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Feature subset selection using heuristic and metaheuristic approaches for diabetes prediction on a binary encoded dataset

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INTERNATIONAL JOURNAL OF MODELING SIMULATION AND SCIENTIFIC COMPUTING 2024年第3期15卷

作者： Kumar, Puneet Bhati, Bhoopesh Singh Dhanaraj, Rajesh Kumar Iwendi, Celestine Balusamy, Balamurugan Bhati, Nitesh Singh Rai, Prerana Chandigarh Univ Dept Comp Sci & Engn Ajitgarh Punjab India Indian Inst Informat Technol Dept Comp Sci & Engn Sonepat India Symbiosis Int Deemed Univ Symbiosis Inst Comp Studies & Res SICSR Pune India Univ Bolton Sch Creat Technol Bolton England Shiv Nadar Univ Delhi NCR Campus Noida India Galgotias Univ Dept Comp Sci & Engn Greater Noida India Chandigarh Univ Ajitgarh Punjab India

The Machine Learning (ML) models are prone to a curse of dimensionality. The dataset with a greater number of features involves more computational cost and it may lead to low performance in the context of prediction accuracy. Therefore, in this research work we have predicted diabetes with more accuracy by using a smaller number of features. The heuristic methods Sequential Forward Selection (SFS), Sequential Backward Selection (SBS) and metaheuristic evolutionary methods - whale optimization algorithm (WOA) and Genetic algorithm (GA) are used for performing feature subset selection. The Gini index is also used as a filter evaluator. The performance of the feature subsets is analyzed by applying three different types of ML models, Random Forest (RF), Multi-Layer Perceptron (MLP) and K-Nearest Neighbor (KNN). We have predicted type-2 diabetes with an accuracy of 96.82%. Also, we have reduced the number of features up to 67.44% i.e., identified 32.56% most relevant features.

关键词： whale optimization algorithm sequential forward selection sequential backward selection Gini index random forest

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Optimized Deep Neuro-Fuzzy Network with MapPeduce Architecture for Acute Lymphoblastic Leukemia Classification and Severity Analysis

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INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2024年第3期24卷 2450028-2450028页

作者： Bai, G. Mercy Venkadesh, P. Noorul Islam Ctr Higher Educ Dept Comp Sci & Engn Kumaracoil Tamil Nadu India VSB Coll Engn Dept CSE Tech Campus Coimbatore India

The most common life-threatening disease, acute lymphoblastic leukemia (ALL), can be lethal within a few weeks if untreated. The early detection and analysis of leukemia is a key dilemma in the field of disease diagnosis, and the methods available for the classification process are time-consuming. To overcome the issues, this paper develops a robust classification technique named Horse Herd whale optimization-enabled Deep Neuro-Fuzzy Network (HHWO-enabled DNFN method) for ALL classification and severity analysis using the MapReduce framework. The input image is first preprocessed and segmented, and the useful features necessary for improving the classification performance are extracted during the mapper phase, known as HHWO, which incorporates Horse Herd optimization algorithm (HOA) and whale optimization algorithm (WOA). Finally, severity analysis of ALL is done to classify the levels of leukemia to offer optimal treatment. As a result, the developed method performed better than other existing methods, achieving superior performance with a greater testing accuracy of 0.959, sensitivity of 0.965, and specificity of 0.966, respectively.

关键词： Acute lymphoblastic leukemia severity analysis deep neuro-fuzzy network Horse Herd optimization whale optimization algorithm

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Optimal design of adaptive and proportional integral derivative controllers using a novel hybrid particle swarm optimization algorithm

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2020年第8期42卷 1492-1510页

作者： Bejarbaneh, Elham Yazdani Ahangarnejad, Arash Hosseinian Bagheri, Ahmad Bejarbaneh, Behnam Yazdani Binh Thai Pham Buyamin, Salinda Shirinzadeh, Fatemeh Univ Teknol Malaysia Fac Elect Engn Skudai 81310 Johor Malaysia Dept Mech Engn Blacksburg VA USA Univ Guilan Fac Mech Engn Dept Dynam Control & Vibrat Rasht Iran Univ Teknol Malaysia Fac Civil Engn Dept Geotech & Transportat Skudai Johor Malaysia Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Ahrar Inst Technol & Higher Educ Dept Elect Engn Rasht Iran

Controlling of a rotational inverted pendulum is considered as a challenging problem, mainly due to the system's inherent nonlinear and unstable dynamics. In fact, the goal of this control is to maintain the pendulum vertically upward regardless of external disturbances. This paper aims to optimally design a model reference adaptive proportional integral derivative (PID) control for rotary inverted pendulum system based on a novel hybrid particle swarm optimization algorithm, combining sine cosine algorithm and levy flight distribution. Evaluation of the performance quality of the proposed adaptive controller is accomplished based on the stabilization and tracking control of rotary inverted pendulum system. In addition, two other PID controllers are designed to get a better understanding of the performance and robustness of the proposed controller. To make a complete comparison, the performance of the hybrid particle swarm optimization algorithm is examined against two other optimization techniques known as simple particle swarm optimization and whale optimization algorithm. Finally, the obtained simulation results demonstrate that the proposed optimal adaptive controller is superior to the other controllers, especially in terms of the transient response characteristics and the magnitude of control output signal.

关键词： Rotational inverted pendulum model reference adaptive PID control particle swarm optimization sine cosine algorithm levy flight whale optimization algorithm

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A WOA-Based optimization Approach for Task Scheduling in Cloud Computing Systems

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IEEE SYSTEMS JOURNAL 2020年第3期14卷 3117-3128页

作者： Chen, Xuan Cheng, Long Liu, Cong Liu, Qingzhi Liu, Jinwei Mao, Ying Murphy, John Zhejiang Ind Polytech Coll Design & Art Shaoxing 312000 Peoples R China Dublin City Univ Sch Comp Dublin D09 NA55 Ireland Shandong Univ Technol Sch Comp Sci & Technol Qingdao 266590 Peoples R China Wageningen Univ Informat Technol Grp NL-6708 PB Wageningen Netherlands Florida A&M Univ Dept Comp & Informat Sci Tallahassee FL 32307 USA Fordham Univ Dept Comp & Informat Sci New York NY 10458 USA Univ Coll Dublin Sch Comp Sci Dublin 4 Ireland

Task scheduling in cloud computing can directly affect the resource usage and operational cost of a system. To improve the efficiency of task executions in a cloud, various metaheuristic algorithms, as well as their variations, have been proposed to optimize the scheduling. In this article, for the first time, we apply the latest metaheuristics whale optimization algorithm (WOA) for cloud task scheduling with a multiobjective optimization model, aiming at improving the performance of a cloud system with given computing resources. On that basis, we propose an advanced approach called Improved WOA for Cloud task scheduling (IWC) to further improve the optimal solution search capability of the WOA-based method. We present the detailed implementation of IWC and our simulation-based experiments show that the proposed IWC has better convergence speed and accuracy in searching for the optimal task scheduling plans, compared to the current metaheuristic algorithms. Moreover, it can also achieve better performance on system resource utilization, in the presence of both small and large-scale tasks.

关键词： Cloud computing metaheuristics multiobjective optimization task scheduling whale optimization algorithm

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Multipoint design optimization for a controlled diffusion airfoil compressor cascade

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2020年第11期234卷 2143-2159页

作者： Huang, Song Yang, Chengwu Han, Ge Zhao, Shengfeng Lu, Xingen Chinese Acad Sci Inst Engn Thermophys Key Lab Light Duty Gas Turbine 11 Beisihuanxi Rd Beijing 100190 Peoples R China Chinese Acad Sci Univ Chinese Acad Sci Beijing Peoples R China

Under high altitude and low Reynolds number conditions, the aerodynamic performance of compressor cascades deteriorates drastically. In this paper, an optimally designed system combining class-shape-transformation method, S1 surface flow solver and whale optimization algorithm was established to achieve for a controlled diffusion airfoil, called MANGHH. The aim of this work is to improve our understanding of the loss mechanism for the original cascade and optimal cascade under different inflow conditions. The study shows that the total pressure loss of the optimal cascade at an angle of attack of -4 degrees, 0 degrees, and 6 degrees decreases by 55.9%, 16.1%, and 16.3%, respectively, compared with the original controlled diffusion airfoil. The range of the available low loss incidence improves significantly. At different incidences, the optimal cascade moves the blade loading forward compared with that of the original controlled diffusion airfoil while reducing the growth rate of the boundary layer thickness, eliminating a wide range of flow separations. The optimal cascade reduces the total pressure loss mainly by reducing trailing edge mixing loss compared with that of the original controlled diffusion airfoil. Under different inlet Mach number conditions, a laminar separation bubble appears on the suction surface of the original controlled diffusion airfoil. As the inlet Mach number increases, the position of the laminar separation bubble moves slightly upstream, while the length and depth of the laminar separation bubble increase. Fortunately, the total pressure loss of the optimal cascade decreases significantly compared with that of the original controlled diffusion airfoil. Under different incoming turbulence intensity conditions, the total pressure loss of the optimal cascade is always lower than that of the original controlled diffusion airfoil. As the incoming turbulence intensity increases, the total pressure loss of the original controlled diffusion airf

关键词： Controlled diffusion airfoil whale optimization algorithm class-shape-transformation method laminar separation bubble turbulence intensity

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WOANet: whale optimized deep neural network for the classification of COVID-19 from radiography images

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BIOCYBERNETICS AND BIOMEDICAL ENGINEERING 2021年第4期41卷 1702-1718页

作者： Murugan, R. Goel, Tripti Mirjalili, Seyedali Chakrabartty, Deba Kumar Natl Inst Technol Silchar Dept Elect & Commun Engn Biomed Imaging Lab BIOMIL Silchar 788010 Assam India Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Silchar Med Coll & Hosp Dept Radiol Silchar 788014 Assam India

Coronavirus Diseases (COVID-19) is a new disease that will be declared a global pandemic in 2020. It is characterized by a constellation of traits like fever, dry cough, dyspnea, fatigue, chest pain, etc. Clinical findings have shown that the human chest Computed Tomography (CT) images can diagnose lung infection in most COVID-19 patients. Visual changes in CT scan due to COVID-19 is subjective and evaluated by radiologists for diagnosis purpose. Deep Learning (DL) can provide an automatic diagnosis tool to relieve radiologists' burden for quantitative analysis of CT scan images in patients. However, DL techniques face different training problems like mode collapse and instability. Deciding on training hyper parameters to adjust the weight and biases of DL by a given CT image dataset is crucial for achieving the best accuracy. This paper combines the backpropagation algorithm and whale optimization algorithm (WOA) to optimize such DL networks. Experimental results for the diagnosis of COVID-19 patients from a comprehensive COVID-CT scan dataset show the best performance compared to other recent methods. The proposed network architecture results were validated with the existing pre-trained network to prove the efficiency of the network. (c) 2021 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.

关键词： Early diagnosis COVID-19 whale optimization algorithm Machine learning Deep learning

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Determining Optimum Location and Sizing of Distributed Generation Systems in a Real Radial Distribution Network

ELECTRICA

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ELECTRICA 2021年第3期21卷 342-351页

作者： Kaya, Anil Bozkurt, Altug Yildiz Tech Univ Grad Sch Sci & Engn Dept Elect Engn Istanbul Turkey Yildiz Tech Univ Dept Elect Engn Istanbul Turkey

In recent years, with the changing concept of electrical energy, distributed generation systems have gained more importance. Distributed generation systems can contribute to distribution networks by increasing energy supply security, reducing power losses, and improving voltage stability. Optimal locations and sizes should be determined to obtain maximum benefit from distributed generation systems that will be connected to distribution networks. This paper presents a multi objective placement and sizing optimization model of distributed generation systems to minimize power losses and voltage deviations. The proposed model is implemented on a 40 bus real radial network using particle swarm algorithm and whale optimization algorithm. The obtained results on different load scenarios using particle swarm algorithm and whale optimization algorithm algorithms are presented comparatively.

关键词： Distributed generation systems particle swarm optimization whale optimization algorithm

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Statistical Load Forecasting Using Optimal Quantile Regression Random Forest and Risk Assessment Index

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IEEE TRANSACTIONS ON SMART GRID 2021年第2期12卷 1467-1480页

作者： Aprillia, Happy Yang, Hong-Tzer Huang, Chao-Ming Natl Cheng Kung Univ Dept Elect Engn Tainan 701 Taiwan Kalimantan Inst Technol Balikpapan 76127 Indonesia Kun Shan Univ Dept Elect Engn Tainan 710 Taiwan

To support daily operation of smart grid, the stochastic load behavior is analyzed by a day-ahead prediction interval (PI) which is built from predictor's probability density function, computed in statistical mean-variance, and achieves a symmetrical PI. However, this approach lacks for intended risk information on the predictors' uncertainty, e.g., weather condition and load variation. This article proposes a novel statistical load forecasting (SLF) using quantile regression random forest (QRRF), probability map, and risk assessment index (RAI) to obtain the actual pictorial of the outcome risk of load demand profile. To know the actual load condition, the proposed SLF is built considering accurate point forecasting results, and the QRRF establishes the PI from various quantiles. To correlate the uncertainty of external factors to the actual load, the probability map computes the most probable quantile happening in the training horizon. Based on the current inputs, the RAI calculates the PI's intended risk. The proposed SLF is verified by Independent System Operator-New England data, compared to benchmark algorithms and Winkler score. The results show that the proposed method can model a more precise load PI along with the risk evaluation, as compared to results of the existing benchmark models.

关键词： Forecasting Load modeling Input variables Uncertainty Predictive models Load forecasting Indexes Statistical load forecasting quantile regression short-term load forecasting discrete wavelet transforms whale optimization algorithm

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Load balancing in cloud environs:Optimal task scheduling via hybrid algorithm

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International Journal of Modeling, Simulation, and Scientific Computing 2021年第2期12卷 40-65页

作者： Shashikant Raghunathrao Deshmukh S.K.Yadav D.N.Kyatanvar Shri JJT University ChudelaJhunJhunuRajasthanIndia

In cloud computing,a lot of challenges like the server failures,loss of confidentiality,improper workloads,*** still bounding the efficiency of cloud systems in real-world *** this reason,many research works are being performed to overcome the shortcoming of existing *** them,load balancing seems to be the most critical issue that worsen the performance of the cloud sector,and hence there necessitates the optimal load balancing with optimal task *** the intention of accomplishing optimal load balancing by effectual task deployment,this paper plans to develop an advanced load balancing model with the assistance acquired from the metaheuristic ***,handling of tasks in cloud system is an NP-hard problem and moreover,nonpreemptive independent tasks are crucial in cloud *** paper goes with the introduction of a new optimal load balancing model by considering three major objectives:minimum makespan,priority,and load balancing,***,a new single-objective function is also defined that incorporates all the three objectives mentioned ***,the deployment of tasks must be optimal and for this a new hybrid optimization algorithm referred as Firefly Movement insistedWOA(FM-WOA)is *** FM-WOA is the conceptual amalgamation of standard whale optimization algorithm(WOA)and Firefly(FF)***,the performances of the proposed FM-WOA model is compared over the conventional models with the intention of proving its efficiency in terms of makespan,task completion(priority),and degree of imbalance as well.

关键词： Cloud computing load balancing task scheduling whale optimization algorithm firefly algorithm

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Machine learning-based single-phase ground fault identification strategy for AC-DC transmission lines

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ELECTRIC POWER SYSTEMS RESEARCH 2023年第1期223卷

作者： Wu, Shuo He, Baina Meng, Fantao Liu, Yang Lin, Xin Dai, Weihan Wei, Yuanlong Wang, Shuo Zhang, Dongjin Shandong Univ Technol Coll Elect & Elect Engn Zibo 255000 Peoples R China Shenyang Univ Technol Key Lab Special Machine & High Voltage Apparat Minist Educ Shenyang 110870 Peoples R China

In this paper, an improved single-phase grounding fault (SPGF) nature identification strategy based on machine learning is proposed. To solve the problem of conventional single-phase automatic reclosure cannot distinguishing the nature of SPGF in AC-DC hybrid lines, the fault phase voltage characteristics of AC lines with DC feed are analyzed, and the voltage harmonic energy feature vector is extracted. The whale optimization algorithm (WOA) designed by orthogonal experiment design (OED) is used to select the model parameters of the least square support vector machine (LSSVM), and a discriminative model for the fault properties of the hybrid transmission line is obtained. The simulation experiments based on PSCAD/EMTDC 4.5.0 platform are conducted for model training and fault identification performance testing, and the results verifies the effectiveness and feasibility of the proposed method. Compared with LSSVM, the fault identification strategy based on OED-WOALSSVM has a higher accuracy in fault identification. In addition, the accuracy of the model identification strategy under different fault locations, parallel compensation degree, transition resistance, and power system noise effects is also verified.

关键词： Single-phase grounding fault Identification strategy Fault phase voltage Harmonic energy whale optimization algorithm Least squares support vector machine

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