2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)最新文献_第3页

Fall simulator for supporting supervised Machine Learning techniques in wearable devices 用于支持可穿戴设备中监督机器学习技术的跌倒模拟器

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194628

Armando Collado Villaverde, Mario Cobos, Pablo Muñoz, M. Rodríguez-Moreno

引用次数: 1

A multilevel mapping based pedestrian model for social robot navigation tasks in unknown human environments 面向未知人类环境下社交机器人导航任务的多层次行人映射模型

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194617

Hasan Kivrak, Furkan Çakmak, Hatice Kose, S. Yavuz

{"title":"A multilevel mapping based pedestrian model for social robot navigation tasks in unknown human environments","authors":"Hasan Kivrak, Furkan Çakmak, Hatice Kose, S. Yavuz","doi":"10.1109/INISTA49547.2020.9194617","DOIUrl":"https://doi.org/10.1109/INISTA49547.2020.9194617","url":null,"abstract":"Social robot navigation aims to generate human-friendly paths in human-robot interactive environments. This paper focuses on maintaining humans' physical safety and mental comfort during robot navigation in an unknown dynamic environment. To achieve this goal, we use a variant of a pedestrian model that is particularly developed for low or average density environments. Design decisions on the representation of the obstacle and pedestrian are important for smooth motion planning. Limiting the local obstacles as a region centered at the robot would be taken into consideration has weaknesses in terms of time complexity because a much detailed map has a great number of cells to be evaluated. The study contributes to the theoretical field with extensions such as the development of the obstacle representation model which aims to overcome the computational cost of the current solutions for smooth motion planning which can be a bottleneck for the entire system. The proposed method is tested on a physical mobile robot in hallway scenario both in real-world environment and simulation, and its success is experimentally shown.","PeriodicalId":124632,"journal":{"name":"2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"32 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":"115694665","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

REDBUL: An Online System for Reverse Engineering of Relational Databases 关系型数据库逆向工程在线系统

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194652

D. Brdjanin, Dragana Vukovic, G. Banjac, Aleksandar Kelec, Igor Dujlovic, Nikola Obradovic, D. Banjac

引用次数: 1

Robustness of Deep Learning Methods for Ocular Fundus Segmentation: Evaluation of Blur Sensitivity 深度学习方法在眼底分割中的鲁棒性:模糊灵敏度评估

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194612

V. Petrovic, Gorana Gojic, D. Dragan, Dušan B. Gajić, Nebojsa Horvat, R. Turovic, A. Oros

{"title":"Robustness of Deep Learning Methods for Ocular Fundus Segmentation: Evaluation of Blur Sensitivity","authors":"V. Petrovic, Gorana Gojic, D. Dragan, Dušan B. Gajić, Nebojsa Horvat, R. Turovic, A. Oros","doi":"10.1109/INISTA49547.2020.9194612","DOIUrl":"https://doi.org/10.1109/INISTA49547.2020.9194612","url":null,"abstract":"This paper analyzes the sensitivity of deep learning methods for ocular fundus segmentation. We use an empirical methodology based on non-adversarial perturbed datasets. The research is motivated by the perceived needs of mass screening and self-administered tests in which autonomous or semi-autonomous artificially intelligent methods are needed and may be given substandard images with focus issues. These substandard pictures are simulated using blurring algorithms of varying designs and kernel sizes which are subjected to a test of inter-network sensitivity. The network's result on an unblurred original is derived from the testing subset of the DRIVE ocular fundus image dataset used as the ground truth. The networks studied were VesselUNet (Ronnenberger et al. and Huang et al.), VesselGAN (Son et al.), and VesselFCNN (Oliveira et al.). Statistical analysis of the resultant n = 3600 sample has determined that the datapoints indicating sensitivity over kernel size can be fitted with a sigmoid (with a maximum final tolerance of 9.33e-6), and that it can be shown, using robust pairwise Holm-corrected comparisons, that VesselUNet is the least sensitive (with p-values <5e-8). The least disruptive was Gaussian blur, and the most disruptive motion blur unaligned with cardinal axes. The analysis gives us cause to believe that there is reason to research this problem with more depth, and to work on developing more robust methods for ocular fundus segmentation.","PeriodicalId":124632,"journal":{"name":"2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"3 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":"123640787","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

Identification of COVID-19 X-ray Images using CNN with Optimized Tuning of Transfer Learning 基于迁移学习优化调谐的CNN识别COVID-19 x射线图像

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194615

Grega Vrbancic, Špela Pečnik, V. Podgorelec

{"title":"Identification of COVID-19 X-ray Images using CNN with Optimized Tuning of Transfer Learning","authors":"Grega Vrbancic, Špela Pečnik, V. Podgorelec","doi":"10.1109/INISTA49547.2020.9194615","DOIUrl":"https://doi.org/10.1109/INISTA49547.2020.9194615","url":null,"abstract":"At this early stage in the COVID-19 epidemic, researchers are looking for all possible insights into the new corona virus SARS-CoV-2. One of the possibilities is an in-depth analysis of X-ray images from COVID-19 patients. We first developed a new adapted classification method that is able to identify COVID-19 patients based on a chest X-ray, and then adopted a local interpretable model-agnostic explanations approach to provide the insights. The classification method uses a grey wolf optimizer algorithm for the purpose of optimizing hyper-parameter values within the transfer learning tuning of a CNN. The trained model is then used to classify a set of X-ray images, upon which the qualitative explanations are performed. The presented approach was tested on a dataset of 842 X-ray images, with the overall accuracy of 94.76%, outperforming both conventional CNN method as well as the compared baseline transfer learning method. The achieved high classification accuracy enabled us to perform a qualitative in-depth analysis, which revealed that there are some regions of greater importance when identifying COVID-19 cases, like aortic arch or carina and right main bronchus. The proposed classification method proved to be very competitive, enabling one to perform an in-depth analysis, necessary to gain qualitative insights into the characteristics of COVID-19 disease.","PeriodicalId":124632,"journal":{"name":"2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"215 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":"128791913","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

Enforcing fairness in logistic regression algorithm 逻辑回归算法公平性的实现

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194676

S. Radovanović, A. Petrović, Boris Delibasic, Milija Suknovic

引用次数: 7

Selection of Countermeasures against Harmful Information based on the Assessment of Semantic Content of Information Objects in the Conditions of Uncertainty 基于不确定条件下信息对象语义内容评估的有害信息对策选择

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194680

I. Parashchuk, E. Doynikova, I. Saenko, Igor Kotenko

引用次数: 1

Image Enhancement Effects On Adult Content Classification 图像增强对成人内容分类的影响

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194646

S. Kalkan, Burak Gözütok, Abdullah Al Nahas, Aysenur Kulunk, Hakki Yagiz Erdinc

引用次数: 2

Machine Learning algorithms approach for Gastrointestinal Polyps classification 胃肠息肉分类的机器学习算法

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194659

Kristijan Cincar, Ioan Sima

引用次数: 1

A Semantic Approach for Domain-Specific Design Patterns Recommendations in CMS-based Web Development 基于cms的Web开发中特定领域设计模式推荐的语义方法

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194622

Vassiliki Gkantouna, Vaios Papaioannou, Giannis Tzimas, Zlatan Sabic

{"title":"A Semantic Approach for Domain-Specific Design Patterns Recommendations in CMS-based Web Development","authors":"Vassiliki Gkantouna, Vaios Papaioannou, Giannis Tzimas, Zlatan Sabic","doi":"10.1109/INISTA49547.2020.9194622","DOIUrl":"https://doi.org/10.1109/INISTA49547.2020.9194622","url":null,"abstract":"Today's web designers are encountering multiple issues regarding website development within specific application domains. These issues can be successfully addressed using Domain-Specific Design Patterns, enhancing the final deliverable quality for the domain under consideration. Having in mind the benefits of using web design patterns, domain ontologies can also complement their identification within the context of a specific application domain. They define the underlying semantic context of a domain, by encapsulating the domain knowledge. To this end, an approach is proposed to support the automated recommendation of candidate web design patterns relying on the respective domain ontology. The website designs, occurring in a particular application domain, are automatically analyzed so that all recurrent patterns among them can be detected. These patterns are validated and matched against domain's ontology, and subsequently evaluated and categorized. Finally, the best-rated design solutions are recommended as candidate design patterns for addressing typical domain design problems. Ultimately, it becomes easier for domain designers to recognize best practices among them and thus identify domain-specific design patterns.","PeriodicalId":124632,"journal":{"name":"2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"12 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":"130765252","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