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IEEE International Conference on Web Services (IEEE ICWS)

作者： Ding, Zeyuan Feng, Binbin Yu, Wangyang Zhang, Bolan Shaanxi Normal Univ Sch Comp Sci Xian Peoples R China Tongji Univ Dept Comp Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9798350368567;9798350368550

As application migration to the cloud becomes the mainstream way of application deployment, application run-time management presents a significant need for large-scale workload prediction technology. Existing large-scale workload approaches commonly construct training sets based on all the training samples generated from the original data to generate prediction models. However, due to the similar behavior between different container instances of the microservice application, training and modeling in this way results in a huge number of redundant samples, which produces a significant redundant training overhead. Therefore, this paper proposes Less, a large-scale workload forecasting model based on multiple sequence compression. First, based on the grouping results of similar containers, a container workload feature recognition algorithm is proposed to determine the common and individual features of container workloads in each prediction period, so as to guide the compression of workload sequences within each group;second, a fitness function that takes into account the common features, individual features, and the number of sequences are designed, and the optimal compressed sequences are solved by the whale optimization algorithm to efficiently reduce the number of redundant training workload sequences, and then the Bidirectional Gated Recurrent Unit model is built and trained based on the compressed sequences, which effectively reduces the model complexity and overhead while ensuring the accuracy. Finally, we validate the comprehensive advantages of Less in terms of accuracy and overhead based on public datasets and verify the effectiveness of each subpart of our model through ablation experiments.

关键词： large-scale multiple sequence compression feature recognition common features individual features whale optimization algorithm fitness function Bi-directional Gated Recurrent Unit

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Climate Impact Prediction: whale-Optimized Conv-XGBoost with Remote Sensing and Sociological Data

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Remote Sensing in Earth Systems Sciences 2024年第4期7卷 443-456页

作者： Jayakarthik, R. Chandrashekhara K.T Sampath, O. Kumar, Deepak Biban, Laxmi Maroor, Jnaneshwar Pai Malluvalasa, S. N. Lakshmi Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Chennai India Department of Information Science and Engineering BMS Institute of Technology and Management Bangalore India Department of CSE RGM College of Engineering and Technology Nandyal India Department of Applied Sciences Manav Rachna International Institute of Research and Studies Faridabad India Department of Botany Pt. C.L.S. Govt. College Haryana Karnal India Department of Business Administration Nitte Institute of Professional Education Nitte (Deemed to be University) Mangaluru Karnataka India Department of Artificial Intelligence & amp Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur District Andhra Pradesh India

Climate change poses significant challenges globally, impacting ecosystems, economies, and societies. Accurate prediction of climate impacts is crucial for developing effective mitigation strategies and informed policy decisions. Traditional predictive models often struggle to capture the complex interactions between environmental and socio-economic factors, necessitating advanced approaches that leverage diverse data sources and sophisticated algorithms. This study presents a robust predictive framework integrating convolutional neural networks (CNN) and XGBoost, optimized by the whale optimization algorithm (WOA). The proposed model leverages high-resolution satellite imagery and socio-economic data, combining spatial feature extraction with advanced machine learning techniques to enhance prediction accuracy and efficiency. We evaluate the model’s performance against baseline models and various optimization techniques, including particle swarm optimization (PSO) and genetic algorithm (GA). The integrated CNN and XGBoost model optimized by WOA significantly outperforms these benchmarks, achieving an accuracy of 98%, with substantial improvements in precision, recall, F1-score, and redundant phrases, and focuses directly on the core points to enhance clarity such as mean squared error (MSE), area under the curve (AUC), specificity, sensitivity, mean absolute error (MAE), and R². The results highlight the efficacy of the proposed approach in capturing complex environmental and socio-economic interactions, providing a powerful tool for climate impact assessment and decision-making. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate Impact Prediction CNN Convolutional Neural Networks GA Genetic algorithm Machine Learning Model optimization Particle Swarm optimization PSO Spatial Feature Extraction whale optimization algorithm WOA XGBoost

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A Novel Map Reduced Based Parallel Feature Selection and Extreme Learning for Micro Array Cancer Data Classification

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WIRELESS PERSONAL COMMUNICATIONS 2022年第2期123卷 1483-1505页

作者： Hira, Swati Bai, Anita Shri Ramdeobaba Coll Engn & Management Dept Comp Sci & Engn Nagpur Maharashtra India CVR Coll Engn Hyderabad Dept Comp Sci & Engn Hyderabad Telangana India

Microarray-based gene expression profiling is an emerging method to predict, classify, diagnose and to treat cancer efficiently. The characteristics of this cancer disease may change frequently which creates large volume of data. In this paper we propose a Novel Map reduced based parallel feature selection and extreme learning for micro array cancer data classification. Initially the gene expression data sets are pre-processed by attribute-wise normalization and setting thresholds on the original data. The second phase uses a wrapper model that uses Adaptive whale optimization algorithm (AWOA) with Nelder-Mead algorithm (NMA) to accomplish the feature (gene) subset selection. Wrapper models are used to describe the selection process of feature sets as a search issue. Here, various combinations are formulated, estimated and compared with other combinations. At last, to demonstrate the effectiveness of the selected genes using the proposed feature selection method, a Regularized Extreme Learning Machine (RELM) classifier is used to classify the gene expression data subsets chosen by AWOA algorithm.

关键词： Gene expression profiling Feature selection whale optimization algorithm Classification whale optimization algorithm

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An optimized human resource management model for cloud-edge computing in the internet of things

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2022年第4期25卷 2527-2539页

作者： Liu, Yishu Zhang, Wenjie Zhang, Qi Norouzi, Monire Guangdong Univ Foreign Studies South China Business Coll Sch Management Guangzhou 510000 Guangdong Peoples R China Nanchang Inst Sci & Technol Sch Artificial Intelligence Nanchang 330000 Jiangxi Peoples R China Fuzhou Univ Int Studies & Trade Sch Econ & Management Fuzhou 350000 Fujian Peoples R China Res Ctr Appl Sci Baku Azerbaijan

The use of cloud-edge technology creates significant potential for cost reduction, efficiency and resource management. These features have encouraged users and organizations to use intelligence federated cloud-edge paradigm in Internet of Things (IoT). Human Resource Management (HRM) is one of the important challenges in federated cloud-edge computing. Since hardware and software resources in the edge environment are allocated for responding human requests, selecting optimal resources based on Quality of Service (QoS) factors is a critical and important issue in the IoT environments. The HRM can be considered as an NP-problem in a way that with proper selection, allocation and monitoring resource, system efficiency increases and response time decreases. In this study, an optimization model is presented for the HRM problem using whale optimization algorithm (WOA) in cloud-edge computing. Experimental results show that the proposed model was able to improve minimum response time, cost of allocation and increasing number of allocated human resources in two different scenarios compared to the other meta-heuristic algorithms.

关键词： Internet of Things (IoT) Edge computing Human resource management and allocation whale optimization algorithm

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Study of energy scheduling and optimal cost management of a new structure CCHP system: A case study supplying energy for a chemical enterprise in Jiangsu Province

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ENERGY REPORTS 2022年 8卷 14499-14515页

作者： Ji, Jie Qin, Jingxin Ji, Rundong Huang, Hui Wang, Fucheng Nazir, Muhammad Shahzad Zhou, Mengxiong Wang, Yaodong Huaiyin Inst Technol Huaiyin 223002 Jiangsu Peoples R China Jiangsu Huashui Engn Detect & Consulting Co Ltd Huaian Peoples R China Univ Durham Durham Energy Inst Durham England

A new structure combined cooling, heating and power (CCHP) system integrating green power applying a control strategy based on a improved whale optimization algorithm is proposed in this paper. This paper fully considers the high energy demand and a large amount of waste heat in chemical enterprises. By absorbing chemical waste heating (mainly from chemical wastewater), coupling wind and photovoltaic equipment, adding cooling, heating, and electricity storage equipment, combined with an energy scheduling strategy, and an efficient CCHP energy system for the target enterprise is realized. The improved whale optimization algorithm is used to optimize cost management. The results show that after the optimization scheduling with the improved whale optimization algorithm, the CCHP system meets the energy demand with higher efficiency at a lower cost. In winter and summer, heat pump recycling chemical wastewater provides 15.4%-57.9% and 11.5%-62.9% of the heating load of the heating network. Meanwhile, the lithium bromide refrigerator in the cooling-net equipment provides an average cooling load of 6.25-58.4% and 7.01-16.64%. Finally, the improved whale optimization algorithm reduces the total cost by 2.4 % and 2.17 %. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by- nc- nd/4.0/).

关键词： CCHP system Chemical enterprises Energy scheduling whale optimization algorithm

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Research on Financial Risk Crisis Prediction of Listed Companies Based on IWOA-BP Neural Network

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JOURNAL OF INTERNET TECHNOLOGY 2022年第5期23卷 955-965页

作者： Li, Sha Chen, Xuan Zhejiang Ind Polytech Coll Finance Branch Shaoxing Peoples R China Zhejiang Ind Polytech Coll Design & Art Branch Shaoxing Peoples R China

To avoid the risk brought by the financial crisis, the Improved whale optimization algorithm-Back Propagation neural network (IWOA-BP) financial crisis early warning model is proposed. This paper selects the data from financial statements of some of the listed Chinese manufacturing companies from 2015-2019 as the research sample. First, the financial data of enterprises are screened by principal component analysis, and the early warning model is constructed from the financial and nonfinancial factors of six indicators: solvency, operating capacity, profitability, development capacity, cash flow and risk level factors. Second, the whale optimization algorithm is optimized by the chaos strategy, as well as by the dynamic weight and sine cosine algorithm. Finally, the improved whale algorithm is optimized for BP neural network parameters. In the simulation experiments, the performance of the improved whale optimization algorithm is substantially improved. In addition, in the empirical analysis, compared to the prediction model with other algorithms, the prediction model of this paper has better results in terms of prediction accuracy.

关键词： Financial risk Crisis prediction whale optimization algorithm BP neural network

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State prediction of hydro-turbine based on WOA-RF-Adaboost

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ENERGY REPORTS 2022年 8卷 13129-13137页

作者： Lan, Chaofeng Song, Bowen Zhang, Lei Fu, Lirong Guo, Xiaoxia Sun, Chao Harbin Univ Sci & Technol Sch Measurement & Commun Engn Harbin Peoples R China Beidahuang Ind Grp Gen Hosp Harbin 150088 Peoples R China Hainan Univ Mech & Elect Engn Coll Haikou 570228 Hainan Peoples R China

Aiming at the problem that the prediction performance of remote operation state of hydro-turbine needs to be improved, this paper uses whale optimization algorithm (WOA) to optimize random forest (RF). It combines it with Adaboost algorithm to propose the prediction model in this paper. Firstly, the signal of hydro-turbine is analyzed by variational modal decomposition (VMD), and the penalty factor and the number of IMF components K of VMD are optimized by fruit fly optimization algorithm (FOA). The arrangement entropy of intrinsic mode function (IMF) components, kurtosis, mean value of original signal are calculated, and the input eigenvector of hydro-turbine state prediction model is constructed;Secondly, the number of split attribute sets and the optimal number of decision trees of RF are optimized by WOA, and multiple WOA-RF models are iteratively trained by Adaboost algorithm to construct WOA-RF-Adaboost state prediction model. The prediction effect of the proposed model and the traditional model is evaluated by correct rate and confusion matrix. The results show that the prediction accuracy of WOA-RF-Adaboost model proposed in this paper is 99.2%, it has good state prediction performance. (C) 2022 The Author(s). Published by Elsevier Ltd.

关键词： Hydro-turbine State prediction Variational modal decomposition Fruit fly optimization algorithm whale optimization algorithm Random forest

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Survival prediction model for right-censored data based on improved composite quantile regression neural network

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第8期19卷 7521-7542页

作者： Qin, Xiwen Yin, Dongmei Dong, Xiaogang Chen, Dongxue Zhang, Shuang Changchun Univ Technol Sch Math & Stat Changchun 130012 Peoples R China

With the development of the field of survival analysis, statistical inference of right-censored data is of great importance for the study of medical diagnosis. In this study, a right-censored data survival prediction model based on an improved composite quantile regression neural network framework, called rcICQRNN, is proposed. It incorporates composite quantile regression with the loss function of a multi-hidden layer feedforward neural network, combined with an inverse probability weighting method for survival prediction. Meanwhile, the hyperparameters involved in the neural network are adjusted using the WOA algorithm, integer encoding and One-Hot encoding are implemented to encode the classification features, and the BWOA variable selection method for high dimensional data is proposed. The rcICQRNN algorithm was tested on a simulated dataset and two real breast cancer datasets, and the performance of the model was evaluated by three evaluation metrics. The results show that the rcICQRNN-5 model is more suitable for analyzing simulated datasets. The One-Hot encoding of the WOA-rcICQRNN-30 model is more applicable to the NKI70 data. The model results are optimal for ?????????????????? L 15 after feature selection for the METABRIC dataset. Finally, we implemented the method for cross-dataset validation. On the whole, the Cindex results using One-Hot encoding data are more stable, making the proposed rcICQRNN prediction model flexible enough to assist in medical decision making. It has practical applications in areas such as biomedicine, insurance actuarial and financial economics.

关键词： right-censored data composite quantile regression neural network whale optimization algorithm feature selection

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Traffic Flow Prediction in 5G-Enabled Intelligent Transportation Systems Using Parameter optimization and Adaptive Model Selection

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SENSORS 2024年第20期24卷 6529页

作者： Vo, Hanh Hong-Phuc Nguyen, Thuan Minh Bui, Khoi Anh Yoo, Myungsik Soongsil Univ Dept Elect Engn Seoul 06978 South Korea Soongsil Univ Sch Elect Engn Seoul 06978 South Korea

This study proposes a novel hybrid method, FVMD-WOA-GA, for enhancing traffic flow prediction in 5G-enabled intelligent transportation systems. The method integrates fast variational mode decomposition (FVMD) with optimization techniques, namely, the whale optimization algorithm (WOA) and genetic algorithm (GA), to improve the accuracy of overall traffic flow based on models tailored for each decomposed sub-sequence. The selected predictive models-long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU)-were considered to capture diverse temporal dependencies in traffic data. This research explored a multi-stage approach, where the decomposition, optimization, and selection of models are performed systematically to improve prediction performance. Experimental validation on two real-world traffic datasets further underscores the method's efficacy, achieving root mean squared errors (RMSEs) of 152.43 and 7.91 on the respective datasets, which marks improvements of 3.44% and 12.87% compared to the existing methods. These results highlight the ability of the FVMD-WOA-GA approach to improve prediction accuracy significantly, reduce inference time, enhance system adaptability, and contribute to more efficient traffic management.

关键词： traffic flow parameter optimization whale optimization algorithm genetic algorithm fast variation mode decomposition

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A whale optimization detection (WOD) method for bladder volume estimation with bioelectrical impedance spectroscopy (BIS)

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第3期35卷 035101-035101页

作者： Hong, Zhiyang Zhu, Jin Wu, Yang Fang, Junyang Ye, Ming Liu, Kai Yao, Jiafeng Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 210016 Peoples R China Soochow Univ Affiliated Hosp 2 Dept Resp & Crit Care Med Suzhou 215004 Peoples R China

A whale optimization detection (WOD) method is presented for rapid bladder volume estimation with bioelectrical impedance spectroscopy (BIS). Firstly, the WOD method for bladder volume estimation is established, which includes a bladder volume estimation electrical equivalent circuit (EEC) model and a rapid fitting strategy. Secondly, an abdominal phantom is constructed, which consists of an abdominal cavity shell and four bladders with volumes of V-b = 30 ml, 100 ml, 200 ml, and 300 ml. Next, BIS experiments are conducted using the phantom with the WOD method fitted to the obtained data. The results demonstrated a high linear correlation coefficient R-2 > 0.987 between the experimental and WOD results. Finally, in vivo testing is performed using both the Caresono HD-5 ultrasonic bladder scanner and the proposed WOD method. The EEC parameters obtained by the WOD method form a mathematical formula that accurately describes the subject's bladder volume, with a detection correlation coefficient r = 98.71%, and the maximum relative error delta = 5.88% between WOD and ultrasound testing results. In conclusion, the proposed method is expected to be applied for rapid monitoring of patients' bladder volume.

关键词： bioelectrical impedance spectroscopy electrical equivalent circuit whale optimization algorithm bladder volume estimation

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