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Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 155-183页

作者： Suganya Athisayamani A.Robert Singh Gyanendra Prasad Joshi Woong Cho School of computing Sastra Deemed to be UniversityThanjavur613401India Department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203India Department of AI and Software Engineering Kangwon National UniversitySamcheok25913Republic of Korea Department of Electronics Information and Communication EngineeringKangwon National UniversitySamcheok25913Republic of Korea

In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological *** is particularly effective for detecting soft tissue ***,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of *** address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI *** manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning *** are three stages for learning;in the first stage,the whole dataset is used to learn the *** the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented *** method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for *** hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning ***-dataset registers maximum classification *** evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.

关键词： MRI tumors classification AlexNet50 transfer learning hyperparameter tuning optimizer

An Enhanced Lung Cancer Detection Approach Using Dual-Model Deep Learning Technique

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 835-867页

作者： Sumaia Mohamed Elhassan Saad Mohamed Darwish Saleh Mesbah Elkaffas Department of information Technology Institute of Graduate Studies and ResearchAlexandria UniversityAlexandria21526Egypt College of computing and information Technology Arab Academy for ScienceTechnology and Maritime TransportAlexandriaP.O.Box 1029Egypt

Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic *** detection of lung tumors significantly increases the chances of successful treatment and ***,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung ***-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate ***,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor *** overcome these disadvantages,dual-model or multi-model approaches can be *** research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of *** automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung ***8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single *** is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to ***,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive *** combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applicati

关键词： Lung cancer detection dual-model deep learning technique data augmentation CNN YOLOv8

Quantum public-key crypto via EPR pairs

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 58-63页

作者： Xiang-Qun Fu Hong-Wei Li Jian-Hong Shi Tan Li Wan-Su Bao Henan Key Laboratory of Quantum information and Cryptography Information Engineering University Synergetic Innovation Center of Quantum information and Quantum Physics University of Science and Technology of China

With the development of quantum computing technology, quantum public-key cryptography is gradually becoming an alternative to the existing classical public-key cryptography. This paper designs a quantum trapdoor one-way function via EPR pairs and quantum measurements. Based on this, a new quantum public-key cryptosystem is presented, which offers forward security,and can resist the chosen-plaintext attack and chosen-ciphertext attack. Compared with the existing quantum public-key cryptos,eavesdropping can be automatically detected in this new quantum public-key cryptosystem under a necessary condition, which is also detailed in the paper.

关键词： quantum public-key crypto trapdoor one-way function EPR pairs

Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 357-384页

作者： Mohd Anjum Naoufel Kraiem Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh Department of Computer Engineering Aligarh Muslim UniversityAligarh202002India College of Computer Science King Khalid UniversityAbha61413Saudi Arabia School of computing Gachon UniversitySeongnam13120Republic of Korea Department of Computer Science and information systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Engineering and information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

关键词： Computer vision feature selection machine learning region detection texture analysis image classification medical images

Multi-Scale Dilated Convolution Network for SPECT-MPI Cardiovascular Disease Classification with Adaptive Denoising and Attenuation Correction

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 299-327页

作者： A.Robert Singh Suganya Athisayamani Gyanendra Prasad Joshi Bhanu Shrestha Department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203Tamil NaduIndia School of computing Sastra Deemed to be UniversityThanjavur613401Tamil NaduIndia Department of Artificial Intelligence&Software Kangwon National UniversitySamcheok25913Republic of Korea Department of information Convergence System Graduate School of Smart ConvergenceKwangwoon UniversitySeoul01897Republic of Korea

Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image *** denoising is done by a U-Net architecture that ensures effective image *** correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of ***,a novel multi-scale diluted convolution(MSDC)network is *** merges the features extracted in different scales and makes the model learn the features more *** scales of filters with size 3×3 are used to extract *** three steps are compared with state-of-the-art *** proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of *** proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.

关键词： SPECT-MPI CAD MSDC denoising attenuation correction classification

A classification-based privacy-preserving decision-making for secure data sharing in Internet of Things assisted applications

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Digital Communications and Networks 2022年第4期8卷 436-445页

作者： Alaa Omran Almagrabi A.K.Bashir Department of information systems Faculty of Computing and Information Technology(FCIT)King Abdulaziz UniversityJeddah21589Saudi Arabia Department of computing and Mathematics Manchester Metropolitan UniversityManchesterUK

The introduction of the Internet of Things(IoT)paradigm serves as pervasive resource access and sharing platform for different real-time *** resource availability,access,and allocation provide a better quality of user experience regardless of the application type and ***,privacy remains an open issue in this ubiquitous sharing platform due to massive and replicated data *** this paper,privacy-preserving decision-making for the data-sharing scheme is *** scheme is responsible for improving the security in data sharing without the impact of replicated resources on communicating *** this scheme,classification learning is used for identifying replicas and accessing granted resources *** on the trust score of the available resources,this classification is recurrently performed to improve the reliability of information *** user-level decisions for information sharing and access are made using the classification of the resources at the time of *** proposed scheme is verified using the metrics access delay,success ratio,computation complexity,and sharing loss.

关键词： Classification learning Data mining IoT Privacy-preserving Resource replication

Pervasive Attentive Neural Network for Intelligent Image Classification Based on N-CDE’s

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Computers, Materials & Continua 2024年第4期79卷 1137-1156页

作者： Anas W.Abulfaraj Department of information systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityRabigh21911Saudi Arabia

The utilization of visual attention enhances the performance of image classification *** attentionbased models have demonstrated notable performance,but many of these models exhibit reduced accuracy when confronted with inter-class and intra-class similarities and ***-Controlled Differential Equations(N-CDE’s)and Neural Ordinary Differential Equations(NODE’s)are extensively utilized within this ***’s possesses the capacity to effectively illustrate both inter-class and intra-class similarities and differences with enhanced *** this end,an attentive neural network has been proposed to generate attention maps,which uses two different types of N-CDE’s,one for adopting hidden layers and the other to generate attention *** distinct attention techniques are implemented including time-wise attention,also referred to as bottom N-CDE’s;and element-wise attention,called topN-CDE’***,a trainingmethodology is proposed to guarantee that the training problem is sufficiently *** classification tasks including fine-grained visual classification andmulti-label classification,are utilized to evaluate the *** proposedmethodology is employed on five publicly available datasets,including CUB-200-2011,ImageNet-1K,PASCAL VOC 2007,PASCAL VOC 2012,and MS *** obtained visualizations have demonstrated that N-CDE’s are better appropriate for attention-based activities in comparison to conventional NODE’s.

关键词： Differential equations neural-controlled DE image classification attention maps N-CDE’s

Feature-Limited Prediction on the UCI Heart Disease Dataset

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Computers, Materials & Continua 2023年第3期74卷 5871-5883页

作者： Khadijah Mohammad Alfadli Alaa Omran Almagrabi Department of information systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Heart diseases are the undisputed leading causes of death ***,the conventional approach of relying solely on the patient’s medical history is not enough to reliably diagnose heart *** potentially indicative factors exist,such as abnormal pulse rate,high blood pressure,diabetes,high cholesterol,*** analyzing these health signals’interactions is challenging and requires years of medical training and ***,this work aims to harness machine learning techniques that have proved helpful for data-driven applications in the rise of the artificial intelligence *** specifically,this paper builds a hybrid model as a tool for data mining algorithms like feature *** goal is to determine the most critical factors that play a role in discriminating patients with heart illnesses from healthy *** contribution in this field is to provide the patients with accurate and timely tentative results to help prevent further complications and heart attacks using minimum *** developed model achieves 84.24%accuracy,89.22%Recall,and 83.49%Precision using only a subset of the features.

关键词： Machine learning feature selection heart disease

A Deep CNN-LSTM-Based Feature Extraction for Cyber-Physical System Monitoring

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Computers, Materials & Continua 2023年第8期76卷 2079-2093页

作者： Alaa Omran Almagrabi Department of information systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

A potential concept that could be effective for multiple applications is a“cyber-physical system”(CPS).The Internet of Things(IoT)has evolved as a research area,presenting new challenges in obtaining valuable data through environmental *** existing work solely focuses on classifying the audio system of CPS without utilizing feature *** study employs a deep learning method,CNN-LSTM,and two-way feature extraction to classify audio systems within *** primary objective of this system,which is built upon a convolutional neural network(CNN)with Long Short Term Memory(LSTM),is to analyze the vocalization patterns of two different species of *** has been demonstrated that CNNs,when combined with mel-spectrograms for sound analysis,are suitable for classifying ambient ***,the data is augmented and ***,the mel spectrogram features are extracted through two-way feature ***,Principal Component Analysis(PCA)is utilized for dimensionality reduction,followed by Transfer learning for audio feature ***,the classification is performed using the CNN-LSTM *** methodology can potentially be employed for categorizing various biological acoustic objects and analyzing biodiversity indexes in natural environments,resulting in high classification *** study highlights that this CNNLSTM approach enables cost-effective and resource-efficient monitoring of large natural *** dissemination of updated CNN-LSTM models across distant IoT nodes is facilitated flexibly and dynamically through the utilization of CPS.

关键词： Cyber-physical system internet of things feature extraction,classification CNN principal component analysis mel spectrograms monitoring deep learning