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Training Autonomous Vehicles in Carla model using Augmented Random Search Algorithm 使用增强随机搜索算法在Carla模型中训练自动驾驶车辆
Journal of Applied Data Sciences Pub Date : 2021-04-23 DOI: 10.47738/jads.v2i2.29
R. Riyanto, Abdul Azis, Tarwoto Tarwoto, W. Deng
{"title":"Training Autonomous Vehicles in Carla model using Augmented Random Search Algorithm","authors":"R. Riyanto, Abdul Azis, Tarwoto Tarwoto, W. Deng","doi":"10.47738/jads.v2i2.29","DOIUrl":"https://doi.org/10.47738/jads.v2i2.29","url":null,"abstract":"CARLA is an open source simulator for autonomous driving research. CARLA has been developed from scratch to support the development, training and validation of autonomous driving systems. In addition to open source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that are created for this purpose and can be used freely. We use CARLA to study the performance of Augmented Random Search (ARS) to autonomous driving: a classic modular pipeline, an end-to-end model trained via imitation learning, and an end-to-end model trained via reinforcement learning. Test the ability of the Augmented Random Search (ARS) algorithm to train driverless cars on data collected from the front cameras per car. In this study, a framework that can be used to train driverless car policy using ARS in Carla will be built. Although effective policies were not achieved after the first round of training, many insights on how to improve these outcomes in the future have been obtained.","PeriodicalId":341738,"journal":{"name":"Journal of Applied Data Sciences","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129107178","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: 1
Comparing Epsilon Greedy and Thompson Sampling model for Multi-Armed Bandit algorithm on Marketing Dataset 市场数据集上多武装强盗算法的Epsilon Greedy和Thompson抽样模型比较
Journal of Applied Data Sciences Pub Date : 2021-04-23 DOI: 10.47738/jads.v2i2.28
Izzatul Umami, Lailia Rahmawati
{"title":"Comparing Epsilon Greedy and Thompson Sampling model for Multi-Armed Bandit algorithm on Marketing Dataset","authors":"Izzatul Umami, Lailia Rahmawati","doi":"10.47738/jads.v2i2.28","DOIUrl":"https://doi.org/10.47738/jads.v2i2.28","url":null,"abstract":"A/B checking is a regular measure in many marketing procedures for e-Commerce companies. Through well-designed A/B research, advertisers can gain insight about when and how marketing efforts can be maximized and active promotions driven. Whilst many algorithms for the problem are theoretically well developed, empirical confirmation is typically restricted. In practical terms, standard A/B experimentation makes less money relative to more advanced machine learning methods. This paper presents a thorough empirical study of the most popular multi-strategy algorithms. Three important observations can be made from our results. First, simple heuristics such as Epsilon Greedy and Thompson Sampling outperform theoretically sound algorithms in most settings by a significant margin. In this report, the state of A/B testing is addressed, some typical A/B learning algorithms (Multi-Arms Bandits) used to optimize A/B testing are described and comparable. We found that Epsilon Greedy, be an exceptional winner to optimize payouts in this situation.","PeriodicalId":341738,"journal":{"name":"Journal of Applied Data Sciences","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122231074","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: 7
Modeling and Forecasting Long-Term Records of Mean Sea Level at Grand Isle, Louisiana: SARIMA, NARNN, and Mixed SARIMA-NARNN Models 路易斯安那州大岛平均海平面长期记录的模拟和预测:SARIMA、NARNN和混合SARIMA-NARNN模式
Journal of Applied Data Sciences Pub Date : 2021-04-17 DOI: 10.47738/jads.v2i2.27
Y. Chi
{"title":"Modeling and Forecasting Long-Term Records of Mean Sea Level at Grand Isle, Louisiana: SARIMA, NARNN, and Mixed SARIMA-NARNN Models","authors":"Y. Chi","doi":"10.47738/jads.v2i2.27","DOIUrl":"https://doi.org/10.47738/jads.v2i2.27","url":null,"abstract":"This study tried to demonstrate the role of time series models in modeling and forecasting process using long-term records of monthly mean sea level from January 1978 to October 2020 at Grand Isle, Louisiana. Following the Box–Jenkins methodology, the ARIMA(1,1,1)(2,0,0)12 with drift model was selected to be the best fit model for the time series, according to its lowest AIC value. Using the LM algorithm, the results revealed that the NARNN model with 9 neurons in the hidden layer and 6 time delays provided the best performance in the nonlinear autoregressive neural network models at its smaller MSE value. The Mixed model, a combination of the SARIMA and NARNN models has both linear and nonlinear modelling capabilities can be a better choice for modelling the time series. The comparative results revealed that the Mixed-LM model with 9 neurons in the hidden layer and 3 time delays yielded higher accuracy than the NARNN-LM model with 9 neurons in the hidden layer and 6 time delays, and the ARIMA(1,1,1)(2,0,0)12 with drift model, according to its lowest MSE in this study. Thus, this study may provide an integrated modelling approach as a decision-making supportive method for formulating local mean sea level forecast in advance. Understanding past sea level is important for the analysis of current and future sea level changes. In order to sustain these observations, research programs utilizing the resulting data should be able to improve significantly our understanding and narrow projections of future sea level rise and variability.","PeriodicalId":341738,"journal":{"name":"Journal of Applied Data Sciences","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125992050","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: 4
Predict high school students' final grades using basic machine learning 使用基本的机器学习预测高中学生的最终成绩
Journal of Applied Data Sciences Pub Date : 2021-01-15 DOI: 10.47738/JADS.V2I1.19
S. Sugiyanto
{"title":"Predict high school students' final grades using basic machine learning","authors":"S. Sugiyanto","doi":"10.47738/JADS.V2I1.19","DOIUrl":"https://doi.org/10.47738/JADS.V2I1.19","url":null,"abstract":"To improve the quality of students, teachers must be able to take precautionary measures to deal with students who are lacking or have the potential to experience deficiency. Student ratings are temporary, however, have a profound impact on students' mental and enthusiasm for learning. As a teacher, it is very important to make predictions in dealing with this matter because if the ranking has been issued, it is too late. By using MAE (Mean Absolute Error), this study got a value of 1.09 which is very close to the value in the test, it showed how close our prediction models were with a very small percentage of error.In this article, we will discuss and make Student grade predictions using basic machine learning, we will also discuss the continuity between student data and machine learning. We hope this research will help teachers, people in the education industry or even parents to know more about predicting student final grades and can help them take preventive action.","PeriodicalId":341738,"journal":{"name":"Journal of Applied Data Sciences","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132510351","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: 2
Exploratory Data Analysis & Booking Cancelation Prediction on Hotel Booking Demands Datasets 基于酒店预订需求数据集的探索性数据分析和预订取消预测
Journal of Applied Data Sciences Pub Date : 2021-01-15 DOI: 10.47738/JADS.V2I1.20
Pujo Hari Saputro, H. Nanang
{"title":"Exploratory Data Analysis & Booking Cancelation Prediction on Hotel Booking Demands Datasets","authors":"Pujo Hari Saputro, H. Nanang","doi":"10.47738/JADS.V2I1.20","DOIUrl":"https://doi.org/10.47738/JADS.V2I1.20","url":null,"abstract":"Online ordering is the latest breakthrough in the hospitality industry, but when it comes to booking cancellations, it has a negative impact on it. To reduce and anticipate an increase in the number of booking cancellations, we developed a booking cancellations prediction model using machine learning interpretable algorithms for hotels. Both models used Random Forest and the Extra Tree Classifier share the highest precision ratios, Random Forest on the other hand has the highest recall ratio, this model predicted 79% of actual positive observations. These results prove that it is possible to predict booking cancellations with high accuracy. These results can also help hotel owners or hotel managers to predict better predictions, improve cancellation regulations, and create new tactics in business.","PeriodicalId":341738,"journal":{"name":"Journal of Applied Data Sciences","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127880884","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: 7
Sentiment Analysis between VADER and EDA for the US Presidential Election 2020 on Twitter Datasets 基于Twitter数据集的2020年美国总统大选中VADER和EDA之间的情感分析
Journal of Applied Data Sciences Pub Date : 2021-01-15 DOI: 10.47738/JADS.V2I1.17
Ria Endsuy
{"title":"Sentiment Analysis between VADER and EDA for the US Presidential Election 2020 on Twitter Datasets","authors":"Ria Endsuy","doi":"10.47738/JADS.V2I1.17","DOIUrl":"https://doi.org/10.47738/JADS.V2I1.17","url":null,"abstract":"The 2020 US Election took place on November 3, 2020, the result of the election was that Joe Biden received 51.4% of the votes, Donald Trump 46.9%, and the rest were other candidates. The period before the election was a time when people conveyed who would vote and conveyed the reasons directly or through social media, especially Twitter through keywords or tags such as #JoeBiden & #DonaldTrump. In this paper, we will compare sentiment analysis and exploratory data analysis against US election data on Twitter. The overall objective of the two case studies is to evaluate the similarity between the sentiment of location-based tweets and on-ground public opinion reflected in election results. In this paper, we find that there are more \"neutral\" sentiments than \"negative\" and \"positive\" sentiments. This study are focused finding sentimental tweets that people say on twitter for both presidential candidate and The dataset used is from and provided by Kaggle and has been updated on November 18, 2020, it is hoped that we hope that the academic community, computational journalists and research practitioners alike can utilize our dataset to study relevant scientific and social problems.","PeriodicalId":341738,"journal":{"name":"Journal of Applied Data Sciences","volume":"668 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115675765","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: 10
Visual Design of Artificial Intelligence Based on the Image Search Algorithm 基于图像搜索算法的人工智能视觉设计
Journal of Applied Data Sciences Pub Date : 2020-12-01 DOI: 10.47738/jads.v1i2.56
Xiaobo Jiang
{"title":"Visual Design of Artificial Intelligence Based on the Image Search Algorithm","authors":"Xiaobo Jiang","doi":"10.47738/jads.v1i2.56","DOIUrl":"https://doi.org/10.47738/jads.v1i2.56","url":null,"abstract":"","PeriodicalId":341738,"journal":{"name":"Journal of Applied Data Sciences","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121084203","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
Analysis of Transaction Data for Modeling the Pattern of Goods Purchase Supporting Goods Location 基于交易数据的商品采购与商品定位模式建模
Journal of Applied Data Sciences Pub Date : 2020-12-01 DOI: 10.47738/jads.v1i2.54
Linda Rosliadewi
{"title":"Analysis of Transaction Data for Modeling the Pattern of Goods Purchase Supporting Goods Location","authors":"Linda Rosliadewi","doi":"10.47738/jads.v1i2.54","DOIUrl":"https://doi.org/10.47738/jads.v1i2.54","url":null,"abstract":"","PeriodicalId":341738,"journal":{"name":"Journal of Applied Data Sciences","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114334074","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: 2
Knowledge Management Strategy by Means of Virtualization in Covid-19 基于虚拟化的新冠肺炎疫情知识管理策略
Journal of Applied Data Sciences Pub Date : 2020-12-01 DOI: 10.47738/jads.v1i2.46
Eissa Mohammed Ali Qhal
{"title":"Knowledge Management Strategy by Means of Virtualization in Covid-19","authors":"Eissa Mohammed Ali Qhal","doi":"10.47738/jads.v1i2.46","DOIUrl":"https://doi.org/10.47738/jads.v1i2.46","url":null,"abstract":"","PeriodicalId":341738,"journal":{"name":"Journal of Applied Data Sciences","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123064964","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: 1
Diagnosis of Preeclampsia in Pregnant Women Based on K-Nearest Neighbor Algorithm 基于k -最近邻算法的孕妇子痫前期诊断
Journal of Applied Data Sciences Pub Date : 2020-12-01 DOI: 10.47738/jads.v1i2.53
R. Hidayat
{"title":"Diagnosis of Preeclampsia in Pregnant Women Based on K-Nearest Neighbor Algorithm","authors":"R. Hidayat","doi":"10.47738/jads.v1i2.53","DOIUrl":"https://doi.org/10.47738/jads.v1i2.53","url":null,"abstract":"","PeriodicalId":341738,"journal":{"name":"Journal of Applied Data Sciences","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115784829","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
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