2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)最新文献

Breast Cancer: Classification of Tumors Using Machine Learning Algorithms 乳腺癌:使用机器学习算法的肿瘤分类

2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2021-06-18 DOI: 10.1109/CIVEMSA52099.2021.9493583

David Hettich, M. Olson, A. Jackson, N. Kaabouch

{"title":"Breast Cancer: Classification of Tumors Using Machine Learning Algorithms","authors":"David Hettich, M. Olson, A. Jackson, N. Kaabouch","doi":"10.1109/CIVEMSA52099.2021.9493583","DOIUrl":"https://doi.org/10.1109/CIVEMSA52099.2021.9493583","url":null,"abstract":"Breast cancer is currently one of the leading causes of death among women worldwide. Masses are considered significant signs of the existence of malignant lesions, as they occur in most breast cancer cases. However, their detection is challenging since masses have large variation in shape, margin, size and are often indistinguishable from surrounding tissue, making the radiologist's task tedious in the case where a significant number of mammograms require fast and accurate interpretation. For these reasons, computer-aided diagnosis (CAD) systems are being developed to make the diagnostic process easier for radiologists. In these systems, segmentation and classification of breast masses in mammograms are important steps. This work aims to evaluate the performance of machine learning techniques in classifying tumors into benign and malignant. The selected techniques were applied on 1663 mammograms from the Digital Database for Screening Mammography. Of the 1663 images, 769 images correspond to malignant cases, and 894 correspond to benign cases. The efficiency of each of the considered techniques was evaluated by using four metrics, namely, the false positive rate, sensitivity, specificity, and accuracy.","PeriodicalId":248405,"journal":{"name":"2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123287944","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

Brain Rhythm Sequencing and Its Application for EEG-based Emotion Recognition 脑节律测序及其在基于脑电图的情绪识别中的应用

2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2021-06-18 DOI: 10.1109/CIVEMSA52099.2021.9493674

Jia Wen Li, S. Barma, S. Pun, M. Vai, F. Wan, Wai Sun Liu, P. Mak

{"title":"Brain Rhythm Sequencing and Its Application for EEG-based Emotion Recognition","authors":"Jia Wen Li, S. Barma, S. Pun, M. Vai, F. Wan, Wai Sun Liu, P. Mak","doi":"10.1109/CIVEMSA52099.2021.9493674","DOIUrl":"https://doi.org/10.1109/CIVEMSA52099.2021.9493674","url":null,"abstract":"A technique based on five brain rhythms (δ, θ, α, β, and γ) presented in a sequential format has been proposed for Electroencephalography (EEG)-based emotion recognition. Its production employs the prominent rhythm having maximum instantaneous power at each 0.2 s timestamp. For this purpose, smoothed pseudo Wigner-Ville distribution (RSPWVD) method is used. In total, 32 subjects from the emotional EEG database (DEAP) are applied for experimental validation, and for each subject, 640 rhythmic features derived from the time-related properties are extracted from 32 channels. After performance evaluation through support vector machine (SVM) classifier, the one that offers the highest accuracy can be found and then denoted as the optimal feature. By this means, the accuracies of EEG-based emotion recognition accomplish 78.36 ± 5.56% for arousal and 75.78 ± 3.73% for valence. Therefore, the results disclosed that a single optimal feature from a representative channel is competent to recognize the emotional EEG data.","PeriodicalId":248405,"journal":{"name":"2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121563060","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

[CIVEMSA 2021 Front cover] [CIVEMSA 2021封面]

2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2021-06-18 DOI: 10.1109/civemsa52099.2021.9493575

引用次数: 0

High-Level Information Fusion in a Mission Planning Context 任务规划环境中的高级信息融合

2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2021-06-18 DOI: 10.1109/CIVEMSA52099.2021.9493579

R. Abielmona, Joel Muteba Kande, M. Harb, J. Berger, E. Petriu

{"title":"High-Level Information Fusion in a Mission Planning Context","authors":"R. Abielmona, Joel Muteba Kande, M. Harb, J. Berger, E. Petriu","doi":"10.1109/CIVEMSA52099.2021.9493579","DOIUrl":"https://doi.org/10.1109/CIVEMSA52099.2021.9493579","url":null,"abstract":"Observation missions often consist of deploying aircrafts, satellites, vessels and ground vehicles to collect imagery of areas of interest. The cost of deploying these platforms to perform missions is enormous. Mission planning is considered to be a precursor to observation missions, it is used to attest their usefulness and optimize them. Image quality prediction is crucial for mission planning. It has the benefit to allow organizations to optimize observation missions so that the collected imagery of missions are of good quality. The most popular method for predicting the quality of imagery is the General Image Quality Equation. This paper presents various methods, each attached to a particular context of observation missions, in order to predict image quality. The presented methods will focus on the use of Artificial Intelligence to predict the quality of images. Research in the sensor fusion field has proven that an image resulting from the fusion of two images from different sensors can provide more information than individual images. Several articles on image quality estimation do not consider the benefit of sensor fusion during mission planning. Therefore, the main innovation of this work will be the prediction of the quality of fused images. The results obtained during the experiments show the effectiveness of the proposed methods.","PeriodicalId":248405,"journal":{"name":"2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115778105","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

Avionics Modelling Environment 航电建模环境

2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2021-06-18 DOI: 10.1109/CIVEMSA52099.2021.9493586

Hüseyin Sagirkaya, Kadriye Polat, Yusuf Erkam Yilmaz

引用次数: 1

Parallel CNN Classification for Human Gait Identification with Optimal Cross Data-set Transfer Learning 基于最优交叉数据集迁移学习的并行CNN分类人体步态识别

2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2021-06-18 DOI: 10.1109/CIVEMSA52099.2021.9493669

Yuanhao Liang, E. H. K. Yeung, Yong Hu

{"title":"Parallel CNN Classification for Human Gait Identification with Optimal Cross Data-set Transfer Learning","authors":"Yuanhao Liang, E. H. K. Yeung, Yong Hu","doi":"10.1109/CIVEMSA52099.2021.9493669","DOIUrl":"https://doi.org/10.1109/CIVEMSA52099.2021.9493669","url":null,"abstract":"Convolutional neural networks (CNN) have been used in multi-scene and cross-view gait recognition. In practice, new dataset and application scenarios continue to emerge, and how to bridge the new to the prior experience is an important optimization problem. Transfer learning has been successfully used to map the model from the source domain to the target domain in many image classification tasks. However, in human gait identification, models are designed to be more complex to improve the feature extraction abilities and spatial-temporal information utilization. GaitSet, for instance, has a global parallel pipeline to collect various-level set information. In these cases, how to fine-tune the model in the target domain does not have a determinate answer. In this paper, we investigate the impact of each layer on parallel-structured CNN. To be specific, we gradually freeze the parameters of GaitSet from its higher layers to the lower and see the fine-tuning performance. We find that such a parallel structure is more robust to the co-adaption phenomenon compared with the single pipeline. Moreover, although the increase of frozen layers has an adverse effect on the accuracy, it can reach the maximum efficiency with only one convolution layer unfrozen.","PeriodicalId":248405,"journal":{"name":"2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"19 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128901279","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

Automatic Face Recognition for Forensic Identification of Persons Deceased in Humanitarian Emergencies 用于人道主义紧急情况下死者法医鉴定的自动人脸识别

2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2021-06-18 DOI: 10.1109/CIVEMSA52099.2021.9493678

R. D. Labati, D. Angelis, B. Bertoglio, C. Cattaneo, F. Scotti, V. Piuri

{"title":"Automatic Face Recognition for Forensic Identification of Persons Deceased in Humanitarian Emergencies","authors":"R. D. Labati, D. Angelis, B. Bertoglio, C. Cattaneo, F. Scotti, V. Piuri","doi":"10.1109/CIVEMSA52099.2021.9493678","DOIUrl":"https://doi.org/10.1109/CIVEMSA52099.2021.9493678","url":null,"abstract":"Forensic scientists often need to identify deceased people. The identification process mainly consists of the analysis of the DNA, dental records, and physical appearance. In humanitarian emergencies, the antemortem documentation needed for forensic analyses may be limited. In this context, face recognition plays a relevant role since antemortem pictures of missing persons are commonly made available by their families. Therefore, automatic recognition systems could be of paramount importance for reducing the search time in databases of face images and for providing a second opinion to the scientists. However, there are only few preliminary studies on automatic face recognition methods for forensic applications, and none of the works in the literature consider problems related to humanitarian emergencies. In this paper, we propose the first study on automatic face recognition for humanitarian emergencies. Specifically, we propose a recognition methodology and we analyze the accuracy of different biometric methods based on deep learning strategies for a real study case. In particular, the considered data regard recent deaths of migrants in Mediterranean Sea. The obtained results are satisfactory, and suggest that automatic recognition methods based on deep learning strategies could be effectively adopted as support tools for forensic identification.","PeriodicalId":248405,"journal":{"name":"2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127409212","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

[Copyright notice] (版权)

2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2021-06-18 DOI: 10.1109/civemsa52099.2021.9493572

引用次数: 0

Automation of Avionics Integration Test 航空电子集成测试自动化

2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2021-06-18 DOI: 10.1109/CIVEMSA52099.2021.9493577

Hüseyin Sagirkaya, Neslihan Doğan Aydemir, Ramazan Altinsoy

引用次数: 1

Active Map-Matching: Teacher-to-Student Knowledge Transfer from Visual-Place-Recognition Model to Next-Best-View Planner for Active Cross-domain Self-localization 主动地图匹配:从视觉位置识别模型到次优视图规划的主动跨域自定位的师生知识转移

2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2021-06-18 DOI: 10.1109/CIVEMSA52099.2021.9493670

Tanaka Kanji

{"title":"Active Map-Matching: Teacher-to-Student Knowledge Transfer from Visual-Place-Recognition Model to Next-Best-View Planner for Active Cross-domain Self-localization","authors":"Tanaka Kanji","doi":"10.1109/CIVEMSA52099.2021.9493670","DOIUrl":"https://doi.org/10.1109/CIVEMSA52099.2021.9493670","url":null,"abstract":"Training a next-best-view (NBV) planner for active cross-domain self-localization is an important and challenging problem. Unlike typical in-domain settings, the planner can no longer assume a constant environment state, but must regard it as a high-dimensional component of the state variable. This study is motivated by the ability of recent visual place recognition (VPR) techniques in recognizing such a high-dimensional environment state in the presence of domain shifts. Our aim is to transfer the state recognition ability from VPR to NBV. However, such a VPR-to-NBV knowledge transfer is a non-trivial task for which no known solution exists. Herein, we propose the use of a reciprocal rank feature, derived from the field of transfer learning, as dark knowledge for transfer. Specifically, our approach is based on the following two observations: (1) The environment state can be compactly represented by a local map descriptor, which is compatible with typical input formats (e.g., image, point cloud, and graph) of VPR systems, and (2) an arbitrary VPR system (e.g., Bayes filter, image retrieval, and deep neural network) can be modeled as a ranking function. Experiments involving nearest neighbor Q-learning show that our approach can yield a practical NBV planner even under severe domain shifts.","PeriodicalId":248405,"journal":{"name":"2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116127302","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