scholarly journals Deep Learning for Anthracnose Diagnosis in Turnip Leaves

2021 ◽  
Author(s):  
Tarun Suresh
Keyword(s):  
Crop Yields ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Agricultural Industry ◽  
Global Food Security ◽  
Svm Model ◽  
Turnip Leaves ◽  
Traditional Approaches ◽  
Fold Cross Validation

Fungal diseases in plants are extremely pressing issues in the agricultural industry, threatening global food security by reducing crop yields and quality. Traditional approaches to disease diagnosis and management have failed to recognize symptoms when they first appear. Leaves of the turnip, a plant of high agricultural value, has been especially affected by the fungal disease Anthracnose. Therefore, this study aimed to develop a novel convolutional neural network that can identify turnip leaves with early symptoms of Anathrosce blight. The model had 4 convolutional blocks and was trained on a custom dataset of 1,470 images, randomly split into 60% train, 20% validation, and 20% test. To compare how the CNN model fared with other machine learning algorithms, a support vector machine(SVM) model was developed and trained with the same image dataset. The CNN model’s accuracy 98.75% compared to the SVM model’s 80.50% accuracy. These results validate the efficacy of the CNN model to accurately identify infected turnip leaves and demonstrate that it can be implemented into a practical disease diagnosis system. Future studies are warranted to improve the model through means such as k-fold cross validation as well as apply the model architecture to other crops and diseases.

Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

2018 ◽  
Vol 1 (1) ◽  
pp. 120-130 ◽  
Author(s):  
Chunxiang Qian ◽  
Wence Kang ◽  
Hao Ling ◽  
Hua Dong ◽  
Chengyao Liang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Environmental Factors ◽  
Cross Validation ◽  
Concrete Strength ◽  
Simulation Method ◽  
Support Vector ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Influence Degree ◽  
Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

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.

A Binary Classifier for the Prediction of EC Numbers of Enzymes

2019 ◽  
Vol 16 (5) ◽  
pp. 383-391 ◽  
Author(s):  
Hao Cui ◽  
Lei Chen
Keyword(s):  
Cross Validation ◽  
Enzyme Commission ◽  
Matthews Correlation Coefficient ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Binary Classifier ◽  
Two Samples ◽  
Functional Classes ◽  
Fold Cross Validation

Background: Identification of Enzyme Commission (EC) number of enzymes is quite important for understanding the metabolic processes that produce enough energy to sustain life. Previous studies mainly focused on predicting six main functional classes or sub-functional classes, i.e., the first two digits of the EC number. Objective: In this study, a binary classifier was proposed to identify the full EC number (four digits) of enzymes. Methods: Enzymes and their known EC numbers were paired as positive samples and negative samples were randomly produced that were as many as positive samples. The associations between any two samples were evaluated by integrating the linkages between enzymes and EC numbers. The classic machining learning algorithm, Support Vector Machine (SVM), was adopted as the prediction engine. Results: The five-fold cross-validation test on five datasets indicated that the overall accuracy, Matthews correlation coefficient and F1-measure were about 0.786, 0.576 and 0.771, respectively, suggesting the utility of the proposed classifier. In addition, the effectiveness of the classifier was elaborated by comparing it with other classifiers that were based on other classic machine learning algorithms. Conclusion: The proposed classifier was quite effective for prediction of EC number of enzymes and was specially designed for dealing with the problem addressed in this study by testing it on five datasets containing randomly produced samples.

scholarly journals Tensor Flow Model in Medical Image Analysis - Review

2021 ◽  
Vol 12 (5) ◽  
pp. 1389-1397
Author(s):  
Raswitha Bandi, Et. al.
Keyword(s):  
Deep Learning ◽  
Medical Image Analysis ◽  
Learning Algorithms ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Techniques ◽  
Vector Machines ◽  
Detection Of Objects ◽  
Medical Analysis

Support Vector Machines, Reinforcement algorithms, artificial neural networks are some of the Machine Learning Algorithms available in Medical Analysis. By using these algorithms, much of the research has been done in analysis of liver cancer for genome classification and identification of lesions. At present, Deep learning algorithms have quickly turned into a strategy for examine CT images. This article presents one of the major deep learning techniques named tensor flow technique to investigate images in scan for the task of visualization of abnormal condition of liver tumor in the context of shape and color towards disease diagnosis. We surveyed the utilization of tensor flow for classifying images, detection of objects, and detection of lesions. In this paper, we mainly concentrated on the study and working of tensor flow in image classification. Also, a summary of the present and future scope in this area has been presented in detail.

scholarly journals Development of CYP3A4 Inhibition Models: Comparisons of Machine-Learning Techniques and Molecular Descriptors

2005 ◽  
Vol 10 (3) ◽  
pp. 197-205 ◽  
Author(s):  
Rieko Arimoto ◽  
Madhu-Ashni Prasad ◽  
Eric M. Gifford
Keyword(s):  
Machine Learning ◽  
High Throughput Screening ◽  
Computational Models ◽  
Similarity Index ◽  
Topological Indices ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Chemical Information ◽  
Svm Model

Computational models of cytochrome P450 3A4 inhibition were developed based on high-throughput screening data for 4470 proprietary compounds. Multiple models differentiating inhibitors (IC50 <3 μM) and noninhibitors were generated using various machine-learning algorithms (recursive partitioning [RP], Bayesian classifier, logistic regression, k-nearest-neighbor, and support vector machine [SVM]) with structural fingerprints and topological indices. Nineteen models were evaluated by internal 10-fold cross-validation and also by an independent test set. Three most predictive models, Barnard Chemical Information (BCI)-fingerprint/SVM, MDL-keyset/SVM, and topological indices/RP, correctly classified 249, 248, and 236 compounds of 291 noninhibitors and 135, 137, and 147 compounds of 179 inhibitors in the validation set. Their overall accuracies were 82%, 82%, and 81%, respectively. Investigating applicability of the BCI/SVM model found a strong correlation between the predictive performance and the structural similarity to the training set. Using Tanimoto similarity index as a confidence measurement for the predictions, the limitation of the extrapolation was 0.7 in the case of the BCI/SVM model. Taking consensus of the 3 best models yielded a further improvement in predictive capability, kappa = 0.65 and accuracy = 83%. The consensus model could also be tuned to minimize either false positives or false negatives depending on the emphasis of the screening.

PhD7FASTER: PREDICTING CLONES PROPAGATING FASTER FROM THE Ph.D.-7 PHAGE DISPLAY PEPTIDE LIBRARY

2014 ◽  
Vol 12 (01) ◽  
pp. 1450005 ◽  
Author(s):  
BEIBEI RU ◽  
PETER A. C. 'T HOEN ◽  
FULEI NIE ◽  
HAO LIN ◽  
FENG-BIAO GUO ◽  
...  
Keyword(s):  
Phage Display ◽  
Peptide Library ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Phage Display Peptide Library ◽  
Phage Display Libraries ◽  
Specific Affinity ◽  
Basic And Applied Research ◽  
Fold Cross Validation ◽  
Better Than

Phage display can rapidly discover peptides binding to any given target; thus, it has been widely used in basic and applied research. Each round of panning consists of two basic processes: Selection and amplification. However, recent studies have showed that the amplification step would decrease the diversity of phage display libraries due to different propagation capacity of phage clones. This may induce phages with growth advantage rather than specific affinity to appear in the final experimental results. The peptides displayed by such phages are termed as propagation-related target-unrelated peptides (PrTUPs). They would mislead further analysis and research if not removed. In this paper, we describe PhD7Faster, an ensemble predictor based on support vector machine (SVM) for predicting clones with growth advantage from the Ph.D.-7 phage display peptide library. By using reduced dipeptide composition (ReDPC) as features, an accuracy (Acc) of 79.67% and a Matthews correlation coefficient (MCC) of 0.595 were achieved in 5-fold cross-validation. In addition, the SVM-based model was demonstrated to perform better than several representative machine learning algorithms. We anticipate that PhD7Faster can assist biologists to exclude potential PrTUPs and accelerate the finding of specific binders from the popular Ph.D.-7 library. The web server of PhD7Faster can be freely accessed at http://immunet.cn/sarotup/cgi-bin/PhD7Faster.pl .

scholarly journals Machine Learning Algorithms for Classification of Gas Sensor Array Dataset

2021 ◽  
Vol 23 (06) ◽  
pp. 721-728
Author(s):  
Rahul . M.G ◽  
◽  
Srujan R Rajanalli ◽  
Sammed Endoli ◽  
Mahantesh Magi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Confusion Matrix ◽  
Principal Component ◽  
Correlated Data ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Redundant Data ◽  
Gas Sensor Array ◽  
Svm Model ◽  
Pca Algorithm

To measure the accuracy of the data being sensed predictive machine learning models have been used. These models take input in the form of datasets and predict the output based on them. By using a large dataset better and efficient predictive models can be designed because a large amount of data can be used to train the model. But having a larger dataset leads to a dimensionality problem. This problem is solved using Dimensionality Reduction Principal Component Analysis(PCA) algorithm. PCA helps to reduce the redundant data or correlated data present in the dataset by which dimensionality of the dataset is reduced. Classifier algorithms like K Nearest Neighbour(KNN), Logistic Regression(LR), Naive Bayes(NB), and Support Vector Machine(SVM) are used which gives output in the form of the confusion matrix. From this confusion matrix, the prediction accuracy of models is decided. From the accuracy measurements, it is found that the SVM model is more accurate(94%) in predicting the output whereas the NB model is the least accurate(60%).

scholarly journals Research of Machine Learning algorithms using K-fold cross validation

2019 ◽  
Vol 8 (6S) ◽  
pp. 215-218
Keyword(s):  
Machine Learning ◽  
Cross Validation ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Validation Data ◽  
Machine Learning Classification ◽  
Fold Cross Validation

In machine learning, Classification is one of the most important research area. Classification allocates the given input to a known category. In this paper different machine algorithms like Logistic regression (LR), Decision tree (DT), Support vector machine (SVM), K nearest neighbors (KNN) were implemented on UCI breast cancer dataset with preprocessing. The models were trained and tested with k-fold cross validation data. Accuracy and run time execution of each classifier are implemented in python.

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 A Comparative study of machine learning algorithms on thyroid disease prediction

2018 ◽  
Vol 7 (2.8) ◽  
pp. 315 ◽  
Author(s):  
Shaik Razia ◽  
P SwathiPrathyusha ◽  
N Vamsi Krishna ◽  
N Sathya Sumana
Keyword(s):  
Machine Learning ◽  
Thyroid Hormones ◽  
Thyroid Disease ◽  
High Capacity ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Learning Techniques

Thyroid illness is a medicinal state that influences the functionality of the thyroid organ that is thyroid gland [1](Guyton, 2011).The indications of thyroid ailment differ basing upon the type. There are four most common varieties: hypothyroidism (low capacity) which is caused due to the insufficiency of the thyroid hormones; hyperthyroidism (high capacity) which is caused due to the existence of the thyroid hormones more than just sufficient, basic variations from the norm, most normally an augmentation of the thyroid organ; and tumors which can be benign or can cause cancer. It is additionally conceivable to have irregular thyroid capacity tests with no clinical side effects [2](Bauer & al, 2013).In this study a comparative thyroid disease diagnosis were performed by using Machine learning techniques that is Support Vector Machine (SVM), Multiple Linear Regression, Naïve Bayes, Decision Trees. For this purpose, thyroid disease dataset gathered from the UCI machine learning database was used.

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