2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)最新文献_第5页

DOTA 2 Win Loss Prediction from Item and Hero Data with Machine Learning 基于机器学习的dota2物品和英雄数据输赢预测

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887525

Stanlly, Fauzan Ardhana Putra, N. N. Qomariyah

{"title":"DOTA 2 Win Loss Prediction from Item and Hero Data with Machine Learning","authors":"Stanlly, Fauzan Ardhana Putra, N. N. Qomariyah","doi":"10.1109/IAICT55358.2022.9887525","DOIUrl":"https://doi.org/10.1109/IAICT55358.2022.9887525","url":null,"abstract":"Video gaming has become a titan in the overall market over the past decade, culminating in an estimated worth almost 180 billion US dollars by 2021. Aside from its growing influence in the overall market, video games have also created a new competitive format called eSports, a format where highly skilled players of certain video games play against each other in a tournament to see who the most skilled are and win a prize at the end. ESports are just one of many reasons why people have become interested in the idea of being able to predict the outcome of any given match between players. In this study, We conducted research on the importance of certain factors in determining the win or loss of any given Defense of the Ancients 2, better known as DOTA 2, match. We found that Item and Hero choices play a large role in winning any given match. From this, we concluded that we would be able to predict a match’s outcome solely based off of these two factors and created models to predict the outcome of any given match. In this study, we will be employing the use of Decision Tree, Random Tree and XGBoost classifiers in order to create our models. In the end, the XGBoost model ended up being our best model, with an accuracy of roughly 93% which can predict an outcome in roughly one minute.","PeriodicalId":154027,"journal":{"name":"2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114825030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Survey of Machine Learning Approaches for Detecting Depression Using Smartphone Data 使用智能手机数据检测抑郁症的机器学习方法的调查

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887526

Zahra Solatidehkordi, Jayroop Ramesh, Michel Pasquier, A. Sagahyroon, F. Aloul

引用次数: 0

AutoSW: a new automated sliding window-based change point detection method for sensor data AutoSW:一种新的基于自动滑动窗口的传感器数据变化点检测方法

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887400

E. B. Nejad, Carla Silva, A. Rodrigues, A. Jorge, I. Dutra

引用次数: 0

Artificial Intelligence in Finance: Possibilities and Threats 金融中的人工智能:可能性与威胁

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887488

Opeoluwa Tosin Eluwole, Segun Akande

{"title":"Artificial Intelligence in Finance: Possibilities and Threats","authors":"Opeoluwa Tosin Eluwole, Segun Akande","doi":"10.1109/IAICT55358.2022.9887488","DOIUrl":"https://doi.org/10.1109/IAICT55358.2022.9887488","url":null,"abstract":"Artificial intelligence (AI) alongside one of its main subsets, machine learning (ML), is no longer a sheer propaganda, it has nearly become a household name, though the use of the term AI by the public and at times technologists is often a misnomer. This paper explores AI and ML, outlining the main categories of extensive ML algorithmic techniques. Importantly, it provides handy timeline and distinction between the duo, whilst also introducing multiple lens views as to their potentials in the finance industry, covering the triad of financial, regulatory and insurance technologies (FinTech, RegTech, InsurTech). Certainly, AI/ML has found practical applications in finance; whether it is generating insights on customer spending, obtaining informed underwriting risk outcomes, detecting anomalous fiscal transactions or interacting with customers using natural language, AI/ML potentials in finance is gaining significant momentum in today’s world of near ubiquity Internet of Things (IoT), advanced computing and telecommunication technologies. Without downplaying the potential capabilities, what is less certain however is whether there are any frontiers to its applications in finance, and whether it will provide panaceas to the pressing challenges, especially in relation to transparency from a collective viewpoint of AI/ML solution design, development and implementation.","PeriodicalId":154027,"journal":{"name":"2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114882248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Recursive Parameter Estimation of Generalized Dirichlet Hidden Markov Models: Application to Occupancy Estimation in Smart Buildings 广义Dirichlet隐马尔可夫模型递归参数估计在智能建筑占用估计中的应用

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887378

Fatemeh Rezapoor Nikroo, Manar Amayri, N. Bouguila

引用次数: 0