An Automated Visa Prediction Technique for Higher Studies Using Machine Learning in the Context of Bangladesh

2021 ◽  
pp. 557-567
Author(s):  
Asif Ahmmed ◽  
Tipu Sultan ◽  
Sk. Hasibul Islam Shad ◽  
Md. Jueal Mia ◽  
Sourov Mazumder
Keyword(s):  
Machine Learning ◽  
Prediction Technique

scholarly journals An Efficient Software Error Prediction and Recommend System

2019 ◽  
Vol 9 (2) ◽  
pp. 5300-5303
Keyword(s):  
Machine Learning ◽  
Modern Technology ◽  
Element Determination ◽  
Software Defect ◽  
Learning Technique ◽  
Prediction Technique ◽  
Information Sets ◽  
Information Set ◽  
Efficient Software

This present paper proposes the while its beginning, and past years software testing has been involved. Modern technology in software is using Artificial Intelligent and machine learning for advancing the technology. According to software engineering various techniques are analysed depending on the required predictions. Here in order to give the importance for the development of software defect prediction technique helps foe testers to focus on modules that defect prone. Depending on the development aspect the literature survey states various techniques based on features that are mostly captured for the prediction of defects. So in this paper we give a novel machine learning technique which is the foremost objective for finding prospective areas defects by considering various parameters like system testing metrics and unique parameters called ‘Component Dependency Score’. By applying element determination method we can reduce the words present in defect information and also there will be an expansion in precision so that both systems can build the additional qualities like precision and reducing defect reports or words. Depending on this new technique we can reduce the defect information sets for getting 71.8 percent exactness for reducing the request. The present issue reducing information to defect and increase the information set of defect in two viewpoints such as all the while diminish the sizes of defect extent and the word extent and to enhance the precision of defect triage. So we propose a mix way for dealing with of attention of issuing for reducing information. This is viewed as an example for purpose of choice highlighting in defecting store house. So we construct a parallel categoriser for expecting the request in determination of applying example and highlighting choices. Here the request for applying occurrence in highlighting the choices is not yet related to the research space.

scholarly journals Intelligent predicting reaction performance in multi-dimensional chemical space using quantile regression forest

2021 ◽  
Vol 87 (2) ◽  
pp. 299-318
Author(s):  
◽  
Jing Dong ◽  
Xuechun Mu ◽  
Zelin Zhang ◽  
Yuqing Zhang ◽  
...  
Keyword(s):  
Organic Chemistry ◽  
Machine Learning ◽  
Chemical Space ◽  
Yield Prediction ◽  
Feature Descriptors ◽  
Amination Reaction ◽  
Prediction Technique ◽  
Experimental Process ◽  
Reaction Performance ◽  
Feature Dimensionality Reduction

Buchwald-Hartwig amination reaction is widely applied in synthetic organic chemistry, which faces tedious and complex experimental process. In 2018, an interesting yield prediction technique is proposed via machine learning (random forest) in Science. However, the method is based on point prediction with many feature descriptors. For tackling these problems, complements and improvements have been made from the perspectives of machine learning and statistics, including feature dimensionality reduction, distributed prediction and visualization, so as to provide accurate and reliable decision information.

scholarly journals Machine Learning Methods for Diabetes Prediction

2019 ◽  
Vol 8 (12) ◽  
pp. 2199-2205
Keyword(s):  
Machine Learning ◽  
Risk Factor ◽  
Research Field ◽  
Support Vector ◽  
Machine Learning Methods ◽  
Health Practitioners ◽  
Prediction Technique ◽  
Diabetes Prediction ◽  
Machine Learning Models

Machine Learning is one of the methods used for task prediction. In the diabetic’s research field, the application of machine learning is emerging since the advantages of approximation on the prediction technique has significantly given insight for many health practitioners. Machine Learning is utilized in order to handle the uncontrollable risk factor by finding a relation between such a risk factor trough prediction. This study aims to review recent machine learning models that have been used in diabetes prediction with respect to the risk factors in order to prevent diabetes. This study compares the performance of the model by justified the accuracy as the baseline to evaluate the model. The result of this review shows that the Random Forest and Support Vector Machine are the most popular technique among researcher. Moreover, from this study, it can be seen that Type 2 Diabetes Mellitus (T2DM) has been a concern by researchers since the incidence of diabetes was increasing in worldwide today that happened from an uncontrollable risk factor

scholarly journals Least Square Regression for Prediction Problems in Machine Learning using R

2018 ◽  
Vol 7 (3.12) ◽  
pp. 960
Author(s):  
Anila. M ◽  
G Pradeepini
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Ordinary Least Squares ◽  
Least Square ◽  
Least Squares Regression ◽  
Ols Regression ◽  
Least Square Regression ◽  
Psychology And Economics ◽  
Prediction Technique ◽  
Prediction Problems

The most commonly used prediction technique is Ordinary Least Squares Regression (OLS Regression). It has been applied in many fields like statistics, finance, medicine, psychology and economics. Many people, specially Data Scientists using this technique know that it has not gone with enough training to apply it and should be checked why & when it can or can’t be applied.It’s not easy task to find or explain about why least square regression [1] is faced much criticism when trained and tried to apply it. In this paper, we mention firstly about fundamentals of linear regression and OLS regression along with that popularity of LS method, we present our analysis of difficulties & pitfalls that arise while OLS method is applied, finally some techniques for overcoming these problems.  

scholarly journals Behavior Prophecy of Stock Trader using Machine Learning Techniques

2019 ◽  
Vol 8 (11) ◽  
pp. 3620-3624
Keyword(s):  
Machine Learning ◽  
Human Behavior ◽  
User Behavior ◽  
Machine Learning Techniques ◽  
Web Based ◽  
Learning Techniques ◽  
Prediction Technique ◽  
Irregular Behavior ◽  
The Web ◽  
Unique Application

The web utilization by users is expanding very rapidly. Users are getting to data and administrations effectively through different media like social correspondence, sight and sound substance, web based trading, banking administrations and so forth. It winds up provoking undertaking to precisely recognize and separate typical and suspicious human behavior conduct. Every unique application need to predict user behavior to forecast and upgrade their administration quality. This work gives the examination of stock trader conduct recognition and expectation. Many Machine Learning (ML) methods and recognizable proof strategies are looked at and examined for stock trader behavior analysis. Their parameters are considered and enhancements are recommended. The proposed procedure portrays stock trader conduct discovery framework. The vital segment examination is the classification and prediction technique used to recognize and understand the typical and irregular behavior of the stock trader.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining
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