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Atherosclerotic plaque classification in carotid ultrasound images using machine learning and explainable deep learning

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Intelligent Medicine 2024年第2期4卷 83-95页

作者： Soni Singh Pankaj K.Jain Neeraj Sharma Mausumi Pohit Sudipta Roy School of Vocational Studies and Applied Sciences Gautam Buddha UniversityGreater NoidaUttar PradeshIndia artificial intelligence and Data Science Jio institute Navi MumbaiMaharashtraIndia School of Biomedical Engineering Indian Institute of TechnologyVaranasiUttar PradeshIndia

Objective The incidence of cardiovascular diseases(CVD)is rising rapidly *** forms of CVD,such as stroke and heart attack,are more common among patients with certain *** development is a major factor underlying cardiovascular events,such as heart attack and stroke,and its early detection may prevent such *** imaging of carotid arteries is a useful method for diagnosis of atherosclerotic plaques;however,an automated method to classify atherosclerotic plaques for evaluation of early-stage CVD is ***,we propose an automated method for classification of high-risk atherosclerotic plaque ultrasound *** Five deep learning(DL)models(VGG16,ResNet-50,GoogLeNet,XceptionNet,and SqueezeNet)were used for automated classification and the results compared with those of a machine learning(ML)-based technique,involving extraction of 23 texture features from ultrasound images and classification using a Support Vector Machine *** enhance model interpretability,output gradient-weighted convolutional activation maps(GradCAMs)were generated and overlayed on original *** A series of indices,including accuracy,sensitivity,specificity,F1-score,Cohen-kappa index,and area under the curve values,were calculated to evaluate model *** output images allowed visualization of the most significant ultrasound image *** GoogLeNet model yielded the highest accuracy(98.20%).Conclusion ML models may be also suitable for applications requiring low computational ***,DL models could be more completely automated than ML models.

关键词： Explainable deep learning Carotid artery Classification VGG 16 ResNet-50 GoogLeNet XceptionNet SqueezeNet

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ContextAug:model-domain failing test augmentation with contextual information

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Frontiers of Computer Science 2024年第2期18卷 43-60页

作者： Zhuo ZHANG Jianxin XUE Deheng YANG Xiaoguang MAO School of Information Technology and Engineering Guangzhou College of CommerceGuangzhou 511363China School of Computer and Information Engineering Institute for Artificial IntelligenceShanghai Polytechnic UniversityShanghai 201209China College of Computer National University of Defense TechnologyChangsha 410073China

In the process of software development,the ability to localize faults is crucial for improving the efficiency of *** speaking,detecting and repairing errant behavior at an early stage of the development cycle considerably reduces costs and development *** have tried to utilize various methods to locate the faulty ***,failing test cases usually account for a small portion of the test suite,which inevitably leads to the class-imbalance phenomenon and hampers the effectiveness of fault ***,in this work,we propose a new fault localization approach named *** obtaining dynamic execution through test cases,ContextAug traces these executions to build an information model;subsequently,it constructs a failure context with propagation dependencies to intersect with new model-domain failing test samples synthesized by the minimum variability of the minority feature *** contrast to traditional test generation directly from the input domain,ContextAug seeks a new perspective to synthesize failing test samples from the model domain,which is much easier to augment test *** conducting empirical research on real large-sized programs with 13 state-of-the-art fault localization approaches,ContextAug could significantly improve fault localization effectiveness with up to 54.53%.Thus,ContextAug is verified as able to improve fault localization effectiveness.

关键词： context fault localization test cases

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Real-Time Detection and Instance Segmentation of Strawberry in Unstructured Environment

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Computers, Materials & Continua 2024年第1期78卷 1481-1501页

作者： Chengjun Wang Fan Ding Yiwen Wang Renyuan Wu Xingyu Yao Chengjie Jiang Liuyi Ling School of artificial intelligence Anhui University of Science and TechnologyHuainan232001China School of Mechanical Engineering Anhui University of Science and TechnologyHuainan232001China

The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting ***-time identification of strawberries in an unstructured envi-ronment is a challenging *** instance segmentation algorithms for strawberries suffer from issues such as poor real-time performance and low *** this end,the present study proposes an Efficient YOLACT(E-YOLACT)algorithm for strawberry detection and segmentation based on the YOLACT *** key enhancements of the E-YOLACT encompass the development of a lightweight attention mechanism,pyramid squeeze shuffle attention(PSSA),for efficient feature ***,an attention-guided context-feature pyramid network(AC-FPN)is employed instead of FPN to optimize the architecture’s ***,a feature-enhanced model(FEM)is introduced to enhance the prediction head’s capabilities,while efficient fast non-maximum suppression(EF-NMS)is devised to improve non-maximum *** experimental results demonstrate that the E-YOLACT achieves a Box-mAP and Mask-mAP of 77.9 and 76.6,respectively,on the custom ***,it exhibits an impressive category accuracy of 93.5%.Notably,the E-YOLACT also demonstrates a remarkable real-time detection capability with a speed of 34.8 *** method proposed in this article presents an efficient approach for the vision system of a strawberry-picking robot.

关键词： YOLACT real-time detection instance segmentation attention mechanism strawberry

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Deciphering the Shared and Specific Drug Resistance Mechanisms of Anaplastic Lymphoma Kinase via Binding Free Energy Computation

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Research 2024年第1期2023卷 663-674页

作者： Yang Yu Zhe Wang Lingling Wang Qinghua Wang Rongfan Tang Sutong Xiang Qirui Deng Tingjun Hou Huiyong Sun Department of Medicinal Chemistry China Pharmaceutical UniversityNanjing 210009JiangsuP.R.China Innovation institute for artificial intelligence in Medicine of Zhejiang University College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058ZhejiangP.R.China

Anaplastic lymphoma kinase(ALK),a tyrosine receptor kinase,has been proven to be associated with the occurrence of numerous *** there have been already at least 3 generations of ALK inhibitors approved by FDA or in clinical trials,the occurrence of various mutations seriously attenuates the effectiveness of the ***,most of the drug resistance mechanisms still remain ***,it is necessary to reveal the bottom reasons of the drug resistance mechanisms caused by the *** this work,on the basis of verifying the accuracy of 2 main kinds of binding free energy calculation methodologies[end-point method of Molecular Mechanics with Poisson-Boltzmann/Generalized Born and Surface Area(MM/PB(GB)SA)and alchemical method of Thermodynamic Integration(TI)],we performed a systematic analysis on the ALK systems to explore the underlying shared and specific drug resistance mechanisms,covering the one-drug-multiple-mutation and multiple-drug-onemutation cases.

关键词： drugs Anaplastic clinical

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An attention-based teacher-student model for multivariate short-term landslide displacement prediction incorporating weather forecast data

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Journal of Mountain Science 2024年第8期21卷 2739-2753页

作者： CHEN Jun HU Wang ZHANG Yu QIU Hongzhi WANG Renchao Sichuan artificial intelligence Research institute Yibin 644000China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Architecture and Civil Engineering Chengdu UniversityChengdu 610106China

Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and ***,traditional methods have the limitation of random selection in sliding window selection and seldom incorporate weather forecast data for displacement prediction,while a single structural model cannot handle input sequences of different lengths at the same *** order to solve these limitations,in this study,a new approach is proposed that utilizes weather forecast data and incorporates the maximum information coefficient(MIC),long short-term memory network(LSTM),and attention mechanism to establish a teacher-student coupling model with parallel structure for short-term landslide displacement *** MIC,a suitable input sequence length is selected for the LSTM *** investigate the influence of rainfall on landslides during different seasons,a parallel teacher-student coupling model is developed that is able to learn sequential information from various time series of different *** teacher model learns sequence information from rainfall intensity time series while incorporating reliable short-term weather forecast data from platforms such as China Meteorological Administration(CMA)and Reliable Prognosis(https://***)to improve the model’s expression capability,and the student model learns sequence information from other time *** attention module is then designed to integrate different sequence information to derive a context vector,representing seasonal temporal attention ***,the predicted displacement is obtained through a linear *** proposed method demonstrates superior prediction accuracies,surpassing those of the support vector machine(SVM),LSTM,recurrent neural network(RNN),temporal convolutional network(TCN),and LSTM-Attention *** achieves a mean absolute error(MAE)of 0.072 mm,root mean square error(RMSE)of 0.096 mm,and pearson correlation coefficients(PC

关键词： Landslide prediction MIC LSTM Attention mechanism Teacher Student model Prediction stability and interpretability

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The inhibitory effect of adenosine on tumor adaptive immunity and intervention strategies

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Acta Pharmaceutica Sinica B 2024年第5期14卷 1951-1964页

作者： Longsheng Wang Jie Zhang Wenxin Zhang Mingming Zheng Hongjie Guo Xiaohui Pan Wen Li Bo Yang Ling Ding Zhejiang Province Key Laboratory of Anti-Cancer Drug Research Institute of Pharmacology and ToxicologyCollege of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China The Innovation institute for artificial intelligence in Medicine Zhejiang UniversityHangzhou 310018China Nanhu Brain-Computer Interface institute Hangzhou 311100China

Adenosine(Ado)is significantly elevated in the tumor microenvironment(TME)compared to normal *** binds to adenosine receptors(AdoRs),suppressing tumor antigen presentation and immune cell activation,thereby inhibiting tumor adaptive *** downregulates major histocompatibility complex II(MHC II)and co-stimulatory factors on dendritic cells(DCs)and macrophages,inhibiting antigen *** suppresses anti-tumor cytokine secretion and T cell activation by disrupting T cell receptor(TCR)binding and signal *** also inhibits chemokine secretion and KCa3.1 channel activity,impeding effector T cell trafficking and infiltration into the tumor ***,Ado diminishes T cell cytotoxicity against tumor cells by promoting immune-suppressive cytokine secretion,upregulating immune checkpoint proteins,and enhancing immune-suppressive cell *** Ado production in the TME can significantly enhance anti-tumor immune responses and improve the efficacy of other *** and clinical development of inhibitors targeting Ado generation or AdoRs is ***,this article will summarize and analyze the inhibitory effects and molecular mechanisms of Ado on tumor adaptive immunity,as well as provide an overview of the latest advancements in targeting Ado pathways in anti-tumor immune responses.

关键词： Adenosine Adenosine receptors Adenosine-generating enzymes Tumor microenvironment Immune suppression Immune escape Clinical treatment Targeting drugs

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Ada-FFL:Adaptive computing fairness federated learning

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CAAI Transactions on intelligence Technology 2024年第3期9卷 573-584页

作者： Yue Cong Jing Qiu Kun Zhang Zhongyang Fang Chengliang Gao Shen Su Zhihong Tian The Cyberspace institute of Advanced Technology Guangzhou UniversityGuangzhouChina Pengcheng Lab ShenzhenChina Carnegie Mellon University PittsburghPennsylvaniaUSA Mohamed Bin Zayed University of artificial intelligence Abu DhabiUnited Arab Emirates

As the scale of federated learning expands,solving the Non-IID data problem of federated learning has become a key challenge of *** existing solutions generally aim to solve the overall performance improvement of all clients;however,the overall performance improvement often sacrifices the performance of certain clients,such as clients with less *** fairness may greatly reduce the willingness of some clients to participate in federated *** order to solve the above problem,the authors propose Ada-FFL,an adaptive fairness federated aggregation learning algorithm,which can dynamically adjust the fairness coefficient according to the update of the local models,ensuring the convergence performance of the global model and the fairness between federated learning *** integrating coarse-grained and fine-grained equity solutions,the authors evaluate the deviation of local models by considering both global equity and individual equity,then the weight ratio will be dynamically allocated for each client based on the evaluated deviation value,which can ensure that the update differences of local models are fully considered in each round of ***,by combining a regularisation term to limit the local model update to be closer to the global model,the sensitivity of the model to input perturbations can be reduced,and the generalisation ability of the global model can be *** numerous experiments on several federal data sets,the authors show that our method has more advantages in convergence effect and fairness than the existing baselines.

关键词： adaptive fariness aggregation fairness federated learning non-IID

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Personality Trait Detection via Transfer Learning

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Computers, Materials & Continua 2024年第2期78卷 1933-1956页

作者： Bashar Alshouha Jesus Serrano-Guerrero Francisco Chiclana Francisco P.Romero Jose A.Olivas Department of Information Technologies and Systems University of Castilla-La ManchaCiudad Real13071Spain institute of artificial intelligence School of Computer Science and InformaticsDe Montfort UniversityThe GatewayLeicesterLE19BHUK

Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains,including education,e-commerce,or human ***-ditional machine learning techniques have been broadly employed for personality trait identification;nevertheless,the development of new technologies based on deep learning has led to new opportunities to improve their *** study focuses on the capabilities of pre-trained language models such as BERT,RoBERTa,ALBERT,ELECTRA,ERNIE,or XLNet,to deal with the task of personality *** models are able to capture structural features from textual content and comprehend a multitude of language facets and complex features such as hierarchical relationships or long-term *** makes them suitable to classify multi-label personality traits from reviews while mitigating computational *** focus of this approach centers on developing an architecture based on different layers able to capture the semantic context and structural features from ***,it is able to fine-tune the previous models using the MyPersonality dataset,which comprises 9,917 status updates contributed by 250 Facebook *** status updates are categorized according to the well-known Big Five personality model,setting the stage for a comprehensive exploration of personality *** test the proposal,a set of experiments have been performed using different metrics such as the exact match ratio,hamming loss,zero-one-loss,precision,recall,F1-score,and weighted *** results reveal ERNIE is the top-performing model,achieving an exact match ratio of 72.32%,an accuracy rate of 87.17%,and 84.41%of *** findings demonstrate that the tested models substantially outperform other state-of-the-art studies,enhancing the accuracy by at least 3%and confirming them as powerful tools for personality *** findings represent substantial a

关键词： Personality trait detection pre-trained language model big five model transfer learning

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DCFNet:Discriminant Correlation Filters Network for Visual Tracking

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Journal of Computer Science & Technology 2024年第3期39卷 691-714页

作者：胡卫明王强高晋李兵 Stephen Maybank State Key Laboratory of Multimodal artificial intelligence Systems Institute of AutomationChinese Academy of Sciences Beijing 100190China Department of Computer Science and Information Systems Birkbeck CollegeLondon WC1E 7HXU.K.

CNN(convolutional neural network)based real time trackers usually do not carry out online network update in order to maintain rapid tracking *** inevitably influences the adaptability to changes in object *** filter based trackers can update the model parameters online in real *** this paper,we present an end-to-end lightweight network architecture,namely Discriminant Correlation Filter Network(DCFNet).A differentiable DCF(discriminant correlation filter)layer is incorporated into a Siamese network architecture in order to learn the convolutional features and the correlation filter *** correlation filter can be efficiently updated *** previous work,we introduced a joint scale-position space to the DCFNet,forming a scale DCFNet which carries out the predictions of object scale and position *** combine the scale DCFNet with the convolutional-deconvolutional network,learning both the high-level embedding space representations and the low-level fine-grained representations for *** adaptability of the fine-grained correlation analysis and the generalization capability of the semantic embedding are complementary for visual *** back-propagation is derived in the Fourier frequency domain throughout the entire work,preserving the efficiency of the *** evaluations on the OTB(Object Tracking Benchmark)and VOT(Visual Object Tracking Challenge)datasets demonstrate that the proposed trackers have fast speeds,while maintaining tracking accuracy.

关键词： correlation filter convolutional neural network(CNN) visual tracking

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A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of Information

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 1045-1075页

作者： Hao Jiang Yuerong Liao Dongdong Zhao Wenjian Luo Xingyi Zhang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei230601China Chongqing Research institute School of Computer Science and Artificial IntelligenceWuhan University of TechnologyWuhan430070China Guangdong Provincial Key Laboratory of Novel Security intelligence Technologies School of Computer Science and TechnologyHarbin Institute of TechnologyShenzhen518055China

Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many *** by the self-nonself discrimination paradigmin the biological immune system,the negative representation of information indicates features such as simplicity and efficiency,which is very suitable for preserving social network ***,we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks,called ***,a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept ***,a negative database-based method is proposed to hide node attributes,so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes,which is crucial to the analysis of social *** evaluate the performance of the AttNetNRI,empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social *** experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding *** experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.

关键词： Attributed social network topology privacy node attribute privacy negative representation of information negative survey negative database

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