scholarly journals Analysis of Earthquake Forecasting in India Using Supervised Machine Learning Classifiers

2021 ◽  
Vol 13 (2) ◽  
pp. 971
Author(s):  
Papiya Debnath ◽  
Pankaj Chittora ◽  
Tulika Chakrabarti ◽  
Prasun Chakrabarti ◽  
Zbigniew Leonowicz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Vital Role ◽  
Earthquake Forecasting ◽  
Supervised Machine Learning ◽  
Learning Technology ◽  
Learning Classifier ◽  
Moderate Earthquake ◽  
Supervised Machine Learning Classifiers ◽  
Simple Logistic ◽  
Classifier Algorithms

Earthquakes are one of the most overwhelming types of natural hazards. As a result, successfully handling the situation they create is crucial. Due to earthquakes, many lives can be lost, alongside devastating impacts to the economy. The ability to forecast earthquakes is one of the biggest issues in geoscience. Machine learning technology can play a vital role in the field of geoscience for forecasting earthquakes. We aim to develop a method for forecasting the magnitude range of earthquakes using machine learning classifier algorithms. Three different ranges have been categorized: fatal earthquake; moderate earthquake; and mild earthquake. In order to distinguish between these classifications, seven different machine learning classifier algorithms have been used for building the model. To train the model, six different datasets of India and regions nearby to India have been used. The Bayes Net, Random Tree, Simple Logistic, Random Forest, Logistic Model Tree (LMT), ZeroR and Logistic Regression algorithms have been applied to each dataset. All of the models have been developed using the Weka tool and the results have been noted. It was observed that Simple Logistic and LMT classifiers performed well in each case.

scholarly journals A Comparison on Supervised and Semi-Supervised Machine Learning Classifiers for Gestational Diabetes Prediction

2021 ◽  
Vol 9 (12) ◽  
pp. 1001-1005
Author(s):  
Lokesh Kola
Keyword(s):  
Machine Learning ◽  
Gestational Diabetes ◽  
Supervised Learning ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
World Health ◽  
Middle Income ◽  
Diabetes Prediction ◽  
Supervised Machine Learning Classifiers ◽  
Health Organization

Abstract: Diabetes is the deadliest chronic diseases in the world. According to World Health Organization (WHO) around 422 million people are currently suffering from diabetes, particularly in low and middle-income countries. Also, the number of deaths due to diabetes is close to 1.6 million. Recent research has proven that the occurrence of diabetes is likely to be seen in people aged between 18 and this has risen from 4.7 to 8.5% from 1980 to 2014. Early diagnosis is necessary so that the disease does not go into advanced stages which is quite difficult to cure. Significant research has been performed in diabetes predictions. As time passes, challenges keep increasing to build a system to detect diabetes systematically. The hype for Machine Learning is increasing day to day to analyse medical data to diagnose a disease. Previous research has focused on just identifying the diabetes without specifying its type. In this paper, we have we have predicted gestational diabetes (Type-3) by comparing various supervised and semi-supervised machine learning algorithms on two datasets i.e., binned and non-binned datasets and compared the performance based on evaluation metrics. Keywords: Gestational diabetes, Machine Learning, Supervised Learning, Semi-Supervised Learning, Diabetes Prediction

scholarly journals Deep Semi-Supervised Learning Improves Universal Peptide Identification of Shotgun Proteomics Data

2020 ◽  
Author(s):  
John T. Halloran ◽  
Gregor Urban ◽  
David Rocke ◽  
Pierre Baldi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Peptide Identification ◽  
Shotgun Proteomics ◽  
Database Search ◽  
Supervised Machine Learning ◽  
Superior Performance ◽  
Support Vector ◽  
Proteomics Data ◽  
Learning Classifier

AbstractSemi-supervised machine learning post-processors critically improve peptide identification of shot-gun proteomics data. Such post-processors accept the peptide-spectrum matches (PSMs) and feature vectors resulting from a database search, train a machine learning classifier, and recalibrate PSMs using the trained parameters, often yielding significantly more identified peptides across q-value thresholds. However, current state-of-the-art post-processors rely on shallow machine learning methods, such as support vector machines. In contrast, the powerful training capabilities of deep learning models have displayed superior performance to shallow models in an ever-growing number of other fields. In this work, we show that deep models significantly improve the recalibration of PSMs compared to the most accurate and widely-used post-processors, such as Percolator and PeptideProphet. Furthermore, we show that deep learning is able to adaptively analyze complex datasets and features for more accurate universal post-processing, leading to both improved Prosit analysis and markedly better recalibration of recently developed database-search functions.

scholarly journals Comparing Supervised Machine Learning Strategies and Linguistic Features to Search for Very Negative Opinions

Information ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 16 ◽  
Author(s):  
Sattam Almatarneh ◽  
Pablo Gamallo
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Empirical Study ◽  
Learning Strategies ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Word Embeddings ◽  
Linguistic Features ◽  
Machine Learning Classifiers ◽  
Supervised Machine Learning Classifiers

In this paper, we examine the performance of several classifiers in the process of searching for very negative opinions. More precisely, we do an empirical study that analyzes the influence of three types of linguistic features (n-grams, word embeddings, and polarity lexicons) and their combinations when they are used to feed different supervised machine learning classifiers: Naive Bayes (NB), Decision Tree (DT), and Support Vector Machine (SVM). The experiments we have carried out show that SVM clearly outperforms NB and DT in all datasets by taking into account all features individually as well as their combinations.

scholarly journals Defending Malicious Script Attacks Using Machine Learning Classifiers

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Nayeem Khan ◽  
Johari Abdullah ◽  
Adnan Shahid Khan
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Malicious Code ◽  
Supervised Machine Learning ◽  
Feature Subset ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Wrapper Method ◽  
Supervised Machine Learning Classifiers ◽  
Client Side

The web application has become a primary target for cyber criminals by injecting malware especially JavaScript to perform malicious activities for impersonation. Thus, it becomes an imperative to detect such malicious code in real time before any malicious activity is performed. This study proposes an efficient method of detecting previously unknown malicious java scripts using an interceptor at the client side by classifying the key features of the malicious code. Feature subset was obtained by using wrapper method for dimensionality reduction. Supervised machine learning classifiers were used on the dataset for achieving high accuracy. Experimental results show that our method can efficiently classify malicious code from benign code with promising results.

scholarly journals Forest Fire Prediction using Machine Learning Models based on DC, Wind and RH

2020 ◽  
Vol 8 (6S) ◽  
pp. 142-143
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Forest Fire ◽  
Random Forest Algorithm ◽  
Learning Models ◽  
Learning Classifier ◽  
Machine Learning Models ◽  
Classifier Algorithms

The paper points out forest fire prediction using machine learning models on the basis of viz. DC, Wind, RH out of the several machine learning classifier algorithms, It is relevant that random forest algorithm generates optimum accuracy(99.61%).

Fake Review Detection Using Machine Learning Techniques

2020 ◽  
Vol 3 (2) ◽  
pp. 46-54
Author(s):  
Abhinandan V. ◽  
Aishwarya C. A. ◽  
Arshiya Sultana
Keyword(s):  
Machine Learning ◽  
Online Reviews ◽  
Vital Role ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Search Results ◽  
Learning Techniques ◽  
The World ◽  
Fake Reviews

Online reviews play a vital role in today's business and commerce. In the world of e-commerce, reviews are the best signs of success and failure. Businesses that have good reviews get a lot of free exposure on websites and pages that have good reviews show up at the top of the search results. Fake reviews are everywhere online. Online fake reviews are the reviews which are written by someone who has not actually used the product or the services. Because of the cut-throat competition, sellers are now willing to resort to unfair means to make their product stand out. This work introduces some supervised machine learning techniques to detect fake online reviews and also be able to block the malicious users who post such reviews.

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