Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Mounir Bouhedda"'
Publikováno v:
Science, Engineering and Technology, Vol 4, Iss 2 (2024)
This study explores the integration of deep learning and Internet of Things (IoT) technologies to enhance healthcare delivery, with a primary focus on improving electrocardiogram (ECG) analysis and real-time patient monitoring systems. The research p
Externí odkaz:
https://doaj.org/article/5ef0f544e6f9461c946e6e91acf83bab
Publikováno v:
Sensors, Vol 13, Iss 3, Pp 2967-2985 (2013)
This paper develops a primitive gas recognition system for discriminating between industrial gas species. The system under investigation consists of an array of eight micro-hotplate-based SnO2 thin film gas sensors with different selectivity patterns
Externí odkaz:
https://doaj.org/article/d9f8ab26e5d949be8122af4362e4c19f
Autor:
Billel Bengherbia, Mohamed el amine Bougheriza, Mounir Bouhedda, Abdelhafid Tobbal, Abdelmoughni Toubal, Hamza Hentabeli
Publikováno v:
2022 International Conference on Electrical Engineering, Computer and Information Technology (ICEECIT).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::08e32bee8a5e7ddce8b504d7a799d065
https://doi.org/10.1109/iceecit55908.2022.10030667
https://doi.org/10.1109/iceecit55908.2022.10030667
Autor:
Mounir Bouhedda
Publikováno v:
Advanced Data Acquisition and Intelligent Data Processing ISBN: 9781003337027
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4473d30a1636851242fabbf26fd68372
https://doi.org/10.1201/9781003337027-9
https://doi.org/10.1201/9781003337027-9
Publikováno v:
Internet of Things. :100830
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3bbefc2c6b3116ac2109e2546d1d182e
https://doi.org/10.1016/j.iot.2023.100830
https://doi.org/10.1016/j.iot.2023.100830
Autor:
Yehya ANIBA, Mounir BOUHEDDA, Mourad BACHENE, Hamza BENYEZZA, Benyoucef KADDOUR, Abdelahani CHABlRA
Publikováno v:
2022 19th International Multi-Conference on Systems, Signals & Devices (SSD).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::84975c15c3d5e7a8437b017ea45f1ee5
https://doi.org/10.1109/ssd54932.2022.9955920
https://doi.org/10.1109/ssd54932.2022.9955920
Publikováno v:
Advanced Smart Computing Technologies in Cybersecurity and Forensics ISBN: 9781003140023
Advanced Smart Computing Technologies in Cybersecurity and Forensics
Advanced Smart Computing Technologies in Cybersecurity and Forensics
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e41c3ba3ff2d990937a52042530261ac
https://doi.org/10.1201/9781003140023-3
https://doi.org/10.1201/9781003140023-3
Autor:
Wahiba Menasri, Manel Djabri, Sarah Chennoufi, Abdellah Skoudarli, Mounir Bouhedda, Omar Benzineb
Publikováno v:
Journal of Visual Communication and Image Representation. 89:103673
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5a1a4e9d7f73c972e92dedae9f58f095
https://doi.org/10.1016/j.jvcir.2022.103673
https://doi.org/10.1016/j.jvcir.2022.103673
Publikováno v:
Drug Delivery and Translational Research. 10:168-184
In this work, topical matrix patches of diclofenac sodium (DS) were formulated by the solvent casting method using different ratios of chitosan (CTS) and kappa carrageenan (KC). Propylene glycol and tween 80 were used as a plasticizer and permeation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b48d96b78a6bd7afa94331c3f590c665
https://doi.org/10.1007/s13346-019-00671-w
https://doi.org/10.1007/s13346-019-00671-w
Autor:
Hichem Tahraoui, Abdeltif Amrane, Abd-Elmouneïm Belhadj, Adh’ya-eddine Hamitouche, Mounir Bouhedda
Publikováno v:
Desalination and Water Treatment
Desalination and Water Treatment, 2021, 217, pp.181-194. ⟨10.5004/dwt.2021.26813⟩
Desalination and Water Treatment, FHF, 2021, 217, pp.181-194. ⟨10.5004/dwt.2021.26813⟩
Desalination and Water Treatment, 2021, 217, pp.181-194. ⟨10.5004/dwt.2021.26813⟩
Desalination and Water Treatment, FHF, 2021, 217, pp.181-194. ⟨10.5004/dwt.2021.26813⟩
International audience; The aim of this work was to use artificial neural networks (ANN) and multiple linear regressions (MLR) models to predict the soluble sulfate content in drinking water. A set of 84 data points were used. For the ANN, 18 neurons
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8be80dbc898a173ab65441e2e577e2b7
https://hal.science/hal-03225482
https://hal.science/hal-03225482