2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA)最新文献_第8页

Real-time synthetic-to-real human detection for robotics applications 机器人应用的实时合成到真实的人体检测

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904394

Maria Tzelepi, C. Symeonidis, N. Nikolaidis, A. Tefas

{"title":"Real-time synthetic-to-real human detection for robotics applications","authors":"Maria Tzelepi, C. Symeonidis, N. Nikolaidis, A. Tefas","doi":"10.1109/IISA56318.2022.9904394","DOIUrl":"https://doi.org/10.1109/IISA56318.2022.9904394","url":null,"abstract":"During the recent years, Deep Learning achieved exceptional performance in various computer vision tasks, paving auspicious research directions for its application in robotics. A key component for its exceptional performance is the availability of sufficient training data. However obtaining such amount of training data constitutes a challenging task, especially considering robotics applications. Thus, synthetic data have recently been regarded as a promising tool to overcoming the data availability problem. In this work we first build a synthetic human dataset, and then we train a lightweight model, capable of operating in real-time for high-resolution input on low-power GPUs, for discriminating between humans and non-humans. The target of this work is to assess the generalization of the model trained on synthetic data, to real data, and also to explore the effect of using (few) real images in the training phase. As it is shown through quantitative and qualitative results the use of only few real images can beneficially affect of the performance of the synthetic-to-real real-time model.","PeriodicalId":217519,"journal":{"name":"2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122118002","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

Optimising Maritime Processes Via Artificial Intelligence: The VesselAI Concept And Use Cases 通过人工智能优化海事流程:VesselAI概念和用例

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904345

S. Mouzakitis, C. Kontzinos, Panagiotis Kapsalis, Ioanna Kanellou, Georgios Kormpakis, Giannis Tsapelas, D. Askounis

{"title":"Optimising Maritime Processes Via Artificial Intelligence: The VesselAI Concept And Use Cases","authors":"S. Mouzakitis, C. Kontzinos, Panagiotis Kapsalis, Ioanna Kanellou, Georgios Kormpakis, Giannis Tsapelas, D. Askounis","doi":"10.1109/IISA56318.2022.9904345","DOIUrl":"https://doi.org/10.1109/IISA56318.2022.9904345","url":null,"abstract":"The beginning of this decade finds artificial intelligence, high performance computing (HPC), and big data analytics in the forefront of digital transformation that is projected to heavily impact various industries and domains. Specifically, the maritime industry, which is already a relatively advanced area concerned to others could stand to gain great benefits from the combination and application of innovative technologies in its practices. This fact, combined with the amount of data generated from naval vessels and sensors points to the direction of AI and big data, two technologies that have the ability to absorb large amounts of data, process them and provide automated and optimised solutions for all maritime stakeholders. Integrating these technologies and tools in a unified system poses various challenges. Under this context, the current publication presents the concept and pilot use cases of VesselAI, an EU-funded project that aims to develop, validate and demonstrate a novel holistic framework based on a combination of the state-of-the-art HPC, Big Data and AI technologies, capable of performing extreme-scale and distributed analytics for fuelling the next-generation digital twins in maritime applications and beyond, including vessel motion and behaviour modelling, analysis and prediction, ship energy system design and optimisation, unmanned vessels, route optimisation and fleet intelligence. The present publication compliments the presentation of the VesselAI project with comprehensive bibliographic research of similar approaches and initiatives that validate the VesselAI concept and prove that there is an unprecedented interest from the research community in applying AI solutions in the maritime industry.","PeriodicalId":217519,"journal":{"name":"2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125376151","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

A Machine Learning Framework for Li-Ion Battery Lifetime Prognostics 锂离子电池寿命预测的机器学习框架

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904407

Afroditi Fouka, Katerina Lepenioti, Alexandros Bousdekis, G. Mentzas

引用次数: 0

AR-based Sustainability in Food Development 基于ar的粮食发展可持续性

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904381

Georgios D. Styliaras, Stavroula Tzima, Victoria Dimou

引用次数: 0

Implementation and Analysis of Quantum Homomorphic Encryption 量子同态加密的实现与分析

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904399

Maxwell Yarter, Glen S. Uehara, A. Spanias

引用次数: 5

A Convolutional Neural Network-based explainable classification method of SPECT myocardial perfusion images in nuclear cardiology 基于卷积神经网络的核心学SPECT心肌灌注图像可解释分类方法

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904340

Nikolaos I. Papandrianos, Anna Feleki, S. Moustakidis, E. Papageorgiou

{"title":"A Convolutional Neural Network-based explainable classification method of SPECT myocardial perfusion images in nuclear cardiology","authors":"Nikolaos I. Papandrianos, Anna Feleki, S. Moustakidis, E. Papageorgiou","doi":"10.1109/IISA56318.2022.9904340","DOIUrl":"https://doi.org/10.1109/IISA56318.2022.9904340","url":null,"abstract":"This study targets on the development of an explainable Convolutional Neural Network (CNN) pipeline in the form of a handcrafted CNN to identify patients’ coronary artery disease status (normal, ischemia or infarction). The proposed RGB-CNN model utilizes various pre- and post-processing tools and deploys a state-of-the-art explainability tool to produce more interpretable predictions in the task of decision making. The provided dataset includes 630 patients’ cases in stress and rest representations and comprises 257 normal, 241 ischemic and 127 infarction cases, previously classified by a doctor. The imaging dataset was split into 20% for testing and 80% for training, whose 15% was further used for validation purposes. Data augmentation was employed to increase generalization. Grad-CAM based color visualization approach was also utilized to provide predictions with interpretability in the detection of ischemia and infarction in SPECT-MPI images, counterbalancing any lack of rationale in the results extracted by CNNs. The proposed model achieved 94,06% accuracy and 0.9541% AUC, demonstrating efficient performance and stability.","PeriodicalId":217519,"journal":{"name":"2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130688334","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

Efficient Named Entity Recognition on Greek Legislation 希腊立法中有效的命名实体承认

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904342

Panteleimon Krasadakis, Evangelos Sinos, Vassilios S. Verykios, E. Sakkopoulos

引用次数: 0

Normalising the Output of Fuzzy Cognitive Maps 模糊认知地图输出的归一化

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904369

Themistoklis Koutsellis, A. Nikas, K. Koasidis, George Xexakis, Christos Petkidis, Anastasios Karamaneas, H. Doukas

{"title":"Normalising the Output of Fuzzy Cognitive Maps","authors":"Themistoklis Koutsellis, A. Nikas, K. Koasidis, George Xexakis, Christos Petkidis, Anastasios Karamaneas, H. Doukas","doi":"10.1109/IISA56318.2022.9904369","DOIUrl":"https://doi.org/10.1109/IISA56318.2022.9904369","url":null,"abstract":"Fuzzy cognitive maps (FCMs) constitute a quasi-quantitative modelling tool with the inherent ability to reduce the computational and data complexity of a represented system, as well as engage experts in the process to introduce human cognition in terms of how a system behaves. However, despite being constructed with and for experts, aiming to assist them into better understanding system dynamics, the interpretation of the semi-quantitative outputs of FCMs has been found challenging. The use of transfer functions in the FCM iterations has led to the distortion of the output values, hampering the qualitative interpretation of the results, and making it difficult for experts to understand the link with the fuzzy input they provided. For this reason, this study introduces a normalisation procedure, following an optimal selection of the $lambda$ parameter of the sigmoid and hyperbolic tangent functions, to enable operating the transfer functions in the “almost linear” area, and then map the output domain into the input domain by a means of a linear transformation. Based on a case study in the energy field, we find that the proposed procedure reduces the distortion caused by the transfer functions, compresses the results and avoids the risk of exaggerating the differences in the output values, and thus builds towards enhancing FCMs’ ability to provide qualitatively interpretable results.","PeriodicalId":217519,"journal":{"name":"2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132876303","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

Solitary Pulmonary Nodule malignancy classification utilising 3D features and semi-supervised Deep Learning 利用三维特征和半监督深度学习的孤立性肺结节恶性分类

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904334

Ioannis D. Apostolopoulos, D. Apostolopoulos, G. Panayiotakis

{"title":"Solitary Pulmonary Nodule malignancy classification utilising 3D features and semi-supervised Deep Learning","authors":"Ioannis D. Apostolopoulos, D. Apostolopoulos, G. Panayiotakis","doi":"10.1109/IISA56318.2022.9904334","DOIUrl":"https://doi.org/10.1109/IISA56318.2022.9904334","url":null,"abstract":"The volumetric representation of Solitary Pulmonary Nodules (SPN) in Computed Tomography (CT) imaging is mandatory, especially for capturing and analysing deep features and having a complete picture of the morphology, the shape of the volume, its distribution in space, and its relationship with the adjacent tissues. Automated deep feature extraction in three dimensional space is a specialisation area of 3D Convolutional Neural Networks (CNN). The extraction of the most representative features of malignant SPN representations, can be achieved with the assistance of CNNs. To evaluate this methodology, a 3D CNN called 3D-LidcNet, is developed in this study. The Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset is utilised to extract 2124 SPNs represented in sets of 2D slices. By concatenating 16 slices for each SPN, 3D nodule representations are constructed. To increase the learning capabilities of the 3D CNN, data augmentation is applied during training. 3D-LidcNet achieves 90.68% accuracy in distinguishing benign from malignant SPNs, coming from the strongly labelled subsets of the dataset (898 unique SPNs). To make full use of the weakly labelled SPNs, a semi-supervised training algorithm is utilised to progressively expand the training dataset with the most confident predictions of the weakly labelled SPNs. This approach succeeds in classifying 1585 SPNs, with an accuracy of 87.44%. Finally, 3D-LidcNet is trained and tested using the complete dataset (2124 SPNs) to distinguish between benign and malignant nodules, achieving an accuracy of 89.68%.","PeriodicalId":217519,"journal":{"name":"2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"24 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130255637","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

Knowledge and skills of the digital transformation of the Greek public school in the post covid era. 后新冠时代希腊公立学校数字化转型的知识和技能。

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904416

Gerasimos Kalogeratos, C. Pierrakeas

{"title":"Knowledge and skills of the digital transformation of the Greek public school in the post covid era.","authors":"Gerasimos Kalogeratos, C. Pierrakeas","doi":"10.1109/IISA56318.2022.9904416","DOIUrl":"https://doi.org/10.1109/IISA56318.2022.9904416","url":null,"abstract":"The present research focuses on the digital transformation the Greek Public School underwent, within a few months, due to the Covid - 19 crisis. The need for a digitally transformed education system that meets the requirements of the 21st century and is characterized using educational and computing technologies [1], was highlighted by the Covid - 19 coronavirus pandemic. Furthermore, the transformation of educational units into learning organizations [2] which are digitally update and, hence, can effectively cope with the new circumstances, was also introduced. The aim of this research is to examine the perceptions the principals of the Primary Schools in the Region of Western Greece have, regarding the need for the Greek Primary School to follow the trends of the new digital era and become sufficient in their use. It, also, examines the principals’ level of ICT knowledge and skills as well as their willingness to respond to the challenges that call for an education system compatible with the new digital era and the necessity of school units to function as learning organizations in the era of the 4th Industrial Revolution. The research was conducted with the use of a questionnaire with close-ended questions. According to the results, the principals were found to have positive attitudes and views towards the available digital tools and platforms used in education in the last two years even though, most of them have low level ICT knowledge and skills. Moreover, they believe that the digital transformation of education is a slow-moving process due to certain obstacles which, also, prevent the transformation of educational units into learning organizations.","PeriodicalId":217519,"journal":{"name":"2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125195362","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