scholarly journals Classification of Neurodegenerative Disease Stages using Ensemble Machine Learning Classifiers

2019 ◽  
Vol 165 ◽  
pp. 66-73 ◽  
Author(s):  
M. Rohini ◽  
D. Surendran
Keyword(s):  
Machine Learning ◽  
Neurodegenerative Disease ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Ensemble Machine Learning ◽  
Disease Stages

Using Machine Learning Classifiers to Assist Healthcare-Related Decisions: Classification of Electronic Patient Records

2012 ◽  
Vol 36 (6) ◽  
pp. 3861-3874 ◽  
Author(s):  
Juliana T. Pollettini ◽  
Sylvia R. G. Panico ◽  
Julio C. Daneluzzi ◽  
Renato Tinós ◽  
José A. Baranauskas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Patient Records ◽  
Electronic Patient Records ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

scholarly journals Prediction of plant lncRNA by ensemble machine learning classifiers

BMC Genomics ◽  
2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Caitlin M. A. Simopoulos ◽  
Elizabeth A. Weretilnyk ◽  
G. Brian Golding
Keyword(s):  
Machine Learning ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Ensemble Machine Learning

AUTOMATED SEGMENTATION OF COVID-19 REGIONS FROM LUNG CT IMAGES USING WATERSHED ALGORITHM AND CLASSIFICATION USING MACHINE LEARNING CLASSIFIERS

2021 ◽  
Author(s):  
Bhargavee Guhan ◽  
S. Sowmiya ◽  
Bukka Shivani ◽  
U. Snekhalatha ◽  
T. Rajalakshmi
Keyword(s):  
Machine Learning ◽  
Medical Imaging ◽  
Ct Images ◽  
Textural Features ◽  
Machine Learning Classifiers ◽  
Fuzzy C Means ◽  
Learning Classifiers ◽  
Lung Ct ◽  
Fuzzy C Means Algorithm

The COVID-19 pandemic originated in Wuhan, China in December 2019 and has since affected over 200 countries worldwide. The highly contagious Coronavirus primarily affects the respiratory system, causing pulmonary inflammation that can be visualized through medical imaging such as CT and X-rays. Conventional testing methods include PCR and antibody tests. Shortage of test kits in hospitals as well as time taken for results to be received can be compensated through medical imaging. Therefore, there is a need for an automated system, which is accurate and robust in detection of Covid-19 from medical radiographs for clinical practice. The objectives of our study are as follows: (i) To segment the lung CT images using a hybrid watershed and fuzzy c-means algorithm. (2) To extract various textural features using the GLCM algorithm. (iii) To implement machine learning classifiers for classification of COVID and non-COVID image classes. Our dataset consisting of 60 chest CT images of COVID-19 and non-COVID-19 patients was pre-processed and segmented using a hybrid watershed and fuzzy c-means algorithm. Then, textural features were extracted from the segmented ROI using the GLCM algorithm. Finally, the images were classified into COVID and non-COVID classes using three machine learning classifiers namely Naïve Bayes, SVM and K-star. Naïve Bayes classifier achieved the highest accuracy of 95%, while SVM achieved 93% accuracy. The ROC curves were also obtained, with AUC of 0.98. Thus, our proposed system has shown promising results in the classification of lung CT images into the two classes namely COVID and non-COVID.

scholarly journals Improved Classification of Blockchain Transactions Using Feature Engineering and Ensemble Learning

2021 ◽  
Vol 14 (1) ◽  
pp. 16
Author(s):  
Chandrashekar Jatoth ◽  
Rishabh Jain ◽  
Ugo Fiore ◽  
Subrahmanyam Chatharasupalli
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Ensemble Learning ◽  
Feature Engineering ◽  
Machine Learning Classifiers ◽  
Blockchain Technology ◽  
Widespread Adoption ◽  
Learning Classifiers ◽  
Correlation Based Feature Selection

Although the blockchain technology is gaining a widespread adoption across multiple sectors, its most popular application is in cryptocurrency. The decentralized and anonymous nature of transactions in a cryptocurrency blockchain has attracted a multitude of participants, and now significant amounts of money are being exchanged by the day. This raises the need of analyzing the blockchain to discover information related to the nature of participants in transactions. This study focuses on the identification for risky and non-risky blocks in a blockchain. In this paper, the proposed approach is to use ensemble learning with or without feature selection using correlation-based feature selection. Ensemble learning yielded good results in the experiments, but class-wise analysis reveals that ensemble learning with feature selection improves even further. After training Machine Learning classifiers on the dataset, we observe an improvement in accuracy of 2–3% and in F-score of 7–8%.

scholarly journals ANALYSIS OF SINGLE AND ENSEMBLE MACHINE LEARNING CLASSIFIERS FOR PHISHING ATTACKS DETECTION

2021 ◽  
Vol 7 (2) ◽  
pp. 44-49
Author(s):  
Oyelakin A. M ◽  
Alimi O. M ◽  
Mustapha I. O ◽  
Ajiboye I. K
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Decision Trees ◽  
Random Forest Algorithm ◽  
Ensemble Techniques ◽  
Learning Classifiers ◽  
Phishing Attacks ◽  
Ensemble Machine Learning

Phishing attacks have been used in different ways to harvest the confidential information of unsuspecting internet users. To stem the tide of phishing-based attacks, several machine learning techniques have been proposed in the past. However, fewer studies have considered investigating single and ensemble machine learning-based models for the classification of phishing attacks. This study carried out performance analysis of selected single and ensemble machine learning (ML) classifiers in phishing classification.The focus is to investigate how these algorithms behave in the classification of phishing attacks in the chosen dataset. Logistic Regression and Decision Trees were chosen as single learning classifiers while simple voting techniques and Random Forest were used as the ensemble machine learning algorithms. Accuracy, Precision, Recall and F1-score were used as performance metrics. Logistic Regression algorithm recorded 0.86 as accuracy, 0.89 as precision, 0.87 as recall and 0.81 as F1-score. Similarly, the Decision Trees classifier achieved an accuracy of 0.87, 0.83 for precision, 0.88 for recall and 0.81 for F1-score. In the voting ensemble, accuracy of 0.92 was achieved. 0.90 was obtained for precision, 0.92 for recall and 0.92 for F1-score. Random Forest algorithm recorded 0.98, 0.97, 0.98 and 0.97 as accuracy, precision, recall and F1-score respectively. From the experimental analyses, Random Forest algorithm outperformed simple averaging classifier and the two single algorithms used for phishing url detection. The study established that the ensemble techniques that were used for the experimentations are more efficient for phishing url identification compared to the single classifiers.  

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