Relevant Independent Variables on MOBA Video Games to Train Machine Learning Algorithms

Multidimensional Data ◽

Data Sets ◽

Network Architectures ◽

Independent Variables ◽

Learning Techniques ◽

Multidimensional Data Sets

Popularity of Multiplayer Online Battle Arena (MOBA) video games has grown considerably, its popularity as well as the complexity of their playability, have attracted the attention in recent years of researchers from various areas of knowledge and in particular how they have resorted to different machine learning techniques. The papers reviewed mainly look for patterns in multidimensional data sets. Furthermore, these previous researches do not present a way to select the independent variables (predictors) to train the models. For this reason, this paper proposes a list of variables based on the techniques used and the objectives of the research. It allows to provide a set of variables to find patterns applied in MOBA videogames. In order to get the mentioned list, the consulted works were grouped by the used machine learning techniques, ranging from rule-based systems to complex neural network architectures. Also, a grouping technique is applied based on the objective of each research proposed.

Software Defect Estimation using Machine Learning Algorithms

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.a5898.0510121 ◽

2021 ◽

Vol 10 (1) ◽

pp. 204-208

Author(s):

Mandi Akif Hussain* ◽

Revoori Veeharika Reddy ◽

Kedharnath Nagella ◽

Vidya S.

Keyword(s):

Machine Learning ◽

Software Engineering ◽

Computer Science ◽

Data Sets ◽

System Software ◽

Software Defect ◽

Learning Techniques ◽

And Training

Software Engineering is a branch of computer science that enables tight communication between system software and training it as per the requirement of the user. We have selected seven distinct algorithms from machine learning techniques and are going to test them using the data sets acquired for NASA public promise repositories. The results of our project enable the users of this software to bag up the defects are selecting the most efficient of given algorithms in doing their further respective tasks, resulting in effective results.

Sentiment Analysis Based on Reviews Using Machine Learning Techniques

Pakistan Journal of Engineering and Technology ◽

10.51846/vol4iss2pp149-152 ◽

2021 ◽

Vol 4 (2) ◽

pp. 149-152

Author(s):

Ameema Sattar ◽

Joddat Fatima

Keyword(s):

Machine Learning ◽

Sentiment Analysis ◽

Data Sets ◽

Sources Of Information ◽

Main Method ◽

Learning Techniques ◽

Clear Result ◽

Digital Time

In our daily life, people’s opinions and experiences are important sources of information. To measure the feeling of people’s opinions the term used that is called sentiment analysis. The text is the main method of communicating on the Internet in modern digital time. Sentiment analysis captures the user’s views, moods, and their opinion related to the specific services provided by the business organization in a real-time. This research focuses on Roman Urdu reviews. It has three basic classes: negative, positive, and neutral where reviews are classified. The proposed method is Analysis of different machine learning algorithms with different datasets has made and a comparison shows, SVM performs the best result on used data sets, a clear result in the form of accuracy, precision, recall, and f1 score shows the results against the specific techniques against the dataset.

Methodology for Analyzing the Traditional Algorithms Performance of User Reviews Using Machine Learning Techniques

Algorithms ◽

10.3390/a13080202 ◽

2020 ◽

Vol 13 (8) ◽

pp. 202

Author(s):

Abdul Karim ◽

Azhari Azhari ◽

Samir Brahim Belhaouri ◽

Ali Adil Qureshi ◽

Maqsood Ahmad

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Data Sets ◽

User Reviews ◽

Almost Everywhere ◽

Document Frequency ◽

Learning Techniques ◽

Google Play

Android-based applications are widely used by almost everyone around the globe. Due to the availability of the Internet almost everywhere at no charge, almost half of the globe is engaged with social networking, social media surfing, messaging, browsing and plugins. In the Google Play Store, which is one of the most popular Internet application stores, users are encouraged to download thousands of applications and various types of software. In this research study, we have scraped thousands of user reviews and the ratings of different applications. We scraped 148 application reviews from 14 different categories. A total of 506,259 reviews were accumulated and assessed. Based on the semantics of reviews of the applications, the results of the reviews were classified negative, positive or neutral. In this research, different machine-learning algorithms such as logistic regression, random forest and naïve Bayes were tuned and tested. We also evaluated the outcome of term frequency (TF) and inverse document frequency (IDF), measured different parameters such as accuracy, precision, recall and F1 score (F1) and present the results in the form of a bar graph. In conclusion, we compared the outcome of each algorithm and found that logistic regression is one of the best algorithms for the review-analysis of the Google Play Store from an accuracy perspective. Furthermore, we were able to prove and demonstrate that logistic regression is better in terms of speed, rate of accuracy, recall and F1 perspective. This conclusion was achieved after preprocessing a number of data values from these data sets.

A Study on Machine Learning in Big Data

Oriental journal of computer science and technology ◽

10.13005/ojcst/10.03.15 ◽

2017 ◽

Vol 10 (3) ◽

pp. 660-663

Author(s):

L. Dhanapriya ◽

Dr. S. MANJU

Keyword(s):

Machine Learning ◽

Big Data ◽

Large Data ◽

Large Data Sets ◽

Data Sets ◽

Huge Data ◽

Learning Techniques ◽

Market Needs

In the recent development of IT technology, the capacity of data has surpassed the zettabyte, and improving the efficiency of business is done by increasing the ability of predictive through an efficient analysis on these data which has emerged as an issue in the current society. Now the market needs for methods that are capable of extracting valuable information from large data sets. Recently big data is becoming the focus of attention, and using any of the machine learning techniques to extract the valuable information from the huge data of complex structures has become a concern yet an urgent problem to resolve. The aim of this work is to provide a better understanding of this Machine Learning technique for discovering interesting patterns and introduces some machine learning algorithms to explore the developing trend.

Using Machine Learning Algorithms on Prediction of Stock Price

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

2020 ◽

Vol 12 (2) ◽

pp. 84-99

Author(s):

Li-Pang Chen

Keyword(s):

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Support Vector ◽

Short Term ◽

Learning Techniques ◽

Historical Database ◽

Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

International Journal of Advanced Research in Computer Science and Software Engineering ◽

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

10.23956/ijarcsse/v7i7/0177 ◽

2017 ◽

Vol 7 (7) ◽

pp. 172

Author(s):

Anantvir Singh Romana

Keyword(s):

Machine Learning ◽

Subsequent Treatment ◽

Support Vector ◽

Disease Prediction ◽

Classification Problems ◽

Learning Techniques ◽

Neural Network Classifiers ◽

Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

Feasibility of Machine Learning Algorithms for Predicting the Deformation of Anodic Titanium Films by Modulating Anodization Processes

Materials ◽

10.3390/ma14051089 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1089

Author(s):

Sung-Hee Kim ◽

Chanyoung Jeong

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Multiclass Classification ◽

Smart Manufacturing ◽

Gradient Boosting ◽

Experimental Conditions ◽

Learning Techniques ◽

Tio2 Nanostructures

This study aims to demonstrate the feasibility of applying eight machine learning algorithms to predict the classification of the surface characteristics of titanium oxide (TiO2) nanostructures with different anodization processes. We produced a total of 100 samples, and we assessed changes in TiO2 nanostructures’ thicknesses by performing anodization. We successfully grew TiO2 films with different thicknesses by one-step anodization in ethylene glycol containing NH4F and H2O at applied voltage differences ranging from 10 V to 100 V at various anodization durations. We found that the thicknesses of TiO2 nanostructures are dependent on anodization voltages under time differences. Therefore, we tested the feasibility of applying machine learning algorithms to predict the deformation of TiO2. As the characteristics of TiO2 changed based on the different experimental conditions, we classified its surface pore structure into two categories and four groups. For the classification based on granularity, we assessed layer creation, roughness, pore creation, and pore height. We applied eight machine learning techniques to predict classification for binary and multiclass classification. For binary classification, random forest and gradient boosting algorithm had relatively high performance. However, all eight algorithms had scores higher than 0.93, which signifies high prediction on estimating the presence of pore. In contrast, decision tree and three ensemble methods had a relatively higher performance for multiclass classification, with an accuracy rate greater than 0.79. The weakest algorithm used was k-nearest neighbors for both binary and multiclass classifications. We believe that these results show that we can apply machine learning techniques to predict surface quality improvement, leading to smart manufacturing technology to better control color appearance, super-hydrophobicity, super-hydrophilicity or batter efficiency.

Improving Reliability Estimation for Individual Numeric Predictions: A Machine Learning Approach

INFORMS Journal on Computing ◽

10.1287/ijoc.2020.1019 ◽

2021 ◽

Author(s):

Gediminas Adomavicius ◽

Yaqiong Wang

Keyword(s):

Machine Learning ◽

General Purpose ◽

Reliability Estimation ◽

Data Sets ◽

Real World Data ◽

Learning Techniques ◽

Reliability Indicator ◽

Machine Learning Approach ◽

Prediction Reliability

Numerical predictive modeling is widely used in different application domains. Although many modeling techniques have been proposed, and a number of different aggregate accuracy metrics exist for evaluating the overall performance of predictive models, other important aspects, such as the reliability (or confidence and uncertainty) of individual predictions, have been underexplored. We propose to use estimated absolute prediction error as the indicator of individual prediction reliability, which has the benefits of being intuitive and providing highly interpretable information to decision makers, as well as allowing for more precise evaluation of reliability estimation quality. As importantly, the proposed reliability indicator allows the reframing of reliability estimation itself as a canonical numeric prediction problem, which makes the proposed approach general-purpose (i.e., it can work in conjunction with any outcome prediction model), alleviates the need for distributional assumptions, and enables the use of advanced, state-of-the-art machine learning techniques to learn individual prediction reliability patterns directly from data. Extensive experimental results on multiple real-world data sets show that the proposed machine learning-based approach can significantly improve individual prediction reliability estimation as compared with a number of baselines from prior work, especially in more complex predictive scenarios.

Heart disease prediction using machine learning techniques : a survey

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.8.10557 ◽

2018 ◽

Vol 7 (2.8) ◽

pp. 684 ◽

Cited By ~ 12

Author(s):

V V. Ramalingam ◽

Ayantan Dandapath ◽

M Karthik Raja

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

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.

Machine Learning for Neurodegenerative Disorder Diagnosis — Survey of Practices and Launch of Benchmark Dataset

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213018500112 ◽

2018 ◽

Vol 27 (03) ◽

pp. 1850011 ◽

Cited By ~ 5

Author(s):

Athanasios Tagaris ◽

Dimitrios Kollias ◽

Andreas Stafylopatis ◽

Georgios Tagaris ◽

Stefanos Kollias

Keyword(s):

Machine Learning ◽

Neurodegenerative Disorder ◽

Developed Countries ◽

Network Architectures ◽

Imaging Data ◽

Learning Techniques ◽

Medical Examinations

Neurodegenerative disorders, such as Alzheimer’s and Parkinson’s, constitute a major factor in long-term disability and are becoming more and more a serious concern in developed countries. As there are, at present, no effective therapies, early diagnosis along with avoidance of misdiagnosis seem to be critical in ensuring a good quality of life for patients. In this sense, the adoption of computer-aided-diagnosis tools can offer significant assistance to clinicians. In the present paper, we provide in the first place a comprehensive recording of medical examinations relevant to those disorders. Then, a review is conducted concerning the use of Machine Learning techniques in supporting diagnosis of neurodegenerative diseases, with reference to at times used medical datasets. Special attention has been given to the field of Deep Learning. In addition to that, we communicate the launch of a newly created dataset for Parkinson’s disease, containing epidemiological, clinical and imaging data, which will be publicly available to researchers for benchmarking purposes. To assess the potential of the new dataset, an experimental study in Parkinson’s diagnosis is carried out, based on state-of-the-art Deep Neural Network architectures and yielding very promising accuracy results.