{"title":"Pedestrian Trajectory Prediction in Graph Representation Using Convolutional Neural Networks","authors":"Bogdan Ilie Sighencea, R. Stanciu, C. Căleanu","doi":"10.1109/SACI55618.2022.9919494","DOIUrl":"https://doi.org/10.1109/SACI55618.2022.9919494","url":null,"abstract":"Predicting the future trajectories of pedestrian in real-world contexts, including video surveillance, self-driving, and robotic systems, is a challenging task due of different trajectory patterns. In this task there are two primary problems: (1) advanced interaction modeling among pedestrians and (2) the specific motion pattern extraction. Pedestrian trajectories are affected not only by another pedestrian, but also by interactions with the environment. To obtain the interactions of pedestrian movements and the active change trend of the environment, there are two components in the proposed method: encoder with a spatial graph neural network for interaction modeling and decoder with a temporal graph neural network for motion pattern extraction. The investigated approach is more compact and efficient than the based method, with a reduced variable size and better accuracy. Furthermore, utilizing two publicly available datasets (ETH and UCY), our model achieves better experimental results considering final displacement error (FDE) and average displacement error (ADE) metrics and predicts more socially appropriate trajectories.","PeriodicalId":105691,"journal":{"name":"2022 IEEE 16th International Symposium on Applied Computational Intelligence and Informatics (SACI)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124950661","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}
Tamara Zuvela, Sara Lazarevic, Sofija Djordjevic, M. Arsenovic, S. Sladojevic
{"title":"Cryptocurrency Price Prediction Using Deep Learning","authors":"Tamara Zuvela, Sara Lazarevic, Sofija Djordjevic, M. Arsenovic, S. Sladojevic","doi":"10.1109/SACI55618.2022.9919554","DOIUrl":"https://doi.org/10.1109/SACI55618.2022.9919554","url":null,"abstract":"After the fall in the price of cryptocurrencies in recent years, bitcoin was increasingly considered an investment vehicle. Due to its very unstable nature and frequent changes, there is an increasing need for good predictions of the values on which later investment decisions will be based. In this project, the use of deep learning algorithms is considered to predict the price of bitcoin, taking into account various parameters that affect the value of bitcoin. The proposed system relies on the LSTM architecture to predict the value for the next N days, based on the previous days' value. For the first phase of the research, historical data on cryptocurrency values was used as a knowledge source. In continuation, a trained model receives values for N days backward to predict the value for the desired day. The second phase of the research involves creating a frontend page of the application that allows users to get a prediction of the value of any cryptocurrency they want, for the period they specify.","PeriodicalId":105691,"journal":{"name":"2022 IEEE 16th International Symposium on Applied Computational Intelligence and Informatics (SACI)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130186651","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}