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Stochastic Augmented-Based Dual-Teaching for Semi-Supervised Medical Image Segmentation

Computers, Materials & Continua 2025年第1期82卷 543-560页

作者： Hengyang Liu Yang Yuan Pengcheng Ren Chengyun Song Fen Luo School of Computer Science and Engineering Chongqing University of TechnologyChongqing400054China College of Artificial intelligence Chongqing Technology and Business UniversityChongqing400067China

Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution ***,current copy-paste methods have three limitations:(1)training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information;(2)low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data;(3)the segmentation performance in low-contrast and local regions is less than *** design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy(SADT),which enhances feature diversity and learns high-quality features to overcome these *** be more precise,SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data,which prevents the loss of rare labeled *** introduce a bi-directional copy-pastemask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in *** the mixed images,Deep-Shallow Spatial Contrastive Learning(DSSCL)is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local *** this procedure,the features retrieved by the Student Network are subjected to a random feature perturbation *** two openly available datasets,extensive trials show that our proposed SADT performs much better than the state-ofthe-art semi-supervised medical segmentation *** only 10%of the labeled data for training,SADT was able to acquire a Dice score of 90.10%on the ACDC(Automatic Cardiac Diagnosis Challenge)dataset.

关键词： Semi-supervised medical image segmentation contrastive learning stochastic augmented

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Transforming Education with Photogrammetry:Creating Realistic 3D Objects for Augmented Reality Applications

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 185-208页

作者： Kaviyaraj Ravichandran Uma Mohan department of Computational intelligence School of ComputingSRM Institute of Science and TechnologyKattankulathur603203India

Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different *** increased use of mobile devices has an even greater *** the demand for AR applications in education continues to increase,educators actively seek innovative and immersive methods to engage students in ***,exploring these possibilities also entails identifying and overcoming existing barriers to optimal educational ***,this surge in demand has prompted the identification of specific barriers,one of which is three-dimensional(3D)*** 3D objects for augmented reality education applications can be challenging and time-consuming for the *** address this,we have developed a pipeline that creates realistic 3D objects from the two-dimensional(2D)*** for augmented and virtual reality can then utilize these created 3D *** evaluated the proposed pipeline based on the usability of the 3D object and performance ***,with 117 respondents,the co-creation team was surveyed with openended questions to evaluate the precision of the 3D object created by the proposed photogrammetry *** analyzed the survey data using descriptive-analytical methods and found that the proposed pipeline produces 3D models that are positively accurate when compared to real-world objects,with an average mean score above *** study adds new knowledge in creating 3D objects for augmented reality applications by using the photogrammetry technique;finally,it discusses potential problems and future research directions for 3D objects in the education sector.

关键词： Augmented reality education immersive learning 3D object creation photogrammetry and StructureFromMotion

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A Support Vector Machine(SVM)Model for Privacy Recommending Data Processing Model(PRDPM)in Internet of Vehicles

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Computers, Materials & Continua 2025年第1期82卷 389-406页

作者： Ali Alqarni department of Computer Science and Artificial intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy *** key requirement for such systems is the preservation of user privacy,ensuring a seamless experience in driving,navigation,and *** privacy needs are influenced by various factors,such as data collected at different intervals,trip durations,and user *** address this,the paper proposes a Support Vector Machine(SVM)model designed to process large amounts of aggregated data and recommend privacy preserving *** model analyzes data based on user demands and interactions with service providers or neighboring *** aims to minimize privacy risks while ensuring service continuity and *** SVMmodel helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy *** results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance.

关键词： Support vector machine big data IoV privacy-preserving

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Comprehensive Review and Analysis on Facial Emotion Recognition:Performance Insights into Deep and Traditional Learning with Current Updates and Challenges

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Computers, Materials & Continua 2025年第1期82卷 41-72页

作者： Amjad Rehman Muhammad Mujahid Alex Elyassih Bayan AlGhofaily Saeed Ali Omer Bahaj Artificial intelligence&Data Analytics Lab College of Computer&Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia MIS department College of business Administration Prince Sattam Bin Abdulaziz UniversityAlKharj11942Saudi Arabia

In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry *** demonstrations and applications in several fields,including computer games,smart homes,expression analysis,gesture recognition,surveillance films,depression therapy,patientmonitoring,anxiety,and others,have brought attention to its significant academic and commercial *** study emphasizes research that has only employed facial images for face expression recognition(FER),because facial expressions are a basic way that people communicate meaning to each *** immense achievement of deep learning has resulted in a growing use of its much architecture to enhance *** review is on machine learning,deep learning,and hybrid methods’use of preprocessing,augmentation techniques,and feature extraction for temporal properties of successive frames of *** following section gives a brief summary of assessment criteria that are accessible to the public and then compares them with benchmark results the most trustworthy way to assess FER-related research topics *** this review,a brief synopsis of the subject matter may be beneficial for novices in the field of FER as well as seasoned scholars seeking fruitful avenues for further *** information conveys fundamental knowledge and provides a comprehensive understanding of the most recent state-of-the-art research.

关键词： Face emotion recognition deep learning hybrid learning CK+ facial images machine learning technological development

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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

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Few-shot learning for screening 2D Ga2CoS4-x supported single-atom catalysts for hydrogen production

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Journal of Energy Chemistry 2025年第1期100卷 665-673页

作者： Nabil Khossossi Poulumi Dey department of Mechanical Engineering (ME) Delft University of Technology

Hydrogen generation and related energy applications heavily rely on the hydrogen evolution reaction（HER）,which faces challenges of slow kinetics and high *** electrocatalysts,particularly single-atom catalysts （SACs） on two-dimensional （2D） materials,are *** study presents a few-shot machine learning （ML） assisted high-throughput screening of 2D septuple-atomic-layer Ga2CoS4-xsupported SACs to predict HER catalytic ***,density functional theory （DFT）calculations showed that 2D Ga2CoS4is inactive for ***,defective Ga2CoS4-x（x=0–0.25）monolayers exhibit excellent HER activity due to surface sulfur vacancies （SVs）,with predicted overpotentials （0–60 mV） comparable to or lower than commercial Pt/C,which typically exhibits an overpotential of around 50 m V in the acidic electrolyte,when the concentration of surface SV is lower than 8.3%.SVs generate spin-polarized states near the Fermi level,making them effective HER *** demonstrate ML-accelerated HER overpotential predictions for all transition metal SACs on 2D *** DFT data from 18 SACs,an ML model with high prediction accuracy and reduced computation time was *** intrinsic descriptor linking SAC atomic properties to HER overpotential was *** study thus provides a framework for screening SACs on 2D materials,enhancing catalyst design.

关键词： Hydrogen production Electrocatalyst 2D material Density functional theory Machine learning Surface sulfur vacancy

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DKP-SLAM:A Visual SLAM for Dynamic Indoor Scenes Based on Object Detection and Region Probability

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Computers, Materials & Continua 2025年第1期82卷 1329-1347页

作者： Menglin Yin Yong Qin Jiansheng Peng College of Automation Guangxi University of Science and TechnologyLiuzhou545000China department of Artificial intelligence and Manufacturing Hechi UniversityHechi546300China Key Laboratory of AI and Information Processing Education Department of Guangxi Zhuang Autonomous RegionHechi546300China Guangxi Key Laboratory of Sericulture Ecology and Applied Intelligent technology School of Chemistry and BioengineeringHechi UniversityHechi546300China

In dynamic scenarios,visual simultaneous localization and mapping(SLAM)algorithms often incorrectly incorporate dynamic points during camera pose computation,leading to reduced accuracy and *** paper presents a dynamic SLAM algorithm that leverages object detection and regional dynamic ***,a parallel thread employs the YOLOX object detectionmodel to gather 2D semantic information and compensate for missed ***,an improved K-means++clustering algorithm clusters bounding box regions,adaptively determining the threshold for extracting dynamic object contours as dynamic points *** process divides the image into low dynamic,suspicious dynamic,and high dynamic *** the tracking thread,the dynamic point removal module assigns dynamic probability weights to the feature points in these *** with geometric methods,it detects and removes the dynamic *** final evaluation on the public TUM RGB-D dataset shows that the proposed dynamic SLAM algorithm surpasses most existing SLAM algorithms,providing better pose estimation accuracy and robustness in dynamic environments.

关键词： Visual SLAM dynamic scene YOLOX K-means++clustering dynamic probability

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Toward understanding the role of genomic repeat elements in neurodegenerative diseases

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Neural Regeneration Research 2025年第3期20卷 646-659页

作者： Zhengyu An Aidi Jiang Jingqi Chen Institute of Science and technology for Brain-Inspired intelligence Fudan UniversityShanghaiChina MOE Frontiers Center for Brain Science Fudan UniversityShanghaiChina MOE Key Laboratory of Computational Neuroscience and Brain-Inspired intelligence Fudan UniversityShanghaiChina Zhangjiang Fudan International Innovation Center ShanghaiChina

Neurodegenerative diseases cause great medical and economic burdens for both patients and society;however, the complex molecular mechanisms thereof are not yet well understood. With the development of high-coverage sequencing technology, researchers have started to notice that genomic repeat regions, previously neglected in search of disease culprits, are active contributors to multiple neurodegenerative diseases. In this review, we describe the association between repeat element variants and multiple degenerative diseases through genome-wide association studies and targeted sequencing. We discuss the identification of disease-relevant repeat element variants, further powered by the advancement of long-read sequencing technologies and their related tools, and summarize recent findings in the molecular mechanisms of repeat element variants in brain degeneration, such as those causing transcriptional silencing or RNA-mediated gain of toxic function. Furthermore, we describe how in silico predictions using innovative computational models, such as deep learning language models, could enhance and accelerate our understanding of the functional impact of repeat element variants. Finally, we discuss future directions to advance current findings for a better understanding of neurodegenerative diseases and the clinical applications of genomic repeat elements.

关键词： Alzheimer's disease ataxia deep learning long-read sequencing neurodegeneration neurodegenerative diseases Parkinson's disease repeat element structural variant

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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

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Evolutionary Particle Swarm Optimization Algorithm Based on Collective Prediction for Deployment of Base Stations

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Computers, Materials & Continua 2025年第1期82卷 345-369页

作者： Jiaying Shen Donglin Zhu Yujia Liu Leyi Wang Jialing Hu Zhaolong Ouyang Changjun Zhou Taiyong Li School of Computer Science and technology Zhejiang Normal UniversityJinhua321004China School of Future Technologies Jiangxi Institute of Applied Science and TechnologyNanchang330000China School of Computing and Artificial intelligence Southwestern University of Finance and EconomicsChengdu611130China

The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real *** development of the Internet of Things(IoT)relies on the support of base stations,which provide a solid foundation for achieving a more intelligent way of *** a specific area,achieving higher signal coverage with fewer base stations has become an urgent ***,this article focuses on the effective coverage area of base station signals and proposes a novel Evolutionary Particle Swarm Optimization(EPSO)algorithm based on collective prediction,referred to herein as *** a new strategy called neighbor-based evolution prediction(NEP)addresses the issue of premature convergence often encountered by *** also employs a strengthening evolution(SE)strategy to enhance the algorithm’s global search capability and efficiency,ensuring enhanced robustness and a faster convergence speed when solving complex optimization *** better adapt to the actual communication needs of base stations,this article conducts simulation experiments by changing the number of base *** experimental results demonstrate thatunder the conditionof 50 ormore base stations,ECPPSOconsistently achieves the best coverage rate exceeding 95%,peaking at 99.4400%when the number of base stations reaches *** results validate the optimization capability of the ECPPSO algorithm,proving its feasibility and *** ablative experiments and comparisons with other algorithms highlight the advantages of ECPPSO.

关键词： Particle swarm optimization effective coverage area global optimization base station deployment

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