Article
Computer Science, Artificial Intelligence
Junbo Lian, Guohua Hui
Summary: This paper introduces the Human Evolutionary Optimization Algorithm (HEOA), which is a metaheuristic algorithm inspired by human evolution. The algorithm divides the global search process into two distinct phases and uses unique search strategies. Comparative analysis with other algorithms demonstrates the effectiveness of HEOA in approximating optimal solutions for complex global optimization problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Chemistry, Multidisciplinary
Jorge M. Cruz-Duarte, Jose C. Ortiz-Bayliss, Ivan Amaya, Nelishia Pillay
Summary: In this work, a heuristic-based solver model called 'unfolded' metaheuristics (uMHs) is proposed for tackling the Metaheuristic Composition Optimisation Problem. The feasibility and effectiveness of this model are demonstrated through experiments, as well as the study of parameter implications on solver performance within the uMH model.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Ke-Jing Du, Jian-Yu Li, Hua Wang, Jun Zhang
Summary: Evolutionary multi-objective multi-task optimization is an emerging paradigm for solving multi-objective multi-task optimization problems using evolutionary computation. This paper proposes treating these problems as multi-objective multi-criteria optimization problems and develops an algorithm framework that utilizes the knowledge of all tasks in the same population. The algorithm selects fitness evaluation functions as criteria, guided by a probability-based selection strategy and an adaptive parameter learning method. Extensive experiments show the effectiveness and efficiency of the proposed algorithm. Treating MO-MTOP as MO-MCOP is a potential and promising direction for solving these problems.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Automation & Control Systems
Yousef Abdi, Mohammad Asadpour, Yousef Seyfari
Summary: In this study, a hybrid micro multi-objective evolutionary algorithm called mu MOSM is proposed to effectively address diversity loss and accelerate the convergence rate in approximating Pareto front solutions.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Yinan Shao, Jerry Chun-Wei Lin, Gautam Srivastava, Dongdong Guo, Hongchun Zhang, Hu Yi, Alireza Jolfaei
Summary: This article introduces a method for optimizing deep reinforcement learning models using neural evolutionary algorithms to solve combinatorial optimization problems. The proposed end-to-end multi-objective neural evolutionary algorithm demonstrates competitive and robust performance on the classic travel salesman problem and knapsack problem, and also performs well in inference time.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Zhe Liu, Fei Han, Qinghua Ling, Henry Han, Jing Jiang
Summary: This paper proposes a many-objective optimization evolutionary algorithm based on the hyper-dominance degree. It quantifies the convergence of each solution using hyper-dominance degree and balances convergence and diversity through tolerance adjusting, reference vectors-based diversity preservation, and population reselection strategies.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Chnoor M. Rahman, Tarik A. Rashid, Aram Mahmood Ahmed, Seyedali Mirjalili
Summary: In this work, a new multi-objective optimization algorithm called multi-objective learner performance-based behavior algorithm is proposed. The proposed algorithm is based on the process of moving graduated students from high school to college, and it produces a set of non-dominated solutions with better accuracy and diversity. Experimental results show that the algorithm outperforms other algorithms in terms of solution quality and processing time.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Yongkuan Yang, Jianchang Liu, Shubin Tan
Summary: Many MOEAs are developed to solve CMOPs, but they encounter low efficiency for steady-state CMOPs. This paper proposes a multi-objective evolutionary algorithm named FACE, which maintains the known feasible solution in the second population and evolves together with the main population. Performance comparisons show the efficiency and scalability of FACE for steady-state CMOPs.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
M. Sri Srinivasa Raju, Saykat Dutta, Rammohan Mallipeddi, Kedar Nath Das
Summary: The existence of constrained multi-objective optimization problems (CMOPs) has led researchers to develop constrained multi-objective evolutionary algorithms (CMOEAs). In order to handle CMOPs with discontinuous feasible regions or infeasible barriers, a novel Dual-Population and Multi-Stage based Constrained Multi-objective Evolutionary Algorithm (CMOEA-DPMS) is proposed, along with a new constraint handling technique (CHT) called decomposition based constraint non-dominating sorting (DCDSort) to maintain feasibility, convergence, and diversity.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Interdisciplinary Applications
Xingyi Yao, Wenhua Li, Xiaogang Pan, Rui Wang
Summary: This study focuses on the multi-objective path planning problem and proposes a new solution-encoding method and environmental selection strategy to address the multi-modal minimum path problems. The experiments prove that the proposed method is effective and efficient for multimodal multi-objective path planning.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Information Systems
Haiping Ma, Haoyu Wei, Ye Tian, Ran Cheng, Xingyi Zhang
Summary: Constrained multi-objective optimization problems are challenging to handle due to the complexities of objectives and constraints. To address this issue, a multi-stage evolutionary algorithm is proposed in this paper, which gradually adds constraints and sorts their handling priority based on their impact on the Pareto front. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art algorithms in dealing with complex constraint problems.
INFORMATION SCIENCES
(2021)
Article
Automation & Control Systems
Yulong Ye, Qiuzhen Lin, Ka-Chun Wong, Jianqiang Li, Zhong Ming, Carlos A. Coello Coello
Summary: This paper proposes a localized decomposition evolutionary algorithm (LDEA) to tackle imbalanced multi-objective optimization problems (MOPs). LDEA assigns a local region for each subproblem using a localized decomposition method and restricts the solution update within the region to maintain diversity. It also speeds up convergence by evolving only the best-associated solution in each subproblem while balancing the population's diversity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Computer Science, Information Systems
Kai Zhang, Chaonan Shen, Juanjuan He, Gary G. Yen
Summary: The proposed MMO-EvoKnee algorithm incorporates MCDM strategy to efficiently search for a complete set of global knee solutions for MMOPs. It outperforms existing state-of-the-art MMOEAs and provides decision makers with well-converged alternative solutions.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Jiawei Yuan, Hai-Lin Liu, Zhaoshui He
Summary: Research has shown that a mixture of feasible and infeasible solutions is beneficial for solving constrained multi-objective optimization problems, and the proposed criterion is more effective in identifying valuable infeasible solutions. The algorithm performs well in dealing with complex CMOPs and can successfully handle problems where the initial population is located in infeasible regions below the Pareto fronts.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Construction & Building Technology
Gongyue Xu, Zemin Feng, Erkuo Guo, Changwang Cai, Huafeng Ding
Summary: This study established a many-objective optimization model of a new type hydraulic shovel named TriRocker, and proposed an improved many-objective differential evolution algorithm to solve the optimization problem. The most satisfactory solution was chosen through multicriteria decision-making method, resulting in a wonderful design of the TriRocker hydraulic shovel.
AUTOMATION IN CONSTRUCTION
(2022)
Article
Construction & Building Technology
James Hey, Peer-Olaf Siebers, Paul Nathanail, Ender Ozcan, Darren Robinson
Summary: Modelling energy retrofit adoption in urban building stocks is important for policymakers. Surrogate models and optimization procedures are insufficient, but recent methods using neural networks can decrease computational costs. By including carbon valuation when training predictive models, the impact of households' changing attitudes to emissions can be analyzed.
JOURNAL OF BUILDING PERFORMANCE SIMULATION
(2023)
Article
Green & Sustainable Science & Technology
Fatma S. S. Alrayes, Mashael M. M. Asiri, Mashael Maashi, Ahmed S. S. Salama, Manar Ahmed Hamza, Sara Saadeldeen Ibrahim, Abu Sarwar Zamani, Mohamed Ibrahim Alsaid
Summary: Artificial intelligence techniques are crucial for the development of smart cities. Intrusion detection systems can provide an effective solution for security in smart environments. This article proposes a intrusion detection method called IDCPRO-DLM, which combines chaotic poor and rich optimization with deep learning models. The IDCPRO-DLM model demonstrates better performance than state-of-the-art approaches, achieving a maximum accuracy of 98.53% on the benchmark CICIDS dataset.
Article
Chemistry, Multidisciplinary
Latifah Almuqren, Mashael S. Maashi, Mohammad Alamgeer, Heba Mohsen, Manar Ahmed Hamza, Amgad Atta Abdelmageed
Summary: This study presents a Explainable Artificial Intelligence Enabled Intrusion Detection Technique for Secure Cyber-Physical Systems (XAIID-SCPS) that integrates feature selection and parameter optimization algorithms and uses the black-box method for accurate classification.
APPLIED SCIENCES-BASEL
(2023)
Article
Oncology
Marwa Obayya, Adeeb Alhebri, Mashael Maashi, Ahmed S. Salama, Anwer Mustafa Hilal, Mohamed Ibrahim Alsaid, Azza Elneil Osman, Amani A. Alneil
Summary: Early diagnosis of skin cancer is crucial for effective treatment. Dermoscopy is a non-invasive method that uses specific equipment to examine the skin and determine the presence of skin cancer. Machine Learning algorithms, such as Convolutional Neural Networks, have been developed to analyze dermoscopic images and classify them as benign or malignant. This study proposes a new Deep Learning-based skin cancer classification method, which has the potential to improve the accuracy and efficiency of diagnosis and produce better outcomes for patients.
Article
Oncology
Hanan Abdullah Mengash, Mohammad Alamgeer, Mashael Maashi, Mahmoud Othman, Manar Ahmed Hamza, Sara Saadeldeen Ibrahim, Abu Sarwar Zamani, Ishfaq Yaseen
Summary: The timely and initial diagnosis of cancer is crucial for reducing the possibility of death. Deep learning and machine learning methods can accelerate cancer recognition and provide a cost-effective way for examination. This study introduces a technique called MPADL-LC3 which uses a marine predator's algorithm with deep learning to properly classify different types of lung and colon cancer based on histopathological images.
Article
Green & Sustainable Science & Technology
Manal Abdullah Alohali, Naif Alasmari, Mashael Maashi, Amal M. Nouri, Mohammed Rizwanullah, Ishfaq Yaseen, Azza Elneil Osman, Amani A. Alneil
Summary: Information technologies have transformed human life in various aspects, contributing to the changing landscape of phishing attacks. This paper proposes a novel metaheuristics deep learning-oriented approach, named MDLPD-SSE, for sustainable and secure phishing detection. The MDLPD-SSE model primarily focuses on identifying phishing websites by preprocessing the input URL, applying feature selection, utilizing the LSTM model, and fine-tuning hyperparameters using the BES optimization methodology. The proposed model achieves an improved accuracy of 95.78%.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2023)
Article
Mathematics
Hadeel Alsolai, Wafa Mtouaa, Mashael S. Maashi, Mahmoud Othman, Ishfaq Yaseen, Amani A. Alneil, Azza Elneil Osman, Mohamed Ibrahim Alsaid
Summary: Next-generation Internet-of-Things applications require large bandwidth, increased network capabilities, and low latency. Using drone-based stations can efficiently address these requirements and improve network performance. The proposed method uses a two-layer optimizer based on a pre-trained VGG-19 model and utilizes non-orthogonal multiple access protocol.
Article
Environmental Sciences
Fatma S. Alrayes, Mashael M. Asiri, Mashael S. Maashi, Mohamed K. Nour, Mohammed Rizwanullah, Azza Elneil Osman, Suhanda Drar, Abu Sarwar Zamani
Summary: The rapid advancement of deep learning technology has led to the development of various network architectures for classification, making it easier to implement intelligent waste classification systems. However, existing waste classification models suffer from issues such as low accuracy and slow processing. This study proposes a Vision Transformer based on Multilayer Hybrid Convolution Neural Network (VT-MLH-CNN) for automatic waste classification, which improves classification accuracy and reduces processing time. The simulation results show that the proposed method outperforms certain current techniques with a simplified network model and higher waste categorization accuracy, achieving a classification accuracy of up to 95.8% on the TrashNet dataset.
Article
Computer Science, Information Systems
Mashael Maashi, Hayam Alamro, Heba Mohsen, Noha Negm, Gouse Pasha Mohammed, Noura Abdelaziz Ahmed, Sara Saadeldeen Ibrahim, Mohamed Ibrahim Alsaid
Summary: This paper introduces a new approach for copy-move forgery detection using a deep transfer learning-based algorithm and search algorithm. The proposed model utilizes NASNet for feature extraction, optimizes network hyperparameters using a deep transfer learning and search algorithm, and classifies image regions using XGBoost. Experimental results demonstrate the superior performance of the proposed method in copy-move forgery detection.
Article
Computer Science, Information Systems
Hadeel Alsolai, Mohammed Rizwanullah, Mashael Maashi, Mahmoud Othman, Amani A. Alneil, Amgad Atta Abdelmageed
Summary: Soil classification is an important topic in many countries, and with the increasing population and demand for food, there is a need for computer-related methods to support agriculturalists. This study introduces a technique that uses deep learning and computer vision approaches to classify soil types. Through simulation analysis, it is shown that this technique can achieve high precision in soil classification.
CMC-COMPUTERS MATERIALS & CONTINUA
(2023)
Article
Computer Science, Information Systems
Mohammad Alamgeer, Hend Khalid Alkahtani, Mashael Maashi, Mahmoud Othman, Anwer Mustafa Hilal, Mohamed Ibrahim Alsaid, Azza Elneil Osman, Amani A. A. Alneil
Summary: Floods are a severe and frequent natural calamity, causing significant economic damage and higher mortality rates. This study introduces an optimal Fuzzy Wavelet Neural Network based Road Damage Detection (OFWNN-RDD) technique for Flooding Management. The technique utilizes remote sensing images, Gabor filtering, DenseNet121 model, and modified barnacles mating optimization for accurate road damage detection, achieving an accuracy of 98.56%.
Article
Mathematics
Manal Abdullah Alohali, Mashael Maashi, Raji Faqih, Hany Mahgoub, Abdullah Mohamed, Mohammed Assiri, Suhanda Drar
Summary: Traffic surveillance systems are used to collect and monitor road network traffic data, which plays a crucial role in Intelligent Transportation Systems (ITS). However, accurate vehicle detection and counting in traffic videos is challenging. This study proposes a deep learning-based vehicle counting and classification model to address this issue.
ELECTRONIC RESEARCH ARCHIVE
(2023)
Article
Computer Science, Information Systems
Mofadal Alymani, Hadeel Alsolai, Mashael Maashi, Adeeb Alhebri, Hussain Alshahrani, Fahd N. Al-Wesabi, Abdullah Mohamed, Manar Ahmed Hamza
Summary: Unmanned aerial vehicles (UAVs) have the potential to perform automatic emergency tasks in marine ecosystems, but the real-time communication between UAVs and base platforms is a challenge. This study introduces a new path planning technique (DFSCSO-PP) based on Cuckoo Search Optimization to optimize data transmission paths and resource allocation in UAV networks.
Article
Mathematics
Mashael S. Maashi, Yasser Ali Reyad Ali, Abdelwahed Motwakel, Amira Sayed A. Aziz, Manar Ahmed Hamza, Amgad Atta Abdelmageed
Summary: Gastric Cancer (GC) is the fifth most common tumor worldwide and early diagnosis is crucial for saving lives. This study proposes an Anas Platyrhynchos Optimizer with Deep Learning-based Gastric Cancer Classification (APODL-GCC) method for categorizing GC using endoscopic images. The experimental results demonstrate that the APODL-GCC technique achieves improved performance compared to other models in GC classification.
ELECTRONIC RESEARCH ARCHIVE
(2023)
Article
Computer Science, Hardware & Architecture
Ali Nauman, Haya Mesfer Alshahrani, Nadhem Nemri, Kamal M. Othman, Nojood O. Aljehane, Mashael Maashi, Ashit Kumar Dutta, Mohammed Assiri, Wali Ullah Khan
Summary: The integration of terrestrial and satellite wireless communication networks offers a practical solution to enhance network coverage, connectivity, and cost-effectiveness. This study introduces a resource allocation framework that leverages local cache pool deployments and non-orthogonal multiple access (NOMA) to improve energy efficiency. Through the use of a multi-agent enabled deep deterministic policy gradient algorithm (MADDPG), the proposed approach optimizes user association, cache design, and transmission power control, resulting in enhanced energy efficiency and reduced time delays compared to existing methods.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
