scholarly journals IDENTIFICATION OF MBTI (MYERS-BRIGGS TYPE INDEX) HUMAN TYPE USING TEXT ON SOCIAL NETWORKS BASED MACHINE LEARNING

2021 ◽  
pp. 136-144
Author(s):  
A. Z. Sunnatilla ◽  
E. S. Nurakhov ◽  
A. A. Myngzhassar
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Literary Analysis ◽  
Support Vector ◽  
Vector Machines ◽  
Myers Briggs ◽  
Personal Qualities ◽  
Online Format

This study aims to create a classifier using machine learning methods that determine the psychological type of people based on the text published on social networks according to the Myers-Briggs Type Index classification. The article is based on the implementation of automation of the task of determining the personality type using machine learning, with an explanation for determining the characteristics of a person using the MBTI personality indicator. The methods of logistic regression, random forest and support vector machines were used, and a literary analysis of similar works was carried out. The article presents the progress of research work and the results of each classifier, as well as an analysis of the approaches used. In the context of the current quarantine restrictions, such studies can be of great help in the selection of personnel in companies due to the transition of people to an online format of work, since the study involves determining the personal qualities of people based on their posts in social networks. In this paper, the most effective machine learning algorithms for the Kazakh language, which are simple to use and do not require a lot of computing power, were used and, accordingly, the results of the work for each method were presented, among these methods, the accuracy and reliability of the classifier for the Kazakh language by the method of support vectors were at a good level.

scholarly journals Heart disease prediction using machine learning techniques : a survey

2018 ◽  
Vol 7 (2.8) ◽  
pp. 684 ◽  
Author(s):  
V V. Ramalingam ◽  
Ayantan Dandapath ◽  
M Karthik Raja
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Complex Data ◽  
Learning Techniques ◽  
Vector Machines ◽  
Supervised Learning Algorithms ◽  
Life Threatening

Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases. This paper presents a survey of various models based on such algorithms and techniques andanalyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.

scholarly journals Research on Parallel Support Vector Machine Based on Spark Big Data Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yao Huimin
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Big Data ◽  
Support Vector Machines ◽  
Cross Validation ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Lambda Architecture ◽  
Vector Machines ◽  
Data Platform

With the development of cloud computing and distributed cluster technology, the concept of big data has been expanded and extended in terms of capacity and value, and machine learning technology has also received unprecedented attention in recent years. Traditional machine learning algorithms cannot solve the problem of effective parallelization, so a parallelization support vector machine based on Spark big data platform is proposed. Firstly, the big data platform is designed with Lambda architecture, which is divided into three layers: Batch Layer, Serving Layer, and Speed Layer. Secondly, in order to improve the training efficiency of support vector machines on large-scale data, when merging two support vector machines, the “special points” other than support vectors are considered, that is, the points where the nonsupport vectors in one subset violate the training results of the other subset, and a cross-validation merging algorithm is proposed. Then, a parallelized support vector machine based on cross-validation is proposed, and the parallelization process of the support vector machine is realized on the Spark platform. Finally, experiments on different datasets verify the effectiveness and stability of the proposed method. Experimental results show that the proposed parallelized support vector machine has outstanding performance in speed-up ratio, training time, and prediction accuracy.

scholarly journals Comparative Performance of Machine Learning Algorithms for Cryptocurrency Forecasting

2018 ◽  
Vol 11 (3) ◽  
pp. 1121 ◽  
Author(s):  
Nor Azizah Hitam ◽  
Amelia Ritahani Ismail
Keyword(s):  
Machine Learning ◽  
Time Series Data ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Support Vector ◽  
Small Range ◽  
Accuracy Rate ◽  
Comparative Performance ◽  
Vector Machines

Machine Learning is part of Artificial Intelligence that has the ability to make future forecastings based on the previous experience. Methods has been proposed to construct models including machine learning algorithms such as Neural Networks (NN), Support Vector Machines (SVM) and Deep Learning. This paper presents a comparative performance of Machine Learning algorithms for cryptocurrency forecasting. Specifically, this paper concentrates on forecasting of time series data. SVM has several advantages over the other models in forecasting, and previous research revealed that SVM provides a result that is almost or close to actual result yet also improve the accuracy of the result itself. However, recent research has showed that due to small range of samples and data manipulation by inadequate evidence and professional analyzers, overall status and accuracy rate of the forecasting needs to be improved in further studies. Thus, advanced research on the accuracy rate of the forecasted price has to be done.

Combination with Machine Learning Algorithms for the Classification in E-Bussiness

2011 ◽  
Vol 230-232 ◽  
pp. 625-628
Author(s):  
Lei Shi ◽  
Xin Ming Ma ◽  
Xiao Hong Hu
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Mathematical Tool ◽  
Vector Machines

E-bussiness has grown rapidly in the last decade and massive amount of data on customer purchases, browsing pattern and preferences has been generated. Classification of electronic data plays a pivotal role to mine the valuable information and thus has become one of the most important applications of E-bussiness. Support Vector Machines are popular and powerful machine learning techniques, and they offer state-of-the-art performance. Rough set theory is a formal mathematical tool to deal with incomplete or imprecise information and one of its important applications is feature selection. In this paper, rough set theory and support vector machines are combined to construct a classification model to classify the data of E-bussiness effectively.

scholarly journals Linear Support Vector Machines for Prediction of Student Performance in School-Based Education

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Nalindren Naicker ◽  
Timothy Adeliyi ◽  
Jeanette Wing
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Student Performance ◽  
State Of The Art ◽  
Learning Algorithms ◽  
The State ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Support Vector ◽  
Vector Machines

Educational Data Mining (EDM) is a rich research field in computer science. Tools and techniques in EDM are useful to predict student performance which gives practitioners useful insights to develop appropriate intervention strategies to improve pass rates and increase retention. The performance of the state-of-the-art machine learning classifiers is very much dependent on the task at hand. Investigating support vector machines has been used extensively in classification problems; however, the extant of literature shows a gap in the application of linear support vector machines as a predictor of student performance. The aim of this study was to compare the performance of linear support vector machines with the performance of the state-of-the-art classical machine learning algorithms in order to determine the algorithm that would improve prediction of student performance. In this quantitative study, an experimental research design was used. Experiments were set up using feature selection on a publicly available dataset of 1000 alpha-numeric student records. Linear support vector machines benchmarked with ten categorical machine learning algorithms showed superior performance in predicting student performance. The results of this research showed that features like race, gender, and lunch influence performance in mathematics whilst access to lunch was the primary factor which influences reading and writing performance.

LOCAL DESCRIPTOR MATCHING WITH SUPPORT VECTOR MACHINES

2010 ◽  
Vol 07 (01) ◽  
pp. 59-80
Author(s):  
D. CHENG ◽  
S. Q. XIE ◽  
E. HÄMMERLE
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Local Descriptor ◽  
Local Descriptors ◽  
Vector Machines ◽  
Three Stages ◽  
Image Transformations

Local descriptor matching is the most overlooked stage of the three stages of the local descriptor process, and this paper proposes a new method for matching local descriptors based on support vector machines. Results from experiments show that the developed method is more robust for matching local descriptors for all image transformations considered. The method is able to be integrated with different local descriptor methods, and with different machine learning algorithms and this shows that the approach is sufficiently robust and versatile.

scholarly journals Machine Learning for Predictions in Academics

2020 ◽  
Vol 8 (5) ◽  
pp. 4624-4627
Keyword(s):  
Machine Learning ◽  
Career Path ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Algorithms ◽  
Bayes Classifier ◽  
Logistics Regression ◽  
Learning Techniques

In recent years, a lot of data has been generated about students, which can be utilized for deciding the career path of the student. This paper discusses some of the machine learning techniques which can be used to predict the performance of a student and help to decide his/her career path. Some of the key Machine Learning (ML) algorithms applied in our research work are Linear Regression, Logistics Regression, Support Vector machine, Naïve Bayes Classifier and K- means Clustering. The aim of this paper is to predict the student career path using Machine Learning algorithms. We compare the efficiencies of different ML classification algorithms on a real dataset obtained from University students.

scholarly journals An prediction of Healthy Diet required to Ease the recovery from Covid-19 using the approach of Machine Learning.

2022 ◽  
Vol 2161 (1) ◽  
pp. 012019
Author(s):  
Rencita Maria Colaco ◽  
Shreya ◽  
N V Subba Reddy ◽  
U Dinesh Acharya
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Machine Learning Algorithms ◽  
Healthy Diet ◽  
Support Vector ◽  
Decision Tree Algorithm ◽  
Huge Number ◽  
Vector Machines ◽  
The World ◽  
Main Factor

Abstract Global terror that has shaken the world named, COVID-19 virus has taken away huge number of lives. According to the research there are lot of recovery cases also. Most important thing to survive from this disease is having good immunity. Everyone does not have same level of immunity. One main factor on which immunity depends is having a healthy diet. If the routine of having healthy diet is maintained, then the immunity to fight against this virus increases. It is much required that people need to be informed about having an healthy diet. Using the dataset of healthy dietary and using various machine learning algorithms we can determine what type of diet one person needs to have. By using algorithms like Random Forest, KNN, logistic regression and Support Vector Machines we can determine the type of diet and probability of recovery. The dataset required for analysis needs to have all the information regarding the diet. Based on the dataset the prediction is taken place by using Decision Tree algorithm. This method of finding the appropriate diet of a particular person based on amount of Sugar level, Blood Pressure and BMI can be the most useful research in this pandemic time.

scholarly journals Machine Learning Prediction of Hospitalization due to COVID-19 based on Self-Reported Symptoms: A Study for Brazil*

2021 ◽  
Author(s):  
Igor Miranda ◽  
Gildeberto Cardoso ◽  
Madhurananda Pahar ◽  
Gabriel Oliveira ◽  
Thomas Niesler
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Health Professionals ◽  
Human Development Index ◽  
Mobile App ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Vector Machines ◽  
Timely Treatment

Predicting the need for hospitalization due to COVID-19 may help patients to seek timely treatment and assist health professionals to monitor cases and allocate resources. We investigate the use of machine learning algorithms to predict the risk of hospitalization due to COVID-19 using the patient's medical history and self-reported symptoms, regardless of the period in which they occurred. Three datasets containing information regarding 217,580 patients from three different states in Brazil have been used. Decision trees, neural networks, and support vector machines were evaluated, achieving accuracies between 79.1% to 84.7%. Our analysis shows that better performance is achieved in Brazilian states ranked more highly in terms of the official human development index (HDI), suggesting that health facilities with better infrastructure generate data that is less noisy. One of the models developed in this study has been incorporated into a mobile app that is available for public use.

scholarly journals Airline Delay Prediction using Machine Learning and Deep Learning Techniques

2020 ◽  
Vol 9 (2) ◽  
pp. 1049-1054
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Short Term Memory ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Maximum Delay ◽  
Learning Techniques ◽  
Delay Prediction

In this paper, we have tried to predict flight delays using different machine learning and deep learning techniques. By using such a model it can be easier to predict whether the flight will be delayed or not. Factors like ‘WeatherDelay’, ‘NASDelay’, ‘Destination’, ‘Origin’ play a vital role in this model. Using machine learning algorithms like Random Forest, Support Vector Machine (SVM) and K-Nearest Neighbors (KNN), the f1-score, precision, recall, support and accuracy have been predicted. To add to the model, Long Short-Term Memory (LSTM) RNN architecture has also been employed. In the paper, the dataset from Bureau of Transportation Statistics (BTS) of the ‘Pittsburgh’ is being used. The results computed from the above mentioned algorithms have been compared. Further, the results were visualized for various airlines to find maximum delay and AUC-ROC curve has been plotted for Random Forest Algorithm. The aim of our research work is to predict the delay so as to minimize loses and increase customer satisfaction.

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