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SVM Classifier - a comprehensive java interface for support vector machine classification of microarray data

BMC BIOINFORMATICS 2006年第s4期7卷 S25-S25页

作者： Pirooznia, Mehdi Deng, Youping Univ So Mississippi Dept Biol Sci Hattiesburg MS 39406 USA

Motivation: Graphical user interface (GUI) software promotes novelty by allowing users to extend the functionality. SVM Classifier is a cross-platform graphical application that handles very large datasets well. The purpose of this study is to create a GUI application that allows SVM users to perform SVM training, classification and prediction. Results: The GUI provides user-friendly access to state-of-the-art SVM methods embodied in the LIBSVM implementation of support vector machine. We implemented the java interface using standard swing libraries. We used a sample data from a breast cancer study for testing classification accuracy. We achieved 100% accuracy in classification among the BRCA1-BRCA2 samples with RBF kernel of SVM. Conclusion: We have developed a java GUI application that allows SVM users to perform SVM training, classification and prediction. We have demonstrated that support vector machines can accurately classify genes into functional categories based upon expression data from DNA microarray hybridization experiments. Among the different kernel functions that we examined, the SVM that uses a radial basis kernel function provides the best performance.

关键词： support vector machine support vector machine Classifier Radial Basic Function Kernel support vector machine algorithm Unknown Data

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Design network intrusion detection system using support vector machine

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2021年第3期34卷 e4689-e4689页

作者： Ajdani, Mahdi Ghaffary, Hamidreza Islamic Azad Univ Dept Comp Sci Ferdows Branch Ferdows Iran

The growing use of the Internet and the existence of vulnerable points in networks have made the usage of intrusion detection systems as one of the most important security elements. This study aimed to present a method to design an analytical framework of detecting destructive data with respect to three factors including time, users' information, and scale. The design can be applied for big data. In the proposed method, to train data, the time has been divided into subperiods exploiting users' review information during each period of time, and the data have been trained. Also, storing methods have been applied for scalability to enhance the speed and reduce the volume of computations. The method used in this study is a combination of hardware-software method to detect destructive data to cluster them (VIRUS TOTAL Dataset). Also, the proposed method applied a new algorithm of modified vector machine, and the efficiency of the algorithm has promoted support vector machine (SVM), designed to operate better than previous methods. The results showed that the proposed method is more acceptable than other previous methods. The results indicated that the method works with the accuracy of 0.97 which can be fairly accepted.

关键词： classification destructive data intrusion support vector machine algorithm

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Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2010年第1期10卷 16-16页

作者： Yu, Wei Liu, Tiebin Valdez, Rodolfo Gwinn, Marta Khoury, Muin J. Ctr Dis Control & Prevent Natl Off Publ Hlth Genom Coordinating Ctr Hlth Promot Atlanta GA 30333 USA

Background: We present a potentially useful alternative approach based on support vector machine (SVM) techniques to classify persons with and without common diseases. We illustrate the method to detect persons with diabetes and pre-diabetes in a cross-sectional representative sample of the U. S. population. Methods: We used data from the 1999-2004 National Health and Nutrition Examination Survey (NHANES) to develop and validate SVM models for two classification schemes: Classification Scheme I (diagnosed or undiagnosed diabetes vs. pre-diabetes or no diabetes) and Classification Scheme II (undiagnosed diabetes or prediabetes vs. no diabetes). The SVM models were used to select sets of variables that would yield the best classification of individuals into these diabetes categories. Results: For Classification Scheme I, the set of diabetes-related variables with the best classification performance included family history, age, race and ethnicity, weight, height, waist circumference, body mass index (BMI), and hypertension. For Classification Scheme II, two additional variables-sex and physical activity-were included. The discriminative abilities of the SVM models for Classification Schemes I and II, according to the area under the receiver operating characteristic (ROC) curve, were 83.5% and 73.2%, respectively. The web-based tool-Diabetes Classifier was developed to demonstrate a user-friendly application that allows for individual or group assessment with a configurable, user-defined threshold. Conclusions: support vector machine modeling is a promising classification approach for detecting persons with common diseases such as diabetes and pre-diabetes in the population. This approach should be further explored in other complex diseases using common variables.

关键词： support vector machine Classification Scheme support vector machine Model Undiagnosed Diabetes support vector machine algorithm

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Hypertensive Disorders of Pregnancy: Kurtosis-Based Classification of Fetal Doppler Ultrasound Signals 5

Hypertensive Disorders of Pregnancy: Kurtosis-Based Classifi...

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5th International Conference on Advances in Biomedical Engineering (ICABME)

作者： Chaaban, Rayan Issa, Walaa Bouakaz, Ayache Zaylaa, Amira J. Beirut Int Univ Dept Biomed Engn Beirut Lebanon Univ Franoise Rabelais Tours Imaging & Brain Lab Tours France Lebanese Univ Fac Med Sci Neorosci Res Ctr Beirut Lebanon Lebanese Univ Fac Publ Hlth 5 Beirut Lebanon

ISBN: (纸本)9781728123141

Hypertensive Disorders of Pregnancy (HDP), a group of medical conditions occurring during pregnancy, have wide-reaching implications on the normal progress of 17% of pregnancies, leading to maternal and perinatal morbidity and mortality. One of the HDP complications is the Intrauterine Growth Restriction (IUGR). IUGR changes the behavior of any feature extracted from Fetal Heart Rates (FHRs). These features if well-selected improve the classification of IUGR. However, the choice of features was reliant on whether it's linear or nonlinear. Also, the classification algorithms such as, K-Means and support vector machine (SVM) used to predict and classify biomedical signals were not optimal, and the best classification algorithm was not yet set. Our aim is to propose a new kurtosis-based combinations of features and explore their effect on HDP and IUGR classification from Doppler Ultrasound FHRs. Features extracted from FHRs were fed into K-means and SVM classification algorithms. The database comprised 50 normal and 50 IUGR FHRs. Results showed that the best extracted features were those based on kurtosis, and the best classification method was the SVM. The best combination result was 67% sensitive, 100% specific and 100% precise to the classification and detection of IUGR and thus HDP. A further future study could test additional combination of features and other classification-based methods to predict IUGR and thus HDP.

关键词： Hypertensive Disorders of Pregnancy Intrauterine Growth Restriction Entropy Features Kurtosis K-means clustering support vector machine algorithm Statistical Analysis

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A comparison of classification methods for predicting chronic fatigue syndrome based on genetic data

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Journal of Translational Medicine 2009年第1期7卷 81-81页

作者： Huang, Lung-Cheng Hsu, Sen-Yen Lin, Eugene Department of Psychiatry National Taiwan University Hospital Yun-Lin Branch Taiwan Graduate Institute of Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Psychiatry Chi Mei Medical Center Liouying Tainan Taiwan Vita Genomics Inc Wugu Shiang Taipei 7 Fl. No. 6 Sec. 1 Jung-Shing Road Taiwan

Background: In the studies of genomics, it is essential select a small number of genes that are more significant than the others for the association studies of disease susceptibility. In this work, our goal was to compare computational tools with and without feature selection for predicting chronic fatigue syndrome (CFS) using genetic factors such as single nucleotide polymorphisms (SNPs) Methods: We employed the dataset that was original to the previous study by the CDC Chronic Fatigue Syndrome Research Group. Touncover relationships between CFS and SNPs, we applied three classification algorithms including naive Bayes, the support machine algorithm, and the C4.5 decision tree algorithm. Furthermore, we utilized feature to identify a subset of influential SNPs. One was the hybrid feature selection approach combining the chi-squared and information-gain methods. The other was the wrapper-based feature selection method. Results: The naive Bayes model with the wrapper-based approach performed maximally among predictive models to infer the disease susceptibility dealing with the complex relationship between CFS and SNPs. Conclusion: We demonstrated that our approach is a promising method to assess the associations between CFS and SNPs. © 2009 Huang licensee BioMed Central Ltd.

关键词： support vector machine Feature Selection Chronic Fatigue Syndrome Chronic Fatigue Syndrome Patient support vector machine algorithm

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Research on the Load_breast_cancer data set under Multiple machine Learning algorithms 24

Research on the Load_breast_cancer data set under Multiple M...

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International Conference on Modeling, Natural Language Processing and machine Learning (CMNM)

作者： Song, Meirui Wu, Shixiao Huang, Rujuan Wuhan Business Univ Wuhan Peoples R China

ISBN: (纸本)9798400709760

breast cancer ranks first in female malignant tumors. Early detection and diagnosis is the key to treatment. This paper uses the open-source load_break_cancer breast cancer data set, mainly uses random forest, support vector machine, logical regression, Gauss naive Bayesian algorithm, BP neural network algorithm, k-neighborhood algorithm and XGBoost algorithm to classify and predict the breast cancer data set, conducts a lot of training and testing on the data set under a variety of machine learning algorithms, analyzes the learning curve in the training process, analyzes the training and testing results, and analyzes the performance of the algorithm processing data, which is of great significance for breast cancer diagnosis and treatment.

关键词： Random Forest algorithm support vector machine algorithm Logistic Regression algorithm Gaussian Naive Bayes algorithm BP Neural Network algorithm k-Neighbor algorithm XGBoost algorithm

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DRIVERS FATIGUE DETECTION AND ALARMING USING EAR AND MOR VALUES 5

DRIVERS FATIGUE DETECTION AND ALARMING USING EAR AND MOR VAL...

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5th International Conference on IoT in Social, Mobile, Analytics and Cloud (I-SMAC)

作者： Geetavani, B. Rao, N. V. Krishna Shalini, N. Reddy, V. Abhishek Amrutha, A. Sultana, Afreen Inst Aeronaut Engn Dept Comp Sci & Engn Hyderabad 500043 India

ISBN: (纸本)9781665426428

There are many accidents that occur daily on highways, roads, and there are many reasons for it to occur. According to surveys done the major cause of the accident was either the driver was not completely awake or they were feeling drowsy and this resulted in accidents either major or minor. Hence, detection of the driver's fatigue and alerting on time is the main motive of this research. Few methods which detect drowsiness are intrusive and distract the driver whereas some methods of heavy sensors are installed which are expensive. Therefore, in this study, a budget-friendly, a real-time driver's fatigue detection system is developed. The proposed system incorporates the Histogram of Oriented Gradients [HOG] algorithm and support vector machine [SVM] algorithm for face detection and Regression Trees algorithm to get the facial landmarks and calculate two major factors which can help us determine the status of driver fatigue. The two major factors that help us determine the driver's fatigue are eye aspect ratio, mouth aspect ratio. Here, this research study will calculate the values first when the driver of the vehicle is completely awake. Such values will be recorded and later it will be set as threshold values. After threshold values are set, the Eye Aspect Ratio and Mouth Opening Ratio are recorded. Then the recorded values are compared with the threshold values. If there are deviations in the values measured then the system sends out an alarm saying that drowsiness is detected. This proposed system helps in the reduction of accidents happening due to Drivers fatigue.

关键词： Fatigue Detection Visual Behaviour Histogram of Oriented Gradients support vector machine algorithm Regression Trees Eye Aspect Ratio Mouth Opening Ratio

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Automated Detection of Polycystic Ovarian Syndrome Using Follicle Recognition

Automated Detection of Polycystic Ovarian Syndrome Using Fol...

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International Conference on Advanced Communication Control and Computing Technologies*

作者： Deshpande, Sharvari S. Wakankar, Asmita Cummins Coll Engn Women Dept Instrumentat & Control Pune Maharashtra India

ISBN: (纸本)9781479939145

Polycystic Ovarian Syndrome (peOS) is one of the most common hormonal disorder present in females in reproductive age group. Early detection and treatment of peos is important since it is often associated with obesity, type 2 diabetes mellitus, and high cholesterol levels. In this paper, automated detection of peos is done by calculating no of follicles in ovarian ultrasound image and then incorporating clinical, biochemical and imaging parameters to classify patients in two groups i.e. normal and peos affected. Number of follicles are detected by ovarian ultrasound image processing using preprocessing which includes contrast enhancement and filtering, feature extraction using Multiscale morphological approach and segmentation. support vector machine algorithm is used for classification which takes into account all the parameters such as body mass index (BMI), hormonal levels, menstrual cycle length and no of follicles detected in ovarian ultrasound image processing. The results obtained are verified by doctors and compared with manual detection. The accuracy obtained for the proposed method is 95%.

关键词： Polycycstic Ovarian syndrome Ultrasound Image Processing Multiscale Morphological Approach support vector machine algorithm

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Computer-Aided Liver Cirrhosis Diagnosis via Automatic Liver Segmentation and machine Learning algorithm

Computer-Aided Liver Cirrhosis Diagnosis via Automatic Liver...

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International Forum on Medical Imaging in Asia

作者： Su, Ting-Yu Yang, Wei-Tse Cheng, Tsu-Chi He, Yi-Fei Yang, Ching-Juei Fang, Yu-Hua Natl Cheng Kung Univ Tainan Dept Biomed Engn Tainan Taiwan Natl Cheng Kung Univ Hosp Dept Diagnost Radiol Tainan Taiwan

ISBN: (数字)9781510627765

ISBN: (纸本)9781510627765

In this paper, a new computer-aided diagnosis system is proposed to automatically diagnose liver cirrhosis based on four-phases CT images, which included non-contrast phase, arterial phase, delay phase and portal venous phase. It is developed for the purpose of discriminating the cirrhosis into mild or severe level by automatic liver segmentation method and classification method using machine learning algorithm. First, the gradient-inverse map of CT images are calculated to derive the relative-smooth features in local area. Then we compared the centroid and area of each binary labeled groups through each slice to quantitatively extract the volume of interest (VOI) of liver automatically. In classification step, some first-order features and texture features are calculated to describe the intensity representation of liver parenchyma. Some parameters are also used to quantify the distribution of intensity in VOI. By the way, we also quantified the shape of VOI and derived some structural features. Finally, the trained support vector machine (SVM) and Neural Network (NN) classifier is applied to classify the subjects into clinical stages of the liver cirrhosis.

关键词： Liver segmentation non-contrast phase arterial phase delay phase and portal venous phase of CT images support vector machine algorithm

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Individual Trip Mode Recognition based on Smartphone GPS Positioning Data

Individual Trip Mode Recognition based on Smartphone GPS Pos...

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International Conference on Intelligent Traffic Systems and Smart City (ITSSC)

作者： Zhang, Xiaolong China Railway First Survey & Design Inst Grp Co L Urban Rail & Architectural Design & Res Inst Xian Peoples R China

ISBN: (数字)9781510652071

ISBN: (纸本)9781510652071;9781510652064

Traditional resident travel survey methods, such as paper questionnaire, telephone interview and mail inquiry, have disadvantage of low data accuracy, difficult organization and limited sampling size. GPS based travel survey method is playing an increasingly important role in modern transportation planning. This paper proposes an innovative method for detecting individual trip mode recognition by using mobile phone GPS positioning data. First, a smartphone GPS sensor-based application is developed for multi-mode travel trajectory data collection. Data characteristics of different trip modes are deeply analyzed and the characterization indexes of different modes are put forward. Second, a support vector machine (SVM) algorithm is proposed for trip mode detection. SVM can map low-dimensional data to high-dimensional space for segmentation, is especially suitable for traffic mode recognition. Results show that the average mode detection accuracy reaches 92% for walk, bike, bus and car. This paper can provide solid data support for urban traffic planning.

关键词： traffic engineering trip mode recognition support vector machine algorithm smartphone GPS data

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