Zobrazeno 1 - 10
of 197
pro vyhledávání: '"Amar Mitiche"'
Publikováno v:
Frontiers in Neuroinformatics, Vol 18 (2024)
The ability to predict the occurrence of an epileptic seizure is a safeguard against patient injury and health complications. However, a major challenge in seizure prediction arises from the significant variability observed in patient data. Common pa
Externí odkaz:
https://doaj.org/article/7230a94cd5124671bdb3399c58a85362
Publikováno v:
IEEE Access, Vol 10, Pp 60141-60150 (2022)
Deep learning has served pattern classification in many applications, with a performance which often well exceeds that of other machine learning paradigms. Yet, in general, deep learning has used computational architectures built, albeit partially, b
Externí odkaz:
https://doaj.org/article/88a9b5015dcb48b69f5d45b8afcda13d
Publikováno v:
IEEE Access, Vol 8, Pp 62841-62854 (2020)
Feature selection is a challenging problem that occurs in the high-dimensional data analysis of many major applications. It addresses the curse of dimensionality by determining a small set of features to represent high-dimensional data without signif
Externí odkaz:
https://doaj.org/article/76768c1a4090487f8ffd5284e2d3a879
Publikováno v:
Applied Sciences, Vol 12, Iss 9, p 4181 (2022)
Monitoring patients at risk of epileptic seizure is critical for optimal treatment and ensuing the reduction of seizure risk and complications. In general, seizure detection is done manually in hospitals and involves time-consuming visual inspection
Externí odkaz:
https://doaj.org/article/6efe8267e5ba44a7b238fc3b5ae8794f
Autor:
Mariem Abid, Amal Khabou, Youssef Ouakrim, Hugo Watel, Safouene Chemcki, Amar Mitiche, Amel Benazza-Benyahia, Neila Mezghani
Publikováno v:
Sensors, Vol 21, Iss 14, p 4713 (2021)
Human activity recognition (HAR) by wearable sensor devices embedded in the Internet of things (IOT) can play a significant role in remote health monitoring and emergency notification to provide healthcare of higher standards. The purpose of this stu
Externí odkaz:
https://doaj.org/article/a509cce4427046a5b8586e1fa9ad81f3
Knee Joint Biomechanical Gait Data Classification for Knee Pathology Assessment: A Literature Review
Publikováno v:
Applied Bionics and Biomechanics, Vol 2019 (2019)
Background. The purpose of this study is to review the current literature on knee joint biomechanical gait data analysis for knee pathology classification. The review is prefaced by a presentation of the prerequisite knee joint biomechanics backgroun
Externí odkaz:
https://doaj.org/article/2651c80d3cf24acb89cadc5f309459c9
Publikováno v:
Applied Sciences, Vol 11, Iss 4, p 1579 (2021)
The purpose of this study is (1) to provide EEG feature complexity analysis in seizure prediction by inter-ictal and pre-ital data classification and, (2) to assess the between-subject variability of the considered features. In the past several decad
Externí odkaz:
https://doaj.org/article/edf31c7348624df59b22d36b584ce8d5
Publikováno v:
PLoS ONE, Vol 13, Iss 10, p e0202348 (2018)
Three-dimensional (3D) knee kinematic data, measuring flexion/extension, abduction/adduction, and internal/external rotation angle variations during locomotion, provide essential information to diagnose, classify, and treat musculoskeletal knee patho
Externí odkaz:
https://doaj.org/article/e8d43e674e0a41e4a6e22d15553fadb7
Publikováno v:
Applied Sciences, Vol 9, Iss 9, p 1741 (2019)
Knee kinematic data consist of a small sample of high-dimensional vectors recording repeated measurements of the temporal variation of each of the three fundamental angles of knee three-dimensional rotation during a walking cycle. In applications suc
Externí odkaz:
https://doaj.org/article/b9f18cb6b65c470a889a6326dc433827
Publikováno v:
Entropy, Vol 14, Iss 12, Pp 2478-2491 (2012)
Recent studies have shown that the Gibbs density function is a good model for visual patterns and that its parameters can be learned from pattern category training data by a gradient algorithm optimizing a constrained entropy criterion. These studies
Externí odkaz:
https://doaj.org/article/c1e21213ddcf42f0ba623f3db538e71e