Suchergebnisse - "Reduced deep convolutional stack autoencoder"

Epileptic Seizure Recognition Using Reduced Deep Convolutional Stack Autoencoder and Improved Kernel RVFLN From EEG Signals

Autoren: Mrutyunjaya Sahani Susanta Kumar Rout Pradipta Kishor Dash

Quelle: IEEE Transactions on Biomedical Circuits and Systems. 15:595-605

Schlagwörter: Epilepsy, Seizures, 0202 electrical engineering, electronic engineering, information engineering, Humans, Electroencephalography, Signal Processing, Computer-Assisted, Neural Networks, Computer, 02 engineering and technology, Child, Algorithms

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/34156948
https://ieeexplore.ieee.org/document/9462442/
https://europepmc.org/article/MED/34156948

Precise single step and multistep short-term photovoltaic parameters forecasting based on reduced deep convolutional stack autoencoder and minimum variance multikernel random vector functional network

Autoren: Mrutyunjaya Sahani Sasmita Choudhury Marif Daula Siddique et al.

Quelle: Engineering Applications of Artificial Intelligence. 136:108935

Epileptic Seizure Recognition Using Reduced Deep Convolutional Stack Autoencoder and Improved Kernel RVFLN From EEG Signals.

Autoren: Sahani, Mrutyunjaya Rout, Susanta Kumar Dash, Pradipta Kishor

Quelle: IEEE Transactions on Biomedical Circuits & Systems; Jun2021, Vol. 16 Issue 3, p595-605, 11p

Blockchain-based framework for secure medical data sharing and disease diagnosis using optimized deep belief networks.

Autoren: Sharma, Nikhil Shambharkar, Prashant Giridhar

Quelle: Cluster Computing; Dec2025, Vol. 28 Issue 16, p1-38, 38p

Schlagwörter: DIAGNOSIS of epilepsy, DATA security, CRYPTOGRAPHY, MEDICAL technology, BLOCKCHAINS, HEALTH information exchanges, DEEP learning, ARTIFICIAL neural networks

A short-term time-series prediction approach for photovoltaic power generation in residential energy systems.

Autoren: Su, Yongyi Zhang, Weirong Zhou, Ning et al.

Quelle: Building Simulation; Oct2025, Vol. 18 Issue 10, p2757-2776, 20p

Understanding Time Series Forecasting: A Fundamental Study.

Autoren: Furizal Ma'arif, Alfian Kariyamin et al.

Quelle: Buletin Ilmiah Sarjana Teknik Elektro (BISTE); Sep2025, Vol. 7 Issue 3, p554-571, 18p

Schlagwörter: FORECASTING, FORECASTING methodology, TREND analysis, DECISION making, ECONOMIC seasonal variations, DATA scrubbing

RDCSAE-RKRVFLN: A unified deep learning framework for robust and accurate DOA estimation.

Autoren: Raiguru, Priyadarshini Swain, Bhanja Kishor Rout, Susanta Kumar et al.

Quelle: Applied Soft Computing. Sep2024, Vol. 162, pN.PAG-N.PAG. 1p.

Schlagwörter: Convolutional neural networks, Standard deviations, Supervised learning, Direction of arrival estimation, Field programmable gate arrays, Deep learning

Detection of human movement by combining supervised machine learning and an embroidered textile capacitance sensor.

Autoren: Kim, Ji-seon Kim, Jooyong

Quelle: Textile Research Journal; Feb2025, Vol. 95 Issue 3/4, p305-322, 18p

Schlagwörter: SUPERVISED learning, MACHINE learning, STANDARD deviations, AUTOENCODERS, ARTIFICIAL intelligence

A unified deep learning framework for image dehazing.

Autoren: More, Anant Lahudkar, S. L.

Quelle: AIP Conference Proceedings; 2024, Vol. 3214 Issue 1, p1-11, 11p

Schlagwörter: MACHINE learning, AIR pollutants, ARTIFICIAL intelligence, LIGHT transmission, LIGHT scattering

Photovoltaics literature survey (No. 193).

Autoren: Hameiri, Ziv

Quelle: Progress in Photovoltaics; Oct2024, Vol. 32 Issue 10, p746-750, 5p

Schlagwörter: SOLAR cell design, CLEAN energy, SILICON solar cells, ENVIRONMENTAL research, DYE-sensitized solar cells, PEROVSKITE, QUANTUM dots

M2SKD: Multi-to-Single Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Systems.

Autoren: Baghersalimi, Saleh Amirshahi, Alireza Forooghifar, Farnaz et al.

Quelle: ACM Transactions on Intelligent Systems & Technology; Oct2024, Vol. 15 Issue 5, p1-31, 31p

Schlagwörter: ARTIFICIAL neural networks, EPILEPSY, EDGE computing, DEEP learning, COMPUTING platforms

Adaptive Bi-LSTM-based Epileptic Seizure Prediction from EEG Signals Using Deep Learning Algorithm.

Autoren: C., Jamunadevi P., Arul

Quelle: Mapana Journal of Sciences; 2024, Vol. 23 Issue 2, p65-82, 18p

Schlagwörter: ARTIFICIAL neural networks, CONVOLUTIONAL neural networks, MACHINE learning, EPILEPSY, SEIZURES (Medicine), DEEP learning, ELECTROENCEPHALOGRAPHY

A novel SVMA and K-NN classifier based optical ML technique for seizure detection.

Autoren: Deepa, N. Naresh, R. Anitha, S. et al.

Quelle: Optical & Quantum Electronics; Nov2023, Vol. 55 Issue 12, p1-34, 34p

Schlagwörter: EPILEPSY, SEIZURES (Medicine), NEUROLOGICAL disorders, SPONTANEOUS combustion, SUPPORT vector machines, PHASE coding, DISCRETE wavelet transforms, HILBERT-Huang transform

Unsupervised feature learning based on autoencoder for epileptic seizures prediction.

Autoren: He, Peng Wang, Linhai Cui, Yaping et al.

Quelle: Applied Intelligence; Sep2023, Vol. 53 Issue 18, p20766-20784, 19p

Schlagwörter: EPILEPSY, DEEP learning, SUPERVISED learning, MACHINE learning, DATA augmentation, NEUROLOGICAL disorders, FALSE alarms

深度学习在癫痫检测中的应用进展. (Chinese)

Alternate Title: Application Progress of Deep Learning in Epilepsy Detection. (English)

Autoren: 张汉明 马金刚 张宁宁 et al.

Quelle: Journal of Computer Engineering & Applications; 5/15/2023, Vol. 59 Issue 10, p35-47, 13p

Schlagwörter: MACHINE learning, DEEP learning, PEOPLE with epilepsy, EPILEPSY, ELECTROENCEPHALOGRAPHY, ETIOLOGY of diseases

Autoencoder-based improved deep learning approach for schizophrenic EEG signal classification.

Autoren: Parija, Sebamai Sahani, Mrutyunjaya Bisoi, Ranjeeta et al.

Quelle: Pattern Analysis & Applications; May2023, Vol. 26 Issue 2, p403-435, 33p

Schlagwörter: DEEP learning, SIGNAL classification, ELECTROENCEPHALOGRAPHY, MACHINE learning, MONARCH butterfly, SCHIZOPHRENIA

Geografische Kategorien: POLAND, MOSCOW (Russia)

Epileptic Seizure Detection Based on Bidirectional Gated Recurrent Unit Network.

Autoren: Zhang, Yanli Yao, Shuxin Yang, Rendi et al.

Quelle: IEEE Transactions on Neural Systems & Rehabilitation Engineering; 2022, Vol. 30, p135-145, 11p

Schlagwörter: EPILEPSY, DIAGNOSIS of epilepsy, WAVELET transforms, INSPECTION & review, MOVING average process

Table of Contents.

Quelle: IEEE Transactions on Biomedical Circuits & Systems; Jun2021, Vol. 16 Issue 3, p365-365, 1p

Biomedical Signals Based Computer-Aided Diagnosis for Neurological Disorders

Autoren: M. Murugappan Yuvaraj Rajamanickam

Resource Type: eBook.

Schlagworte: Nervous system--Diseases--Diagnosis, Medical informatics

Categories: TECHNOLOGY & ENGINEERING / Biomedical, COMPUTERS / Artificial Intelligence / Computer Vision & Pattern Recognition, MEDICAL / Radiology, Radiotherapy & Nuclear Medicine, SCIENCE / Life Sciences / Neuroscience, TECHNOLOGY & ENGINEERING / Signals & Signal Processing, MEDICAL / Informatics