scholarly journals Machine Learning-Based Presymptomatic Detection of Rice Sheath Blight Using Spectral Profiles

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Anna O. Conrad ◽  
Wei Li ◽  
Da-Young Lee ◽  
Guo-Liang Wang ◽  
Luis Rodriguez-Saona ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Disease Diagnosis ◽  
Sheath Blight ◽  
Machine Learning Algorithms ◽  
Plant Diseases ◽  
Support Vector ◽  
Rice Sheath Blight ◽  
Spectral Profiles ◽  
Testing Accuracy

Early detection of plant diseases, prior to symptom development, can allow for targeted and more proactive disease management. The objective of this study was to evaluate the use of near-infrared (NIR) spectroscopy combined with machine learning for early detection of rice sheath blight (ShB), caused by the fungus Rhizoctonia solani. We collected NIR spectra from leaves of ShB-susceptible rice (Oryza sativa L.) cultivar, Lemont, growing in a growth chamber one day following inoculation with R. solani, and prior to the development of any disease symptoms. Support vector machine (SVM) and random forest, two machine learning algorithms, were used to build and evaluate the accuracy of supervised classification-based disease predictive models. Sparse partial least squares discriminant analysis was used to confirm the results. The most accurate model comparing mock-inoculated and inoculated plants was SVM-based and had an overall testing accuracy of 86.1% (N=72), while when control, mock-inoculated, and inoculated plants were compared the most accurate SVM model had an overall testing accuracy of 73.3% (N=105). These results suggest that machine learning models could be developed into tools to diagnose infected but asymptomatic plants based on spectral profiles at the early stages of disease development. While testing and validation in field trials are still needed, this technique holds promise for application in the field for disease diagnosis and management.

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 Machine Learning Classification of Port Scanning and DDoS Attacks: A Comparative Analysis

2021 ◽  
Vol 40 (1) ◽  
pp. 215-229
Author(s):  
Muhammad Aamir ◽  
Syed Sajjad Hussain Rizvi ◽  
Manzoor Ahmed Hashmani ◽  
Muhammad Zubair ◽  
Jawwad Ahmed . Usman
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Denial Of Service ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Subjective Rating ◽  
Support Vector ◽  
Ddos Attacks ◽  
Testing Accuracy

Cyber security is one of the major concerns of today’s connected world. For all the platforms of today’s communication technology such as wired, wireless, local and remote access, the hackers are present to corrupt the system functionalities, circumvent the security measures and steal sensitive information. Amongst many techniques of hackers, port scanning and Distributed Denial of Service (DDoS) attacks are very common. In this paper, the benefits of machine learning are taken into consideration for classification of port scanning and DDoS attacks in a mix of normal and attack traffic. Different machine learning algorithms are trained and tested on a recently published benchmark dataset (CICIDS2017) to identify the best performing algorithms on the data which contains more recent vectors of port scanning and DDoS attacks. The classification results show that all the variants of discriminant analysis and Support Vector Machine (SVM) provide good testing accuracy i.e. more than 90%. According to a subjective rating criterion mentioned in this paper, 9 algorithms from a set of machine learning experiments receive the highest rating (good) as they provide more than 85% classification (testing) accuracy out of 22 total algorithms. This comparative analysis is further extended to observe training performance of machine learning models through k-fold cross validation, Area Under Curve (AUC) analysis of the Receiver Operating Characteristic (ROC) curves, and dimensionality reduction using the Principal Component Analysis (PCA). To the best of our knowledge, a comprehensive comparison of various machine learning algorithms on CICIDS2017 dataset is found to be deficient for port scanning and DDoS attacks while considering such recent features of attack.

scholarly journals Prediction of Multidrug-Resistant Tuberculosis Using Machine Learning Algorithms in SWAT, Pakistan

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Mian Haider Ali ◽  
Dost Muhammad Khan ◽  
Khalid Jamal ◽  
Zubair Ahmad ◽  
Sadaf Manzoor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Public Health Problem ◽  
Multidrug Resistant ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
The Status ◽  
Mdr Tb ◽  
Selection Operator

In this paper, we have focused on machine learning (ML) feature selection (FS) algorithms for identifying and diagnosing multidrug-resistant (MDR) tuberculosis (TB). MDR-TB is a universal public health problem, and its early detection has been one of the burning issues. The present study has been conducted in the Malakand Division of Khyber Pakhtunkhwa, Pakistan, to further add to the knowledge on the disease and to deal with the issues of identification and early detection of MDR-TB by ML algorithms. These models also identify the most important factors causing MDR-TB infection whose study gives additional insights into the matter. ML algorithms such as random forest, k-nearest neighbors, support vector machine, logistic regression, leaset absolute shrinkage and selection operator (LASSO), artificial neural networks (ANNs), and decision trees are applied to analyse the case-control dataset. This study reveals that close contacts of MDR-TB patients, smoking, depression, previous TB history, improper treatment, and interruption in first-line TB treatment have a great impact on the status of MDR. Accordingly, weight loss, chest pain, hemoptysis, and fatigue are important symptoms. Based on accuracy, sensitivity, and specificity, SVM and RF are the suggested models to be used for patients’ classifications.

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.

scholarly journals Comparison of Coronary Heart Disease Prediction models using various Machine Learning Algorithms

2021 ◽  
Author(s):  
Sunil Kr. Tiwari ◽  
◽  
Suresh Kumar Garg ◽  
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Prediction Models ◽  
Health Sector ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Soft Computing Method ◽  
Preventive Medication ◽  
Heart Disease Diagnosis

In the health sector, Data Analytics and Machine Learning (ML) methods are taking over role of skill and experience of a doctor especially in diagnosing diseases and preventive health measures. The health care industry is collecting very large amount of data related to patients, his medical history for preventive medication and diagnosing disease well in time and more accurately. In this paper, a comparison of five classification machine learning methods viz. Decision Tree, Random Forests, Support Vector Machine, Artificial Neural Network and Fuzzy Logic based soft computing method is done for heart disease diagnosis on the basis of data available on public domain. Out of 76 parameters collected for a patient, only 15 medical parameters such as blood pressure, sex, age, obesity and cholesterol level are used for predicting heart disease of patients.

A Topical Survey: Applications of Machine Learning in Medical Issues

2020 ◽  
Vol 17 (11) ◽  
pp. 5010-5019
Author(s):  
Chapala Maharana ◽  
Bijan Bihari Mishra ◽  
Ch. Sanjeev Kumar Dash
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Image Segmentation ◽  
Learning Algorithms ◽  
Disease Diagnosis ◽  
Lesion Detection ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
Medical Issues

Computational Intelligence methods have replaced almost all real world applications with high accuracy within the given time period. Machine Learning approaches like classification, feature selection, feature extraction have solved many problems of different domain. They use different ML models implemented with suitable ML tool or combination of tools from NN (Neural Network), SVM (Support Vector Machine), DL (Deep Learning), ELM (Extreme Learning Machine). The model is used for training with known data along with ML algorithms (fuzzy logic, genetic algorithm) to optimize the accuracy for different medical issues for example gene expression and image segmentation for information extraction and disease diagnosis, health monitoring, disease treatment. Most of the medical problems are solved using recent advances in AI (Artificial Intelligence) technologies with the biomedical systems development (e.g., Knowledge based Decision Support Systems) and AI technologies with medical informatics science. AI based methods like machine learning algorithms implemented models are increasingly found in real life applications ex. healthcare, natural calamity detection and forecasting. There are the expert systems handled by experts for knowledge gain which is used in decision making applications. The ML models are found in different medical applications like disease diagnosis (ex. cancer prediction, diabetics disease prediction) and for treatment of diseases (ex. in diabetics disease the reduction in mean glucose concentration following intermittent gastric feeds). The feature selection ML method is used for EEG classification for detection of the severity of the disease in heart related diseases and for identification of genes in different disorder like autism disorder. The ML models are found in health record systems. There are other applications of ML approaches found in image segmentation, tissue extraction, image fragmentation for disease diagnosis (ex. lesion detection in breast cancer for malignancy) and then treatment of those diseases. ML models are found in mobile health treatment, treatment of psychology patients, treatment of dumb patients etc. Medical data handling is the vital part of health care systems for the development of AI systems which can again be solved by machine learning approaches. The ML approaches for medical issues have used ensemble methods or combinations of machine learning tools and machine learning algorithms to optimize the result with good accuracy value at a faster rate.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

scholarly journals Predicting Future Occurrence of Acute Hypotensive Episodes Using Noninvasive and Invasive Features

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 445-451
Author(s):  
Yifei Sun ◽  
Navid Rashedi ◽  
Vikrant Vaze ◽  
Parikshit Shah ◽  
Ryan Halter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Real World ◽  
Short Term Memory ◽  
Model Performance ◽  
Learning Technologies ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Continuous Map

ABSTRACT Introduction Early prediction of the acute hypotensive episode (AHE) in critically ill patients has the potential to improve outcomes. In this study, we apply different machine learning algorithms to the MIMIC III Physionet dataset, containing more than 60,000 real-world intensive care unit records, to test commonly used machine learning technologies and compare their performances. Materials and Methods Five classification methods including K-nearest neighbor, logistic regression, support vector machine, random forest, and a deep learning method called long short-term memory are applied to predict an AHE 30 minutes in advance. An analysis comparing model performance when including versus excluding invasive features was conducted. To further study the pattern of the underlying mean arterial pressure (MAP), we apply a regression method to predict the continuous MAP values using linear regression over the next 60 minutes. Results Support vector machine yields the best performance in terms of recall (84%). Including the invasive features in the classification improves the performance significantly with both recall and precision increasing by more than 20 percentage points. We were able to predict the MAP with a root mean square error (a frequently used measure of the differences between the predicted values and the observed values) of 10 mmHg 60 minutes in the future. After converting continuous MAP predictions into AHE binary predictions, we achieve a 91% recall and 68% precision. In addition to predicting AHE, the MAP predictions provide clinically useful information regarding the timing and severity of the AHE occurrence. Conclusion We were able to predict AHE with precision and recall above 80% 30 minutes in advance with the large real-world dataset. The prediction of regression model can provide a more fine-grained, interpretable signal to practitioners. Model performance is improved by the inclusion of invasive features in predicting AHE, when compared to predicting the AHE based on only the available, restricted set of noninvasive technologies. This demonstrates the importance of exploring more noninvasive technologies for AHE prediction.

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