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DeepSVDNet:A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images

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Computer Systems Science & Engineering 2024年第2期48卷 511-528页

作者： Anas Bilal Azhar Imran Talha Imtiaz Baig Xiaowen Liu Haixia Long abdulkareem Alzahrani Muhammad Shafiq College of Information Science and Technology Hainan Normal UniversityHaikou571158China Department of Creative Technologies Air UniversityIslamabadPakistan School of Life Science and Technology University of Electronic Science and Technology of China UESTCChengduChina School of Science and Technology University of Management and Technology UMTLahorePunjabPakistan Faculty of Computer Science&Information Technology Al Baha UniversityAl BahaSaudi Arabia School of Information Engineering Qujing Normal UniversityQujingChina

Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness ***,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to *** paper proposes a novel VTDR detection and classification model that combines different models through majority *** proposed methodology involves preprocessing,data augmentation,feature extraction,and classification *** use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for *** tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,*** proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.

关键词： Diabetic retinopathy(DR) fundus images(FIs) support vector machine(SVM) medical image analysis convolutional neural networks(CNN) singular value decomposition(SVD) classification

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

An Optimal Distribution of RSU for Improving Self-Driving Vehicle Connectivity

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Computers, Materials & Continua 2022年第2期70卷 3311-3319页

作者： Khattab Alheeti abdulkareem Alaloosy Haitham Khalaf abdulkareem Alzahrani Duaa Al_Dosary Department of Computer Networking System College of Computer Sciences and Information TechnologyUniversity of AnbarRamadiIraq College of Medicine University of AnbarRamadiIraq Department of Computer Science Faculty of Computer Science and Information TechnologyAlbaha UniversityAlbahaSaudi Arabia College of Computer Sciences and Information Technology University of AnbarRamadiIraq

Self-driving and semi-self-driving cars play an important role in our daily *** effectiveness of these cars is based heavily on the use of their surrounding areas to collect sensitive and vital ***,external infrastructures also play significant roles in the transmission and reception of control data,cooperative awareness messages,and caution *** this case,roadside units are considered one of themost important communication *** distribution of these infrastructures will overburden the spread of self-driving vehicles in terms of cost,bandwidth,connectivity,and radio coverage *** this paper,a new distributed roadside unit is proposed to enhance the performance and connectivity of these ***,this approach is based primarily on k-means to find the optimal location of each roadside *** addition,this approach supports dynamicmobility with a long period of connectivity for each ***,this system can adapt to various locations(e.g.,highways,rural areas,urban environments).The simulation results of the proposed system are reflected in its efficiency and ***,the system can achieve a high connectivity rate with a low error rate while reducing costs.

关键词： Self-driving cars roadside unit autonomous vehicles distributed systems connectivity

The Synthetic and Mechanical Properties of a Silica Matrix Cermet Composite

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Journal of Wuhan University of Technology(Materials Science) 2022年第3期37卷 423-428页

作者： Sufian H Humeedi Saadoon M abdulkareem Salih Y Darweesh Department of Physics College of ScienceTikrit UniversityTikritIraq Physics Department College of EducationSamaraa UniversitySamaraaIraq Physics Department College of Education TuzKhurmatuTikrit UniversityTikritIraq

A cermet composite was prepared by employing thermal spraying flame onto stainless steel surfaces of(304)*** 25,50%of aluminum was mixed with matrix of(10%Al_(2)O_(3)-SiO_(2)).A spray system consistis of oxygen barrel,acetylene and spray gun that contains powders feeder at different spray distances(5,10,15,20 and 25 cm).The thermal treatment of the samples resulting from spraying was performed at 1100℃for 1.5 *** physical examinations(porosity and adhesion),mechanical tests(hardness and wear rate)and Atomic Force Microscope(AFM)test were performed before and after thermal *** test results showed that the best mixing rate was(50%)and the best spray distance was(15cm)where the quality of coating layer improved after thermal treatment at 1100℃for 1.5 hr with a porosity ratio reduced from 11.2%to 6.6%,and adhesion strength improved with the increase in the alumina content to 50%.The results demonstrated that the best adhesion value after thermal treatment was 30.98 *** hardness of coating layer increased from 45.78 to 54.88 HV;while the lowest rate of wear was at 50%of alumina with a spray distance of ***,there was a clear improvement based on AFM within grain size,root mean square and roughness rate.

关键词： spraying by flame ceramic oxides composites atomic force microscope

Core-scale modeling of surfactant-assisted spontaneous water imbibition in carbonates

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Petroleum 2022年第4期8卷 458-465页

作者： Xingang Bu Ming Han abdulkareem M.AlSofi Beijing Research Center Aramco AsiaBeijing100102China EXPEC Advanced Research Center Saudi AramcoDhahran31311Saudi Arabia

This work numerically investigates surfactant effects on spontaneous water imbibition in oil-wet *** open boundary core-scale imbibition model with 9×9×10 gridblocks was used in UTCHEM to simulate carbonate core plug exposure to a vast water *** simulation models were developed based on surfactant-assisted imbibition tests that were conducted in secondary and tertiary oil production modes using Amott cells at 75℃.Capillary and gravity forces were captured by history matching the *** history matching,the inputs for surfactant adsorption and diffusion,capillary pressure and relative permeability were *** tertiary mode,the surfactants-assisted imbibition process presents the performance in mixed-wet state rather than oil-wet state,which is governed by wettability alteration.A simulation model for surfactant-assisted imbibition in secondary mode was used to investigate the effects of various factors including interfacial tension(IFT)reduction,wettability alteration,adsorption,volume of surrounding water and capillary *** simulation results suggest that surfactant-assisted water imbibition in secondary mode is gravity dominant,which is facilitated by both IFT reduction and wettability alteration caused by addition of proper *** from water imbibition in water-wet core,it presents vertically dominant oil flow with a hemispherical oil-rich area and uneven remaining oil *** is obvious that sufficient surfactant supply in vast water is required to make effective imbibition,in consideration of surfactant consumption and changes in concentration *** core-scale modeling provides insights of surfactant-assisted imbibition in initially oil-wet carbonates and helps scale up the application in a cost-effective way.

关键词： Surfactant Imbibition Core-scale modeling Simulation Wettability alteration Interfacial tension

PCF-multimode/endless fiber sensor for respiratory rate monitoring

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Optoelectronics Letters 2023年第1期19卷 8-13页

作者： Haneen D.abdulkareem Ali A.Alwahib Bushara R.Mahdi Laser and Optoelectronics Department University of Technology-IraqBaghdadIraq Department of Physics College of ScienceUniversity of BaghdadBaghdadIraq

A respiratory monitoring system was proposed based on optical *** sensor consists of photonic crystal fiber(PCF)spliced with multimode fiber(MMF)to fabricate Michelson *** sensor is inserted into an oxygen mask tube and then put on the nose and mouth to monitor the respiratory rate(RR)for different *** results showed that when breathing increased,the intensity of light *** intensity of light in the sleeping case of breathing was more intense than that when the intensity of working sport was *** working sport case was a higher transmittance than that of *** sensitivity of the sensor was also measured to be 231.9 pm/RH.

关键词： fiber multimode interferometer

Aspect-Based Sentiment Analysis for Social Multimedia:A Hybrid Computational Framework

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Computer Systems Science & Engineering 2023年第8期46卷 2415-2428页

作者： Muhammad Rizwan Rashid Rana Saif Ur Rehman Asif Nawaz Tariq Ali Azhar Imran abdulkareem Alzahrani Abdullah Almuhaimeed University Institute of Information Technology–PMAS Arid Agriculture University Rawalpindi46000Pakistan Department of Creative Technologies Air UniversityIslamabad44000Pakistan Computer Engineering and Science Department Faculty of Computer Science and Information TechnologyAl Baha UniversityAl Baha65799Saudi Arabia Digital Health Institute King Abdulaziz City for Science and TechnologyRiyadh11442Saudi Arabia

People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events,public products and the latest *** share their thoughts and feelings about various topics,including products,news,blogs,*** user reviews and tweets,sentiment analysis is used to discover opinions and *** polarity is a term used to describe how sentiment is ***,neutral and negative are all examples of *** area is still in its infancy and needs several critical *** and hidden emotions can detract from the accuracy of traditional *** methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative *** existing strategies are *** proposed model incorporates aspect extraction,association rule mining and the deep learning technique Bidirectional EncoderRepresentations from Transformers(BERT).Aspects are extracted using Part of Speech Tagger and association rulemining is used to associate aspects with opinion ***,classification was performed using *** proposed approach attained an average of 89.45%accuracy,88.45%precision and 85.98%recall on different datasets of products and *** results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.

关键词： Aspects deep learning lexicon sentiments reviews

Telemedicine and Smart Healthcare—The Role of Artificial Intelligence, 5G, Cloud Services, and Other Enabling Technologies

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International Journal of Communications, Network and System Sciences 2023年第3期16卷 31-51页

作者： Taofik Ahmed Suleiman abdulkareem Adinoyi Department of Medical Imaging and Computing University of Girona Girona Spain Graduate School of Science and Technology Medical Imaging and Applications (MAIA) University of Burgundy Le Creusot France Department of Electrical and Information Engineering MAIA University of Cassino and Southern Lazio Cassino Italy Research and Innovation Department academy from stc Saudi Telecommunication Company (stc) Riyadh Saudi Arabia

This paper discusses telemedicine and the employment of advanced mobile technologies in smart healthcare delivery. It covers the technological advances in connected smart healthcare, including the roles of artificial intelligence, machine learning, 5G and IoT platforms, and other enabling technologies. It also presents the challenges and potential risks that could arise from delivering connected smart healthcare services. Healthcare delivery is witnessing revolutions engineered by the developments in mobile connectivity and the plethora of platforms, applications, sensors, devices, and equipment that go along with it. Human society is evolving fast in response to these technological developments, which are also pushing the connectivity-providing sector to create and adopt new waves of network technologies. Consequently, new communications technologies have been introduced into the healthcare system and many novel applications have been developed to make it easier for sharing data in various forms and volumes within health-related services. These applications have also made it possible for telemedicine to be effectively adopted. This paper provides an overview of some of the recent developments within the space of mobile connectivity and telemedicine.

关键词： Telemedicine Smart Healthcare 5G Artificial Intelligence Machine Learning Internet-of-Medical-Things

Path Integral Quantization of Doubly Supersymmetric Model

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Journal of Applied Mathematics and Physics 2022年第2期10卷 245-253页

作者： Hanaa.abdulkareem Elegla Nasser Ismail Farahat Physics Department Islamic University of Gaza Gaza Palestine

The Hamilton-Jacobi formalism is used to discuss the path integral quantization of the double supersymmetric models with the spinning superparticle in the component and superfield form. The equations of motion are obtained as total differential equations in many variables. The equations of motion are integrable, and the path integral is obtained as an integration over the canonical phase space coordinates.

关键词： Hamilton-Jacobi Formalism Singular Lagrangian Superparticle Superfield Supersymmetry