Pub Date : 2022-07-28DOI: 10.1109/IAICT55358.2022.9887525
Stanlly, Fauzan Ardhana Putra, N. N. Qomariyah
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.
在过去的十年里，电子游戏已经成为整个市场的巨人，到2021年，电子游戏的价值估计将达到1800亿美元。除了在整个市场中越来越大的影响力外，电子游戏还创造了一种新的竞争形式，即电子竞技，在这种形式中，某些电子游戏的高技能玩家在比赛中相互对抗，看看谁的技能最强，并在最后赢得奖品。电子竞技只是人们对能够预测玩家之间任何给定比赛结果的想法感兴趣的众多原因之一。在这项研究中，我们研究了决定《Defense of the Ancients 2》(即DOTA 2)比赛输赢的某些因素的重要性。我们发现道具和英雄的选择在赢得任何一场比赛中都扮演着重要角色。由此，我们得出结论，我们将能够仅基于这两个因素预测比赛结果，并创建模型来预测任何给定比赛的结果。在本研究中，我们将使用决策树、随机树和XGBoost分类器来创建我们的模型。最终，XGBoost模型成为了我们的最佳模型，其准确率约为93%，可以在大约一分钟内预测结果。
{"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}
Pub Date : 2022-07-28DOI: 10.1109/IAICT55358.2022.9887526
Zahra Solatidehkordi, Jayroop Ramesh, Michel Pasquier, A. Sagahyroon, F. Aloul
Depression is one of the most common mental health issues worldwide and has only become more widespread after the emergence of the Covid-19 pandemic. Although depression can be treated through various methods, it often goes undiagnosed and therefore untreated, forcing individuals to go through life with a condition that is nothing short of debilitating. With mobile phones being an integral part of people’s lives, they can provide valuable information about a person’s habits and behaviors, which can then be used to detect depressive tendencies. This paper provides a review of several studies conducted in recent years on the possibility of using machine learning and smartphone data to detect depression.
{"title":"A Survey of Machine Learning Approaches for Detecting Depression Using Smartphone Data","authors":"Zahra Solatidehkordi, Jayroop Ramesh, Michel Pasquier, A. Sagahyroon, F. Aloul","doi":"10.1109/IAICT55358.2022.9887526","DOIUrl":"https://doi.org/10.1109/IAICT55358.2022.9887526","url":null,"abstract":"Depression is one of the most common mental health issues worldwide and has only become more widespread after the emergence of the Covid-19 pandemic. Although depression can be treated through various methods, it often goes undiagnosed and therefore untreated, forcing individuals to go through life with a condition that is nothing short of debilitating. With mobile phones being an integral part of people’s lives, they can provide valuable information about a person’s habits and behaviors, which can then be used to detect depressive tendencies. This paper provides a review of several studies conducted in recent years on the possibility of using machine learning and smartphone data to detect depression.","PeriodicalId":154027,"journal":{"name":"2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"8 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":"114225976","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}
Pub Date : 2022-07-28DOI: 10.1109/IAICT55358.2022.9887400
E. B. Nejad, Carla Silva, A. Rodrigues, A. Jorge, I. Dutra
Change point detection methods try to find any sudden changes in the patterns and features of a given time series. In this paper a new change point detection method is presented, where the window width is automatically calculated. The proposed algorithm, AutoSW, is based on a Sliding Window search method of the Python ruptures package and uses a subset of statistical concepts to compute a possibly optimal window width. The proposed algorithm is compared with some other popular methods such as PELT using different real-world and synthetic time series. Results show that AutoSW can perform better than PELT producing a better set of change points in the time series tested.
{"title":"AutoSW: a new automated sliding window-based change point detection method for sensor data","authors":"E. B. Nejad, Carla Silva, A. Rodrigues, A. Jorge, I. Dutra","doi":"10.1109/IAICT55358.2022.9887400","DOIUrl":"https://doi.org/10.1109/IAICT55358.2022.9887400","url":null,"abstract":"Change point detection methods try to find any sudden changes in the patterns and features of a given time series. In this paper a new change point detection method is presented, where the window width is automatically calculated. The proposed algorithm, AutoSW, is based on a Sliding Window search method of the Python ruptures package and uses a subset of statistical concepts to compute a possibly optimal window width. The proposed algorithm is compared with some other popular methods such as PELT using different real-world and synthetic time series. Results show that AutoSW can perform better than PELT producing a better set of change points in the time series tested.","PeriodicalId":154027,"journal":{"name":"2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"2 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":"114435509","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}
Pub Date : 2022-07-28DOI: 10.1109/IAICT55358.2022.9887488
Opeoluwa Tosin Eluwole, Segun Akande
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.
{"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}
Pub Date : 2022-07-28DOI: 10.1109/IAICT55358.2022.9887378
Fatemeh Rezapoor Nikroo, Manar Amayri, N. Bouguila
Hidden Markov model (HMM) is a classic machine learning technique to model sequences. Analyzing the characteristics of this model has been extensively studied in the past. In this paper we go through parameter estimation of HMM. We apply recursive technique in order to be able to handle real time data without suffering from extensive time complexity and memory usage in calculation. In this context, we investigate recursive parameter estimation of generalized Dirichlet (GD) HMM via the expectation-maximization (EM) framework. The GD HMM is shown to be an interesting alternative to the Dirichlet HMM. Extensive simulations based on synthetic and real data to estimate occupancy in smart buildings show the effectiveness of the recursive approach for parameter estimation.
{"title":"Recursive Parameter Estimation of Generalized Dirichlet Hidden Markov Models: Application to Occupancy Estimation in Smart Buildings","authors":"Fatemeh Rezapoor Nikroo, Manar Amayri, N. Bouguila","doi":"10.1109/IAICT55358.2022.9887378","DOIUrl":"https://doi.org/10.1109/IAICT55358.2022.9887378","url":null,"abstract":"Hidden Markov model (HMM) is a classic machine learning technique to model sequences. Analyzing the characteristics of this model has been extensively studied in the past. In this paper we go through parameter estimation of HMM. We apply recursive technique in order to be able to handle real time data without suffering from extensive time complexity and memory usage in calculation. In this context, we investigate recursive parameter estimation of generalized Dirichlet (GD) HMM via the expectation-maximization (EM) framework. The GD HMM is shown to be an interesting alternative to the Dirichlet HMM. Extensive simulations based on synthetic and real data to estimate occupancy in smart buildings show the effectiveness of the recursive approach for parameter estimation.","PeriodicalId":154027,"journal":{"name":"2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"10 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":"125039295","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}
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