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Your search keyword '"Bouazzi, Imen"' showing total 28 results
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28 results on '"Bouazzi, Imen"'

3. Impact of Geo-positioning Information in Limiting Forest Fire Damage

Author

Zaidi, Monji M., Alhamdi, Khaled Abdulaziz, Bouazzi, Imen R., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Choudrie, Jyoti, editor, Mahalle, Parikshit N., editor, Perumal, Thinagaran, editor, and Joshi, Amit, editor

Published
2023
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6. Adaptive Tuning Parameters for Data Transmission over IEEE-802.15.4 Wireless Sensors Network

Author

Bouazzi, Imen, Zaidi, Monji, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Fong, Simon, editor, Dey, Nilanjan, editor, and Joshi, Amit, editor

Published
2021
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13. Magnetic field and dissipation effects on mixed convection viscous fluid flow by a channel in the presence of porous medium and heat generation/absorption phenomenon.

Author

Xin, Xiao, Masthanaiah, Y., Rushikesava, A., Tarakaramu, Nainaru, Abdullaev, Sherzod, Khan, M. Ijaz, and Bouazzi, Imen Rashid

Subjects
VISCOUS flow, CHANNEL flow, FLUID flow, MAGNETIC field effects, POROUS materials
Abstract

In this study, we investigate the phenomenon of mixed convection in viscous fluid flow by a vertical channel, considering the presence of magneto‐hydro‐dynamics and porosity. Mixed convection occurs when both buoyancy forces and external forces, such as pumps or fans, influence the flow behavior. Understanding and accurately predicting mixed convection is crucial for optimizing heat exchanger design and performance. To model the temperature equation, we utilize the concept of the first law of thermodynamics. Additionally, we incorporate effects such as Joule heating, heat generation, and radiative heat flux in the energy equation modeling. The resulting physical liquid equations are solved using the shooting technique with the RKF (Runge–Kutta–Fehlberg) scheme. We present graphical representations of the flow variables, discussing their behavior in detail. The introduction provides an overview of the paper's roadmap, while the conclusion highlights the main and significant results obtained from our study. We found that, the temperature is more when liquid motion in between channel for large numerical values of thermal radiation parameter and Reynolds number. [ABSTRACT FROM AUTHOR]

Published
2024
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14. Optimizing Multi-Tier Cellular Networks With Deep Learning for 6G Consumer Electronics Communications

Author

Ali, Sher, Hassan, Muhammad Abul, Granelli, Fabrizio, Wang, Wei, Sampedro, Gabriel Avelino, Hejaili, Abdullah Al, and Bouazzi, Imen

Abstract

The imminent arrival of 6th Generation (6G) consumer electronics wireless networks heralds a paradigm shift in communication necessitated by diverse quality-of-service (QoS) demands. Meeting these demands efficiently requires novel strategies, particularly within multi-tier cellular networks encompassing femto cells, pico cells, macro cells, and device-to-device (D2D) links. However, managing channel assignments and computational loads in such underlay networks poses challenges. Additionally, due to the inherent randomness, selecting a communication mode between cellular and D2D and associating users with specific access points are complex tasks. Leveraging advancements in artificial intelligence (AI), we propose novel Deep Learning (DL) techniques for joint mode selection, user association, and channel assignment (MUC) in C-RAN (multi-Tier). Using deep neural networks (DNNs), our approach streamlines decision-making without exhaustive computations. To train the DNN model, we introduce an efficient Individual Channel Allocation (ICA) algorithm, a path-loss-based user-association method, and a heuristic mode selection technique collectively named JMUC. These techniques are applied in a random environment for a large number of times, and each time, their outputs are added to a set of samples, which is then used to train our DNN model using a supervised DL technique. Simulation outcomes demonstrate the effectiveness of the presented DL-based JMUC approach.

Published
2024
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