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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Kordnoori, Shirin Alqammaz, Abdullah Sabeti, Maliheh Boostani, Reza Mostafaei, Hamidreza Agha Banihashemi, Saeed Seyed Islamic Azad University North Tehran Branch Department of Computer Engineering Tehran Iran Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Shiraz University Faculty of Electrical and Computer Engineering Cse and It Departmnet Shiraz Iran Islamic Azad University Shiraz University North Tehran Branch Department of Statistics Tehran Iran Islamic Azad University North Tehran Branch Department of Mathematics Tehran Iran

ISBN: (纸本)9798350373363

To address the urgent need for intelligent systems capable of simultaneously identifying COVID-19 cases and delineating lung infection regions in CT scan images, we introduce a multi-task architecture. Initially, our model, an enhanced U-Net with a classification branch, aims to balance segmentation and classification tasks using binary preprocessing algorithms. The CBAM attention module further improves task coherence. Rigorous evaluation demonstrates our model's superiority over previous studies, achieving an impressive 95% classification accuracy and a Dice index of 88.37 ± 0.01 for segmentation, supported by robust metrics. We also extend our multi-task framework to complex U-Net++ and ResUNet architectures with positive outcomes. Future exploration includes integrating ensemble learning within this framework. Our work signifies significant progress in enhancing diagnostic capabilities in complex medical image analysis, elevating standards for COVID-19 detection and lung infection analysis through multi-task learning and shared feature extraction. © 2023 IEEE.

关键词： COVID-19

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Chimp Optimization Algorithm Based Feature Selection with Machine Learning for Medical Data Classification

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computer Systems Science & engineering 2023年第12期47卷 2791-2814页

作者： Firas Abedi Hayder M.A.Ghanimi Abeer D.Algarni Naglaa F.Soliman Walid El-Shafai Ali Hashim Abbas Zahraa H.Kareem Hussein Muhi Hariz Ahmed Alkhayyat Department of Mathematics College of EducationAl-Zahraa University for WomenKarbalaIraq Biomedical Engineering Department College of EngineeringUniversity ofWarith Al-AnbiyaaKarbalaIraq Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt College of Information Technology Imam Jaafar Al-Sadiq UniversityAl-Muthanna66002Iraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Computer Engineering Department Mazaya University CollegeDhi QarIraq College of Technical Engineering the Islamic UniversityNajafIraq

Datamining plays a crucial role in extractingmeaningful knowledge fromlarge-scale data repositories,such as data warehouses and *** rule mining,a fundamental process in data mining,involves discovering correlations,patterns,and causal structures within *** the healthcare domain,association rules offer valuable opportunities for building knowledge bases,enabling intelligent diagnoses,and extracting invaluable information *** paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System(MLARMC-HDMS).The MLARMC-HDMS technique integrates classification and association rule mining(ARM)***,the chimp optimization algorithm-based feature selection(COAFS)technique is employed within MLARMC-HDMS to select relevant *** by the foraging behavior of chimpanzees,the COA algorithm mimics their search strategy for ***,the classification process utilizes stochastic gradient descent with a multilayer perceptron(SGD-MLP)model,while the Apriori algorithm determines attribute *** propose a COA-based feature selection approach for medical data classification using machine learning *** approach involves selecting pertinent features from medical datasets through COA and training machine learning models using the reduced feature *** evaluate the performance of our approach on various medical datasets employing diverse machine learning *** results demonstrate that our proposed approach surpasses alternative feature selection methods,achieving higher accuracy and precision rates in medical data classification *** study showcases the effectiveness and efficiency of the COA-based feature selection approach in identifying relevant features,thereby enhancing the diagnosis and treatment of various *** provide further validation,we conduct detailed experiments on a benchmark

关键词： Association rule mining data classification healthcare data machine learning parameter tuning data mining feature selection MLARMC-HDMS COA stochastic gradient descent Apriori algorithm

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On an attack on RSA with small CRT-exponents

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Science China(Information Sciences) 2010年第8期53卷 1511-1518页

作者： HAN LiDong 1,2 , WANG XiaoYun 3 & XU GuangWu 3,4 1 Key Laboratory of Cryptologic Technology and Information security, Ministry of Education, Shandong University, Jinan 250100, China 2 School of mathematics, Shandong University, Jinan 250100, China 3 Institute for Advanced Study, Tsinghua University, Beijing 100084, China 4 department of Electrical engineering and computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA 1. Key Laboratory of Cryptologic Technology and Information Security Ministry of Education Shandong University Jinan 250100 China2. School of Mathematics Shandong University Jinan 250100 China3. Institute for Advanced Study Tsinghua University Beijing 100084 China4. Department of Electrical Engineering and Computer Science University of Wisconsin-Milwaukee Milwaukee WI 53201 USA

This paper concerns the RSA system with private CRT-exponents. Since Chinese remainder representation provides efficiency in computation, such system is of some practical significance. In this paper, an existing attack to small private CRT-exponents is analyzed. It is indicated that this attack makes nice use of lattice in RSA analysis, but some argument does not hold in general. Several counterexamples are constructed. Refinements and more precise statements of the attack are given.

关键词： RSA CRT cryptanalysis continued fraction lattice reduction

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Detection of Liver Disease Using Machine Learning Approach 5

Detection of Liver Disease Using Machine Learning Approach

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Kshirsagar, Pravin Ramdas Reddy, Dhoma Harshavardhan Dhingra, Mallika Dhabliya, Dharmesh Gupta, Ankur G H Raisoni College of Engineering Department of Artificial Intelligence Maharashtra Nagpur India Trainee Security Analyst JunoClinic Davman Technology Services Private Limited Maharashtra Mumbai India Manipal University Jaipur Department of Mathematics and Statistics Rajasthan Jaipur India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Vaish College of Engineering Department of Computer Science and Engineering Haryana Rohtak India

ISBN: (纸本)9798350398267

For more effective therapy, it's critical to get an early diagnosis of liver illness. Due to the disease's modest symptoms, it is a very difficult challenge for medical experts to forecast the disease in its early stages. Frequently, the symptoms show up only when it's too late. This study uses machine learning techniques to enhance the detection of liver illness in an effort to solve this problem. The major goal of this study is to distinguish between liver patients and healthy people using classification algorithms. The prevalence of liver illness has been rising globally in the twenty-first century. According to the most recent survey data, the death rate from liver disease has increased by almost 2 million per year globally. 3.5% of deaths globally are caused by liver disease generally. As one of the most fatal diseases, chronic liver disease can be readily cured with early diagnosis and therapy. The lifespan of a patient with Chronic Liver Disease (CLD) would increase due to the rapid development of artificial intelligence (AI), including various machine learning algorithms like SVM, K-mean clustering, KNN, Random forest, Logistic regression, etc. The foundation of this research is the use of machine learning algorithms to predict liver disease. Pre-processing, feature extraction, and classification are just a few of the stages that go into predicting liver disease. In this study, a hybrid classification system is suggested for predicting liver illness. © 2022 IEEE.

关键词： Logistic regression

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A Scalable Platform to Collect, Store, Visualize and Analyze Big Data in Real- Time 3

A Scalable Platform to Collect, Store, Visualize and Analyze...

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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Kshirsagar, Pravin Ramdas Reddy, Dhoma Harshavardhan Dhingra, Mallika Dhabliya, Dharmesh Gupta, Ankur S B Jain College of Engineering and Technology Department of Artificial Intelligence Maharashtra Nagpur India Trainee Security Analyst JunoClinic Davman Technology Services Private Limited Maharashtra Mumbai India Manipal University Jaipur Department of Mathematics and Statistics Rajasthan Jaipur India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Vaish College of Engineering Department of Computer Science and Engineering Haryana Rohtak India

ISBN: (纸本)9798350336238

Examination and evaluation of sentiment The most popular area for dissecting and extracting knowledge from communication data from many sources, such as Facebook, Instagram, Twitter, Amazon, and so on, is known as mining. It plays a fundamental role in enabling the organizations to work productively on advancing the business system and gaining the most comprehensive understanding of the buyer's feedback on their product. It entails calculating a person's behavior in terms of his buying preferences and then placing yourself in his shoes about the commercial aspect of a relationship. You may think of this component as a situation, person, blog post, or tangible experience. Appraisals, checks, and Reflections created by customers may be divided into smaller, more notable pieces for usage by large businesses. The analysis of comparable purchasing behavior may be used to understand the needs of the customer and foresee future opportunities to assist them. E-business Associations may track the uses and passions associated with their Particulars via this internal assessment and adopt better marketing mechanism to give a tailored shopping experience for their customers, so improving their hierarchical advantage. By using the Programming interface key, Twitter data for this article was collected from Twitter. Similarly, we really want to finish setting up the systems. Additionally, the system supported NLP approaches, thus we wish to implement the calculation as a crucial backslide. The exploratory problems exhibit perfection. © 2023 IEEE.

关键词： Big data

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Efficient Privacy-Preserving ML for IoT: Cluster-Based Split Federated Learning Scheme for Non-IID Data 7

Efficient Privacy-Preserving ML for IoT: Cluster-Based Split...

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7th Cyber security in Networking Conference, CSNet 2023

作者： Arafeh, Mohamad Wazzeh, Mohamad Ould-Slimane, Hakima Talhi, Chamseddine Mourad, Azzam Otrok, Hadi Department of Software and It Engineering Montreal Canada Department of Mathematics and Computer Science Canada New York University Division of Science Abu Dhabi United Arab Emirates Cyber Security Systems and Applied Ai Research Center Lebanese American University Department of Csm Beirut Lebanon Khalifa University Department of Eecs Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350342871

This paper proposes a scheme addressing the challenges of integrating privacy-preserving distributed machine learning in the Internet of Things (IoT) context while improving the efficiency of the learning process and accelerating the convergence of the model. Specifically, it addresses the presence of non-independent and identically distributed (Non-IID) data and device heterogeneity, which occurs during parallel training and are common characteristics of IoT environments. In this context, SplitFed is a privacy-preserving technique that combines Federated Learning (FL) and Split Learning (SL) to enable fast and privacy-preserving model training on IoT devices. Through parallelism, FL allows fast and parallel model training. Furthermore, SL enables weak devices to train models through model splitting. However, SplitFed suffers from the increasing convergence time due to the FL parallelism introducing Non-IID and stragglers issues. In this paper, we aim to address the aforementioned problems by proposing a clustering parallelization scheme. As such, similar clients are combined based on their weight divergence and considered a single FL unit addressing the Non-IID problem. Moreover, to improve time efficiency and overcome the stragglers' presence, we offer a second clustering stage that separates clients by efficiency and uses FedAsync for model aggregation. © 2023 IEEE.

关键词： Internet of things

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Computational Intelligence Driven Secure Unmanned Aerial Vehicle Image Classification in Smart City Environment

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computer Systems Science & engineering 2023年第12期47卷 3127-3144页

作者： Firas Abedi Hayder M.A.Ghanimi Abeer D.Algarni Naglaa F.Soliman Walid El-Shafai Ali Hashim Abbas Zahraa H.Kareem Hussein Muhi Hariz Ahmed Alkhayyat Department of Mathematics College of EducationAl-Zahraa University forWomenKarbalaIraq Biomedical Engineering Department College of EngineeringUniversity ofWarith Al-AnbiyaaKarbalaIraq Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt College of Information Technology Imam Jaafar Al-Sadiq UniversityAl-Muthanna66002Iraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Computer Engineering Department Mazaya University CollegeDhi QarIraq College of Technical Engineering The Islamic UniversityNajafIraq

Computational intelligence(CI)is a group of nature-simulated computationalmodels and processes for addressing difficult real-life *** CI is useful in the UAV domain as it produces efficient,precise,and rapid ***,unmanned aerial vehicles(UAV)developed a hot research topic in the smart city *** the benefits of UAVs,security remains a major challenging *** addition,deep learning(DL)enabled image classification is useful for several applications such as land cover classification,smart buildings,*** paper proposes novel meta-heuristics with a deep learning-driven secure UAV image classification(MDLS-UAVIC)model in a smart city *** purpose of the MDLS-UAVIC algorithm is to securely encrypt the images and classify them into distinct class *** proposedMDLS-UAVIC model follows a two-stage process:encryption and image *** encryption technique for image encryption effectively encrypts the UAV ***,the image classification process involves anXception-based deep convolutional neural network for the feature extraction ***,shuffled shepherd optimization(SSO)with a recurrent neural network(RNN)model is applied for UAV image classification,showing the novelty of the *** experimental validation of the MDLS-UAVIC approach is tested utilizing a benchmark dataset,and the outcomes are examined in various *** achieved a high accuracy of 98%.

关键词： Computational intelligence unmanned aerial vehicles deep learning metaheuristics smart city image encryption image classification

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A Review on Application of Deep Learning in Natural Language Processing 5

A Review on Application of Deep Learning in Natural Language...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

ISBN: (纸本)9798350398267

Natural Language Processing (NLP) is a developing method utilized in building different sorts of Artificial Intelligence (AI) that is available in today's time. More intellectual applications will tend to be a primary goal for ongoing and upcoming research. The requirement and desire for data-driven strategies for automatic semantic analysis have risen as a result of recent improvements in processing capacity as well as the accessibility of enormous several linguistic records. A boom in the applications throughout the previous several years of deep learning approaches has advanced the area of natural language processing. This review offers a succinct summary of deep learning architectures and techniques as well as a basic introduction to the area. Our goal is to develop a theoretical study of numerous sectors where NLP may have a significant impact and completely alter the situation with its automated approaches. Everyone is interested in investing in it since it is a hot issue. An in-depth investigation of NLP and its field is used to create these applications. Natural language processing (NLP) trends and its constituent parts are covered in the introduction to this article before it discusses applications for NLP, its emergence, and related issues. © 2022 IEEE.

关键词： Deep learning

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A Review on Comparative study of 4G, 5G and 6G Networks 5

A Review on Comparative study of 4G, 5G and 6G Networks

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Kshirsagar, Pravin Ramdas Reddy, Dhoma Harshavardhan Dhingra, Mallika Dhabliya, Dharmesh Gupta, Ankur G H Raisoni College of Engineering Department of Artificial Intelligence Maharashtra Nagpur India Trainee Security Analyst JunoClinic Davman Technology Services Pvt. Ltd Maharashtra Mumbai India Manipal University Jaipur Department of Mathematics and Statistics Rajasthan Jaipur India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Vaish College of Engineering Department of Computer Science and Engineering Haryana Rohtak India

ISBN: (纸本)9798350398267

In the previous several years, wireless telecommunications technology has seen a significant change. The term 'generation' in the context of wireless communication typically describes to a shift in the essential characteristics of the assistance being offered, such as transmission technologies, bit rates, frequency bands, channel frequency bandwidth, and data transfer capacity. One of the most active technological fields that is expanding too quickly is the wireless age. There is a need to implement such technologies that can be integrated and adjusted in order to produce a more advanced and unified system because there are numerous superior technologies available. In this work, we provide a summary of several wireless telecommunications technologies, in particular, 4G, 5G, and 6G Networks, and a detailed comparison between them. © 2022 IEEE.

关键词： Data transfer

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Hardware Performance Evaluation of SHA-3 Candidate Algorithms

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Journal of Information security 2012年第2期3卷 69-76页

作者： Yaser Jararweh Lo’ai Tawalbeh Hala Tawalbeh Abidalrahman Moh’d Computer Science Department Jordan University of Science and Technology(CHiS) Irbid Jordan Director of Cryptographic Hardware and Information Security Lab (CHiS) Computer Engineering DepartmentJordan University of Science and Technology Irbid Jordan Engineering Mathematics & Internetworking Dalhousie University Halifax Canada

Secure Hashing Algorithms (SHA) showed a significant importance in today’s information security applications. The National Institute of Standards and Technology (NIST), held a competition of three rounds to replace SHA1 and SHA2 with the new SHA-3, to ensure long term robustness of hash functions. In this paper, we present a comprehensive hardware evaluation for the final round SHA-3 candidates. The main goal of providing the hardware evaluation is to: find the best algorithm among them that will satisfy the new hashing algorithm standards defined by the NIST. This is based on a comparison made between each of the finalists in terms of security level, throughput, clock frequancey, area, power consumption, and the cost. We expect that the achived results of the comparisons will contribute in choosing the next hashing algorithm (SHA-3) that will support the security requirements of applications in todays ubiquitous and pervasive information infrastructure.

关键词： Information security Secure Hash Algorithm (SHA) Hardware Performance FPGA

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