scholarly journals Machine Learning Approaches: From Theory to Application in Schizophrenia

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Elisa Veronese ◽  
Umberto Castellani ◽  
Denis Peruzzo ◽  
Marcella Bellani ◽  
Paolo Brambilla
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Support Vector Machine ◽  
Clinical Practice ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Healthy Controls ◽  
Learning Approaches ◽  
Neuroimaging Data ◽  
Comparison Of The Results

In recent years, machine learning approaches have been successfully applied for analysis of neuroimaging data, to help in the context of disease diagnosis. We provide, in this paper, an overview of recent support vector machine-based methods developed and applied in psychiatric neuroimaging for the investigation of schizophrenia. In particular, we focus on the algorithms implemented by our group, which have been applied to classify subjects affected by schizophrenia and healthy controls, comparing them in terms of accuracy results with other recently published studies. First we give a description of the basic terminology used in pattern recognition and machine learning. Then we separately summarize and explain each study, highlighting the main features that characterize each method. Finally, as an outcome of the comparison of the results obtained applying the described different techniques, conclusions are drawn in order to understand how much automatic classification approaches can be considered a useful tool in understanding the biological underpinnings of schizophrenia. We then conclude by discussing the main implications achievable by the application of these methods into clinical practice.

Theoretical Approaches of Machine Learning to Schizophrenia

2018 ◽  
Vol 6 (2) ◽  
pp. 155-168 ◽  
Author(s):  
Naresh Babu Bynagari ◽  
Takudzwa Fadziso
Keyword(s):  
Machine Learning ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Healthy Controls ◽  
Theoretical Approaches ◽  
Automatic Categorization ◽  
Learning Techniques ◽  
Key Characteristics ◽  
Neuroimaging Data

Machine learning techniques have been successfully used to analyze neuroimaging data in the context of disease diagnosis in recent years. In this study, we present an overview of contemporary support vector machine-based methods developed and used in psychiatric neuroimaging for schizophrenia research. We focus in particular on our group's algorithms, which have been used to categorize schizophrenia patients and healthy controls, and compare their accuracy findings to those of other recently published studies. First, we'll go over some basic pattern recognition and machine learning terms. Then, for each study, we describe and discuss it independently, emphasizing the key characteristics that distinguish each approach. Finally, conclusions are reached as a result of comparing the data obtained using the various methodologies presented to determine how beneficial automatic categorization systems are in understanding the molecular underpinnings of schizophrenia. The primary implications of applying these approaches in clinical practice are then discussed.

scholarly journals Biomarkers of Eating Disorders Using Support Vector Machine Analysis of Structural Neuroimaging Data: Preliminary Results

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Antonio Cerasa ◽  
Isabella Castiglioni ◽  
Christian Salvatore ◽  
Angela Funaro ◽  
Iolanda Martino ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Support Vector Machine ◽  
Eating Disorders ◽  
Occipital Cortex ◽  
Brain Regions ◽  
Magnetic Resonance Images ◽  
Support Vector ◽  
Individual Level ◽  
Neuroimaging Data

Presently, there are no valid biomarkers to identify individuals with eating disorders (ED). The aim of this work was to assess the feasibility of a machine learning method for extracting reliable neuroimaging features allowing individual categorization of patients with ED. Support Vector Machine (SVM) technique, combined with a pattern recognition method, was employed utilizing structural magnetic resonance images. Seventeen females with ED (six with diagnosis of anorexia nervosa and 11 with bulimia nervosa) were compared against 17 body mass index-matched healthy controls (HC). Machine learning allowed individual diagnosis of ED versus HC with an Accuracy ≥ 0.80. Voxel-based pattern recognition analysis demonstrated that voxels influencing the classification Accuracy involved the occipital cortex, the posterior cerebellar lobule, precuneus, sensorimotor/premotor cortices, and the medial prefrontal cortex, all critical regions known to be strongly involved in the pathophysiological mechanisms of ED. Although these findings should be considered preliminary given the small size investigated, SVM analysis highlights the role of well-known brain regions as possible biomarkers to distinguish ED from HC at an individual level, thus encouraging the translational implementation of this new multivariate approach in the clinical practice.

scholarly journals Predicting Student’s Performance Using Machine Learning Algorithm

2021 ◽  
pp. 53-58
Author(s):  
Sheela Rani P ◽  
Dhivya S ◽  
Dharshini Priya M ◽  
Dharmila Chowdary A
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbors

Machine learning is a new analysis discipline that uses knowledge to boost learning, optimizing the training method and developing the atmosphere within which learning happens. There square measure 2 sorts of machine learning approaches like supervised and unsupervised approach that square measure accustomed extract the knowledge that helps the decision-makers in future to require correct intervention. This paper introduces an issue that influences students' tutorial performance prediction model that uses a supervised variety of machine learning algorithms like support vector machine , KNN(k-nearest neighbors), Naïve Bayes and supplying regression and logistic regression. The results supported by various algorithms are compared and it is shown that the support vector machine and Naïve Bayes performs well by achieving improved accuracy as compared to other algorithms. The final prediction model during this paper may have fairly high prediction accuracy .The objective is not just to predict future performance of students but also provide the best technique for finding the most impactful features that influence student’s while studying.

scholarly journals PSO-Based Support Vector Machine with Cuckoo Search Technique for Clinical Disease Diagnoses

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaoyong Liu ◽  
Hui Fu
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Cuckoo Search ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Svm Model ◽  
Two Stages ◽  
Best Parameters

Disease diagnosis is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM), particle swarm optimization (PSO), and cuckoo search (CS). The new method consists of two stages: firstly, a CS based approach for parameter optimization of SVM is developed to find the better initial parameters of kernel function, and then PSO is applied to continue SVM training and find the best parameters of SVM. Experimental results indicate that the proposed CS-PSO-SVM model achieves better classification accuracy and F-measure than PSO-SVM and GA-SVM. Therefore, we can conclude that our proposed method is very efficient compared to the previously reported algorithms.

Pattern Recognition of Chatter Gestation Based on Hybrid PCA-SVM

2011 ◽  
Vol 120 ◽  
pp. 190-194 ◽  
Author(s):  
Qiang Shao ◽  
Chang Jian Feng
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Support Vector Machine ◽  
Vibration Signal ◽  
Cutting Process ◽  
Support Vector ◽  
Recognition Method ◽  
Chatter Prediction ◽  
Dynamic Patterns

To distinguish chatter gestation, chatter recognition method based on hybrid PCA(Principal Compenent Analysis) and SVM(Support Vector Machine) is proposed for dynamic patterns of chatter gestation in cutting process. At first, FFT features are extracted from the vibration signal of cutting process, then FFT vectors are presorted and introduced to PCA-SVM for machine learning and classification. Finally the results of chatter gestation recognition and chatter prediction experiments are presented and show that the method proposed is effective.

scholarly journals Artificial neural networks as a tool for pattern recognition and electrofacies analysis in Polish palaeozoic shale gas formations

2019 ◽  
Vol 67 (6) ◽  
pp. 1991-2003 ◽  
Author(s):  
Edyta Puskarczyk
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Support Vector Machine ◽  
P Wave ◽  
Well Logs ◽  
Support Vector ◽  
Western Slope ◽  
Data Set ◽  
Artificial Neural ◽  
Gas Bearing

Abstract Unconventional oil and gas reservoirs from the lower Palaeozoic basin at the western slope of the East European Craton were taken into account in this study. The aim was to supply and improve standard well logs interpretation based on machine learning methods, especially ANNs. ANNs were used on standard well logging data, e.g. P-wave velocity, density, resistivity, neutron porosity, radioactivity and photoelectric factor. During the calculations, information about lithology or stratigraphy was not taken into account. We apply different methods of classification: cluster analysis, support vector machine and artificial neural network—Kohonen algorithm. We compare the results and analyse obtained electrofacies. Machine learning method–support vector machine SVM was used for classification. For the same data set, SVM algorithm application results were compared to the results of the Kohonen algorithm. The results were very similar. We obtained very good agreement of results. Kohonen algorithm (ANN) was used for pattern recognition and identification of electrofacies. Kohonen algorithm was also used for geological interpretation of well logs data. As a result of Kohonen algorithm application, groups corresponding to the gas-bearing intervals were found. Analysis showed diversification between gas-bearing formations and surrounding beds. It is also shown that internal diversification in gas-saturated beds is present. It is concluded that ANN appeared to be a useful and quick tool for preliminary classification of members and gas-saturated identification.

scholarly journals Kynurenine and Hemoglobin as Sex-Specific Variables in COVID-19 Patients: A Machine Learning and Genetic Algorithms Approach

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2197
Author(s):  
Jose M. Celaya-Padilla ◽  
Karen E. Villagrana-Bañuelos ◽  
Juan José Oropeza-Valdez ◽  
Joel Monárrez-Espino ◽  
Julio E. Castañeda-Delgado ◽  
...  
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Immune Response ◽  
Support Vector Machine ◽  
Clinical Manifestations ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Men And Women ◽  
Metabolic Alterations

Differences in clinical manifestations, immune response, metabolic alterations, and outcomes (including disease severity and mortality) between men and women with COVID-19 have been reported since the pandemic outbreak, making it necessary to implement sex-specific biomarkers for disease diagnosis and treatment. This study aimed to identify sex-associated differences in COVID-19 patients by means of a genetic algorithm (GALGO) and machine learning, employing support vector machine (SVM) and logistic regression (LR) for the data analysis. Both algorithms identified kynurenine and hemoglobin as the most important variables to distinguish between men and women with COVID-19. LR and SVM identified C10:1, cough, and lysoPC a 14:0 to discriminate between men with COVID-19 from men without, with LR being the best model. In the case of women with COVID-19 vs. women without, SVM had a higher performance, and both models identified a higher number of variables, including 10:2, lysoPC a C26:0, lysoPC a C28:0, alpha-ketoglutaric acid, lactic acid, cough, fever, anosmia, and dysgeusia. Our results demonstrate that differences in sexes have implications in the diagnosis and outcome of the disease. Further, genetic and machine learning algorithms are useful tools to predict sex-associated differences in COVID-19.

scholarly journals Support vector machine in the anticipation of currency markets

2018 ◽  
Vol 7 (2.7) ◽  
pp. 66
Author(s):  
V Lalithendra Nadh ◽  
G Syam Prasad
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Stock Market ◽  
Support Vector ◽  
Learning Approaches ◽  
Currency Markets ◽  
Machine Learning Methods ◽  
Conventional Models

Various researchers have done an expansive research within the domain of stock market anticipation. The majority of the anticipated models is confronting some pivotal troubles because of the likelihood of the market. Numerous normal models are accurate when the data is linear. In any case, the expectation in view of nonlinear data could be a testing movement. From past twenty years with the progression of innovation and the artificial intelligence, including machine learning approaches like a Support Vector Machine it becomes conceivable to estimate in light of nonlinear data. Modern researchers are combining GA (Genetic Algorithm) with SVM to achieve highly precise outcomes. This analysis compares the SVM and ESVM with other conventional models and other machine learning methods in the domain of currency market prediction. Finally, the consequence of SVM when compared with different models it is demonstrated that SVM is the premier for foreseeing.

scholarly journals Simple quantum circuit for pattern recognition based on nearest mean classifier

2016 ◽  
Vol 2 (2) ◽  
Author(s):  
Gharib M Subhi ◽  
Azeddine Messikh
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Pattern Recognition ◽  
Support Vector Machine ◽  
Quantum Circuit ◽  
Support Vector ◽  
Bloch Sphere ◽  
Optimal Hyperplane ◽  
Simple Quantum ◽  
Quantum Matrix

Machine learning plays a key role in many applications such as data mining and image recognition.Classification is one subcategory under machine learning. In this paper we propose a simple quantum circuitbased on the nearest mean classifier to classified handwriting characters. Our circuit is a simplified circuit fromthe quantum support vector machine [Phys. Rev. Lett. 114, 140504 (2015)] which uses quantum matrix inversealgorithm to find optimal hyperplane that separated two different classes. In our case the hyperplane is foundusing projections and rotations on the Bloch sphere.

scholarly journals Automatic Recognition of Asphalt Pavement Cracks Based on Image Processing and Machine Learning Approaches: A Comparative Study on Classifier Performance

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Nhat-Duc Hoang ◽  
Quoc-Lam Nguyen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Comparative Study ◽  
Asphalt Pavement ◽  
Classification Performance ◽  
Support Vector ◽  
Learning Approaches ◽  
Pavement Condition

Periodic surveys of asphalt pavement condition are very crucial in road maintenance. This work carries out a comparative study on the performance of machine learning approaches used for automatic pavement crack recognition. Six machine learning approaches, Naïve Bayesian Classifier (NBC), Classification Tree (CT), Backpropagation Artificial Neural Network (BPANN), Radial Basis Function Neural Network (RBFNN), Support Vector Machine (SVM), and Least Squares Support Vector Machine (LSSVM), have been employed. Additionally, Median Filter (MF), Steerable Filter (SF), and Projective Integral (PI) have been used to extract useful features from pavement images. In the feature extraction phase, performance comparison shows that the input pattern including the diagonal PIs enhances the classification performance significantly by creating more informative features. A simple moving average method is also employed to reduce the size of the feature set with positive effects on the model classification performance. Experimental results point out that LSSVM has achieved the highest classification accuracy rate. Therefore, this machine learning algorithm used with the feature extraction process proposed in this study can be a very promising tool to assist transportation agencies in the task of pavement condition survey.

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