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International Conference on Computing, Communication and Networking Technologies

作者： Parag Verma Shadab Khan Shayan Anwar Sunil B. Mane Department of Computer Engineering & IT College of Engineering Pune

An unauthorized activity on the network is called network intrusion and device or software application which monitors the network parameters in order to detect such an intrusion is called network intrusion detection system (NIDS). With high rise in malicious activities on the internet, it is extremely important for NIDS to quickly and correctly identify any kind of malicious activity on the network. Moreover, the system must refrain from raising false alarms in case of normal usage detected as malicious. This paper proposes use of machine learning classification algorithms - XGBoost and AdaBoost with and without clustering to train a model for NIDS. The models are trained and tested using NSL KDD dataset and the results are an improvement over the previous works related to intrusion detection on the same dataset.

关键词： Intrusion detection XGBoost AdaBoost Machine learning Decision trees classification algorithm Clustering algorithm Gradient boosting

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Texture classification using Combination of LBP and GLRLM Features along with KNN and Multiclass SVM classification 2

Texture Classification using Combination of LBP and GLRLM Fe...

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2nd International Conference on Communication Control and Intelligent Systems (CCIS)

作者： Das, Sourajit Jena, Uma Ranjan Veer Surendra Sai Univ Technol Dept Elect & Telecommun Engn Sambalpur India

ISBN: (纸本)9781509032105

The paper presents a unique combination of texture feature extraction techniques which can be used in image texture analysis. Setting the prime objective of classifying different texture images, the Local Binary Pattern (LBP) and a modified form of Gray Level Run Length Matrix (GLRLM) are implemented initially. The next phase involves use of combination of the former two methods to extract improved features. The feature vectors were obtained by defining the features on the transformed images. These texture features are classified using two classification algorithms, KNN and multiclass SVM. The results of above feature extraction techniques with individual classifiers have been compared. The comparison yields that the combination of LBP and GLRLM texture features shows better classification rate than the features obtained from individual feature extraction techniques. Among the classifiers, Support Vector Machine has better classification rate than the Nearest Neighbor approach for the texture classification.

关键词： Image Texture Analysis classification algorithm Feature Extraction Nearest Neighbor Support Vector Machine

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An EMG Knowledge-Based System for Leg Strength classification and Vertical Jump Height Estimation of Female Netball Players 8th

An EMG Knowledge-Based System for Leg Strength Classificatio...

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8th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Yahya, Umar Senanayake, S. M. N. Arosha Lai, Daphne Univ Brunei Darussalam Fac Sci Mot Anal Lab Jalan Tungku Link BE-1410 Bandar Seri Begawan Brunei

ISBN: (纸本)9783662493908;9783662493892

This study proposes a framework for the design and implementation of a leg strength Knowledge-Based classification System based on pattern sets obtained from the statistical analysis of Electromyography (EMG) features (Peak, IEMG, Peak time) extracted from eight different lower limb muscles during a single leg vertical jump test of female netball players. The system employs a novel classification algorithm that takes into account the test subject's anthropometry profile (height, body mass) and age, with respect to pattern sets in the knowledge-base. The algorithm is also able to estimate the vertical jump height with which the test subject attained the corresponding EMG data set. Results indicate that the system is not only able to accurately classify a subject's overall leg strength category (strength category weight >= 66.67 %), but also is able to reveal the individual muscle strength classification (strength category weight >= 62.5 %). The results offer conditioning coaches and trainers deeper insights into muscle-specific strength variations of players.

关键词： classification algorithm Knowledge-based system Strength classification Anthropometry profile Feature extraction Pattern set identification Strength category weight

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GLOBAL LAND COVER classification USING MODIS SURFACE REFLECTANCE PROSUCTS 23

GLOBAL LAND COVER CLASSIFICATION USING MODIS SURFACE REFLECT...

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23rd Congress of the International-Society-for-Photogrammetry-and-Remote-Sensing (ISPRS)

作者： Fukue, Kiyonari Shimoda, Haruhisa Tokai Univ Res & Informat Ctr Shibuya Ku 2-28-4 Tomigaya Tokyo 151 Japan

The objective of this study is to develop high accuracy land cover classification algorithm for Global scale by using multi-temporal MODIS land reflectance products. In this study, time-domain co-occurrence matrix was introduced as a classification feature which provides time-series signature of land covers. Further, the non-parametric minimum distance classifier was introduced for time domain co-occurrence matrix, which performs multi-dimensional pattern matching for time-domain co-occurrence matrices of a classification target pixel and each classification classes. The global land cover classification experiments have been conducted by applying the proposed classification method using 46 multi-temporal(in one year) SR(Surface Reflectance) and NBAR(Nadir BRDF-Adjusted Reflectance) products, respectively. IGBP 17 land cover categories were used in our classification experiments. As the results, SR and NEAR products showed similar classification accuracy of 99%.

关键词： Global Land Cover classification algorithm Multi-temporal Multi-spectral Co-occurrence Matrix Non-Parametric Classifier

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Method for Detection of Airborne UEs Based on LTE Radio Measurements 28

Method for Detection of Airborne UEs Based on LTE Radio Meas...

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28th Annual IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

作者： Wigard, Jeroen Amorim, Rafhael Nguyen, Huan C. Kovacs, Istvan Z. Mogensen, Preben Nokia Bell Labs Aalborg Denmark Aalborg Univ Wireless Commun Networks Aalborg Denmark

ISBN: (纸本)9781538635315

Unmanned Aerial Vehicles (UAVs) are expected to be connected through cellular networks. As the radio characteristics are different for airborne UEs compared to terrestrial UEs, it is beneficial to identify whether a UE is airborne (on a UAV) or on the ground, such that interference and mobility management can be optimized for UAVs separately from terrestrial UEs. In this paper, we present a classification algorithm using existing LTE UE radio measurements to identify whether a UE is airborne or terrestrial. The method is verified with LTE measurements made in a rural area at different heights, including terrestrial measurements and it is shown that the method in 3 out of the 4 different measurement cases can detect a UE to be airborne with 99% likelihood, while the fourth case still can classify a UE correctly in 95% of the cases. The right classification can further be improved by taking multiple consecutive samples into account before making a classification decision.

关键词： UAV detection classification algorithm LTE radio measurement

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Advances in Classifier Evaluation: Novel Insights for an Electric Data-Driven Motor Diagnosis

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IEEE ACCESS 2016年 4卷 7028-7038页

作者： Martin-Diaz, Ignacio Morinigo-Sotelo, Daniel Duque-Perez, Oscar Romero-Troncoso, Rene De J. Univ Valladolid Escuela Ingn Ind E-47002 Valladolid Spain Univ Guanajuato CA Telemat DICIS Leon 37320 Spain

Fault diagnosis of inductions motors has received much attention recently. Most of the works use data obtained either from the time domain or by applying advanced techniques in the frequency domain. Some researchers have employed a considerable effort in designing sophisticated algorithms to achieve the best performance of the diagnosis system. However, some contributions in the field have not taken advantage of the benefits that a good evaluation stage can bring to the developing of classifiers for fault diagnosis. In this paper, novel insights for the classifier evaluation are presented to promote better assessment practices in the field of electric machine diagnosis based on supervised classification. A case of study consisting of a motor with a broken rotor bar is described to analyze the performance of two classifiers by using scores focused on the fault detection. Also, different error estimation methods are considered to obtain unbiased predictive performances. Two statistical tests are also discussed to confirm the significance of the results under a single data set.

关键词： Broken rotor bar classification algorithm condition monitoring electric machines fault diagnosis performance evaluation

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Adaptive algorithm for classifying LiDAR data into water and land points by multifeature statistics

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JOURNAL OF APPLIED REMOTE SENSING 2016年第4期10卷

作者： Wei, Li-Xin Yang, Biao Jiang, Jian-Ping Cao, Guan-Zhong Hohai Univ Inst Remote Sensing & Spatial Informat Engn Sch Earth Sci & Engn 8 Focheng West Rd Nanjing 211100 Jiangsu Peoples R China Hydrol & Water Resources Survey Lower Changjiang River Bur Changjiang Water Resources Commiss 66 Dama Rd Nanjing 210011 Jiangsu Peoples R China

Classifying light detection and ranging (LiDAR) data into water and land points is an issue for the application of low-attitude airborne LiDAR (e.g., digital terrain model generation and river shoreline extraction). To solve the problem of distinguishing the water points from the land points in complex landscapes, an adaptive classification algorithm of water LiDAR point clouds is proposed, which consists of the following steps. First, the descriptors of local terrain slope and point density are designed by analyzing the characteristics of low-altitude airborne LiDAR water point clouds. Then Bayes' theorem is introduced to establish membership functions of the elevation, slope, and density. Next, the adaptive weights of the individual membership functions are determined according to the t-test of the independent samples of water and land points. Finally, a classification model based on multifeature statistics is obtained, and the adaptive classification threshold of the model is determined by the probability density of the training samples. Typical experiments conducted in the middle-lower Yangtze River riparian zone indicate that water classification accuracies higher than 99% are obtained by this algorithm, even in complex landscapes with mudflats and inland plains. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： light detection and ranging low-attitude airborne laser scanning water point clouds classification algorithm digital terrain model

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Human performance modeling for manufacturing based on an improved KNN algorithm

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2016年第1-4期84卷 473-483页

作者： Li, Ni Kong, Haipeng Ma, Yaofei Gong, Guanghong Huai, Wenqing Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

Human performance is a key factor in a manufacturing system. Behavior modeling is a very important but difficult problem when describing human activities. Performance modeling based on data mining is an effective way to help managers predict personnel's capabilities and to recruit appropriate new staff with relevant skills, which can be significant to ensure an enterprise's competitiveness. The K-nearest neighbor (KNN) algorithm is the most common classification algorithm in cases of no prior knowledge of data distribution. In the paper, an improved KNN algorithm was proposed to cope with the human performance prediction problem with the improvements in three aspects, which are the neighboring distance calculation based on entropy, the classification determination strategy, and the quantitative description method of human performance. The improved KNN algorithm was proved to have better classification ability in comparison with other seven typical KNN algorithms and five classic classification algorithms using data sets from the University of California, Irvine (UCI) machine learning repository. The improved KNN algorithm was further tested with a real-world case of performance prediction, and the effectiveness of the algorithm was confirmed.

关键词： Improved KNN Human performance modeling Data mining classification algorithm Behavior modeling

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Recognition of air-conditioner operation from indoor air temperature and relative humidity by a data mining approach

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ENERGY AND BUILDINGS 2016年 111卷 233-241页

作者： Zhou, Hao Qiao, Lifeng Jiang, Yi Sun, Hejiang Chen, Qingyan Tianjin Univ Sch Environm Sci & Engn Tianjin Key Lab Indoor Air Environm Qual Control Tianjin 300072 Peoples R China Intelligent Green Solut Ltd Beijing 100084 Peoples R China Purdue Univ Sch Mech Engn W Lafayette IN 47907 USA

The variety of occupant behaviors in buildings have led to a significant mismatch between simulated building energy performance and measured one. It is crucial to collect real occupant behaviors in buildings to achieve accurate simulation purpose, although there exists a great challenge due to the cost of monitoring devices and privacy concerns. This study proposed an inexpensive and minimally intrusive method, to recognize behavior information from environment parameters by data mining approach. To validate this method, experiments were conducted in three bedrooms. Two types of classification algorithms were developed to recognize AC operations from the experiment data of indoor air temperature and relative humidity. Two types of recognition rules were generated from algorithm training in one dataset, and tested in the other datasets. Based on the testing results, the performance of the two algorithms were evaluated and compared. The results indicated that the C4.5 decision tree algorithm was not suitable for mining AC operations, while the curve description algorithm had good performance in processing the time-series curves of air temperature and relative humidity. Through this experiment, it is confirmed that AC operations can be recognized from indoor air temperature and relative humidity by data mining approach. The main contribution of this study is that a promising approach was developed, which is inexpensive and minimally intrusive on gathering and interpreting information about occupants' daily behaviors. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Occupant behavior Air-conditioner operation Data mining classification algorithm C4.5 decision tree Curve description Time-series curve

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Research on Node Detection algorithm of Tomato Seedlings Based on Digital Image

Research on Node Detection Algorithm of Tomato Seedlings Bas...

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2017 2nd International Conference on Computer, Mechatronics and Electronic Engineering(CMEE 2017)

作者： Xue-guan ZHAO Xiu WANG Jin-wei ZHANG Kai JIANG Shuai SU Beijing Research Center for Intelligent Equipment Technology in Agricultural National Engineering Research Center Information Technology in Agricultural

According to a large number of studies, the internode length is sensitive to environmental stress. In the quality testing of tomato seedlings, optimization algorithm is proposed to extract tomato seedling stem node, using line scan algorithm to extract the main stem area, improve the accuracy of the main stem region selection, and finally through the bag of words model extract main stem node, this method has good robustness. Through the test, it is found that the accuracy of main stem node detection is lowest at fourth nodes, which is 81%.These results demonstrate that our method has the ability to evaluate the vigor of tomato seedlings quickly and accurately.

关键词： Image processing Seedling detection classification algorithm Machine vision

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