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Multi-Target Tracking Resources Allocation Using Multi-Agent Modeling and Auction Algorithm
Published 2023Subjects: Get full text
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2
Bird’s Eye View feature selection for high-dimensional data
Published 2023“…This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. …”
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Multi-Objective Task Allocation Via Multi-Agent Coalition Formation
Published 2012Subjects: Get full text
doctoralThesis -
4
Information reconciliation through agent controlled graph model. (c2018)
Published 2018“…Our approach provides a damage assessment and recovery algorithm that is based on agents and graphs.…”
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masterThesis -
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Socially Motivated Approach to Simulate Negotiation Process
Published 2014Get full text
doctoralThesis -
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Metallic coating thickness assessment over nonmagnetic metals using eddy current technology
Published 2019Get full text
doctoralThesis -
8
A Hybrid Deep Learning Model Using CNN and K-Mean Clustering for Energy Efficient Modelling in Mobile EdgeIoT
Published 2023“…The proposed method, existing weighted clustering algorithm (WCA), and agent-based secure enhanced performance approach (AB-SEP) are tested over the network dataset. …”
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Multi Agent Reinforcement Learning Approach for Autonomous Fleet Management
Published 2019Get full text
doctoralThesis -
10
Single channel speech denoising by DDPG reinforcement learning agent
Published 2025“…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. …”
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Resources Allocation for Drones Tracking Utilizing Agent-Based Proximity Policy Optimization
Published 2023“…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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12
Current trends and future orientation in diagnosing lung pathologies: A systematic survey
Published 2025“…This study examined the current state-of-the-art methods and offers a comprehensive analysis of their advantages and disadvantages from an engineering perspective. …”
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Accuracy of an internationally validated genetic-guided warfarin dosing algorithm compared to a clinical algorithm in an Arab population
Published 2024“…Each patient provided a saliva sample. DNA was extracted, purified and genotyped for <i>VKORC−1639 G>A, CYP2C9*2, CYP2C9*3 </i>and<i> CYP4F2*3</i>. …”
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Multi-Agent Meta Reinforcement Learning for Reliable and Low-Latency Distributed Inference in Resource-Constrained UAV Swarms
Published 2025“…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. …”
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16
An Auction-Based Scheduling Approach for Minimizing Latency in Fog Computing Using 5G Infrastructure
Published 2020Get full text
doctoralThesis -
17
Exploring Semi-Supervised Learning Algorithms for Camera Trap Images
Published 2022Get full text
doctoralThesis -
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A conjugate self-organizing migration (CSOM) and reconciliate multi-agent Markov learning (RMML) based cyborg intelligence mechanism for smart city security
Published 2023“…Moreover, the Reconciliate Multi-Agent Markov Learning (RMML) based classification algorithm is used to predict the intrusion with its appropriate classes. …”
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XBeGene: Scalable XML Documents Generator by Example Based on Real Data
Published 2012“…Inspired by the query-by-example paradigm in information retrieval, Our generator system i)allows the user to provide her own sample XML documents as input, ii) analyzes the structure, occurrence frequencies, and content distributions for each XML element in the user input documents, and iii) produces synthetic XML documents which closely concur, in both structural and content features, to the user's input data. …”
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