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Severity Based Light-Weight Encryption Model for Secure Medical Information System

computers, Materials & Continua 2023年第3期74卷 5691-5704页

作者： Firas Abedi Subhi R.M.Zeebaree Zainab Salih Ageed Hayder M.A.Ghanimi Ahmed Alkhayyat Mohammed A.M.Sadeeq Sarmad Nozad Mahmood Ali S.Abosinnee Zahraa H.Kareem Ali Hashim Abbas Waleed Khaild Al-Azzawi Mustafa Musa Jaber Mohammed Dauwed Department of Mathematics College of EducationAl-Zahraa University for WomenKarbalaIraq Energy Eng.Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq Biomedical Engineering Department College of EngineeringUniversity of Warith Al-AnbiyaaKarbalaIraq College of Technical Engineering The Islamic UniversityNajafIraq ITM Department Technical College of AdministrationDuhok Polytechnic UniversityDuhokIraq Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityIraq Altoosi University College NajafIraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq College of Information Technology Imam Ja’afar Al-Sadiq UniversityAl-Muthanna66002Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi UniversityBagdadIraq Department of Medical Instruments Engineering Techniques Al-Turath University CollegeBaghdad10021Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi UniversityBaghdad10021Iraq Department of Medical Instrumentations Techniques Engineering Dijlah University CollegeBaghdadIraq

As the amount of medical images transmitted over networks and kept on online servers continues to rise,the need to protect those images digitally is becoming increasingly ***,due to the massive amounts of multimedia and medical pictures being exchanged,low computational complexity techniques have been *** commonly used algorithms offer very little security and require a great deal of communication,all of which add to the high processing costs associated with using ***,a deep learning classifier is used to classify records according to the degree of concealment they *** images that aren’t needed can be saved by using this method,which cuts down on security *** is one of the most effective methods for protecting medical images after this *** and dispersion are two fundamental encryption processes.A new encryption algorithm for very sensitive data is developed in this *** splitting with image blocks is nowdeveloped by using Zigzag patterns,rotation of the image blocks,and random permutation for scrambling the *** that,this research suggests a Region of Interest(ROI)technique based on selective picture *** the first step,we use an active contour picture segmentation to separate the ROI from the Region of Background(ROB).Permutation and diffusion are then carried out using a Hilbert curve and a Skew Tent *** all of the blocks have been encrypted,they are combined to create encrypted *** investigational analysis is carried out to test the competence of the projected ideal with existing techniques.

关键词： Deep learning encryption medical images scrambling security skew tent map rotation zigzag pattern

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Wavelet and modified VGG-16 deep learning to identify COVID-19 based on X-ray images

Wavelet and modified VGG-16 deep learning to identify COVID-...

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2022 International Conference on Data Science and Intelligent Computing, ICDSIC 2022

作者： Kareem, Omar Sedqi Al-Sulaifanie, Ahmed Khorsheed University of Duhok Dept. of Electrical and Computer Engineering Kurdistan Region Duhok Iraq Duhok Polytechnic University Dept. of Public Health Kurdistan Region Duhok Iraq

ISBN: (纸本)9798350334883

COVID-19 is caused by the SARS coronavirus 2 family (SARS-CoV-2). A quick antibody or antigen test can detect the presence of COVID-19, but further testing is needed to confirm a positive result. Radiologists use chest X-rays to diagnose chest diseases early. The proposed system integrates discrete wavelet transformation and deep learning to help radiologists categorise conditions. Wavelets break down images into multiple spatial resolutions depending on a high pass and low pass frequency components and efficiently extract characteristics from lung X-rays. Here, we use a hybrid wavelet-CNN model to diagnose lung X-rays. The proposed CNN model is trained and verified on different source COVID 19 chest X-ray images for binary and three classes. The proposed studies suggest significant improvement in outcomes, with the best parameters achieving 99.42% accuracy and 96.43% accuracy for binary and three classes. The depiction of feature maps shows that our suggested network collected features from the corona virus-affected lung properly. Results suggest that the proposed model is successful enough for COVID 19 diagnosis. © 2022 IEEE.

关键词： COVID-19

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Privacy Preserving Blockchain with Optimal Deep Learning Model for Smart Cities

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computers, Materials & Continua 2022年第12期73卷 5299-5314页

作者： K.Pradeep Mohan Kumar Jenifer Mahilraj D.Swathi R.Rajavarman Subhi R.M.Zeebaree Rizgar RZebari Zryan Najat Rashid Ahmed Alkhayyat Department of Computing Technologies SRM Institute of Science and TechnologyKattankulathurChennai603203India Department of Computer Science and Information Technology School of Engineering and TechnologyKebridehar UniversityKebridehar250Ethiopia Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTiruchirappalli621112India Energy Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq Computer Network Department Technical College of InformaticsSulaimani Polytechnic UniversitySulaimaniIraq College of Technical Engineering The Islamic UniversityNajafIraq

Recently,smart cities have emerged as an effective approach to deliver high-quality services to the people through adaptive optimization of the available *** the advantages of smart cities,security remains a huge challenge to be ***,Intrusion Detection System(IDS)is the most proficient tool to accomplish security in this ***,blockchain exhibits significance in promoting smart city designing,due to its effective characteristics like immutability,transparency,and *** order to address the security problems in smart cities,the current study designs a Privacy Preserving Secure Framework using Blockchain with Optimal Deep Learning(PPSF-BODL)*** proposed PPSFBODL model includes the collection of primary data using sensing ***,z-score normalization is also utilized to transform the actual data into useful ***,Chameleon Swarm Optimization(CSO)with Attention Based Bidirectional Long Short TermMemory(ABiLSTM)model is employed for detection and classification of *** is employed for optimal hyperparameter tuning of ABiLSTM *** the same time,Blockchain(BC)is utilized for secure transmission of the data to cloud *** cloud server is a decentralized,distributed,and open digital ledger that is employed to store the transactions in different methods.A detailed experimentation of the proposed PPSF-BODL model was conducted on benchmark dataset and the outcomes established the supremacy of the proposed PPSFBODL model over recent approaches with a maximum accuracy of 97.46%.

关键词： Blockchain smart city security intrusion detection system chameleon swarm optimization deep learning parameter tuning

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Performance Monitoring and Controlling of Multicore Shared-Memory Parallel Processing Systems 3

Performance Monitoring and Controlling of Multicore Shared-M...

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3rd Information Technology to Enhance e-Learning and Other Application, IT-ELA 2022

作者： Haji, Lailan M Zeebaree, Subhi R. M. Ageed, Zainab Salih Ahmed, Omar M. Sadeeq, Mohammed A. M. Shukur, Hanan M. University of Zakho Faculty of Science Computer Science Dep. Duhok Iraq Technical College of Engineering Duhok Polytechnic University Energy Eng. Dept. Duhok Iraq College of Science Nawroz University Computer Science Dept. Duhok Iraq Zakho Technical Institute Duhok Polytechnic University Information Technology Dept. Duhok Iraq Technical College of Administration Duhok Polytechnic University ITM Dept. Duhok Iraq Al-Kitab University Computer Engineering Dept. Kirkuk Iraq

ISBN: (纸本)9798350334968

The field of research known as 'parallel processing' examines architectural and algorithmic methods for increasing the efficiency and other desirable qualities (such as cost-effectiveness and reliability) of computers by exploiting many forms of concurrency. The development of multi-core CPUs has opened the door to novel strategies for boosting system performance via parallelism. Increasing the number of concurrent threads greatly affects the time cost. While increased parallelism might reduce calculation time, the associated synchronization costs would likely be prohibitive for most applications. In this article, we'll go through the first half of the process, which involves gathering requirements and designing a prototype;the second half, which involves monitoring and managing a program that can run on a wide range of multicore system architectures, including those with (2, 4, and 8 CPUs). A shared-memory parallel processing method was proposed and built to show off the system's full potential. The implications of multi-core systems on overall program execution time and parallel processing in shared-memory systems provide the backbone of this investigation. © 2022 IEEE.

关键词： Cost effectiveness

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Clustering Document based on Semantic Similarity Using Graph Base Spectral Algorithm 5

Clustering Document based on Semantic Similarity Using Graph...

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5th International Conference on engineering Technology and its Applications, IICETA 2022

作者： Ibrahim, Rowaida Khalil Zeebaree, Subhi R. M. Jacksi, Karwan Ahmed, Sarkar Hasan Mohammed, Shapol M. Zebari, Rizgar R. Alkhayyat, Ahmed Rashid, Zryan Najat University of Zakho Computer Science Dept. Duhok Iraq Duhok Polytechnic University Energy Engineering Dept. Duhok Iraq Sulaimani Polytechnic University Computer Networks Dept. Sulaimani Iraq Tishk International University Computer Engineering Dept. Erbil Iraq Islamic University Najaf Technical Engineering Dept. Najaf Iraq

ISBN: (纸本)9781665472159

The Internet's continued growth has resulted in a significant rise in the amount of electronic text documents. Grouping these materials into meaningful collections has become crucial. The old approach of document compilation based on statistical characteristics and categorization relied on syntactic rather than semantic information. This article introduces a unique approach for classifying texts based on their semantic similarity. The graph-based approach is depended an efficient technique been utilized for clustering. This is performed by extracting document summaries called synopses from the Wikipedia and IMDB databases and grouping thus downloaded documents, then utilizing the NLTK dictionary to generate them by making some important preprocessing to make it more convenient to use. Following that, a vector space is modelled using TFIDF and converted to TFIDF matrix as numeric form, and clustering is accomplished using Spectral methods. The results are compared with previews work. © 2022 IEEE.

关键词： Vector spaces

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Performance Measurement of Distributed Systems via Single-Host Parallel Requesting using (Single, Multi and Pool) Threads 3

Performance Measurement of Distributed Systems via Single-Ho...

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3rd Information Technology to Enhance e-Learning and Other Application, IT-ELA 2022

作者： Jghef, Yousif Sufyan Zeebaree, Subhi R. M. Ageed, Zainab Salih Shukur, Hanan M. College of Engineering Knowledge University Computer Engineering Dept. Erbil Iraq Technical College of Engineering Duhok Polytechnic University Energy Eng. Dept. Duhok Iraq College of Science Nawroz University Computer Science Dept. Duhok Iraq

ISBN: (纸本)9798350334968

Distributed systems and cloud computing are essential in IT (ICT). Scholars have not contemplated merging distributed systems and cloud computing to measure execution time in milliseconds and capacity in gigabytes. Distributed Systems and Cloud Computing Execute Time and Capacity Methodology This title covered two networking types. Multi-scenario distributed networking begins. A TCP socket links a client-server application (Transmission Control Protocol). This example also networks between computers. In another example, Remote Method Invocation Technology (RMI) links subservers to the main server to handle all client inquiries. Second-generation networking involves distributed systems and cloud computing. Google's API downloaded files to my machine and sent them to the main server. Other examples include using numerous computers or a client server software on the same machine. Multi-client and server threads helped the system find correct results. Pool threads were used elsewhere. This thesis examines execution time and file capacity. The system showed how cloud computing affects distributed systems in two ways to apply many scenarios and draw conclusions (execution time and capacity). This project utilizes TCP sockets, RMI, and API. Threads and a hash table software structure were used to load data into the system between the client and the server. © 2022 IEEE.

关键词： Cloud computing

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Challenges and Opportunities in Healthcare and Industrial IoT: A Comparative Analysis

Challenges and Opportunities in Healthcare and Industrial Io...

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Emerging Technologies for Dependable Internet of Things (ICETI), International Conference on

作者： Zaid Berjis Siddeeq Y. Ameen Mohammed H. Al-Jammas Electrical and Computer Engineering Department College of Engineering University of Duhok Duhok Iraq Energy Engineering Department College of Technical Engineering Duhok Polytechnic University Duhok Iraq Computer and Information Engineering Department College of Electronics Ninevah University Mosul Iraq

ISBN: (数字)9798331533557

ISBN: (纸本)9798331533564

The Internet of Things (IoT) has changed many industries by enabling smart devices to transmit data, operate autonomously, and interact in real-time. Among its most prominent applications are in healthcare and industrial sectors, where IoT is classified into two classes: massive Machine-Type Communication (mMTC) and Ultra-Reliable Low Latency Communication (URLLC). While mMTC devices prioritize low energy consumption and can handle large-scale deployments with minimal data rates, URLLC devices are focused on mission-critical applications requiring ultra-low latency and high reliability. This paper examines the challenges and opportunities of IoT implementation in healthcare and industrial sectors, focusing on interoperability, data security, and scalability issues. The objective is to provide actionable solutions for the effective integration of IoT technologies. The investigation shows that in healthcare, mMTC is utilized for non-critical monitoring, while URLLC supports critical tasks like remote surgery. In industrial settings, mMTC improves processes like supply chain management, while URLLC enables automation and real-time control of machinery. Despite these opportunities, challenges such as data privacy, security, interoperability, and reliability remain. Addressing these issues is crucial for unlocking the full potential of IoT in healthcare and industrial sectors.

关键词： Automation Supply chain management Surgery Medical services Ultra reliable low latency communication Real-time systems Internet of Things Reliability Smart devices Interoperability

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A Development of Edge Computing Method in Integration with IOT System for Optimizing and To Produce Energy Efficiency System

A Development of Edge Computing Method in Integration with I...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Renas Rajab Asaad Ahmed Alaa Hani Amira Bibo Sallow Saman Mohammed Abdulrahman Hawar Bahzad Ahmad R. M. Subhi Department of Computer Science Nawroz University Duhok Iraq Department of Computer Science College of Science Nawroz University Duhok KRG Iraq ZeebareeEnergy Engineering Department Technical College of Engineering Duhok Polytechnic University Duhok Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

The number of the Internet of Things (IoT) devices is growing, leading to increased generation of data at a level that has never been witnessed from the network edge. These pervading are through different industries: in the likes of healthcare, transportation, manufacturing, and smart cities, explicit needs call for effective data processing and analysis at the edge. This paper discusses the idea of edge computing in an IoT context, focusing on how this can best be optimized in terms of minimizing the two aforementioned areas to improve overall system performance. The traditional approach to data processing in IoT systems, which is cloud-centric,json, often experiences latency problems because data has to travel over distances to servers that are centralized for analysis. More so, the continuous transmission of voluminous raw data into the cloud is very consuming of the energy and weighs down the network bandwidth. That is where the edge computing comes into place, filling this gap through the relocation of computational tasks near the source, thereby reducing latency that translates to relieving the network from congestion. This paper tries to reflect key principles and benefits of edge computing in an IoT environment based on a comprehensive review of existing literature and relevant case studies. Near IoT devices, edge nodes enable computational capabilities.

关键词： Industries Scalability Ecosystems Medical services Energy efficiency Internet of Things Servers

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Agility of in Deep Learning Algorithms in Developing Conflicting Attacks Protections and to Secure Vulnerabilities

Agility of in Deep Learning Algorithms in Developing Conflic...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Renas Rajab Asaad Saman Mohammed Abdulrahman Ahmed Alaa Hani Hawar Bahzad Ahmad Amira Bibo Sallow Subhi R. M Zeebaree Department of Computer Science Nawroz University Duhok Iraq Department of Computer Science College of Science Nawroz University Duhok KRG Iraq Energy Engineering Department Technical College of Engineering Duhok Polytechnic University Duhok Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Deep learning methods have brought revolution to a variety of applications: from image recognition to natural language processing, and many others. However, their most important vulnerability, when used in adversarial conditions, is a matter of concern that remains up to today. Most importantly, adversarial attacks insinuate the design of input in a way to be capable of bringing errors in the model, hence exposing significant vulnerabilities. This paper investigates the nature of such vulnerabilities and proposes the enhancement of model robustness through comprehensive methodologies. First of all, adversaries would be considered under the threat landscape and classified according to their complexity and impact, should one materialize. The second section would detail all available defense mechanisms, again classified based on strengths and limitations. We propose using the ensemble of adversaries, regularization methods, and dynamic network architecture. We validate this using the empirical testing of the technique over several datasets and models, subjected to a battery of rigorous tests.

关键词： Deep learning Training Heuristic algorithms Computational modeling Network architecture Robustness Protection

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A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef Mohammed Dilovan Asaad Zebari Mazin Abed Mohammed Diyar Qader Zeebaree Haydar Abdulameer Marhoon Karrar Hameed Abdulkareem Seifedine Kadry Wattana Viriyasitavat Jan Nedoma Radek Martinek Department of Information Technology Lebanese French UniversityErbilIraq Department of Computer Science University of ZakhoZakhoKurdistan RegionIraq Department of Computer Science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq Department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq Department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic Department of Cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic Department of Computer Network and Information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Information and Communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq Department of Applied Data Science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

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