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7th International Conference on Soft Computing in Data Science (SCDS)

作者： Wang, Dan Wang, Ting Qiannan Normal Univ Nationalities Sch Math & Stat Duyun 558000 Guizhou Peoples R China Baoji Univ Arts & Sci Sch Comp Sci Baoji 721007 Peoples R China Key Lab Complex Syst & Intelligent Optimizat Guiz Duyun 558000 Guizhou Peoples R China

ISBN: (纸本)9789819904044;9789819904051

In the realm of multi-source privacy data protection, federated learning is now one of the most popular study topics. When the data being used is not local, its architecture has the ability to train a common model that can satisfy the needs of many parties. On the other hand, there are circumstances in which local model parameters are challenging to incorporate and cannot be used for security purposes. As a result, a federated ensemble algorithm that is based on deep learning has been presented, and both deep learning and ensemble learning have been used within the context of federated learning. Using the various integrated algorithms that integrate local model parameters, which improve the accuracy of the model and take into account the security of multi-source data, the accuracy of the local model can be improved by optimizing the parameters of the local model. This in turn improves the accuracy of the local model. The results of the experiments show that, in comparison to conventional multi-source data processing technology, the accuracy of the algorithm in the training model for the MNIST dataset, the digits dataset, the letter dataset, and thewine dataset is improved by 1%, 8%, 1%, and 1%, respectively, and the accuracy is guaranteed. Additionally, accuracy is guaranteed. It also improves the security of data and models that come from more than one source, which is a very useful feature.

关键词： Federated learning Federated ensemble algorithm Deep neural network model ensemble algorithm Deep learning

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Short-term Wind Speed Prediction with ensemble algorithm

Short-term Wind Speed Prediction with Ensemble Algorithm

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Chinese Automation Congress (CAC)

作者： Long, Yitao Zhang, Runfeng Tianjin Univ Sch Mech Engn Tianjin Peoples R China

ISBN: (纸本)9781728176871

Wind energy is a crucial component of renewable energy. Accurate prediction of wind speed will contribute a lot to the energy generation. This paper uses the data of wind speed in Belnoit, USA, studying the methods of predicting short-term wind speed and its direction. Firstly, different machine learning methods were compared to obtain initial solution, then the hybrid models using different ensemble learning algorithm also proposed. To valid the prediction accuracy and stability, various types of evaluation indicators were listed and calculated. The results shows that different models could capture different information, so we need to use several evaluation indicators to have a more exhaustive image about each model. Moreover, a better initial prediction with single model will allow us have a general result in prediction at first. Finally, ensemble learning could combines advantages of each model and reduce errors in the prediction process.

关键词： wind speed prediction machine learning ensemble algorithm

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Chronic Kidney Disease Prediction using Machine Learning ensemble algorithm

Chronic Kidney Disease Prediction using Machine Learning Ens...

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International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

作者： Nikhila Ctr PG Studies Dept Elect & Commun Syst Mysuru Karnataka India

ISBN: (数字)9781728185293

ISBN: (纸本)9781728185293

Chronic Kidney Disease is one among the noncontagious illnesses that affect most of the individual in the world. The main factors of risk for the Chronic Kidney Disease are Diabetes, Heart Ailment, Hypertension. The Chronic Kidney Disease shows no symptoms in the early stages and most of the cases are diagnosed in the advanced stage. This leads to delayed treatment to the patient which may be fatal. Machine learning technique provides an efficient way in the prediction of Chronic Kidney Disease at the earliest stage. In this paper, four ensemble algorithms are used to diagnose the patient with Chronic Kidney Disease at the earlier stages. The machine learning models are evaluated based on seven performance metrics including Accuracy, Sensitivity, Specificity, F1-Score, and Mathew Correlation Coefficient. Based on the evaluation the AdaBoost and Random Forest performed the best in terms of accuracy, precision, Sensitivity compared to Gradient Boosting and Bagging. The AdaBoost and Random Forest also showed the Mathew Correlation Coefficient and Area Under the curve scores of 100%. The machine learning model proposed in this paper will provide an efficient way to prevent Chronic Kidney diseases by enabling the medical practitioners to diagnose the disease at an early stage.

关键词： AdaBoost Gradient Boosting Random Forest ensemble algorithm Machine Learning

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Prediction of OPEC Carbon Dioxide Emissions Using K-Means Clustering and ensemble algorithm 6th

Prediction of OPEC Carbon Dioxide Emissions Using K-Means Cl...

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6th International Conference on Soft Computing and Data Mining (SCDM)

作者： Lasisi, Ayodele Zin, Nur Ariffin Mohd Ghazali, Rozaida Agagu, Modupe King Khalid Univ Dept Comp Sci Coll Comp Sci Abha Saudi Arabia Univ Tun Hussein Onn Malaysia Fac Comp Sci & Informat Technol Parit Raja 86400 Johor Malaysia Olusegun Agagu Univ Sci & Technol Dept Comp Sci Okitipupa Ondo Nigeria

ISBN: (纸本)9783031669644;9783031669651

The rapid increase in the concentration of carbon dioxide (CO2) polluting the atmosphere induces global warming and climate change. This is detrimental to human health and their natural habitat. Thus, it is imperative to proffer measures in analyzing and predicting the emissions of CO2. This research suggests using an ensemble approach with fuzzy nearest neighbor, sequential minimal optimization, and logistic regression to predict global CO2 emissions. The K-means algorithm divides data into groups of similar and relevant patterns. Simulation findings show that the proposed model outperforms techniques such as multi-layer perceptron, fuzzy ownership nearest neighbor, and random forest. It also improves CO2 forecast accuracy.

关键词： ensemble algorithm Carbon dioxide emissions Fuzzy nearest neighbor Sequential minimal optimization Logistic regression

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Numerical analysis of a second order ensemble algorithm for numerical approximation of stochastic Stokes-Darcy equations

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2022年第0期406卷 113934-113934页

作者： Jiang, Nan Qiu, Changxin Univ Florida Dept Math Gainesville FL 32611 USA Ningbo Univ Sch Math & Stat Ningbo 315211 Peoples R China

Numerical approximation of stochastic Stokes-Darcy equations usually requires repeated sampling of the random hydraulic conductivity tensor and then simulating flow ensembles. In this setting, we propose an efficient, second order, ensemble algorithm for fast computation of the whole set of realizations of the stochastic Stokes-Darcy model corresponding to different random hydraulic conductivity tensor samples. The ensemble algorithm only requires the solution of two linear systems that have the same constant coefficient matrices for all realizations. We give a complete long time stability and convergence analysis for the method. Numerical experiments are presented to support theoretical results and demonstrate the application of the method.(c) 2021 Elsevier B.V. All rights reserved.

关键词： Stokes-Darcy equations Uncertainty quantification ensemble algorithm Finite element method Partitioned method

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Improving the Regional Applicability of Satellite Precipitation Products by ensemble algorithm

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REMOTE SENSING 2018年第4期10卷 577-577页

作者： Muhammad, Waseem Yang, Hanbo Lei, Huimin Muhammad, Ajmal Yang, Dawen Tsinghua Univ Dept Hydraul Engn State Key Lab Hydrosci & Engn Beijing 100084 Peoples R China Univ Engn & Technol Ctr Excellence Water Resource Engn Lahore 54890 Pakistan Univ Engn & Technol Dept Agr Engn Peshawar 25120 Pakistan

Satellite-based precipitation products (e. g., Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) and its predecessor, Tropical Rainfall Measuring Mission (TRMM)) are a critical source of precipitation estimation, particularly for a region with less, or no, hydrometric networking. However, the inconsistency in the performance of these products has been observed in different climatic and topographic diverse regions, timescales, and precipitation intensities and there is still room for improvement. Hence, using a projected ensemble algorithm, the regional precipitation estimate (RP) is introduced here. The RP concept is mainly based on the regional performance weights derived from the Mean Square Error (MSE) and the precipitation estimate from the TRMM product, that is, TRMM 3B42 (TR), real-time (late) (IT) and the research (post-real-time) (IR) products of IMERG. The overall results of the selected contingency table (e.g., Probability of detection (POD)) and statistical indices (e.g., Correlation Coefficient (CC)) signposted that the proposed RP product has shown an overall better potential to capture the gauge observations compared with the TR, IR, and IT in five different climatic regions of Pakistan from January 2015 to December 2016, at a diurnal time scale. The current study could be the first research providing preliminary feedback from Pakistan for global precipitation measurement researchers by highlighting the need for refinement in the IMERG.

关键词： satellite observation regional estimates ensemble algorithm TRMM

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An improved energy-low clustering hierarchy protocol based on ensemble algorithm

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2020年第7期32卷 e5575-e5575页

作者： Xie, Penghua Lv, Ming Zhao, Juanjuan Taiyuan Univ Technol Shanxi Key Lab Precis Machining Taiyuan Peoples R China Taiyuan Univ Technol Coll Informat & Comp Taiyuan 030024 Peoples R China

LEACH (low-energy adaptive clustering hierarchy) protocol is an important part of wireless sensor network (WSN) and consisting of a good deal of energy-constrained nodes. LEACH is widely used but also faces various challenges and constraints. The biggest constraint is that the energy is limited and the network life is finite. In the article, an improved ensemble algorithm based on energy-efficient LEACH is proposed. The ensemble algorithm benefits from the cuckoo search algorithm (CS), glowworm swarm optimization (GSO), and bacterial foraging optimization (BFO). The paper enhances local and global search capabilities by modify the CS GSO and BFO algorithm. To verify results, this paper performs statistical comparatives with oriented cuckoo search (OCS), bat algorithm with characteristics of Levy flights, particle swarm optimization, and adaptive differential evolution algorithm. Comparison results shows that the new method is effective. Moreover, the research also showed the universal applicability of the ensemble strategy in solving diverse problems by using above different optimization algorithms. To further exploit our mind, this paper applies the improved coupling algorithm to LEACH for selecting the cluster-head node. Computational experiments illustrate that the method proposed in this paper can provide very competitive results compared to these bio-inspired LEACH, both in terms of reducing the network energy consumption and improving the lifecycle of nodes.

关键词： bacterial foraging optimization (BFO) cuckoo search algorithm (CS) ensemble algorithm glowworm swarm optimization (GSO) LEACH WSN

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A Combined Strategy of Improved Variable Selection and ensemble algorithm to Map the Growing Stem Volume of Planted Coniferous Forest

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REMOTE SENSING 2021年第22期13卷 4631-4631页

作者： Xu, Xiaodong Lin, Hui Liu, Zhaohua Ye, Zilin Li, Xinyu Long, Jiangping Cent South Univ Forestry & Technol Res Ctr Forestry Remote Sensing & Informat Engn Changsha 410004 Peoples R China Key Lab Forestry Remote Sensing Based Big Data & Changsha 410004 Peoples R China Key Lab State Forestry Adm Forest Resources Manag Changsha 410004 Peoples R China

Remote sensing technology is becoming mainstream for mapping the growing stem volume (GSV) and overcoming the shortage of traditional labor-consumed approaches. Naturally, the GSV estimation accuracy utilizing remote sensing imagery is highly related to the variable selection methods and algorithms. Thus, to reduce the uncertainty caused by variables and models, this paper proposes a combined strategy involving improved variable selection with the collinearity test and the secondary ensemble algorithm to obtain the optimally combined variables and extract a reliable GSV from several base models. Our study extracted four types of alternative variables from the Sentinel-1A and Sentinel-2A image datasets, including vegetation indices, spectral reflectance variables, backscattering coefficients, and texture features. Then, an improved variable selection criterion with the collinearity test was developed and evaluated based on machine learning algorithms (classification and regression trees (CART), k-nearest neighbors (KNN), support vector regression (SVR), and artificial neural network (ANN)) considering the correlation between variables and GSV (with random forest (RF), distance correlation coefficient (DC), maximal information coefficient (MIC), and Pearson correlation coefficient (PCC) as evaluation metrics), and the collinearity among the variables. Additionally, we proposed a secondary ensemble with an improved weighted average approach (IWA) to estimate the reliable forest GSV using the first ensemble models constructed by Bagging and AdaBoost. The experimental results demonstrated that the proposed variable selection criterion efficiently obtained the optimal combined variable set without affecting the forest GSV mapping accuracy. Specifically, considering the first ensemble, the relative root mean square error (rRMSE) values ranged from 21.91% to 30.28% for Bagging and 23.33% to 31.49% for AdaBoost, respectively. After the secondary ensemble involving the IWA, t

关键词： growing stem volume sentinel variable selection ensemble algorithm

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Privacy-preserving with data optimisation in social networks using ensemble algorithm and K-neural network

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INTERNATIONAL JOURNAL OF ENGINEERING SYSTEMS MODELLING AND SIMULATION 2023年第4期14卷 214-224页

作者： Kaarthi, P. S. Arun Sathiyabama, S. Periyar Univ Dept Comp Sci Salem Tamilnadu India Thiruvalluvar Govt Arts Coll Dept Comp Sci Rasipuram Tamilnadu India

A huge collection of data is being produced for each second due to advance technology development and its innovation in the social media. Monitoring the system and network, securing lines and servers are coming to end by using various mechanism. The data accuracy has been increased by the K-nearest neighbour (KNN) classification. A deep learning technique which is a neural network is used for the detection of attacks being done by hackers or fraudulent users. The proposed model uses a programming language called Python which has packages of Scikit-learn, Tensorflow and Seaborn. We have also developed and found the accuracy rate gets increased by the deep learning model and so the attacks made on the social networks have been avoided as much as feasible.

关键词： social networking data mining intrusion detection system IDS data optimisation deep learning techniques clustering-based IDS ensemble algorithm KNN classification

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APPLICATION OF ensemble algorithm INTEGRATING MULTIPLE CRITERIA FEATURE SELECTION IN CORONARY HEART DISEASE DETECTION

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BIOMEDICAL ENGINEERING-APPLICATIONS BASIS COMMUNICATIONS 2017年第6期29卷

作者： Qin, Cai-Jie Guan, Qiang Wang, Xin-Pei SanMing Univ Inst Informat Engn Sanming 365004 Peoples R China ShanDong Univ Sch Control Sci & Engn Jinan 250000 Shandong Peoples R China

Conventional coronary heart disease (CHD) detection methods are expensive, rely much on doctors' subjective experience, and some of them have side effects. In order to obtain rapid, high-precision, low-cost, non-invasive detection results, several methods in machine learning were attempted for CHD detection in this paper. The paper adopted multiple evaluation criteria to measure features, combined with heuristic search strategy and seven common classification algorithms to verify the validity and the importance of feature selection (FS) in the Z-Alizadeh Sani CHD dataset. On this basis, a novelty algorithm integrating multiple FS methods into the ensemble algorithm (ensemble algorithm based on multiple feature selection, EA-MFS) was further proposed. The algorithm adopted Bagging approach to increase data diversity, used the aforementioned MFS methods for functional perturbation, employed major voting method to carry out the decision results, and performed selective integration in terms of the difference of base classifiers in the ensemble process. Compared with the single FS method, the EA-MFS algorithm could comprehensively describe the relationship of features, enhance the classification effect, and displayed better robustness. That meant the EA-MFS algorithm could reduce the dependence on dataset and strengthen the stability of the algorithm, all of which were of great significance for the clinical application of machine learning algorithm in coronary heart disease detection.

关键词： Feature selection ensemble algorithm Coronary heart disease

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