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A New Benchmark and Algorithm for Clothes-Changing Video Person Re-Identification

IEEE Transactions on Information Forensics and Security 2025年 20卷 1993-2005页

作者： Wang, Likai Zhang, Xiangqun Han, Ruize Wei, Yanjie Wang, Song Feng, Wei Tianjin University College of Intelligence and Computing Tianjin300350 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518107 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

Person re-identification (Re-ID) is a classical computer vision task and has significant applications for public security and information forensics. Recently, long-term Re-ID with clothes-changing has attracted increasing attention. However, existing methods mainly focus on image-based setting, where richer temporal information is overlooked. In this paper, we focus on the relatively new yet practical problem of Clothes-Changing Video-based Re-ID (CCVReID), which is less studied. First, given the dataset shortage, we build two new benchmark datasets for CCVReID problem, including a large-scale synthetic video dataset and a real-world one, both containing human sequences with various clothing changes. Moreover, we systematically study this problem by simultaneously considering the classical appearance feature and temporal feature contained in the video. We develop a dual-branch fusion framework that makes use of the information from both clothes-aware appearance feature and clothes-free gait feature. For better information fusion, a confidence-guided re-ranking strategy is proposed to adaptively balance the weight of these two categories of features. We have released the benchmark and code proposed in this work to the public at https://***/kkw98/CCVReID. © 2025 IEEE.

关键词： Reamers

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Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

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Computers, Materials & Continua 2023年第6期75卷 5255-5270页

作者： JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Safa Alsafari Ayman Yafoz Romany F.Mansour Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080002Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080002Colombia Department of Computer Science and AI Faculty of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the *** X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and *** radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer *** lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is *** current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on *** data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s ***,the OSDL model is applied to classify the CXRs under different severity levels based on CXR *** learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the *** model,applied in this study,was validated using the COVID-19 *** experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.

关键词： Artificial intelligence chest X-ray COVID-19 optimized synergic deep learning preprocessing public health

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Image-Based Extraction of Prescription Information using OCR-Tesseract 2

Image-Based Extraction of Prescription Information using OCR...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Ponnuru, Mahesh Ponmalar, Sridevi P. Likhitha, A. Sree, Tanu B. Chaitanya, Guna G. School of Computing SRM Institute of Science and Technology Potheri603203 India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Potheri603203 India School of Computer Science Vellore Institute of Technology Amaravathi522237 India

The paper presents advancements in healthcare data capture through the application of image-based extraction techniques, which include sophisticated image processing techniques such as resizing and adaptive thresholding, for prescription information. With the increasing digitization of medical records, automating the extraction of relevant data from prescription documents has become crucial. This research explores the utilization of image processing and optical character recognition (OCR) methodologies to extract prescription information accurately. By converting prescription documents into image format and employing OCR algorithms, the text content is extracted and parsed for critical details such as medication names, dosages, and patient instructions. Notably, our methodology excels in overcoming limitations associated with handwritten documents, achieving an impressive accuracy rate of 98%. This image-based approach offers a streamlined and efficient method for capturing prescription data, reducing manual data entry efforts, and minimizing potential errors. Experimental evaluations demonstrate the effectiveness and accuracy of the proposed approach, highlighting its potential to enhance healthcare data capture and improve patient care. © 2024 Elsevier B.V.. All rights reserved.

关键词： Optical character recognition

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Yield Optimization for Exothermic Batch Process Using Particle Swarm Optimization 6

Yield Optimization for Exothermic Batch Process Using Partic...

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6th IEEE International Conference on Artificial Intelligence in engineering and Technology, IICAIET 2024

作者： Tan, Min Keng Bansing, Matthew Laurentius Xu, Huiyi Chuo, Helen Sin Ee Tham, Heng Jin Teo, Kenneth Tze Kin Universiti Malaysia Sabah Modelling Simulation & Computing Laboratory Faculty of Engineering Kota Kinabalu Malaysia School of Marine Science and Engineering Hainan Tropical Ocean University Hainan China

ISBN: (纸本)9798350389692

Modern advancements in optimizing processes have been explored by various researchers, particularly for exothermic batch processes, which are prolific in the chemical and pharmaceutical industries. This paper proposes using artificial intelligence, specifically particle swarm optimization (PSO), to enhance these exothermic batch processes. Given that most exothermic batch processes utilize generic model control (GMC), the PSO algorithm will primarily focus on optimizing the control of the GMC within the process. The precise control of the controllers will aim to maintain the reactor temperature during the processes, ensuring stability and efficiency. This approach leverages AI to improve process performance, highlighting the potential of PSO in industrial applications. The proposed algorithm is shown to be able to produce a result of an increase of 4.5% of production of the desired product, while decreasing by-product production of 97%. The time it takes for the production of the desired product is also improved by 11.8%. This shows an efficiency increase of the entire exothermic batch processes, The results show that an improvement of the efficiency of the process makes the proposed algorithm a great method to improve the controller of the processes. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Lightweight Blockchain Prototype for Food Supply Chain Management 17

Lightweight Blockchain Prototype for Food Supply Chain Manag...

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17th International Conference on Security of Information and Networks, SIN 2024

作者： Rusakov, Alexey Moradpoor, Naghmeh Akbarzadeh, Aida School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom Norwegian University of Science and Technology Faculty of Information Technology and Electrical Engineering Gjovik Norway

ISBN: (纸本)9798331509736

The modern food supply chain often involves multiple layers of participants spread across different countries and continents. This complex system offers significant benefits to businesses worldwide;however, it also presents several challenges. One major problem is the inability to trace the product flow back to its origin, a critical issue in many industries. Another issue is the lack of trust among supply chain participants. Blockchain technology can help address these and other challenges faced by the supply chain industry. However, it is surprising that, globally, there are still not many examples of the technology's adoption, with most projects remaining in the pilot stage. This paper explores the field of custom blockchain design tailored to specific applications, with a focus on supply chain operations in the food industry. It includes the development of a lightweight yet fully featured Python prototype for a decentralized blockchain system. In addition to common features like block validation and state updates, the prototype includes a newly designed type of transaction tailored specifically for supply chain operations. These transactions eliminate the need for smart contracts, making the system more lightweight compared to general-purpose blockchain platforms such as Ethereum and less prone to security vulnerabilities. The prototype is designed as a public blockchain network, with Proof of Work selected as the consensus algorithm. The novelty of this research work lies in advancing the concept of a custom blockchain solution for the food industry. The key elements of the prototype have been unit tested. The overall evaluation was completed using a Python script that simulates product flow through an example supply chain, allowing product provenance to be determined by tracing the product flow back to its origin. © 2024 IEEE.

关键词： Food industry

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Road Traffic Accident Pattern Analysis with Data Mining 12

Road Traffic Accident Pattern Analysis with Data Mining

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12th International Conference on Awareness science and Technology, iCAST 2023

作者： Zhe Chai, Albe Bing Lau, Bee Theng Tsun Tee, Mark Kit McCarthy, Chris Faculty of Engineering Computing & Science Swinburne University of Technology Sarawak Campus Kuching Malaysia Department of Computer Science & Software Engineering Swinburne University of Technology Melbourne Australia

ISBN: (纸本)9798350324693

The discovery of Road Traffic Accident (RTA) patterns is vital to formulate mitigation strategies based on the characteristics of RTA. Various studies have applied association rule mining for RTA pattern discovery. However, limited research has conducted a comparative analysis of RTA patterns in different regions. This work is necessary to identify the similarities and differences of RTA characteristics between different regions, especially the developed and developing countries with different RTA trends in fatality rates. This work explored the application of association rule mining (FP-Growth algorithm) to discover and compare the RTA patterns for developed and developing countries. The Orange Data Mining software is used to discover RTA patterns for Ethiopia (12,317 samples) and the UK (United Kingdom) (1,048,575 samples), representing developing and developed countries. Eventually, findings showed similarities and differences in RTA patterns between Ethiopia and the UK. Road characteristics and conditions are the common factors contributing to RTA severity levels, while other crucial factors include the light condition and speed limit. In conclusion, the discovery of RTA patterns in different regions is beneficial to assist in formulating strategies to reduce the risk of RTA. Future work is necessary to discover the RTA patterns from different perspectives, such as seasonal or periodical variations of RTA patterns. © 2023 IEEE.

关键词： Association rules

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Optimized Evaluation of Mobile Base Station by Modern Topological Invariants

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Computers, Materials & Continua 2023年第1期74卷 363-378页

作者： Khalid Hamid Muhammad Waseem Iqbal Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Due to a tremendous increase in mobile traffic,mobile operators have started to restructure their networks to offload their *** directions will lead to fundamental changes in the design of future Fifthgeneration(5G)cellular *** the formal reason,the study solves the physical network of the mobile base station for the prediction of the best characteristics to develop an enhanced network with the help of graph *** number that can be uniquely calculated by a graph is known as a graph *** the last two decades,innumerable numerical graph invariants have been portrayed and used for correlation *** any case,no efficient assessment has been embraced to choose,how much these invariants are connected with a network *** paper will talk about two unique variations of the hexagonal graph with great capability of forecasting in the field of optimized mobile base station topology in setting with physical *** K-banhatti sombor invariants(KBSO)and Contrharmonic-quadratic invariants(CQIs)are newly introduced and have various expectation characteristics for various variations of hexagonal graphs or *** the hexagonal networks are used in mobile base stations in layered,forms called *** review settled the topology of a hexagon of two distinct sorts with two invariants KBSO and CQIs and their reduced *** deduced outcomes can be utilized for the modeling of mobile cellular networks,multiprocessors interconnections,microchips,chemical compound synthesis and memory interconnection *** results find sharp upper bounds and lower bounds of the honeycomb network to utilize the Mobile base station network(MBSN)for the high load of traffic and minimal traffic also.

关键词： Hexagonal networks k-banhatti sombor invariants maple network graph artificial intelligence contraharmonic-quadratic invariants honeycomb

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Occluded Person Re-Identification using Region-based Convolutional Neural Networks 23

Occluded Person Re-Identification using Region-based Convolu...

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3rd International Conference on Human Machine Interaction, ICHMI 2023

作者： Islam, Atiqul Tsun, Mark Tee Kit Bee Theng, Lau Chua, Caslon Faculty of Engineering Computing & Science Swinburne University of Technology Sarawak Campus Kuching Malaysia Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia

ISBN: (纸本)9798400700088

The ability to continuously follow a target person in a dynamically changing environment remains a major challenge that indoor companion robots confront. Ongoing human following is complicated by close similarity matching and target occlusions. Person Re-Identification (re-ID) is a computer vision task that can help with the problem of intentional or unintentional occlusion of the subject or person of interest. Numerous research has been conducted on re-ID where the basic assumption is that the gallery set will only have non-occluded person images. This paper examines the problem from a human detection perspective and surveys the state-of-the-art of person re-ID, their achievement and shortcomings. A region-based convolutional neural network (R-CNN) Re-ID is proposed in this research. The findings from the prototyping and evaluation demonstrate that the proposed person Re-ID model is reliable for indoor re-ID situations. Thus, future works in this direction include investigating other factors, such as the number of persons in a scene and varying viewing angles of the probed target person, which can impact the performance of the proposed re-ID model. © 2023 ACM.

关键词： Convolutional neural networks

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Aircraft parameter estimation using a stacked long short-term memory network and Levenberg-Marquardt method

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Chinese Journal of Aeronautics 2024年第2期37卷 123-136页

作者： Zhe HUI Yinan KONG Weigang YAO Gang CHEN School of Aerospace Science and Technology Xidian UniversityXi’an 710071China Computation Aerodynamics Institution China Aerodynamics Research and Development CenterMianyang 621000China Faculty of Computing Engineering and MediaDe Montfort UniversityLeicester LE19BHUnited Kingdom School of Aerospace Engineering Xi’an Jiaotong UniversityXi’an 710049China

To effectively estimate the unknown aerodynamic parameters from the aircraft’s flight data,this paper proposes a novel aerodynamic parameter estimation method incorporating a stacked Long Short-Term Memory(LSTM)network model and the Levenberg-Marquardt(LM)*** stacked LSTM network model was designed to realize the aircraft dynamics modeling by utilizing a frame of nonlinear functional mapping based entirely on the measured input-output data of the aircraft system without requiring explicit postulation of the *** LM method combines the already-trained LSTM network model to optimize the unknown aerodynamic *** proposed method is applied by using the real flight data,generated by ATTAS aircraft and a bio-inspired morphing Unmanned Aerial Vehicle(UAV).The investigation reveals that for the two different flight data,the designed stacked LSTM network structure can maintain the efficacy of the network prediction capability only by appropriately adjusting the dropout rates of its hidden layers without changing other network parameters(i.e.,the initial weights,initial biases,number of hidden cells,time-steps,learning rate,and number of training iterations).Besides,the proposed method’s effectiveness and potential are demonstrated by comparing the estimated results of the ATTAS aircraft or the bio-inspired morphing UAV with the corresponding reference values or wind-tunnel results.

关键词： Parameter estimation LSTM network model LM method Aerodynamic parameters Flight data Aircraft dynamics modeling Network prediction capability Network parameters

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Machine Learning Prediction Based Integrated Smart Energy Management System to Improve Home Energy Efficiency 21

Machine Learning Prediction Based Integrated Smart Energy Ma...

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21st International Symposium on Distributed computing and Applications for Business engineering and science, DCABES 2022

作者： Al-Adaileh, Ahmed Khaddaj, Souheil Kingston University Science Engineering & Computing Faculty London United Kingdom

ISBN: (纸本)9781665454629

This paper proposes an integrated smart energy management system that applies different machine learning regression techniques to gather, enhance, and prepare various relevant data taken from the surrounding environment to predict and schedule the running periods of one of the schedulable appliances in the household. The system was applied to a case study with encouraging results showing energy consumption reduction rates up to 36%. © 2022 IEEE.

关键词： Energy management

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