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Articles 1 - 10 of 10
Full-Text Articles in Physical Sciences and Mathematics
Robust Human Activity Recognition Using Lesser Number Of Wearable Sensors, Di Wang, Edwin Candinegara, Junhui Hou, Ah-Hwee Tan, Chunyan Miao
Robust Human Activity Recognition Using Lesser Number Of Wearable Sensors, Di Wang, Edwin Candinegara, Junhui Hou, Ah-Hwee Tan, Chunyan Miao
Research Collection School Of Computing and Information Systems
In recent years, research on the recognition of human physical activities solely using wearable sensors has received more and more attention. Compared to other types of sensory devices such as surveillance cameras, wearable sensors are preferred in most activity recognition applications mainly due to their non-intrusiveness and pervasiveness. However, many existing activity recognition applications or experiments using wearable sensors were conducted in the confined laboratory settings using specifically developed gadgets. These gadgets may be useful for a small group of people in certain specific scenarios, but probably will not gain their popularity because they introduce additional costs and they are …
Anomaly Detection For A Water Treatment System Using Unsupervised Machine Learning, Jun Inoue, Yoriyuki Yamagata, Yuqi Chen, Christopher M. Poskitt, Jun Sun
Anomaly Detection For A Water Treatment System Using Unsupervised Machine Learning, Jun Inoue, Yoriyuki Yamagata, Yuqi Chen, Christopher M. Poskitt, Jun Sun
Research Collection School Of Computing and Information Systems
In this paper, we propose and evaluate the application of unsupervised machine learning to anomaly detection for a Cyber-Physical System (CPS). We compare two methods: Deep Neural Networks (DNN) adapted to time series data generated by a CPS, and one-class Support Vector Machines (SVM). These methods are evaluated against data from the Secure Water Treatment (SWaT) testbed, a scaled-down but fully operational raw water purification plant. For both methods, we first train detectors using a log generated by SWaT operating under normal conditions. Then, we evaluate the performance of both methods using a log generated by SWaT operating under 36 …
An Ameliorated Prediction Of Drug–Target Interactions Based On Multi-Scale Discrete Wavelet Transform And Network Features, Cong Shen, Yijie Ding, Jijun Tang, Xinying Xu, Fei Guo
An Ameliorated Prediction Of Drug–Target Interactions Based On Multi-Scale Discrete Wavelet Transform And Network Features, Cong Shen, Yijie Ding, Jijun Tang, Xinying Xu, Fei Guo
Faculty Publications
The prediction of drug–target interactions (DTIs) via computational technology plays a crucial role in reducing the experimental cost. A variety of state-of-the-art methods have been proposed to improve the accuracy of DTI predictions. In this paper, we propose a kind of drug–target interactions predictor adopting multi-scale discrete wavelet transform and network features (named as DAWN) in order to solve the DTIs prediction problem. We encode the drug molecule by a substructure fingerprint with a dictionary of substructure patterns. Simultaneously, we apply the discrete wavelet transform (DWT) to extract features from target sequences. Then, we concatenate and normalize the target, drug, …
Application Of Support Vector Machine Modeling And Graph Theory Metrics For Disease Classification, Jessica M. Rudd
Application Of Support Vector Machine Modeling And Graph Theory Metrics For Disease Classification, Jessica M. Rudd
Published and Grey Literature from PhD Candidates
Disease classification is a crucial element of biomedical research. Recent studies have demonstrated that machine learning techniques, such as Support Vector Machine (SVM) modeling, produce similar or improved predictive capabilities in comparison to the traditional method of Logistic Regression. In addition, it has been found that social network metrics can provide useful predictive information for disease modeling. In this study, we combine simulated social network metrics with SVM to predict diabetes in a sample of data from the Behavioral Risk Factor Surveillance System. In this dataset, Logistic Regression outperformed SVM with ROC index of 81.8 and 81.7 for models with …
Time-Series Link Prediction Using Support Vector Machines, Proceso L. Fernandez Jr, Jan Miles Co
Time-Series Link Prediction Using Support Vector Machines, Proceso L. Fernandez Jr, Jan Miles Co
Department of Information Systems & Computer Science Faculty Publications
The prominence of social networks motivates developments in network analysis, such as link prediction, which deals with predicting the existence or emergence of links on a given network. The Vector Auto Regression (VAR) technique has been shown to be one of the best for time-series based link prediction. One VAR technique implementation uses an unweighted adjacency matrix and five additional matrices based on the similarity metrics of Common Neighbor, Adamic-Adar, Jaccard’s Coefficient, Preferential Attachment and Research Allocation Index. In our previous work, we proposed the use of the Support Vector Machines (SVM) for such prediction task, and, using the same …
Support Vector Machine And Its Application To Regression And Classification, Xiaotong Hu
Support Vector Machine And Its Application To Regression And Classification, Xiaotong Hu
MSU Graduate Theses
Support Vector machine is currently a hot topic in the statistical learning area and is now widely used in data classification and regression modeling. In this thesis, we introduce the basic idea for support vector machine, its application in the classification area including both linear and nonlinear parts, and the idea of support vector regression contains the comparison of loss functions and the usage of kernel function. Two real life examples, which are taken from R package, are also provided for both classification and regression part respectively, talking about classification of glass type and prediction for Ozone pollution.
Generalized Referenceless Image Quality Assessment Framework Using Texture Energy Measures And Pattern Strength Features, Jayashri Bagade, Kulbir Singh, Yogesh Dandawate
Generalized Referenceless Image Quality Assessment Framework Using Texture Energy Measures And Pattern Strength Features, Jayashri Bagade, Kulbir Singh, Yogesh Dandawate
Turkish Journal of Electrical Engineering and Computer Sciences
Referenceless image quality assessment is a challenging and critical problem in today's multimedia applica\-tions. Texture patterns in images are normally at high frequencies compared to lower ones. Due to the effect of distortions during acquisition, compression, and transmission, texture deviation artifacts are generated that cause a granular effect in the image. Other artifacts, such as blocking, affect high frequencies in an image, causing distorted edges. Combining the analysis of texture deviation and other artifacts helps in determining the quality of an image. The proposed approach uses variation in the energy of pixels to quantify the quality of an image. These …
Classifications Of Disturbances Using Wavelet Transform And Support Vector Machine, Neda Hajibandeh, Faramarz Faghihi, Hossein Ranjbar, Hesam Kazari
Classifications Of Disturbances Using Wavelet Transform And Support Vector Machine, Neda Hajibandeh, Faramarz Faghihi, Hossein Ranjbar, Hesam Kazari
Turkish Journal of Electrical Engineering and Computer Sciences
This paper proposes a new method to detect and classify all kinds of faults, capacitor switching, and load switching in a power system network based on wavelet transform and support vector machines (SVMs). In this regard, a sample of a power system is simulated via MATLAB/Simulink, and by reading the voltage of the point of common coupling and using the wavelet transform, the differences of the outputs of the wavelet transform are investigated. The SVM approach is employed to distinguish the type of the transient (capacitor switching, fault, and/or load switching) in use for the high level outputs of the …
Support Vector Machines For Predicting The Hamstring And Quadriceps Muscle Strength Of College-Aged Athletes, Mehmet Fati̇h Akay, Fati̇h Abut, Ebru Çeti̇n, İmdat Yarim, Boubacar Sow
Support Vector Machines For Predicting The Hamstring And Quadriceps Muscle Strength Of College-Aged Athletes, Mehmet Fati̇h Akay, Fati̇h Abut, Ebru Çeti̇n, İmdat Yarim, Boubacar Sow
Turkish Journal of Electrical Engineering and Computer Sciences
Hamstring and quadriceps muscles are essential for the performance of athletes in various sport branches. Hamstring muscles control running activities and stabilize the knee during turns or tackles, while quadriceps muscles play an important role in jumping and kicking. Although hamstring and quadriceps muscle strength in athletes can be accurately measured using isokinetic dynamometry, practical difficulties, such as the requirement of nonportable and costly equipment as well as a long period of measurement time, motivate the researcher to predict hamstring and quadriceps muscle strength using promising machine-learning methods. The purpose of this study is to build prediction models for estimating …
Breast-Region Segmentation In Mri Using Chest Region Atlas And Svm, Aida Fooladivanda, Shahriar Baradaran Shokouhi, Nasrin Ahmadinejad
Breast-Region Segmentation In Mri Using Chest Region Atlas And Svm, Aida Fooladivanda, Shahriar Baradaran Shokouhi, Nasrin Ahmadinejad
Turkish Journal of Electrical Engineering and Computer Sciences
An important step for computerized analysis of breast magnetic resonance imaging (MRI) is segmentation of the breast region. Due to the similar signal intensity of fibroglandular tissue and the chest wall, the segmentation process is difficult for breasts with fibroglandular tissue connected to the chest wall. In order to overcome this challenge, a new framework is presented that relies on a chest region atlas. The proposed method first detects the approximated breast-chest wall boundary using an intensity-based operation. A support vector machine (SVM) then determines the connectivity of fibroglandular tissue to the chest wall by the extracted features from the …