Article
Computer Science, Information Systems
Lijun Sun, Yan Xin, Tianfei Chen, Binbin Feng
Summary: A feature selection method based on clustering hybrid binary cuckoo search is proposed to improve the accuracy in rolling bearing fault diagnosis. The method utilizes Hilbert-Huang transform to extract fault features, applies a clustering hybrid initialization technique for feature selection, and introduces a mutation strategy based on Levy flight. The experimental results demonstrate the effectiveness of the proposed method.
Article
Computer Science, Information Systems
Liyun Fu, Houyao Zhu, Chengyun Zhang, Haibin Ouyang, Steven Li
Summary: The HHSDE algorithm combines the strengths of DE algorithm and harmony search algorithm, introducing new mutation operation, self-adaptive parameter adjustment strategy, and evaluation method. It outperforms other algorithms on CEC2005 benchmark functions.
Article
Computer Science, Artificial Intelligence
Xiangbo Qi, Zhonghu Yuan, Yan Song
Summary: Integrating heterogeneous biological-inspired strategies and mechanisms into one algorithm can avoid the shortcomings of single algorithm. The proposed integrated cuckoo search optimizer (ICSO) and multi-objective version MOICSO demonstrate the effectiveness of the integrated mechanism and the superior performance of the algorithm.
PEERJ COMPUTER SCIENCE
(2021)
Article
Green & Sustainable Science & Technology
Keshav Jha, Akhil Gupta, Abdulatif Alabdulatif, Sudeep Tanwar, Calin Ovidiu Safirescu, Traian Candin Mihaltan
Summary: Wireless technology is facing the challenge of always best connected (ABC) service. This study develops a new hybrid algorithm to optimize the performance of heterogeneous wireless systems. The proposed hybrid model demonstrates better performance compared to individual algorithms, resulting in increased throughput.
Article
Computer Science, Artificial Intelligence
Hu Peng, Zhaogan Zeng, Changshou Deng, Zhijian Wu
Summary: Cuckoo search algorithm is effective but can get trapped in local optimum due to unitary search strategy. To overcome this, a multi-strategy serial CS algorithm (MSSCS) is proposed with new learning strategies based on cuckoo's behavior, aiming to enhance performance.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Mingwei Wang, Shuai Xiong, Maolin Chen, Peipei He
Summary: A novel waveform decomposition technique based on wavelet function and differential cuckoo search algorithm is proposed in this study, which is successfully applied to airborne FWL point cloud data. Experimental results demonstrate that the decomposed waveforms have reasonable convergence rate and feature characterization, and waveform parameters are used as features to recognize different objects in the point cloud.
Article
Multidisciplinary Sciences
Yan Xiong, Ziming Zou, Jiatang Cheng
Summary: The cuckoo search algorithm based on cloud model effectively configures the step size factor and adapts to changing optimization problems. Simulation experiments show that this algorithm outperforms other CS and non-CS algorithms.
SCIENTIFIC REPORTS
(2023)
Article
Mathematics
Mohamed Abdel-Basset, Reda Mohamed, Nazeeruddin Mohammad, Karam Sallam, Nour Moustafa
Summary: The study introduces an improved cuckoo search algorithm (ICSA) with a convergence speed strategy to accelerate solving nonlinear equations for better outcomes. Experimental results reveal that ICSA outperforms other algorithms in terms of convergence speed and final accuracy, making it a promising alternative to existing algorithms.
Article
Computer Science, Hardware & Architecture
Faisal Alkhateeb, Bilal H. Abed-alguni, Mohammad Hani Al-rousan
Summary: The hybrid optimization algorithm DCSA tackles the JSSP scheduling problem by discretizing solutions, showing faster convergence and shorter computational time in experiments compared to other popular optimization-based scheduling algorithms.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Thanh Cuong-Le, Hoang-Le Minh, Samir Khatir, Magd Abdel Wahab, Minh Thi Tran, Seyedali Mirjalili
Summary: In this paper, a new Cuckoo search algorithm NMS-CS is proposed, which outperforms the original CS in convergence rate and accuracy by modifying the step mechanism. Through analysis of 23 benchmark functions, NMS-CS shows superior performance compared to CS. Furthermore, NMS-CS demonstrates good performance on engineering design problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Bo Yuan, Deji Chen
Summary: This paper establishes an optimization model for the parameters of the VG equation, using the DECS algorithm to solve the parameter optimization problem of the soil moisture characteristic curve equation. Through analysis of experimental data, it is found that the DECS hybrid algorithm has better exploration and development capabilities compared to the cuckoo search algorithm.
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Wenguan Luo, Xiaobing Yu
Summary: This paper discusses the importance of carbon neutrality in the task of social development and proposes a Reinforcement Learning-based Modified Cuckoo Search algorithm (RLMCS) to solve the economic dispatch problem. Experimental results demonstrate that RLMCS is more competitive and robust in solving standard and valve-point effects economic dispatch problems.
KNOWLEDGE-BASED SYSTEMS
(2022)
Review
Computer Science, Artificial Intelligence
Shuzhi Gao, Yue Gao, Yimin Zhang, Tianchi Li
Summary: The study proposed a self-adaptive multi-strategy cuckoo search algorithm (MSACS) for solving optimization problems, achieving the best results on 17 common benchmark functions and performing well on the remaining 11 functions. The improved algorithm was also successfully applied to optimizing a ball screw driving system model, reducing the peak acceleration of the screw and improving the reasonableness of the crank angle's peak acceleration.
APPLIED SOFT COMPUTING
(2021)
Article
Chemistry, Physical
Qiuchan Bai, Hao Li
Summary: This study identifies dependable and accurate variables of solid oxide fuel cell (SOFC) using the cuckoo search grey wolf optimization (CSGWO) algorithm, which shows superior performance compared to other optimization algorithms. The CSGWO algorithm has the lowest MSE values for parameter estimation in the SOFC models.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2022)
Article
Computer Science, Information Systems
Jiaxiang Zhang, Linwei Li, Huanlong Zhang, Fengxian Wang, Yangyang Tian
Summary: A novel sparrow search algorithm (NSSA) combined with spawning technology is proposed in this paper to effectively improve the problem of the algorithm falling into local optimization. The NSSA replaces the traditional stochastic method with the good point set theory to find the initial individual, integrates the spawning strategy of the cuckoo algorithm into the discoverer stage, and uses Levy flight and Brownian motion to disturb the position of the sparrow dimension by dimension. Simulation results show the effectiveness and superiority of the proposed scheme.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Industrial
Rajan Mondal, Subhajit Das, Subhash Chandra Das, Ali Akbar Shaikh, Asoke Kumar Bhunia
Summary: This paper investigates a partially backlogged inventory model with interval uncertainty, considering the availability of advance payment and discounts. The objective function is transformed into interval-valued and solved using c-r optimization technique and three variants of QPSO. The feasibility of the proposed model is demonstrated through numerical examples.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE-OPERATIONS & LOGISTICS
(2023)
Article
Engineering, Industrial
Subhajit Das, Md Sadikur Rahman, Ali Akbar Shaikh, Asoke Kumar Bhunia, Ali Ahmadian
Summary: The goal of this work is two-fold: (i) to theoretically develop optimality conditions for a variational problem with interval uncertainty, and (ii) to apply the established results in a production inventory model with interval uncertainty. The necessary and sufficient optimality conditions for the interval-valued variational problem (IVVP) are proposed using interval order relations. A production inventory model is formulated considering interval-valued time-dependent production and demand rates. The optimal policy of the proposed model is studied using the established optimality conditions.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE-OPERATIONS & LOGISTICS
(2023)
Article
Mathematics, Applied
Subhajit Das, Md Sadikur Rahman, Ali Akbar Shaikh, Asoke Kumar Bhunia, Ioannis Konstantaras
Summary: This work explores the Laplace and inverse transforms of interval valued functions and emphasizes on their properties under interval flexibility. The formal definition of interval Laplace transform is proposed, along with derived properties and the existence conditions. The study also discusses crucial results related to switching points of the interval Laplace transform and provides numerical examples. Finally, the definition of inverse transform for interval valued functions is introduced, and its application is demonstrated in a production inventory model under interval uncertainty.
MATHEMATICAL METHODS IN THE APPLIED SCIENCES
(2023)
Article
Engineering, Mechanical
Subhashis Das, Madhurima Mukherjee, Argha Mondal, Kshitish Ch. Mistri, Sanat Kumar Mahato, M. A. Aziz-Alaoui
Summary: This article demonstrates an analytical approach to show the emerging traveling pulses for the local evolution of a set of diffusively coupled dynamical equations representing neuronal impulses. The derived dynamics governing the traveling pulses solution is described in a space-time reference frame with a two-dimensional excitable Hindmarsh-Rose (H-R) type oscillator. The conditions that allow us to describe explicitly the nature of propagating traveling pulses are deduced. The obtained results reveal the possibility of collective behavior for information processing in excitable systems.
NONLINEAR DYNAMICS
(2023)
Article
Computer Science, Artificial Intelligence
Md Sadikur Rahman, Amalesh Kumar Manna, Ali Akbar Shaikh, Ioannis Konstantaras, Asoke Kumar Bhunia
Summary: The concepts of generalized Hukuhara difference and interval differential equation have significant applications in various research fields, including optimization, information theory, and inventory control. This paper focuses on the application of Hukuhara difference and interval differential equation in inventory management, proposing an inventory model for imperfect production process under warranty-dependent demand and carbon tax regulatory mechanism. By utilizing the interval arithmetic, the generalized Hukuhara difference, and the existence and uniqueness theorem of interval differential equation, the corresponding average profit function is obtained. A center-radius optimization technique is introduced to maximize the average profit, and numerical examples are solved using different variants of quantum-behaved particle swarm optimization algorithms.
Article
Computer Science, Artificial Intelligence
Goutam Mandal, Nirmal Kumar, Avijit Duary, Ali Akbar Shaikh, Asoke Kumar Bhunia
Summary: The key to avoiding local optima in solving optimization problems using metaheuristic algorithms is to enhance exploration and exploitation. This can be achieved through processes like enhancing search agents and hybridization. This study introduces a novel hybrid algorithm based on the strategies of group league and knock-out system using the quantum particle swarm optimization (QPSO) metaheuristic algorithm.
Article
Computer Science, Interdisciplinary Applications
Subhajit Das, Goutam Mandal, Amalesh Kumar Manna, Ali Akbar Shaikh, Asoke Kumar Bhunia
Summary: In the past few decades, government regulations on emissions have led to a lack of motivation among small and moderate manufacturing industries. Consumers nowadays prefer eco-friendly products, making it difficult for manufacturing companies to make appropriate marketing decisions in a highly competitive market. This study aims to develop a production system model to benefit manufacturers dealing with these issues by using interval modelling and optimal control techniques. The model investigates the impact of emission reduction technology and the greenness index on the revenue of the system, providing insights for manufacturers to set up effective marketing policies.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Biology
Prasenjit Mahato, Sanat Kumar Mahato, Subhashis Das
Summary: We propose and study a susceptible-exposed-infected-recovered (XY ZW) epidemic model with saturated treatment function. The effect of delayed treatment on disease transmission is examined by considering a saturated treatment function in the epidemic system. The existence and uniqueness of the positive solution as well as stochastic boundedness, permanence, and extinction of the model are investigated. Numerical simulations are performed to illustrate the results, and the sensitivity analysis of the basic reproduction number is conducted.
JOURNAL OF BIOLOGICAL SYSTEMS
(2023)
Article
Engineering, Mechanical
Subhashis Das, Sanat Kumar Mahato, Argha Mondal, Eva Kaslik
Summary: This article introduces a three-dimensional fractional-order slow-fast prey-predator model to explore the impact of pest-control strategy on integrated pest management. The dynamics of the prey-predator system and its qualitative properties are analyzed using a fractional-order model. The stability and the occurrence of Hopf bifurcations in the model are found to be influenced by the fractional-order exponent.
NONLINEAR DYNAMICS
(2023)
Article
Automation & Control Systems
Subhajit Das, Hachen Ali, Ali Akbar Shaikh, Asoke Kumar Bhunia
Summary: Considering the impact of emission on the environment, reduction of emission during the production process has become increasingly important for manufacturing companies. In order to survive in the competitive market, manufacturing companies must offer various facilities to consumers. This study presents an imperfect manufacturing inventory model that takes into account the linear increase in customer demand with emission reduction levels and the nonlinear decrease with item price. The goal is to maximize the average profit by solving optimal control problems and using the Hamiltonian maximum principle.
OPTIMAL CONTROL APPLICATIONS & METHODS
(2023)
Article
Biology
Prasenjit Mahato, Sanat Kumar Mahato, Subhashis Das, Partha Karmakar
Summary: In this article, the dynamical properties of the susceptible-vaccinated-infected-susceptible (SVIS) epidemic system with saturated incidence rate and vaccination strategies are studied. The existence and uniqueness of the stochastic system are examined by constructing a suitable Lyapunov function. A critical value R-s* is established with respect to the basic reproduction number R* of the deterministic system. Under the condition of R-s* > 1, a unique ergodic stationary distribution is investigated, representing the long-term persistence of the disease. The main focus of the study is the analysis of the probability density function of the stochastic system around the quasi-endemic equilibrium. The existence of the ergodic stationary distribution and density function under R-s* > 1 can explain all the dynamical behavior of the disease persistence. The condition for disease extinction is derived, and numerical results and sensitivities of the biological parameters are discussed to support the theoretical study. Results and conclusions are highlighted.
THEORY IN BIOSCIENCES
(2023)
Article
Engineering, Multidisciplinary
Subhashis Das, Sanat Kumar Mahato, Prasenjit Mahato
Summary: In this paper, a mathematical model for COVID-19 is developed, dividing the population into six classes. The concept of shield immunity is utilized to get back to normal, considering the interaction between recovered and susceptible/infected individuals. The model's stability and optimal control are analyzed, and simulations are conducted with real data to predict different scenarios.
JOURNAL OF APPLIED NONLINEAR DYNAMICS
(2023)
Article
Engineering, Multidisciplinary
Md Sadikur Rahman, Subhash Chandra Das, Md. Al-Amin Khan, Ali Akbar Shaikh, Asoke Kumar Bhunia
Summary: This paper discusses the importance of advance payment and the lifetime of an item in the business sector and proposes a non-deterministic inventory model to address these issues. The models consider variable holding cost, variable demand, and advertisement frequency under an advance payment policy. The interval optimization problems related to both models are solved using interval mathematics and interval order relations. Two examples are used to validate the models and sensitivity analyses are performed to study the impact of parameter deviations on optimality. The paper concludes by providing potential management insights and future research directions.
INTERNATIONAL JOURNAL OF MODELLING AND SIMULATION
(2023)
Article
Mathematics, Applied
Subhashis Das, Prasenjit Mahato, Sanat Kumar Mahato
Summary: A non-linear mathematical model is proposed in this paper to study the dynamics of disease transmission via pests. The crisp model is converted to a fuzzy model and a graded mean integration technique is used for defuzzification. The model is compared with a stochastic model and the existence, uniqueness, and boundedness of the solution are discussed. Equilibrium points, local stability, global stability analysis, and Hopf bifurcations are investigated. Numerical experiments with MATLAB are conducted, and the sensitivities of the control parameters are analyzed graphically.
DIFFERENTIAL EQUATIONS AND DYNAMICAL SYSTEMS
(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)
