Article
Computer Science, Information Systems
Yi Liu, Wei Qin, Jinhui Zhang, Mengmeng Li, Qibin Zheng, Jichuan Wang
Summary: In this paper, a novel multi-objective variant of ant lion optimizer is proposed, which addresses the issues of updating the archive and selecting elite individuals and ant lions by introducing a new measure and the concept of time weight. Experimental results demonstrate that the proposed algorithm outperforms other methods in terms of performance and time complexity.
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
(2021)
Article
Computer Science, Information Systems
Wei Chen, Panlong Yang, Wei Zhao, Linna Wei
Summary: Coverage optimization is an important research topic in wireless sensor networks. In this study, an improved ant lion optimizer (IALO) is proposed to solve the coverage optimization problem. IALO enhances population diversity and accelerates convergence speed by alternately executing Cuckoo Search and Cauchy mutation. Additionally, differential evolution is introduced to improve the convergence accuracy of the algorithm. Experimental results demonstrate the effectiveness of IALO in achieving higher coverage rate and reducing deployment cost in wireless sensor networks.
WIRELESS COMMUNICATIONS & MOBILE COMPUTING
(2022)
Article
Mathematics
Olympia Roeva, Dafina Zoteva, Gergana Roeva, Velislava Lyubenova
Summary: In this study, the ant lion optimizer (ALO) and genetic algorithm (GA) were hybridized for the first time, creating the novel ALO-GA hybrid algorithm. Through testing on benchmark functions and parameter identification in an Escherichia coli cultivation process, the ALO-GA algorithm demonstrated superior global optimization ability compared to other competing algorithms.
Article
Computer Science, Information Systems
Chengfeng Peng, Zhantao Li, Hongyang Zhong, Xiang Li, Anping Lin, Yong Liao
Summary: With the increasing automation rate of workshops and the significance of energy consumption, more and more enterprises are required not only to make scheduling decisions on production equipment but also to consider whether the scheduling of transportation equipment supports workshop production decisions. Since both workshop production scheduling and transportation scheduling are NP-hard problems, an efficient algorithm is necessary to improve workshop productivity. To solve this problem, a manufacturing-transportation multi-objective joint scheduling optimization mathematical model is established based on problem structure, production environment, and optimization objectives. The proposed algorithm incorporates a design idea of memetic algorithm (MA) and non-dominated sorting genetic algorithm-II (NSGA-II) as the basis framework, along with an effective encoding scheme, initialization method, and neighborhood search mechanism. The algorithm's parameter design is completed through variance analysis, and its advantages in solving the problem are verified by comparing and analyzing it with other algorithms in terms of hypervolume and Set Coverage (SC).
Article
Multidisciplinary Sciences
Xiaofeng Yue, Hongbo Zhang
Summary: This work introduces an improved ant lion optimizer called BIALO, which combines a novel inertial weight, the local search part of the bat algorithm, and the invasive weed optimization algorithm to search for the best solutions in industrial image enhancement. Tests show that BIALO outperforms other metaheuristic algorithms in achieving better outcomes.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2021)
Review
Computer Science, Interdisciplinary Applications
Laith Abualigah, Mohammad Shehab, Mohammad Alshinwan, Seyedali Mirjalili, Mohamed Abd Elaziz
Summary: The Ant Lion Optimizer (ALO) is a novel metaheuristic swarm-based approach introduced by Mirjalili in 2015 to emulate the hunting behavior of ant lions in nature. It aims to enhance the performance of functional and efficient during the optimization process by finding the minimum or maximum values to solve a certain problem. Metaheuristic algorithms have become a research focus with the introduction of decision-making and asses the benefits in solving various optimization problems.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Nima Khodadadi, Laith Abualigah, Seyedali Mirjalili
Summary: The single-objective version of the Stochastic Paint Optimizer (SPO) has been modified to address multi-objective optimization problems and is now known as MOSPO. SPO utilizes color theory, the color wheel, and color combination methods to achieve excellent exploration and exploitation capabilities. By using four simple color combination rules without internal parameters and incorporating principles like a fixed-sized external archive, the recommended MOSPO technique differs from the original single-objective SPO. Furthermore, a leader selection feature has been added to SPO to accommodate multi-objective optimization. Testing performed on various mathematical and engineering design problems demonstrates that MOSPO outperforms other multi-objective optimization algorithms such as MOPSO, MSSA, and multi-objective ant lion optimizer in terms of precision and uniformity. Based on different performance metrics including generational distance, inverted generational distance, maximum spread, and spacing, the proposed algorithm consistently produces high-quality Pareto fronts with competitive convergence.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Green & Sustainable Science & Technology
Guoqiang Niu, Xiaoyong Li, Xin Wan, Xinzhong He, Yinzhong Zhao, Xiaohui Yi, Chen Chen, Liang Xujun, Guangguo Ying, Mingzhi Huang
Summary: This paper proposes a novel dynamic optimization control method based on multi-objective ant lion optimization and deep learning algorithm, which can optimize energy consumption and effluent quality simultaneously in the wastewater treatment processes. By using a deep belief network model to predict the objective function, and solving it with the ant lion optimization method under constraints, the optimal solution is selected by an intelligent decision system. Finally, the proportional integral controllers are used to track and control the optimal dynamic parameters. The experimental results demonstrate that this method significantly reduces energy consumption and meets the standards of effluent quality parameters.
JOURNAL OF CLEANER PRODUCTION
(2022)
Article
Thermodynamics
Wenbin Gu, Zhuo Li, Min Dai, Minghai Yuan
Summary: The paper investigates the multi-objective permutation flow shop scheduling problem and proposes a hybrid cuckoo search algorithm to efficiently solve such problems. By designing a series of methods and rules, the algorithm's performance and efficiency are improved.
ADVANCES IN MECHANICAL ENGINEERING
(2021)
Article
Biology
Shankar Thawkar, Satish Sharma, Munish Khanna, Law Kumar Singh
Summary: The article introduces a hybrid feature selection method based on the Butterfly optimization algorithm and the Ant Lion optimizer for the design and development of a computer-based system for breast cancer detection. The method outperforms original algorithms in terms of accuracy, sensitivity, specificity, and error rates, demonstrating high performance and robustness in breast cancer diagnosis.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Computer Science, Artificial Intelligence
Lijun He, Wenfeng Li, Raymond Chiong, Mehdi Abedi, Yulian Cao, Yu Zhang
Summary: This study presents an EMOJaya algorithm for solving multi-objective job-shop scheduling problems, which achieves higher performance and more high-quality scheduling schemes through the application of grey entropy parallel analysis and opposition-based learning strategies.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Tamer F. Abdelmaguid
Summary: This paper introduces two heuristic methods based on NSGA-II and MOGWO for addressing a bi-objective dynamic multiprocessor open shop scheduling problem, focusing on the generation of Pareto optimal solutions, with findings indicating that NSGA-II generally outperforms MOGWO in various settings.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Jaza M. Abdullah, Tarik A. Rashid, Bestan B. Maaroof, Seyedali Mirjalili
Summary: This paper proposes a multi-objective variant of the fitness dependent optimizer (FDO) called multi-objective fitness dependent optimizer (MOFDO), which is equipped with all five types of knowledge as in FDO. MOFDO is tested on standard benchmarks and compared to other algorithms, revealing its superiority in most cases. Additionally, MOFDO is applied to real-world engineering problems and provides well-distributed feasible solutions.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Energy & Fuels
Sadhan Gope, Subhojit Dawn, Shreya Shree Das, Hemakumar Reddy Galiveeti
Summary: This study focuses on the role of wind farms in mitigating transmission congestion by providing additional power in wind-integrated restructured power systems. By using a generator shift factor-based method with the objective of maximizing social welfare, the Ant Lion Optimizer algorithm is employed to determine the optimal placement of wind farms in the system, showing its effectiveness in solving the problem at hand.
ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS
(2021)
Article
Automation & Control Systems
Gongjie Xu, Qiang Bao, Hongliang Zhang
Summary: This paper addresses the multi-objective green scheduling problem of integrated flexible job shop and automated guided vehicles (AGVs). A multi-objective mixed-integer programming model is formulated to minimize energy consumption and makespan simultaneously. An efficient heuristic algorithm (EHA) is designed to solve the model, and experimental results show that the EHA outperforms other comparison algorithms in terms of solution quality.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Operations Research & Management Science
Alireza Goli, Ali Ala, Seyedali Mirjalili
Summary: This paper proposes a mathematical formulation and solution method to optimize organ transplant supply chain under shipment time uncertainty. The proposed model considers the fuzzy uncertainty of organ demands and transportation time, and the simulation-based optimization using credibility theory. The numerical results show that the optimal credibility level is between 0.2 and 0.6 in all tested cases.
ANNALS OF OPERATIONS RESEARCH
(2023)
Review
Computer Science, Interdisciplinary Applications
Ali Ala, Vladimir Simic, Muhammet Deveci, Dragan Pamucar
Summary: This review paper focuses on the healthcare simulation appointment scheduling system (SASS), aims to highlight challenges and research topics in appointment scheduling, and provides a comprehensive summary of existing research in modeling healthcare issues via simulation. By analyzing a large number of articles, several approaches related to SASS are identified, and the study suggests future research should focus on improving the efficiency of simulation optimization methods.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Article
Environmental Sciences
Sasan Zahmatkesh, Mahmoud Kiannejad Amiri, Seyed Peiman Ghorbanzade Zaferani, Mohammad Reza Sarmasti Emami, Mostafa Hajiaghaei-Keshteli, Munirah D. Albaqami, Ammar Mohamed Tighezza, Maryam Shafahi, Ning Han
Summary: This study evaluated the effects of novel polycarbonate ultrafiltration, aluminum oxide nanoparticle (Al2O3-NPs) volume fraction, temperature, and water/ethylene glycol (EG) ratio on the thermophysical properties of the membrane. The results showed that the addition of 5%-10% Al(2)O(3)-NPs improved the performance of the polycarbonate membrane. Machine learning methods were used to analyze the effects of Al2O3-NPs volume fraction, temperature, and water/EG ratio on the membrane.
Review
Environmental Sciences
Sasan Zahmatkesh, Mostafa Hajiaghaei-Keshteli, Awais Bokhari, Suresh Sundaramurthy, Balamurugan Panneerselvam, Yousof Rezakhani
Summary: Both aquatic and terrestrial ecosystems are threatened by toxic wastewater. The unique properties of nanomaterials are being thoroughly studied for sewage treatment, as they can effectively remove organic matter, fungi, and viruses. Advanced oxidation processes and the large effective contact area of nanomaterials contribute to successful wastewater treatment.
ENVIRONMENTAL RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Abhijit Saha, Vladimir Simic, Tapan Senapati, Svetlana Dabic-Miletic, Ali Ala
Summary: This article addresses the critical problem of prioritizing zero-emission last-mile delivery solutions for sustainable city logistics, providing practical guidelines for city logistics companies to decarbonize urban freight distribution. It introduces a novel multicriteria group decision-making methodology using dual hesitant fuzzy sets. The methodology includes improved operations on DHF elements, a new model for measuring criteria weights, and a method for rational aggregation of preferences. A case study for an Austrian logistics company in Serbia demonstrates the applicability of the methodology and recommends electric light commercial vehicles as the best LMD solution.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Chemistry, Multidisciplinary
Mostafa Azizi, Targol Teymourian, Termeh Teymoorian, Mohammad Gheibi, Elaheh Kowsari, Mostafa Hajiaghaei-Keshteli, Seeram Ramakrishna
Summary: In this study, cellular lightweight concrete waste was used as a novel adsorbent to remove the antibiotic amoxicillin from water. The key factors of the adsorption process were studied and the ideal conditions for removal were determined. Different isotherm and kinetic models were employed to analyze the adsorption process, with the Freundlich model showing the best fit. The adsorbent material showed effective capacity for amoxicillin removal even after multiple cycles. Additionally, competitive adsorption between amoxicillin and other contaminants was investigated.
RESEARCH ON CHEMICAL INTERMEDIATES
(2023)
Article
Geosciences, Multidisciplinary
Omid Zabihi, Maryam Siamaki, Mohammad Gheibi, Mehran Akrami, Mostafa Hajiaghaei-Keshteli
Summary: Decision Support System (DSS) is used to manage man-made and natural phenomena, such as flood disasters, and achieve Sustainable Development Goals. The study designs different stages of a novel DSS system for monitoring, predicting, and controlling floods, using machine learning computations and multi-criteria decision-making techniques. The integration of artificial intelligence and Ward computations helps analyze heterogeneous rainfall data in different provinces of Iran, and prioritize strategies for early decision-making in flood disasters through Pre-FA, DFA, and Post-FA activities.
INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION
(2023)
Article
Automation & Control Systems
Behzad Mosallanezhad, Fatemeh Gholian-Jouybari, Leopoldo Eduardo Cardenas-Barron, Mostafa Hajiaghaei-Keshteli
Summary: The COVID-19 pandemic has disrupted supply chains, making it difficult for nations to provide necessary medical supplies. This study proposes a supply chain network model for COVID-19 Pandemic Wastes (CPWs) using an IoT platform, taking sustainability into account as objective functions.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Fatemeh Gholian-Jouybari, Omid Hashemi-Amiri, Behzad Mosallanezhad, Mostafa Hajiaghaei-Keshteli
Summary: In recent decades, the increase in global population has led to higher demands for agricultural and food products. This has resulted in increased production in the agricultural food supply chain network to address food security concerns. However, excessive production has led to issues such as greenhouse gas emissions and increased water consumption. This study develops a mathematical model to improve sustainability in the agricultural food supply chain network and proposes solution methods to address uncertainty and complexity.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Gholamreza Haseli, Ramin Ranjbarzadeh, Mostafa Hajiaghaei-Keshteli, Saeid Jafarzadeh Ghoushchi, Aliakbar Hasani, Muhammet Deveci, Weiping Ding
Summary: The increasing economic pressures and competition in markets have forced CEOs to make strategic decisions on developing and selling the right products to the right customers. In order to do this, companies need to understand the criteria that lead to customer loyalty. This study introduces a new method, called the HECON method, which considers the halo effect in customer decisions and uses convolutional neural networks (CNN) to assess the weight of product loyalty criteria.
INFORMATION SCIENCES
(2023)
Article
Green & Sustainable Science & Technology
Syed Mohd Muneeb, Zainab Asim, Mostafa Hajiaghaei-Keshteli, Haidar Abbas
Summary: This paper presents an integrated supplier selection and distribution model for refurbished products, aiming to minimize cost, carbon footprints, and maximize revenue. Goal programming is used to generate the results, showing optimal allocation of orders to suppliers and creation of distribution patterns. This study provides a comprehensive approach to supplier selection, order allocation, and product distribution, addressing the objectives of minimizing cost and carbon footprints, and maximizing profit.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2023)
Article
Construction & Building Technology
Ali Ala, Vladimir Simic, Dragan Pamucar, Chiranjibe Jana
Summary: Energy security is crucial, and sustainable, low-cost, emissions-conscious development presents significant challenges for energy resources. We introduce a bi-objective optimization framework to ensure long-term security and resilience in sustainable energy planning, considering important social factors. An advanced neutrosophic-based weighting approach is used to handle conflicting objectives and experts' views on energy security and costs. The MOGWO and LWT methods are applied to solve the sustainable energy strategy problems, with the neutrosophic-based MOGWO outperforming LWT. A case study in Belgium demonstrates the model's real-life applicability. By 2030, solar and wind energy utilization rates will reach 80% and 90% respectively.
SUSTAINABLE CITIES AND SOCIETY
(2023)
Article
Business
Gholamreza Haseli, Ilkin Yaran Ogel, Fatih Ecer, Mostafa Hajiaghaei-Keshteli
Summary: Smart jewelry, as an extension of wearable technology, has gained attention from women who seek both style and functionality. This research addresses the lack of women-centered approaches in existing studies on smart jewelry and the need for determining evaluation criteria and selecting the best alternatives for women. Through a review of literature and expert opinions, three main criteria and seventeen subcriteria are identified. The findings reveal that technology-related criteria are prioritized by women, with activity tracking, price, and mobile alert being the most critical subcriteria. Based on the overall evaluation, Ringly Luxe Smart Bracelet is determined as the best smart jewelry for women. This research contributes practically by guiding women in their selection processes and providing insights for smart jewelry designers and producers. The proposed framework also considers the reliability of decision-makers and experts, adding methodological value.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2023)
Article
Social Sciences, Interdisciplinary
Mahdi Yousfi Nejad Attari, Ali Asghar Moslemi Beirami, Ali Ala, Ensiye Neyshabouri Jami
Summary: Healthcare System Management (HSM) is a technique to integrate technology in public hospitals and improve hospital services. This study presents a hybrid decision making model combining AHP and ANP methods to evaluate HSM application in resource-limited settings. The proposed method increases decision efficiency and reliability in HSM.
EVALUATION AND PROGRAM PLANNING
(2023)
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)
