scholarly journals A Simplistic Overview of Machine Learning

2021 ◽  
pp. 184-187
Author(s):  
Rajesh Yadav
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised And Unsupervised Learning ◽  
Training Examples ◽  
Learning Types

While dealing with machine learning, a computer learns first to perform a roles/task by learning a set of training examples. The computer performs then the same task along with data it hasn't found before. This paper presents a brief overview of machine-learning types along with instances. The paper also covers differences between supervised and unsupervised learning.

scholarly journals Feature Selection Optimization for Highlighting Opinions Using Supervised and Unsupervised Learning on Arabic Language

2021 ◽  
Vol 10 (2) ◽  
pp. 636-642
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Text Mining ◽  
Unsupervised Learning ◽  
Arabic Language ◽  
Machine Learning Techniques ◽  
Business Decision ◽  
Supervised And Unsupervised Learning ◽  
Proposed Model ◽  
The Impact

Text mining utilizes machine learning (ML) and natural language processing (NLP) for text implicit knowledge recognition, such knowledge serves many domains as translation, media searching, and business decision making. Opinion mining (OM) is one of the promised text mining fields, which are used for polarity discovering via text and has terminus benefits for business. ML techniques are divided into two approaches: supervised and unsupervised learning, since we herein testified an OM feature selection(FS)using four ML techniques. In this paper, we had implemented number of experiments via four machine learning techniques on the same three Arabic language corpora. This paper aims at increasing the accuracy of opinion highlighting on Arabic language, by using enhanced feature selection approaches. FS proposed model is adopted for enhancing opinion highlighting purpose. The experimental results show the outperformance of the proposed approaches in variant levels of supervisory,i.e. different techniques via distinct data domains. Multiple levels of comparison are carried out and discussed for further understanding of the impact of proposed model on several ML techniques.

scholarly journals Diving Deeper into Models

2018 ◽  
pp. 121-133
Author(s):  
Alberto Traverso ◽  
Frank J. W. M. Dankers ◽  
Biche Osong ◽  
Leonard Wee ◽  
Sander M. J. van Kuijk
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Prediction Model ◽  
Unsupervised Learning ◽  
Learning Objectives ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
Supervised And Unsupervised Learning

AbstractPre-requisites to better understand the chapter: knowledge of the major steps and procedures of developing a clinical prediction model.Logical position of the chapter with respect to the previous chapter: in the last chapters, you have learned how to develop and validate a clinical prediction model. You have been learning logistic regression as main algorithm to build the model. However, several different more complex algorithms can be used to build a clinical prediction model. In this chapter, the main machine learning based algorithms will be presented to you.Learning objectives: you will be presented with the definitions of: machine learning, supervised and unsupervised learning. The major algorithms for the last two categories will be introduced.

scholarly journals Data-Driven Signal–Noise Classification for Microseismic Data Using Machine Learning

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1499
Author(s):  
Sungil Kim ◽  
Byungjoon Yoon ◽  
Jung-Tek Lim ◽  
Myungsun Kim
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Test Data ◽  
Active Faults ◽  
Learning Methods ◽  
Hydraulic Stimulation ◽  
Signal Noise ◽  
Supervised And Unsupervised Learning ◽  
Microseismic Data ◽  
Noise Classification

It is necessary to monitor, acquire, preprocess, and classify microseismic data to understand active faults or other causes of earthquakes, thereby facilitating the preparation of early-warning earthquake systems. Accordingly, this study proposes the application of machine learning for signal–noise classification of microseismic data from Pohang, South Korea. For the first time, unique microseismic data were obtained from the monitoring system of the borehole station PHBS8 located in Yongcheon-ri, Pohang region, while hydraulic stimulation was being conducted. The collected data were properly preprocessed and utilized as training and test data for supervised and unsupervised learning methods: random forest, convolutional neural network, and K-medoids clustering with fast Fourier transform. The supervised learning methods showed 100% and 97.4% of accuracy for the training and test data, respectively. The unsupervised method showed 97.0% accuracy. Consequently, the results from machine learning validated that automation based on the proposed supervised and unsupervised learning applications can classify the acquired microseismic data in real time.

A machine learning framework for enhancing digital experiences in cultural heritage

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abdelhak Belhi ◽  
Abdelaziz Bouras ◽  
Abdulaziz Khalid Al-Ali ◽  
Sebti Foufou
Keyword(s):  
Machine Learning ◽  
Cultural Heritage ◽  
Unsupervised Learning ◽  
Research Work ◽  
Data Set ◽  
Content Type ◽  
Data Enrichment ◽  
Supervised And Unsupervised Learning ◽  
Visual Reconstruction ◽  
Cultural Data

PurposeDigital tools have been used to document cultural heritage with high-quality imaging and metadata. However, some of the historical assets are totally or partially unlabeled and some are physically damaged, which decreases their attractiveness and induces loss of value. This paper introduces a new framework that aims at tackling the cultural data enrichment challenge using machine learning.Design/methodology/approachThis framework focuses on the automatic annotation and metadata completion through new deep learning classification and annotation methods. It also addresses issues related to physically damaged heritage objects through a new image reconstruction approach based on supervised and unsupervised learning.FindingsThe authors evaluate approaches on a data set of cultural objects collected from various cultural institutions around the world. For annotation and classification part of this study, the authors proposed and implemented a hierarchical multimodal classifier that improves the quality of annotation and increases the accuracy of the model, thanks to the introduction of multitask multimodal learning. Regarding cultural data visual reconstruction, the proposed clustering-based method, which combines supervised and unsupervised learning is found to yield better quality completion than existing inpainting frameworks.Originality/valueThis research work is original in sense that it proposes new approaches for the cultural data enrichment, and to the authors’ knowledge, none of the existing enrichment approaches focus on providing an integrated framework based on machine learning to solve current challenges in cultural heritage. These challenges, which are identified by the authors are related to metadata annotation and visual reconstruction.

scholarly journals Credit Card Fraud Detection using Supervised and Unsupervised Learning

2021 ◽  
Author(s):  
Vikas Thammanna Gowda
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Credit Card ◽  
Learning Algorithms ◽  
Fraud Detection ◽  
Machine Learning Algorithms ◽  
Credit Card Fraud ◽  
Unsupervised Machine Learning ◽  
Supervised And Unsupervised Learning ◽  
Credit Card Use

In the present monetary situation, credit card use has gotten normal. These cards allow the user to make payments online and even in person. Online payments are very convenient, but it comes with its own risk of fraud. With the expanding number of credit card users, frauds are also expanding at the same rate. Some machine learning algorithms can be applied to tackle this problem. In this paper an evaluation of supervised and unsupervised machine learning algorithms has been presented for credit card fraud detection.

scholarly journals Rank Prediction for Articles and Conference Papersusing Machine Learning Techniques

2019 ◽  
Vol 8 (4) ◽  
pp. 9746-9750
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Supervised And Unsupervised Learning ◽  
Learning Techniques ◽  
Day By Day

Searching for an optimal article which was given highest and best priority is quite harder based on requirements. Ranking is one of the best measure or a method to get the best rated and optimal article or a conference or a research paper through this huge Internet World. As Technology been increasing day by day Artificial Intelligence is the first step to get through any problem for a solution Machine learning is also an important aspect of Artificial Intelligence. Machine Learning is best known for classifying, categorizing and predicting. Rank prediction can be done through many different algorithm implementations in machine learning. But choosing the best is important for accurate results. This paper gives the most accurate results of algorithms that can be used for rank predictions for articles. To simplify and resolve this problem, solutions were given in many different ways but to achieve accuracy is necessary, in previous models this is given using supervised learning only. We proposed this research work with perfect results using both supervised and unsupervised learning. Neural Networks is the best algorithm in supervised learning for classifying and predicting within data. In unsupervised learning we used K-means clustering because of grouping the data. This work helps the user(s) for optimal search of an article and also gives a competitive spirit for author to get into the top, totally this is implemented using Machine Learning Techniques of Neural Networks, K-Means Algorithm which is a mixture of supervised and unsupervised learning for predicting ranks.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

Application of Machine Learning in Animal Disease Analysis and Prediction

2020 ◽  
Vol 15 ◽  
Author(s):  
Shuwen Zhang ◽  
Qiang Su ◽  
Qin Chen
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Support Vector ◽  
Animal Disease ◽  
Human Beings ◽  
Animal Diseases ◽  
Disease Analysis

Abstract: Major animal diseases pose a great threat to animal husbandry and human beings. With the deepening of globalization and the abundance of data resources, the prediction and analysis of animal diseases by using big data are becoming more and more important. The focus of machine learning is to make computers learn how to learn from data and use the learned experience to analyze and predict. Firstly, this paper introduces the animal epidemic situation and machine learning. Then it briefly introduces the application of machine learning in animal disease analysis and prediction. Machine learning is mainly divided into supervised learning and unsupervised learning. Supervised learning includes support vector machines, naive bayes, decision trees, random forests, logistic regression, artificial neural networks, deep learning, and AdaBoost. Unsupervised learning has maximum expectation algorithm, principal component analysis hierarchical clustering algorithm and maxent. Through the discussion of this paper, people have a clearer concept of machine learning and understand its application prospect in animal diseases.

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