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Cloud Security Service for Identifying Unauthorized User Behaviour

computers, Materials & Continua 2022年第2期70卷 2581-2600页

作者： D.Stalin David Mamoona Anam Chandraprabha Kaliappan S.Arun Mozhi Selvi Dilip Kumar Sharma Pankaj Dadheech Sudhakar Sengan Department of Computer Science and Engineering IFET College of EngineeringVillupuram605108Tamil NaduIndia Department of Computer Sciences and Software Engineering Faculty of Basic and Applied SciencesInternational Islamic UniversityIslamabadPakistan Department of Information Technology Bannari Amman Institute of TechnologySathyamangalam638401Tamil NaduIndia Department of Computer Science Engineering DMI St.John the Baptist UniversityMangochiLilongweMalawi Department of Mathematics Jaypee University of Engineering and TechnologyGuna473226Madhya PradeshIndia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement and Gramothan(SKIT)Jaipur302017RajasthanIndia Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia

Recently,an innovative trend like cloud computing has progressed quickly in *** a background of distributed networks,the extensive sprawl of internet resources on the Web and the increasing number of service providers helped cloud computing technologies grow into a substantial scaled Information Technology service *** cloud computing environment extracts the execution details of services and systems from end-users and ***,through the system’s virtualization accomplished using resource pooling,cloud computing resources become more *** attempt to design and develop a solution that assures reliable and protected authentication and authorization service in such cloud environments is described in this *** the help of multi-agents,we attempt to represent Open-Identity(ID)design to find a solution that would offer trustworthy and secured authentication and authorization services to software services based on the *** research aims to determine how authentication and authorization services were provided in an agreeable and preventive *** on attack-oriented threat model security,the evaluation *** considering security for both authentication and authorization systems,possible security threats are analyzed by the proposed security systems.

关键词： Cloud computing user behaviour access control security model

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SUT: a new multi-purpose synthetic dataset for Farsi document image analysis 13

SUT: a new multi-purpose synthetic dataset for Farsi documen...

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13th International Conference on computer and Knowledge engineering, ICCKE 2023

作者： Shabaninia, Elham Eslami, Fatemeh Sadat Afkari-Fahandari, Ali Nezamabadi-Pour, Hossein Graduate University of Advanced Technology Faculty of Sciences and Modern Technologies Department of Applied Mathematics Kerman Iran Sirjan University of Technology Department of Computer Engineering Sirjan Iran Shahid Bahonar University of Kerman Department of Electrical Engineering Kerman Iran

ISBN: (纸本)9798350330151

This paper introduces a new large-scale dataset for Farsi document images, named SUT, which aims to tackle the challenges associated with obtaining diverse and substantial ground-truth data for supervised models in document image analysis (DIA) tasks, like document image classification, text detection and recognition, and information retrieval. The dataset comprises 62,453 images that have been categorized into 21 distinct classes, including identity documents featuring synthetically generated personal information superimposed on various backgrounds. The dataset also includes corresponding files with labeling information for the images. The ground-truth data is organized in CSV files containing image file paths and associated information about the embedded data. To demonstrate the efficacy of the SUT dataset in DIA tasks, it was utilized for document classification (achieving an accuracy of 86% using a convolutional neural network) and OCR (achieving a CER of 0.083 and 0.072 using Tesseract and EasyOCR engines, respectively). The SUT dataset serves as an esteemed asset for scholars engaged in the development and assessment of supervised models in Farsi document image analysis. © 2023 IEEE.

关键词： Optical character recognition

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Heuristic computing with sequential quadratic programming for solving a nonlinear hepatitis B virus model

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mathematics and computers in Simulation 2023年第1期212卷 234-248页

作者： Umar, Muhammad Sabir, Zulqurnain Raja, Muhammad Asif Zahoor Baskonus, Haci Mehmet Ali, Mohamed R. Shah, Nehad Ali Department of Mathematics and Statistics Hazara University Mansehra Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Faculty of Education Harran University Sanliurfa Turkey Department of Mathematics Benha Faculty of Engineering Benha University Egypt Faculty of Engineering and Technology Future University Cairo Egypt Department of Mechanical Engineering Sejong University Seoul05006 Korea Republic of Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon

This current study presents a nonlinear system of the hepatitis B virus infection by using the strength of artificial neural network (ANN) together with the competences of global and local search efficiencies of genetic algorithm (GA) and sequential quadratic programming scheme (SQPS), i.e., ANN-GA-SQPS. An error function is designed to use the mathematical form of the hepatitis B virus differential model and its initial conditions. The optimization of the error function is performed by using the hybridization efficiency of the GA-SQPS for the hepatitis B virus infection disease model. For the competence of ANN-GA-SQPS, the matching of obtained and the Adams numerical solutions has been observed. The absolute error is found in good measures for solving a nonlinear hepatitis B virus infection disease model. Moreover, statistical measures using different indices for 50 independent executions and 30 variables have been performed to check constancy, reliability, and effectiveness of the ANN-GA-SQPS. © 2023 International Association for mathematics and computers in Simulation (IMACS)

关键词： Genetic algorithms

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Cubature Kalman Fusion Filtering Under Amplify-and-Forward Relays With Randomly Varying Channel Parameters

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 356-368页

作者： Jiaxing Li Zidong Wang Jun Hu Hongli Dong Hongjian Liu the Department of Applied Mathematics Harbin University of Science and Technology IEEE the Department of Computer Science Brunel University London the School of Automation Harbin University of Science and Technology the Artificial Intelligence Energy Research Institute Northeast Petroleum University the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University the National Key Laboratory of Continental Shale Oil of China Northeast Petroleum University the Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of Education Anhui Polytechnic University the School of Mathematics and Physics Anhui Polytechnic University

In this paper, the problem of cubature Kalman fusion filtering(CKFF) is addressed for multi-sensor systems under amplify-and-forward(AaF) relays. For the purpose of facilitating data transmission, AaF relays are utilized to regulate signal communication between sensors and filters. Here, the randomly varying channel parameters are represented by a set of stochastic variables whose occurring probabilities are permitted to exhibit bounded uncertainty. Employing the spherical-radial cubature principle, a local filter under AaF relays is initially constructed. This construction ensures and minimizes an upper bound of the filtering error covariance by designing an appropriate filter gain. Subsequently, the local filters are fused through the application of the covariance intersection fusion rule. Furthermore, the uniform boundedness of the filtering error covariance's upper bound is investigated through establishing certain sufficient conditions. The effectiveness of the proposed CKFF scheme is ultimately validated via a simulation experiment concentrating on a three-phase induction machine.

关键词： Amplify-and-forward (AaF) relays covariance intersection fusion cubature Kalman filtering multi-sensor systems uniform boundedness

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Anomaly detection using deep learning approach for IoT smart city applications

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Multimedia Tools and Applications 2025年第17期84卷 17929-17949页

作者： Shibu, S. Kirubakaran, S. Remamany, Krishna Priya Ahamed, Suhail Chitra, L. Kshirsagar, Pravin R. Tirth, Vineet Department of Electronics and Communication Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Science and Engineering CMR College of Engineering and Technology Telangana Hyderabad501401 India Engineering Department College of Engineereing and Technology University of Technology an Applied Sciences PC Musandam811 Oman Machine Learning Software Engineer Paragon Semvox GmbH Saarland Kirkel Germany Department of Electrical and Electronics Engineering Aarupadai Veedu Institute of Technology Vinayaka Missions Research Foundation Tamil Nadu Chennai603104 India Department of Electronics & Telecommunication Engineering J D College of Engineering & Management Maharashtra Nagpur India Department of Mechanical Engineering College of Engineering King Khalid University Asir Abha61421 Saudi Arabia

With the advancements of IoT devices, many smart applications start to rule this era. In particular, smart cities has been adapted and realized by many countries around the world. In smart cities, vas amount of data is generated at every second. This vast data need a transmission medium which could be wireless standard. However, security is the main concern in such applications since the smart transmission always binds with anomalies. The existing anomaly detection systems need improvement in accuracy due to inefficient feature extraction and selection procedure. This paper proposes an accurate anomaly detection technique that built upon deep learning approach. We proposed a Combined Deep Q-Learning (CDQL) algorithm for anomaly detection. Priory, optimal features are selected by using Spider Monkey Optimizer (SMO). With the optimal features, CDQL detects anomalies accurately. In addition, the CDQL algorithm learns the environment in order to monitor the network data continuously. This continuous monitoring and optimum features helps in accuracy improvement up to 98%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Reinforcement learning

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Extended Deep Learning Algorithm for Improved Brain Tumor Diagnosis System

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Intelligent Automation & Soft Computing 2024年第1期39卷 33-55页

作者： M.Adimoolam K.Maithili N.M.Balamurugan R.Rajkumar S.Leelavathy Raju Kannadasan Mohd Anul Haq Ilyas Khan ElSayed M.Tag El Din Arfat Ahmad Khan Department of Computer Engineering Government Polytechnic CollegeSevvapetTiruvallurTamil Nadu602025India Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyHyderabadTelangana500075India Department of Computer Science and Engineering Sri Venkateswara College of EngineeringSriperumbudurChennai602117India Department of Computer Science and Engineering Annamalai UniversityChidambaramTamil Nadu608002India Department of Computer Science and Engineering Aarupadai Veedu Institute of TechnologyVinayaka Mission Research FoundationPaiyanurTamil Nadu603104India Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudur602117India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Electrical Engineering Department Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)*** various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhancement,particularly accuracy,sensitivity,false positive and false negative,to improve the brain tumor prediction system ***,this work proposed an Extended Deep Learning Algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative *** addition,these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network(CNN)way further using the SPSS tool,and respective graphical illustrations were *** results were that the mean performance measures for the proposed EDLA algorithm were calculated,and those measured were accuracy(97.665%),sensitivity(97.939%),false positive(3.012%),and false negative(3.182%)for ten *** in the case of the CNN,the algorithm means accuracy gained was 94.287%,mean sensitivity 95.612%,mean false positive 5.328%,and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms,including CNN,and ensures symmetrically improved *** EDLA algorithm introduces novelty concerning its performance and particular activation *** proposed method will be utilized effectively in brain tumor detection in a precise and accurate *** algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses *** the quantity of dataset records is enormous,then themethod’s computation power has to be updated.

关键词： Brain tumor extended deep learning algorithm convolution neural network tumor detection deep learning

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Early Prediction of Thoracic Diseases Using Rough Set Theory and Machine Learning 3rd

Early Prediction of Thoracic Diseases Using Rough Set Theory...

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3rd International Conference on Machine Learning, Internet of Things and Big Data, ICMIB 2023

作者： Hota, Radhanath Dash, Sachikanta Mishra, Sujogya Pattnaik, P.K. Pradhan, Sipali Department of Computer Science College of Basic Science and Humanities OUAT Odisha Bhubaneswar India Department of Computer Science and Engineering GIET University Gunupur Odisha Bhubaneswar India Department of Mathematics and Humanities Odisha University of Technology and Research Odisha Bhubaneswar India Department of Computer Science RBVRR Women’s College Hyderabad India

ISBN: (纸本)9789819939312

An unexpected demise due to cardiac arrest is a significant physical anomaly and is responsible for several deaths. Death due to unexpected cardiac arrest has no significant symptoms, and cardiac arrest has no initial symptoms. In most cases, a person may suffer from cardiac arrest, despite having a normal electrocardiogram (ECG). In this work, we have used two concepts, i.e., rough set theory (RST) to find the significant symptoms of cardiac arrest and support vector machines (SVM) to predict cardiac arrest. Our work aims to predict unexpected cardiac arrest less than half an hour before its occurrence. We have validated our claim using statistical techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electrocardiograms

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The Role of Predictive Maintenance Optimization Techniques in Enhancing Industrial Productivity 10

The Role of Predictive Maintenance Optimization Techniques i...

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10th International Conference on Optimization and Applications, ICOA 2024

作者： Er-Ratby, Mohamed Kobi, Abdessamad Sadraoui, Youssef Kadiri, Moulay Saddik National School of Applied Sciences Sultan Moulay Slimane University LASTI Laboratory of Science and Engineering Techniques Khouribga Morocco Polytech Angers University of Angers LARIS Angevin Laboratory for Systems Engineering Research Angers France National School of Applied Sciences Sultan Moulay Slimane University LIPIM Laboratory of Process Engineering Computer Science and Mathematics Khouribga Morocco

ISBN: (纸本)9798350387353

The convergence between advanced technology and industrial production holds significant potential to enhance operational efficiency, reduce maintenance costs, and minimize unexpected disruptions. Artificial intelligence, in particular, plays a crucial role in predicting machine failures, thereby contributing to the optimization of operational performance and maximizing overall company output. However, current challenges in maintenance and production are numerous. High maintenance costs are often a major concern for companies, as they can significantly eat into profit margins. Unexpected downtime, caused by sudden machine failures, leads to substantial production losses, affecting not only revenue but also customer satisfaction and the company's reputation. Furthermore, the increasing complexity of industrial systems makes the management of maintenance and production even more difficult, necessitating more advanced and integrated solutions. This article explores the various applications of artificial intelligence in the manufacturing industry, highlighting its impact on business performance. It also examines the steps necessary for implementing predictive maintenance, from data collection and processing to the establishment of predictive models. The ultimate goal is to improve equipment availability, optimize operational performance, and maximize overall company output. © 2024 IEEE.

关键词： Predictive maintenance

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Existence of solutions to the generalized periodic fractional boundary value problem

Existence of solutions to the generalized periodic fractiona...

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作者： Fečkan, Michal Marynets, Kateryna Wang, JinRong Department of Mathematical Analysis and Numerical Mathematics Comenius University in Bratislava Bratislava Slovakia Mathematical Institute of Slovak Academy of Sciences Bratislava Slovakia Delft Institute of Applied Mathematics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Department of Mathematics Guizhou University Guiyang China

We study a boundary value problem for a Caputo-type fractional differential equation subjected to periodic boundary conditions. For an auxiliary problem with the simplified right-hand side, we explicitly construct its unique solution. In addition, based on the theory of the topological index, we prove existence of at least one solution to the original problem. © 2023 The Authors. Mathematical Methods in the applied Sciences published by John Wiley & Sons Ltd.

关键词： Boundary value problems

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Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

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computer Modeling in engineering & Sciences 2024年第2期138卷 1981-2004页

作者： Shaik Mahaboob Basha Victor Hugo Cde Albuquerque Samia Allaoua Chelloug Mohamed Abd Elaziz Shaik Hashmitha Mohisin Suhail Parvaze Pathan Department of Electronics and Communication Engineering N.B.K.R.Institute of Science&TechnologyVidyanagarAndhra Pradesh524413India Department of Teleinformatics Engineering Federal University of CearáFortaleza60455-970Brazil Information Technology Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Mathematics Faculty of ScienceZagazig UniversityZagazig44519Egypt Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos13-5053Lebanon Department of Avionics Indian Institute of Space Science and TechnologyThiruvananthapuramKerala695547India Department of Veterinary Clinical Sciences University of MinnesotaSt.PaulMinnesota55108USA

Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 ***,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning *** study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal *** information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering *** learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding *** ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different ***,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased *** analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered ***,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.

关键词： Chest radiography(CXR)image COVID-19 classifier machine learning random forest texture analysis

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