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Diabetic Retinopathy Detection: A Hybrid Intelligent Approach

computers, Materials & Continua 2024年第9期80卷 4561-4576页

作者： Atta Rahman Mustafa Youldash Ghaida Alshammari Abrar Sebiany Joury Alzayat Manar Alsayed Mona Alqahtani Noor Aljishi Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of Computer Engineering(CE) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia

Diabetes is a serious health condition that can cause several issues in human body organs such as the heart and kidney as well as a serious eye disease called diabetic retinopathy(DR).Early detection and treatment are crucial to prevent complete blindness or partial vision *** detection methods,which involve ophthalmologists examining retinal fundus images,are subjective,expensive,and ***,this study employs artificial intelligence(AI)technology to perform faster and more accurate binary classifications and determine the presence of *** this regard,we employed three promising machine learning models namely,support vector machine(SVM),k-nearest neighbors(KNN),and Histogram Gradient Boosting(HGB),after carefully selecting features using transfer learning on the fundus images of the Asia Pacific Tele-Ophthalmology Society(APTOS)(a standard dataset),which includes 3662 images and originally categorized DR into five levels,now simplified to a binary format:No DR and DR(Classes 1-4).The results demonstrate that the SVM model outperformed the other approaches in the literature with the same dataset,achieving an excellent accuracy of 96.9%,compared to 95.6%for both the KNN and HGB *** approach is evaluated by medical health professionals and offers a valuable pathway for the early detection of DR and can be successfully employed as a clinical decision support system.

关键词： Diabetic retinopathy transfer learning machine learning fundus images binary classification APTOS

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Deep Learning Based Signal Detection for Quadrature Spatial Modulation System

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China Communications 2024年第10期21卷 78-85页

作者： Shu Dingyun Peng Yuyang Yue Ming Fawaz AL-Hazemi Mohammad Meraj Mirza School of Computer Science and Engineering Macao University of Science and TechnologyMacaoChina Department of Computer and Network Engineering University of JeddahSaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia

With the development of communication systems, modulation methods are becoming more and more diverse. Among them, quadrature spatial modulation(QSM) is considered as one method with less capacity and high efficiency. In QSM, the traditional signal detection methods sometimes are unable to meet the actual requirement of low complexity of the system. Therefore, this paper proposes a signal detection scheme for QSM systems using deep learning to solve the complexity problem. Results from the simulations show that the bit error rate performance of the proposed deep learning-based detector is better than that of the zero-forcing(ZF) and minimum mean square error(MMSE) detectors, and similar to the maximum likelihood(ML) detector. Moreover, the proposed method requires less processing time than ZF, MMSE,and ML.

关键词： bit error rate complexity deep learning quadrature spatial modulation

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Multi-Level Knowledge Engineering Approach for Mapping Implicit Aspects to Explicit Aspects

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computers, Materials & Continua 2022年第2期70卷 3491-3509页

作者： Jibran Mir Azhar Mahmood Shaheen Khatoon Computer Science Department Shaheed Zulfikar Ali Bhutto Institute of Science and Technology(SZABIST)Faculty of Computing and Engineering SciencesIslamabadPakistan Department of Computer Science Capital University of Science&TechnologyFaculty of ComputingIslamabad College of Computer Science and Information Technology King Faisal UniversitySaudi Arabia

Aspect’s extraction is a critical task in aspect-based sentiment analysis,including explicit and implicit aspects *** extensive research has identified explicit aspects,little effort has been put forward on implicit aspects extraction due to the complexity of the ***,existing research on implicit aspect identification is widely carried out on product reviews targeting specific aspects while neglecting sentences’dependency ***,in this paper,a multi-level knowledge engineering approach for identifying implicit movie aspects is *** proposed method first identifies explicit aspects using a variant of BiLSTM and CRF(Bidirectional Long Short Memory-Conditional Random Field),which serve as a memory to process dependent sentences to infer implicit *** can identify implicit aspects from four types of sentences,including independent and three types of dependent *** study is evaluated on a largemovie reviews dataset with 50k *** experimental results showed that the explicit aspect identification method achieved 89%F1-score and implicit aspect extraction methods achieved 76%*** addition,the proposed approach also performs better than the state-of-the-art techniques(NMFIAD andML-KB+)on the product review dataset,where it achieved 93%precision,92%recall,and 93%F1-score.

关键词： Movie NEs(named entities) aspects opinion words annotation process memory implicit aspects implicit aspects mapping word embedding and BiLSTM

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Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

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Chinese Physics B 2024年第4期33卷 418-433页

作者：周新卫蒋东华 Jean De Dieu Nkapkop Musheer Ahmad Jules Tagne Fossi Nestor Tsafack 吴建华 Department of Information Engineering Gongqing CollegeNanchang UniversityJiujiang 332020China School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 511400China Department of Electrical Engineering and Industrial Computing University Institute of TechnologyDoualaCameroon Department of Computer Engineering Jamia Millia IslamiaNew Delhi 110025India Department of Physics Faculty of ScienceUniversity of YaoundeCameroon Electrical Engineering Department and Industrial Computing of ISTAMA University of DoualaDoualaCameroon

Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function,a brand-new tristable locally active memristor model is first proposed in this ***,a novel four-dimensional fractional-order memristive cellular neural network(FO-MCNN)model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its ***,its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation ***,it is used toward secure communication application *** it as the pseudo-random number generator(PRNG),a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing(ASR-CS)***,the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.

关键词： cellular neural network memristor hardware circuit compressive sensing privacy data protection

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Simulating a Chern Insulator with C=±2 on Synthetic Floquet Lattice

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Chinese Physics Letters 2024年第9期41卷 1-8页

作者： Ling-Xiao Lei Wei-Chen Wang Guang-Yao Huang Shun Hu Xi Cao Xin-Fang Zhang Ming-Tang Deng Ping-Xing Chen Institute for Quantum Science and Technology College of ScienceNational University of Defense TechnologyChangsha 410073China Institute for Quantum Information&State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China Greatwall Quantum Laboratory Changsha 410006China Hefei National Laboratory Hefei 230088China

The synthetic Floquet lattice,generated by multiple strong drives with mutually incommensurate frequencies,provides a powerful platform for quantum simulation of topological *** this study,we propose a 4-band tight-binding model of the Chern insulator with a Chern number C=±2 by coupling two layers of the half Bernevig–Hughes–Zhang lattice and subsequently mapping it onto the Floquet lattice to simulate its topological *** determine the Chern number of our Floquet-version model,we extend the energy pumping method proposed by Martin et al.[2017 ***.X 7041008]and the topological oscillation method introduced by Boyers et al.[2020 ***.125160505],followed by numerical simulations for both *** simulation results demonstrate the successful extraction of the Chern number using either of these methods,providing an excellent prediction of the phase diagram that closely aligns with the theoretical one derived from the original bilayer half Bernevig–Hughes–Zhang ***,we briefly discuss a potential experimental implementation for our *** work demonstrates significant potential for simulating complex topological matter using quantum computing platforms,thereby paving the way for constructing a more universal simulator for non-interacting topological quantum states and advancing our understanding of these intriguing phenomena.

关键词： properties topological quantum

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Enhanced Multimodal Physiological Signal Analysis for Pain Assessment Using Optimized Ensemble Deep Learning

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computer Modeling in Engineering & sciences 2025年第5期143卷 2459-2489页

作者： Karim Gasmi Olfa Hrizi Najib Ben Aoun Ibrahim Alrashdi Ali Alqazzaz Omer Hamid Mohamed O.Altaieb Alameen E.M.Abdalrahman Lassaad Ben Ammar Manel Mrabet Omrane Necibi Department of Computer Science College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Faculty of Computing and Information Al-Baha UniversityAl-Baha65528Saudi Arabia REGIM-Lab National School of Engineers of SfaxUniversity of SfaxSfax3038Tunisia College of Computing and Information Technology University of BishaBisha61922Saudi Arabia Cybersecurity Department College of Engineering and Information TechnologyBuraydah Private CollegesBuraydah51418 Saudi Arabia Department of Computer Science Prince Sattam bin Abdulaziz UniversityAl-Kharj1942Saudi Arabia

The potential applications of multimodal physiological signals in healthcare,pain monitoring,and clinical decision support systems have garnered significant attention in biomedical *** self-reporting is the foundation of conventional pain assessment methods,which may be *** learning is a promising alternative to resolve this limitation through automated pain *** paper proposes an ensemble deep-learning framework for pain *** framework makes use of features collected from electromyography(EMG),skin conductance level(SCL),and electrocardiography(ECG)*** integrate Convolutional Neural Networks(CNN),Long Short-Term Memory Networks(LSTM),Bidirectional Gated Recurrent Units(BiGRU),and Deep Neural Networks(DNN)*** then aggregate their predictions using a weighted averaging ensemble technique to increase the classification’s *** improve computing efficiency and remove redundant features,we use Particle Swarm Optimization(PSO)for feature *** enables us to reduce the features’dimensionality without sacrificing the classification’s *** improved accuracy,precision,recall,and F1-score across all pain levels,the experimental results show that the suggested ensemble model performs better than individual deep learning *** our experiments,the suggested model achieved over 98%accuracy,suggesting promising automated pain assessment ***,due to differences in validation protocols,comparisons with previous studies are still *** deep learning and feature selection techniques significantly improves model generalization,reducing overfitting and enhancing classification *** evaluation was conducted using the BioVid Heat Pain Dataset,confirming the model’s effectiveness in distinguishing between different pain intensity levels.

关键词： Pain assessment ensemble learning deep learning optimal algorithm feature selection

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Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

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computers, Materials & Continua 2023年第2期74卷 3151-3166页

作者： Marwa Obayya Nadhem NEMRI Lubna A.Alharbi Mohamed K.Nour Mrim M.Alnfiai Mohammed Abdullah Al-Hagery Nermin M.Salem Mesfer Al Duhayyim Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science and Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia

With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has *** evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based *** Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in *** to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from *** such scenario computer-assisted automated diagnostic tools are important for classification of ECG *** current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECGSignal Classification(IBADL-BECGC)*** accomplish this,the proposed IBADL-BECGC model initially pre-processes the input ***,IBADL-BECGC model applies NasNet model to derive the features from test ECG *** addition,Improved Bat Algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet ***,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification *** presented IBADL-BECGC model was experimentally validated utilizing benchmark *** comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.

关键词： Data science ECG signals improved bat algorithm deep learning biomedical data data classification machine learning

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Leveraging Transformers for Detection of Arabic Cyberbullying on Social Media: Hybrid Arabic Transformers

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computers, Materials & Continua 2025年第5期83卷 3165-3185页

作者： Amjad A.Alsuwaylimi Zaid S.Alenezi Department of Computer Science College of ScienceNorthern Border UniversityArar91431Saudi Arabia Information Technology Management Northern Border UniversityArar91431Saudi Arabia

Cyberbullying is a remarkable issue in the Arabic-speaking world,affecting children,organizations,and *** efforts have been made to combat this problem through proposed models using machine learning(ML)and deep learning(DL)approaches utilizing natural language processing(NLP)methods and by proposing relevant ***,most of these endeavors focused predominantly on the English language,leaving a substantial gap in addressing Arabic *** the complexities of the Arabic language,transfer learning techniques and transformers present a promising approach to enhance the detection and classification of abusive content by leveraging large and pretrained models that use a large ***,this study proposes a hybrid model using transformers trained on extensive Arabic *** then fine-tunes the hybrid model on a newly curated Arabic cyberbullying dataset collected from social media platforms,in particular ***,the following two hybrid transformer models are introduced:the first combines CAmelid Morphologically-aware pretrained Bidirectional Encoder Representations from Transformers(CAMeLBERT)with Arabic Generative Pre-trained Transformer 2(AraGPT2)and the second combines Arabic BERT(AraBERT)with Cross-lingual Language Model-RoBERTa(XLM-R).Two strategies,namely,feature fusion and ensemble voting,are employed to improve the model performance *** results,measured through precision,recall,F1-score,accuracy,and AreaUnder the Curve-Receiver Operating Characteristic(AUC-ROC),demonstrate that the combined CAMeLBERT and AraGPT2 models using feature fusion outperformed traditional DL models,such as Long Short-Term Memory(LSTM)and Bidirectional Long Short-Term Memory(BiLSTM),as well as other independent Arabic-based transformer models.

关键词： Cyberbullying transformers pre-trained models arabic cyberbullying detection deep learning

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Distance Matrix and Markov Chain Based Sensor Localization in WSN

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computers, Materials & Continua 2022年第5期71卷 4051-4068页

作者： Omaima Bamasaq Daniyal Alghazzawi Surbhi Bhatia Pankaj Dadheech Farrukh Arslan Sudhakar Sengan Syed Hamid Hassan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversitySaudi Arabia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipurRajasthanIndia University of Engineering and Technology LahorePakistan Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveliTamilnaduIndia

Applications based on Wireless Sensor Networks(WSN)have shown to be quite useful in monitoring a particular geographic area of *** geometries of the surrounding environment are essential to establish a successful WSN *** it is literally hard because constructing a localization algorithm that tracks the exact location of Sensor Nodes(SN)in a WSN is always a challenging *** this research paper,Distance Matrix and Markov Chain(DM-MC)model is presented as node localization technique in which Distance Matrix and Estimation Matrix are used to identify the position of the *** method further employs a Markov Chain Model(MCM)for energy optimization and interference *** are performed against two well-known models,and the results demonstrate that the proposed algorithm improves performance by using less network resources when compared to the existing *** probability is used in the Markova chain to sustain higher energy ***,the proposed Distance Matrix and Markov Chain model decrease energy use by 31%and 25%,respectively,compared to the existing DV-Hop and CSA *** experimental results were performed against two proven models,Distance VectorHop Algorithm(DV-HopA)and Crow Search Algorithm(CSA),showing that the proposed DM-MC model outperforms both the existing models regarding localization accuracy and Energy Consumption(EC).These results add to the credibility of the proposed DC-MC model as a better model for employing node localization while establishing a WSN framework.

关键词： Wireless sensor network resource optimization routing distance matrix Markov chain

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Artificial Intelligence Based Smart Routing in Software Defined Networks

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computer Systems science & Engineering 2023年第2期44卷 1279-1293页

作者： C.Aswini M.L.Valarmathi Department of Information Technology Government College of TechnologyCoimbatore641013India Department of Computer Science and Engineering Dr.Mahalingam College of Engineering and TechnologyPollachi642003India

In a non-static information exchange network,routing is an overly com-plex task to perform,which has to satisfy all the needs of the *** Deﬁned Network(SDN)is the latest and widely used technology in the future communication networks,which would provide smart routing that is visible *** various features of routing are supported by the information centric network,which minimizes the congestion in the dataﬂow in a network and pro-vides the content awareness through its mined *** to the advantages of the information centric network,the concepts of the information-centric net-work has been used in the paper to enable an optimal routing in the software-deﬁned *** there are many advantages in the information-centric network,there are some disadvantages due to the non-static communication prop-erties,which affects the routing in *** this regard,artiﬁcial intelligence meth-odology has been used in the proposed approach to solve these difﬁculties.A detailed analysis has been conducted to map the content awareness with deep learning and deep reinforcement learning with *** novel aligned internet investigation technique has been proposed to process the deep reinforcement *** performance evaluation of the proposed systems has been con-ducted among various existing approaches and results in optimal load balancing,usage of the bandwidth,and maximization in the throughput of the network.

关键词： Content aware routing software deﬁned networks deep learning load balancing

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