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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Qumsiyeh, Emma Yousef, Malik Salah, Zaidoun Jayousi, Rashid Al-Quds University Department of Computer Science and Information Technology Jerusalem Palestine Zefat Academic College Galilee Digital Health Research Center Department of Information Systems Zefat Israel Arab American University Molecular Genetics and Genetic Toxicology Ramallah Palestine

ISBN: (纸本)9798350337488

Identifying disease-disease associations (DDAs) is critical in medicine and systems biology, providing valuable insights into the complex relationships among different diseases. Recent advances in systems biology and the increasing availability of diverse biological data have provided opportunities for such research. However, the heterogeneity of the data makes it difficult to identify disease associations. To develop effective methods for predicting DDAs, it is necessary to leverage data at the molecular level. In this study, utilizing the Grouping-Scoring-Modeling approach, we have developed a statistical technique to compute a semantic similarity metric between the disease under study and other diseases. We have also used Jaccard similarity to compute similarity among disease groups. We have considered GediNETPro, using machine learning techniques, to record, over the Monte Carlo cross-validation, all the ranks based on the scores assigned by the S component to each disease group. The k-means clustering algorithm is applied to these recorded ranks for each disease group to detect patterns of similar diseases by placing them into coherent clusters. A cluster score (named as cluster-score) is assigned for each cluster by averaging the ranks of its members. Notably, the lowest cluster score is the more significant one. The semantic approach is applied to the top-ranked clusters to detect a semantic relationship between the disease under investigation and other diseases (groups). In addition, we have applied diversity metrics based on the Jaccard measurement to each disease cluster to support the semantic findings. A high value of diversity indicates a greater likelihood of detecting distinct groups with semantic relationships. This study enhances our understanding of disease associations and increases our ability to improve treatment strategies. This is especially important in a context where disease associations go beyond the scope of genetic factors. © 2023 IEEE.

关键词： disease-disease associations diversity metrics GediNETPro Grouping-Scoring-Modeling approach Jaccard index Jaccard similarity k-means clustering algorithm machine learning Monte Carlo cross-validation semantic similarity

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Energy-efficient trajectory planning for amulti-UAV-assisted mobile edge computing system

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Frontiers of Information Technology & Electronic Engineering 2020年第12期21卷 1713-1725页

作者： Pei-qiu HUANG Yong WANG ke-zhi WANG School of Automation Central South UniversityChangsha 410083China Department of Computer and Information Sciences Northumbria UniversityNewcastle NE 18STUK

We study a mobile edge computing system assisted by multiple unmanned aerial vehicles(UAVs),where the UAVs act as edge servers to provide computing services for Internet of Things *** goal is to minimize the energy consumption of this system by planning the trajectories of *** problem is difficult to address because when planning the trajectories,we need to consider not only the order of stop points(SPs),but also their deployment(including the number and locations)and the association between UAVs and *** tackle this problem,we present an energy-efficient trajectory planning algorithm(TPA)which comprises three *** the first phase,a differential evolution algorithm with a variable population size is adopted to update the number and locations of SPs at the same *** the second phase,the k-means clustering algorithm is employed to group the given SPs into a set of clusters,where the number of clusters is equal to th at of UAVs and each cluster contains all SPs visited by the same *** the third phase,to quickly generate the trajectories of UAVs,we propose a low-complexity greedy method to construct the order of SPs in each *** with other algorithms,the effectiveness of TPA is verified on a set of instances at different scales.

关键词： Multiple unmanned aerial vehicles Mobile edge computing Trajectory planning Differential evolution k-means clustering algorithm Greedy method

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An unsupervised feature extractionl method for classification of hyperspectral images 22

An unsupervised feature extractionl method for classificatio...

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22nd Iranian Conference on Electrical Engineering, ICEE 2014

作者： Imani, Maryam Ghassemian, Hassan Faculty of Electrical and Computer Engineering Tarbiat Modares University Iran

ISBN: (纸本)9781479944095

Hyperspectral images provide huge volume of spectral information for classification of land cover classes. Feature reduction plays an important role as a pre-processing step in classification of high dimensional data. Because of limited available training samples, unsupervised feature extraction is a proper selection for reduction of feature space. We propose an unsupervised feature extraction method in this paper that is called boundary clustering based feature extraction (BCFE). In the BCFE, at first using a clustering algorithm, data is clustered. We use the k-means clustering algorithm in this paper. After clustering, by training of SVM with using the obtained clusters, boundary samples of clusters are calculated. These boundary samples are used for discriminant analysis in the proposed feature extraction method. The experimental results on two real hyperspectral images show the advantage of BCFE in comparison with the most conventional feature extraction methods such as principal component analysis (PCA) and linear discriminant analysis (LDA). © 2014 IEEE.

关键词： feature extraction hyperspectral imaging image classification support vector machines unsupervised learning BCFE method k-means clustering algorithm LDA PCA SVM training boundary clustering based feature extraction high dimensional data classification hyperspectral image classification land cover class classification linear discriminant analysis principal component analysis spectral information unsupervised feature extraction method Accuracy Educational institutions Feature extraction Hyperspectral imaging Principal component analysis Support vector machines Training extraction-clustering-boundary feature sample-classification-hyperspectral image

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Error Prediction method of Electronic Transformers Based on RBF Neural Network 13

Error Prediction method of Electronic Transformers Based on ...

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2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI)

作者： Wu Qiao Xu Xin Yang Shihai Lu Shufeng Chen Gang Electric Power Research Institute Jiangsu Electric Power Company School of Electrical Engineering Southeast University

ISBN: (纸本)9781509050369;9781509050352

With the gradual application of electronic transformers in the intelligent substation, the influence of the typical disturbing source on the electronic transformers is gradually to the industry’s attention. In order to ensure the accuracy and reliability of electronic transformers, it is necessary to determine the error of the electronic transformers accurately through the study of the influence factors of the measurement error of the electronic transformers. In this regard, this paper proposes a transformer error prediction method which combines RBF neural network prediction with k-means clustering algorithm. First, the historical data are classified by k-means clustering. Then, train the prediction model with the historical data clustered. Take the influencing factors such as temperature, load capacity and magnetic field as the independent variables and the error as the prediction target to establish the prediction model. The prediction model is suitable for the analysis of a large number of data and can accurately predict the error. After using this model to analyze the long-term experimental data of Dongshan substation in 2016, the relative error of the error result is around 5%, which is relatively and verifies the validity of the model.

关键词： Electronic transformers k-means clustering algorithm RBF neural network long-term experimental data

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A Cluster Analysis of Power Load Forecasting Based on Power Consumption Data and Meteorological Data 4

A Cluster Analysis of Power Load Forecasting Based on Power ...

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4th International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2024

作者： Wei, Yi Tang, Zhong-Sheng College of Intelligent Networking and New Energy Vehicles Geely University of China Sichuan Province Chengdu City China

ISBN: (纸本)9798350378542

With the increasing of the big data volume for power consumption data and the complexity of meteorological data, it has become more difficult to cope with the power load forecasting in the recent energy crisis. To address this issue, we analyze the power consumption data and meteorological data in this paper, which would be applied in developing a more effective power load management for the intelligent power systems. We develop a hybrid day-ahead load forecasting models based on k-means clustering algorithm well-known neural networks Long Short-Term Memory (LSTM)). To study the impact of meteorological factors on power load forecasting, a cluster analysis is also conducted in this work to classify temperature data and rainfall data, which aims to mine the relationship between power consumption data and meteorological data. In the experiments on the public data sets, the performance evaluation of the proposed method is compared with the commonly used algorithms for power load forecasting. In the numerical results, it is shown that the proposed method performs higher accuracy and applicability compared with the traditional methods. © 2024 IEEE.

关键词： k-means clustering algorithm load forecasting Neural network Smart grid

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Complexity Analysis of Lumped Parameter Models: Development of Complexity Reduction algorithm

Complexity Analysis of Lumped Parameter Models: Development ...

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作者： YUXI LI Reykjavik University

学位级别：硕士

Lumped parameter models have been shown to be a useful tool for geothermal reservoir analysis and production planning. Tank models are a common form of lumped parameter models, incorporating tanks of given capacitance partially filled with fluid. Between the tanks are connections with given conductance, that allow fluid to flow between connected tanks with different fluid levels. In this thesis, we analyze how tank models of varying complexity compare in terms of accuracy and utility. An algorithm called Complexity Reduction algorithm (CRA) is developed that automatically finds those models that are likely to be the best by choosing a certain path through the model space. Since in general, it is reasonable to expect that a complex model is able to give an accurate fitting result and the optimum model indicated by CRA only has a medium complexity, a switch-back method is developed to decrease the training error of the complex models. Also, in some cases, there is a large number of production wells that are producing hot water, which will lead to a situation where very many parameters needed to be estimated, since the number of parameters grows quadratically in terms of the number of tanks. The k-means clustering algorithm is shown to be suitable for finding an initial production tank configuration under such situations. Real data from the Laugarnes geothermal field and Reykir geothermal area in Iceland is shown in the thesis. The results show that the newly developed algorithm provides insights into model selection for lumped parameter models. The accuracy of both history-matched and predicted drawdown for lumped parameter models of varying complexity and the results by using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) as an indicator for model selection have been shown.

关键词： Geothermal reservoir modeling Lumped-parameter models Complexity analysis Information criterion Complexity Reduction algorithm Switch-back method Model selection k-means clustering algorithm

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Real-time fault detection approach of software under big data environment

Real-time fault detection approach of software under big dat...

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2015 International Conference on Automation,Mechanical Control and Computational Engineering(AMCCE 2015)

作者： Jian xianrui Chengdu vocational and technical college

ISBN: (纸本)9781510803084

For large data environment, the traditional k-means clustering algorithm flaw in the software in real-time fault detection process, we propose a k-means clustering software intelligent real-time fault detection method is improved. The k-means clustering algorithm and particle swarm optimization combined during the iterative process, the combination of k-means to optimize the upcoming PSO algorithm offspring individuals use k-means clustering to obtain local optimal solution calculates and uses these individuals continue to participate in an iterative process, so that the algorithm can improve the convergence speed, avoid falling into local optimal solution, to obtain accurate software fault signal characteristics. Experimental results show that the use of kmeans tilt feature extraction software, intelligent real-time fault detection algorithm can effectively improve the accuracy of fault detection, and achieved satisfactory results. On the condition of big data, traditional k-mean cluster algorithm showed some flaws of software fault detection in realtime way. In this research, an improved k-mean cluster method was proposed for the purpose of testing software fault in intellectual and real-time way. This improved method combined k-mean cluster algorithm with Particle cluster algorithm. k-mean cluster were used in the optimizing procedure among the iterative process. Individuals from Particle cluster algorithm were computed by using k-mean algorithm and obtained the locally optimal solution, and then these individuals continue to participate in the iterative processing. This improved converging rate of the algorithm, avoided falling into the locally optimal solution and finally got the accurate features of software fault signal. Results showed that this testing method with using the tilt feature of k-mean algorithm improved detection accuracy effectively.

关键词： k-means clustering algorithm feature extraction real-time software fault detection

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Software Technology Immersive Education Research and Practice Based on Spark Platform 3

Software Technology Immersive Education Research and Practic...

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3rd IEEE International Conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Genrang, Zheng Chunping, Huang Zhongshan Polytechnic Department of Information Engineering Guangdong Zhongshan China

ISBN: (纸本)9798350380989

From the perspective of education informatization, a teaching resource sharing system with huge storage space and computation amount is established. When the user accesses the teaching materials, it constructs the database with a large number of users through instant access to the data. This project intends to study an optimization scheme that does not require any intermediary output based on Hadoop and combined with Spark's parallel computing mode, so that it can make better use of Spark's iterative computing capability. It realizes the efficient recommendation of teaching resources. A hierarchical resource model based on collaborative filtering is proposed. The unrated resources predicted in this way are imprecise. A time function is added to the prediction process for non-rated resources to reflect the change in user interest over time. The index function is used to describe the change of time. The practicability of the Spark-based in the recommendation of teaching resources are verified by the research. © 2024 IEEE.

关键词： collaborative filtering Educational resources recommendation k-means clustering algorithm recommendation system spark

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Research on ASIC for multi-speaker isolated word recognition

Research on ASIC for multi-speaker isolated word recognition

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2nd International Conference on ASIC

作者： Xiong, B Sun, YH Institute of Microelectronics Tsinghua University Beijing China

ISBN: (纸本)7543909405

The ASIC for multi-speaker speech recognition is design in this paper. The LPC-derived cepstral coefficients are chosen as speech features. Templates are trained by k-means clustering algorithm. Two stage recognition system can not only improve recognition accuracy, but also reduce the delay. The first stage of recognition system uses speech spectrum difference(SSD) algorithm. The second stage uses DTW. The whole recognition system is design into ASIC on high level with VHDL and simulated in Powerview.

关键词： speech recognition linear predictive coding application specific integrated circuits cepstral analysis ASIC multi-speaker isolated word recognition LPC cepstral coefficients k-means clustering algorithm template training speech spectrum difference algorithm DTW high level design VHDL Powerview simulation

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Error Prediction method of Electronic Transformers Based on RBF Neural Network

Error Prediction method of Electronic Transformers Based on ...

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13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI)

作者： Wu Qiao Xu Xin Yang Shihai Lu Shufeng Chen Gang Jiangsu Elect Power Co Elect Power Res Inst Nanjing 211103 Jiangsu Peoples R China Southeast Univ Sch Elect Engn Nanjing 210096 Jiangsu Peoples R China

ISBN: (纸本)9781509050352

With the gradual application of electronic transformers in the intelligent substation, the influence of the typical disturbing source on the electronic transformers is gradually to the industry's attention. In order to ensure the accuracy and reliability of electronic transformers, it is necessary to determine the error of the electronic transformers accurately through the study of the influence factors of the measurement error of the electronic transformers. In this regard, this paper proposes a transformer error prediction method which combines RBF neural network prediction with k-means clustering algorithm. First, the historical data are classified by k-means clustering. Then, train the prediction model with the historical data clustered. Take the influencing factors such as temperature, load capacity and magnetic field as the independent variables and the error as the prediction target to establish the prediction model. The prediction model is suitable for the analysis of a large number of data and can accurately predict the error. After using this model to analyze the long-term experimental data of Dongshan substation in 2016, the relative error of the error result is around 5%, which is relatively and verifies the validity of the model.

关键词： Electronic transformers k-means clustering algorithm RBF neural network long-term experimental data

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