft prevention,is discussed in this *** STMS uses technologies based on the IoE and PLAs to continuously and remotely observe,control,and monitor *** STMS is familiar with the platform used by the global positioning system;as a result,the STMS can maintain a real-time record of current vehicle *** information is utilized to locate the vehicle in an accident or *** findings of the STMS system are promising for precisely identifying crashes,evaluating incident severity,and locating vehicles after collisions have ***,we formulate an ad hoc STMS network communication scenario to evaluate the efficacy of data communication by utilizing various network parameters,such as round-trip time(RTT),data packet transmission,data packet reception,and *** our experimentation,we obtained an improved communication efficiency for STMS across multiple PLAs compared to the standard greedy routing and traditional AODV *** framework facilitates adaptable solutions with communication competence by deploying Proactive PLAs in a cloud-connected smart vehicular environment.

关键词： Artificial intelligence(AI) proactive AI-based learning agents(PLA) internet of everything(IoE) smart things monitoring system(STMS) cloud processing system driving monitoring assistance system(MAS) smart vehicles

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Weather Classification for Autonomous Vehicles under Adverse Conditions Using Multi-Level Knowledge Distillation

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Computers, Materials & Continua 2024年第12期81卷 4327-4347页

作者： Parthasarathi Manivannan Palaniyappan Sathyaprakash Vaithiyashankar Jayakumar Jayakumar Chandrasekaran Bragadeesh Srinivasan Ananthanarayanan Md Shohel Sayeed School of Computing SASTRA Deemed to be UniversityThanjavur613401India School of Computer science&Engineering Presidency UniversityBengaluru560064India Dr.A.P.J.Abdul Kalam School of Engineering Garden City UniversityBengaluru560049India Independent Researcher OxnardCA 93036USA Faculty of Information Science and Technology Multimedia UniversityMelaka75450Malaysia

Achieving reliable and efficient weather classification for autonomous vehicles is crucial for ensuring safety and operational ***,accurately classifying diverse and complex weather conditions remains a significant *** advanced techniques such as Vision Transformers have been developed,they face key limitations,including high computational costs and limited generalization across varying weather *** challenges present a critical research gap,particularly in applications where scalable and efficient solutions are needed to handle weather phenomena’intricate and dynamic nature in *** address this gap,we propose a Multi-level Knowledge Distillation(MLKD)framework,which leverages the complementary strengths of state-of-the-art pre-trained models to enhance classification performance while minimizing computational ***,we employ ResNet50V2 and EfficientNetV2B3 as teacher models,known for their ability to capture complex image features and distil their knowledge into a custom lightweight Convolutional Neural Network(CNN)student *** framework balances the trade-off between high classification accuracy and efficient resource consumption,ensuring real-time applicability in autonomous *** Response-based Multi-level Knowledge Distillation(R-MLKD)approach effectively transfers rich,high-level feature representations from the teacher models to the student model,allowing the student to perform robustly with significantly fewer parameters and lower computational *** proposed method was evaluated on three public datasets(DAWN,BDD100K,and CITS traffic alerts),each containing seven weather classes with 2000 samples per *** results demonstrate the effectiveness of MLKD,achieving a 97.3%accuracy,which surpasses conventional deep learning *** work improves classification accuracy and tackles the practical challenges of model complexity,resource consumption,and real-time deployment,of

关键词： EfficientNetV2B3 multi-level knowledge distillation RestNet50V2 weather classification

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A survey of machine learning techniques for detecting and diagnosing COVID-19 from imaging

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Quantitative Biology 2022年第2期10卷 188-207页

作者： Aishwarza Panday Muhammad Ashad Kabir Nihad Karim Chowdhury Department of Computer Science&Engineering Stamford UniversityDhaka 1217Bangladesh School of Computing and Mathematics Charles Sturt UniversityNSW 2795Australia Department of Computer Science&Engineering University of ChittagongChittagong 4349Bangladesh

Background: Due to the limited availability and high cost of the reverse transcription-polymerase chain reaction (RT- PCR) test, many studies have proposed machine learning techniques for detecting COVID-19 from medical imaging. The purpose of this study is to systematically review, assess and synthesize research articles that have used different machine learning techniques to detect and diagnose COVID-19 from chest X-ray and CT scan ***: A structured literature search was conducted in the relevant bibliographic databases to ensure that the survey solely centered on reproducible and high-quality research. We selected papers based on our inclusion ***: In this survey, we reviewed 98 articles that fulfilled our inclusion criteria. We have surveyed a complete pipeline of chest imaging analysis techniques related to COVID-19, including data collection, pre-processing, feature extraction, classification, and visualization. We have considered CT scans and X-rays as both are widely used to describe the latest developments in medical imaging to detect ***: This survey provides researchers with valuable insights into different machine learning techniques and their performance in the detection and diagnosis of COVID-19 from chest imaging. At the end, the challenges and limitations in detecting COVID-19 using machine learning techniques and the future direction of research are discussed.

关键词： COVID-19 machine learning deep learning detection classification diagnosing X-ray CT scan

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Crystal structure guided machine learning for the discovery and design of intrinsically hard materials

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Journal of Materiomics 2022年第3期8卷 678-684页

作者： Russlan Jaafreh Tamer Abuhmed Jung-Gu Kim Kotiba Hamad School of Advanced Materials Science&Engineering Sungkyunkwan UniversitySuwon16419South Korea College of Computing and Informatics Sungkyunkwan UniversitySuwon16419South Korea

In this work,a machine learning(ML)model was created to predict intrinsic hardness of various compounds using their crystal *** this purpose,an initial dataset,containing the hardness values of 270 compounds and counterpart applied loads,was employed in the learning *** on various features generated using crystal information,an ML model,with a high accuracy(R^(2)=0.942),was built using extreme gradient boosting(XGB)*** validations conducted by hardness measurements of various compounds,including MSi_(2)(M=Nb,Ce,V,and Ta),Al_(2)O_(3),and FeB_(4),showed that the XGB model was able to reproduce load-dependent hardness behaviors of these *** addition,this model was also used to predict the behavior based on prototype crystal structures that are randomly substituted with elements.

关键词： Machine learning XGB algorithm Intrinsic hardness Crystal chemistry OQMD ICSD MP

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PSO-DBNet for Peak-to-Average Power Ratio Reduction Using Deep Belief Network

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Computer Systems science & engineering 2023年第5期45卷 1483-1493页

作者： A.Jameer Basha M.Ramya Devi S.Lokesh P.Sivaranjani D.Mansoor Hussain Venkat Padhy Department of Computer Science and Engineering Hindusthan Institute of TechnologyCoimbatore641032India Department of Computer Science and Engineering Hindusthan College of Engineering and TechnologyCoimbatore641032India Department of Electronics and Communication Engineering Kongu Engineering CollegeErode638060India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Science&Engineering VIT Bhopal UniversityBhopal466114India

Data transmission through a wireless network has faced various signal problems in the past *** orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at various frequency bands.A recent wireless communication network uses OFDM in longterm evolution(LTE)and 5G,among *** main problem faced by 5G wireless OFDM is distortion of transmission signals in the *** transmission loss is called peak-to-average power ratio(PAPR).This wireless signal distortion can be reduced using various *** study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless *** transmit sequence(PTS)helps in the fast transfer of data in wireless *** is merged with deep belief neural network(DBNet)for the efficient processing of signals in wireless 5G *** indicates that the proposed system outperforms other existing ***,PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm ***,the specified design supports in improving the proposed PAPR reduction architecture.

关键词： 5G wireless network orthogonal frequency division multiplexing signal distortion peak to average power ratio partial transmit sequence deep belief network

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An Improved Steganographic Scheme Using the Contour Principle to Ensure the Privacy of Medical Data on Digital Images

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Computer Systems science & engineering 2023年第8期46卷 1563-1576页

作者： R.Bala Krishnan D.Yuvaraj P.Suthanthira Devi Varghese S.Chooralil N.Rajesh Kumar B.Karthikeyan G.Manikandan srinivasa ramanujan centre sastra deemed universitykumbakonamtamil naduindia department of computer science cihan university–duhokkurdistan regioniraq department of information technology st.joseph’s college of engineeringchennaitamil naduindia department of computer science&engineering rajagiri school of engineering&technologykakkanadkeralaindia school of computing sastra deemed universitythanjavurtamil naduindia

With the improvement of current online communication schemes,it is now possible to successfully distribute and transport secured digital Content via the communication channel at a faster transmission *** steganography and cryptography concepts are used to achieve the goal of concealing secret Content on a media and encrypting it before *** of the techniques mentioned above aid in the confidentiality of feature *** proposed approach concerns secret content embodiment in selected pixels on digital image layers such as Red,Green,and *** private Content originated from a medical client and was forwarded to a medical practitioner on the server end through the *** K-Means clustering principle uses the contouring approach to frame the pixel clusters on the image *** content embodiment procedure is performed on the selected pixel groups of all layers of the image using the Least Significant Bit(LSB)substitution technique to build the secret Content embedded image known as the stego image,which is subsequently transmitted across the internet medium to the server *** experimental results are computed using the inputs from“Open-Access Medical Image Repositories(***)”and demonstrate the scheme’s impudence as the Content concealing procedure progresses.

关键词： Contouring secret content embodiment least significant bit embedding medical data preservation secret content congregation pixel clustering

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Explainable Artificial Intelligence(XAI)Model for Cancer Image Classification

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Computer Modeling in engineering & sciences 2024年第10期141卷 401-441页

作者： Amit Singhal Krishna Kant Agrawal Angeles Quezada Adrian Rodriguez Aguiñaga Samantha Jiménez Satya Prakash Yadav Department of Computer Science&Engineering Raj Kumar Goel Institute of TechnologyGhaziabad201017India Department of School of Computing Science and Engineering Galgotias UniversityGreater Noida203201India Tecnológico Nacional de México Campus Tijuana Baja California22414México Universidad Autónoma de Baja California TijuanaBaja California22414México Department of Computer Science and Engineering G.LBajaj Institute of Technology and Management(GLBITM)Affiliated to Dr.A.P.J.Abdul Kalam Technical UniversityLucknow201306India School of Computer Science Engineering and Technology(SCSET) Bennett UniversityGreater Noida201310India

The use of Explainable Artificial Intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare *** is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these *** models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial ***,the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer *** research presents an advanced investigation of XAI models to classify cancer *** describes the different levels of explainability and interpretability associated with XAI models and the challenges faced in deploying them in healthcare *** addition,this study proposes a novel framework for cancer image classification that incorporates XAI models with deep learning and advanced medical imaging *** proposed model integrates several techniques,including end-to-end explainable evaluation,rule-based explanation,and useradaptive *** proposed XAI reaches 97.72%accuracy,90.72%precision,93.72%recall,96.72%F1-score,9.55%FDR,9.66%FOR,and 91.18%*** will discuss the potential applications of the proposed XAI models in the smart healthcare *** will help ensure trust and accountability in AI-based decisions,which is essential for achieving a safe and reliable smart healthcare environment.

关键词： Explainable artificial intelligence artificial intelligence XAI healthcare cancer image classification

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A Search for Radio Pulsars in Supernova Remnants Using FAST with One Pulsar Discovered

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Chinese Physics Letters 2024年第2期41卷 133-136页

作者：张振闫文明袁建平王娜白俊涛温志刚李保达解进涛赵德王榆斌翟楠楠 Xinjiang Astronomical Observatory Chinese Academy of SciencesUrumqi 830011China University of Chinese Academy of Sciences Beijing 100049China Key Laboratory of Radio Astronomy and Technology(Chinese Academy of Sciences) Beijing 100101China Xinjiang Key Laboratory of Radio Astrophysics Urumqi 830011China College of Physics GuiZhou UniversityGuizhou 550025China Research Center for Intelligent Computing Platforms Zhejiang LaboratoryHangzhou 311100China School of Physical Science and Technology Xinjiang UniversityUrumqi 830046China School of Physics and Electronic Engineering Sichuan University of Science&EngineeringZigong 643000China

We report the results of a search for radio pulsars in five supernova remnants(SNRs)with the FAST *** observations were made using the 19-beam receiver in“snapshot”*** integration time for each pointing was 10 *** discovered a new pulsar,PSR J1845–0306,which has a spin period of 983.6 ms and a dispersion measure of 444.6±2.0 cm^(−3)·pc,in observations of SNR G29.6+*** judge the association between the pulsar and the SNR,further verification is *** also re-detected some known pulsars in the data from SNRs G29.6+0.1 and G29.7–*** pulsars were detected in the observations of the other three SNRs.

关键词： observations Pulsar pulsar

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A robust approach for road traffic prediction using Markov chain model 7

A robust approach for road traffic prediction using Markov c...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Kumar, M. Ranjith Kamisetty, Sri Surya Shobith Syedayan Reddy, Nikhileswar Raghunanda Vediyappan Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Mathematics Chennai India Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science&Engineering Chennai India DMI-St.John The Baptist University Govindan Department of Mathematics Mangochi Malawi

ISBN: (纸本)9798350371406

Traffic congestion is a persistent problem in metropolitan areas that leads to delays, higher fuel usage, and pollution of the environment. Accurate traffic prediction is crucial for effective transportation management and urban planning. Markov Chain models have become increasingly popular for modeling traffic dynamics. These models treat traffic conditions as discrete states, representing congestion levels, and use historical data to estimate transition probabilities between states. Various Markov Chain models, including first-order, hidden Markov models, and higher-order models, have been designed in order to estimate traffic. These models consider the current state to predict future traffic conditions, addressing the complexity of urban traffic systems. © 2023 IEEE.

关键词： Hidden Markov models

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