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International Conference on Computational science and Computational Intelligence (CSCI)

作者： Akinkunle Akinola Akoh Atadoga Kimberlyn Brooks Vibhuti Chandna Robert C. Green Data Science Program Bowling Green State University Bowling Green OH USA Dept. of Computer Science Bowling Green State University Bowling Green OH USA

To determine whether Ohio college re-opening plans were effective in controlling the spread of COVID-19, cumulative case counts by county were gathered to compare various metrics related to the spread of COVID-19 cases between counties with NCAA colleges and counties without NCAA colleges. Various non-parametric statistical tests were used to determine if the samples were similar, and the analysis found the differences were statistically significant. Metropolitan and non-metropolitan groupings were also added to further subdivide the data set, but the analysis found no statistically significant differences in this case.

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Development of Portable Temperature and Air Quality Detector for Preventing Covid-19 1

Development of Portable Temperature and Air Quality Detector...

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1st International Conference on computer science and Artificial Intelligence, ICCSAI 2021

作者： Budiharto, Widodo Irwansyah, Edy Dewanti, Retno Santoso Gunawan, Alexander Agung Widhyatmoko, Danu Sembodo, Jarot Soeroso Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Binus Business School Management Department Jakarta Indonesia Bina Nusantara University School of Design Dept. of Visual Communication Design Jakarta Indonesia Bina Nusantara University Master of Information System Program BINUS Graduate Program Jakarta Indonesia

ISBN: (纸本)9781728133331

Due to Covid-19, body temperature measurement is mandatory and become an important consideration in determining whether an individual is healthy or not. This paper presents the development of portable temperature and air quality detector to prevent suspect of Covid-19 with the main symptom is the body of temperature above 38° Celsius. We propose an algorithm and architecture used for temperature detector with maximum distance 80cm and CO2 and Volatile Organic Compounds (VOC) measurement are as indicator of good air quality. Based on experiment, we can detect temperature accurately until 0.3° Celsius using digital temperature sensor MLX90614 comparing with commercial one, furthermore the system able to give information about the quality of air, allowing/not allowing someone to enter a room with accuracy 92.5%. © 2021 IEEE.

关键词： Air quality

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Predicting Stress and Providing Counterfactual Explanations: A Pilot Study on Caregivers

Predicting Stress and Providing Counterfactual Explanations:...

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International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)

作者： Kei Shibuya Zachary D King Maryam Khalid Han Yu Yufei Shen Khadija Zanna Ryan L Brown Marzieh Majd Christopher P Fagunders Akane Sano Dept. of Electrical and Computer Engineering Rice University Houston U.S. Biometrics Research Laboratories. NEC Corporation Tokyo Japan Dept. of Electrical and Computer Engineering The University of Texas Austin U.S Dept. of Psychiatry and Behavioral Sciences University of California San Francisco San Francisco U.S Dept. of Psychiatry Harvard Medical School Boston U.S Dept. of Psychiatry Mood and Psychosis Research Program Brigham and Women’s Hospital Boston U.S Dept. of Psychological Science Rice University Houston U.S

Caregiving for spouses with Alzheimer’s disease or related dementias (ADRD) is one of the most stressful experiences. Evidence-based treatments for caregivers who have a high risk of mental health issues are needed. In this study, we designed models for predicting changes in perceived stress scale (PSS) (increase/not increase) in one week and generated some examples of counterfactual (’what-if’) explanations to change the stress state for helping manage stress. Using self-report (positive and negative affect and sleep quality) and sensor data (heart rate, sleep, and steps) collected in 132 week-long study sessions from 57 participants, we compared explainable PSS change prediction models (Random Forest, XGBoost, LightGBM, EBM, and Neural Network) along with ’what-if’ explanations. First, we developed machine learning models for classifying the change in PSS scores before and after the session period. Second, we identified feature importance using our explainable models. Our results showed that XGBoost performed the best with an accuracy of 0.79 and an F1 score of 0.78 for predicting changes in perceived stress. Our results also showed that minimum heart rate, mean steps per day, and negative affect are the most predictive features. Our preliminary counterfactual examples about sleep parameters would be able to provide suggestions for improving one’s health. We discussed our ideas to provide better suggestions using DiCE.

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Multi-Center CNN-Based Spine Segmentation from T2W MRI Using Small Amounts of Data

Multi-Center CNN-Based Spine Segmentation from T2W MRI Using...

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IEEE International Symposium on Biomedical Imaging

作者： Satheshkumar Kaliyugarasan Magnhild H. Dagestad Evin I. Papalini Erling Andersen John-Anker Zwart Helena Brisby Hanna Hebelka Ansgar Espeland Kerstin M. Lagerstrand Alexander S. Lundervold Dept. of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Norway Dept. of Radiology MMIV Haukeland University Hospital Norway Dept. of Radiology Haukeland University Hospital Norway Dept. of Clinical Medicine University of Bergen Norway Dept. of Medical Physics and Biomedical Engineering Sahlgrenska University Hospital Sweden Inst. of Clinical Sciences The Sahlgrenska Academy at the University of Gothenburg Sweden Dept. of Clinical Engineering Haukeland University Hospital Norway Faculty of Medicine University of Oslo Dept. of Research and Innovation Division of Clinical Neuroscience Oslo University Hospital Dept. of Orthopaedics Sahlgrenska University Hospital Sweden Dept. of Radiology Sahlgrenska University Hospital Gothenburg Sweden

Segmentation of the spinal tissues on MRI is the basis for quantitative analyses, but time-consuming if done manually. In this work, we construct a pipeline for automatic vertebrae segmentation from T2w MRI scans, assessing performance and generalizability by external validation. Our study used 15 scans from one site (Haukeland University Hospital, HUH) and 10 scans from another (Sahlgrenska University Hospital, SUH). MRI experts manually delineated the vertebral bodies Th12-L5 on all the HUH data and a subset of six scans from SUH. We trained multiple convolutional neural networks, assessing the performance in an experimental design tailored to small-data contexts and also on external data. Our best model achieved a mean Dice score of 0.899. This is comparable to results in the literature, but our system required much less training data. 1 .

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Evaluation of sar to optical image translation using conditional generative adversarial network for cloud removal in a crop dataset 24

Evaluation of sar to optical image translation using conditi...

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2021 24th ISPRS Congress Commission III: Imaging Today, Foreseeing Tomorrow

作者： Christovam, L.E. Shimabukuro, M.H. Galo, M.L.B.T. Honkavaara, E. Graduate Program in Cartographic Sciences São Paulo State University Brazil Dept. of Mathematics and Computer Science São Paulo State University Brazil Dept. of Cartography São Paulo State University Brazil Dept. of Remote Sensing and Photogrammetry Finnish Geospatial Research Institute in National Land Survey of Finland Finland

Most methods developed to map crop fields with high-quality are based on optical image time-series. However, often accuracy of these approaches is deteriorated due to clouds and cloud shadows, which can decrease the availably of optical data required to represent crop phenological stages. In this sense, the objective of this study was to implement and evaluate the conditional Generative Adversarial Network (cGAN) that has been indicated as a potential tool to address the cloud and cloud shadow removal;we also compared it with the Witthaker Smother (WS), which is a well-known data cleaning algorithm. The dataset used to train and assess the methods was the Luis Eduardo Magalhães benchmark for tropical agricultural remote sensing applications. We selected one MSI/Sentinel-2 and C-SAR/Sentinel-1 image pair taken in days as close as possible. A total of 5000 image pair patches were generated to train the cGAN model, which was used to derive synthetic optical pixels for a testing area. Visual analysis, spectral behaviour comparison, and classification were used to evaluate and compare the pixels generated with the cGAN and WS against the pixel values from the real image. The cGAN provided consistent pixel values for most crop types in comparison to the real pixel values and outperformed the WS significantly. The results indicated that the cGAN has potential to fill cloud and cloud shadow gaps in optical image time-series. © 2021 International Archives of the Photogrammetry, Remote Sensing and Spatial Information sciences - ISPRS Archives. All rights reserved.

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Students behaviors and factors affecting online learning during the COVID-19 situation Based on Machine Learning

Students behaviors and factors affecting online learning dur...

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International Conference on Computational science and Computational Intelligence (CSCI)

作者： Jaruwan Chutrtong Narumon Boonman Supatchalee Sirichokworrakit Kunyanuth Kularbphettong Dept. of Industrail Microbiology Faculty of Science and Technology Saun Sunandha Rajabhat University Bangkok Thailand Dept. of Biology Faculty of Science and Technology Saun Sunandha Rajabhat University Bangkok Thailand Dept. of foodscience and technology Faculty of Science and Technology Saun Sunandha Rajabhat University Bangkok Thailand Computer Science Program Suan Sunandha Rajabhat University Bangkok Thailand

The objectives of this research paper are study online learning behaviors, the success factors of online learning and perspectives on online learning during the Covid-19 situation. It is quasi experimental research. The population is 127 students of the Faculty of science and Technology, Suan Sunandha Rajabhat university. The sample group enrolled in microbiology courses, semesters 1 and 2 of 2020 academic year. Research tool is a questionnaire. This paper has two main objectives: to analyze the behavior of students and to predict learning behaviors through the online system by using SVM, and KNN. Overall academic success on study is also at moderate levels. The experiment shows that the support vector machine (SVM) model has the best and most stable effect. The average recall rate, precision rate, and accuracy rate reached 80.81%, 84.75%, and 81.83%, respectively.

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Uncovering Brain Network Modules in Major Depression During Naturalistic Music Perception with Block Term Decomposition

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IEEE Transactions on Cognitive and Developmental Systems 2024年

作者： Zhu, Yongjie Hao, Yuxing Liu, Jia Cong, Fengyu Dalian Univ. of Tech. School of Biomedical Engineering China Univ. of Helsinki Dept. of Computer Science Finland Aalto University Dept. of Neuroscience and Biomedical Engineering Finland Dalian University of Technology School of Biomedical Engineering Faculty of Medicine Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education China University of Jyväskylä Faculty of Information Technology Jyväskylä Finland Dalian Univ. of Tech. School of Biomedical Engineering Faculty of Medicine China

Major depressive disorder (MDD) has been linked to altered brain networks and might be relieved by music therapy. Yet, the neurophysiological basis, especially the functional network mechanism, of music therapy on depression remains poorly understood. Here, we apply a novel dynamic module detection method based on block term decomposition (BTD) to examine the reorganization of time-varying topological network structures during music listening in MDD using electroencephalography (EEG). Specifically, temporal adjacency matrices generated using a sliding-window technique form a three-way tensor. The multilinear rank-(L, L, 1) BTD is applied to directly derive hidden network modules with specific time evolution from the temporally concatenated tensors for each frequency band. After temporal correlation analysis with musical features extracted from music stimuli, we identify several frequency-dependent network modules with specific temporal patterns modulated by musical features. These modular networks encompass subnetworks of default mode, frontoparietal, language, and sensorimotor networks involved in the delta, alpha, and beta bands, exhibiting significantly different modulations by music between healthy control and MDD groups. These results implicate that the altered oscillatory modular networks might affect the dynamic processing of musical features for MDD patients, which could offer valuable perspectives on revealing the neural mechanisms of music therapy for MDD. © 2016 IEEE.

关键词： Tensors

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Spam Text Detection using Machine Learning Model

Spam Text Detection using Machine Learning Model

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International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

作者： Mahasak Ketcham Thittaporn Ganokratanaa Patiyuth Pramkeaw Narumol Chumuang Dept. of Information Technology Management King Mongkut's University of Technology North Bangkok Bangkok Thailand Applied Computer Science Program King Mongkut's University of Technology Thonburi Bangkok Thailand Media Technology Program King Mongkut's University of Technology Thonburi Bangkok Thailand Dept. of Digital Media Technology Muban Chombueng Rajabhat University (MCRU) Thailand

This research presents a classification between spam and non-spam messages by removing duplicate sentences or words. meaningless word and various marks The data is then classified by machine learning techniques and compared by differences between the data sets. Quantitative transformations were performed on each model to find the most efficient model. which can filter spam messages efficiently and quickly By getting the best comparison results in terms of information. quantitative conversion and model use The experimental results showed that Of all the tests, the model that performed best was Random Forest, with an average accuracy of 97

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Application of the Internet of Everything in Educational Institutions

Application of the Internet of Everything in Educational Ins...

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2021 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2021

作者： Yuan Chou, Tsung Lai, Wen-Hsing National Kaohsiung University of Science and Technology Ph.D.Program In Enginering Seience and Technology College of Enginering Kaohsiung Taiwan National Kaohsiung University of Science and Technology Dept. of Computer and Communication Engineering Kaohsiung Taiwan

ISBN: (纸本)9781665419512

Developments in network infrastructure and progress in sensor devices have brought the rise of the Internet of Everything (IoE). The technologies and applications are characterized by overall perception, tight connection, and intelligent responses. This study explored the application of the IoE in education such that these features can be utilized in educational institutions and areas designated for educational services. Focus was placed on the use of the Smart Manager in school attendance tracking, notification system, and security monitoring system for more broad and comprehensive campus safety. © 2021 IEEE.

关键词： Internet of Everything

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Early Detection of Mild Cognitive Impairment using Neuropsychological Data and Machine Learning Techniques

Early Detection of Mild Cognitive Impairment using Neuropsyc...

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2020 IEEE Conference on Big Data and Analytics, ICBDA 2020

作者： Almubark, Ibrahim Alsegehy, Samah Jiang, Xiong Chang, Lin-Ching Catholic University of America Dept. of Electrical Engineering and Computer Science WashingtonDC United States Penn State University Dept. of Computer Science and Engineering University ParkPA United States Georgetown University Medical Center Dept. of Neuroscience WashingtonDC United States

ISBN: (纸本)9781728192468

Individuals with mild cognitive impairment (MCI) have a much higher risk of developing Alzheimer's disease (AD) or other types of dementia. Early detection of MCI is critical to identify these high-risk candidates for proper management and early interventional treatment. In this paper, a neuropsychological dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI), one of the largest public AD database, was used. Four different machine learning algorithms were investigated to classify MCI from demographically matched cognitively normal (CN) older adults. In total, there were 620 subjects, including 391 MCI and 229 CN. A comprehensive neuropsychological battery was administered to all subjects, and 3 tests were selected for this study: Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog), Mini-Mental State Examination (MMSE), and Functional Activities Questionnaire (FAQ). Of all the classifiers tested, the Support Vector Machine (SVM) classifier with radial basis function (RBF) kernel had the best performance for MCI detection, with an average classification accuracy of 88.06% and the area under the receiver operating characteristic curve (AUC) of 0.945. © 2020 IEEE.

关键词： Neuroimaging

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