Article
Computer Science, Artificial Intelligence
Caijie Shang, Dong Zhang, Yan Yang
Summary: A gradient-based method different from bionic algorithms is proposed in this paper for optimal multilevel thresholds search in image segmentation. Experiments show that this method is efficient in computation and has equal or better performance of segmentation compared to other algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Biology
Caiyang Yu, Yixi Wang, Chenwei Tang, Wentao Feng, Jiancheng Lv
Summary: This paper proposes an automatic U-Net Neural Architecture Search (NAS) algorithm named EU-Net, which utilizes the differential evolutionary (DE) algorithm to segment critical information in medical images. The proposed algorithm, through the use of variable-length strategy and DE algorithm, can automatically search for neural network architecture, significantly enhancing the accuracy of image interpretation and diagnosis.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Engineering, Chemical
Laith Abualigah, Ali Diabat, Putra Sumari, Amir H. Gandomi
Summary: This paper proposes a new multilevel thresholding approach based on the Evolutionary Arithmetic Optimization Algorithm (AOA) called DAOA, which uses Differential Evolution technique to enhance local research of AOA and achieve better results in multilevel thresholding. The efficiency of the proposed DAOA method is evaluated and compared to other methods, showing superior performance and ranking first in all test cases.
Article
Automation & Control Systems
Nand Kishor Yadav, Mukesh Saraswat
Summary: This paper introduces a new method for RGB-Depth image segmentation, which achieves optimized fuzzy clustering and segmentation through a random Henry gas solubility optimization algorithm and superpixel aggregation. Evaluating its performance on a standard dataset, the proposed method outperforms traditional methods.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Mathematics, Interdisciplinary Applications
Ruishuai Chai
Summary: This paper investigates the common pepper noise in grayscale images, and proposes a method that combines improved filtering algorithms with the OTSU algorithm to effectively enhance the quality of grayscale images.
Article
Engineering, Biomedical
Xiao Yang, Rui Wang, Dong Zhao, Fanhua Yu, Ali Asghar Heidari, Zhangze Xu, Huiling Chen, Abeer D. Algarni, Hela Elmannai, Suling Xu
Summary: Breast cancer is the most prevalent malignancy threatening human health, and early screening is crucial for improving treatment success and reducing mortality. Computer-aided technology plays a key role in the analysis and diagnosis of breast cancer real images, and high-quality medical segmentation images can enhance lesion area detection accuracy. This study proposed an enhanced differential evolution algorithm for threshold search, which accelerates convergence and reduces premature convergence. Experimental results showed that the proposed method outperformed other methods in breast cancer image segmentation.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Computer Science, Information Systems
Le Yan, Jianjun Chen, Qi Li, Jiafa Mao, Weiguo Sheng
Summary: Preserving an appropriate population diversity is critical for the performance of evolutionary algorithms. In this paper, a Co-evolutionary niching strategy (CoEN) is proposed to dynamically evolve appropriate niching methods and incorporate them into differential evolution (DE) to maintain population diversity. Extensive testing on benchmark functions from CEC2019 and CEC2014 demonstrates the significance of the proposed CoEN, showing that incorporating CoEN allows the resulting DE to achieve better or competitive performance compared to related algorithms.
Article
Computer Science, Information Systems
Xiangxiao Lei, Honglin Ouyang
Summary: A novel kernel-based intuitionistic fuzzy clustering method, combining an improved grey wolf optimizer, was proposed for image segmentation. This method can handle noise, improve algorithm robustness, and address local optima problems through differential mutation.
Article
Chemistry, Analytical
Wysterlanya K. P. Barros, Leonardo A. Dias, Marcelo A. C. Fernandes
Summary: This study implements the Otsu automatic image thresholding algorithm on FPGA for real-time processing of high-resolution images. By optimizing processing time through parallelization, the proposed hardware achieved a high speedup compared to similar works.
Article
Automation & Control Systems
Rifat Kurban, Ali Durmus, Ercan Karakose
Summary: This study uses multiple metaheuristic algorithms for multilevel color image thresholding, with MPA and TFWO outperforming other algorithms in terms of image quality evaluation and CPU time consumption.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Information Systems
K. P. Baby Resma, Madhu S. Nair
Summary: A novel multilevel thresholding algorithm utilizing the meta-heuristic Krill Herd Optimization algorithm is proposed for image segmentation, reducing computational time and demonstrating superior performance through comparative analysis with other bio-inspired techniques.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Essam H. Houssein, Marwa M. Emam, Abdelmgeid A. Ali
Summary: The paper presents the IChOA algorithm for breast cancer segmentation using thermography images and achieves valuable and accurate results in the optimization process.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Essam H. Houssein, Marwa M. Emam, Abdelmgeid A. Ali
Summary: This study proposes an MRFO-OBL algorithm based on opposition-based learning for image segmentation of COVID-19 CT images, demonstrating higher quality and robustness compared to other algorithms, and performs exceptionally well across all evaluation metrics.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Huan Xiong, Mengyang Yu, Li Liu, Fan Zhu, Jie Qin, Fumin Shen, Ling Shao
Summary: This paper proposes a novel generalized optimization method called Alternating Binary Matrix Optimization (ABMO) for solving binary optimization problems (BOPs). ABMO can handle BOPs with different constraints and loss functions, and solves them by iteratively decomposing the original problem into small optimization problems with closed-form solutions.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Jakub Kudela
Summary: The article highlights the critical issue in evolutionary computation where some frequently used benchmark functions have their optima in the center of the feasible set, posing challenges in algorithm analysis. Through analyzing seven recently published methods, it was found that the presence of a center-bias operator enables easy identification of optima in the center of the benchmark set, rendering comparisons with methods lacking this bias meaningless. The computational performance comparison of these methods with established ones like 'differential evolution' and 'particle swarm optimization' showed varied results with only one new method consistently outperforming the old ones.
NATURE MACHINE INTELLIGENCE
(2022)
Article
Engineering, Biomedical
Phornpot Chainok, Karla de Jesus, Leandro Coelho, Helon Vicente Hultmann Ayala, Mateus Gheorghe de Castro Ribeiro, Ricardo J. Fernandes, Joao Paulo Vilas-Boas
Summary: The purpose of this study was to predict the performance determinant factors of 15m backstroke-to-breaststroke turning using machine-learning models and comparing linear and tree-based models. The collected data revealed that the best models showed similar performance in different turning techniques, with balanced contributions between turn-in and turn-out variables.
SPORTS BIOMECHANICS
(2023)
Review
Computer Science, Artificial Intelligence
Luiza Scapinello Aquino da Silva, Yan Lieven Souza Lucio, Leandro dos Santos Coelho, Viviana Cocco Mariani, Ravipudi Venkata Rao
Summary: The Jaya Algorithm, a population-based optimization method, has become a valuable tool in swarm intelligence. This paper provides a comprehensive review and bibliometric study of the algorithm's applicability and variants, emphasizing its versatility. The study aims to inspire new researchers to utilize this simple and efficient algorithm for problem-solving. Evaluation: 8/10.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Allan Christian Krainski Ferrari, Gideon Villar Leandro, Leandro dos Santos Coelho, Myriam Regattieri De Biase Silva Delgado
Summary: This work proposes a fuzzy mechanism to improve the convergence of the rat swarm optimizer algorithm. The proposed fuzzy model uses the normalized fitness and population diversity as input. The results show that the fuzzy mechanism improves convergence and is competitive with other metaheuristics.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2023)
Article
Energy & Fuels
Anne Carolina Rodrigues Klaar, Stefano Frizzo Stefenon, Laio Oriel Seman, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: The energy price has a significant impact on investment and economic development. Forecasting future energy prices can support industrial planning and help avoid economic recession.
Article
Thermodynamics
Silvio Cesar de Lima Nogueira, Stephan Hennings Och, Luis Mauro Moura, Eric Domingues, Leandro dos Santos Coelho, Viviana Cocco Mariani
Summary: This study aimed to develop a reliable framework for predicting diesel engine emissions by using a novel Random Forest (RF) model. The RF model accurately predicted the output signals of the diesel engine, demonstrating its viability, effectiveness, and competitive performance.
Article
Thermodynamics
Stefano Frizzo Stefenon, Laio Oriel Seman, Luiza Scapinello Aquino, Leandro dos Santos Coelho
Summary: This paper proposes a Seq2Seq LSTM neural network model with an attention mechanism and wavelet transform for reservoir level prediction. The proposed approach outperforms other models and provides accurate and timely predictions of water levels, allowing for better decision-making in dam management under emergency conditions.
Article
Computer Science, Artificial Intelligence
Alan Naoto Tabata, Alessandro Zimmer, Leandro dos Santos Coelho, Viviana Cocco Mariani
Summary: This study used synthetic datasets from the CARLA simulator and real-world dataset from WAYMO Open to train and evaluate computer vision algorithms. An efficient automated method for pedestrian and vehicle identification and counting was developed, which can quickly identify target features among many images and output formatted results.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Laio Oriel Seman, Stefano Frizzo Stefenon, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: This paper evaluates a time series of leakage current from a high-voltage laboratory experiment using porcelain pin-type insulators. Time series forecasting is performed with ensemble learning approaches, and the results show that applying these approaches enhances the performance of the machine learning models in predicting breakdowns in the electrical power system.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2023)
Article
Chemistry, Analytical
Guilherme Augusto Silva Surek, Laio Oriel Seman, Stefano Frizzo Stefenon, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: This paper aims to evaluate and map the current scenario of human actions in red, green, and blue videos using deep learning models. A semi-supervised learning approach is employed to evaluate a residual network (ResNet) and a vision transformer architecture (ViT). The results obtained using a bi-dimensional ViT structure demonstrated great performance in human action recognition, achieving an accuracy of 96.7% on the HMDB51 dataset.
Article
Chemistry, Analytical
Matheus Henrique Dal Molin Ribeiro, Ramon Gomes da Silva, Jose Henrique Kleinubing Larcher, Andre Mendes, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: This paper proposes a new hybrid framework combining STACK ensemble learning and a JADE algorithm for nonlinear system identification. The model performs well in decoding EEG signals, achieving an average explanation of 94.50% and 67.50% of data variability, and outperforms other methods in terms of accuracy.
Article
Chemistry, Analytical
Stefano Frizzo Stefenon, Laio Oriel Seman, Nemesio Fava Sopelsa Neto, Luiz Henrique Meyer, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: This paper presents a novel hybrid method for fault prediction based on the time series of leakage current of contaminated insulators. The proposed CFRW-GMDH method, with a root-mean-squared error of 3.44x10(-12), outperformed other models in fault prediction. This approach can provide power utilities with a reliable tool for monitoring insulator health and predicting failures, thereby enhancing the reliability of the power supply.
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)
