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Method for detecting abnormal behaviour of users based on selective clustering ensemble

IET NETWORKS 2018年第2期7卷 85-90页

作者： Du, Juan Xie, Ping Zhu, Junlong Zheng, Ruijuan Wu, Qingtao Zhang, Mingchuan Henan Univ Sci & Technol Coll Informat Engn Luoyang Henan Peoples R China

With the development of cloud computing in the mobile communications industry, the credibility between users and the cloud has become an important obstacle to the development of mobile cloud services. Therefore, the user's abnormal behaviour detection is particularly important. The authors propose a selective clustering ensemble algorithm based on fractal dimension. In their proposed algorithm, they firstly use the sliding window to dynamically obtain data to improve the accuracy of user behaviour acquisition. Secondly, they use the initial clustering stage and incremental clustering stage to produce clustered members. Thirdly, they also use the Duun_index to select the clustered members, then the selection of high-quality clustered members with the voting algorithm to get the final result. Finally, they use the average difference of change to determine whether the current behaviour is abnormal based on the clustering results. The experimental results show that the proposed scheme has a better performance than the traditional clustering algorithm in clustering accuracy. Moreover, their algorithm can improve the detection rate and accuracy rate, respectively.

关键词： pattern clustering behavioural sciences computing cloud computing mobile computing user abnormal behaviour detection cloud computing mobile cloud services selective clustering ensemble algorithm fractal dimension sliding window user behaviour acquisition incremental clustering stage Duun_index voting algorithm detection rate improvement accuracy rate improvement

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Brake fault diagnosis using a voting ensemble of machine learning classifiers

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RESULTS IN ENGINEERING 2024年 23卷

作者： Viswanathan, Sivagurunathan Sridharan, Naveen Venkatesh Rakkiyannan, Jegadeeshwaran Vaithiyanathan, Sugumaran Vellore Inst Technol Sch Mech Engn SMEC Chennai India Lulea Univ Technol Div Operat & Maintenance Engn Lulea Sweden

Brake fault diagnosis is a crucial aspect of enhancing driving safety, as various faults such as air in brake fluid, brake oil spill, reservoir leak, mechanical fade and distinct types of brake pad wear can compromise vehicle safety. This study presents a method for timely fault detection by analyzing vibration signals. Vibration signals for normal and faulty conditions were captured using a hydraulic brake test setup equipped with a piezoelectric transducer and data acquisition system. Feature extraction was performed using an auto-regressive moving average (ARMA) model. The performance of five different classifiers namely, random forest (RF), Naive Bayes (NB), instance-based k-nearest neighbours (IBk), logistic model trees (LMT) and J48 decision tree was evaluated. The LMT classifier achieved the highest accuracy at 95.00 % followed by IBk, RF, J48 and NB with accuracies of 92.00 %, 90.00 %, 90.00 % and 87.00 %. To further improve the diagnosis accuracy, a voting-based ensemble approach was employed by combining two, three, four and five classifiers with the application of five different voting strategies. The results obtained showcase that a combination of three classifiers LMT, IBk and NB utilizing the majority voting rule yielded an enhanced classification accuracy of 98.00 % highlighting the effectiveness of this ensemble method in brake fault diagnosis. A similar classification accuracy of 98.00 % was achieved by four (LMT-IBk-RF-J48) and five (IBk-RF-MLP-J48-NB) classifier ensembles;however, considering the computational complexity three classifier ensembles were selected. Whilst two classifier ensembles (IBk-J48) achieved a maximum classification accuracy of 96.00 %.

关键词： Machine learning voting algorithm Brake fault diagnosis ARMA Ensemble learning Classifier ensemble

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Methods of orientating different Resolution Satellite Images with the existing Vector Maps

Methods of orientating different Resolution Satellite Images...

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7th Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR) - Remote Sensing Image Processing, Geographic Information Systems, and Other Applications

作者： Lu, Luping Zhang, Yong Zhang, Yongjun Zhang, Zuxun Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China

ISBN: (纸本)9780819485809

In this paper, the different methods of orientating different resolution satellite images with the existing vector maps are introduced. To moderate high resolution satellite image, such as SPOT5 image, the vector road maps are often used for the orientation, and the vector lines in the maps usually represent the road central lines. And to higher resolution satellite image, such as QuickBird image, the vector lines in the maps usually represent the road edges, instead. Beside that, the different extend of details of the images makes it necessary to handle them with different methods. Because in very high resolution satellite images a lot of disturbing image features will be extracted along with the wanted one. A voting algorithm is employed to solve the problem, the approach is based on the previous work where an edge-based voting strategy was studied(1). The voting algorithm has the advantages of globally optimal and robust to noise. It can directly estimate the transformation parameters, meanwhile providing the potential matches of edge points, these matches can then be used in calculating the accurate orientation parameters, and providing the chance of change detection since the unchanged objects can be marked out with this method.

关键词： high-resolution satellite imagery vector map auto-orientation voting algorithm generalized point photogrammetry

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A Synergistic Framework for Geographic Question Answering

A Synergistic Framework for Geographic Question Answering

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7th IEEE International Conference on Semantic Computing (ICSC)

作者： Chen, Wei Fosler-Lussier, Eric Xiao, Ningchuan Raje, Satyajeet Ramnath, Rajiv Sui, Daniel Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA Ohio State Univ Dept Geog Columbus OH 43210 USA

ISBN: (纸本)9780769551197

QA (question answering) systems designated for answering in-depth geographic questions are highly demanded but not quite available. Previous research has visited various individual aspects of a QA system but few synergistic frameworks have been proposed. This paper investigates the nature of geographic question formation and observes their unique linguistic structures that can be semantically translated into a spatial query. We create a new task of solving non-trivial questions using GIS (Geographic Information System) and test it with an associated corpus. A dynamic programming algorithm is developed for classification and voting algorithm for verification. Two types of ontologies are integrated for disambiguating and discriminating spatial terms. PostGIS serves as the GIS backend to provide domain expertise for spatial reasoning. Results show that exact answers can be returned quickly and correctly by our system. Contrast classification results in improved accuracy compared with the baseline which proves the effectiveness of proposed methods.

关键词： nlp gis question answering ontology voting algorithm machine learning dynamic programming spatial SQL

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Byzantine Fault Tolerant algorithm based on Vote

Byzantine Fault Tolerant Algorithm based on Vote

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International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)

作者： Wang, Haiyong Guo, Kaixuan Nanjing Univ Posts & Telecommun Coll Comp & Sci Nanjing Peoples R China

ISBN: (纸本)9781728125428

Practical Byzantine Fault Tolerance (PBFT) algorithm is a popular solution for establishing consensus in blockchain systems. However, there are some issues in the PBFT algorithm, such as high energy consumption, low efficiency, and poor scalability. These problems are not solved even in some of its improved algorithms, such as Byzantine consensus algorithm based on Gossip protocol (GBC) and Credit Practical Byzantine Fault Tolerance (CBPFT) algorithms. To address these issues, this paper proposes a Byzantine Fault Tolerant algorithm based on Vote algorithm (vBFT). The nodes in the network are divided into three types of with different responsibilities by the vBFT algorithm, which are client, slave, and master nodes. The state between nodes could be dynamically adjusted at any time. As shown our simulations and analysis, the proposed algorithm has significantly improved performance in terms of dynamics, energy consumption, fault tolerance and low-latency compared with consensus algorithms such as PBFT, GBC and CBPFT algorithm.

关键词： blockchain Byzantine fault tolerance voting algorithm

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Sleep Stage Classification for Managing Nocturnal Enuresis through Effective Configuration

Sleep Stage Classification for Managing Nocturnal Enuresis t...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Lee, Sangyeop Moon, Junhyung Lee, Taeho Kye, Saewon Lee, Kyoungwoo Lee, Yong Seung Shin, Seung-chul Yonsei Univ Dept Comp Sci Seoul South Korea Yonsei Univ Coll Med Dept Urol Seoul South Korea Yonsei Univ Coll Med Urol Sci Inst Seoul South Korea Yonsei Univ Sch Elect & Elect Engn Seoul South Korea

ISBN: (纸本)9781538616451

Various studies have examined the quality of one's sleep and further investigated several sleep disorders. In those investigations, accurately classifying one's sleep into the standardized sleep stages is important. The conventional classification heavily depends on the manual examination of each expert on one's physiological signals during the sleep. Therefore, various automatic classification models have been proposed using the machine learning. Although they properly classify the sleep stages on average, there have been few investigations to specifically improve the classification accuracy of certain stages. Accurate determination of several stages considerably correlating with a disorder gives us a more effective hint to conquer the disorder. Accordingly, we propose a configured classification model focusing on the interesting sleep stages related to a challenging sleep disorder, the nocturnal enuresis. We consider the deterministic physiological signals of the interesting stages when training the classifiers. Further, the proposed system utilizes recurrent neural network to effectively learn the sequential feature of the physiological data. Our proposed system achieves the classification accuracy by 83.6% over the data. In particular, technique presents up to 15.5% higher accuracy to differentiate interesting stages than the support vector machine approach for the nocturnal enuresis.

关键词： Sleep stage automatic classification recurrent neural networks nocturnal enuresis voting algorithm

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Registration of CBERS-02B Satellite Imagery in quick GIS Updating

Registration of CBERS-02B Satellite Imagery in quick GIS Upd...

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7th Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR) - Remote Sensing Image Processing, Geographic Information Systems, and Other Applications

作者： Zhang, Zuxun Lu, Luping Tao, Pengjie Zhang, Yong Zhang, Yongjun Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China

ISBN: (纸本)9780819485809

Three key techniques in registration of CBERS-02B image are introduced in this paper. Including an improved image matching strategy aided by a voting algorithm, the voting algorithm directly estimates the transformation parameters of image instead of finding the one to one corresponding, making the image matching much more efficient and reliable. Beside that, the method of handling the interior deformation problem caused by the incorrect splicing of three sub CCD arrays is introduced. At last, a virtual combination of satellite image technique is introduced, the adjacent satellite images in the same orbit are virtually combined to form a large image which is taken as the orientation unit, purposed on accelerating the orientation process speed and decreasing the probability of orientation failure. With the techniques mentioned above, the two main problems about CBERS-02B image: the inaccurate ephemeris parameters and the poor internal accuracy caused by the incorrect splicing of three sub CCD arrays are solved successfully, the absolute positioning accuracy reaches 1: 10000 mapping accuracy and the internal accuracy reaches sub-pixel after the orientation process. It indicates that extensive usage of Chinese high resolution satellite imagery may become possible and practical.

关键词： Chinese High Resolution Satellite Imagery voting algorithm Image Matching Self-Calibration Virtual combination

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Mechanical Sensor FTC using Sub-Optimal Sliding Mode Observer for Electrical Vehicle Induction Motor Drive 13

Mechanical Sensor FTC using Sub-Optimal Sliding Mode Observe...

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13th International Multi-Conference on Systems, Signals & Devices (SSD)

作者： Raisemche, A. Chaibet, A. Boukhnifer, M. Diallo, D. ESTACA Ecole Ingenieurs 12 Ave Paul Delouvrier F-78066 St Quentin En Yveline France GeePs Lab Genie Elect & Elect Paris 11 Rue Joliot Curie F-91192 Gif Sur Yvette France

ISBN: (纸本)9781509012916

In this paper, we propose an experimental study for hybrid voting algorithm based on observer and robust control design for two mechanical sensor faults. The proposed strategy is applied for the induction motor speed drive of electrical vehicle powertrain. To adopt the best performance method for electrical vehicle application, we illustrate the effectiveness of the hybrid voting algorithm approach with the New European Driving Cycle (NEDC) speed profile. The experimental results demonstrate the effectiveness of the proposed Input/Output FTC architecture.

关键词： Fault Tolerant Control Induction Motor Electric Vehicle Sliding Mode voting algorithm

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A Novel Vector Representation Model for Text Mining Based on Enhancing Features

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JOURNAL OF INTERNET TECHNOLOGY 2015年第3期16卷 475-484页

作者： Chen, Heng Jin, Hai Zhao, Feng Chen, Hanhua Fang, Fei Huazhong Univ Sci & Technol Serv Comp Technol & Syst Lab Wuhan Peoples R China Huazhong Univ Sci & Technol Cluster & Grid Comp Lab Wuhan Peoples R China

Semantics relationships such as polysemy and synonymy etc. contained in text documents can be used to enhance the features in the vector representation. Previous feature representation methods rarely consider such semantic relations to improve mining precision, because taking semantic relations into account greatly depends on the learning process. In this paper, we present a novel document representation method which utilizes semantic relations to enhance the features. To obtain semantic relations, we use the external resource of Wikipedia. Moreover, a term-concept mapping structure is constructed to describe features. When concepts are extracted from document, Markov random walk model is used to handle the semantic relationships between the concepts and related concepts are voted to enhance the features. We conduct comprehensive experiment using 20-newsgroup corpus and Reuters corpus. Results show that the proposed method has good performance in text mining.

关键词： Feature representation Semantic relation Semantic concept voting algorithm

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An efficient computer vision-based approach for acute lymphoblastic leukemia prediction

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2022年 16卷 1083649页

作者： Almadhor, Ahmad Sattar, Usman Al Hejaili, Abdullah Ghulam Mohammad, Uzma Tariq, Usman Ben Chikha, Haithem Jouf Univ Coll Comp & Informat Sci Dept Comp Engn & Networks Sakaka Saudi Arabia Beaconhouse Natl Univ Dept Management Sci Lahore Pakistan Univ Tabuk Fac Comp & Informat Technol Comp Sci Dept Tabuk Saudi Arabia Int Islamic Univ Dept Comp Sci & Software Engn Islamabad Pakistan Prince Sattam Bin Abdulaziz Univ Dept Management Informat Syst Al Kharj Saudi Arabia

Leukemia (blood cancer) diseases arise when the number of White blood cells (WBCs) is imbalanced in the human body. When the bone marrow produces many immature WBCs that kill healthy cells, acute lymphocytic leukemia (ALL) impacts people of all ages. Thus, timely predicting this disease can increase the chance of survival, and the patient can get his therapy early. Manual prediction is very expensive and time-consuming. Therefore, automated prediction techniques are essential. In this research, we propose an ensemble automated prediction approach that uses four machine learning algorithms K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB). The C-NMC leukemia dataset is used from the Kaggle repository to predict leukemia. Dataset is divided into two classes cancer and healthy cells. We perform data preprocessing steps, such as the first images being cropped using minimum and maximum points. Feature extraction is performed to extract the feature using pre-trained Convolutional Neural Network-based Deep Neural Network (DNN) architectures (VGG19, ResNet50, or ResNet101). Data scaling is performed by using the MinMaxScaler normalization technique. Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and Random Forest (RF) as feature Selection techniques. Classification machine learning algorithms and ensemble voting are applied to selected features. Results reveal that SVM with 90.0% accuracy outperforms compared to other algorithms.

关键词： leukemia prediction acute lymphocytic leukemia feature extraction feature selection machine learning voting algorithm

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