2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML)最新文献

{"title":"Energy Efficient Smart Buildings: LSTM Neural Networks for Time Series Prediction","authors":"Idil Sülo, Seref Recep Keskin, Gulustan Dogan, T. Brown","doi":"10.1109/Deep-ML.2019.00012","DOIUrl":"https://doi.org/10.1109/Deep-ML.2019.00012","url":null,"abstract":"Considering the human resources, time and energy expenditures in the modern technology of today, efficient use of resources provides significant advantages in many ways. As a result of this, the role of intelligent building systems, which are part of the campuses and cities, is becoming much more important day by day. The purpose of these building systems is to ensure that the resources and systems are efficiently used in order to provide comfortable living conditions to the people. For this purpose, in this paper, we investigate the ways to improve the efficiency of the energy used by these buildings. In this study, we use the Long Short Term Memory (LSTM) neural network model to analyze the energy expenditures of the buildings that reside in the campuses of the City University of New York (CUNY). With the help of the neural network model that had been developed, we aim to predict the energy consumption values of these buildings in order to obtain energy efficient smart buildings.","PeriodicalId":228378,"journal":{"name":"2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129478904","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}

{"title":"Evaluation of Deep Learning Techniques in Sentiment Analysis from Twitter Data","authors":"Dionysis Goularas, Sani Kamis","doi":"10.1109/Deep-ML.2019.00011","DOIUrl":"https://doi.org/10.1109/Deep-ML.2019.00011","url":null,"abstract":"This study presents a comparison of different deep learning methods used for sentiment analysis in Twitter data. In this domain, deep learning (DL) techniques, which contribute at the same time to the solution of a wide range of problems, gained popularity among researchers. Particularly, two categories of neural networks are utilized, convolutional neural networks(CNN), which are especially performant in the area of image processing and recurrent neural networks (RNN) which are applied with success in natural language processing (NLP) tasks. In this work we evaluate and compare ensembles and combinations of CNN and a category of RNN the long short-term memory (LSTM) networks. Additionally, we compare different word embedding systems such as the Word2Vec and the global vectors for word representation (GloVe) models. For the evaluation of those methods we used data provided by the international workshop on semantic evaluation (SemEval), which is one of the most popular international workshops on the area. Various tests and combinations are applied and best scoring values for each model are compared in terms of their performance. This study contributes to the field of sentiment analysis by analyzing the performances, advantages and limitations of the above methods with an evaluation procedure under a single testing framework with the same dataset and computing environment.","PeriodicalId":228378,"journal":{"name":"2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129536121","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}

{"title":"[Copyright notice]","authors":"","doi":"10.1109/deep-ml.2019.00003","DOIUrl":"https://doi.org/10.1109/deep-ml.2019.00003","url":null,"abstract":"","PeriodicalId":228378,"journal":{"name":"2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122792894","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}

{"title":"An Application of a Deep Learning Algorithm for Automatic Detection of Unexpected Accidents Under Bad CCTV Monitoring Conditions in Tunnels","authors":"Kyu-Beom Lee, H. Shin","doi":"10.1109/Deep-ML.2019.00010","DOIUrl":"https://doi.org/10.1109/Deep-ML.2019.00010","url":null,"abstract":"In this paper, Object Detection and Tracking System (ODTS) in combination with a well-known deep learning network, Faster Regional Convolution Neural Network (Faster R-CNN), for Object Detection and Conventional Object Tracking algorithm will be introduced and applied for automatic detection and monitoring of unexpected events on CCTVs in tunnels, which are likely to (1) Wrong-Way Driving (WWD), (2) Stop, (3) Person out of vehicle in tunnel (4) Fire. ODTS accepts a video frame in time as an input to obtain Bounding Box (BBox) results by Object Detection and compares the BBoxs of the current and previous video frames to assign a unique ID number to each moving and detected object. This system makes it possible to track a moving object in time, which is not usual to be achieved in conventional object detection frameworks. A deep learning model in ODTS was trained with a dataset of event images in tunnels to Average Precision (AP) values of 0.8479, 0.7161 and 0.9085 for target objects: Car, Person, and Fire, respectively. Then, based on a trained deep learning model, the ODTS based Tunnel CCTV Accident Detection System was tested using four accident videos which including each accident. As a result, the system can detect all accidents within 10 seconds. The more important point is that the detection capacity of ODTS could be enhanced automatically without any changes in the program codes as the training dataset becomes rich.","PeriodicalId":228378,"journal":{"name":"2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132940755","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}

{"title":"A Collaborative Recommendation System for Online Courses Recommendations","authors":"Raghad Obeidat, R. Duwairi, Ahmad Al-Aiad","doi":"10.1109/Deep-ML.2019.00018","DOIUrl":"https://doi.org/10.1109/Deep-ML.2019.00018","url":null,"abstract":"In this paper, we present a collaborative recommender system that recommends online courses for students based on similarities of students' course history. The proposed system employs data mining techniques to discover patterns between courses. Consequently, we have noticed that clustering students into similar groups based on their respective course selections play a vital role in generating association rules of high quality when compared with the association rules generated using the whole set of courses and students. In particular, the Apriori algorithm was used to generate association rules; once using the whole dataset and once using the clusters which are formed based on students' choices of courses. The results reveal that the coverage of the rules generated on clusters are better. Also, to assess the effect of course dependency on recommendations, we applied the SPADE algorithm on course sequences. The results are in harmony with the results obtained when Apriori was applied.","PeriodicalId":228378,"journal":{"name":"2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130963412","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}

{"title":"Message from the DEEP-ML 2019 Chairs","authors":"","doi":"10.1109/deep-ml.2019.00005","DOIUrl":"https://doi.org/10.1109/deep-ml.2019.00005","url":null,"abstract":"It is our great pleasure to welcome you to the International Conference on Deep Learning and Machine Learning in Emerging Applications (DEEP-ML 2019), which is held during 26-28 August 2019, in Istanbul, Turkey. In modern computing and related areas, deep learning and machine learning have become the state-of-the-art at providing models, methods, tools and techniques for developing autonomous and intelligent systems which can revolutionize industrial and commercial applications in various fields such as online commerce, intelligent transportation, healthcare and medicine, security, manufacturing, education, games, and various other industrial applications. Google, for example, exploits the techniques of deep learning in voice and image recognition applications, while Amazon uses such techniques in helping customers in their online purchase decisions. The Deep-ML conference provides a leading forum for researchers, developers, practitioners, and professional from public sectors and industries in order to meet and share latest solutions and ideas in solving cutting edge problems in modern information society and economy. It will foster discussions and ideas to inspire participants from different disciplines in order to initiate and establish collaborations within and across disciplines for the further development in the area of deep and machine learning. The conference comprises a set of tracks that focus on specific challenges in deep learning and machine learning and the emerging application areas. The main tracks include; deep and machine learning models and techniques, deep and machine learning for big data analytics, deep and machine learning for data mining and knowledge: deep and machine learning for computing and network platforms, and deep and machine learning application areas. A number of authors have submitted their contributions to the Deep-ML 2019 from different countries across the world. Turkey, being the host country, came top in terms of the submission rate. A total of 48 papers were submitted. Each paper has been reviewed by multiple reviewers of the program committee. Based on the review, the program chairs have selected 14 papers for the technical program which represents 29% as the accepted rate. In addition to the technical paper sessions, the conference also features a hands-on practical workshop on deep learning. We greatly appreciate the efforts and contributions of the workshop organizer, Serpil Üstebay. We are greatly indebted to different people for their contributions in the successful organization of the various activities of the Deep-ML 2019 conference such as the devising the technical program, planning the social events and the local arrangements. We would like to thank Joao Gama and Edwin Lughofer (General Co-Chairs), Filipe Portela (Workshop Coordinator), Esra N. Yolacan (Publicity Chair), Lin Guan (Journal Special Issue Coordinator) and Muhammad Younas (Publication Chair). We are very grateful of the Local Organ","PeriodicalId":228378,"journal":{"name":"2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114110800","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}