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Novel Rifle Number Recognition Based on Improved YOLO in Military Environment

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Computers, Materials & Continua 2024年第1期78卷 249-263页

作者： Hyun Kwon Sanghyun Lee department of artificial intelligence and data science Korea Military AcademySeoulKorea Graduate School of Information Security Korea Advanced Institute of Science and TechnologyDaejeonKorea

Deep neural networks perform well in image recognition,object recognition,pattern analysis,and speech recog-nition.In military applications,deep neural networks can detect equipment and recognize objects.In military equipment,it is necessary to detect and recognize rifle management,which is an important piece of equipment,using deep neural networks.There have been no previous studies on the detection of real rifle numbers using real rifle image datasets.In this study,we propose a method for detecting and recognizing rifle numbers when rifle image data are insufficient.The proposed method was designed to improve the recognition rate of a specific dataset using data fusion and transfer learningmethods.In the proposed method,real rifle images and existing digit images are fusedas trainingdata,andthe final layer is transferredto theYolov5 algorithmmodel.The detectionand recognition performance of rifle numbers was improved and analyzed using rifle image and numerical datasets.We used actual rifle image data(K-2 rifle)and numeric image datasets,as an experimental environment.TensorFlow was used as the machine learning library.Experimental results show that the proposed method maintains 84.42% accuracy,73.54% precision,81.81% recall,and 77.46% F1-score in detecting and recognizing rifle numbers.The proposed method is effective in detecting rifle numbers.

关键词： Machine learning deep neural network rifle number recognition detection

Correlation analysis between lung ultrasound scores and pulmonary arterial systolic pressure in patients with acute heart failure admitted to the emergency intensive care unit

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Journal of Intensive Medicine 2024年第1期4卷 125-132页

作者： Ping Xu Basma Nasr Liang Li Wenbin Huang Wei Liu Xuelian Wang Emergency department Zigong Fourth People’s HospitalZigongChina Institute of Medical Big data Zigong Academy of Artificial Intelligence and Big Data for Medical ScienceZigongChina artificial intelligence Key Laboratory of Sichuan Province ZigongChina department of Cardiology First Affiliated Hospital of Dalian Medical UniversityDalianChina

Background:No convenient,inexpensive,and non-invasive screening tools exist to identify pulmonary hypertension(PH)-left heart disease(LHD)patients during the early stages of the disease course.This study investigated whether different methods of lung ultrasound(LUS)could be used for the initial investigation of PH-LHD.Methods:This was a single-center prospective observational study which was performed in the Zigong Fourth People’s Hospital.We consecutively enrolled patients with heart failure(HF)admitted to the emergency intensive care unit from January 2018 to May 2020.Transthoracic echocardiography and LUS were performed within 24 h before discharge.We used the Spearman coefficient for correlation analysis between ultrasound scores and pulmonary arterial systolic pressure(PASP).Bland-Altman plots were generated to inspect possible bias,and receiver operating characteristic(ROC)curves were calculated to assess the relationship between ultrasound scores and an intermediate and high echocardiographic probability of PH-LHD.Results:Seventy-one patients were enrolled in this study,with an overall median age of 79(interquartile range:71.5–84.0)years.Among the 71 patients,36(50.7%)cases were male,and 26(36.6%)had an intermediate and high echocardiographic probability of PH.All four LUS scores in patients with an intermediate and high probability of PH were significantly higher than in patients with a low probability of PH(P<0.05).The correlation coefficient(r)between different LUS scoring methods and PASP was moderate for the 6-zone(r=0.455,P<0.001),8-zone(r=0.385,P=0.001),12-zone(r=0.587,P<0.001),and 28-zone(r=0.535,P<0.001)methods.In Bland-Altman plots,each of the four LUS scoring methods had a good agreement with PASP(P<0.001).The 8-zone and 12-zone methods showed moderately accurate discriminative values in differentiating patients with an intermediate and high echocardiographic probability of PH(P<0.05).

关键词： Acute heart failure Pulmonary arterial systolic pressure Pulmonary hypertension Left heart disease Lung ultrasound

A Tutorial on Federated Learning from Theory to Practice:Foundations,Software Frameworks,Exemplary Use Cases,and Selected Trends

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 824-850页

作者： M.Victoria Luzón Nuria Rodríguez-Barroso Alberto Argente-Garrido Daniel Jiménez-López Jose M.Moyano Javier Del Ser Weiping Ding Francisco Herrera department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain department of Computer science and artificial intelligence Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain department of Communications Engineering University of the Basque Country(UPV/EHU)and also with TECNALIABasque Research&Technology Alliance(BRTA)Spain School of Information science and Technology Nantong UniversityNantong 226019China

When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized environment.FL allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third parties.This paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data sources.By virtue of FL,models can be learned from all such distributed data sources while preserving data privacy.The aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software frameworks.Furthermore,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL landscape.The ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.

关键词： data privacy distributed machine learning federated learning software frameworks

Enhancing Identity Protection in Metaverse-Based Psychological Counseling System

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Computers, Materials & Continua 2024年第1期78卷 617-632页

作者： Jun Lee Hanna Lee Seong Chan Lee Hyun Kwon department of Game Software Hoseo UniversityAsan-si31499Korea Yatav Enter Seoul04799Korea department of artificial intelligence and data science Korea Military AcademySeoul01805Korea

Non-face-to-face psychological counseling systems rely on network technologies to anonymize information regard-ing client identity.However,these systems often face challenges concerning voice data leaks and the suboptimal communication of the client’s non-verbal expressions,such as facial cues,to the counselor.This study proposes a metaverse-based psychological counseling system designed to enhance client identity protection while ensuring efficient information delivery to counselors during non-face-to-face counseling.The proposed systemincorporates a voicemodulation function that instantlymodifies/masks the client’s voice to safeguard their identity.Additionally,it employs real-time client facial expression recognition using an ensemble of decision trees to mirror the client’s non-verbal expressions through their avatar in the metaverse environment.The system is adaptable for use on personal computers and smartphones,offering users the flexibility to access metaverse-based psychological counseling across diverse environments.The performance evaluation of the proposed system confirmed that the voice modulation and real-time facial expression replication consistently achieve an average speed of 48.32 frames per second or higher,even when tested on the least powerful smartphone configurations.Moreover,a total of 550 actual psychological counseling sessions were conducted,and the average satisfaction rating reached 4.46 on a 5-point scale.This indicates that clients experienced improved identity protection compared to conventional non-face-to-face metaverse counseling approaches.Additionally,the counselor successfully addressed the challenge of conveying non-verbal cues from clients who typically struggled with non-face-to-face psychological counseling.The proposed systemholds significant potential for applications in interactive discussions and educational activities in the metaverse.

关键词： Metaverse counseling system face tracking identity protection

A hierarchical clustering approach for colorectal cancer molecular subtypes identification from gene expression data

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Intelligent Medicine 2024年第1期4卷 43-51页

作者： Shivangi Raghav Aastha Suri Deepika Kumar Aakansha Aakansha Muskan Rathore Sudipta Roy department of Computer science&Engineering Bharati Vidyapeeth’s College of EngineeringNew DelhiIndia artificial intelligence&data science Jio InstituteNavi Mumbai410206India

Background Colorectal cancer(CRC)is the second leading cause of cancer fatalities and the third most common human disease.Identifying molecular subgroups of CRC and treating patients accordingly could result in better therapeutic success compared with treating all CRC patients similarly.Studies have highlighted the significance of CRC as a major cause of mortality worldwide and the potential benefits of identifying molecular subtypes to tailor treatment strategies and improve patient outcomes.Methods This study proposed an unsupervised learning approach using hierarchical clustering and feature selection to identify molecular subtypes and compares its performance with that of conventional methods.The proposed model contained gene expression data from CRC patients obtained from Kaggle and used dimension reduction techniques followed by Z-score-based outlier removal.Agglomerative hierarchy clustering was used to identify molecular subtypes,with a P-value-based approach for feature selection.The performance of the model was evaluated using various classifiers including multilayer perceptron(MLP).Results The proposed methodology outperformed conventional methods,with the MLP classifier achieving the highest accuracy of 89%after feature selection.The model successfully identified molecular subtypes of CRC and differentiated between different subtypes based on their gene expression profiles.Conclusion This method could aid in developing tailored therapeutic strategies for CRC patients,although there is a need for further validation and evaluation of its clinical significance.

关键词： Machine learning Colorectal cancer Feature selection Classification Clustering

Weighted Probability Density Estimator with Updated Bandwidths

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Journal of Systems science & Complexity 2024年第2期37卷 886-906页

作者： WU Yi YU Wei WANG Xuejun PRAKASA RAO B L S School of Big data and artificial intelligence Chizhou UniversityChizhou 247000China School of Big data and Statistics Anhui UniversityHefei 230601China CR Rao Advanced Institute of Mathematics Statistics and Computer ScienceHyderabad 500046India

In this paper,the authors study a class of weighted version of probability density estimator.It is shown that the weighted estimator contains some existing estimators of probability density,no matter they are recursive or non-recursive.Some statistical results including weak consistency,strong consistency,rate of strong consistency,and asymptotic normality are established under some mild conditions.Moreover,the random weighted estimator is also investigated.Some numerical simulations and a real data analysis are presented to study the numerical performances of the estimators.

关键词： Asymptotic normality consistency numerical simulation probability density real data analysis updated bandwidths weighted estimator

CAW-YOLO:Cross-Layer Fusion and Weighted Receptive Field-Based YOLO for Small Object Detection in Remote Sensing

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Computer Modeling in Engineering & sciences 2024年第6期139卷 3209-3231页

作者： Weiya Shi Shaowen Zhang Shiqiang Zhang College of artificial intelligence and Big data Henan University of TechnologyZhengzhou450001China College of Information science and Engineering Henan University of TechnologyZhengzhou450001China

In recent years,there has been extensive research on object detection methods applied to optical remote sensing images utilizing convolutional neural networks.Despite these efforts,the detection of small objects in remote sensing remains a formidable challenge.The deep network structure will bring about the loss of object features,resulting in the loss of object features and the near elimination of some subtle features associated with small objects in deep layers.Additionally,the features of small objects are susceptible to interference from background features contained within the image,leading to a decline in detection accuracy.Moreover,the sensitivity of small objects to the bounding box perturbation further increases the detection difficulty.In this paper,we introduce a novel approach,Cross-Layer Fusion and Weighted Receptive Field-based YOLO(CAW-YOLO),specifically designed for small object detection in remote sensing.To address feature loss in deep layers,we have devised a cross-layer attention fusion module.Background noise is effectively filtered through the incorporation of Bi-Level Routing Attention(BRA).To enhance the model’s capacity to perceive multi-scale objects,particularly small-scale objects,we introduce a weightedmulti-receptive field atrous spatial pyramid poolingmodule.Furthermore,wemitigate the sensitivity arising from bounding box perturbation by incorporating the joint Normalized Wasserstein Distance(NWD)and Efficient Intersection over Union(EIoU)losses.The efficacy of the proposedmodel in detecting small objects in remote sensing has been validated through experiments conducted on three publicly available datasets.The experimental results unequivocally demonstrate the model’s pronounced advantages in small object detection for remote sensing,surpassing the performance of current mainstream models.

关键词： Small object detection attention mechanism cross-layer fusion discrete cosine transform

Mathematics of multi-agent learning systems at the interface of game theory and artificial intelligence

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science China(Information sciences) 2024年第6期67卷 445-447页

作者： Long WANG Feng FU Xingru CHEN Center for Systems and Control College of EngineeringPeking University Center for Multi-Agent Research Institute for Artificial IntelligencePeking University department of Mathematics Dartmouth College department of Biomedical data science Geisel School of Medicine at Dartmouth School of science Beijing University of Posts and Telecommunications

Evolutionary game theory (EGT) and artificial intelligence(AI) are two fields that,at first glance,might seem distinct,but they have notable connections and intersections.The former focuses on the evolution of behaviors (or strategies)in a population,where individuals interact with others and update their strategies based on imitation (or social learning).

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Part-Whole Relational Few-Shot 3D Point Cloud Semantic Segmentation

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Computers, Materials & Continua 2024年第3期78卷 3021-3039页

作者： Shoukun Xu Lujun Zhang Guangqi Jiang Yining Hua Yi Liu School of Computer science and artificial intelligence Aliyun School of Big Data and School of SoftwareChangzhou UniversityChangzhou213164China department of Computer science University of AberdeenKing’s CollegeAberdeenAB243FXUK

This paper focuses on the task of few-shot 3D point cloud semantic segmentation.Despite some progress,this task still encounters many issues due to the insufficient samples given,e.g.,incomplete object segmentation and inaccurate semantic discrimination.To tackle these issues,we first leverage part-whole relationships into the task of 3D point cloud semantic segmentation to capture semantic integrity,which is empowered by the dynamic capsule routing with the module of 3D Capsule Networks(CapsNets)in the embedding network.Concretely,the dynamic routing amalgamates geometric information of the 3D point cloud data to construct higher-level feature representations,which capture the relationships between object parts and their wholes.Secondly,we designed a multi-prototype enhancement module to enhance the prototype discriminability.Specifically,the single-prototype enhancement mechanism is expanded to the multi-prototype enhancement version for capturing rich semantics.Besides,the shot-correlation within the category is calculated via the interaction of different samples to enhance the intra-category similarity.Ablation studies prove that the involved part-whole relations and proposed multi-prototype enhancement module help to achieve complete object segmentation and improve semantic discrimination.Moreover,under the integration of these two modules,quantitative and qualitative experiments on two public benchmarks,including S3DIS and ScanNet,indicate the superior performance of the proposed framework on the task of 3D point cloud semantic segmentation,compared to some state-of-the-art methods.

关键词： Few-shot point cloud semantic segmentation CapsNets