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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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作者 Amjad Rehman Muhammad Mujahid +2 位作者 Alex Elyassih Bayan AlGhofaily Saeed Ali Omer Bahaj 《Computers, Materials & Continua》 SCIE EI 2025年第1期41-72,共32页
In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent demonstrations and applications in several fi... In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent 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 importance.This 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 other.The immense achievement of deep learning has resulted in a growing use of its much architecture to enhance efficiency.This 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 data.The 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 statistically.In 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 investigation.The information conveys fundamental knowledge and provides a comprehensive understanding of the most recent state-of-the-art research. 展开更多
关键词 Face emotion recognition deep learning hybrid learning CK+ facial images machine learning technological development
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The Internet of Things under Federated Learning:A Review of the Latest Advances and Applications
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作者 Jinlong Wang Zhenyu Liu +2 位作者 Xingtao Yang Min Li Zhihan Lyu 《Computers, Materials & Continua》 SCIE EI 2025年第1期1-39,共39页
With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices ge... With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious challenge.Federated learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing.This review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy protection.In this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy security.Next,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning quality.We also list various application scenarios of FL on the IoT.Finally,we propose several open research challenges and possible solutions. 展开更多
关键词 Federated learning Internet of Things SENSORS machine learning privacy security
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Inspires effective alternatives to backpropagation:predictive coding helps understand and build learning
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作者 Zhenghua Xu Miao Yu Yuhang Song 《Neural Regeneration Research》 SCIE CAS 2025年第11期3215-3216,共2页
Artificial neural networks are capable of machine learning by simulating the hiera rchical structure of the human brain.To enable learning by brain and machine,it is essential to accurately identify and correct the pr... Artificial neural networks are capable of machine learning by simulating the hiera rchical structure of the human brain.To enable learning by brain and machine,it is essential to accurately identify and correct the prediction errors,referred to as credit assignment(Lillicrap et al.,2020).It is critical to develop artificial intelligence by understanding how the brain deals with credit assignment in neuroscience. 展开更多
关键词 ASSIGNMENT learning enable
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Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models
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作者 Shanmugasundaram Hariharan D.Anandan +3 位作者 Murugaperumal Krishnamoorthy Vinay Kukreja Nitin Goyal Shih-Yu Chen 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第1期91-122,共32页
Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment planning.Liver Tumors(LTs)vary significantly in size,shape,and location,and can present wi... Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment planning.Liver Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and challenging.This review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and resilience.Performance metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical contexts.The review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging conditions.It suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular methods.This paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges. 展开更多
关键词 Liver tumor detection liver tumor segmentation image processing liver tumor diagnosis feature extraction tumor classification deep learning machine learning
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SensFL:Privacy-Preserving Vertical Federated Learning with Sensitive Regularization
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作者 Chongzhen Zhang Zhichen Liu +4 位作者 Xiangrui Xu Fuqiang Hu Jiao Dai Baigen Cai Wei Wang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第1期385-404,共20页
In the realm of Intelligent Railway Transportation Systems,effective multi-party collaboration is crucial due to concerns over privacy and data silos.Vertical Federated Learning(VFL)has emerged as a promising approach... In the realm of Intelligent Railway Transportation Systems,effective multi-party collaboration is crucial due to concerns over privacy and data silos.Vertical Federated Learning(VFL)has emerged as a promising approach to facilitate such collaboration,allowing diverse entities to collectively enhance machine learning models without the need to share sensitive training data.However,existing works have highlighted VFL’s susceptibility to privacy inference attacks,where an honest but curious server could potentially reconstruct a client’s raw data from embeddings uploaded by the client.This vulnerability poses a significant threat to VFL-based intelligent railway transportation systems.In this paper,we introduce SensFL,a novel privacy-enhancing method to against privacy inference attacks in VFL.Specifically,SensFL integrates regularization of the sensitivity of embeddings to the original data into the model training process,effectively limiting the information contained in shared embeddings.By reducing the sensitivity of embeddings to the original data,SensFL can effectively resist reverse privacy attacks and prevent the reconstruction of the original data from the embeddings.Extensive experiments were conducted on four distinct datasets and three different models to demonstrate the efficacy of SensFL.Experiment results show that SensFL can effectively mitigate privacy inference attacks while maintaining the accuracy of the primary learning task.These results underscore SensFL’s potential to advance privacy protection technologies within VFL-based intelligent railway systems,addressing critical security concerns in collaborative learning environments. 展开更多
关键词 Vertical federated learning PRIVACY DEFENSES
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Federated Learning’s Role in Next-Gen TV Ad Optimization
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作者 Gabriela Dobrita Simona-Vasilica Oprea Adela Bâra 《Computers, Materials & Continua》 SCIE EI 2025年第1期675-712,共38页
In the rapidly evolving landscape of television advertising,optimizing ad schedules to maximize viewer engagement and revenue has become significant.Traditional methods often operate in silos,limiting the potential in... In the rapidly evolving landscape of television advertising,optimizing ad schedules to maximize viewer engagement and revenue has become significant.Traditional methods often operate in silos,limiting the potential insights gained from broader data analysis due to concerns over privacy and data sharing.This article introduces a novel approach that leverages Federated Learning(FL)to enhance TV ad schedule optimization,combining the strengths of local optimization techniques with the power of global Machine Learning(ML)models to uncover actionable insights without compromising data privacy.It combines linear programming for initial ads scheduling optimization with ML—specifically,a K-Nearest Neighbors(KNN)model—for predicting ad spot positions.Taking into account the diversity and the difficulty of the ad-scheduling problem,we propose a prescriptivepredictive approach in which first the position of the ads is optimized(using Google’s OR-Tools CP-SAT)and then the scheduled position of all ads will be the result of the optimization problem.Second,this output becomes the target of a predictive task that predicts the position of new entries based on their characteristics ensuring the implementation of the scheduling at large scale(using KNN,Light Gradient Boosting Machine and Random Forest).Furthermore,we explore the integration of FL to enhance predictive accuracy and strategic insight across different broadcasting networks while preserving data privacy.The FL approach resulted in 8750 ads being precisely matched to their optimal category placements,showcasing an alignment with the intended diversity objectives.Additionally,there was a minimal deviation observed,with 1133 ads positioned within a one-category variance from their ideal placement in the original dataset. 展开更多
关键词 Ad scheduling prescriptive-predictive approach federated learning KNN
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Prediction of Shear Bond Strength of Asphalt Concrete Pavement Using Machine Learning Models and Grid Search Optimization Technique
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作者 Quynh-Anh Thi Bui Dam Duc Nguyen +2 位作者 Hiep Van Le Indra Prakash Binh Thai Pham 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第1期691-712,共22页
Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Ext... Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Extra Trees(ET),and Light Gradient Boosting Machine(LGBM),to predict SBS based on easily determinable input parameters.Also,the Grid Search technique was employed for hyper-parameter tuning of the ML models,and cross-validation and learning curve analysis were used for training the models.The models were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat rate.Model validation was performed using three statistical criteria:the coefficient of determination(R2),the Root Mean Square Error(RMSE),and the mean absolute error(MAE).Additionally,SHAP analysis was also used to validate the importance of the input variables in the prediction of the SBS.Results show that these models accurately predict SBS,with LGBM providing outstanding performance.SHAP(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on SBS.Consequently,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design. 展开更多
关键词 Shear bond asphalt pavement grid search OPTIMIZATION machine learning
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Early identification of stroke through deep learning with multi-modal human speech and movement data
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作者 Zijun Ou Haitao Wang +9 位作者 Bin Zhang Haobang Liang Bei Hu Longlong Ren Yanjuan Liu Yuhu Zhang Chengbo Dai Hejun Wu Weifeng Li Xin Li 《Neural Regeneration Research》 SCIE CAS 2025年第1期234-241,共8页
Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are... Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting. 展开更多
关键词 artificial intelligence deep learning DIAGNOSIS early detection FAST SCREENING STROKE
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Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate
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作者 Suganya Athisayamani A.Robert Singh +1 位作者 Gyanendra Prasad Joshi Woong Cho 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第1期155-183,共29页
In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue... In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue anomalies.Traditionally,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of data.To address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI scans.This manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning methods.There are three stages for learning;in the first stage,the whole dataset is used to learn the features.In 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 dataset.This 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 analysis.Various 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 process.HWBA-dataset registers maximum classification performance.We evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%. 展开更多
关键词 MRI TUMORS CLASSIFICATION AlexNet50 transfer learning hyperparameter tuning OPTIMIZER
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A Rapid Adaptation Approach for Dynamic Air‑Writing Recognition Using Wearable Wristbands with Self‑Supervised Contrastive Learning
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作者 Yunjian Guo Kunpeng Li +4 位作者 Wei Yue Nam‑Young Kim Yang Li Guozhen Shen Jong‑Chul Lee 《Nano-Micro Letters》 SCIE EI CAS 2025年第2期417-431,共15页
Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities.Unlike existing approaches that often focus on static gestures and require extensive labeled data,the pro... Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities.Unlike existing approaches that often focus on static gestures and require extensive labeled data,the proposed wearable wristband with selfsupervised contrastive learning excels at dynamic motion tracking and adapts rapidly across multiple scenarios.It features a four-channel sensing array composed of an ionic hydrogel with hierarchical microcone structures and ultrathin flexible electrodes,resulting in high-sensitivity capacitance output.Through wireless transmission from a Wi-Fi module,the proposed algorithm learns latent features from the unlabeled signals of random wrist movements.Remarkably,only few-shot labeled data are sufficient for fine-tuning the model,enabling rapid adaptation to various tasks.The system achieves a high accuracy of 94.9%in different scenarios,including the prediction of eight-direction commands,and air-writing of all numbers and letters.The proposed method facilitates smooth transitions between multiple tasks without the need for modifying the structure or undergoing extensive task-specific training.Its utility has been further extended to enhance human–machine interaction over digital platforms,such as game controls,calculators,and three-language login systems,offering users a natural and intuitive way of communication. 展开更多
关键词 Wearable wristband Self-supervised contrastive learning Dynamic gesture Air-writing Human-machine interaction
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Industrial Control Anomaly Detection Based on Distributed Linear Deep Learning
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作者 Shijie Tang Yong Ding Huiyong Wang 《Computers, Materials & Continua》 SCIE EI 2025年第1期1129-1150,共22页
As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and... As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and fast and accurate attack detection techniques are crucial.The key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time series.To address this issue,we propose an anomaly detection method based on distributed deep learning.Our method uses a bilateral filtering algorithm for sequential sequences to remove noise in the time series,which can maintain the edge of discrete features.We use a distributed linear deep learning model to establish a sequential prediction model and adjust the threshold for anomaly detection based on the prediction error of the validation set.Our method can not only detect abnormal attacks but also locate the sensors that cause anomalies.We conducted experiments on the Secure Water Treatment(SWAT)and Water Distribution(WADI)public datasets.The experimental results show that our method is superior to the baseline method in identifying the types of attacks and detecting efficiency. 展开更多
关键词 Anomaly detection CPS deep learning MLP(multi-layer perceptron)
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Hybrid Deep Learning Approach for Automating App Review Classification:Advancing Usability Metrics Classification with an Aspect-Based Sentiment Analysis Framework
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作者 Nahed Alsaleh Reem Alnanih Nahed Alowidi 《Computers, Materials & Continua》 SCIE EI 2025年第1期949-976,共28页
App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their products.Automating the analysis of these reviews is vital for efficient review management.While t... App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their products.Automating the analysis of these reviews is vital for efficient review management.While traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior performance.This research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and satisfaction.We propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification accuracy.Comparative analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,respectively.Thesignificant contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews dataset.These advancements provide valuable insights for software developers to enhance usability and drive user-centric application development. 展开更多
关键词 Requirements Engineering(RE) app review analysis usabilitymetrics hybrid deep learning BERT-BiLSTM-CNN
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Interpretable Machine Learning Method for Compressive Strength Prediction and Analysis of Pure Fly Ash-based Geopolymer Concrete
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作者 SHI Yuqiong LI Jingyi +1 位作者 ZHANG Yang LI Li 《Journal of Wuhan University of Technology(Materials Science)》 SCIE EI CAS 2025年第1期65-78,共14页
In order to study the characteristics of pure fly ash-based geopolymer concrete(PFGC)conveniently,we used a machine learning method that can quantify the perception of characteristics to predict its compressive streng... In order to study the characteristics of pure fly ash-based geopolymer concrete(PFGC)conveniently,we used a machine learning method that can quantify the perception of characteristics to predict its compressive strength.In this study,505 groups of data were collected,and a new database of compressive strength of PFGC was constructed.In order to establish an accurate prediction model of compressive strength,five different types of machine learning networks were used for comparative analysis.The five machine learning models all showed good compressive strength prediction performance on PFGC.Among them,R2,MSE,RMSE and MAE of decision tree model(DT)are 0.99,1.58,1.25,and 0.25,respectively.While R2,MSE,RMSE and MAE of random forest model(RF)are 0.97,5.17,2.27 and 1.38,respectively.The two models have high prediction accuracy and outstanding generalization ability.In order to enhance the interpretability of model decision-making,we used importance ranking to obtain the perception of machine learning model to 13 variables.These 13 variables include chemical composition of fly ash(SiO_(2)/Al_(2)O_(3),Si/Al),the ratio of alkaline liquid to the binder,curing temperature,curing durations inside oven,fly ash dosage,fine aggregate dosage,coarse aggregate dosage,extra water dosage and sodium hydroxide dosage.Curing temperature,specimen ages and curing durations inside oven have the greatest influence on the prediction results,indicating that curing conditions have more prominent influence on the compressive strength of PFGC than ordinary Portland cement concrete.The importance of curing conditions of PFGC even exceeds that of the concrete mix proportion,due to the low reactivity of pure fly ash. 展开更多
关键词 machine learning pure fly ash geopolymer compressive strength feature perception
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An Enhanced Lung Cancer Detection Approach Using Dual-Model Deep Learning Technique
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作者 Sumaia Mohamed Elhassan Saad Mohamed Darwish Saleh Mesbah Elkaffas 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第1期835-867,共33页
Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of suc... Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of successful treatment and survival.However,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung tissue.Single-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 detection.Furthermore,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor characteristics.To overcome these disadvantages,dual-model or multi-model approaches can be employed.This 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 tumors.CNNs 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 cancer.YOLOv8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single image.This is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to detect.Furthermore,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 datasets.The combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applications.The CNN achieved an accuracy of 97.67%in classifying lung tissues into healthy and cancerous categories.The YOLOv8 model achieved an Intersection over Union(IoU)score of 0.85 for tumor localization,reflecting high precision in detecting and marking tumor boundaries within the images.Finally,the incorporation of synthetic images generated by DCGAN led to a 10%improvement in both the CNN classification accuracy and YOLOv8 detection performance. 展开更多
关键词 Lung cancer detection dual-model deep learning technique data augmentation CNN YOLOv8
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High-throughput screening of CO_(2) cycloaddition MOF catalyst with an explainable machine learning model
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作者 Xuefeng Bai Yi Li +3 位作者 Yabo Xie Qiancheng Chen Xin Zhang Jian-Rong Li 《Green Energy & Environment》 SCIE EI CAS 2025年第1期132-138,共7页
The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF str... The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction. 展开更多
关键词 Metal-organic frameworks High-throughput screening Machine learning Explainable model CO_(2)cycloaddition
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Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing
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作者 Mohd Anjum Naoufel Kraiem +2 位作者 Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第1期357-384,共28页
Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify sp... Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The 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 dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset. 展开更多
关键词 Computer vision feature selection machine learning region detection texture analysis image classification medical images
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A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare
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作者 Vajratiya Vajrobol Geetika Jain Saxena +6 位作者 Amit Pundir Sanjeev Singh Akshat Gaurav Savi Bansal Razaz Waheeb Attar Mosiur Rahman Brij B.Gupta 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第1期49-90,共42页
Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Num... Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Numerous applications have been developed to support the challenges in intelligent healthcare systems.However,because mental health data is sensitive,privacy concerns have emerged.Federated learning has gotten some attention.This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems.It explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health applications.This research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security concerns.The survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact. 展开更多
关键词 DEPRESSION emotional recognition intelligent healthcare systems mental health federated learning stress detection sleep behaviour
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Machine Learning Techniques in Predicting Hot Deformation Behavior of Metallic Materials
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作者 Petr Opela Josef Walek Jaromír Kopecek 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第1期713-732,共20页
In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot al... In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot always provide sufficiently reliable solutions.Nevertheless,Machine Learning(ML)techniques,which offer advanced regression tools to address complicated engineering issues,have been developed and widely explored.This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials.The ML-based regression methods of Artificial Neural Networks(ANNs),Support Vector Machine(SVM),Decision Tree Regression(DTR),and Gaussian Process Regression(GPR)are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon steel.Although the GPR method has not been used for such a regression task before,the results showed that its performance is the most favorable and practically unrivaled;neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis. 展开更多
关键词 Machine learning Gaussian process regression artificial neural networks support vector machine hot deformation behavior
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An Iterated Greedy Algorithm with Memory and Learning Mechanisms for the Distributed Permutation Flow Shop Scheduling Problem
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作者 Binhui Wang Hongfeng Wang 《Computers, Materials & Continua》 SCIE EI 2025年第1期371-388,共18页
The distributed permutation flow shop scheduling problem(DPFSP)has received increasing attention in recent years.The iterated greedy algorithm(IGA)serves as a powerful optimizer for addressing such a problem because o... The distributed permutation flow shop scheduling problem(DPFSP)has received increasing attention in recent years.The iterated greedy algorithm(IGA)serves as a powerful optimizer for addressing such a problem because of its straightforward,single-solution evolution framework.However,a potential draw-back of IGA is the lack of utilization of historical information,which could lead to an imbalance between exploration and exploitation,especially in large-scale DPFSPs.As a consequence,this paper develops an IGA with memory and learning mechanisms(MLIGA)to efficiently solve the DPFSP targeted at the mini-malmakespan.InMLIGA,we incorporate a memory mechanism to make a more informed selection of the initial solution at each stage of the search,by extending,reconstructing,and reinforcing the information from previous solutions.In addition,we design a twolayer cooperative reinforcement learning approach to intelligently determine the key parameters of IGA and the operations of the memory mechanism.Meanwhile,to ensure that the experience generated by each perturbation operator is fully learned and to reduce the prior parameters of MLIGA,a probability curve-based acceptance criterion is proposed by combining a cube root function with custom rules.At last,a discrete adaptive learning rate is employed to enhance the stability of the memory and learningmechanisms.Complete ablation experiments are utilized to verify the effectiveness of the memory mechanism,and the results show that this mechanism is capable of improving the performance of IGA to a large extent.Furthermore,through comparative experiments involving MLIGA and five state-of-the-art algorithms on 720 benchmarks,we have discovered that MLI-GA demonstrates significant potential for solving large-scale DPFSPs.This indicates that MLIGA is well-suited for real-world distributed flow shop scheduling. 展开更多
关键词 Distributed permutation flow shop scheduling MAKESPAN iterated greedy algorithm memory mechanism cooperative reinforcement learning
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Text-Image Feature Fine-Grained Learning for Joint Multimodal Aspect-Based Sentiment Analysis
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作者 Tianzhi Zhang Gang Zhou +4 位作者 Shuang Zhang Shunhang Li Yepeng Sun Qiankun Pi Shuo Liu 《Computers, Materials & Continua》 SCIE EI 2025年第1期279-305,共27页
Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimo... Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal features.Given this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for JMASA.First,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect terms.Then,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature learning.Thereby,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and enhanced.Experiments on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods. 展开更多
关键词 Multimodal sentiment analysis aspect-based sentiment analysis feature fine-grained learning graph convolutional network adjective-noun pairs
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