检索结果-内蒙古大学图书馆

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2023年第3期38卷 1299-1306页

作者： Akalin, Fatma Yumusak, Nejat Sakarya Univ Fac & Informat Sci Dept Informat Syst Engn TR-54187 Sakarya Turkiye Sakarya Univ Fac & Informat Sci Dept Comp Engn TR-54187 Sakarya Turkiye

In this study, it is realized to differentiate ALL, AML and MLL malignancies, which are the types of leukaemia, using the microarray dataset. The flow chart related to the classification of malignancies is given in Figure A. Figure A. Flow chart related to the classification of ALL, AML and MLL malignanciesPurpose: Classification of ALL, AML and MLL malignancies using LSTM neural network architecture on microarray *** and Methods: In this study, the microarray dataset obtained from https://*** /biolab/supp/bi-cancer/projections/ is used. Microarray dataset, which has dimensional superiority, creates a complex and costly situation. For this reason, it is planned to facilitate the analysis process with computer -aided systems. In the presented study;ALL, AML and MLL malignancies are analyzed with an artificial intelligence-supported system. Firstly, qualified disease-associated genes are selected from the microarray dataset using the whale optimization algorithm in this scope. Then, ALL, AML and MLL malignancies are classified using the LSTM neural network on selected potential genes. This present study which has a simple hierarchy and low computational complexity produced an efficient ***: This study classified ALL, AML and MLL malignancies using the microarray dataset. In this context, LSTM neural network architecture was used on the potential genes selected with the whale optimization algorithm and an accuracy rate of 89.883% was ***: Diagnostic and prognostic prediction of leukaemia disease is important in terms of determining the category of the disease and planning the treatment process. In the current study conducted for this purpose, ALL, AML and MLL leukaemia types were distinguished successfully.

关键词： Microarray dataset whale optimization algorithm LSTM neural network

来源：评论

学校读者我要写书评

暂无评论

A single-vendor multi-buyer inventory model with multiple raw material and quality degradation: A case study on agri-food industry

引用

RESULTS IN CONTROL AND optimization 2024年 14卷

作者： Utama, Dana Marsetiya Kusuma, Iludy Rosetta Amallynda, Ikhlasul Baroto, Teguh Jauhari, Wakhid Ahmad Univ Muhammadiyah Malang Dept Ind Engn Kota Malang Indonesia Univ Sebelas Maret Dept Ind Engn Surakarta Indonesia

Managing degraded-quality materials in a supply chain with a single-vendor and multi- buyers is prevalent and critical in modern industry. This research addresses the single-vendor multi-buyer problem by incorporating multiple raw materials and quality degradation to maximize joint total profit (JTP). The proposed model utilizes the whale optimization algorithm (WOA) for optimization, with empirical data derived from an agri-food industry case study in Indonesia. Experimental findings demonstrate the superior effectiveness of WOA compared to Chim optimization algorithm (ChOA), Genetic algorithm (GA), (GWO), Particle Swarm optimization (PSO), and heuristic algorithm in solving the single-vendor multi-buyer problem. Sensitivity analysis reveals noteworthy insights: increased demand boosts total profit and production cycle efficiency, while elevated shipping costs for finished products negatively impact JTP by reducing production efficiency. Moreover, escalating quality degradation rates decrease JTP and production cycle time. This study significantly contributes to optimizing single-vendor and multi-buyer operations by addressing multiple raw materials and quality degradation, offering valuable insights into sensitivity analysis for critical factors in the agri-food industry.

关键词： Quality degradation Vendor-buyer inventory model Supply chain whale optimization algorithm Multiple materials

来源：评论

学校读者我要写书评

暂无评论

Tomato storage quality predicting method based on portable electronic nose system combined with WOA-SVM model

引用

JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2023年第4期17卷 3654-3664页

作者： Zhou, Weixiang Lian, Junbo Zhang, Jingyu Mei, Zhenghao Gao, Yuanyuan Hui, Guohua Zhejiang A&F Univ Sch Math & Comp Sci Key Lab Forestry Sensing Technol & Intelligent Equ Dept ForestryKey Lab Forestry Intelligent Monitor Hangzhou 311300 Peoples R China

In this paper, a portable electronic nose (e-nose) system, combined with the support vector machine model characterized by whale optimization algorithm (WOA-SVM), was developed for tomato storage quality prediction. K-nearest neighbor (KNN) model, decision tree (DT) model, support vector machine (SVM) model and support vector machine combined with loop optimization algorithm (LOA-SVM) model were constructed to provide a comparison for the WOA-SVM model. E-nose responses at different storage times were collected for 21 days. Results demonstrated that the prediction accuracy of the WOA-SVM model reached 99.81%, which was high than other models. This method presented some advantages including rapid analysis, good repeatability and high accuracy.

关键词： Tomato Quality prediction Electronic nose Support vector machine whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Performance evaluation and comparative analysis of Crowwhale-energy and trust aware multicast routing algorithm

引用

WEB INTELLIGENCE 2023年第3期21卷 271-291页

作者： Shende, Dipali K. Angal, Yogesh S. PCCOER Pune Maharashtra India Bhivarabai Sawant Inst Technol & Res Wagholi Pune India

Multipath routing helps to establish various quality of service parameters, which is significant in helping multimedia broadcasting in the Internet of Things (IoT). Traditional multicast routing in IoT mainly concentrates on ad hoc sensor networking environments, which are not approachable and vigorous enough for assisting multimedia applications in an IoT environment. For resolving the challenging issues of multicast routing in IoT, Crowwhale-energy and trust-aware multicast routing (Crowwhale-ETR) have been devised. In this research, the routing performance of Crowwhale-ETR is analyzed by comparing it with optimization-based routing, routing protocols, and objective functions. Here, the optimization-based algorithm, namely the SpiderMonkey optimization algorithm (SMO), whale optimization algorithm (WOA), Dolphin Echolocation optimization (DEO) algorithm, Water Wave optimization (WWO) algorithm, Crow Search algorithm (CSA), and, routing protocols, like Ad hoc On-Demand Distance Vector (AODV), CTrust-RPL, Energy-Harvesting-Aware Routing algorithm (EHARA), light-weight trust-based Quality of Service (QoS) routing, and Energy-awareness Load Balancing-Faster Local Repair (ELB-FLR) and the objective functions, such as energy, distance, delay, trust, link lifetime (LLT) and EDDTL (all objectives) are utilized for comparing the performance of Crowwhale-ETR. In addition, the performance of Crowwhale-ETR is analyzed in terms of delay, detection rate, energy, Packet Delivery Ratio (PDR), and throughput, and it achieved better values of 0.539 s, 0.628, 78.42%, 0.871, and 0.759 using EDDTL as fitness.

关键词： Spider monkey optimization dolphin echolocation water wave optimization crow search algorithm whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Designing Sustainable Closed-Loop Supply Chain Network Based on a Circular Economy Approach: Under Uncertainty During the Post-COVID Era

引用

Circular Economy and Sustainability 2025年 1-43页

作者： Abbasi, Sina Rokhva, Shayan Farahmand, Kazem Ghasemi, Peiman Shahab, Erfan Department of Industrial Engineering Lahijan Branch Islamic Azad University Lahijan Iran Department of Information Technology Faculty of Industrial and System Engineering Tarbiat Modares University Tehran Iran Department of Economics Faculty of Economics and Administrative Sciences Ferdowsi University of Mashhad Mashhad Iran Department of Business Decisions and Analytics University of Vienna Kolingasse 14-16 Vienna 1090 Austria Department of Industrial Engineering Toronto Metropolitan University Toronto Canada

Sustainable closed-loop supply chain networks (SCLSCNs) are a key component of the circular economy and aim to reduce waste and increase resource efficiency. The multi-product SCLSCN under uncertainty during the post-COVID pandemic is presented in this study. After establishing a mathematical model for the design of a closed-loop network (CLN), a series of potential scenarios are used to account for parameter uncertainty. In this paper, a hybrid metaheuristic method is proposed to tackle this NP-hard problem for solving the multi-objective (MO) optimization. We refer to the hybrid algorithm as HWS (a hybrid method of WOA and SA). It is an optimizer that uses the relatively new whale optimization algorithm (WOA) and the widely used simulated annealing (SA) technique. Hybrid metaheuristics are one of the most interesting current trends in MO optimization. The aim of utilizing SA is to increase the yield by searching the most promising regions found by the WOA algorithm. Analytical and numerical data are given to illustrate the reliability and suitability of the proposed model using real case studies in the electronics industry. Finally, a sensitivity analysis is conducted to assess how consumer demand affects different aspects of sustainability. The results emphasize that the achievement of economic, environmental, and social goals is significantly affected by COVID-19. The scope of the economic aspect thus tends to increase with increasing changes in customer demand in the post-COVID-19. The bad social effects increased due to the number of days lost due to illness. These effects continue in the post-disaster period. On the other hand, carbon dioxide (CO₂) emissions have decreased due to lockdowns during the disaster, leading to greater health and it continues after COVID-19. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Circular closed-loop supply chain Multi-objective optimization Simulated annealing Uncertain programming whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Intervention-Aware Epidemic Prediction by Enhanced whale optimization 1

引用

15th International Conference on Knowledge Science, Engineering, and Management (KSEM)

作者： Zhao, Songwei Song, Jiuman Du, Xinqi Liu, Tianyi Chen, Huiling Chen, Hechang Jilin Univ Sch Artificial Intelligence Changchun Peoples R China Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun Peoples R China Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou Zhejiang Peoples R China

ISBN: (数字)9783031109867

ISBN: (纸本)9783031109867;9783031109850

In recent decades, new epidemics have seriously endangered people's lives and are now the leading cause of death in the world. The prevention of pandemic diseases has therefore become a top priority today. However, effective prevention remains a difficult challenge due to factors such as transmission mechanisms, lack of documentation of clinical outcomes, and population control. To this end, this paper proposes a susceptible-exposed-infected-quarantined (hospital or home)-recovered (SEIQHR) model based on human intervention strategies to simulate and predict recent outbreak transmission trends and peaks in Changchun, China. In this study, we introduce Levy operator and random mutation mechanism to reduce the possibility of the algorithm falling into a local optimum. The algorithm is then used to identify the parameters of the model optimally. The validity and adaptability of the proposed model are verified by fitting experiments to the number of infections in cities in China that had COVID-19 outbreaks in previous periods (Nanjing, Wuhan, and Xi'an), where the peaks and trends obtained from the experiments largely match the actual situation. Finally, the model is used to predict the direction of the disease in Changchun, China, for the coming period. The results indicated that the number of COVID-19 infections in Changchun would peak around April 3 and continue to decrease until the end of the outbreak. These predictions can help the government plan countermeasures to reduce the expansion of the epidemic.

关键词： Epidemic prediction Intervention strategy SEIQHR model whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A novel hybrid whale-Nelder-Mead algorithm for optimization of design and manufacturing problems

引用

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2019年第12期105卷 5091-5104页

作者： Yildiz, Ali Riza Bursa Uludag Univ Fac Engn Dept Automot Engn TR-16059 Bursa Turkey

This paper introduces a new hybrid optimization algorithm (HWOANM) based on the Nelder-Mead local search algorithm (NM) and whale optimization algorithm (WOA). The aim of hybridization is to accelerate global convergence speed of the whale algorithm for solving manufacturing optimization problems. The main objective of our study on hybridization is to accelerate the global convergence rate of the whale algorithm to solve production optimization problems. This paper is the first research study of both the whale algorithm and HWOANM for the optimization of processing parameters in manufacturing processes. The HWOANM is evaluated using the well-known benchmark problems such as cantilever beam problem, welded beam problem, and three-bar truss problem. Finally, a grinding manufacturing optimization problem is solved to investigate the performance of the HWOANM. The results of the HWOANM for both the design and manufacturing problems solved in this paper are compared with other optimization algorithms presented in the literature such as the ant colony algorithm, genetic algorithm, scatter search algorithm, differential evolution algorithm, particle swarm optimization algorithm, simulated annealing algorithm, artificial bee colony algorithm, improved differential evolution algorithm, harmony search algorithm, hybrid particle swarm algorithm, teaching-learning-based optimization algorithm, cuckoo search algorithm, grasshopper optimization algorithm, salp swarm optimization algorithm, mine blast algorithm, gravitational search algorithm, ant lion optimizer, multi-verse optimizer, whale optimization algorithm, and the Harris hawks optimization algorithm. The results show that the HWOANM provides better exploration and exploitation properties, and can be considered as a promising new algorithm for optimizing both design and manufacturing optimization problems.

关键词： Hybrid algorithm whale optimization algorithm Nelder-Mead Design Manufacturing

来源：评论

学校读者我要写书评

暂无评论

RF fingerprint extraction and device recognition algorithm based on multi-scale fractal features and APWOA-LSSVM

引用

EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2023年第1期2023卷 131页

作者： Feng, Wenjiang Li, Yuan Wu, Chongchong Zhang, Juntao Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China State Grid Chongqing Elect Power Co Informat Commun Branch Chongqing 401320 Peoples R China

RF fingerprints can be used for device identification and network access authentication. An RF fingerprint extraction and device identification algorithm based on multiscale fractal features and APWOA-LSSVM is proposed. First, a Hilbert transform is performed on the original RF signal, and differential operations are performed on the I/Q signal. Then, the variational modal decomposition is performed on the differentially processed I/Q signals separately to obtain a number of band-limited Intrinsic Modal Function (IMF) components. The fractal box dimension of each IMF component is calculated separately as the first-dimensional RF fingerprint. The multi-fractal spectrum of the original RF signal is further computed by Multi-fractal Detrended Fluctuation Analysis (MFDFA) as the second-dimensional RF fingerprint. The RF fingerprint feature vector consists of a combination of the first- and second-dimensional RF fingerprints and is used to train the LSSVM model. Since the penalty factor and kernel parameter width directly affect the recognition accuracy and generalization ability of the LSSVM model, APWOA is used to optimize the hyperparameters of the LSSVM model. Finally, the LSSVM model is constructed using the optimal hyperparameters and the performance of the model is verified on a test dataset. The experimental results show that the proposed model achieves an average recognition accuracy of 99.13% for 16 Bluetooth devices. Multi-scale fractal features consisting of fractal box dimensions and multiple fractal dimensions are more beneficial in the recognition of wireless devices as compared to single features. In addition, the proposed model possesses superior recognition capabilities when compared to classical machine learning algorithms.

关键词： Radio frequency fingerprint Feature extraction Classification and recognition Least squares support vector machine whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Comparison of optimization algorithms based on swarm intelligence applied to convolutional neural networks for face recognition

引用

International Journal of Hybrid Intelligent Systems 2022年第3-4期18卷 161-171页

作者： Melin, Patricia Sánchez, Daniela Castillo, Oscar Tijuana Institute of Technology Tijuana Mexico

In this work, a comparison of optimization techniques based on swarm intelligence to design Convolutional Neural Networks is performed. The optimization techniques used in this comparison are Grey Wolf Optimizer (GWO) and whale optimization algorithm (WOA). The algorithms design convolutional neural networks (CNNs) architectures applied to face recognition. These techniques were chosen due to their similarity in their processes to find optimal results, such as their searching of prey. The design of CNNs consists of the number of layers (convolutional and fully connected), number and size of the filters, neurons fully connected, batch size, epoch, and algorithm for the learning phase. The simulation results are compared, using a different number of images for the learning phase to know which technique has a better performance using a smaller number of images to CNN design. © 2022 - IOS Press. All rights reserved.

关键词： Convolutional neural networks face recognition grey wolf optimizer swarm intelligence whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

An Indirect State-of-Health Estimation Method for Lithium-Ion Battery Based on Correlation Analysis and Long Short-Term Memory Network 8th

An Indirect State-of-Health Estimation Method for Lithium-Io...

引用

8th International Symposium on Artificial Intelligence and Robotics (ISAIR)

作者： Zhang, Zhiying Meng, Gong Wang, Shenhang Bai, Xiaojun Fu, Yanfang Xian Technol Univ Sch Comp Sci & Engn Xian Peoples R China Beijing Aerosp Automat Control Inst Beijing Peoples R China

ISBN: (纸本)9789819991082;9789819991099

State of Health (SOH) is a key indicator to describe battery's health status, which is important in terms of extending battery life, reducing failures and losses. Battery capacity are commonly used indicators for SOH estimation, but it's difficult to monitor capacity online because of battery working conditions. To overcome this challenge, this paper proposes an indirect method for SOH estimation. The study found a correlation between battery capacity and measured voltage during discharge. This correlation was confirmed using correlation analysis methods. The measured voltage sequence of the battery discharge phase is chosen as a health indicator to measure SOH. An LSTM network was used to create a model predicting battery capacity based on the health indicator. optimization of LSTM model hyperparameters using whale optimization algorithm. The proposed method achieved a mean absolute percentage error of 0.53%, 11.38% higher than the traditional method.

关键词： state of health lithium-ion battery correlation analysis long short-term memory network whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：