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Behaviour recognition based on the integration of multigranular motion features in the Internet of Things

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Digital Communications and Networks 2024年第3期10卷 666-675页

作者： Lizong Zhang Yiming Wang Ke Yan Yi Su Nawaf Alharbe Shuxin Feng school of computer science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China Beijing Institute of Remote Sensing Equipment Beijing100039China Applied college Taibah UniversityMedina42353Saudi Arabia

With the adoption of cutting-edge communication technologies such as 5G/6G systems and the extensive development of devices,crowdsensing systems in the Internet of Things(IoT)are now conducting complicated video analysis tasks such as behaviour *** applications have dramatically increased the diversity of IoT ***,behaviour recognition in videos usually requires a combinatorial analysis of the spatial information about objects and information about their dynamic actions in the temporal *** recognition may even rely more on the modeling of temporal information containing short-range and long-range motions,in contrast to computer vision tasks involving images that focus on understanding spatial ***,current solutions fail to jointly and comprehensively analyse short-range motions between adjacent frames and long-range temporal aggregations at large scales in *** this paper,we propose a novel behaviour recognition method based on the integration of multigranular(IMG)motion features,which can provide support for deploying video analysis in multimedia IoT crowdsensing *** particular,we achieve reliable motion information modeling by integrating a channel attention-based short-term motion feature enhancement module(CSEM)and a cascaded long-term motion feature integration module(CLIM).We evaluate our model on several action recognition benchmarks,such as HMDB51,Something-Something and *** experimental results demonstrate that our approach outperforms the previous state-of-the-art methods,which confirms its effective-ness and efficiency.

关键词： Behaviour recognition Motion features Attention mechanism Internet of things Crowdsensing

Measurement of acceleration and angular velocity of particles during a 3D silo discharge

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Particuology 2024年第9期92卷 61-71页

作者： Tongtong Mu Yiming Li Quan Chen Ran Li Hui Yang college of Medical Instrument Shanghai University of Medicine and Health SciencesShanghai201318China school of Optical-Electrical and computer Engineering University of Shanghai for Science and TechnologyShanghai200093China

Accurate measurement of the three-dimensional(3D)movement of discrete particles is crucial for comprehending complex granular rheology in *** this paper,the acceleration and angular velocity of particles in 3D silos are measured by using a spherical detector based on inertial technology and magnetic positioning *** acceleration of particles is the largest in the center of silos,which suggest that the resistance generated by friction and extrusion is the ***,the angular velocity distribution follows lognormal function except for particles near the *** correlation between acceleration and angular velocity is opposite in different flow *** reveals for the first time that the extent to which the resultant force on the particles affects their rotational motion is related to the flow *** results have practical significance for regulating the granular flow pattern and optimizing the structural design of silos.

关键词： Granularflow Silo Angular velocity Acceleration

Laguerre Series Expansion for Scale Functions and Its Applications in Risk Theory

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Acta Mathematicae Applicatae Sinica 2024年

作者： Jia-yi XIE Zhen-yu CUI Zhi-min ZHANG school of Business Stevens Institute of Technology college of Mathematics science Chongqing Normal University college of Mathematics and statistics Chongqing University

We propose an exact explicit closed-form Laguerre series expansion formula to compute the q-scale function of a spectrally negative L′evy process (SNLP), and other functions associated to the scale function for the first time. The proposed closed-form formula for the scale function has many applications in applied probability and in particular in the Le′vy insurance risk theory. We shall show that the new series expansion formulas can be used to express the expected discounted penalty functions, the moments of the present value of total dividend payments as well as the time value of Parisian ruin in the L′evy risk models.

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GliomaCNN: An Effective Lightweight CNN Model in Assessment of Classifying Brain Tumor from Magnetic Resonance Images Using Explainable AI

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computer Modeling in Engineering & sciences 2024年第9期140卷 2425-2448页

作者： Md.Atiqur Rahman Mustavi Ibne Masum Khan Md Hasib M.F.Mridha Sultan Alfarhood Mejdl Safran Dunren Che Department of computer science and Engineering Ahsanullah University of Science and TechnologyDhaka1208Bangladesh Department of computer science and Software Engineering The University of Western AustraliaPerthWA 6009Australia Department of computer science American International University-BangladeshDhaka1229Bangladesh Department of computer science College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia school of Computing Southern Illinois UniversityCarbondale62901USA

Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global *** study addresses the pressing issue of brain tumor classification using Magnetic resonance imaging(MRI).It focuses on distinguishing between Low-Grade Gliomas(LGG)and High-Grade Gliomas(HGG).LGGs are benign and typically manageable with surgical resection,while HGGs are malignant and more *** research introduces an innovative custom convolutional neural network(CNN)model,*** stands out as a lightweight CNN model compared to its *** research utilized the BraTS 2020 dataset for its *** with the gradient-boosting algorithm,GliomaCNN has achieved an impressive accuracy of 99.1569%.The model’s interpretability is ensured through SHapley Additive exPlanations(SHAP)and Gradient-weighted Class Activation Mapping(Grad-CAM++).They provide insights into critical decision-making regions for classification *** challenges in identifying tumors in images without visible signs,the model demonstrates remarkable performance in this critical medical application,offering a promising tool for accurate brain tumor diagnosis which paves the way for enhanced early detection and treatment of brain tumors.

关键词： Deep learning magnetic resonance imaging convolutional neural networks explainable AI boosting algorithm ablation

A Comprehensive Survey for Privacy-Preserving Biometrics: Recent Approaches, Challenges, and Future Directions

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computers, Materials & Continua 2024年第2期78卷 2087-2110页

作者： Shahriar Md Arman Tao Yang Shahadat Shahed Alanoud AlMazroa Afraa Attiah Linda Mohaisen school of computer science and Technology Zhejiang Gongshang UniversityHangzhou310018China school of computer science and Technology Hangzhou Dianzi UniversityHangzhou310018China college of computer and Information sciences Princess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia

The rapid growth of smart technologies and services has intensified the challenges surrounding identity authenti-cation *** credentials are increasingly being used for verification due to their advantages over traditional methods,making it crucial to safeguard the privacy of people’s biometric data in various *** paper offers an in-depth exploration for privacy-preserving techniques and potential threats to biometric *** proposes a noble and thorough taxonomy survey for privacy-preserving techniques,as well as a systematic framework for categorizing the field’s existing *** review the state-of-the-art methods and address their advantages and limitations in the context of various biometric modalities,such as face,fingerprint,and eye *** survey encompasses various categories of privacy-preserving mechanisms and examines the trade-offs between security,privacy,and recognition performance,as well as the issues and future research *** aims to provide researchers,professionals,and decision-makers with a thorough understanding of the existing privacy-preserving solutions in biometric recognition systems and serves as the foundation of the development of more secure and privacy-preserving biometric technologies.

关键词： Biometric modalities biometric recognition biometric security privacy-preserving security threats

Multi-Objective Equilibrium Optimizer for Feature Selection in High-Dimensional English Speech Emotion Recognition

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computers, Materials & Continua 2024年第2期78卷 1957-1975页

作者： Liya Yue Pei Hu Shu-Chuan Chu Jeng-Shyang Pan Fanli Business school Nanyang Institute of TechnologyNanyang473004China school of computer and Software Nanyang Institute of TechnologyNanyang473004China college of computer science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung413310Taiwan

Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by *** number of features acquired with acoustic analysis is extremely high,so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition *** proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum ***,we use the information gain and Fisher Score to sort the features extracted from ***,we employ a multi-objective ranking method to evaluate these features and assign different importance to *** with high rankings have a large probability of being ***,we propose a repair strategy to address the problem of duplicate solutions in multi-objective feature selection,which can improve the diversity of solutions and avoid falling into local *** random forest and K-nearest neighbor classifiers,four English speech emotion datasets are employed to test the proposed algorithm(MBEO)as well as other multi-objective emotion identification *** results illustrate that it performs well in inverted generational distance,hypervolume,Pareto solutions,and execution time,and MBEO is appropriate for high-dimensional English SER.

关键词： Speech emotion recognition filter-wrapper high-dimensional feature selection equilibrium optimizer multi-objective

A multi-stream network for retrosynthesis prediction

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Frontiers of computer science 2024年第2期18卷 231-233页

作者： Qiang ZHANG Juan LIU Wen ZHANG Feng YANG Zhihui YANG Xiaolei ZHANG Institute of Artificial Intelligence School of Computer ScienceWuhan UniversityWuhan 430072China college of Informatics Huazhong Agricultural UniversityWuhan 430072China

1 Introduction Retrosynthesis aims to predict a set of reactants for producing given molecules[1],which plays a significant part in the biochemistry field,such as molecular pathway design and drug *** existing methods perform better in solving this problem,most of these methods[2,3]only describe the molecules from one kind of perspective,such as they use Simplified Molecular Input Line Entry System(SMILES)strings[4]to represent the atomic components and the relationship between atoms of molecules or leverage Extended Connectivity Fingerprints(ECFPs)represent the information of the molecular sub-structure composition via binary vectors[5].

关键词： synthesis reactant hough

Left-invariant metrics on a class of solvable Lie Groups

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Acta Mathematica Sinica,English Series 2024年

作者： Zhi Qi CHEN Ke YAN Hui ZHANG school of Mathematics and statistics Guangdong University of Technology college of science National University of Defense Technology school of Mathematics Southeast University

Left-invariant Riemannian metrics on Lie groups G_(RHn-1)×R and GRH2×Rn-2（n ≥ 3） have been classified by Hiroshi, Takahara and Tamaru. It is easily seen that G_(RHn-1)×R and GRH2×Rn-2（n ≥ 3） have the same automorphism group, which is denoted by L_n. In this paper, we first classify n-dimensional simply connected Lie groups with automorphism group L_n, then we classify the leftinvariant Riemannian metrics on such Lie groups. As an application, we get the m-quasi Einstein metrics.

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Quasi-shadowing for Z^(d)-actions

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Acta Mathematica Sinica,English Series 2024年第6期40卷 1563-1580页

作者： Juan PAN Xian Kun REN Yun Hua ZHOU school of Mathematics and statistics Chongqing Technology and Business UniversityChongqing 400067P.R.China college of Mathematics and statistics Chongqing UniversityChongqing 401331P.R.China

A diffeomorphism is non-uniformly partially hyperbolic if it preserves an ergodic measure with at least one zero Lyapunov *** prove that a C^(1)-smooth Z^(d)-action has the quasishadowing property if one of the genera... 详细信息

关键词： Quasi-shadowing Z^(d)-action non-uniformly partially hyperbolic ergodic measure

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