Article
Mathematics
Messaoud Aloui, Faical Hamidi, Houssem Jerbi, Mohamed Omri, Dumitru Popescu, Rabeh Abbassi
Summary: This study utilizes the chaotic krill herd algorithm and a hybrid technique to address the challenging problem of estimating the largest attraction domain for nonlinear systems, by developing an intelligent approach based on quadratic Lyapunov functions to compute and characterize the attraction domain, avoiding possible false solutions from the nonlinear optimization solver.
Article
Multidisciplinary Sciences
Sinan A. Salih, AbdulRahman Alsewari, H. A. S. Wahab, Mustafa K. A. Mohammed, Tarik Rashid, Debashish Das, Shadi Basurra
Summary: Data clustering is a data mining technique that classifies similar objects into groups based on their similarities. The Black Hole Algorithm (BHA) is a nature-based optimization algorithm that has shown better performance than other algorithms in solving optimization problems. This paper proposes a multi-population version of BHA called MBHA, which achieves improved performance by generating a set of best solutions. Experimental results show that MBHA outperforms BHA and comparable algorithms in terms of accuracy and robustness, and it is suitable for data clustering problems.
Article
Computer Science, Artificial Intelligence
Moussa Mohsenpourian, Hadi Asharioun, Niloufar Mosharafian
Summary: Researchers have explored a novel approach using the Krill Herd Algorithm to train Fuzzy Inference Systems and found promising performance, especially for imbalanced data.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
Zahra Asghari Varzaneh, Soodeh Hosseini, Mohammad Masoud Javidi
Summary: Feature selection is important for improving the performance of classification by removing useless features from the data set in machine learning problems. This paper proposes an improved version of Horse herd Optimization Algorithm (HOA) called BHOA as a wrapper-based feature selection method. S-Shaped and V-Shaped transfer functions are considered to convert continuous search space to discrete search space. Furthermore, the Power Distance Sums Scaling approach is used to control selection pressure, exploration, and exploitation capabilities. The implementation results on 17 standard benchmark datasets demonstrate the efficiency of the proposed method based on the V-shaped transfer functions compared to other transfer functions and other wrapper-based feature selection algorithms.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
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, Information Systems
Kabeer Ahmed Bhatti, Sohail Asghar, Sheneela Naz
Summary: A wireless sensor network (WSN) consisting of limited-resource devices faces network congestion due to inconsistent transmission rates, resulting in decreased throughput, increased packet loss, and energy depletion. Existing congestion control strategies lack performance and quality of service. This article proposes a multi-objective fuzzy krill herd algorithm (MFKHA) to optimize the source sending rate and control network congestion. Extensive simulations using MATLAB show that the MFKHA algorithm outperforms other cutting-edge meta-heuristic algorithms, improving sending rate, throughput, fairness, and reducing packet loss, delay, queue size, energy usage, and congestion.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Civil
Yetis Bulent Sonmezer, Ersin Korkmaz
Summary: Soil liquefaction, a type of disaster that often occurs during earthquakes, causes the highest number of casualties among natural disasters. This study developed analytical equation models to predict the probability of soil liquefaction, using earthquake and ground parameters determined from real field conditions. Among the developed models, the Exponential model showed the most accurate results in predicting the occurrence of liquefaction, based on earthquake magnitude, fine grain ratio, effective stress, standard penetration test impact number, and maximum ground acceleration parameters.
GEOMECHANICS AND ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Laith Abualigah, Bisan Alsalibi, Mohammad Shehab, Mohammad Alshinwan, Ahmad M. Khasawneh, Hamzeh Alabool
Summary: This paper introduces a novel feature selection method to address the issue of high-dimensional features in text clustering. By utilizing swarm-based optimization techniques and a parallel membrane framework, the proposed method enhances clustering performance and outperforms other optimization algorithms on benchmark datasets.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2021)
Article
Computer Science, Artificial Intelligence
Halil Bilal, Ferruh Ozturk
Summary: This study introduces a novel chaotic krill herd (CKH) optimization algorithm by incorporating chaos theory, which demonstrates superior performance in optimizing rubber bushing stiffness.
Article
Computer Science, Information Systems
Lamees Mohammad Dalbah, Mohammed Azmi Al-Betar, Mohammed A. Awadallah, Raed Abu Zitar
Summary: The capacitated vehicle routing problem is a scheduling problem that has been tackled using metaheuristic optimization algorithms. In this study, the Coronavirus Herd Immunity Optimizer (CHIO) is modified to efficiently solve this problem, achieving competitive results when compared to other algorithms.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Weifeng Gao, Zhifang Wei, Maoguo Gong, Gary G. Yen
Summary: This article proposes a decomposition differential evolution algorithm based on radial basis function to solve multimodal optimization problems. The algorithm decomposes the problem into multiple global optimization subproblems and solves them using population update strategy and local RBF surrogate models.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Johan Ofverstedt, Joakim Lindblad, Natasa Sladoje
Summary: Multimodal image alignment is the process of finding spatial correspondences between images formed by different imaging techniques or under different conditions. This article proposes an efficient algorithm based on frequency domain cross-correlation for computing mutual information (MI) for all discrete displacements. The proposed algorithm is shown to be equivalent to a direct method while offering superior runtime performance. Additionally, the article presents a multimodal image alignment method for transformation models with few degrees of freedom. The method is evaluated on benchmark datasets and outperforms alternative methods, including both local optimization of MI and recent deep learning-based approaches.
PATTERN RECOGNITION LETTERS
(2022)
Article
Computer Science, Hardware & Architecture
Aakanksha Sharaff, Chandramani Kamal, Siddhartha Porwal, Surbhi Bhatia, Kuljeet Kaur, Mohammad Mehendi Hassan
Summary: This paper explores fraudulent activities related to spam messages and proposes a SMS spam filtering model based on the theory of Artificial Immune System, achieving a high accuracy of 96% through optimization algorithms and feature extraction techniques. The use of Krill Herd Optimization (KHO) algorithm for feature selection and integration with Dendritic Cell Algorithm (DCA) to improve efficiency is highlighted. Comparative results with state-of-the-art machine learning classifiers show the effectiveness of the proposed model.
Article
Chemistry, Multidisciplinary
Tingyao Wu, Di Wu, Heming Jia, Nuohan Zhang, Khaled H. Almotairi, Qingxin Liu, Laith Abualigah
Summary: This paper introduces the Gorilla Troops Optimizer (GTO) and its limitations, and proposes an improved version called Modified Gorilla Troops Optimizer (MGTO) with strategies including QIBAS, TLBO, and QRBL. Experimental results demonstrate the competitive performance and promising prospects of MGTO on various benchmark functions and engineering problems.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Multidisciplinary
Lihong Yu, Linyang Xie, Chunmei Liu, Song Yu, Yongxia Guo, Kejun Yang
Summary: This study focuses on the research of kidney beans and explores the optimization of the BP neural network model using chaos theory and krill herd algorithm. The C-KHA-BP model shows high accuracy in predicting the yield of kidney beans, and the results provide a new approach for similar models in the field of grain production.
ALEXANDRIA ENGINEERING JOURNAL
(2022)
Review
Computer Science, Artificial Intelligence
Iman Rahimi, Fang Chen, Amir H. Gandomi
Summary: This paper presents a review and analysis of machine learning forecasting models for COVID-19. It includes a scientometric analysis of literature and discusses the classification, evaluation criteria, and solution approaches for these models. The paper concludes with a discussion of the findings.
NEURAL COMPUTING & APPLICATIONS
(2023)
Correction
Computer Science, Interdisciplinary Applications
Bisan Alsalibi, Seyedali Mirjalili, Laith Abualigah, Rafaa Ismael Yahya, Amir H. Gandomi
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Review
Computer Science, Interdisciplinary Applications
Iman Rahimi, Amir H. Gandomi, Fang Chen, Efren Mezura-Montes
Summary: This study analyzes scholarly literature on constraint-handling techniques for single-objective and multi-objective population-based algorithms. The results show that the constraint-handling techniques for multi-objective optimization have received less attention compared to single-objective optimization. Genetic algorithms, differential evolutionary algorithms, and particle swarm intelligence are identified as the most promising algorithms for such optimization. Future research work is anticipated to increase in Engineering, Computer Science, and Mathematics.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Editorial Material
Engineering, Multidisciplinary
Amir H. Gandomi, Christian Soize, James R. Stewart
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Agriculture, Multidisciplinary
Mohsen Mousavi, Mohammad Sadegh Taskhiri, Amir H. Gandomi
Summary: The problem of hole-defect detection in standing trees is successfully solved using ultrasonic signals and machine learning algorithms. The proposed method, which includes Variational Mode Decomposition and PCA, achieves high accuracy in classifying wood materials based on their health state. A one-dimensional convolutional neural network (1D-CNN) is also employed to generalize the solution to the classification problem of standing trees in fields.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Article
Engineering, Civil
Qianyun Zhang, Sun Ho Ro, Zhe Wan, Saeed Babanajad, John Braley, Kaveh Barri, Amir H. Alavi
Summary: This study improves the IR-based delamination detection method by focusing on data collection and data interpretation. Automated inspection data collection is achieved using UAVs equipped with IR sensors, while a pixel-level deep learning method is developed for automatic delamination detection. The results show that the proposed method is highly accurate and efficient.
TRANSPORTATION RESEARCH RECORD
(2023)
Article
Energy & Fuels
Iman Rahimi, Mohammad Reza Nikoo, Amir H. Gandomi
Summary: Hybrid energy systems are considered as viable alternatives for power production in Australia. This study proposes an optimal integrated renewable energy system model for five major Australian cities to fulfill their electrical energy demands.
ENERGY STRATEGY REVIEWS
(2023)
Review
Automation & Control Systems
Laith Abualigah, Essam Said Hanandeh, Raed Abu Zitar, Cuong-Le Thanh, Samir Khatir, Amir H. Gandomi
Summary: This paper reviews the application of metaheuristics in optimizing sustainable supply chain management. It explores the potential of metaheuristics to improve the sustainability, efficiency, and competitiveness of the supply chain. The paper provides an overview of sustainable supply chain management principles and challenges, introduces the concept of metaheuristics, reviews various algorithms applied in this field, analyzes their effectiveness, and identifies key success factors. It also provides recommendations for future research and highlights the potential of metaheuristics in promoting sustainable supply chain management.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Marine
Alireza Sadat Hosseini, Amir Kabiri, Amir H. Gandomi, Mehdi Shafieefar
Summary: The response of berm breakwaters to wave forces was investigated through rebuild and cumulative experiments. A new method was proposed to study the berm breakwaters recession by considering datasets collected from both types of experiments. The Multi-Objective Genetic Programming approach was used to analyze the data and create a prediction model, which showed reliable results compared to implicit formulas in the literature.
Article
Engineering, Marine
Mostafa Gandomi, Moharram Dolatshahi Pirooz, Banafsheh Nematollahi, Mohammad Reza Nikoo, Iman Varjavand, Talal Etri, Amir H. Gandomi
Summary: This study proposed a stochastic multi-criteria decision-making model to optimize the geometry of permeable breakwaters by using a combination of the non-dominated sorting genetic algorithm-II (NSGA-II) and the multi-layer perceptron neural network (MLP-NN). The risk-based model, considering the uncertainties in wave characteristics, determined the optimal trade-offs between wave transmission, wave reflection, and rockfill materials volume using the conditional value-at-risk (CVaR) method. The model was experimentally applied for risk analysis of a permeable breakwater under different wave heights and confidence levels. The study achieved a rating of 8 out of 10 in terms of importance.
Article
Computer Science, Information Systems
Boyu Li, Ting Guo, Ruimin Li, Yang Wang, Amir H. Gandomi, Fang Chen
Summary: Intelligent transportation system (ITS) is an important symbol of smart cities, aiming to provide sustainable and efficient services to residents. Railway systems, playing a vital role in ITS, have integrated with multiple IoT devices to monitor real-time inbound passenger flows and ensure pedestrian safety. However, the consolidation of real-time information from IoT sources and accurate estimation of future flow face challenges such as coarse-grained data and dynamic interchanged passengers. To overcome these challenges, a two-stage self-adaptive model is proposed for accurately and timely predicting passenger flow in metropolitan railway systems. The model includes a self-attention-based prediction model and a real-time fine-tuning model that combine offline deep learning and real-time allocation.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Syed Thouheed Ahmed, Syed Muzamil Basha, Manikandan Ramachandran, Mahmoud Daneshmand, Amir H. Gandomi
Summary: The autonomous connected ambulance (ACA) is an essential requirement in the healthcare sector's demand-supply management. However, the traditional prototypes for such unmanned vehicles do not meet the demands of advanced communication technologies. Therefore, there is an urgent need to design a route resource recommendation (R3) protocol for ACA under Edge-AI. This article proposes a dedicated and novel ACA-R3 protocol to address the issues of connectivity and resource management in ACA, with the primary objective of improving emergency services.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Automation & Control Systems
Qianyun Zhang, Kaveh Barri, Hao Yu, Zhe Wan, Wenyun Lu, Jianzhe Luo, Amir H. Alavi
Summary: Harnessing the power of natural evolution, a concept called evolving metamaterial (EM) is introduced to directly evolve thousands of metastructures with unknown structures and new modes of operation. By randomly creating an initial population of parent metamaterial entities and passing their genetic material to offspring through variation, reproduction, and selection, desired metamaterial configurations emerge. The proposed approach demonstrates the capability to explore both 2D and 3D mechanical metamaterial structures with specific properties such as maximum bulk modulus and minimum Poisson's ratio.
ADVANCED INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Ahmad Taheri, Keyvan RahimiZadeh, Amin Beheshti, Jan Baumbach, Ravipudi Venkata Rao, Seyedali Mirjalili, Amir H. Gandomi
Summary: In this paper, a novel evolutionary optimization algorithm called Partial Reinforcement Optimizer (PRO) is introduced. The PRO algorithm is based on the psychological theory of partial reinforcement effect (PRE) and is mathematically modeled to solve global optimization problems. Experimental results demonstrate that the PRO algorithm outperforms existing meta-heuristic algorithms in terms of accuracy and robustness.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Khan Muhammad, Javier Del Ser, Naercio Magaia, Ramon Fonseca, Tanveer Hussain, Amir H. Gandomi, Mahmoud Daneshmand, Victor Hugo C. de Albuquerque
Summary: With the increasing popularity of smart devices and their need for data, edge computing and edge learning have become powerful tools. However, edge learning faces challenges such as latency sensitivity and resource consumption. This study proposes a prioritization framework for video data based on edge learning, which can reduce resource usage. Additionally, communication aspects related to edge learning are critically examined.
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.
