2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)最新文献_第5页

Differential Privacy Practice on Diagnosis of COVID-19 Radiology Imaging Using EfficientNet 基于EfficientNet的COVID-19影像学诊断差异隐私实践

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194651

Z. Müftüoğlu, M. A. Kizrak, T. Yildirim

{"title":"Differential Privacy Practice on Diagnosis of COVID-19 Radiology Imaging Using EfficientNet","authors":"Z. Müftüoğlu, M. A. Kizrak, T. Yildirim","doi":"10.1109/INISTA49547.2020.9194651","DOIUrl":"https://doi.org/10.1109/INISTA49547.2020.9194651","url":null,"abstract":"Medical sciences are an important application area of artificial intelligence. Healthcare requires meticulousness in the whole process from collecting data to processing. It should also be handled in terms of data quality, data size, and data privacy. Various data are used within the scope of the COVID-19 outbreak struggle. Medical and location data collected from mobile phones and wearable devices are used to prevent the spread of the epidemic. In addition to this, artificial intelligence approaches are presented by using medical images in order to identify COVID-19 infected people. However, studies should be carried out by taking care not to endanger the security of the data, people, and countries needed for these useful applications. Therefore, differential privacy (DP) application, which was an interesting research subject, has been included in this study. CXR images have been collected from COVID-19 infected 139 and a total of 373 public data sources were used for a diagnostic concept. It has been trained with EfficientNet- B0, a recent and robust deep learning model, and proposal the possibility of infected with an accuracy of 94.7%. Other evaluation parameters were also discussed in detail. Despite the data constraint, this performance showed that it can be improved by augmenting the dataset. The most important aspect of the study was the proposal of differential privacy practice for such applications to be reliable in real-life use cases. With this view, experiments were repeated with DP applied images and the results obtained were presented. Here, Private Aggregation of Teacher Ensembles (PATE) approach was used to ensure privacy assurance.","PeriodicalId":124632,"journal":{"name":"2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127625588","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: 12

End-To-End Learning from Demonstation for Object Manipulation of Robotis-Op3 Humanoid Robot 基于Robotis-Op3仿人机器人对象操作演示的端到端学习

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194630

Simge Nur Aslan, Recep Ozalp, A. Uçar, C. Güzelı̇ş

{"title":"End-To-End Learning from Demonstation for Object Manipulation of Robotis-Op3 Humanoid Robot","authors":"Simge Nur Aslan, Recep Ozalp, A. Uçar, C. Güzelı̇ş","doi":"10.1109/INISTA49547.2020.9194630","DOIUrl":"https://doi.org/10.1109/INISTA49547.2020.9194630","url":null,"abstract":"Humanoid robots are deployed ranging from houses and hotels to healthcare and industry environments to help people. Robots can be easily programed by users to predefined tasks such as walking, grasping, stand-up, and shake-up. However, in these days, all robots are expected to learn itself from the obtained experience by watching the environment and people in there. In this study, it is aimed for Robotis-Op3 humanoid robot to grasp the objects by learning from demonstrations based on vision. A new algorithm is proposed for this purpose. Firstly, the robot is manipulated from user commands and the raw images from the camera of Robotis-Op3 are collected. Secondly, a semantic segmentation algorithm is applied to detect and recognize the objects. A new model using Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs) is then proposed to learn the user demonstrations. The results were compared in terms of training time, performance, and model complexity. Simulation results showed that new models produced a high performance for object manipulation.","PeriodicalId":124632,"journal":{"name":"2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130521346","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: 3

Sentiment Analysis Based Churn Prediction in Mobile Games using Word Embedding Models and Deep Learning Algorithms 使用词嵌入模型和深度学习算法的基于情感分析的手机游戏流失预测

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194624

Z. H. Kilimci, Hasan Yörük, S. Akyokuş

{"title":"Sentiment Analysis Based Churn Prediction in Mobile Games using Word Embedding Models and Deep Learning Algorithms","authors":"Z. H. Kilimci, Hasan Yörük, S. Akyokuş","doi":"10.1109/INISTA49547.2020.9194624","DOIUrl":"https://doi.org/10.1109/INISTA49547.2020.9194624","url":null,"abstract":"Customer churn is one of the most important problems for many industries, including banking, telecommunications, and gaming. In the gaming market, it is observed that the demand on game applications rises with the usage of mobile devices such as smartphones. Because of this, it is important to predict when players tend to leave a game. Studies so far focus on churn prediction in mobile or online games by analyzing demographic, economic, and behavioral data about their customers. In this work, we introduce a sentiment analysis-based churn prediction model in mobile games using word embedding models and deep learning algorithms. To the best of our knowledge, this is the first study to evaluate the churn tendency of customers by analyzing sentiments of players from their comments on games using deep learning and word embedding models. For this purpose, we use deep learning algorithms for classification and word embedding models for text representation. The applied deep learning algorithms include convolutional neural networks, recurrent neural networks, long short-term memory networks. Word2Vec, GloVe, and FastText word embedding models are employed for text representation. To demonstrate the impact of proposed model, comprehensive experiments are carried out on Turkish four different game datasets. The experiment results show that sentiment analysis of players in mobile games can be powerful indicator in terms of predicting customer churn.","PeriodicalId":124632,"journal":{"name":"2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114516617","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

The Characteristics of Virtual Reality Usage in Educational Systems 虚拟现实在教育系统中的应用特点

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194682

V. Aleksić, D. Politis

引用次数: 1

Identification of Computer Displays Through Their Electromagnetic Emissions Using Support Vector Machines 利用支持向量机识别计算机显示器的电磁发射

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194634

H. S. Efendioglu, Ulas Asik, Cantürk Karadeniz

{"title":"Identification of Computer Displays Through Their Electromagnetic Emissions Using Support Vector Machines","authors":"H. S. Efendioglu, Ulas Asik, Cantürk Karadeniz","doi":"10.1109/INISTA49547.2020.9194634","DOIUrl":"https://doi.org/10.1109/INISTA49547.2020.9194634","url":null,"abstract":"As a TEMPEST information security problem, electromagnetic emissions from the computer displays can be captured, and reconstructed using signal processing techniques. It is necessary to identify the display type to intercept the image of the display. To determine the display type not only significant for attackers but also for protectors to prevent display compromising emanations. This study relates to the identification of the display type using Support Vector Machines (SVM) from electromagnetic emissions emitted from computer displays. After measuring the emissions using receiver measurement system, the signals were processed and training/test data sets were formed and the classification performance of the displays was examined with the SVM. Moreover, solutions for a better classification under real conditions have been proposed. Thus, one of the important step of the display image capture can accomplished by automatically identification the display types. The performance of the proposed method was evaluated in terms of confusion matrix and accuracy, precision, F1-score, recall performance measures.","PeriodicalId":124632,"journal":{"name":"2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116604115","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

Single Object Trackers in OpenCV: A Benchmark OpenCV中的单对象跟踪器:基准测试

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194647

Adnan Brdjanin, Nadja Dardagan, Dzemil Dzigal, Amila Akagic

引用次数: 8

Toddler Tracking System with Face Recognition and Object Tracking Using Deep Neural Network 基于深度神经网络的人脸识别和目标跟踪幼儿跟踪系统

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194666

Hanife Güney, Melek Aydin, M. Taskiran, N. Kahraman

{"title":"Toddler Tracking System with Face Recognition and Object Tracking Using Deep Neural Network","authors":"Hanife Güney, Melek Aydin, M. Taskiran, N. Kahraman","doi":"10.1109/INISTA49547.2020.9194666","DOIUrl":"https://doi.org/10.1109/INISTA49547.2020.9194666","url":null,"abstract":"Toddlers tend to have approach objects that may be dangerous to them at home because of their natural curiosity. When families are sleeping or doing housework, unpredictable injuries may be happening. Horrible, irreversible accidents may occur in less than no time. Not only for this reason but also with the technological evolution, systems related digital parenting have gained importance. Smart technology is adopted by modern parents to provide their children safety. Considering all these situations, the need to design a toddler tracking system for helping the parents has emerged. In this article, a toddler tracking system using Deep Neural Network has been proposed. The proposed system is based on face recognition and object tracking algorithms and created using pre-trained neural networks. The system is based on recognizing the toddlers' faces and following all toddler's movements in the house. When the toddler is getting close dangerous places, tools, furniture, etc, the system alerts the user with the warning system. The proposed system is tested by using the toddlers' data which is collected before and 80.7% test accuracy has been obtained. The experimental result showed that the proposed method has achieved sufficient performance to be compared with state-of-the-art studies.","PeriodicalId":124632,"journal":{"name":"2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133319217","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: 3

Optimally Efficient Locomotion of Snake Robot 蛇形机器人的最优高效运动

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194621

Y. A. Baysal, I. Altas

引用次数: 5

Bearing fault detection using deep neural network and weighted ensemble learning for multiple motor phase current sources 基于深度神经网络和加权集成学习的多电机相电流源轴承故障检测

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194618

Tobias Wagner, Sara Sommer

{"title":"Bearing fault detection using deep neural network and weighted ensemble learning for multiple motor phase current sources","authors":"Tobias Wagner, Sara Sommer","doi":"10.1109/INISTA49547.2020.9194618","DOIUrl":"https://doi.org/10.1109/INISTA49547.2020.9194618","url":null,"abstract":"The objective of this study is the automatic bearing fault detection of permanent magnet synchronous motors (PMSM) using phase current data. Our research proposes a method using sensor fusion to improve the information quantity as well as the gained quality from all available sensor sources using a multi stage workflow. As an initial feature extraction stage a deep neural network architecture based on a 1D-CNN-LSTM is applied on the raw current data to create baseline probability distributions. Then, probability merging is applied to combine the results of all available baseline classifiers to a new feature matrix which is considered as the feature-set for the final classification stage which is build up on an multi learner ensemble of k-Nearest-Neighbor classifiers. To give all ensemble participants a factor of trust, the ensemble predictions are weighted using either average or an optimized weighting. The proposed method reached accuracies of 98,93% on a public available open source benchmark bearing fault dataset. The main contributions of our research are: 1) Tackling the problem of automatic bearing fault detection for PMSMs based on multiple phase current data 2) Improving the final classification results through optimized weighting giving each classifier a factor of trust 3) Reducing the cross working condition accuracy loss by means of an ensemble reaching a higher level of generalization between different domains","PeriodicalId":124632,"journal":{"name":"2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123125034","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: 8

DeepKAF: A Heterogeneous CBR & Deep Learning Approach for NLP Prototyping DeepKAF:用于NLP原型的异构CBR和深度学习方法

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194679

Kareem Amin, S. Kapetanakis, Nikolaos Polatidis, K. Althoff, A. Dengel

{"title":"DeepKAF: A Heterogeneous CBR & Deep Learning Approach for NLP Prototyping","authors":"Kareem Amin, S. Kapetanakis, Nikolaos Polatidis, K. Althoff, A. Dengel","doi":"10.1109/INISTA49547.2020.9194679","DOIUrl":"https://doi.org/10.1109/INISTA49547.2020.9194679","url":null,"abstract":"With widespread modernization, digitization and transformations of most of industries, Artificial Intelligence (AI) has become the key enabler in that modernization journey. AI offers substantial capabilities to solve new problems and optimise existing solutions specialising on specific problems and learning from different domains. AI solutions can be either explainable or black box ones with the latter being urged to improve since they cannot trust. Case-based Reasoning (CBR) is an explainable AI approach where solutions are provided along with relevant explanations in terms of why a solution was selected. However, CBR, like most other explainable approaches, has several limitations in terms of scalability, large data volumes, domain complexity, that reduce its ability to scale any CBR system in industrial applications. In this paper, we provide a heterogeneous CBR framework - DeepKAF where we combine CBR paradigm with Deep Learning architectures to solve complicated Natural Language Processing (NLP) problems (eg. mixed language and grammatically incorrect text).DeepKAF is built based on continuous research in the area of Deep Learning and CBR. DeepKAF has been implemented and used across different domains, test use cases and research models as an ensemble deep learning and CBR Architecture.","PeriodicalId":124632,"journal":{"name":"2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125400015","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