scholarly journals Data Aggregation and Terror Group Prediction using Machine Learning Algorithms

2019 ◽  
Vol 8 (4) ◽  
pp. 1467-1469 ◽  
Keyword(s):  
Machine Learning ◽  
Detailed Analysis ◽  
Data Aggregation ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Classification Algorithms ◽  
Terrorist Groups ◽  
Different Sources

This paper is about to introduce a proposed system that examines growth or decay of the terrorist groups by the time, active locations, types of attack they carry out, motive targets, Weapon mastery and availability and many parameters to analyze the patterns and hidden structures in their activity and to predict the occasion and type of their future attack. We have done a detailed analysis of data we get from different sources and we also performed different classification algorithms on the available data to find the chances of probable attack on different regions.Based on results finding which of the algorithms works with highest accuracy.

scholarly journals Cyber Bullying Detection for Twitter Using ML Classification Algorithms

2021 ◽  
Vol 9 (11) ◽  
pp. 24-29
Author(s):  
Muskan Patidar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Natural Language ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Cyber Bullying ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Classification Algorithms

Abstract: Social networking platforms have given us incalculable opportunities than ever before, and its benefits are undeniable. Despite benefits, people may be humiliated, insulted, bullied, and harassed by anonymous users, strangers, or peers. Cyberbullying refers to the use of technology to humiliate and slander other people. It takes form of hate messages sent through social media and emails. With the exponential increase of social media users, cyberbullying has been emerged as a form of bullying through electronic messages. We have tried to propose a possible solution for the above problem, our project aims to detect cyberbullying in tweets using ML Classification algorithms like Naïve Bayes, KNN, Decision Tree, Random Forest, Support Vector etc. and also we will apply the NLTK (Natural language toolkit) which consist of bigram, trigram, n-gram and unigram on Naïve Bayes to check its accuracy. Finally, we will compare the results of proposed and baseline features with other machine learning algorithms. Findings of the comparison indicate the significance of the proposed features in cyberbullying detection. Keywords: Cyber bullying, Machine Learning Algorithms, Twitter, Natural Language Toolkit

scholarly journals Structural Health Monitoring using Varied Machine Learning Algorithms

2018 ◽  
Vol 7 (3.12) ◽  
pp. 793 ◽  
Author(s):  
B Shanthi ◽  
Mahalakshmi N ◽  
Shobana M
Keyword(s):  
Machine Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Classification Algorithms ◽  
Structural Health

Structural Health Monitoring is essential in today’s world where large amount of money and labour are involved in building a structure. There arises a need to periodically check whether the built structure is strong and flawless, also how long it will be strong and if not how much it is damaged. These information are needed so that the precautions can be made accordingly. Otherwise, it may result in disastrous accidents which may take away even human lives. There are various methods to evaluate a structure. In this paper, we apply various classification algorithms like J48, Naive Bayes and many other classifiers available, to the dataset to check on the accuracy of the prediction determined by all of these classification algorithms and ar-rive at the conclusion of the best possible classifier to say whether a structure is damaged or not.  

scholarly journals Performance Evaluation of Different Machine Learning Classification Algorithms for Disease Diagnosis

2021 ◽  
Vol 12 (6) ◽  
pp. 1-28
Author(s):  
Munder Abdulatef Al-Hashem ◽  
Ali Mohammad Alqudah ◽  
Qasem Qananwah
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Performance Metrics ◽  
Confusion Matrix ◽  
Learning Algorithms ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Machine Learning Classification

Knowledge extraction within a healthcare field is a very challenging task since we are having many problems such as noise and imbalanced datasets. They are obtained from clinical studies where uncertainty and variability are popular. Lately, a wide number of machine learning algorithms are considered and evaluated to check their validity of being used in the medical field. Usually, the classification algorithms are compared against medical experts who are specialized in certain disease diagnoses and provide an effective methodological evaluation of classifiers by applying performance metrics. The performance metrics contain four criteria: accuracy, sensitivity, and specificity forming the confusion matrix of each used algorithm. We have utilized eight different well-known machine learning algorithms to evaluate their performances in six different medical datasets. Based on the experimental results we conclude that the XGBoost and K-Nearest Neighbor classifiers were the best overall among the used datasets and signs can be used for diagnosing various diseases.

scholarly journals Integrating Feature Selection and Multiclass Classification for Sport Result Prediction

2019 ◽  
Vol 9 (1) ◽  
pp. 1339-1342
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Learning Algorithms ◽  
Multiclass Classification ◽  
Machine Learning Algorithms ◽  
Classification Algorithms ◽  
Learning Models ◽  
Sports Fans ◽  
Player Performance ◽  
Machine Learning Models

Machine learning (ML) has become the most predominant methodology that shows good results in the classification and prediction domains. Predictive systems are being employed to predict events and its results in almost every walk of life. The field of prediction in sports is gaining importance as there is a huge community of betters and sports fans. Moreover team owners and club managers are struggling for Machine learning models that could be used for formulating strategies to win matches. Numerous factors such as results of previous matches, indicators of player performance and opponent information are required to build these models. This paper provides an analysis of such key models focusing on application of machine learning algorithms to sport result prediction. The results obtained helped us to elucidate the best combination of feature selection and classification algorithms that render maximum accuracy in sport result prediction.

An Extensive Text Mining Study for the Turkish Language

2021 ◽  
pp. 272-306
Author(s):  
Durmuş Özkan Şahin ◽  
Erdal Kılıç
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Text Mining ◽  
Language Processing ◽  
Information Gain ◽  
Learning Algorithms ◽  
Feature Selection Method ◽  
Machine Learning Algorithms ◽  
Classification Algorithms ◽  
Chi Square

In this study, the authors give both theoretical and experimental information about text mining, which is one of the natural language processing topics. Three different text mining problems such as news classification, sentiment analysis, and author recognition are discussed for Turkish. They aim to reduce the running time and increase the performance of machine learning algorithms. Four different machine learning algorithms and two different feature selection metrics are used to solve these text classification problems. Classification algorithms are random forest (RF), logistic regression (LR), naive bayes (NB), and sequential minimal optimization (SMO). Chi-square and information gain metrics are used as the feature selection method. The highest classification performance achieved in this study is 0.895 according to the F-measure metric. This result is obtained by using the SMO classifier and information gain metric for news classification. This study is important in terms of comparing the performances of classification algorithms and feature selection methods.

An analysis of PCOS disease prediction model using machine learning classification algorithms

2020 ◽  
Vol 15 ◽  
Author(s):  
Shivani Aggarwal ◽  
Kavita Pandey
Keyword(s):  
Machine Learning ◽  
Insulin Resistance ◽  
Feature Selection ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Classification Algorithms ◽  
Metabolic Abnormalities ◽  
Related Disorder ◽  
Machine Learning Classification

Background: Polycystic ovary syndrome is commonly known as PCOS and it is surprising that it affects up to 18% of women in reproductive age. PCOS is the most usually occurring hormone-related disorder. Some of the symptoms of PCOS are irregular periods, increased facial and body hair growth, attain more weight, darkening of skin, diabetes and trouble conceiving (infertility). It also came into light that patients suffering from PCOS also possess a range of metabolic abnormalities. Due to metabolic abnormalities, some disorder may occur which increase the risk of insulin resistance, type 2 diabetes and impaired glucose tolerance (a sign of prediabetes). Family members of women suffering from PCOS are also at higher hazardous level for developing the same metabolic abnormalities. Obesity and overweight status contribute to insulin resistance in PCOS. Objective: In the modern era, there are several new technologies available to diagnose PCOS and one of them is Machine learning algorithms because they are exposed to new data. These algorithms learn from past experiences to produce reliable and repeatable decisions. In this article, Machine learning algorithms are used to identify the important features to diagnose PCOS. Methods: Several classification algorithms like Support vector machine (SVM), Logistic Regression, Gradient Boosting, Random Forest, Decision Tree and K-Nearest Neighbor (KNN) are uses well organized test datasets for classify huge records. Initially a dataset of 541 instances and 41 attributes has been taken to apply the prediction models and a manual feature selection is done over it. Results: After the feature selection, a set of 12 attributes has been identified which plays a crucial role in diagnosing PCOS. Conclusion: There are several researches progressing in the direction of diagnosing PCOS but till now the relevant features are not identify for the same.

scholarly journals Classification of Liver Patient Dataset Using Machine Learning Algorithms

2018 ◽  
Vol 7 (3.34) ◽  
pp. 323
Author(s):  
S Muthuselvan ◽  
S Rajapraksh ◽  
K Somasundaram ◽  
K Karthik
Keyword(s):  
Machine Learning ◽  
Medical Information ◽  
Learning Algorithms ◽  
Medical Industry ◽  
Machine Learning Algorithms ◽  
Classification Algorithms ◽  
Successful Implementation ◽  
Computer Aided ◽  
Aided Diagnosis

Prediction of the disease in the human being is the very long and difficult process in early days. Now a days, computer aided diagnosis is the important role in the medical industry for predicting, analyzing and storing medical information with the images. In this paper will discuss and classify the liver patients with the help of the liver patient dataset with the help of the machine learning algorithms. WEKA is the software used here for implement the some of the classification algorithms with the data selected from the liver disease dataset. After the successful implementation of the all the algorithms, the best algorithms selected from the output of the all the algorithms execution. 

scholarly journals Easyml: Easily Build and Evaluate Machine Learning Models

2017 ◽  
Author(s):  
Woo-Young Ahn ◽  
Paul Hendricks ◽  
Nathaniel Haines
Keyword(s):  
Machine Learning ◽  
Best Practices ◽  
Linear Models ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Classification Algorithms ◽  
Vector Machines ◽  
Barrier To Entry

AbstractThe easyml (easy machine learning) package lowers the barrier to entry to machine learning and is ideal for undergraduate/graduate students, and practitioners who want to quickly apply machine learning algorithms to their research without having to worry about the best practices of implementing each algorithm. The package provides standardized recipes for regression and classification algorithms in R and Python and implements them in a functional, modular, and extensible framework. This package currently implements recipes for several common machine learning algorithms (e.g., penalized linear models, random forests, and support vector machines) and provides a unified interface to each one. Importantly, users can run and evaluate each machine learning algorithm with a single line of coding. Each recipe is robust, implements best practices specific to each algorithm, and generates a report with details about the model, its performance, as well as journal-quality visualizations. The package’s functional, modular, and extensible framework also allows researchers and more advanced users to easily implement new recipes for other algorithms.

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