Article
Chemistry, Applied
Valber Elias de Almeida, David Douglas de Sousa Fernandes, Paulo Henrique Goncalves Dias Diniz, Adriano de Araujo Gomes, Germano Veras, Roberto Kawakami Harrop Galvao, Mario Cesar Ugulino Araujo
Summary: This paper introduces a new algorithm, PCA-DP-LDA, for solving classification problems of food data using PCA and LDA. Compared with conventional methods, PCA-DP-LDA achieves more parsimonious and interpretable results, with similar or better classification performance.
Article
Chemistry, Analytical
Sunil Kumar Prabhakar, Harikumar Rajaguru, Semin Ryu, In Cheol Jeong, Dong-Ok Won
Summary: Manual sleep stage scoring is a hectic task, leading to the development of automated sleep stage classification systems. This study proposes a holistic strategy combining clustering, dimensionality reduction, feature extraction and selection, and deep learning for sleep stage classification. The methodology surpasses previous studies in terms of classification accuracy, reporting a high accuracy of 93.51% even for a six-class classification problem.
Article
Computer Science, Artificial Intelligence
Qurrat Ul Ain, Harith Al-Sahaf, Bing Xue, Mengjie Zhang
Summary: This study analyzes GP-based approaches to skin image classification, which improve the performance of machine learning classification algorithms by constructing features, thereby enhancing diagnostic efficiency and assisting dermatologists in diagnosis.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Adam Lysiak, Miroslaw Szmajda
Summary: This study compared nine feature evaluation methods by evaluating features from ten different datasets and training various classifiers. The results indicated that the method based on the average overlap between feature values is best suited for applications with limited computational power, while the two-sample t-test method may be preferable for datasets known to be normally distributed.
Article
Computer Science, Artificial Intelligence
Dalal Al-Alimi, Zhihua Cai, Mohammed A. A. Al-Qaness, Abdelghani Dahou, Eman Ahmed Alawamy, Sakinatu Issaka
Summary: In hyperspectral image processing, dimensionality reduction methods are crucial for reducing complexity and improving classification accuracy. The newly introduced Compression and Reinforced Variation (CRV) method shows promising results in reducing data dimension while improving classification accuracy.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Imam Mustafa Kamal, Hyerim Bae
Summary: Dimensionality reduction plays a crucial role in classification, object detection, and pattern recognition tasks. Autoencoder, as a state-of-the-art non-linear dimensionality reduction method, can be problematic in preserving distinctive information. In this study, we propose a supervised and cooperative super-encoder (SE) network to tackle this issue and achieve effective extraction of separable latent code.
PATTERN RECOGNITION
(2022)
Review
Computer Science, Artificial Intelligence
Julliano Trindade Pintas, Leandro A. F. Fernandes, Ana Cristina Bicharra Garcia
Summary: The systematic literature review (SLR) assessed 1376 unique papers on feature selection methods in text classification published in the past eight years. Through mapping different aspects of proposed methods and identifying main characteristics of experiments, the SLR helps researchers develop new studies and position them in the context of existing literature.
ARTIFICIAL INTELLIGENCE REVIEW
(2021)
Article
Computer Science, Artificial Intelligence
Laura Moran-Fernandez, Veronica Bolon-Canedo
Summary: In this study, the performance of various feature selection approaches are compared to random selection to determine the most effective strategy. The findings indicate that correlation-based feature selection is the most effective strategy regardless of the scenario.
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jefferson G. Martins, Luiz E. S. Oliveira, Daniel Weingaertner, Andersson Barison, Gerlon A. R. Oliveira, Luciano M. Liao
Summary: Forests are being exploited disorderly and many species are endangered, prompting the need for a spatial distribution plan. Researchers facing a lack of representative databases can benefit from introducing new databases and proposing selection strategies to improve outcomes.
Article
Computer Science, Information Systems
Husam Ali Abdulmohsin, Hala Bahjat Abdul Wahab, Abdul Mohssen Jaber Abdul Hossen
Summary: Feature selection is a crucial process in pattern recognition and machine learning, as it can improve classification speed and accuracy, and reduce system complexity. In this work, a new statistical method is proposed to select features based on performance value quality and recognition value strength, ranking features by final weight and removing those below a predefined threshold. Experiments show that the proposed method outperforms traditional FS methods in terms of system complexity and performance.
CMC-COMPUTERS MATERIALS & CONTINUA
(2021)
Article
Computer Science, Artificial Intelligence
Dalal AL-Alimi, Mohammed A. Al-qaness, Zhihua Cai, Eman Ahmed Alawamy
Summary: This study introduces a novel feature reduction method called improving distribution analysis (IDA) to enhance data distribution, reduce complexity, and accelerate performance in hyperspectral images (HSIs). The experimental results demonstrate that IDA performs admirably in achieving these goals.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Heba Mamdouh Farghaly, Tarek Abd El-Hafeez
Summary: The feature selection problem is a significant challenge in pattern recognition, especially for classification tasks. This work explores the use of association analysis in data mining to select meaningful features and proposes a novel feature selection technique for text classification. The technique effectively reduces redundant information while achieving high accuracy using only 6% of the features.
Article
Computer Science, Artificial Intelligence
Imam Mustafa Kamal, Hyerim Bae
Summary: This paper proposes an auto-classifier that automatically utilizes dimensionality reduction to improve the generalization accuracy. It contains classifier and generator networks and uses a cooperative learning mechanism to achieve the objectives of classification and data reconstruction. Experimental results show that the accuracy of this classifier is highly competitive.
PATTERN RECOGNITION LETTERS
(2022)
Review
Computer Science, Information Systems
Afnan M. Alhassan, Wan Mohd Nazmee Wan Zainon
Summary: Early diagnosis of chronic diseases is crucial in reducing mortality rates, while feature selection and dimensionality reduction techniques play vital roles in improving accuracy of diagnosis systems. Classification techniques can effectively predict chronic diseases, with the development of intelligent adaptive systems making diagnosis more efficient.
Article
Engineering, Multidisciplinary
Zicheng Zhang
Summary: This paper proposes a hybrid system for speech emotion recognition, which adopts a two-stage design concept and utilizes random forest algorithm and logistic regression algorithm for feature importance evaluation. Experimental results show that the proposed method achieves satisfactory sentiment classification performance.
ALEXANDRIA ENGINEERING JOURNAL
(2021)
Article
Computer Science, Artificial Intelligence
Essam H. Houssein, Mohamed H. Hassan, Mohamed A. Mahdy, Salah Kamel
Summary: This paper proposes an enhanced version of Equilibrium Optimizer (EO) called Enhanced Equilibrium Optimizer (EEO) for solving global optimization and optimal power flow (OPF) problems. The proposed algorithm improves upon the original EO by introducing a new performance reinforcement strategy with the Levy Flight mechanism. The efficacy of the EEO algorithm is demonstrated through comparisons with other algorithms on the CEC'20 test suite, as well as its application to the OPF problem on the IEEE 30-bus test system. The results show that the EEO algorithm outperforms other methods by providing better optimized solutions.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Essam H. Houssein, Mohamed H. Hassan, Salah Kamel, Kashif Hussain, Fatma A. Hashim
Summary: This study proposes an alternative algorithm called LFD-OBL, which integrates Opposition-based learning (OBL) operator into the Levy Flight Distribution (LFD) to address the drawbacks of the canonical LFD. The proposed approach shows superiority over other algorithms in various experiments.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Essam H. Houssein, Mosa E. Hosney, Waleed M. Mohamed, Abdelmgeid A. Ali, Eman M. G. Younis
Summary: Feature selection is an important step in data preprocessing for data mining and machine learning. Traditional methods often fail to find the optimal solution due to the large search space, leading to the development of hybrid techniques. This study proposes a modified hunger games search algorithm (mHGS) to address optimization and feature selection problems.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Mansur Khasanov, Salah Kamel, Essam Halim Houssein, Claudia Rahmann, Fatma A. Hashim
Summary: This paper proposes an improved version of the AEO algorithm called AEO-OBL, which aims to enhance the performance of the original AEO. It is applied to determine the optimal allocation of DG units in RDNs, taking into account the stochastic nature of renewable DGs. The proposed AEO-OBL algorithm includes strategies to prevent getting trapped in local optima and achieves better results compared to other algorithms.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Emre Celik, Nihat Ozturk, Essam H. Houssein
Summary: This paper investigates the use of energy storage devices (ESDs) as back-up sources to escalate load frequency control (LFC) of power systems (PSs). The PS models implemented here are 2-area linear and nonlinear non-reheat thermal PSs besides 3-area nonlinear hydro-thermal PS. PID controller is employed as secondary controller in each control area and ESDs such as battery energy storage system, flywheel energy storage system and ultra-capacitor are employed to assist LFC task during crest load disturbances.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Majdi Mafarja, Thaer Thaher, Jingwei Too, Hamouda Chantar, Hamza Turabieh, Essam H. Houssein, Marwa M. Emam
Summary: This paper proposes an effective feature selection (FS) method, Multi-strategy Gray Wolf Optimizer (MSGWO), for biological data classification. By utilizing multiple exploration and exploitation strategies during the optimization process, MSGWO demonstrates superiority in feature selection and addressing search problems, resulting in better classification accuracy and feature selection performance compared to other algorithms.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Imene Neggaz, Nabil Neggaz, Hadria Fizazi
Summary: Due to advancements in technology and the widespread use of mobile applications, facial analysis has become a key research area in computer vision. This article presents a gender recognition system based on an improved Archimedes optimization algorithm, which outperforms other optimizers in terms of accuracy and statistical testing. The proposed method is evaluated using two datasets, demonstrating its effectiveness in gender recognition.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Biology
Marwa M. Emam, Essam H. Houssein, Rania M. Ghoniem
Summary: In this paper, an enhanced reptile search algorithm (RSA) is proposed for global optimization and optimal thresholding values for multilevel image segmentation are selected. The RSA algorithm is improved by enhancing the solution quality and incorporating a scale factor, and its performance is verified through experiments.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Essam H. Houssein, Mohammed R. Saad, Abdelmgeid A. Ali, Hassan Shaban
Summary: The multi-objective gorilla troops optimizer (MOGTO) is proposed to address multi-objective optimization issues. It is evaluated using the CEC 2020 test bed and large-scale wireless sensor networks, and outperforms other optimization models in terms of various indicators.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Mathematics
Essam H. Houssein, Awny Sayed
Summary: This study proposes two stages to enhance the Beluga Whale Optimization (BWO) algorithm, namely Opposition-Based Learning (OBWO) and a combination of Dynamic Candidate Solution (DCS) and OBWO based on k-Nearest Neighbor (kNN) classifier referred to as OBWOD. Experimental results show that the OBWOD algorithm is competitive in solving real-world problems and performs well in classifying medical datasets.
Article
Computer Science, Artificial Intelligence
Marwa M. Emam, Hoda Abd El-Sattar, Essam H. Houssein, Salah Kamel
Summary: This paper presents a new and improved optimization algorithm called the modified Orca Predation Algorithm (mOPA). The mOPA is based on the original OPA but incorporates two enhancing strategies: Levy flight and opposition-based learning. It aims to enhance search efficiency and overcome the limitations of the original OPA for global optimization problems. Comparative analysis shows that the mOPA outperforms benchmark methods in terms of computational speed and overall performance.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Dincer Maden, Emre Celik, Essam H. Houssein, Gulshan Sharma
Summary: Estimating model parameters of solar photovoltaic (PV) cells/modules using real current-voltage (I-V) data is crucial for the performance of PV systems. The squirrel search algorithm (SSA) is employed as a powerful tool to optimize the parameters of the PV models. The results show that SSA outperforms other approaches and can effectively handle industrial PV modules. This new method offers a practical tool to enhance the effectiveness of PV systems.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Essam H. Houssein, Mosa E. Hosney, Marwa M. Emam, Eman M. G. Younis, Abdelmgeid A. Ali, Waleed M. Mohamed
Summary: In recent years, medical data analysis has become crucial for accurate diagnoses of various diseases. Soft computing techniques, such as swarm algorithms and machine learning methods, have emerged as superior approaches. Feature selection is essential for extracting optimal features and reducing data dimensions.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Essam H. Houssein, Mosa E. Hosney, Diego Oliva, Eman M. G. Younis, Abdelmgeid A. Ali, Waleed M. Mohamed
Summary: This paper proposes a wrapper feature selection approach that combines the rat swarm optimization algorithm with genetic operators to improve classification accuracy and reduce the number of features. The approach converts the continuous search space into a discrete space using transfer functions, achieving a balance between local and global search. Experimental results demonstrate the efficiency and effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Mohamed A. Tolba, Essam H. Houssein, Mohammed Hamouda Ali, Fatma A. Hashim
Summary: This paper proposes an innovative method for evaluating the appropriate allocation of DSTATCOM in power distribution grids (PDGs) to reduce power losses, relieve voltage deviation, and lessen annual costs. It introduces a modified Capuchin search algorithm (mCapSA) and an analytic hierarchy process method to achieve optimal DSTATCOM allocation.
NEURAL COMPUTING & APPLICATIONS
(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)