scholarly journals A New Method for Recognizing Cytokines Based on Feature Combination and a Support Vector Machine Classifier

Molecules ◽  
2018 ◽  
Vol 23 (8) ◽  
pp. 2008 ◽  
Author(s):  
Zhe Yang ◽  
Juan Wang ◽  
Zhida Zheng ◽  
Xin Bai
Keyword(s):  
Support Vector Machine Classifier ◽  
New Method ◽  
Evolutionary Information ◽  
Support Vector ◽  
Feature Combination ◽  
Medical Field ◽  
Low Sensitivity ◽  
Matthew’S Correlation Coefficient ◽  
Sensitivity Specificity ◽  
Better Than

Research on cytokine recognition is of great significance in the medical field due to the fact cytokines benefit the diagnosis and treatment of diseases, but the current methods for cytokine recognition have many shortcomings, such as low sensitivity and low F-score. Therefore, this paper proposes a new method on the basis of feature combination. The features are extracted from compositions of amino acids, physicochemical properties, secondary structures, and evolutionary information. The classifier used in this paper is SVM. Experiments show that our method is better than other methods in terms of accuracy, sensitivity, specificity, F-score and Matthew’s correlation coefficient.

scholarly journals SVM-based switching filter hardware design for mixed noise reduction in digital images using high-level synthesis tools

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Image Quality ◽  
Support Vector Machine Classifier ◽  
Digital Images ◽  
High Level Synthesis ◽  
Support Vector ◽  
Hardware Accelerator ◽  
Mixed Noise ◽  
High Level ◽  
Switching Filter ◽  
Better Than

Impulse and Gaussian are the two most common types of noise that affect digital images due to imperfections in the imaging process, compression, storage and communication. The conventional filtering approaches, however, reduce the image quality in terms of sharpness and resolution while suppressing the effects of noise. In this work, a machine learning-based filtering structure has been proposed preserves the image quality while effectively removing the noise. Specifically, a support vector machine classifier is employed to detect the type of noise affecting each pixel to select an appropriate filter. The choice of filters includes Median and Bilateral filters of different kernel sizes. The classifier is trained using example images with known noise parameters. The proposed filtering structure has been shown to perform better than the conventional approaches in terms of image quality metrics. Moreover, the design has been implemented as a hardware accelerator on an FPGA device using high-level synthesis tools.

scholarly journals Supervised Learning Based Classification of Cardiovascular Diseases

2021 ◽  
Vol 20 ◽  
pp. 24-34
Author(s):  
Arif Hussain ◽  
Hassaan Malik ◽  
Muhammad Umar Chaudhry
Keyword(s):  
Machine Learning ◽  
Model Building ◽  
Matthews Correlation Coefficient ◽  
Early Stage ◽  
Support Vector ◽  
Logistics Regression ◽  
Efficiency And Effectiveness ◽  
Sensitivity Specificity ◽  
Better Than

Detecting cardiovascular disease (CVD) in the early stage is a difficult and crucial process. The objective of this study is to test the capability of machine learning (ML) methods for accurately diagnosing the CVD outcomes. For this study, the efficiency and effectiveness of four well renowned ML classifiers, i.e., support vector machine (SVM), logistics regression (LR), naive Bayes (NB), and decision tree (J48), are measured in terms of precision, sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC), correctly and incorrectly classified instances, and model building time. These ML classifiers are applied on publically available CVD dataset. In accordance with the measured result, J48 performs better than its competitor classifiers, providing significant assistance to the cardiologists.

scholarly journals Modified Support Vector Machine for Detecting Stress Level Using EEG Signals

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Richa Gupta ◽  
M. Afshar Alam ◽  
Parul Agarwal
Keyword(s):  
Stress Level ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Mental Strain ◽  
Feature Extraction And Selection ◽  
Whale Optimization ◽  
Optimal Kernel ◽  
Sensitivity Specificity ◽  
Better Than

Stress is categorized as a condition of mental strain or pressure approaches because of upsetting or requesting conditions. There are various sources of stress initiation. Researchers consider human cerebrum as the primary wellspring of stress. To study how each individual encounters stress in different forms, researchers conduct surveys and monitor it. The paper presents the fusion of 5 algorithms to enhance the accuracy for detection of mental stress using EEG signals. The Whale Optimization Algorithm has been modified to select the optimal kernel in the SVM classifier for stress detection. An integrated set of algorithms (NLM, DCT, and MBPSO) has been used for preprocessing, feature extraction, and selection. The proposed algorithm has been tested on EEG signals collected from 14 subjects to identify the stress level. The proposed approach was validated using accuracy, sensitivity, specificity, and F1 score with values of 96.36%, 96.84%, 90.8%, and 97.96% and was found to be better than the existing ones. The algorithm may be useful to psychiatrists and health consultants for diagnosing the stress level.

scholarly journals PMSVM: An Optimized Support Vector Machine Classification Algorithm Based on PCA and Multilevel Grid Search Methods

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Yukai Yao ◽  
Hongmei Cui ◽  
Yang Liu ◽  
Longjie Li ◽  
Long Zhang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Roc Curve ◽  
Support Vector Machine Classifier ◽  
Data Preprocessing ◽  
Parameters Optimization ◽  
Experimental Results ◽  
Support Vector ◽  
Grid Search ◽  
Search Methods ◽  
Sensitivity Specificity

We propose an optimized Support Vector Machine classifier, named PMSVM, in which System Normalization, PCA, and Multilevel Grid Search methods are comprehensively considered for data preprocessing and parameters optimization, respectively. The main goals of this study are to improve the classification efficiency and accuracy of SVM. Sensitivity, Specificity, Precision, and ROC curve, and so forth, are adopted to appraise the performances of PMSVM. Experimental results show that PMSVM has relatively better accuracy and remarkable higher efficiency compared with traditional SVM algorithms.

Comparison of Processing Chains Based on Support Vector Machine Classifier for Hyperspectral Image Classification

2012 ◽  
Vol 433-440 ◽  
pp. 646-649
Author(s):  
Jee Cheng Wu ◽  
Bo Kai Lin ◽  
Gwo Chyang Tsuei
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Hyperspectral Image ◽  
Test Sample ◽  
Support Vector ◽  
Feature Transformation ◽  
Hyperspectral Image Classification ◽  
Transformation Methods ◽  
Noise Fraction ◽  
Better Than

In this research, we compared the performance of different processing chains resulting from combinations of spatial denoising filters, unsupervised feature transformation methods, and a support vector machine classifier. Two different training and test sample scenarios were investigated and conducted on an AVIRIS image over the Indian Pines region in Indiana, USA. The results showed that by using the process chain (adaptive enhanced Lee filter, maximum noise fraction, and support vector machine) the classification accuracies of the kappa coefficient were better than those of the best previously published techniques.

scholarly journals Epileptic EEG signal classifications based on DT-CWT and SVM classifier

2021 ◽  
Author(s):  
Deivasigamani S ◽  
◽  
Senthilpari C ◽  
Wong Hin Yong ◽  
Rajesh P.K. ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
The Other ◽  
Support Vector ◽  
Svm Classifier ◽  
Medical Procedure ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Sensitivity Specificity ◽  
The Mind

Contamination in human cerebrum causes the mind issue which is as Epilepsy. The contaminated territory in the cerebrum area creates the unpredictable example signals as focal signs and the other sound locales in the mind produce the standard example signals as non-focal sign. Henceforth, the discovery of focal signs from the non-focal signs is a significant for epileptic medical procedure in epilepsy patients. This paper proposes a straightforward and proficient technique for EEG (Electroencephalogram) signals orders utilizing SVM (Support Vector Machine) classifier. The exhibition of the proposed EEG signals characterization framework is assessed as far as Sensitivity, Specificity, and Accuracy.

scholarly journals Identification of DNA-Binding Proteins Using Support Vector Machine with Sequence Information

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xin Ma ◽  
Jiansheng Wu ◽  
Xiaoyun Xue
Keyword(s):  
Support Vector Machine ◽  
Dna Binding ◽  
Binding Proteins ◽  
Query Protein ◽  
Dna Binding Proteins ◽  
Evolutionary Information ◽  
Support Vector ◽  
Sequence Information ◽  
Novel Approach ◽  
Matthew’S Correlation Coefficient

DNA-binding proteins are fundamentally important in understanding cellular processes. Thus, the identification of DNA-binding proteins has the particularly important practical application in various fields, such as drug design. We have proposed a novel approach method for predicting DNA-binding proteins using only sequence information. The prediction model developed in this study is constructed by support vector machine-sequential minimal optimization (SVM-SMO) algorithm in conjunction with a hybrid feature. The hybrid feature is incorporating evolutionary information feature, physicochemical property feature, and two novel attributes. These two attributes use DNA-binding residues and nonbinding residues in a query protein to obtain DNA-binding propensity and nonbinding propensity. The results demonstrate that our SVM-SMO model achieves 0.67 Matthew's correlation coefficient (MCC) and 89.6% overall accuracy with 88.4% sensitivity and 90.8% specificity, respectively. Performance comparisons on various features indicate that two novel attributes contribute to the performance improvement. In addition, our SVM-SMO model achieves the best performance than state-of-the-art methods on independent test dataset.

OFFLINE YORÙBÁ HANDWRITTEN WORD RECOGNITION USING GEOMETRIC FEATURE EXTRACTION AND SUPPORT VECTOR MACHINE CLASSIFIER

2020 ◽  
Vol 5 (2) ◽  
pp. 504
Author(s):  
Matthias Omotayo Oladele ◽  
Temilola Morufat Adepoju ◽  
Olaide ` Abiodun Olatoke ◽  
Oluwaseun Adewale Ojo
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Word Recognition ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Support Vector ◽  
Geometric Features ◽  
Total Length ◽  
Yoruba Language

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

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