Multi-Target Tracking Resources Allocation Using Multi-Agent Modeling and Auction Algorithm حسب De Rochechouart, Maxence

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Socially Motivated Approach to Simulate Negotiation Process حسب Hammadih, Mohammed Zaher

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A Fast and Robust Gas Recognition Algorithm Based on Hybrid Convolutional and Recurrent Neural Network حسب Xiaofang Pan (1895950)

Multi-Objective Task Allocation Via Multi-Agent Coalition Formation حسب Amer, Noha Tarek

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Information reconciliation through agent controlled graph model. (c2018) حسب Saba, Rita

"…Our approach provides a damage assessment and recovery algorithm that is based on agents and graphs.…"
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CNN and HEVC Video Coding Features for Static Video Summarization حسب Issa, Obada

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Novel algorithms for accurate DNA base-calling حسب Mohammed, Omniyah Gul

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Predicting Patient ICU Readmission Using Recurrent Neural Networks With Long Short-Term Memory حسب Nadia Rasheed (22997257)

Deep Learning-Based Coding Strategy for Improved Cochlear Implant Speech Perception in Noisy Environments حسب Billel Essaid (22047578)

A Hybrid Deep Learning Model Using CNN and K-Mean Clustering for Energy Efficient Modelling in Mobile EdgeIoT حسب Dhananjay Bisen (19482454)

"…The proposed method, existing weighted clustering algorithm (WCA), and agent-based secure enhanced performance approach (AB-SEP) are tested over the network dataset. …"

On the Optimization of Band Gaps in Periodic Waveguides حسب Jamil Renno (14070771)

"…<h3 dir="ltr">Purpose</h3><p dir="ltr">This work applies a computational framework for vibration attenuation in periodic structures by combining the established wave and finite element (WFE) method with nature-inspired optimization algorithms. …"

Multi Agent Reinforcement Learning Approach for Autonomous Fleet Management حسب Alhusin, Mohammed Omer Alamin

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Single channel speech denoising by DDPG reinforcement learning agent حسب Sania Gul (18272227)

"…In this paper, a novel SD algorithm is presented based on the deep deterministic policy gradient (DDPG) agent; an off-policy reinforcement learning (RL) agent with a continuous action space. …"

Resources Allocation for Drones Tracking Utilizing Agent-Based Proximity Policy Optimization حسب De Rochechouart, Maxence

"…This paper presents a reinforcement learning agent-based model that works by incorporating the MESA environment with the Stone Soup radar systems simulator. …"
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Competitive learning/reflected residual vector quantization for coding angiogram images حسب Mourn, W.A.H.

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Automatic Video Summarization Using HEVC and CNN Features حسب Issa, Obada

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Multi-Agent Meta Reinforcement Learning for Reliable and Low-Latency Distributed Inference in Resource-Constrained UAV Swarms حسب Marwan Dhuheir (19170898)

"…Given the complexity of the LDTP solution for managing online requests, we propose a real-time, lightweight solution using multi-agent meta-reinforcement learning. Our approach is tested on CNN networks and benchmarked against state-of-the-art conventional reinforcement learning algorithms. …"

An Auction-Based Scheduling Approach for Minimizing Latency in Fog Computing Using 5G Infrastructure حسب Fahmy, Ahmed

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Block constrained pressure residual preconditioning for two-phase flow in porous media by mixed hybrid finite elements حسب Stefano Nardean (14151900)

"…This preconditioner, denoted as Block CPR (BCPR), is specifically designed for Lagrange multipliers-based flow models, such as those generated by Mixed Hybrid Finite Element (MHFE) approximations. An original MHFE-based formulation of the two-phase flow model is taken as a reference for the development of the BCPR preconditioner, in which the set of system unknowns comprises both element and face pressures, in addition to the cell saturations, resulting in a $$3\times 3$$ 3 × 3 block-structured Jacobian matrix with a $$2\times 2$$ 2 × 2 inner pressure problem. …"

Evolutionary algorithms for state justification in sequential automatic test pattern generation حسب El-Maleh, Aiman H.

"…Sequential circuit test generation using deterministic, fault-oriented algorithms is highly complex and time consuming. New approaches are needed to enhance the existing techniques, both to reduce execution time and improve fault coverage. …"
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