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An Architecture for Simplified and Automated Machine Learning

Jittapoo Poolwan, Sucha Smanchat
2018 International Journal of Electrical and Computer Engineering (IJECE)  
In this paper, we propose an architecture for simplified and automated machine learning process currently supporting the data classification task.  ...  However, knowledge in machine learning and technical skill are usually required to prepare data and perform machine learning tasks.  ...  RESEARCH METHOD As mentioned earlier, to automate the machine learning process, it is necessary to automate several steps including the approximation of data type, dataset preprocessing, machine learning  ... 
doi:10.11591/ijece.v8i5.pp2994-3002 fatcat:xffjhu7yprcrxhzdsjvqbo4r2a

Machine Learning Techniques and Testing

Ashish Verma, Kandy Arora, Aarush Sharma, Neelu Chaudhary
2019 Zenodo  
In this paper, we have discussed about various algorithms of the machine learning.  ...  These algorithms are used in various processes like image automated medical diagnostics, online advertising, robot incomotion etc.  ...  Background/Literature Review Machine Learning algorithms are Supervised Learning The supervised machine learning algorithms are the algorithms in which given dataset is divided into train & test dataset  ... 
doi:10.5281/zenodo.2615118 fatcat:uskijdpthfdufo2t5st6mw3sqy

Sentiment Analysis of Impact of Technology on Employment from Text on Twitter

Shahzad Qaiser, Nooraini Yusoff, Farzana Kabir Ahmad, Ramsha Ali
2020 International Journal of Interactive Mobile Technologies  
Naïve Bayes (NB) classifier on the text.  ...  This study aims to analyze people's sentiments about the impact of technology on employment and advancements in technologies and build a machine learning classifier to classify the sentiments.  ...  Analyzing sentiment using WordNet in Rapid Miner Building machine learning classifier After getting the labeled text, now a supervised machine learning classifier, i.e., Naïve Bayes can be trained so  ... 
doi:10.3991/ijim.v14i07.10600 fatcat:dphhmwkyajd4tnfeumgxmyqtgu

A Survey on Prediction Techniques of Heart Disease using Machine Learning

Mangesh Limbitote, Pimpri Chinchwad College of Engineering ,Pune
2020 International Journal of Engineering Research and  
The discussed machine learning algorithms are Decision Tree, SVM, ANN, Naive Bayes, Random Forest, KNN. The algorithms are compared on the basis of features.  ...  Furthermore, an in-depth analysis of the most relevant machine learning techniques available on the literature for heart disease prediction is briefly elaborated.  ...  Now a days there are too many automated techniques to detect the heart disease like data mining, machine learning, deep learning, etc.  ... 
doi:10.17577/ijertv9is060298 fatcat:tmb6whdmp5eozi6ddg4dgyfepe

An exploratory study of a text classification framework for Internet-based surveillance of emerging epidemics

Manabu Torii, Lanlan Yin, Thang Nguyen, Chand T. Mazumdar, Hongfang Liu, David M. Hartley, Noele P. Nelson
2011 International Journal of Medical Informatics  
In this study, we investigated automated detection of articles relevant to disease outbreaks using machine learning classifiers.  ...  Methods-Naïve Bayes and Support Vector Machine (SVM) classifiers were trained on 149 relevant and 149 or more randomly sampled unlabeled articles.  ...  Machine learning text classifiers-Among other machine learning algorithms, naïve Bayes and SVM are two of the most widely used text classification algorithms.  ... 
doi:10.1016/j.ijmedinf.2010.10.015 pmid:21134784 pmcid:PMC3904285 fatcat:2v7upizrgzf3xeww553rdycebe

Automating quranic verses labeling using machine learning approach

A. Adeleke, N. Samsudin, A. Mustapha, S. Ahmad Khalid
2019 Indonesian Journal of Electrical Engineering and Computer Science  
Automated text classification (ATC) is a well-known technique in machine learning.  ...  However, in recent times, with the advancement in information technology and machine learning, several classification algorithms have been developed for the purpose of text classification tasks.  ...  In this paper, we presented an automated machine learning approach for classifying the input Quranic verses.  ... 
doi:10.11591/ijeecs.v16.i2.pp925-931 fatcat:drkgr5tipje4bgb2oeg33buz7u

Some Investigations on Machine Learning Techniques for Automated Text Categorization

Bhagirath Prajapati, Sanjay Garg, N C Chauhan
2013 International Journal of Computer Applications  
This paper discusses the Naïve Bayes, Rocchio, k-Nearest Neighborhood and Support Vector Machine methods within machine learning paradigm for automated text categorization of given documents in predefined  ...  Machine learning technique is an approach by which we can train automated classifier to classify the documents with minimum human assistance.  ...  Automated text categorization with machine learning gained a prominent status in the information systems field.  ... 
doi:10.5120/12340-8617 fatcat:jom2wztpfrdghc6vphb6dmiqm4

Automating the Classification of Complexity of Medical Decision-Making in Patient-Provider Messaging in a Patient Portal

Lina Sulieman, Jamie R. Robinson, Gretchen P. Jackson
2020 Journal of Surgical Research  
Our analysis demonstrated that machine learning models have better performance than the model that did not use machine learning.  ...  We compared the performance of the models to using only the number of medical terms without training a machine learning model.  ...  Machine learning algorithm Parameter name Parameter possible values Multinomial Naïve Bayes Smoothing parameter alpha 0-1, 0 no smoothing Fitting prior: Learning class prior probabilities True  ... 
doi:10.1016/j.jss.2020.05.039 pmid:32570124 pmcid:PMC7303623 fatcat:xbsx54lntzb5tcwfrqvdy5evy4

Active Learning for Automated Visual Inspection of Manufactured Products [article]

Elena Trajkova, Jože M. Rožanec, Paulien Dam, Blaž Fortuna, Dunja Mladenić
2021 arXiv   pre-print
In this research, we compare three active learning approaches and five machine learning algorithms applied to visual defect inspection with real-world data provided by Philips Consumer Lifestyle BV.  ...  In addition, artificial intelligence enables higher degrees of automation, reducing overall costs and time required for defect inspection.  ...  ., histograms) from machine component images and compared the performance of the Näive Bayes and C4.5 models.  ... 
arXiv:2109.02469v1 fatcat:f7wpqc2ykbe2ha5oxofmmewbcu

Arabic Text Categorization using Machine Learning Approaches

Riyad Alshammari
2018 International Journal of Advanced Computer Science and Applications  
Thus, in this paper, an investigation of the impact of the preprocessing methods concerning the performance of three machine learning algorithms, namely, Naïve Bayesian, DMNBtext and C4.5 is conducted.  ...  Results show that the DMNBtext learning algorithm achieved higher performance compared to other machine learning algorithms in categorizing Arabic text.  ...  In this paper, an automated categorization system has been introduced based on machine learning algorithms for Arabic text documents.  ... 
doi:10.14569/ijacsa.2018.090332 fatcat:ap477n2nzzcbvolrcbvmwnkolu

Domain Specific Automated Triaging System for Bug Classification

Heena Singla, Gitika Sharma, Sumit Sharma
2016 Indian Journal of Science and Technology  
Different classification algorithms namely-Linear Discriminant Analysis (LDA), Naive Bayes (NB) to predict the performance measures are used.  ...  Author applied machine learning algorithm i.e. Naïve Bayes for finding similarity between the expertise of the project developers and the new bug record.  ...  For example, if a search engine is searching query "what is Machine learning" then search engine will find a lot of irrelevant page containing terms "what" and "and" , however "Machine " and "learning"  ... 
doi:10.17485/ijst/2016/v9i33/97891 fatcat:xe2yzxpynna4vawxt3jjaeznla

Machine learning technique for morphological classification of galaxies at z<0.1 from the SDSS [article]

I.B. Vavilova, D.V. Dobrycheva, M.Yu. Vasylenko, A.A. Elyiv, O.V. Melnyk, V. Khramtsov
2020 arXiv   pre-print
We present results of a binary automated morphological classification of galaxies conducted by human labeling, multiphotometry, and supervised Machine Learning methods.  ...  The methods of Support Vector Machine and Random Forest with Scikit-learn machine learning in Python provide the highest accuracy for the binary galaxy morphological classification: 96.4% correctly classified  ...  We evaluated the accuracy of different supervised Machine learning methods to be applied to the binary automated morphological classification of galaxies (Naive Bayes, Random Forest, Support Vector Machines  ... 
arXiv:1712.08955v2 fatcat:ucenkhz7hzezdelenbwfdvxjee

Predicting Risk through Artificial Intelligence Based on Machine Learning Algorithms: A Case of Pakistani Nonfinancial Firms

Shamsa Khalid, Muhammad Anees Khan, M.S. Mazliham, Muhammad Mansoor Alam, Nida Aman, Muhammad Tanvir Taj, Rija Zaka, Muhammad Jehangir, Atila Bueno
2022 Complexity  
So, in this study, we used financial ratios for accurate risk assessment and for the automation of corporate risk management by developing machine learning algorithms using techniques, namely, random forest  ...  , decision tree, naïve Bayes, and KNN.  ...  Profound learning and machine learning (ML) come under the heading of the artificial intelligence.  ... 
doi:10.1155/2022/6858916 fatcat:wsbvfh6rubdmvdbpxj4fddtwt4

Brain Tumor Detection based on Machine Learning Algorithms

Komal Sharma, Akwinder Kaur, Shruti Gujral
2014 International Journal of Computer Applications  
In this paper, tumor is detected in brain MRI using machine learning algorithms.  ...  learning algorithm.  ...  In this paper, an efficient automated classification technique for brain MRI is proposed using machine learning algorithms.  ... 
doi:10.5120/18036-6883 fatcat:gcf76og54rdynjyzlur556whsy

A Comparison of Machine Learning Approaches for the Automated Classification of Dementia [chapter]

Herbert Jelinek, David Cornforth, Patricia Waley, Eduardo Fernandez, Wayne Robinson
2002 Lecture Notes in Computer Science  
In our second experiment, automated classification was attempted using five machine learning algorithms (Nearest Neighbour, Naive Bayes, Decision Tree Induction, CMAC, and Decision Table) .  ...  Automated classification is a common goal of machine learning, and consists of assigning a class label to a set of measurements.  ...  In our second experiment, automated classification was attempted using five machine learning algorithms (Nearest Neighbour, Naive Bayes, Decision Tree Induction, CMAC, and Decision Table) .  ... 
doi:10.1007/3-540-36187-1_70 fatcat:r3uiy7nujzg23c7ntzsadnddnq
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