Zobrazeno 1 - 10 of 174 pro vyhledávání: '"Keiichi Horio"'
1
Akademický článek
Epilepsy EEG classification using morphological component analysis
Autor: Arindam Gajendra Mahapatra, Balbir Singh, Hiroaki Wagatsuma, Keiichi Horio
Publikováno v: EURASIP Journal on Advances in Signal Processing, Vol 2018, Iss 1, Pp 1-12 (2018)
Abstract In this paper, we have proposed an application of sparse-based morphological component analysis (MCA) to address the problem of classification of the epileptic seizure using time series electroencephalogram (EEG). MCA was employed to decompo
Externí odkaz: https://doaj.org/article/77e6d68307a94cba9799e586514b6333
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2
Evaluation of Estimating Preschool Children’s Verbal Communication Skills in Group Discussions
Autor: Hiroki Yamaguchi, Keiichi Horio
Publikováno v: 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS).
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::e23dbdbe603bfb997e608b01a1b5e1c7
https://doi.org/10.1109/scisisis55246.2022.10002023
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3
Akademický článek
Self-Calibration Algorithm for a Pressure Sensor with a Real-Time Approach Based on an Artificial Neural Network
Autor: Ahmed M. M. Almassri, Wan Zuha Wan Hasan, Siti Anom Ahmad, Suhaidi Shafie, Chikamune Wada, Keiichi Horio
Publikováno v: Sensors, Vol 18, Iss 8, p 2561 (2018)
This paper presents a novel approach to predicting self-calibration in a pressure sensor using a proposed Levenberg Marquardt Back Propagation Artificial Neural Network (LMBP-ANN) model. The self-calibration algorithm should be able to fix major prob
Externí odkaz: https://doaj.org/article/7ddf29096d7547c5af255743de485714
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4
Predicting Disease Activity for Biologic Selection in Rheumatoid Arthritis
Autor: Yoshiya Tanaka, Keiichi Horio, Kazuhisa Nakano, Morio Yamauchi
Publikováno v: Computer Science & Information Technology (CS & IT).
In this article, we implemented a regression model and conducted experiments for predicting disease activity using data from 1929 rheumatoid arthritis patients to assist in the selection of biologics for rheumatoid arthritis. On modelling, the missin
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::20808c0dec9fc73f105c945edf489d72
https://doi.org/10.5121/csit.2020.101913
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5
Tuna Nutriment Tracking using Trajectory Mapping in Application to Aquaculture Fish Tank
Autor: Hilmil Pradana, Keiichi Horio
Publikováno v: 2020 Digital Image Computing: Techniques and Applications (DICTA).
The cost of fish feeding is usually around 40 percent of total production cost. Estimating a state of fishes in a tank and adjusting an amount of nutriments play an important role to manage cost of fish feeding system. Our approach is based on tracki
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f79929aeff452abfe86a6d2358d01325
https://doi.org/10.1109/dicta51227.2020.9363387
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6
Wave Height and Peak Wave Period Prediction Using Recurrent Neural Networks
Autor: Manzoor Ahmed Hashmani, Kazuki Osawa, Hiroki Yamaguchi, Keiichi Horio, Muhammad Umair
Publikováno v: 2020 International Conference on Computational Intelligence (ICCI).
In this paper, we applied a recurrent neural network to predict a wave height and a peak wave period for next 24 hours from only those last 24 hours. We adopted LSTM as the network structure and used statistic gradient decent method and adaptive mome
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::e3bd7b360c30c17bc2a3129934e5088f
https://doi.org/10.1109/icci51257.2020.9247805
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8
Classification of ictal and interictal EEG using RMS frequency, dominant frequency, root mean instantaneous frequency square and their parameters ratio
Autor: Keiichi Horio, Arindam Gajendra Mahapatra
Publikováno v: Biomedical Signal Processing and Control. 44:168-180
In this work, we have proposed to use root mean square (RMS) frequency fr and dominant frequency fd along with the ratio of their contributing parameters as features for classification of interictal and ictal electroencephalogram (EEG). Empirical mod
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8dcfffa3882578ae55963f1e8f698d1a
https://doi.org/10.1016/j.bspc.2018.04.007
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