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Support vector machine

Ming Li, Vivek Narayan, Ritu R Gill, Jyothi P Jagannathan, Maria F Barile, Feng Gao, Raphael Bueno, Jagadeesan Jayender
OBJECTIVE: The purposes of this study are to develop quantitative imaging biomarkers obtained from high-resolution CTs for classifying ground-glass nodules (GGNs) into atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC); to evaluate the utility of contrast enhancement for differential diagnosis; and to develop and validate a support vector machine (SVM) to predict the GGN type. MATERIALS AND METHODS: The heterogeneity of 248 GGNs was quantified using custom software...
October 18, 2017: AJR. American Journal of Roentgenology
Igor Vidić, Liv Egnell, Neil P Jerome, Jose R Teruel, Torill E Sjøbakk, Agnes Østlie, Hans E Fjøsne, Tone F Bathen, Pål Erik Goa
BACKGROUND: Diffusion-weighted MRI (DWI) is currently one of the fastest developing MRI-based techniques in oncology. Histogram properties from model fitting of DWI are useful features for differentiation of lesions, and classification can potentially be improved by machine learning. PURPOSE: To evaluate classification of malignant and benign tumors and breast cancer subtypes using support vector machine (SVM). STUDY TYPE: Prospective. SUBJECTS: Fifty-one patients with benign (n = 23) and malignant (n = 28) breast tumors (26 ER+, whereof six were HER2+)...
October 16, 2017: Journal of Magnetic Resonance Imaging: JMRI
Fares Al-Shargie, Tong Boon Tang, Nasreen Badruddin, Masashi Kiguchi
Mental stress has been identified as one of the major contributing factors that leads to various diseases such as heart attack, depression, and stroke. To avoid this, stress quantification is important for clinical intervention and disease prevention. This study aims to investigate the feasibility of exploiting electroencephalography (EEG) signals to discriminate between different stress levels. We propose a new assessment protocol whereby the stress level is represented by the complexity of mental arithmetic (MA) task for example, at three levels of difficulty, and the stressors are time pressure and negative feedback...
October 18, 2017: Medical & Biological Engineering & Computing
Guk Bae Kim, Kyu-Hwan Jung, Yeha Lee, Hyun-Jun Kim, Namkug Kim, Sanghoon Jun, Joon Beom Seo, David A Lynch
This study aimed to compare shallow and deep learning of classifying the patterns of interstitial lung diseases (ILDs). Using high-resolution computed tomography images, two experienced radiologists marked 1200 regions of interest (ROIs), in which 600 ROIs were each acquired using a GE or Siemens scanner and each group of 600 ROIs consisted of 100 ROIs for subregions that included normal and five regional pulmonary disease patterns (ground-glass opacity, consolidation, reticular opacity, emphysema, and honeycombing)...
October 17, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
Yue Cui, Min Zhang, Danshi Wang, Siming Liu, Ze Li, Gee-Kung Chang
An effective bit-based support vector machine (SVM) is proposed as a non-parameter nonlinear mitigation approach in the millimeter-wave radio-over-fiber (RoF) mobile fronthaul (MFH) system for various modulation formats. First, we analyze the impairments originated from nonlinearities in the millimeter-wave RoF system. Then we introduce the operation principle of the bit-based SVM detector. As a classifier, the SVM can create nonlinear decision boundaries by kernel function to mitigate the distortions caused by both linear and nonlinear noise...
October 16, 2017: Optics Express
Zhilong Wang, Min Zhang, Danshi Wang, Chuang Song, Min Liu, Jin Li, Liqi Lou, Zhuo Liu
In this paper, we propose a performance monitoring and failure prediction method in optical networks based on machine learning. The primary algorithms of this method are the support vector machine (SVM) and double exponential smoothing (DES). With a focus on risk-aware models in optical networks, the proposed protection plan primarily investigates how to predict the risk of an equipment failure. To the best of our knowledge, this important problem has not yet been fully considered. Experimental results showed that the average prediction accuracy of our method was 95% when predicting the optical equipment failure state...
August 7, 2017: Optics Express
Yue Yang, Yongjiang Wu, Weili Li, Xuesong Liu, Jiyu Zheng, Wentao Zhang, Yong Chen
Near infrared (NIR) spectroscopy coupled with chemometrics was used to discriminate the geographical origin of Herba Epimedii in this work. Four different classification models, namely discriminant analysis (DA), back propagation neural network (BPNN), K-nearest neighbor (KNN), and support vector machine (SVM), were constructed, and their performances in terms of recognition accuracy were compared. The results indicated that the SVM model was superior over the other models in the geographical origin identification of Herba Epimedii...
October 10, 2017: Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
Zhe Ju, Juhe Sun, Yanjie Li, Li Wang
Glycation is a nonenzymatic post-translational modification which has been found to be involved in various biological processes and closely associated with many metabolic diseases. The accurate identification of glycation sites is important to understand the underlying molecular mechanisms of glycation. As the traditional experimental methods are often labor-intensive and time-consuming, it is desired to develop computational methods to predict glycation sites. In this study, a novel predictor named BPB_GlySite is proposed to predict lysine glycation sites by using bi-profile bayes feature extraction and support vector machine algorithm...
October 12, 2017: Computational Biology and Chemistry
Shiwei Ma, Qi Song, Huan Tao, Andrew Harrison, Shaobo Wang, Wei Liu, Shoukai Lin, Ziding Zhang, Yufang Ai, Huaqin He
Rice blast disease caused by the fungus Magnaporthe grisea (M. grisea) is one of the most serious diseases for the cultivated rice Oryza sativa (O. sativa). A key factor causing rice blast disease and defense might be protein-protein interactions (PPIs) between rice and fungus. In this research, we have developed a computational pipeline to predict PPIs between blast fungus and rice. After cross-prediction by interolog-based and domain-based method, we achieved 532 potential PPIs between 27 fungus proteins and 236 rice proteins...
October 11, 2017: Briefings in Bioinformatics
Zhiyong Dong, Ranfeng Wang, Minqiang Fan, Xiang Fu
Flotation is an important part of coal preparation, and the flotation column is widely applied as efficient flotation equipment. This process is complex and affected by many factors, with the froth depth and reagent dosage being two of the most important and frequently manipulated variables. This paper proposes a new method of switching and optimizing control for the coal flotation process. A hybrid model is built and evaluated using industrial data. First, wavelet analysis and principal component analysis (PCA) are applied for signal pre-processing...
2017: PloS One
Brenda V Canizo, Leticia B Escudero, María B Pérez, Roberto G Pellerano, Rodolfo G Wuilloud
The feasibility of the application of chemometric techniques associated with multi-element analysis for the classification of grape seeds according to their provenance vineyard soil was investigated. Grape seed samples from different localities of Mendoza province (Argentina) were evaluated. Inductively coupled plasma mass spectrometry (ICP-MS) was used for the determination of twenty-nine elements (Ag, As, Ce, Co, Cs, Cu, Eu, Fe, Ga, Gd, La, Lu, Mn, Mo, Nb, Nd, Ni, Pr, Rb, Sm, Te, Ti, Tl, Tm, U, V, Y, Zn and Zr)...
March 1, 2018: Food Chemistry
Lisheng Wei, Quan Gan, Tao Ji
The issue of an automated approach for detecting cervical cancer is proposed to improve the accuracy of recognition. Firstly, the cervical cancer histology source images are needed to use image preprocessing for reducing the impact brought by noise of images as well as the impact on subsequent precise feature extraction brought by irrelevant background. Secondly, the images are grouped into ten vertical images and the information of texture feature is extracted by Grey Level Co-occurrence Matrix (GLCM). GLCM is an effective tool to analyze the features of texture...
October 16, 2017: Computer Assisted Surgery (Abingdon, England)
Tailong Lei, Huiyong Sun, Yu Kang, Feng Zhu, Hui Liu, Wenfang Zhou, Zhe Wang, Dan Li, Youyong Li, Tingjun Hou
Xenobiotic chemicals and their metabolites are mainly excreted out of our bodies by the The urinary tract is the main organ excreting xenobiotic chemicals and their metabolites through the urine. Chemical-induced urinary tract toxicity is one of the main reasons that cause failure during drug development, and it is a common adverse event for medications, natural supplements and environmental chemicals. Despites its importance, there are only a few in silico models for assessing urinary tract toxicity for a large number of compounds with diverse chemical structures...
October 16, 2017: Molecular Pharmaceutics
Summer G Goodson, Sarah White, Alicia M Stevans, Sanjana Bhat, Chia-Yu Kao, Scott Jaworski, Tamara R Marlowe, Martin Kohlmeier, Leonard McMillan, Steven H Zeisel, Deborah A O'Brien
The ability to accurately monitor alterations in sperm motility is paramount to understanding multiple genetic and biochemical perturbations impacting normal fertilization. Computer-aided sperm analysis (CASA) of human sperm typically reports motile percentage and kinematic parameters at the population level, and uses kinematic gating methods to identify subpopulations such as progressive or hyperactivated sperm. The goal of this study was to develop an automated method that classifies all patterns of human sperm motility during in vitro capacitation following the removal of seminal plasma...
October 4, 2017: Biology of Reproduction
Pi Guo, Tao Liu, Qin Zhang, Li Wang, Jianpeng Xiao, Qingying Zhang, Ganfeng Luo, Zhihao Li, Jianfeng He, Yonghui Zhang, Wenjun Ma
BACKGROUND: In China, dengue remains an important public health issue with expanded areas and increased incidence recently. Accurate and timely forecasts of dengue incidence in China are still lacking. We aimed to use the state-of-the-art machine learning algorithms to develop an accurate predictive model of dengue. METHODOLOGY/PRINCIPAL FINDINGS: Weekly dengue cases, Baidu search queries and climate factors (mean temperature, relative humidity and rainfall) during 2011-2014 in Guangdong were gathered...
October 16, 2017: PLoS Neglected Tropical Diseases
Jing Zhang, Songhan Lu, Xiangzhou Wang, Xiaohui Du, Guangming Ni, Juanxiu Liu, Lin Liu, Yong Liu
Identifying fungi in microscopic leucorrhea images provides important information for evaluating gynecological diseases. Subjective judgment and fatigue can greatly affect recognition accuracy. This paper proposes an automatic identification system to detect fungi in leucorrhea images that incorporates a convolutional neural network, the histogram of oriented gradients algorithm, and a binary support vector machine. In experiments, the detection rate of the positive samples was as high as 99.8%. The experimental results demonstrate the effectiveness of the proposed method and its potential as a primary software component of a completely automated system...
September 1, 2017: Journal of the Optical Society of America. A, Optics, Image Science, and Vision
Hoda Mohammadzade, Benyamin Ghojogh, Sina Faezi, Mahdi Shabany
Locating critical objects is crucial in various security applications and industries. For example, in security applications, such as in airports, these objects might be hidden or covered under shields or secret sheaths. Millimeter-wave images can be utilized to discover and recognize the critical objects out of the hidden cases without any health risk due to their non-ionizing features. However, millimeter-wave images usually have waves in and around the detected objects, making object recognition difficult...
June 1, 2017: Journal of the Optical Society of America. A, Optics, Image Science, and Vision
Yunlong Yu, Zhong Ji, Jichang Guo, Yanwei Pang
Zero-shot learning (ZSL) endows the computer vision system with the inferential capability to recognize new categories that have never seen before. Two fundamental challenges in it are visual-semantic embedding and domain adaptation in cross-modality learning and unseen class prediction steps, respectively. This paper presents two corresponding methods named Adaptive STructural Embedding (ASTE) and Self-PAced Selective Strategy (SPASS) for both challenges. Specifically, ASTE formulates the visual-semantic interactions in a latent structural support vector machine framework by adaptively adjusting the slack variables to embody different reliablenesses among training instances...
October 12, 2017: IEEE Transactions on Neural Networks and Learning Systems
Youyuan Li, Yingping Zhuang
Peptide mass fingerprinting continues to play an important role in current proteomics studies based on its good performance in sample throughput, specificity for single peptides, and insensitive to unexpected post-translational modifications as compared with . Here, we proposed and evaluated the use of feature-matching pattern-based support vector machines (SVMs) for robust protein identification. This approach is now facilitated with an updated web server (fmpRPMF) incorporated with several newly developed or improved modules and workflows allowing identification of proteins from data...
October 13, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Elise A Piazza, Marius Cătălin Iordan, Casey Lew-Williams
The voice is the most direct link we have to others' minds, allowing us to communicate using a rich variety of speech cues [1, 2]. This link is particularly critical early in life as parents draw infants into the structure of their environment using infant-directed speech (IDS), a communicative code with unique pitch and rhythmic characteristics relative to adult-directed speech (ADS) [3, 4]. To begin breaking into language, infants must discern subtle statistical differences about people and voices in order to direct their attention toward the most relevant signals...
October 11, 2017: Current Biology: CB
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