Big Cats Classification Based on Body Covering

2021 ◽  
Vol 5 (5) ◽  
pp. 984-991
Author(s):  
Fernanda Januar Pratama ◽  
Wikky Fawwaz Al Maki ◽  
Febryanti Sthevanie
Keyword(s):  
Median Filter ◽  
Computation Time ◽  
Optimal Level ◽  
Input Image ◽  
The Body ◽  
Classification Model ◽  
Support Vector ◽  
Feature Extraction Method ◽  
Animal Populations ◽  
Segmentation Image

The reduced habitat owned by an animal has a very bad impact on the survival of the animal, resulting in a continuous decrease in the number of animal populations especially in animals belonging to the big cat family such as tigers, cheetahs, jaguars, and others. To overcome the decline in the animal population, a classification model was built to classify images that focuses on the pattern of body covering possessed by animals. However, in designing an accurate classification model with an optimal level of accuracy, it is necessary to consider many aspects such as the dataset used, the number of parameters, and computation time. In this study, we propose an animal image classification model that focuses on animal body covering by combining the Pyramid Histogram of Oriented Gradient (PHOG) as the feature extraction method and the Support Vector Machine (SVM) as the classifier. Initially, the input image is processed to take the body covering pattern of the animal and converted it into a grayscale image. Then, the image is segmented by employing the median filter and the Otsu method. Therefore, the noise contained in the image can be removed and the image can be segmented. The results of the segmentation image are then extracted by using the PHOG and then proceed with the classification process by implementing the SVM. The experimental results showed that the classification model has an accuracy of 91.07%.  

scholarly journals Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques

Medicina ◽  
2021 ◽  
Vol 57 (6) ◽  
pp. 527
Author(s):  
Vijay Vyas Vadhiraj ◽  
Andrew Simpkin ◽  
James O’Connell ◽  
Naykky Singh Singh Ospina ◽  
Spyridoula Maraka ◽  
...  
Keyword(s):  
Decision Making ◽  
Sensitivity And Specificity ◽  
Thyroid Nodules ◽  
Median Filter ◽  
Ultrasound Image ◽  
Multiple Instance Learning ◽  
Image Features ◽  
Classification Model ◽  
Support Vector ◽  
Accuracy Score

Background and Objectives: Thyroid nodules are lumps of solid or liquid-filled tumors that form inside the thyroid gland, which can be malignant or benign. Our aim was to test whether the described features of the Thyroid Imaging Reporting and Data System (TI-RADS) could improve radiologists’ decision making when integrated into a computer system. In this study, we developed a computer-aided diagnosis system integrated into multiple-instance learning (MIL) that would focus on benign–malignant classification. Data were available from the Universidad Nacional de Colombia. Materials and Methods: There were 99 cases (33 Benign and 66 malignant). In this study, the median filter and image binarization were used for image pre-processing and segmentation. The grey level co-occurrence matrix (GLCM) was used to extract seven ultrasound image features. These data were divided into 87% training and 13% validation sets. We compared the support vector machine (SVM) and artificial neural network (ANN) classification algorithms based on their accuracy score, sensitivity, and specificity. The outcome measure was whether the thyroid nodule was benign or malignant. We also developed a graphic user interface (GUI) to display the image features that would help radiologists with decision making. Results: ANN and SVM achieved an accuracy of 75% and 96% respectively. SVM outperformed all the other models on all performance metrics, achieving higher accuracy, sensitivity, and specificity score. Conclusions: Our study suggests promising results from MIL in thyroid cancer detection. Further testing with external data is required before our classification model can be employed in practice.

scholarly journals Semantic feature extraction method for hyperspectral crop classification

2021 ◽  
Vol 23 (1) ◽  
pp. 387
Author(s):  
M. C. Girish Baabu ◽  
Padma M. C.
Keyword(s):  
Feature Extraction ◽  
Hyperspectral Image ◽  
Feature Representation ◽  
Classification Model ◽  
Support Vector ◽  
Spectral Correlation ◽  
Feature Extraction Method ◽  
Mixed Pixel ◽  
Computational Overhead ◽  
Crop Classification

<span>Hyperspectral imaging (HSI) is composed of several hundred of narrow bands (NB) with high spectral correlation and is widely used in crop classification; thus induces time and space complexity, resulting in high computational overhead and Hughes phenomenon in processing these images. Dimensional reduction technique such as band selection and feature extraction plays an important part in enhancing performance of hyperspectral image classification. However, existing method are not efficient when put forth in noisy and mixed pixel environment with dynamic illumination and climatic condition. Here the proposed Sematic Feature Representation based HSI (SFR-HSI) crop classification method first employ Image Fusion (IF) method for finding meaningful features from raw HSI spectrally. Second, to extract inherent features that keeps spatially meaningful representation of different crops by eliminating shading elements. Then, the meaningful feature set are used for training using Support vector machine (SVM). Experiment outcome shows proposed HSI crop classification model achieves much better accuracies and Kappa coefficient performance. </span>

scholarly journals Extraction of the Major Features of Brain Signals using Intelligent Networks

2021 ◽  
Vol 15 (1) ◽  
pp. 71-88
Author(s):  
Shirin Salarian ◽  
Amir Shahab Shahabi
Keyword(s):  
Median Filter ◽  
Evaluation Criteria ◽  
The Body ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Diagnostic Systems ◽  
Eeg Signals ◽  
Data Set ◽  
P300 Component ◽  
Medical Software

The brain-computer interface is considered one of the main tools for implementing and designing smart medical software. The analysis of brain signal data, called EEG, is one of the main tasks of smart medical diagnostic systems. While EEG signals have many components, one of the most important brain activities pursued is the P300 component. Detection of this component can help detect abnormalities and visualize the movement of organs of the body. In this research, a new method for processing EEG signals is proposed with the aim of detecting the P300 component. Major features were extracted from the BCI Competition IV EEG data set in a number of steps, i.e. normalization with the purpose of noise reduction using a median filter, feature extraction using a recurrent neural network, and classification using Twin Support Vector Machine. Then, a series of evaluation criteria were used to validate the proposed approach and compare it with similar methods. The results showed that the proposed approach has high accuracy.

Quantitative inspection for identifying broken coal mine wire rope based on wavelet packet sparse representation

2021 ◽  
Vol 63 (2) ◽  
pp. 102-110
Author(s):  
Junying Zhou ◽  
Jie Tian ◽  
Peng Cheng ◽  
Xu Li ◽  
Decheng Wang
Keyword(s):  
Sparse Representation ◽  
Matching Pursuit ◽  
Wavelet Packet ◽  
Median Filter ◽  
Recognition Rate ◽  
Wire Rope ◽  
Classification Model ◽  
Filter Method ◽  
Support Vector ◽  
Reconstructed Signal

The magnetic flux leakage (MFL) signal of steel wire rope is easily affected by background noise, rope strands and so on. A preprocessing method for the damage signal based on wavelet packet sparse representation is proposed. This method is suitable for the damage signal of the wire rope. The original signal is decomposed into three layers of wavelet packets and the wavelet packet coefficients are sparsely represented by the matching pursuit (MP) and orthogonal matching pursuit (OMP) algorithms. The signal-to-noise ratio (SNR) of the reconstructed signal is much higher than that obtained through the wavelet threshold shrinkage method, the median filter method and the singular value difference spectrum method. The proposed method can significantly improve the noise reduction effect of the damage signal. A principal component analysis (PCA)-based particle swarm optimisation support vector machine (PSO-SVM) model for quantitative recognition is proposed. Seven global eigenvalues and wavelet packet energy entropy details of damage signals are extracted as effective eigenvalues. The eight eigenvalues are used as the input for the SVM that is designed and trained. A PSO-SVM classification model based on PCA is proposed. The results show that the recognition rate of the SVM is 94.73%. The quantitative recognition accuracy is improved.

Multi-layer Hybrid Classification Model of COVID-19 Chest X-ray Images v1

2021 ◽  
Author(s):  
Thanakorn Poomkur ◽  
Thakerng Wongsirichot
Keyword(s):  
Machine Learning ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Computational Time ◽  
Support Vector ◽  
X Ray ◽  
Learning Techniques ◽  
Hybrid Classification ◽  
Chest X Ray ◽  
Segmentation Image

The coronavirus disease of 2019 (COVID-19) has been declared a pandemic and has raised worldwide concern. Lung inflammation and respiratory failure are commonly observed in moderate-to-severe cases. Chest X-ray imaging is compulsory for diagnosis, and interpretation is commonly performed by skilled medical specialists. Many studies have been conducted using machine learning approaches such as Deep Learning (DL) with acceptable accuracy. However, other dimensions such as computational time were less discussed. Thus, our work is motivated to design anew computer-aided diagnosis (CADx) tool for identifying chest X-ray images of COVID-19 infection using machine learning techniques including Decision Tree (DT), Support Vector Machine (SVM), and Neural Networks (NNs). Our work is designed with the concept of multi-layer classification architecture and performs with minimal computational time and acceptable classification results. First, image segmentation, image enhancement and feature extraction techniques are performed. Second, machine learning techniques are selected based on classification performance. Finally, selected machine learning techniques are assembled into a multi-layer hybrid classification model for COVID-19 (MLHC-COVID-19). Specifically, the MLHC-COVID-19 consists of two layers, Layer I: Healthy and Unhealthy; Layer II: COVID-19 and non-COVID-19.

A Novel Efficient Approach for the Screening of New Abnormal Blood Vessels in Color Fundus Images

2014 ◽  
Vol 573 ◽  
pp. 808-813 ◽  
Author(s):  
B. Ramasubramanian ◽  
S. Selvaperumal
Keyword(s):  
Blood Vessels ◽  
Median Filter ◽  
Computation Time ◽  
Support Vector ◽  
Svm Classifier ◽  
Fundus Images ◽  
Blood Vessel Detection ◽  
Average Computation Time ◽  
Color Fundus Image ◽  
Color Fundus Images

Reliable detection of abnormal vessels in color fundus image is still a great issue in medical image processing. An Efficient and robust approach for automatic detection of abnormal blood vessels in digital color fundus images is presented in this paper. First, the fundus images are preprocessed by applying a 3x3 median filter. Then, the images are segmented using a novel morphological operation. To classify these segmented image into normal and abnormal, seven features based on shape, contrast, position and density are extracted. Finally, these features are classified using a non-linear Support Vector Machine (SVM) Classifier. The average computation time for blood vessel detection was less than 2.4sec with a success rate of 99%. The performance of our proposed method is measured on publically available DRIVE and STARE database.

A Hybrid Approach for Automated Plant Leaf Recognition Using Hybrid Texture Features and Machine Learning-Based Classifiers

2021 ◽  
Vol 13 (2) ◽  
pp. 27-39
Author(s):  
Upendra Kumar ◽  
Shashank Yadav ◽  
Esha Tripathi
Keyword(s):  
Machine Learning ◽  
Hybrid Approach ◽  
Texture Features ◽  
Classification Model ◽  
Automated System ◽  
Support Vector ◽  
Feature Extraction Method ◽  
Plant Recognition ◽  
Low Efficiency ◽  
Ann Classifier

Automated plant recognition performs a significant role in various applications used by environmental experts, chemists, and botany experts. Humans can recognize plants manually, but it is a prolonged and low-efficiency process. This paper introduces an automated system for recognizing plant species based on leaf images. A hybrid texture and colour-based feature extraction method was applied on digital leaf images to produce robust feature, and a further classification model was developed. A combination of machine learning methods, such as SVM (support vector machine), KNN (k-nearest neighbours), and ANN (artificial neural network), was applied on dataset for plant classification. This dataset contains 32 types of leaves. The outcomes of this work proved that success rate of plant recognition can be enhanced up to 94% with ANN classifier when both shape and colour features are utilized. Automatic recognition of plants is useful for medicine, foodstuff, and reduction of chemical wastage during crop spraying. It is also useful for identification and preservation of species.

scholarly journals Penentuan Tes Kepribadian Calon Mahasiswa Berdasarkan Sidik Jari Menggunakan Minutie dan Support Vector Machine

2020 ◽  
Vol 4 (1) ◽  
pp. 28-32
Author(s):  
Lukman Hakim
Keyword(s):  
Support Vector Machine ◽  
Input Image ◽  
The Body ◽  
Training Data ◽  
Support Vector ◽  
Human Being ◽  
Prospective Students ◽  
Study Program ◽  
Svm Algorithm ◽  
Fingerprint Pattern

Every human being is given its own uniqueness by an almighty god, one of which is a part of the body organs such as the fingerprint pattern of the hand, the fingerprint pattern of each human being determines personality, this can be known from many previous studies, which use fingerprints or someone's detection by the police to capture the perpetrators with the biometry approach in the form of footprint fingerprint records attached to other objects. Determination of a person's personality can be known through fingerprints, and also can adjust prospective students in choosing the study program correctly. Fingerprint student personality identification application provides convenience in determining the choice of prospective students of the study program. The minutie method and the Support Vector Machine algorithm are used in clustering personalities according to training data in the application. The minutie test on the fingerprint pattern shows 100% compatibility, with a precision input image source. SVM algorithm in testing reached 80,9% in grouping personality types accordingly.

Comparison of SVM, RF and SGD Methods for Determination of Programmer's Performance Classification Model in Social Media Activities

2020 ◽  
Vol 4 (2) ◽  
pp. 329-335
Author(s):  
Rusydi Umar ◽  
Imam Riadi ◽  
Purwono
Keyword(s):  
Social Media ◽  
Gradient Descent ◽  
Classification Model ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Svm Algorithm ◽  
Vector Machines ◽  
Performance Patterns ◽  
A Company

The failure of most startups in Indonesia is caused by team performance that is not solid and competent. Programmers are an integral profession in a startup team. The development of social media can be used as a strategic tool for recruiting the best programmer candidates in a company. This strategic tool is in the form of an automatic classification system of social media posting from prospective programmers. The classification results are expected to be able to predict the performance patterns of each candidate with a predicate of good or bad performance. The classification method with the best accuracy needs to be chosen in order to get an effective strategic tool so that a comparison of several methods is needed. This study compares classification methods including the Support Vector Machines (SVM) algorithm, Random Forest (RF) and Stochastic Gradient Descent (SGD). The classification results show the percentage of accuracy with k = 10 cross validation for the SVM algorithm reaches 81.3%, RF at 74.4%, and SGD at 80.1% so that the SVM method is chosen as a model of programmer performance classification on social media activities.

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