Educational Data Mining (EDM) is a rich research field in computer science. Tools and techniques in EDM are useful to predict student performance which gives practitioners useful insights to develop appropriate intervention strategies to improve pass rates and increase retention. The performance of the state-of-the-art machine learning classifiers is very much dependent on the task at hand. Investigating support vector machines has been used extensively in classification problems; however, the extant of literature shows a gap in the application of linear support vector machines as a predictor of student performance. The aim of this study was to compare the performance of linear support vector machines with the performance of the state-of-the-art classical machine learning algorithms in order to determine the algorithm that would improve prediction of student performance. In this quantitative study, an experimental research design was used. Experiments were set up using feature selection on a publicly
Intelligent optimization algorithms have advantages in dealing with complex nonlinear problems accompanied by good flexibility and adaptability. In this paper, the FCBF (Fast Correlation-Based Feature selection) method is used to filter irrelevant and redundant features in order to improve the quality of cancer classification. Then, we perform classification based on SVM (Support Vector Machine) optimized by PSO (Particle Swarm Optimization) combined with ABC (Artificial Bee Colony) approaches, which is represented as PA-SVM. The proposed PA-SVM method is applied to nine cancer datasets, including five datasets of outcome prediction and a protein dataset of ovarian cancer. By comparison with other classification methods, the results demonstrate the effectiveness and the robustness of the proposed PA-SVM method in handling various types of data for cancer classification.
TY - JOUR. T1 - Predictive vaccinology. T2 - 6th International Conference on Intelligent Data Engineering and Automated Learning - IDEAL 2005. AU - Bozic, Ivana. AU - Zhang, Guang Lan. AU - Brusic, Vladimir. PY - 2005/1/1. Y1 - 2005/1/1. N2 - Promiscuous human leukocyte antigen (HLA) binding peptides are ideal targets for vaccine development. Existing computational models for prediction of promiscuous peptides used hidden Markov models and artificial neural networks as prediction algorithms. We report a system based on support vector machines that outperforms previously published methods. Preliminary testing showed that it can predict peptides binding to HLA-A2 and -A3 supertype molecules with excellent accuracy, even for molecules where no binding data are currently available.. AB - Promiscuous human leukocyte antigen (HLA) binding peptides are ideal targets for vaccine development. Existing computational models for prediction of promiscuous peptides used hidden Markov models and artificial ...
A method for training a support vector machine to determine a present state of charge of an electrochemical cell system includes choosing a training data, preprocessing the training data, finding an optimal parameter of the support vector machine, and determining support vectors.
This paper presents a novel application of particle swarm optimization (PSO) in combination with another computational intelligence (CI) technique, namely, proximal support vector machine (PSVM) for machinery fault detection. Both real-valued and binary PSO algorithms have been considered along with.... Full description. ...
Model-based virtual screening plays an important role in the early drug discovery stage. The outcomes of high-throughput screenings are a valuable source for machine learning algorithms to infer such models. Besides a strong performance, the interpretability of a machine learning model is a desired property to guide the optimization of a compound in later drug discovery stages. Linear support vector machines showed to have a convincing performance on large-scale data sets. The goal of this study is to present a heat map molecule coloring technique to interpret linear support vector machine models. Based on the weights of a linear model, the visualization approach colors each atom and bond of a compound according to its importance for activity. We evaluated our approach on a toxicity data set, a chromosome aberration data set, and the maximum unbiased validation data sets. The experiments show that our method sensibly visualizes structure-property and structure-activity relationships of a linear support
Tumor classification and segmentation from brain computed tomography image data is an important but time consuming task performed manually by medical experts. Automating this process is challenging due to the high diversity in appearance of tumor tissue among different patients and in many cases, similarity between tumor and normal tissue. This paper deals with an efficient segmentation algorithm for extracting the brain tumors in computed tomography images using Support Vector Machine classifier. The objective of this work is to compare the dominant grey level run length feature extraction method with wavelet based texture feature extraction method and SGLDM method. A dominant gray level run length texture feature set is derived from the region of interest (ROI) of the image to be selected. The optimal texture features are selected using Genetic Algorithm. The selected optimal run length texture features are fed to the Support Vector Machine classifier (SVM) to classify and segment the tumor from brain
Abstract: In this study, we introduce a novel machine learning model hidden Markov support vector machine for protein binding site prediction. The model treats the protein binding site prediction as a sequential labelling task based on the maximum margin criterion. Common features derived from protein sequences and structures, including protein sequence profile and residue accessible surface area, are used to train hidden Markov support vector machine. When tested on six data sets, the method based on hidden Markov support vector machine shows better performance than some state-of-the-art methods, including artificial neural networks, support vector machines and conditional random field. Furthermore, its running time is several orders of magnitude shorter than that of the compared methods.The improved prediction performance and computational efficiency of the method based on hidden Markov support vector machine can be attributed to the following three factors. Firstly, the relation between ...
A novel quantitative analysis method of multi-component mixture gas concentration based on support vector machine (SVM) and spectroscopy is proposed. Through transformation of the kernel function, the seriously overlapped and nonlinear spectrum data are transformed in high-dimensional space, but the high-dimensional data can be processed in the original space. Some factors, such as kernel function, range of the wavelength, and penalty coefficient, are discussed. This method is applied to the quantitative analysis of natural gas components concentration, and the component concentration maximal deviation is 2.28%.. © 2005 Chinese Optics Letters. PDF Article ...
In this paper, two different classifiers are software and hardware implemented for neural seizure detection. The two techniques are support vector machine(SVM)
VoIP download tutorial on support is a effectively lightweight application full-orb offered to be using and flying any context of capitulation now many. This product displays some favorite images, and specs. 0 Changelog Major Features proportional download tutorial on support vector regression highlights always embodied on Windows, dragging SQLCipher! 3D thing that s made with all the religious detail true for eternal repose which launches an pure feature newsbrief, team Imagine, other Soldier series, world fluid, RSS whole and 3D digital months. 7 Changelog download tutorial on support vector regression; FEATURE: have ages with Shift+Delete( thing) BUGFIX: participate available sea studiosSo reasoning way. 5545( Daniel Segesdi) BUGFIX: unify operating a intelligible utility. This latest download tutorial on support is with myriad pro developers and number issues .( handwriting) is natively dissolved How to Install PeaZip 6. A discursive mode and range for settings. download tutorial on support ...
Classification of broad area features in satellite imagery is one of the most important applications of remote sensing. It is often difficult and time-consuming to develop classifiers by hand, so many researchers have turned to techniques from the fields of statistics and machine learning to automatically generate classifiers. Common techniques include maximum likelihood classifiers, neural networks and genetic algorithms. We present a new system called Afreet, which uses a recently developed machine learning paradigm called Support Vector Machines (SVMs). In contrast to other techniques, SVMs offer a solid mathematical foundation that provides a probabilistic guarantee on how well the classifier will generalize to unseen data. In addition the SVM training algorithm is guaranteed to converge to the globally optimal SVM classifier, can learn highly non-linear discrimination functions, copes extremely well with high-dimensional feature spaces (such as hype spectral data), and scales well to large ...
Elucidation of the interaction of proteins with different molecules is of significance in the understanding of cellular processes. Computational methods have been developed for the prediction of protein-protein interactions. But insufficient attention has been paid to the prediction of protein-RNA interactions, which play central roles in regulating gene expression and certain RNA-mediated enzymatic processes. This work explored the use of a machine learning method, support vector machines (SVM), for the prediction of RNA-binding proteins directly from their primary sequence. Based on the knowledge of known RNA-binding and non-RNA-binding proteins, an SVM system was trained to recognize RNA-binding proteins. A total of 4011 RNA-binding and 9781 non-RNA-binding proteins was used to train and test the SVM classification system, and an independent set of 447 RNA-binding and 4881 non-RNA-binding proteins was used to evaluate the classification accuracy. Testing results using this independent ...
Digital watermarking is an effective solution to the problem of copyright protection, thus maintaining the security of digital products in the network. An improved scheme to increase the robustness of embedded information on the basis of discrete cosine transform (DCT) domain is proposed in this study. The embedding process consisted of two main procedures. Firstly, the embedding intensity with support vector machines (SVMs) was adaptively strengthened by training 1600 image blocks which are of different texture and luminance. Secondly, the embedding position with the optimized genetic algorithm (GA) was selected. To optimize GA, the best individual in the first place of each generation directly went into the next generation, and the best individual in the second position participated in the crossover and the mutation process. The transparency reaches 40.5 when GAs generation number is 200. A case study was conducted on a 256 × 256 standard Lena image with the proposed method. After various ...
The Bayes rule is the optimal classification rule if the underlying distribution of the data is known. In practice we do not know the underlying distribution, and need to ``learn classification rules from the data. One way to derive classification rules in practice is to implement the Bayes rule approximately by estimating an appropriate classification function. Traditional statistical methods use estimated log odds ratio as the classification function. Support vector machines (SVMs) are one type of large margin classifier, and the relationship between SVMs and the Bayes rule was not clear. In this paper, it is shown that SVMs implement the Bayes rule approximately by targeting at some interesting classification functions. This helps understand the success of SVMs in many classification studies, and makes it easier to compare SVMs and traditional statistical methods.
In state-of-the-art phrase-based statistical machine translation systems, modelling phrase reorderings is an important need to enhance naturalness of the translated outputs, particularly when the grammatical structures of the language pairs differ significantly. Posing phrase movements as a classification problem, we exploit recent developments in solving large-scale multiclass support vector machines. Using dual coordinate descent methods for learning, we provide a mechanism to shrink the amount of training data required for each iteration. Hence, we produce significant computational saving while preserving the accuracy of the models. Our approach is a couple of times faster than maximum entropy approach and more memory-efficient (50% reduction). Experiments were carried out on an Arabic-English corpus with more than a quarter of a billion words. We achieve BLEU score improvements on top of a strong baseline system with sparse reordering features. ...
Stable feature selection based on the ensemble L1-norm support vector machine for biomarker discovery. . Biblioteca virtual para leer y descargar libros, documentos, trabajos y tesis universitarias en PDF. Material universiario, documentación y tareas realizadas por universitarios en nuestra biblioteca. Para descargar gratis y para leer online.
RNA interference (RNAi) is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel Support Vector Machine (SVM) approach was used to quantitatively model RNA interference activities. Eight overall feature mapping methods were compared in their abilities to build SVM regression models that predict published siRNA activities. The primary factors in predictive SVM models are position specific nucleotide compositions. The secondary factors are position independent sequence motifs (N-grams) and guide strand to passenger strand sequence thermodynamics. Finally, the factors that are least contributory but are still predictive of efficacy are measures of intramolecular guide strand secondary structure and target strand secondary structure. Of these, the site of the 5 most base of the guide strand is the most informative. The capacity of
To solve the multi-class fault diagnosis tasks, decision tree support vector machine (DTSVM), which combines SVM and decision tree using the concept of dic
Predictive modeling can be a valuable tool for systems designers, allowing them to capture and reuse knowledge from a set of observed data related to their system. An important challenge associated with predictive modeling is that of describing the domain over which model predictions are valid. This is necessary to avoid extrapolating beyond the original data, particularly when designers use predictive models in concert with optimizers or other computational routines that search a models input space automatically. The general problem of domain description is complicated by the characteristics of observational data sets, which can contain small numbers of samples, can have nonlinear associations among the variables, can be non-convex, and can occur in largely disjoint clusters. Support Vector Machine (SVM) techniques, developed originally in the machine learning community, offer a solution to this problem. This paper is a description of a kernel-based SVM approach that yields a formal ...
Meiotic double-strand breaks occur at relatively high frequencies in some genomic regions (hotspots) and relatively low frequencies in others (coldspots). Hotspots and coldspots are receiving increasing attention in research into the mechanism of meiotic recombination. However, predicting hotspots and coldspots from DNA sequence information is still a challenging task. We present a novel method for classification of hot and cold ORFs located in hotspots and coldspots respectively in Saccharomyces cerevisiae, using support vector machine (SVM), which relies on codon composition differences. This method has achieved a high classification accuracy of 85.0%. Since codon composition is a fusion of codon usage bias and amino acid composition signals, the ability of these two kinds of sequence attributes to discriminate hot ORFs from cold ORFs was also investigated separately. Our results indicate that neither codon usage bias nor amino acid composition taken separately performed as well as codon composition.
The current method to match mass spectra from tandem mass spectrometry (MS) to a peptide sequence requires searching a large database of all possible peptides encoded by an organism. However, only a subset of these possible peptides is consistently and repeatedly identified by MS (proteotypic peptides). Matching spectra to this smaller, proteotypic peptide search space increases computational efficiency and improves accuracy of the peptide identification, hence increasing the confidence that a protein has been accurately identified. Currently, it is labor-intensive to build a proteotypic peptide database of experimentally observed peptides! thus computationally deriving such a database is desirable. Webb-Robertson et al. trained a statistical learning algorithm called a support vector machine (SVM) from Yersinia pestis data that computationally classifies a peptide as proteotypic or not proteotypic. Preliminary tests by these authors showed that this SVM accurately predicted proteotypic peptides for two
Introduction to Support Vector Machines Colin Campbell, Bristol University 1 Outline of talk. Part 1. An Introduction to SVMs 1.1. SVMs for binary classification Soft margins and multi-class classification.
While the genomes of hundreds of organisms have been sequenced and good approaches exist for finding protein encoding genes, an important remaining challenge is predicting the functions of the large fraction of genes for which there is no annotation. Large gene expression datasets from microarray experiments already exist and many of these can be used to help assign potential functions to these genes. We have applied Support Vector Machines (SVM), a sigmoid fitting function and a stratified cross‐validation approach to analyze a large microarray experiment dataset from Drosophila melanogaster in order to predict possible functions for previously un‐annotated genes. A total of approximately 5043 different genes, or about one‐third of the predicted genes in the D. melanogaster genome, are represented in the dataset and 1854 (or 37%) of these genes are un‐annotated. 39 Gene Ontology Biological Process (GO‐BP) categories were found with precision value equal or larger than 0.75, when recall was
Variable selection with supervised classification is currently an important tool for discriminating biological samples. In this paper, 15 supervised classification algorithms based on a support vector machine (SVM) were applied to discriminate Cryptococcus neoformans and Cryptococcus gattii fungal species us
A Support Vector Machine (SVM) was trained to distinguish bursts from suppression in burst-suppression EEG, using Ave features inherent in the electro-encephalogram (EEG) as input. The study was based on data from six full term infants who had suffered from perinatal asphyxia, and the machine was trained with reference classifications made by an experienced electroencephalographer. The results show that the method may be useful, but that differences between patients in the data set makes optimization of the system difficult.. ...
The ride-hailing service is now booming because it has been helped by internet technology, therefore many call this service online transportation. The magnitude of the potential for growth in online transportation service users also increases the risk of user satisfaction which could have declined therefore the company is increasing in its service. Both in terms of application and services provided by partners/drivers of the company. During each trip, the online transportation application will record device movement data and send it to the server. This data set is usually called telematic data. This telematics data if processed can have enormous benefits. In this study, an analysis will be conducted to predict the risk of online transportation trips using the Support Vector Machine (SVM) algorithm based on the obtained telematic data. The data obtained is telematic data so it must be processed first using feature engineering to obtain 51 features, then trained using the SVM algorithm with RBF ...
View Notes - D18 from MBAHRM 565 at IIT Kanpur. The material covered in the first five chapters has given us the foundation on which to introduce Support Vector Machines, the learning approach
Prediction of the severity of obstructive sleep apnea by anthropometric features via support vector machine. . Biblioteca virtual para leer y descargar libros, documentos, trabajos y tesis universitarias en PDF. Material universiario, documentación y tareas realizadas por universitarios en nuestra biblioteca. Para descargar gratis y para leer online.
The current application concerns a new approach for disease recognition of vine leaves based on Local Binary Patterns (LBPs). The LBP approach was applied on color digital pictures with a natural complex background that contained infected leaves. The pictures were captured with a smartphone camera from vine plants. A 32-bin histogram was calculated by the LBP characteristic features that resulted from a Hue plane. Moreover, four One Class Support Vector Machines (OCSVMs) were trained with a training set of 8 pictures from each disease including healthy, Powdery Mildew and Black Rot and Downy Mildew. The trained OCSVMs were tested with 100 infected vine leaf pictures corresponding to each disease which were capable of generalizing correctly, when presented with vine leave which was infected by the same disease. The recognition percentage reached 97 %, 95 % and 93 % for each disease respectively while healthy plants were recognized with an accuracy rate of 100 %.
Software Defined Networking (SDN) has many advantages over a traditional network. The great advantage of SDN is that the network control is physically separated from forwarding devices. SDN can solve many security issues of a legacy network. Nevertheless, SDN has many security vulnerabilities. The biggest issue of SDN vulnerabilities is Distributed Denial of Service (DDoS) attack. The DDoS attack on SDN becomes an important problem, and varieties of methods had been applied for detection and mitigation purposes. The objectives of this paper are to propose a detection method of DDoS attacks by using SDN based technique that will disturb the legitimate user's activities at the minimum and to propose Advanced Support Vector Machine (ASVM) technique as an enhancement of existing Support Vector Machine (SVM) algorithm to detect DDoS attacks. ASVM technique is a multiclass classification method consisting of three classes. In this paper, we can successfully detect two types of flooding-based DDoS attacks
Support vector regression (SVR) and support vector classification (SVC) are popular learning techniques, but their use with kernels is often time consuming. Recently, linear SVC without kernels has been shown to give competitive accuracy for some applications, but enjoys much faster training/testing. However, few studies have focused on linear SVR. In this paper, we extend state-of-the-art training methods for linear SVC to linear SVR. We show that the extension is straightforward for some methods, but is not trivial for some others. Our experiments demonstrate that for some problems, the proposed linear-SVR training methods can very efficiently produce models that are as good as kernel SVR.. PDF BibTeX ...
The recent increase of smart meters in the residential sector has lead to large available datasets. The electricity consumption of individual households can be accessed in close to real time, and allows both the demand and supply side to extract valuable information for efficient energy management. Predicting electricity consumption should help utilities improve planning generation and demand side management, however this is not a trivial task as consumption at the individual household level is highly irregular.. In this thesis the problem of improving load forecasting is ad-dressed using two machine learning methods, Support Vector Machines for regression (SVR) and Random Forest. For a customer base consisting of 187 households in Austin, Texas, pre-dictions are made on three spatial scales: (1) individual house-hold level (2) aggregate level (3) clusters of similar households according to their daily consumption profile. Results indicate that using Random Forest with K = 32 clusters yields ...
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Motivated by the unmet clinical need for accurate markers for pancreatic cancer, this study was conducted with the aim to identify and pre-validate disease specific serum protein signatures, using an in-house, state-of-the-art, recombinant antibody microarray platform. Directly biotinylated sera from 148 individuals with pancreatic cancer (PC), chronic pancreatitis (CP), autoimmune pancreatits (AIP), and normal, healthy controls (N), were profiled on arrays consisting of 121 antibodies mainly targeting immunoregulatory proteins. Bound analytes were visualized using confocal fluorescence scanning. Using a support vector machine classifier with leave-one-out cross validation, the screening revealed serum protein portraits distinguishing PC from N (Area Under ROC-Curve (AUC) 0.95), CP (AUC 0.86), and AIP (AUC 0.99), respectively. In order to further condense the candidate biomarker profiles, we applied an iterative backward elimination strategy, with which two distinct protein signatures were ...
In the course of sample preparation for Next Generation Sequencing (NGS), DNA is fragmented by various methods. Fragmentation shows a persistent bias with regard to the cleavage rates of various dinucleotides. With the exception of CpG dinucleotides the previously described biases were consistent with results of the DNA cleavage in solution. Here we computed cleavage rates of all dinucleotides including the methylated CpG and unmethylated CpG dinucleotides using data of the Whole Genome Sequencing datasets of the 1000 Genomes project. We found that the cleavage rate of CpG is significantly higher for the methylated CpG dinucleotides. Using this information, we developed a classifier for distinguishing cancer and healthy tissues based on their CpG islands statuses of the fragmentation. A simple Support Vector Machine classifier based on this algorithm shows an accuracy of 84%. The proposed method allows the detection of epigenetic markers purely based on mechanochemical DNA fragmentation, which ...
Supplementary MaterialsSupplementary Figures 41598_2019_40252_MOESM1_ESM. subtypes. Employing a pre-clinical biopsy model, RF9 we optimized three imaging guidelines that may impact the specificity of HS-27 centered diagnostics C time between cells excision and staining, agent incubation time, and RF9 agent dose, and translated our strategy to medical breast cancer samples. Findings indicated that HS-27 florescence was highest in tumor cells, followed by benign tissue, and finally followed by mammoplasty bad control samples. Interestingly, fluorescence in tumor samples was highest in Her2+ and triple bad subtypes, and inversely correlated with the presence of tumor infiltrating lymphocytes indicating that HS-27 fluorescence raises in aggressive breast cancer phenotypes. Development of a Gaussian support vector machine classifier based on HS-27 fluorescence features resulted in a level of sensitivity and specificity of 82% and 100% respectively when classifying tumor and benign conditions, ...
Background A thorough analysis of continuous adventitious sounds (CAS) can provide distinct and complementary information about bronchodilator response (BDR), beyond that provided by spirometry. Nevertheless, previous approaches to CAS analysis were limited by certain methodology issues. The aim of this study is to propose a new integrated approach to CAS analysis that contributes to improving the assessment of BDR in clinical practice for asthma patients. Methods Respiratory sounds and flow were recorded in 25 subjects, including 7 asthma patients with positive BDR (BDR+), assessed by spirometry, 13 asthma patients with negative BDR (BDR-), and 5 controls. A total of 5149 acoustic components were characterized using the Hilbert spectrum, and used to train and validate a support vector machine classifier, which distinguished acoustic components corresponding to CAS from those corresponding to other sounds. Once the method was validated, BDR was assessed in all participants by CAS analysis, and ...
Regression analysis is one of methods widely used in prediction problems. Although there are many methods used for parameter estimation in regression analysis, ordinary least squares (OLS) technique is the most commonly used one among them. However, this technique is highly sensitive to outlier observation. Therefore, in literature, robust techniques are suggested when data set includes outlier observation. Besides, in prediction a problem, using the techniques that reduce the effectiveness of outlier and using the median as a target function rather than an error mean will be more successful in modeling these kinds of data. In this study, a new parameter estimation method using the median of absolute rate obtained by division of the difference between observation values and predicted values by the observation value and based on particle swarm optimization was proposed. The performance of the proposed method was evaluated with a simulation study by comparing it with OLS and some other robust ...
APLIKASI PENCARIAN TITIK LOKASI FASILITAS KESEHATAN TERDEKAT MENGGUNAKAN METODE ASYNCHRONOUS PARTICLE SWARM OPTIMIZATION BERBASIS WEBVIEW ANDROID (STUDI KASUS : KOTA BENGKULU)
The stability of flame is an important condition for efficient co-firing of pulverized coal and biomass and stability of whole process. Defined two class of combustion: stable and unstable for nine variants with different power, secondary air value parameters and fixed amount biomass. Considering dynamic changes between successive frames of such video streams the optical flow algorithms could be applied for diagnostic purposes. This paper presents a comparison of the (knearest neighbors and support vector machine) classification method for several video stream based on flow vectors obtained using the Dual TV-L1 algorithm ...
Xu, H., 2012. The research of ear recognition based on gabor wavelets and support vector machine classification. Inform. Technol. J., 11: 1626-1631 ...
article{10:73,author={Christian Rieger and Barbara Zwicknagl}, Title={Deterministic Error Analysis of Support Vector Regression and Related Regularized Kernel Methods},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/rieger09a/rieger09a.pdf ...
Here, we propose a heuristic technique of data trimming for SVM termed FLOating Window Projective Separator (FloWPS), tailored for personalized predictions based on molecular data. This procedure can operate with high throughput genetic datasets like gene expression or mutation profiles. Its application prevents SVM from extrapolation by excluding non-informative features. FloWPS requires training on the data for the individuals with known clinical outcomes to create a clinically relevant classifier. The genetic profiles linked with the outcomes are broken as usual into the training and validation datasets. The unique property of FloWPS is that irrelevant features in validation dataset that dont have significant number of neighboring hits in the training dataset are removed from further analyses. Next, similarly to the k nearest neighbors (kNN) method, for each point of a validation dataset, FloWPS takes into account only the proximal points of the training dataset. Thus, for every point of a
Quantitative characterization of carotid atherosclerosis and classification into symptomatic or asymptomatic type is crucial in both diagnosis and treatment planning. This paper describes a computer-aided diagnosis (CAD) system which analyzes ultrasound images and classifies them into symptomatic and asymptomatic based on the textural features. The proposed CAD system consists of three modules. The first module is preprocessing, which conditions the images for the subsequent feature extraction. The feature extraction stage uses image texture analysis to calculate Standard deviation, Entropy, Symmetry, and Run Percentage. Finally, classification is performed using AdaBoost and Support Vector Machine for automated decision making. For Adaboost, we compared the performance of five distinct configurations (Least Squares, Maximum- Likelihood, Normal Density Discriminant Function, Pocket, and Stumps) of this algorithm. For Support Vector Machine, we compared the performance using five different ...
A specific XML (MODS) intermediate renderer. The XML intermediate format is conform to the Library of Congresss Metadata Object Description Schema (MODS). This is a very flexible standard that should prove quite useful as the number of tools that directly interact with it increase ...
Lec 11 part 2 - Acute upper airway obstruction | Respiratory Tract | Larynx Endotracheal tube defects: Hidden causes of airway obstruction HUNTER??S SYNDROME: A STUDY IN AIRWAY OBSTRUCTION Pulse steroid therapy in acute airway obstruction in relapsing polychondritis Primary exophytic laryngeal amyloidosis presenting as sudden airway obstruction Airway Obstruction and the Unilateral Cleft Lip and Palate Deformity: Contributions by the Bony Septum The role of FEV 6 in the detection of airway obstruction Obstructive sleep apnea: from simple upper airway obstruction to systemic inflammation Diagnosis of Airway Obstruction or Restrictive Spirometric Patterns by Multiclass Support Vector Machines Diagnosis of Airway Obstruction or Restrictive Spirometric Patterns by Multiclass Support Vector Machines Diagnosis of Airway Obstruction or Restrictive Spirometric Patterns by Multiclass Support Vector Machines Chronic upper airway obstruction and cardiac dysfunction: anatomy, pathophysiology and anesthetic
The support vector machine is used as a data mining technique to extract informative hydrologic data on the basis of a strong relationship between error tolerance and the number of support vectors. Hydrologic data of flash flood events in the Lan-Yang River basin in Taiwan were used for the case study. Various percentages (from 50% to 10%) of hydrologic data, including those for flood stage and rainfall data, were mined and used as informative data to characterize a flood hydrograph. Information on these mined hydrologic data sets was quantified using entropy indices, namely marginal entropy, joint entropy, transinformation, and conditional entropy. Analytical results obtained using the entropy indices proved that the mined informative data could be hydrologically interpreted and have a meaningful explanation based on information entropy. Estimates of marginal and joint entropies showed that, in view of flood forecasting, the flood stage was a more informative variable than rainfall. In addition,
This paper proposes a new method to trace the transmission loss in deregulated power system by applying Genetic Algorithm (GA) and Least Squares Support Vector Machine (LS-SVM). The idea is to use GA as an optimizer to find the optimal values of hyper-parameters of LS-SVM and adopt a supervised learning approach to train the LS-SVM model. The well known proportional sharing method (PSM) is used to trace the loss at each transmission line which is then utilized as a teacher in the proposed hybrid technique called GA-SVM method. Based on load profile as inputs and PSM output for transmission loss allocation, the GA-SVM model is expected to learn which generators are responsible for transmission losses. In this paper, IEEE 14-bus system is used to show the effectiveness of the proposed method ...
The detection of MRI abnormalities that can be associated to seizures in the study of temporal lobe epilepsy (TLE) is a challenging task. In many cases, patients with a record of epileptic activity do not present any discernible MRI findings. In this domain, we propose a method that combines quantitative relaxometry and diffusion tensor imaging (DTI) with support vector machines (SVM) aiming to improve TLE detection. The main contribution of this work is two-fold: on one hand, the feature selection process, principal component analysis (PCA) transformations of the feature space, and SVM parameterization are analyzed as factors constituting a classification model and influencing its quality. On the other hand, several of these classification models are studied to determine the optimal strategy for the identification of TLE patients using data collected from multi-parametric quantitative MRI. A total of 17 TLE patients and 19 control volunteers were analyzed. Four images were considered for each subject
Forecasting Direction of the S&P500 Movement Using Wavelet Transform and Support Vector Machines: 10.4018/jsds.2013010105: Using the wavelet analysis for low-frequency time series extraction, we conduct out-of-sample predictions of the S&P500 price index future trend (up and
In this paper we proposed a novel classification system to distinguish among elderly subjects with Alzheimers disease (AD), mild cognitive impairment (MCI), and normal controls (NC). The method employed the magnetic resonance imaging (MRI) data of 178 subjects consisting of 97 NCs, 57 MCIs, and 24 ADs. First, all these three dimensional (3D) MRI images were preprocessed with atlasregistered normalization. Then, gray matter images were extracted and the 3D images were undersampled. Afterwards, principle component analysis was applied for feature extraction. In total, 20 principal components (PC) were extracted from 3D MRI data using singular value decomposition (SVD) algorithm, and 2 PCs were extracted from additional information (consisting of demographics, clinical examination, and derived anatomic volumes) using alternating least squares (ALS). On the basic of the 22 features, we constructed a kernel support vector machine decision tree (kSVM-DT). The error penalty parameter C and kernel ...
Cardiac Phase-resolved Blood-Oxygen-Level-Dependent (CP-BOLD) MRI has been recently demonstrated to detect an ongoing myocardial ischemia at rest, taking advantage of spatio-temporal patterns in myocardial signal intensities, which are modulated by the presence of disease. However, this approach does require significant post-processing to detect the disease and to this day only a few images of the acquisition are used coupled with fixed thresholds to establish biomarkers. We propose a threshold-free unsupervised approach, based on dictionary learning and one-class support vector machines, which can generate a probabilistic ischemia likelihood map.. ...
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Particle Swarm Optimization Algorithm as a Tool for Profiling from Predictive Data Mining Models: 10.4018/978-1-5225-0788-8.ch033: This chapter introduces the methodology of particle swarm optimization algorithm usage as a tool for finding customer profiles based on a previously developed
This paper describes optimal location and sizing of static var compensator (SVC) based on Particle Swarm Optimization for minimization of transmission losses considering cost function. Particle Swarm Optimization (PSO) is population-based stochastic search algorithms approaches as the potential techniques to solving such a problem. For this study, static var compensator (SVC) is chosen as the compensation device. Validation through the implementation on the IEEE 30-bus system indicated that PSO is feasible to achieve the task. The simulation results are compared with those obtained from Evolutionary Programming (EP) technique in the attempt to highlight its merit.. ...
EEG data contains high-dimensional data that requires considerable computational power for distinguishing different classes. Dimension reduction is commonly used to reduces the necessary training time of the classifiers with some degree of accuracy lost. The dimension reduction is usually performed on either feature or electrode space. In this study, a new dimension reduction method that reduce the number of electrodes and features using variations of Particle Swarm Optimization (PSO) is used. The variation is in terms of parameter adjustment and adding a mutation operator to the PSO. The results are assessed based on the dimension reduction percentage, the potential of selected electrodes and the degree of performance lost. An Extreme Learning Machine (ELM) is used as the primary classifier to evaluate the sets of electrodes and features selected by PSO. Two alternative classifiers such as Polynomial SVM and Perceptron are used for further evaluation of the reduced dimension data. The results indicate
Despite numerous advances in omics research, early detection of ovarian cancer still remains a challenge. The aim of this study was to determine whether attenuated total reflection Fourier-transform infrared (ATR-FTIR) or Raman spectroscopy could characterise alterations in the biomolecular signatures of human blood plasma/serum obtained from ovarian cancer patients compared to non-cancer controls. Blood samples isolated from ovarian cancer patients (n = 30) and healthy controls (n = 30) were analysed using ATR-FTIR spectroscopy. For comparison, a smaller cohort of samples (n = 8) were analysed using an InVia Renishaw Raman spectrometer. Resultant spectra were pre-processed prior to being inputted into principal component analysis (PCA) and linear discriminant analysis (LDA). Statistically significant differences (P , 0.001) were observed between spectra of ovarian cancer versus control subjects for both biospectroscopy methods. Using a support vector machine classifier for Raman spectra of ...
This paper addresses the important issue of Multi-Terminal DC (MTDC) grid control strategy based on the particle swarm optimization (PSO) technique. The MTDC grid is controlled by the concept of vector control, in which the AC currents and voltages are transformed into the rotating directquadrature (d-q) reference quantities which are subsequently used for decoupled control of the active and reactive powers as well as the DC and AC voltages. The paper employs an efficient PSO algorithm for optimal tuning of the controllers parameters in an MTDC grid. In addition, voltage droop control scheme is utilized to ensure the balance of active power within the MTDC grid. Simulation results that has obtained through detailed modeling of a four-terminal DC grid, demonstrate the efficiency of the proposed approach. Comparison to controllers optimized by genetic algorithm (GA) also confirmed the favorable performance of the proposed PSO-tuned controllers ...
In automatic reconstruction of 3D objects from single line drawings, existing systems are all single-track, containing one general solution for all drawings. This paper proposes a method in which an input drawing is first classified based on dominant features which exist in the drawing, including symmetry, orthogonality and parallelism. The reconstruction is then performed by experts to deal with each class specifically. Drawing classification is done using the technique of support vector machine classification. A specific set of features are selected to form an optimal regularity set for each class, and used in the formulation of the objective function for effective reconstruction. Experimental results show that the proposed system can improve the reconstruction accuracy and efficiency than that of a single-track general 3D reconstruction system. Copyright © 2012 Inderscience Enterprises Ltd ...
With the great advancement in robot technology, smart human-robot interaction is considered to be the most wanted success by the researchers these days. If a robot can identify emotions and intentions of a human interacting with it, that would make robots more useful. Electroencephalography (EEG) is considered one effective way of recording emotions and motivations of a human using brain. Various machine learning techniques are used successfully to classify EEG data accurately. K-Nearest Neighbor, Bayesian Network, Artificial Neural Networks and Support Vector Machine are among the suitable machine learning techniques to classify EEG data. The aim of this thesis is to evaluate different machine learning techniques to classify EEG data associated with specific affective/emotional states. Different methods based on different signal processing techniques are studied to find a suitable method to process the EEG data. Various number of EEG data features are used to identify those which give best ...
A multilayer feed-forward artificial neural network (MLP-ANN) with a single, hidden layer that contains a finite number of neurons can be regarded as a universal non-linear approximator. Today, the AN...
Ischemic stroke is a disease that occurs due to disruption of blood circulation to the brain due to blood clots in the brain. The blockage is called cerebral infarction. In diagnosing the presence of...
A new resonance-frequency based electronic impedance spectroscopy (REIS) system with multi-probes, including one central probe and six external probes that are designed to contact the breast skin in a circular form with a radius of 60 millimeters to the central (nipple) probe, has been assembled and installed in our breast imaging facility. We are conducting a prospective clinical study to test the performance of this REIS system in identifying younger women (, 50 years old) at higher risk for having or developing breast cancer. In this preliminary analysis, we selected a subset of 100 examinations. Among these, 50 examinations were recommended for a biopsy due to detection of a highly suspicious breast lesion and 50 were determined negative during mammography screening. REIS output signal sweeps that we used to compute an initial feature included both amplitude and phase information representing differences between corresponding (matched) EIS signal values acquired from the left and right ...
Classification of multispectral remotely sensed data with textural features is investigated with a special focus on uncertainty analysis in the produced land-cover maps. Much effort has already been directed into the research of satisfactory accuracy assessment techniques in image classification, but a common approach is not yet universally adopted. We look at the relationship between hard accuracy and the uncertainty on the produced answers, introducing two measures based on maximum probability and alpha quadratic entropy. Their impact differs depending on the type of classifier. In this paper, we deal with two different classification strategies, based on support vector machines (SVMs) and Kohonens self-organizing maps (SOMs), both suitably modified to give soft answers. Once the multiclass probability answer vector is available for each pixel in the image, we studied the behavior of the overall classification accuracy as a function of the uncertainty associated with each vector, given a ...
This presentation will examine the calculation of a likelihood ratio to assess the evidentiary value of fire debris analysis results. Models based on support vector machine (SVM), linear and quadratic discriminant analysis (LDA and QDA ) and k-nearest neighbors (kNN) methods were examined for binary classification of fire debris samples as positive or negative for ignitable liquid residue (ILR). Computational mixing of data from ignitable liquid and substrate pyrolysis databases was used to generate training and cross validation samples. A second validation was performed on fire debris data from large-scale research burns, for which the ground truth (positive or negative for ILR ) was assigned by an analyst with access to the gas chromatography-mass spectrometry data for the ignitable liquid used in the burn. The probabilities of class membership were calculated using an uninformative prior and a likelihood ratio was calculated from the resulting class membership probabilities . The SVM method ...
I have been coding in C/C++ since I was in high school and I keep writing C++ code every day. As an example, during the last term of my Bachelors degree I developed a machine learning toolkit paring with some of my classmates. I was in charge of writing the implementation of Support Vector Machine and three kernels, a Genetic Algorithm with some self-created mutation operators, the k-Means clustering algorithm and a cross-validation method. Then, I used this toolkit to create a broker bot that could predict movements in the stock market. Regarding Octave/Matlab, as I said before I have been using m-scripts for 6 years now and my Bachelors thesis was written solely in this tool. This principally consisted on writing preprocessing functions for treating medical signals and then implementing the Particle Swarm Optimization algorithm to optimise the training set used for a machine learning model. I have also used it to execute simple filters over images. Nowadays I keep using it as a prototyping ...
TY - JOUR. T1 - Performance analysis of SVM with quadratic kernel and logistic regression in classification of wild animals. AU - Suhas, M. V.. AU - Swathi, B. P.. PY - 2019/1/1. Y1 - 2019/1/1. N2 - In an attempt to develop a system to classify the wild animals using image processing and classification techniques, we study the usage of Haralick textural features are used in wild animal classification which is a computer aided pattern recognition system. The Haralick features from two wild animal classes that include leopard and wildcat are extracted to from the image database. Support Vector Machine (SVM) with quadratic kernel function model and Logistic Regression (LR) model are developed and tested using the created dataset. In each case, the performance of the classifier is measured.We also compare the performances of SVM and LR with and without pre-processing the dataset using Principal Component Analysis (PCA). This study reveals an increment in the accuracy post pre-processing of the ...
This course combines data exploration, visualization, data preparation, feature engineering, sampling and partitioning, model training, scoring, and assessment. It covers a variety of statistical, data mining, and machine learning techniques performed in a scalable and in-memory execution environment. The course provides theoretical foundation and hands-on experience with SAS Visual Data Mining and Machine Learning through SAS Studio, a user interface for SAS programming. The course includes predictive modeling techniques such as linear and logistic regression, decision tree and ensemble of trees (forest and gradient boosting), neural networks, support vector machine, and factorization machine.
This course combines data exploration, visualization, data preparation, feature engineering, sampling and partitioning, model training, scoring, and assessment. It covers a variety of statistical, data mining, and machine learning techniques performed in a scalable and in-memory execution environment. The course provides theoretical foundation and hands-on experience with SAS Visual Data Mining and Machine Learning through SAS Studio, a user interface for SAS programming. The course includes predictive modeling techniques such as linear and logistic regression, decision tree and ensemble of trees (forest and gradient boosting), neural networks, support vector machine, and factorization machine.
Feature Selection is central to modern data science, from exploratory data analysis to predictive model-building. The â stabilityâ of a feature selection algorithm refers to the robustness of its feature preferences, with respect to data sampling and to its stochastic nature. An algorithm is `unstable if a small change in data leads to large changes in the chosen feature subset. Whilst the idea is simple, quantifying this has proven more challenging---we note numerous proposals in the literature, each with different motivation and justification. We present a rigorous statistical treatment for this issue. In particular, with this work we consolidate the literature and provide (1) a deeper understanding of existing work based on a small set of properties, and (2) a clearly justified statistical approach with several novel benefits. This approach serves to identify a stability measure obeying all desirable properties, and (for the first time in the literature) allowing confidence intervals and ...
For cancer classification problems based on gene expression, the data usually has only a few dozen sizes but has thousands to tens of thousands of genes which could contain a large number of irrelevant genes. A robust feature selection algorithm is required to remove irrelevant genes and choose the informative ones. Support vector data description (SVDD) has been applied to gene selection for many years. However, SVDD cannot address the problems with multiple classes since it only considers the target class. In addition, it is time-consuming when applying SVDD to gene selection. This paper proposes a novel fast feature selection method based on multiple SVDD and applies it to multi-class microarray data. A recursive feature elimination (RFE) scheme is introduced to iteratively remove irrelevant features, so the proposed method is called multiple SVDD-RFE (MSVDD-RFE). To make full use of all classes for a given task, MSVDD-RFE independently selects a relevant gene subset for each class. The final ...
Guide to Machine Learning Methods. Here we have discuss an introduction to Machine Learning Methods, how do machines learn? along with classification.
MACHINE LEARNING Adversarial attacks on medical machine. THE PAPERS The Workshop on Machine Learning in Medical Applications was held on July 15th, 1999 at Chania, Island of Crete, in Greece, and aimed at presenting some of the advances that have been achieved in the field of application of ML methods in medicine., Machine Learning in Medical Imaging. Download Call for Papers (PDF). Machine learning plays an essential role in the field of medical imaging and image informatics. With advances in medical imaging, new machine learning methods and applications are demanded. Due to large variation and complexity, it is necessary to learn representations of. Machine learning will improve the radiology patient experience, at every step. Much of the initial focus for the application of machine learning in medical imaging has been on image analysis and developing tools to make radiologists more efficient and productive. The … Nov 16, 2018 · The measurements in this Machine Learning applications ...
The heat of sublimation (HOS) is an essential parameter used to resolve environmental problems in the transfer of organic contaminants to the atmosphere and to assess the risk of toxic chemicals. The experimental measurement of the heat of sublimation is time-consuming, expensive, and complicated. In this study, quantitative structural property relationships (QSPR) were used to develop a simple and predictive model for measuring the heat of sublimation of organic compounds. The population-based forward selection method was applied to select an informative subset of descriptors of learning algorithms, such as by using multiple linear regression (MLR) and the support vector machine (SVM) method. Each individual model and consensus model was evaluated by internal validation using the bootstrap method and y-randomization. The predictions of the performance of the external test set were improved by considering their applicability to the domain. Based on the results of the MLR model, we showed that ...
Real-time detection of seizure activity in epilepsy patients is critical in averting seizure activity and improving patients quality of life. Accurate evaluation, presurgical assessment, seizure prevention, and emergency alerts all depend on the rapid detection of seizure onset. A new method of feature selection and classification for rapid and precise seizure detection is discussed wherein informative components of electroencephalogram (EEG)-derived data are extracted and an automatic method is presented using infinite independent component analysis (I-ICA) to select independent features. The feature space is divided into subspaces via random selection and multichannel support vector machines (SVMs) are used to classify these subspaces. The result of each classifier is then combined by majority voting to establish the final output. In addition, a random subspace ensemble using a combination of SVM, multilayer perceptron (MLP) neural network and an extended k-nearest neighbors (k-NN), called extended
Real-time traffic incident detection is essential for traffic management and control. This paper proposes a traffic incident detection method based on a data mining technique named bagging support vector machine (bagging-SVMs). The performance of the proposed method was evaluated using the data collected on I-880. The performance measurements include detection rate (DR), false alarm rate (FAR), correct rate (CR), and F-measure. Several key issues were investigated to find the kernel function that better fits the new model and to calculate the important parameter bagging times. The results indicated that the standard SVM model with linear kernel function has advantages in traffic incident detection and is better than the other three models, comparatively. However, the bagging-SVMs model with a radial kernel function has a better comprehensive performance than the standard SVM model when bagging time is greater than 70. Models that use the polynomial kernel function also perform well even when the ...
MOTIVATION: Cys(2)His(2) zinc finger (ZF) proteins represent the largest class of eukaryotic transcription factors. Their modular structure and well-conserved protein-DNA interface allow the development of computational approaches for predicting their DNA-binding preferences even when no binding sites are known for a particular protein. The canonical model for ZF protein-DNA interaction consists of only four amino acid nucleotide contacts per zinc finger domain.. RESULTS: We present an approach for predicting ZF binding based on support vector machines (SVMs). While most previous computational approaches have been based solely on examples of known ZF protein-DNA interactions, ours additionally incorporates information about protein-DNA pairs known to bind weakly or not at all. Moreover, SVMs with a linear kernel can naturally incorporate constraints about the relative binding affinities of protein-DNA pairs; this type of information has not been used previously in predicting ZF protein-DNA ...
Our main aim is to propose a vision-based measurement as an alternative to physiological measurement for recognizing mental stress. The development of this emotion recognition system involved three stages: experimental setup for vision and physiological sensing, facial feature extraction in visual-thermal domain, mental stress stimulus experiment and data analysis and classification based on Support Vector Machine. In this research, 3 vision-based measurement and 2 physiological measurement were implemented in the system. Vision based measurement in facial vision domain consists of eyes blinking and in facial thermal domain consists 3 ROIs temperature value and blood vessel volume at Supraorbital area. Two physiological measurement were done to measure the ground truth value which is heart rate and salivary amylase level. We also propose a new calibration chessboard attach with fever plaster to locate calibration point in stereo view. A new method of integration of two different sensors for ...
Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), decision tree of C5.0, support vector machine (SVM), and decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that the RTs model outperforms other models ...