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4th International Conference on computer Vision, Application, and Algorithm, CVAA 2024

作者： Jiang, Wei Hu, Bin State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Department of Computer Science College of Computer Science & Technology Guizhou University Guiyang550025 China Artificial Intelligence Research Institute of Guizhou University Guizhou University Guiyang550025 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

In high-density crowd, a unique visual motion effect called stop-and-go wave occurs, which could evolve to trampling and compression incidents. However, few computational models have been reported for stop-and-go wave perception form the angle of computer vision. On the bases of the neural structures of the locusts’ vision systems and the frequency resonance properties of the interneuronal (INs) neurons in locust’s brains, this paper investigates a bio-inspired visual neural network (sgWPNN) for stop-and-go wave perception in high-density crowd scenes. The proposed sgWPNN firstly visually stabilizes the image frames;and then converts the spatial intensity visual information to the frequency domain for extracting crowd’s motion cues;finally, sgWPNN output membrane potential impulses to identify the occurring of stop-and-go wave in the field of view. Videos filmed high-density crowd activities in two scenarios demonstrate the effectiveness of sgWPNN in the perception against to the unique stop-and-go wave effect in moving crowd scenes. This work discusses the biological inspired processing of dynamic visual information in crowd activity perception, which can provide some new ideas for crowd activity detection and behavior analysis. © 2025 SPIE.

关键词： Frequency domain analysis

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Beyond administrative reports: a deep learning framework for classifying and monitoring crime and accidents leveraging large-scale online news

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Neural Computing and Applications 2025年第10期37卷 7183-7205页

作者： Tuarob, Suppawong Tatiyamaneekul, Phonarnun Pongpaichet, Siripen Tawichsri, Tanisa Noraset, Thanapon Faculty of Information and Communication Technology Mahidol University Nakhon Pathom Thailand Department of Computer Science University College London London United Kingdom Puey Ungphakorn Institute for Economic Research Bank of Thailand Bangkok Thailand

The escalating prevalence of violent crimes and accidents underscores the urgent need for efficient and timely monitoring systems. Traditional methods reliant on administrative reports often suffer from significant delays. This paper proposes CRIMSON, a novel framework that leverages large-scale online news to provide real-time insights into crime and accident trends. CRIMSON utilizes a multi-label classification technique that leverages a fine-tuned, pre-trained, cross-lingual language model to accurately categorize news articles. Our experimental results, conducted on a substantial dataset of Thai news articles, demonstrate superior performance, achieving an average F1 score of 86%. Beyond classification, CRIMSON aggregates categorized news into real-time statistics, revealing strong correlations between news-reported incidents and official crime data. This study pioneers online news as a reliable and timely crime and accident monitoring source, offering valuable insights for law enforcement, policymakers, and researchers. © The Author(s) 2025.

关键词： Deep learning

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research hotspots and trend analysis of text generation for explainable recommendation based on CiteSpace 6

Research hotspots and trend analysis of text generation for ...

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6th International Conference on Advanced Electronic Materials, computers, and Software Engineering, AEMCSE 2023

作者： Meng, Wenjun Xu, Dawei Yu, Runde School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Huadian E-commerce Technology Co. LTD China College of Cybersecurity Changchun University Changchun China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781510668249

Bibliometric analysis was applied to retrieve the international core journals on the subject of text generation for an explainable recommendation from 2000 to 2022. CiteSpace, a visualization-based analysis tool, was used to analyze the research status and recent development of text generation for explainable recommendations by mapping the co-occurrence of high-frequency keywords and citation bursts. The results show that in the past 22 years, the number of international articles on text generation for an explainable recommendation has been on the rise with more publications especially compared with that in China, calling for further exchange and collaboration among scholars and institutions over the world to facilitate the research progress. "Personalized"recommendation has been exerting its influence in text generation for explainable recommendations, which effectively gain the trust of users, and increase the persuasiveness and satisfaction of the recommendation system by providing recommendations with explainable texts. Text processing in Deep Learning has now been widely used for explainable recommendations and will throw its weight further in the future. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Text processing

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An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

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computer Modeling in Engineering & sciences 2024年第3期138卷 2029-2059页

作者： Sheetal Sharma Kamali Gupta DeepaliGupta Shalli Rani Gaurav Dhiman Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjab140401India PG Department of Information Technology Goswami Ganesh Dutta Sanatan Dharma CollegeChandigarh160030India Department of Computer Science Government Bikram College of CommercePatialaIndia Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon University Centre for Research and Development Department of Computer Science and EngineeringChandigarh UniversityMohali140413India Department of Computer Science and Engineering Graphic Era Deemed to be UniversityDehradun248002India Division of Research and Development Lovely Professional UniversityPunjab144001India

The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making themmore intelligent and connected. However, this advancement comes with challenges related to the effectiveness ofIoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensuretheir proper functionality. The success of smart systems relies on their seamless operation and ability to handlefaults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore,sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart *** address these concerns, various techniques and algorithms can be employed to enhance the performance ofIoT devices through effective fault detection. This paper conducted a thorough review of the existing literature andconducted a detailed *** analysis effectively links sensor errors with a prominent fault detection techniquecapable of addressing them. This study is innovative because it paves theway for future researchers to explore errorsthat have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essentialfactors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and theircorresponding recommended techniques. Additionally, the paper presents amethodical overview of fault detectiontechniques employed in smart devices, including themetrics used for evaluation. Furthermore, the paper examinesthe body of academic work related to sensor faults and fault detection techniques within the domain. This reflectsthe growing inclination and scholarly attention of researchers and academicians toward strategies for fault detectionwithin the realm of the Internet of Things.

关键词： Error fault detection techniques sensor faults outliers Internet of Things

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Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

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computers, Materials & Continua 2023年第7期76卷 993-1007页

作者： A.Manju R.Kaladevi Shanmugasundaram Hariharan Shih-Yu Chen Vinay Kukreja Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang Department of Computer Science and Engineering SRM Institute of Science and TechnologyRamapuramChennaiIndia Department of Computer Science and Engineering Saveetha Engineering CollegeChennaiIndia Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabadIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin 64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin 64002Taiwan Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Computing Science University of AberdeenAberdeenUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh 12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh 11437Saudi Arabia School of Computer&Communication Engineering Changsha University of Science&TechnologyChangshaChina

The medical community has more concern on lung cancer *** experts’physical segmentation of lung cancers is time-consuming and needs to be *** research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning ***-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor *** application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation *** research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold *** Honey hook process categorizes lung cancer based on characteristics retrieved using several *** this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using *** proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)*** study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal *** contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the *** is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.

关键词： Lung tumor deep wave auto encoder decision tree classifier deep neural networks extraction techniques

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Multi-scale network for single image deblurring based on ensemble learning module

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Multimedia Tools and Applications 2025年第11期84卷 9045-9064页

作者： Wu, WenBo Pan, Yun Su, Na Wang, JingTao Wu, ShaoChuan Xu, ZeKun Yu, YouJian Liu, YaPeng State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China School of Computer and Cyberspace Security Communication University of China Beijing100024 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China College of Computer and Control Engineering Northeast Forestry University Harbin150040 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China School of Computer Science and Technology Anhui University Hefei230601 China

In this paper, we have identified two primary issues with current multi-scale image deblurring methods. On the one hand, the blurring scale is ignored. On the other hand, the context information of images is not fully utilized, and the spatial detail information of images is lost. To better solve the above problems, on top of that, this paper proposes a multi-scale network for single image deblurring. The designed network contains two parts: 1) Ensemble Learning Model: The Weak Learning Model in this module connects multiple weak learners in series to increase network depth. Moreover, through multi-scale feature ensemble module, the output features and intermediate features of different weak learner are integrated, and finally strong learner is used to enhance the features with semantic information at different scales;2) Spatial Feature Enhancement Module: This module can preserve spatial detail information of images and compensate for the disadvantages of losing image spatial details due to continuous downsampling in the Ensemble Learning Model structure. Our proposed deblurring model has been extensively evaluated on several benchmark datasets, and the model achieves superior performance compared to the most advanced deblurring methods. In particular, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values are 33.57 dB and 0.965 on the GoPro dataset, respectively, with PSNR and SSIM improving by 0.49 dB and 0.002 over NAFNet, and 0.65 dB and 0.004 over Restormer. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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FewFine: Few-shot Malware Traffic Classification Via Transfer Learning based on Fine-tuning Strategy 19

FewFine: Few-shot Malware Traffic Classification Via Transfe...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Liu, Xingtong Shen, Meng Cui, Laizhong Ye, Ke Jia, Jizhe Yue, Guangchun School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China Shenzhen University College of Computer Science and Software Engineering Shenzhen China School of Computer Science Beijing Institute of Technology Beijing China

ISBN: (纸本)9798350346558

Malware traffic is constantly evolving and remains destructive. The detection and classification of malware traffic is crucial for maintaining cyberspace security. Only by swiftly and accurately detecting and classifying malware traffic can user privacy and cyberspace security be effectively *** this paper, we propose FewFine, an approach for few-shot malware traffic classification based on transfer learning. We initially pre-train a detection model and two classification models with substantial quantity of malware and application traffic samples. For classifying new types of malware traffic accurately and promptly, we utilize transfer learning based on fine-tuning strategy and freeze several blocks in the pre-trained model. Utilizing prior knowledge from the pre-trained models, we leverage few samples of novel classes to perform accurate malware detection and classification. We execute extensive experiments on publicly available datasets to evaluate the effectiveness of FewFine. In model pre-training, with considerable number of samples, the accuracy of malware detection and classification can reach 0.99. The pre-trained models are saved for fine-tuning. When detecting and classifying novel malware traffic, FewFine can achieve the accuracy of 0.95 leveraging only 10 samples per class through fine-tuning the pre-trained model. It outperforms methods under comparison in terms of accuracy and efficiency. © 2022 IEEE.

关键词： Knowledge management

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Design of Neural Network Based Wind Speed Prediction Model Using GWO

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computer Systems science & Engineering 2022年第2期40卷 593-606页

作者： R.Kingsy Grace R.Manimegalai Department of Computer Science and Engineering Sri Ramakrishna Engineering CollegeCoimbatore641022India Department of Computer Science and Engineering PSG Institute of Technology and Applied ResearchCoimbatore641062India

The prediction of wind speed is imperative nowadays due to the increased and effective generation of wind *** power is the clean,free and conservative renewable *** is necessary to predict the wind speed,to implement wind power *** paper proposes a new model,named WT-GWO-BPNN,by integrating Wavelet Transform(WT),Back Propagation Neural Network(BPNN)and GreyWolf Optimization(GWO).The wavelet transform is adopted to decompose the original time series data(wind speed)into approximation and detailed ***-BPNN is applied to predict the wind *** is used to optimize the parameters of back propagation neural network and to improve the convergence *** work uses wind power data of six months with 25,086 data points to test and verify the performance of the proposed *** proposed work,WT-GWO-BPNN,predicts the wind speed using a three-step procedure and provides better *** Absolute Error(MAE),Mean Squared Error(MSE),Mean absolute percentage error(MAPE)and Root mean squared error(RMSE)are calculated to validate the performance of the proposed *** results demonstrate that the proposed model has better performance when compared to other methods in the literature.

关键词： Wind speed wavelet transform back propagation neural network grey wolf optimization time series

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On the design of an AI-driven secure communication scheme for internet of medical things environment

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Digital Communications and Networks 2023年第5期9卷 1080-1089页

作者： Neha Garg Rajat Petwal Mohammad Wazid D.P.Singh Ashok Kumar Das Joel J.P.C.Rodrigues Department of Computer Science and Engineering Graphic Era Deemed to be UniversityDehradun248002India Center for Security Theory and Algorithmic ResearchInternational Institute of Information TechnologyHyderabad500032India College of Computer Science and Technology China University of Petroleum(East China)Qingdao266555China Instituto de Telecomunicaçoes 6201-001CovilhaPortugal

The Internet of Medical Things(IoMT)is a collection of smart healthcare devices,hardware infrastructure,and related software applications,that facilitate the connection of healthcare information technology system via the *** is also called IoT in healthcare,facilitating secure communication of remote healthcare devices over the Internet for quick and flexible analysis of healthcare *** other words,IoMT is an amalgam of medical devices and applications,which improves overall healthcare ***,this system is prone to securityand privacy-related attacks on healthcare ***,providing a robust security mechanism to prevent the attacks and vulnerability of IoMT is *** mitigate this,we proposed a new Artificial-Intelligence envisioned secure communication scheme for *** discussed network and threat models provide details of the associated network arrangement of the IoMT devices and attacks relevant to ***,we provide the security analysis of the proposed scheme to show its security against different possible ***,a comparative study of the proposed scheme with other similar schemes is *** results show that the proposed scheme outperforms other similar schemes in terms of communication and computation costs,and security and functionality ***,we provide a pragmatic study of the proposed scheme to observe its impact on various network performance parameters.

关键词： Internet of Medical Things(IoMT) Security Authentication and key agreement Artificial Intelligence(AI) Big data analytics

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A circuit area optimization of MK-3 S-box

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Cybersecurity 2025年第1期8卷 61-71页

作者： Yanjun Li Weiguo Zhang Yiping Lin Jian Zou Jian Liu Information Industry Information Security Evaluation Center The 15th Research Institute of China Electronic Technology Group CorporationBeijing 100083China Henan Key Laboratory of Network Cryptography Technology Zhengzhou 450001China Beijing Electronic Science and Technology Institute No.7 Fufeng RoadFengtai DistricBeijing 100083China College of Computer and Data Science Fuzhou UniversityFuzhou 350108China

In MILCOM 2015,Kelly et *** the authentication encryption algorithm MK-3,which applied the 16-bit *** paper aims to implement the 16-bit S-box with less circuit ***,we classifed the irreducible polynomials over F_(2n)into three *** we compared the logic gates required for multiplication over the fnite feld constructed by the three types of irreducible *** to the comparison result,we constructed the composite felds, F_((2^(4))^(2))and F_((2^(8))^(2)).Based on the isomorphism of fnite felds,the operations over F_(2)^(16)can be conducted over F_((2^(8))^(2)).Similarly,elements over F28 can be mapped to the corresponding elements over F_((2^(4)))^(2).Next,the SAT solver was used to optimize the operations over smaller feld F_(2)^(4).At last,the architecture of the optimized MK-3 S-box was worked *** with the implementation proposed by the original designer,the circuit area of the MK-3 S-box in this paper is reduced by at least 55.9%.

关键词： irreducible operations optimization

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