Article
Computer Science, Artificial Intelligence
Kushal Kanti Ghosh, Ritam Guha, Suman Kumar Bera, Neeraj Kumar, Ram Sarkar
Summary: Feature selection is a core concept in machine learning and data mining, aiming to eliminate irrelevant or partially relevant features to improve model performance. Researchers have applied various meta-heuristic optimization techniques to overcome the limitations of traditional approaches. The new FS approach based on the Manta ray foraging optimization algorithm outperforms state-of-the-art methods in terms of classification accuracy and number of features selected.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Environmental Sciences
Yilin Chen, Bo Gao, Tao Lu, Hui Li, Yiqi Wu, Dejun Zhang, Xiangyun Liao
Summary: This article presents an improved dragonfly algorithm combined with a directed differential operator for feature selection. By adaptively adjusting the step size, designing a new differential operator, and updating the directed differential operator, the proposed method enhances the search capability and convergence speed. Experimental results demonstrate that the proposed algorithm outperforms other representative algorithms in terms of both convergence speed and solution quality.
Article
Computer Science, Artificial Intelligence
Kulandaivel Balakrishnan, Ramasamy Dhanalakshmi, Gopalakrishnan Seetharaman
Summary: The African vulture optimization algorithm (AVOA) is a metaheuristic algorithm that imitates the eating and movement patterns of African vultures. It has been developed to address continuous optimization problems. To solve discrete search space problems, a binary version of AVOA (BAVOA) is proposed for feature selection problems in classification tasks. Experimental results show that BAVOA outperforms conventional binary metaheuristic algorithms in terms of classification accuracy, fitness function, number of selected features, and converging ability.
Article
Computer Science, Artificial Intelligence
Min Zhang, Jie-Sheng Wang, Jia-Ning Hou, Hao-Ming Song, Xu-Dong Li, Fu-Jun Guo
Summary: In this paper, a ReliefF-guided novel binary equilibrium optimizer (RG-NBEO) is proposed for feature selection. The proposed method can effectively improve the classification accuracy while reducing the dimensionality of the dataset.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Jingwei Too, Seyedali Mirjalili
Summary: This article proposed a novel feature selection method HLBDA, using a hyper learning strategy to enhance the algorithm performance, and compared it with multiple datasets, demonstrating the superior effectiveness of HLBDA in improving classification accuracy and reducing the number of selected features.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Chemistry, Multidisciplinary
Kunti Robiatul Mahmudah, Fatma Indriani, Yukiko Takemori-Sakai, Yasunori Iwata, Takashi Wada, Kenji Satou
Summary: Converting binary features into numerical ones can improve the performance of oversampling methods in classification tasks. Through experiments, it was observed that converting binary features into numerical features before applying oversampling methods resulted in maximum improvements of 35.11% in accuracy and 42.17% in F1-score.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Information Systems
Cun Ji, Mingsen Du, Yanxuan Wei, Yupeng Hu, Shijun Liu, Li Pan, Xiangwei Zheng
Summary: Time series classification is widely used in various domains, including EEG/ECG classification, device anomaly detection, and speaker authentication. Despite the existence of many methods, selecting intuitive temporal features for accurate classification remains a challenge. Therefore, this paper proposes a new method called TSC-RTF, which utilizes random temporal features, and shows that it can compete with state-of-the-art methods.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Mostafa Khojastehnazhand, Mozaffar Roostaei
Summary: This study used a machine vision system and texture feature extraction methods to classify seven varieties of wheat in the East Azerbaijan Province of Iran. By utilizing unsupervised and supervised methods, along with feature extraction, the different wheat varieties were identified with over 95% accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Engineering, Biomedical
Sireesha Moturi, S. N. Tirumala Rao, Srikanth Vemuru
Summary: Disease prediction is crucial for individuals to lead a healthy life, and advancements in data mining offer potential in this area. Despite its advantages, current systems face challenges in efficiency and information security, affecting their practical utility.
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
(2021)
Article
Computer Science, Artificial Intelligence
Xinkai Yang, Luhan Zhen, Zhanshan Li
Summary: This paper presents a high-dimensional feature selection method based on the Binary Golden Eagle Optimizer algorithm combined with Initialization of Feature Number Subspace. Experimental results show that this method achieves significant improvements on most datasets and demonstrates superior performance.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Prachi Agrawal, Talari Ganesh, Diego Oliva, Ali Wagdy Mohamed
Summary: This study presents the challenging task of searching for the optimal feature subset from original datasets using metaheuristic algorithms in machine learning. The recently developed GSK algorithm demonstrates excellent performance in solving feature selection problems, with the use of population reduction schemes and transfer functions to enhance efficiency.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
An-Da Li, Bing Xue, Mengjie Zhang
Summary: This paper proposes an improved sticky binary PSO algorithm for feature selection problems, which aims to enhance evolutionary performance through new mechanisms such as an initialization strategy, dynamic bits masking, and genetic operations. Experimental results show that ISBPSO achieves higher accuracy with fewer features and reduces computation time compared to benchmark PSO-based FS methods.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Victor Hugo da Silva Muniz, Joao Baptista de Oliveira e Souza Filho
Summary: This paper discusses the importance of music genre in music recommendations and presents a method to improve system performance through the generation of new handcrafted features and feature selection.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Theory & Methods
Yuanjin Xu, Ming Wei, M. M. Kamruzzaman
Summary: Classification, recognition, and quality assessment of aerial images depend on detecting and identifying discriminative visual features. A novel method is proposed to explore quality-related and topological cues to mitigate the challenges posed by image quality and topological structures. This method shows effective prediction of aerial image categories and outperforms other state-of-the-art algorithms.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Yousef Sharafi, Mohammad Teshnehlab
Summary: This study introduces an opposition-based binary competitive optimization algorithm for feature selection, utilizing a time-varying V-shape transfer function and an opposition-based learning mechanism to improve population diversity. Experimental results based on classification error rate and number of selected features show that the proposed algorithm outperforms other binary optimization algorithms in finding an optimal subset of features.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Zaid Abdi Alkareem Alyasseri, Ahamad Tajudin Khader, Mohammed Azmi Al-Betar, Xin-She Yang, Mazin Abed Mohammed, Karrar Hameed Abdulkareem, Seifedine Kadry, Imran Razzak
Summary: A multi-objective flower pollination algorithm is proposed in this study to solve the EEG signal denoising problem using wavelet transform. The algorithm optimizes the denoising parameters based on two measurement criteria, minimum mean squared error and maximum signal-to-noise ratio. Experimental results show that the proposed method achieves good performance.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Bilal H. Abed-alguni, Noor Aldeen Alawad, Mohammed Azmi Al-Betar, David Paul
Summary: This paper proposes improved binary versions of the Sine Cosine Algorithm (SCA) for the Feature Selection (FS) problem. By introducing Opposition Based Learning (OBL), Variable Neighborhood Search (VNS), Laplace distribution, and Refraction Learning (RL), the binary SCA algorithm has been successfully improved. The experimental results show that the improved IBSCA3 algorithm performs well in terms of classification accuracy and fitness values, outperforming other algorithms.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Mohammed Azmi Al-Betar, Mohammed A. Awadallah, Iyad Abu Doush, Osama Ahmad Alomari, Ammar Kamal Abasi, Sharif Naser Makhadmeh, Zaid Abdi Alkareem Alyasseri
Summary: This paper boosts the learning process of multilayer perceptron (MLP) neural network using hybrid metaheuristic optimization algorithms. Six versions of memetic algorithms (MAs) replace the gradient descent learning mechanism of MLP, and adaptive beta-hill climbing (A beta HC) is hybridized with six population-based metaheuristics. The results show that the proposed MA versions excel the original algorithms, with hybrid grey wolf optimization (HGWO) outperforming all other MA versions.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Environmental
Fadl A. Essa, Mohamed Abd Elaziz, Mohammed Azmi Al-Betar, Ammar H. Elsheikh
Summary: The study investigated the performance of a reverse osmosis unit integrated with a recovery energy system, under different operating system pressures and recovery ratios. A hybrid machine learning model using LSTM neural network optimized by AHA was developed to predict permeate flow and power saving of the reverse osmosis unit. The optimized model showed significantly improved prediction accuracy compared to the pure model, with high coefficient of determination values.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2023)
Article
Engineering, Multidisciplinary
Mohammed Azmi Al-Betar, Mohammed A. Awadallah, Sharif Naser Makhadmeh, Iyad Abu Doush, Raed Abu Zitar, Samah Alshathri, Mohamed Abd Elaziz
Summary: This paper proposes a hybridized version of the Harris Hawks Optimizer (HHO) with adaptive-hill-climbing optimizer for solving economic load dispatch (ELD) problems. The proposed method achieves significant performance in various ELD cases and can be considered as an efficient alternative for solving ELD problems.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Malik Sh. Braik, Mohammed A. Awadallah, Mohammed Azmi Al-Betar, Abdelaziz I. Hammouri, Raed Abu Zitar
Summary: An Enhanced Chameleon Swarm Algorithm (ECSA) is proposed to solve non-convex Economic Load Dispatch (ELD) problems by integrating roulette wheel selection and Levy flight methods. The performance of ECSA is shown to outperform other methods on complex benchmark functions.
APPLIED INTELLIGENCE
(2023)
Review
Computer Science, Interdisciplinary Applications
Sharif Naser Makhadmeh, Mohammed Azmi Al-Betar, Ammar Kamal Abasi, Mohammed A. Awadallah, Iyad Abu Doush, Zaid Abdi Alkareem Alyasseri, Osama Ahmad Alomari
Summary: This paper reviews and summarizes the studies that utilize the butterfly optimization algorithm (BOA) for optimization problems. It introduces the basic concepts, inspiration, and mathematical model of BOA, and categorizes the studies into different adaptation forms. The advantages, drawbacks, and future directions of BOA in dealing with optimization problems are analyzed and summarized.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Majdi Mafarja, Thaer Thaher, Mohammed Azmi Al-Betar, Jingwei Too, Mohammed A. Awadallah, Iyad Abu Doush, Hamza Turabieh
Summary: Software Fault Prediction (SFP) is an important process to detect faulty components of software early in the development life cycle. This paper proposes a machine learning framework for SFP, comparing the performance of seven classifiers and improving the results through dimensionality reduction and optimization strategies.
APPLIED INTELLIGENCE
(2023)
Review
Computer Science, Interdisciplinary Applications
Mohammed Azmi Al-Betar, Mohammed A. Awadallah, Sharif Naser Makhadmeh, Zaid Abdi Alkareem Alyasseri, Ghazi Al-Naymat, Seyedali Mirjalili
Summary: The Marine Predators Algorithm (MPA) is a nature-inspired optimizer based on the foraging mechanisms of ocean predators. It has become popular for its derivative-free, parameterless, and easy-to-use features, leading to its wide application in various optimization problems. This review paper analyzes the growth and performance of MPA based on 102 research papers. It discusses the inspirations and theoretical concepts of MPA, focusing on its convergence behavior. The review also examines the versions of MPA proposed to improve its performance on real-world optimization problems and explores the diverse optimization applications using MPA as the main solver.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Review
Computer Science, Interdisciplinary Applications
Mohammed A. Awadallah, Mohammed Azmi Al-Betar, Iyad Abu Doush, Sharif Naser Makhadmeh, Ghazi Al-Naymat
Summary: This paper reviews the latest versions and applications of sparrow search algorithm (SSA), a rapidly growing swarm-based algorithm proposed in 2020. SSA is inspired by the foraging behavior of sparrows. It has been widely used for optimization problems in various research fields. The paper highlights the growth of SSA, its theoretical features, and discusses the different extended versions to overcome premature convergence and enhance diversity. It also presents multi-objective SSA and analyzes the research gaps in the convergence behavior of SSA.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Iyad Abu Doush, Khalid Sultan, Mohammed Azmi Al-Betar, Zainab Almeraj, Zaid Abdi Alkareem Alyasseri, Mohammed A. Awadallah
Summary: This study aims to identify performance criteria for comparing automatic web accessibility evaluation tools (WAET) and determine the possibility of automatically testing SC based on current technologies. It also explores ways to reduce mistakenly reported errors through WAET. WCAG 2.1 SC level-A, AA, and AAA were analyzed, and the results can guide developers to enhance their tools by utilizing cutting-edge technologies, as well as provide performance indicators for measuring WAET's performance.
CCF TRANSACTIONS ON PERVASIVE COMPUTING AND INTERACTION
(2023)
Article
Engineering, Biomedical
Malik Shehadeh Braik, Abdelaziz I. Hammouri, Mohammed A. Awadallah, Mohammed Azmi Al-Betar, Khalaf Khtatneh
Summary: This paper presents a hybrid model for feature selection, using an improved swarm algorithm and k-nearest neighbor classifier to select optimal feature subsets. The proposed method demonstrates superior classification performance compared to other methods, indicating its potential in exploring the feature space and identifying the most useful features for classification tasks.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Computer Science, Information Systems
Mohammed Azmi Al-Betar, Iyad Abu Doush, Sharif Naser Makhadmeh, Ghazi Al-Naymat, Osama Ahmad Alomari, Mohammed A. Awadallah
Summary: This survey paper comprehensively analyzes the performance and applications of Equilibrium Optimizer (EO), comparing it with eight other well-established methods. Different versions and applications of EO are discussed, highlighting their pros and cons, and suggesting future research directions.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Ghazi Al-Naymat, Mariam Khader, Mohammed Azmi Al-Betar, Raghda Hriez, Ali Hadi
Summary: This paper introduces a parallel approximated variant called MR-VDENCLUE, which is capable of discovering clusters with varying densities and can handle big datasets.
INTELLIGENT SYSTEMS AND APPLICATIONS, VOL 1
(2023)
Article
Computer Science, Artificial Intelligence
Hao Yang, Min Wang, Zhengfei Yu, Hang Zhang, Jinshen Jiang, Yun Zhou
Summary: In this paper, a novel method called CSTTA is proposed for test time adaptation (TTA), which utilizes confidence-based optimization and sample reweighting to better utilize sample information. Extensive experiments demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Jin Liu, Ju-Sheng Mi, Dong-Yun Niu
Summary: This article focuses on a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). The logic of decision implication based on OE operators is described, and a method for obtaining the canonical basis for decision implications is given. The completeness, nonredundancy, and optimality of the canonical basis are proven. Additionally, a method for generating true premises based on OE operators is proposed.
KNOWLEDGE-BASED SYSTEMS
(2024)
Review
Computer Science, Artificial Intelligence
Kun Bu, Yuanchao Liu, Xiaolong Ju
Summary: This paper discusses the importance of sentiment analysis and pre-trained models in natural language processing, and explores the application of prompt learning. The research shows that prompt learning is more suitable for sentiment analysis tasks and can achieve good performance.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xu Wu, Yang Liu, Jie Tian, Yuanpeng Li
Summary: This paper proposes a blockchain-based privacy-preserving trust management architecture, which adopts federated learning to train task-specific trust models and utilizes differential privacy to protect device privacy. In addition, a game theory-based incentive mechanism and a parallel consensus protocol are proposed to improve the accuracy of trust computing and the efficiency of consensus.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zaiyang Yu, Prayag Tiwari, Luyang Hou, Lusi Li, Weijun Li, Limin Jiang, Xin Ning
Summary: This study introduces a 3D view-based approach that effectively handles occlusions and leverages the geometric information of 3D objects. The proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Yongliang Shi, Runyi Yang, Zirui Wu, Pengfei Li, Caiyun Liu, Hao Zhao, Guyue Zhou
Summary: Neural implicit representations have gained attention due to their expressive, continuous, and compact properties. However, there is still a lack of research on city-scale continual implicit dense mapping based on sparse LiDAR input. In this study, a city-scale continual neural mapping system with a panoptic representation is developed, incorporating environment-level and instance-level modeling. A tailored three-layer sampling strategy and category-specific prior are proposed to address the challenges of representing geometric information in city-scale space and achieving high fidelity mapping of instances under incomplete observation.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ruihan Hu, Zhi-Ri Tang, Rui Yang, Zhongjie Wang
Summary: Mesh data is crucial for 3D computer vision applications worldwide, but traditional deep learning frameworks have struggled with handling meshes. This paper proposes MDSSN, a simple mesh computation framework that models triangle meshes and represents their shape using face-based and edge-based Riemannian graphs. The framework incorporates end-to-end operators inspired by traditional deep learning frameworks, and includes dedicated modules for addressing challenges in mesh classification and segmentation tasks. Experimental results demonstrate that MDSSN outperforms other state-of-the-art approaches.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Buliao Huang, Yunhui Zhu, Muhammad Usman, Huanhuan Chen
Summary: This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Junchen Ye, Weimiao Li, Zhixin Zhang, Tongyu Zhu, Leilei Sun, Bowen Du
Summary: Modeling multivariate time series has long been a topic of interest for scholars in various fields. This paper introduces MvTS, an open library based on Pytorch, which provides a unified framework for implementing and evaluating these models. Extensive experiments on public datasets demonstrate the effectiveness and universality of the models reproduced by MvTS.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Reham R. Mostafa, Ahmed M. Khedr, Zaher Al Aghbari, Imad Afyouni, Ibrahim Kamel, Naveed Ahmed
Summary: Feature selection is crucial in classification procedures, but it faces challenges in high-dimensional datasets. To overcome these challenges, this study proposes an Adaptive Hybrid-Mutated Differential Evolution method that incorporates the mechanics of the Spider Wasp Optimization algorithm and the concept of Enhanced Solution Quality. Experimental results demonstrate the effectiveness of the method in terms of accuracy and convergence speed, and it outperforms contemporary cutting-edge algorithms.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ti Xiang, Pin Lv, Liguo Sun, Yipu Yang, Jiuwu Hao
Summary: This paper introduces a Track Classification Model (TCM) based on marine radar, which can effectively recognize and classify shipping tracks. By using a feature extraction network with multi-feature fusion and a dataset production method to address missing labels, the classification accuracy is improved, resulting in successful engineering application in real scenarios.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zhihao Zhang, Yuan Zuo, Chenghua Lin, Junjie Wu
Summary: This paper proposes a novel unsupervised context-aware quality phrase mining framework called LMPhrase, which is built upon large pre-trained language models. The framework mines quality phrases as silver labels using a parameter-free probing technique on the pre-trained language model BERT, and formalizes the phrase tagging task as a sequence generation problem by fine-tuning on the Sequence to-Sequence pre-trained language model BART. The results of extensive experiments show that LMPhrase consistently outperforms existing competitors in two different granularity phrase mining tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Kemal Buyukkaya, M. Ozan Karsavuran, Cevdet Aykanat
Summary: The study aims to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. A hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme is proposed to achieve scalability up to hundreds of processors. Experimental results on real-world benchmark datasets show that the proposed algorithm achieves 6x higher throughput on sparse datasets compared to the state-of-the-art, while achieving comparable throughput on relatively dense datasets.
KNOWLEDGE-BASED SYSTEMS
(2024)