A comparative study on image-based snake identification using machine learning

AbstractAutomated snake image identification is important from different points of view, most importantly, snake bite management. Auto-identification of snake images might help the avoidance of venomous snakes and also providing better treatment for patients. In this study, for the first time, it’s been attempted to compare the accuracy of a series of state-of-the-art machine learning methods, ranging from the holistic to neural network algorithms. The study is performed on six snake species in Lar National Park, Tehran Province, Iran. In this research, the holistic methods [k-nearest neighbors (kNN), support vector machine (SVM) and logistic regression (LR)] are used in combination with a dimension reduction approach [principle component analysis (PCA) and linear discriminant analysis (LDA)] as the feature extractor. In holistic methods (kNN, SVM, LR), the classifier in combination with PCA does not yield an accuracy of more than 50%, But the use of LDA to extract the important features significantly improves the performance of the classifier. A combination of LDA and SVM (kernel = 'rbf') is achieved to a test accuracy of 84%. Compared to holistic methods, convolutional neural networks show similar to better performance, and accuracy reaches 93.16% using MobileNetV2. Visualizing intermediate activation layers in VGG model reveals that just in deep activation layers, the color pattern and the shape of the snake contribute to the discrimination of snake species. This study presents MobileNetV2 as a powerful deep convolutional neural network algorithm for snake image classification that could be used even on mobile devices. This finding pave the road for generating mobile applications for snake image identification.

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Analisis Perbandingan Algoritma SVM, KNN, dan CNN untuk Klasifikasi Citra Cuaca

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2021824553 ◽

2021 ◽

Vol 8 (2) ◽

pp. 311

Author(s):

Mohammad Farid Naufal

Keyword(s):

Neural Network ◽

Machine Learning ◽

Computer Vision ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Cross Validation ◽

Nearest Neighbors ◽

Support Vector ◽

Classification Algorithms ◽

K Nearest Neighbors

Cuaca merupakan faktor penting yang dipertimbangkan untuk berbagai pengambilan keputusan. Klasifikasi cuaca manual oleh manusia membutuhkan waktu yang lama dan inkonsistensi. Computer vision adalah cabang ilmu yang digunakan komputer untuk mengenali atau melakukan klasifikasi citra. Hal ini dapat membantu pengembangan self autonomous machine agar tidak bergantung pada koneksi internet dan dapat melakukan kalkulasi sendiri secara real time. Terdapat beberapa algoritma klasifikasi citra populer yaitu K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan Convolutional Neural Network (CNN). KNN dan SVM merupakan algoritma klasifikasi dari Machine Learning sedangkan CNN merupakan algoritma klasifikasi dari Deep Neural Network. Penelitian ini bertujuan untuk membandingkan performa dari tiga algoritma tersebut sehingga diketahui berapa gap performa diantara ketiganya. Arsitektur uji coba yang dilakukan adalah menggunakan 5 cross validation. Beberapa parameter digunakan untuk mengkonfigurasikan algoritma KNN, SVM, dan CNN. Dari hasil uji coba yang dilakukan CNN memiliki performa terbaik dengan akurasi 0.942, precision 0.943, recall 0.942, dan F1 Score 0.942. AbstractWeather is an important factor that is considered for various decision making. Manual weather classification by humans is time consuming and inconsistent. Computer vision is a branch of science that computers use to recognize or classify images. This can help develop self-autonomous machines so that they are not dependent on an internet connection and can perform their own calculations in real time. There are several popular image classification algorithms, namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). KNN and SVM are Machine Learning classification algorithms, while CNN is a Deep Neural Networks classification algorithm. This study aims to compare the performance of that three algorithms so that the performance gap between the three is known. The test architecture is using 5 cross validation. Several parameters are used to configure the KNN, SVM, and CNN algorithms. From the test results conducted by CNN, it has the best performance with 0.942 accuracy, 0.943 precision, 0.942 recall, and F1 Score 0.942.

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Music Emotions Recognition by Machine Learning With Cognitive Classification Methodologies

International Journal of Cognitive Informatics and Natural Intelligence ◽

10.4018/ijcini.2017100105 ◽

2017 ◽

Vol 11 (4) ◽

pp. 80-92

Author(s):

Junjie Bai ◽

Kan Luo ◽

Jun Peng ◽

Jinliang Shi ◽

Ying Wu ◽

...

Keyword(s):

Machine Learning ◽

Affective Computing ◽

Classification Problem ◽

Machine Learning Algorithms ◽

Support Vector ◽

Cognitive Computing ◽

Bayes Classifier ◽

K Nearest Neighbors ◽

Linear Discriminant ◽

Neuro Fuzzy

Music emotions recognition (MER) is a challenging field of studies addressed in multiple disciplines such as musicology, cognitive science, physiology, psychology, arts and affective computing. In this article, music emotions are classified into four types known as those of pleasing, angry, sad and relaxing. MER is formulated as a classification problem in cognitive computing where 548 dimensions of music features are extracted and modeled. A set of classifications and machine learning algorithms are explored and comparatively studied for MER, which includes Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Neuro-Fuzzy Networks Classification (NFNC), Fuzzy KNN (FKNN), Bayes classifier and Linear Discriminant Analysis (LDA). Experimental results show that the SVM, FKNN and LDA algorithms are the most effective methodologies that obtain more than 80% accuracy for MER.

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Using Machine Learning to Grade the Mango’s Quality Based on External Features Captured by Vision System

Applied Sciences ◽

10.3390/app10175775 ◽

2020 ◽

Vol 10 (17) ◽

pp. 5775

Author(s):

Nguyen Truong Minh Long ◽

Nguyen Truong Thinh

Keyword(s):

Machine Learning ◽

Vision System ◽

Human Perception ◽

Poor Quality ◽

Machine Learning Algorithms ◽

Internal Quality ◽

Support Vector ◽

K Nearest Neighbors ◽

Linear Discriminant ◽

Length Width

Nowadays, mangoes and other fruits are classified according to human perception of low productivity, which is a poor quality of classification. Therefore, in this study, we suggest a novel evaluation of internal quality focused on external features of mango as well as its weight. The results show that evaluation is more effective than using only one of the external features or weight combining an expensive nondestructive (NDT) measurement. Grading of fruits is implemented by four models of machine learning as Random Forest (RF), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). Models have inputs such as length, width, defect, weight, and outputs being mango classifications such as grade G1, G2, and G3. The unstructured data of 4983 of captured images combining with load-cell signals are transferred to structured data to generate a completed dataset including density. The data normalization and elimination of outliers (DNEO) are used to create a better dataset which prepared for machine learning algorithms. Moreover, an unbiased performance estimate for the training process carried out by the nested cross-validation (NCV) method. In the experiment, the methods of machine learning have high accurate over 87.9%, especially the model of RF gets 98.1% accuracy.

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New approaches for predicting outcomes in patients with atrial fibrillation

Jounal of arrhythmology ◽

10.35336/va-2020-e-28-32 ◽

2020 ◽

Vol 27 ◽

pp. 28-32

Author(s):

N. A. Novikova ◽

M. Yu. Gilyarov ◽

A. Yu. Suvorov ◽

A. Yu. Kuchina

Keyword(s):

Neural Network ◽

Machine Learning ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Test Group ◽

Learning Technologies ◽

Support Vector ◽

K Nearest Neighbor ◽

Cardiovascular Risks ◽

Linear Discriminant

Aim: we aimed to assess the capabilities of “machine learning” methods in predicting remote outcomes in patients with non-valvular atrial fi brillation (AF).Methods. From 2015 to 2016 234 patients with non-valvular AF were included in the study (median age 72 (65; 79) years; 50.0% men). During the median follow-up of 2.9 (2.7; 3.2) years 42 patients died, 9 patients had non-fatal acute cerebral circulatory disorders and 3 patients had non-fatal myocardial infarction (MI). These events in 52 subjects (22.2% from all patients included) were combined into a combined endpoint (death and a nonfatal cardiovascular accident at the stage of remote observation). The first 184 patients comprised a “training” group. The next 50 patients formed the “test” group. The following methods of «machine learning» were used in the analysis: classifi cation trees, linear discriminant analysis, the k-nearest neighbor method, support vectors method, neural network.Results. Long-term outcomes were influenced by age, known traditional risk factors for cardiovascular diseases, the presence of these diseases, changes in intracardiac hemodynamics and heart chambers as evaluated by echocardiography, the presence of concomitant anemia, advanced stages of chronic kidney disease, and the administration of drugs associated with a more severe cardiovascular disease progression (amiodarone, digoxin). The best prognosis was created using the model of linear discriminant analysis, the complex neural network model, and the support vector machine.Conclusion. Modern methods aimed at prognosis estimation seem to be of importance in cardiology. These methods include big data analysis and machine learning technologies. The methods require further evaluation and confirmation, and in the future they may allow correcting cardiovascular risks, using data from real clinical practice and evidence-based medicine at the same time.

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Using Item Response Theory for Explainable Machine Learning in Predicting Mortality in the Intensive Care Unit: Case-Based Approach

Journal of Medical Internet Research ◽

10.2196/20268 ◽

2020 ◽

Vol 22 (9) ◽

pp. e20268

Author(s):

Adrienne Kline ◽

Theresa Kline ◽

Zahra Shakeri Hossein Abad ◽

Joon Lee

Keyword(s):

Neural Network ◽

Machine Learning ◽

Intensive Care Unit ◽

Logistic Regression ◽

Intensive Care ◽

Item Response ◽

Nearest Neighbors ◽

K Nearest Neighbors ◽

Linear Discriminant ◽

Case Based

Background Supervised machine learning (ML) is being featured in the health care literature with study results frequently reported using metrics such as accuracy, sensitivity, specificity, recall, or F1 score. Although each metric provides a different perspective on the performance, they remain to be overall measures for the whole sample, discounting the uniqueness of each case or patient. Intuitively, we know that all cases are not equal, but the present evaluative approaches do not take case difficulty into account. Objective A more case-based, comprehensive approach is warranted to assess supervised ML outcomes and forms the rationale for this study. This study aims to demonstrate how the item response theory (IRT) can be used to stratify the data based on how difficult each case is to classify, independent of the outcome measure of interest (eg, accuracy). This stratification allows the evaluation of ML classifiers to take the form of a distribution rather than a single scalar value. Methods Two large, public intensive care unit data sets, Medical Information Mart for Intensive Care III and electronic intensive care unit, were used to showcase this method in predicting mortality. For each data set, a balanced sample (n=8078 and n=21,940, respectively) and an imbalanced sample (n=12,117 and n=32,910, respectively) were drawn. A 2-parameter logistic model was used to provide scores for each case. Several ML algorithms were used in the demonstration to classify cases based on their health-related features: logistic regression, linear discriminant analysis, K-nearest neighbors, decision tree, naive Bayes, and a neural network. Generalized linear mixed model analyses were used to assess the effects of case difficulty strata, ML algorithm, and the interaction between them in predicting accuracy. Results The results showed significant effects (P<.001) for case difficulty strata, ML algorithm, and their interaction in predicting accuracy and illustrated that all classifiers performed better with easier-to-classify cases and that overall the neural network performed best. Significant interactions suggest that cases that fall in the most arduous strata should be handled by logistic regression, linear discriminant analysis, decision tree, or neural network but not by naive Bayes or K-nearest neighbors. Conventional metrics for ML classification have been reported for methodological comparison. Conclusions This demonstration shows that using the IRT is a viable method for understanding the data that are provided to ML algorithms, independent of outcome measures, and highlights how well classifiers differentiate cases of varying difficulty. This method explains which features are indicative of healthy states and why. It enables end users to tailor the classifier that is appropriate to the difficulty level of the patient for personalized medicine.

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NEW METHODS FOR PREDICTING OUTCOMES AND COMPLICATIONS IN PATIENTS WITH ATRIAL FIBRILLATION

Jounal of arrhythmology ◽

10.35336/va-2019-2-49-50 ◽

2019 ◽

Vol 26 (2(96)) ◽

pp. 45-50

Author(s):

N. A. Novikova ◽

M. Yu. Gilyarov ◽

A. Yu. Suvorov ◽

A. Yu. Kuchina

Keyword(s):

Neural Network ◽

Machine Learning ◽

Atrial Fibrillation ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Test Group ◽

Learning Technologies ◽

Support Vector ◽

K Nearest Neighbor ◽

Linear Discriminant

Aim: assessment of the capabilities of “machine learning” methods in predicting remote outcomes in patients with non-valvular atrial fibrillation (AF).Methods. From 2015 to 2016 234 patients with non-valvular AF were included in the study (median age 72 (65; 79) years; 50.0% men). During the median follow-up of 2.9 (2.7; 3.2) years 42 patients died, 9 patients had non-fatal acute cerebral circulatory disorders and 3 patients had non-fatal myocardial infarction (MI). These events in 52 subjects (22.2% from all patients included) were combined into a combined endpoint (death and a nonfatal cardiovascular accident at the stage of remote observation). The first 184 patients comprised a “training” group. The next 50 patients formed the “test” group. The following methods of «machine learning» were used in the analysis: classification trees, linear discriminant analysis, the k-nearest neighbor method, support vectors method, neural network.Results. Long-term outcomes were influenced by age, known traditional risk factors for cardiovascular diseases, the presence of these diseases, changes in intracardiac hemodynamics and heart chambers as evaluated by echocardiography, the presence of concomitant anemia, advanced stages of chronic kidney disease, and the administration of drugs associated with a more severe cardiovascular disease progression (amiodarone, digoxin). The best prognosis was created using the model of linear discriminant analysis, the complex neural network model, and the support vector machine.Conclusion. Modern methods aimed at prognosis estimation seem to be of great potential for cardiology. These methods include big data analysis and machine learning technologies. The methods require further evaluation and con firmation, and in the future they may allow correcting cardiovascular risks, using data from real clinical practice and evidence-based medicine at the same time.

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Combined In-silico and Machine Learning Approaches Toward Predicting Arrhythmic Risk in Post-infarction Patients

Frontiers in Physiology ◽

10.3389/fphys.2021.745349 ◽

2021 ◽

Vol 12 ◽

Author(s):

Mary M. Maleckar ◽

Lena Myklebust ◽

Julie Uv ◽

Per Magne Florvaag ◽

Vilde Strøm ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

In Silico ◽

Data Augmentation ◽

Nearest Neighbors ◽

Patient Data ◽

Patient Specific ◽

Support Vector ◽

Geometric Features ◽

K Nearest Neighbors

Background: Remodeling due to myocardial infarction (MI) significantly increases patient arrhythmic risk. Simulations using patient-specific models have shown promise in predicting personalized risk for arrhythmia. However, these are computationally- and time- intensive, hindering translation to clinical practice. Classical machine learning (ML) algorithms (such as K-nearest neighbors, Gaussian support vector machines, and decision trees) as well as neural network techniques, shown to increase prediction accuracy, can be used to predict occurrence of arrhythmia as predicted by simulations based solely on infarct and ventricular geometry. We present an initial combined image-based patient-specific in silico and machine learning methodology to assess risk for dangerous arrhythmia in post-infarct patients. Furthermore, we aim to demonstrate that simulation-supported data augmentation improves prediction models, combining patient data, computational simulation, and advanced statistical modeling, improving overall accuracy for arrhythmia risk assessment.Methods: MRI-based computational models were constructed from 30 patients 5 days post-MI (the “baseline” population). In order to assess the utility biophysical model-supported data augmentation for improving arrhythmia prediction, we augmented the virtual baseline patient population. Each patient ventricular and ischemic geometry in the baseline population was used to create a subfamily of geometric models, resulting in an expanded set of patient models (the “augmented” population). Arrhythmia induction was attempted via programmed stimulation at 17 sites for each virtual patient corresponding to AHA LV segments and simulation outcome, “arrhythmia,” or “no-arrhythmia,” were used as ground truth for subsequent statistical prediction (machine learning, ML) models. For each patient geometric model, we measured and used choice data features: the myocardial volume and ischemic volume, as well as the segment-specific myocardial volume and ischemia percentage, as input to ML algorithms. For classical ML techniques (ML), we trained k-nearest neighbors, support vector machine, logistic regression, xgboost, and decision tree models to predict the simulation outcome from these geometric features alone. To explore neural network ML techniques, we trained both a three - and a four-hidden layer multilayer perceptron feed forward neural networks (NN), again predicting simulation outcomes from these geometric features alone. ML and NN models were trained on 70% of randomly selected segments and the remaining 30% was used for validation for both baseline and augmented populations.Results: Stimulation in the baseline population (30 patient models) resulted in reentry in 21.8% of sites tested; in the augmented population (129 total patient models) reentry occurred in 13.0% of sites tested. ML and NN models ranged in mean accuracy from 0.83 to 0.86 for the baseline population, improving to 0.88 to 0.89 in all cases.Conclusion: Machine learning techniques, combined with patient-specific, image-based computational simulations, can provide key clinical insights with high accuracy rapidly and efficiently. In the case of sparse or missing patient data, simulation-supported data augmentation can be employed to further improve predictive results for patient benefit. This work paves the way for using data-driven simulations for prediction of dangerous arrhythmia in MI patients.

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A Deep Learning Method for Prediction of Cardiovascular Disease Using Convolutional Neural Network

Revue d intelligence artificielle ◽

10.18280/ria.340510 ◽

2020 ◽

Vol 34 (5) ◽

pp. 601-606

Author(s):

Tulasi Krishna Sajja ◽

Hemantha Kumar Kalluri

Keyword(s):

Neural Network ◽

Machine Learning ◽

Cardiovascular Disease ◽

Deep Learning ◽

Convolutional Neural Network ◽

Early Stage ◽

Support Vector ◽

Learning Approach ◽

K Nearest Neighbors ◽

Traditional Approaches

Heart disease is a very deadly disease. Worldwide, the majority of people are suffering from this problem. Many Machine Learning (ML) approaches are not sufficient to forecast the disease caused by the virus. Therefore, there is a need for one system that predicts disease efficiently. The Deep Learning approach predicts the disease caused by the blocked heart. This paper proposes a Convolutional Neural Network (CNN) to predict the disease at an early stage. This paper focuses on a comparison between the traditional approaches such as Logistic Regression, K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machine (SVM), Neural Networks (NN), and the proposed prediction model of CNN. The UCI machine learning repository dataset for experimentation and Cardiovascular Disease (CVD) predictions with 94% accuracy.

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Analysis of Machine Learning Algorithms for Diagnosis of Diffuse Lung Diseases

Methods of Information in Medicine ◽

10.1055/s-0039-1681086 ◽

2018 ◽

Vol 57 (05/06) ◽

pp. 272-279 ◽

Cited By ~ 1

Author(s):

Isadora Cardoso ◽

Eliana Almeida ◽

Hector Allende-Cid ◽

Alejandro Frery ◽

Rangaraj Rangayyan ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Lung Diseases ◽

Gaussian Mixture ◽

Machine Learning Algorithms ◽

Support Vector ◽

Data Set ◽

Linear Discriminant ◽

Diffuse Lung Diseases ◽

Diffuse Lung

Computational Intelligence Re-meets Medical Image Processing A Comparison of Some Nature-Inspired Optimization Metaheuristics Applied in Biomedical Image Registration Background Diffuse lung diseases (DLDs) are a diverse group of pulmonary disorders, characterized by inflammation of lung tissue, which may lead to permanent loss of the ability to breathe and death. Distinguishing among these diseases is challenging to physicians due their wide variety and unknown causes. Computer-aided diagnosis (CAD) is a useful approach to improve diagnostic accuracy, by combining information provided by experts with Machine Learning (ML) methods. Objectives Exploring the potential of dimensionality reduction combined with ML methods for diagnosis of DLDs; improving the classification accuracy over state-of-the-art methods. Methods A data set composed of 3252 regions of interest (ROIs) was used, from which 28 features were extracted per ROI. We used Principal Component Analysis, Linear Discriminant Analysis, and Stepwise Selection – Forward, Backward, and Forward-Backward to reduce feature dimensionality. The feature subsets obtained were used as input to the following ML methods: Support Vector Machine, Gaussian Mixture Model, k-Nearest Neighbor, and Deep Feedforward Neural Network. We also applied a Deep Convolutional Neural Network directly to the ROIs. Results We achieved the maximum reduction from 28 to 5 dimensions using LDA. The best classification results were obtained by DFNN, with 99.60% of overall accuracy. Conclusions This work contributes to the analysis and selection of features that can efficiently characterize the DLDs studied.

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Cell Cycle-Regulated Genes Classification using Machine Learning and Deep Learning Techniques on Processed Microarrays Images

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2021/861022021 ◽

2021 ◽

Vol 10 (2) ◽

pp. 1100-1107

Keyword(s):

Neural Network ◽

Machine Learning ◽

Cell Cycle ◽

Deep Learning ◽

New Technologies ◽

Gene Evolution ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Support Vector ◽

Test Accuracy

Nowadays, machine learning and deep learning algorithms, are considered as new technologies increasingly used in the biomedical field. Machine learning is a branch of Artificial Intelligence that aims to automatically find patterns in existing data. A new Machine Learning subfield, the deep learning theory, has emerged. It deals with object recognition in images. In this paper, our goal is DNA Microarrays’analysis with these algorithms to classify two genes’ types. The first class represents cell cycle regulated genes and the second is non cell cycle regulated ones. In the current state of the art, the researchers are processing the numerical data associated to gene evolution to achieve this classification. Here, we propose a new and different approach, based on the microarrays images’ treatment. To classify images, we use three machine learning algorithms which are: Support Vector Machine, KNearest Neighbors and Random Forest Classifier. We also use the Convolutional Neural Network and the fully connected neural network algorithms. Experiments demonstrate that our approaches outperform the state of art by a margin of 14.73 per cent by using machine learning algorithms and a margin of 22.39 per cent by using deep learning models. Our models accomplish real time test accuracy of ~ 92.39 % at classifying using CNNand 94.73% using machine learning algorithms.

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