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2022 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2022

作者： Na, Younghoon Kim, Dongyoung Kim, Dong-Kyu Lee, Jeong-Gun Division of Software Hallym University Chuncheon Korea Republic of Dept. of Computer Engineering Hallym University Chuncheon Korea Republic of Department of Otorhinolaryngology-Head and Neck Surgery Chuncheon Sacred Heart Hospital Hallym University College of Medicine Chuncheon Korea Republic of Institute of New Frontier Research Division of Big Data and Artificial Intelligence Chuncheon Sacred Heart Hospital Chuncheon Korea Republic of

ISBN: (数字)9781665464345

ISBN: (纸本)9781665464345

In this paper, we conduct a comparative analysis of sleep stage classification for patients having different levels of obstructive sleep apnea (OSA). For the analysis, we use 10 bio-signal channels: 4 EEG (Electroencephalogram) channels (F3-M2, F4-M1, C3-M2, and C4-M1), 2 EOG (Electrooculogram) channels (E1-M2 and E2-M1), and 4 other bio-channels (EMG, ECG, Flow, and Abdomen). In this work, in particular, we consider OSA severity for training and testing. Then, we investigated the detailed impacts of the OSA severity on the accuracy performance of a modern deep learning model with single channel and multiple channels. © 2022 IEEE.

关键词： Deep learning

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Entropy-Based Model Generalization for Sleep Stage Classification 13

Entropy-Based Model Generalization for Sleep Stage Classific...

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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Kim, Dongyoung Na, Younghoon Woo, Yunhee Kim, Dong-Kyu Lee, Jeong-Gun Hallym University Dept. of Computer Engineering Chuncheon Korea Republic of Hallym University Division of Software Chuncheon Korea Republic of Chuncheon Sacred Heart Hospital Hallym University College of Medicine Department of Otorhinolaryngology-Head and Neck Surgery Chuncheon Korea Republic of Institute of New Frontier Research and Division of Big Data and Artificial Intelligence Chuncheon Sacred Heart Hospital Chuncheon Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

The most recent researches on sleep stage classification have been focused on a model architecture to attain high classification accuracy. Sleep stage classification is performed by human sleep experts and consequently there are some inconsistencies of labels among the scoring experts. To improve robustness of the sleep stage classification model, it is essential to consider those noisy labels which have been commonly mentioned in typical image classification problems as well. To partly solve the problem, we employ 'entropy minimization' in a loss function. By including the entropy, we can make a generalized model with regard to the label noises. To validate the effectiveness of the use of entropy on model generalization, we use two different datasets gathered from two institutions. We train a model only using a dataset obtained from one institution. Then we test the model using the dataset of another institution to investigate the accuracy improvement by the generalization. © 2022 IEEE.

关键词： Entropy

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Putting Robots in Context: Challenging the Influence of Voice and Empathic Behaviour on Trust

Putting Robots in Context: Challenging the Influence of Voic...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Marta Romeo Ilaria Torre Sébastien Le Maguer Angelo Cangelosi Iolanda Leite School of Mathematical and Computer Sciences Heriot-Watt University Edinburgh UK School of Computer Science University of Manchester Manchester UK Division of Interaction Design and Software Engineering Chalmers University of Technology Gothenburg Sweden Division of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden ADAPT Centre Trinity College Dublin Dublin Ireland

Trust is essential for social interactions, including those between humans and social artificial agents, such as robots. Several robot-related factors can contribute to the formation of trust. However, previous work has often treated trust as an absolute concept, whereas it is highly context-dependent, and it is possible that some robot-related features will influence trust in some contexts, but not in others. In this paper, we present the results of two video-based online studies aimed at investigating the role of robot voice and empathic behaviour on trust formation in a general context as well as in a task-specific context. We found that voice influences trust in the specific context, with no effect of voice or empathic behaviour in the general context. Thus, context mediated whether robot-related features play a role in people’s trust formation towards robots.

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Experimentation of human activity recognition by using accelerometer data based on lstm 15th

Experimentation of human activity recognition by using accel...

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15th International Conference on Future Information Technology, Future Tech 2020

作者： Sa-nguannarm, Phataratah Elbasani, Ermal Kim, Bongjae Kim, Eung-Hee Kim, Jeong-Dong Division of Computer Science and Engineering Sun Moon University Asan Korea Republic of Department of Global Software Engineering Sun Moon University Asan Korea Republic of Genome-based BioIT Convergence Institute Sun Moon University Asan Korea Republic of

ISBN: (纸本)9789811593086

The majority of studies on Human Activity Recognition (HAR) were conducted utilizing video and/or image data which caused critical problems such as privacy exposure and storage capacity issues. To tackle these issues, we propose a Long Short-Term Memory (LSTM) architecture for HAR taking accelerometer data acquired from smartphones as its input values. The experimental results showed the outstanding performance of the proposed model against a convolutional neural network (CNN) based model which is one of the state-of-art methods for HAR. More precisely, it was observed that the accuracy of the LSTM based model is about 12% superior to that of the CNN-based model. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Audio Adversarial Example Detection Using the Audio Style Transfer Learning Method

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IEEE Access 2023年 1-1页

作者： Kwon, Hyun Lee, Kangjun Ryu, Junyeol Lee, Jun Department of Artificial Intelligence and Data Science Korea Military Academy Seoul Republic of Korea Department of Software Science and Engineering Kunsan National University South Korea Department of systems & Mechanical Engineering Korea Military Academy Seoul Korea Division of Computer Information and Science Hoseo University Asan-si South Korea

In this paper, we propose a method for defending against audio adversarial examples that operates by applying audio style transfer learning. The proposed method has the effect of maintaining the classification result produced by the target model and removing the adversarial noise by changing only the style while maintaining the content of the input audio sample. In an experimental evaluation using the Mozilla Common Voice dataset as the test data source and TensorFlow as the machine learning library, the proposed method improved the target model’s accuracy on the adversarial examples from 2.1% to 79.2% while maintaining its accuracy on the original samples at 81.4%. Author

关键词： Speech recognition

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An Applied Artificial Intelligence Technique for Early-Stage Alzheimer's Disease Prediction

An Applied Artificial Intelligence Technique for Early-Stage...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Hassan Ali Hussain Imtiaz Attique Ur Rehman Sabeen Javaid Tahir Muhammad Ali Menwa Alshammeri Azka Mir Himanshu Kumar Department of Software Engineering University of Sialkot Sialkot Pakistan Department of Computer Science Gulf University for Science and Technology Kuwait Department of Computer Science College of CIS Al Jouf University Saudia Arabia Division of Research and Development Lovely Professional University Phagwara India KKR and KSR Institute of Technology and Sciences Guntur Andhra Pradesh India

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Alzheimer's disease is a neurodegenerative disorder in which central nervous cells gradually die that poses a significant health challenge globally, especially in older people. As life expectancy increases which suggests a large amount of society will be affected. This increasing number of Alzheimer's cases demands the urgency to develop predictive models for early intervention as the real effect of Alzheimer's shows very late. Although there are no treatments capable of reversing the natural pathological changes, we can delay the development of Alzheimer's. Our goal is to, detect this neurodegenerative disease before it becomes more rooted, we can help the patient identify the disease at an early age so they can adapt to their new condition and perform treatments to help manage symptoms. We used the OASIS dataset which consists of multiple features collection of subjects whose age varies from 18 to 96. This research focuses on employing supervised machine learning, we have used various classifiers such as Decision trees, Naïve Bayes, k-NN, random forest, and logistic regression, and evaluated each classification's effectiveness using several well-known performance indicators to forecast Alzheimer's disease status. We used Rapid Miner to test different models and found that the Naïve Bayes classifier achieves the highest accuracy at 97.5% this model has a superior predictive capability and outperforms alternative classifiers. The insights gained from this research contribute to advancing early diagnosis and intervention for Alzheimer's disease.

关键词： Pathology Logistic regression Computational modeling Predictive models Market research Bayes methods Delays Decision trees Alzheimer's disease Random forests

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Gradient Compression and Correlation Driven Federated Learning for Wireless Traffic Prediction

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IEEE Transactions on Cognitive Communications and Networking 2024年

作者： Zhang, Chuanting Zhang, Haixia Dang, Shuping Shihada, Basem Alouini, Mohamed-Slim Shandong University School of Software Jinan250101 China Shandong University School of Control Science and Engineering Jinan250061 China Shandong University Shandong Key Laboratory of Wireless Communication Technologies Jinan250061 China University of Bristol Department of Electrical and Electronic Engineering BristolBS8 1UB United Kingdom Computer Electrical and Mathematical Science and Engineering Division Thuwal23955-6900 Saudi Arabia

Wireless traffic prediction plays an indispensable role in cellular networks to achieve proactive adaptation for communication systems. Along this line, Federated Learning (FL)-based wireless traffic prediction at the edge attracts enormous attention because of the exemption from raw data transmission and enhanced privacy protection. However FL-based wireless traffic prediction methods still rely on heavy data transmissions between local clients and the server for local model updates. Besides, how to model the spatial dependencies of local clients under the framework of FL remains uncertain. To tackle this, we propose an innovative FL algorithm that employs gradient compression and correlation-driven techniques, effectively minimizing data transmission load while preserving prediction accuracy. Our approach begins with the introduction of gradient sparsification in wireless traffic prediction, allowing for significant data compression during model training. We then implement error feedback and gradient tracking methods to mitigate any performance degradation resulting from this compression. Moreover, we develop three tailored model aggregation strategies anchored in gradient correlation, enabling the capture of spatial dependencies across diverse clients. Experiments have been done with two real-world datasets and the results demonstrate that by capturing the spatio-temporal characteristics and correlation among local clients, the proposed algorithm outperforms the state-of-the-art algorithms and can increase the communication efficiency by up to two orders of magnitude without losing prediction accuracy. © 2015 IEEE.

关键词： Prediction models

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COVIDX: computer-aided diagnosis of COVID-19 and its severity prediction with raw digital chest X-ray scans

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Quantitative Biology 2022年第2期10卷 208-220页

作者： Wajid Arshad Abbasi Syed Ali Abbas Saiqa Andleeb Maryum Bibi Fiaz Majeed Abdul Jaleel Muhammad Naveed Akhtar Computational Biology and Data Analysis Lab Department of Computer Sciences&Information TechnologyKing Abdullah CampusUniversity of Azad Jammu&KashmirMuzaffarabadAJ&K 13100Pakistan Biotechnology Lab Department of ZoologyKing Abdullah CampusUniversity of Azad Jammu&KashmirMuzaffarabadAJ&K 13100Pakistan Department of Software Engineering University of GujratGujrat 50700Pakistan Department of Computer Science (RCET)UETLahore 54000Pakistan Computational and Internet Services Division Pakistan Institute of Engineering and Applied Sciences(PIEAS)Islamabad 44000Pakistan

Background: Coronavirus disease (COVID-19) is a contagious infection caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) and it has infected and killed millions of people across the ***: In the absence or inadequate provision of therapeutic treatments of COVID-19 and the limited convenience of diagnostic techniques, there is a necessity for some alternate spontaneous screening systems that can easily be used by the physicians to rapidly recognize and isolate the infected patients to circumvent onward surge. A chest X-ray (CXR) image can effortlessly be used as a substitute modality to diagnose the ***: In this study, we present an automatic COVID-19 diagnostic and severity prediction system (COVIDX) that uses deep feature maps of CXR images along with classical machine learning algorithms to identify COVID-19 and forecast its severity. The proposed system uses a three-phase classification approach (healthy vs unhealthy, COVID-19 vs pneumonia, and COVID-19 severity) using different conventional supervised classification ***: We evaluated COVIDX through 10-fold cross-validation, by using an external validation dataset, and also in a real setting by involving an experienced radiologist. In all the adopted evaluation settings, COVIDX showed strong generalization power and outperforms all the prevailing state-of-the-art methods designed for this ***: Our proposed method (COVIDX), with vivid performance in COVID-19 diagnosis and its severity prediction, can be used as an aiding tool for clinical physicians and radiologists in the diagnosis and follow-up studies of COVID-19 infected ***: We made COVIDX easily accessible through a cloud-based webserver and python code available at https://***/view/wajidarshad/software and https://***/wajidarshad/covidx, respectively.

关键词： coronavirus COVID-19 radiology machine learning chest X-ray contagious infection

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AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Vehicle Designs

arXiv

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arXiv 2023年

作者： Cobb, Adam D. Roy, Anirban Elenius, Daniel Heim, F. Michael Swenson, Brian Whittington, Sydney Walker, James D. Bapty, Theodore Hite, Joseph Ramani, Karthik McComb, Christopher Jha, Susmit Computer Science Laboratory SRI International United States Mechanical Engineering Division Southwest Research Institute United States Intelligent Systems Division Southwest Research Institute United States Institute of Software Integrated Systems Vanderbilt University United States School of Mechanical Engineering Purdue University United States Department of Mechanical Engineering Carnegie Mellon University United States

We present AircraftVerse, a publicly available aerial vehicle design dataset. Aircraft design encompasses different physics domains and, hence, multiple modalities of representation. The evaluation of these cyber-physical system (CPS) designs requires the use of scientific analytical and simulation models ranging from computer-aided design tools for structural and manufacturing analysis, computational fluid dynamics tools for drag and lift computation, battery models for energy estimation, and simulation models for flight control and dynamics. AircraftVerse contains 27,714 diverse air vehicle designs - the largest corpus of engineering designs with this level of complexity. Each design comprises the following artifacts: a symbolic design tree describing topology, propulsion subsystem, battery subsystem, and other design details;a STandard for the Exchange of Product (STEP) model data;a 3D CAD design using a stereolithography (STL) file format;a 3D point cloud for the shape of the design;and evaluation results from high fidelity state-of-the-art physics models that characterize performance metrics such as maximum flight distance and hover-time. We also present baseline surrogate models that use different modalities of design representation to predict design performance metrics, which we provide as part of our dataset release. Finally, we discuss the potential impact of this dataset on the use of learning in aircraft design and, more generally, in CPS. AircraftVerse is accompanied by a data card, and it is released under Creative Commons Attribution-ShareAlike (CC BY-SA) license. The dataset is hosted at https://***/record/6525446, baseline models and code at https://***/SRI-CSL/AircraftVerse, and the dataset description at https://***/. © 2023, CC BY-SA.

关键词： computer aided design

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Feature-centric registration of large deformed images using transformers and correlation distance

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computers in Biology and Medicine 2025年 184卷 109356-109356页

作者： Kim, Heeyeon Lee, Minkyung Kim, Bohyoung Shin, Yeong-Gil Chung, Minyoung School of Software Soongsil University 369 Sangdo-RoDongjak-Gu Seoul06978 Korea Republic of Department of Computer Science and Engineering Seoul National University 1 Gwanak-roGwanak-gu Seoul08826 Korea Republic of Division of Biomedical Engineering Hankuk University of Foreign Studies 81 Oedae-ro Mohyeon-myeon Cheoin-gu Gyeonggi-do Yongin-si17035 Korea Republic of

In deformable medical image registration, both a robust backbone registration network and a suitable similarity metric are essential. This paper introduces a robust registration network combined with a feature-based loss function, specifically designed to handle large deformations and address the challenge of the absence of ground truth data. Tackling large deformations typically requires either expanding the receptive field or breaking down extensive deformations into smaller, more manageable ones. We address this challenge through two key network components: the coarse-to-fine estimation of the target displacement vector field (DVF) and the integration of the Transformer's feature attention mechanism. To further enhance registration performance, we propose a novel feature correlation-based distance metric that leverages the symmetric properties of the correlation matrix to efficiently exploit feature correlations. Additionally, by utilizing the features extracted directly from the registration network, we eliminate the need for additional feature extraction networks. Experimental results demonstrate that our feature correlation-based loss function is particularly effective in achieving accurate registration in the absence of ground truth data. Our method has proven successful in both mono-modality abdomen CT registration and brain MRI atlas registration, leading to improvements in Dice similarity coefficient and other evaluation metrics. © 2024 Elsevier Ltd

关键词： Distribution transformers

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