Search Results - (( iot application swarm algorithm ) OR ( its application learning algorithm ))*
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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
Published 2022“…Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. …”
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Dynamic Task Offloading Algorithm for optimising IoT network quality of service in the Mobile-Fog-Cloud System
Published 2023“…The application of the Internet of Things (IoT) is increasing to almost all aspects of human endevour. …”
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Resource-Efficient Coverage Path Planning for UAV-Based Aerial IoT Gateway
Published 2023“…As a result, the Energy Efficient Coverage Path Planning (EECPP) algorithm has been proposed. The EECPP is composed of two algorithms: the Stop Point Prediction Algorithm using K-Means, and Path Planning Algorithm using Particle Swarm Optimization. …”
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Lightweight ontology architecture for QoS aware service discovery and semantic CoAP data management in heterogeneous IoT environment
Published 2026“…User requests, transmitted via the Constrained Application Protocol (CoAP), are semantically enriched and matched to QoS parameters using Dynamic Shannon Entropy optimized with the Salp Swarm Algorithm. …”
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Resource-efficient coverage path planning for unmanned aerial vehicle based aerial gateway in LoRaWAN
Published 2022“…Due to finite resource, multiple issues need to be considered in designing such system, including AG flight time, coverage radius and the achievable data rate of the ground-to-air system, thus an Energy Efficient Coverage Path Planning (EECPP) algorithm has been proposed. EECPP consist of two algorithms which is Stop Point Prediction Algorithm using K-Means, which finding the stop point for the AG after grouping the IDs into clusters, and Path Planning Algorithm using Particle Swarm Optimization which connect all of the stop point in shortest route. …”
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Intelligent Optimization Techniques for Optimal Power Flow using Interline Power Flow Controller
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Effect of chaos noise on the learning ability of back propagation algorithm in feed forward neural network
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A modified generalized RBF model with EM-based learning algorithm for medical applications
Published 2006“…Radial Basis Function (RBF) has been widely used in different fields, due to its fast learning and interpretability of its solution. …”
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SLIDING WINDOW TRAINING ALGORITHMS USING MLP-NETWORK FOR CORRELATED AND LOST PACKET DATA
Published 2012“…This thesis gives a systematic investigation of various MLP learning mainly Sliding Window (SW) learning mode which is treated as the adaptation of offline algorithms into online application Consequently this thesis reviews various offline algorithms including: batch backpropagation, nonlinear conjugate gradient, limited memory and full-memory Broyden, Fletcher, Goldfarb and Shanno algorithms and different forms of the latest proposed bimary ensemble learning. …”
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Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection
Published 2020“…Two main improvements were included into the original SSA algorithm to alleviate its drawbacks and adapt it for feature selection problems. …”
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Ensemble dual recursive learning algorithms for identifying flow with leakage
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Active force control with iterative learning control algorithm for a vehicle suspension
Published 2013“…The research focuses on the application of an active force control (AFC) strategy with iterative learning control (ILC) algorithms to compensate for the various introduced road profiles or 'disturbances' in a quarter car suspension system as an improvement to ride comfort performance. …”
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Learning Algorithm effect on Multilayer Feed Forward Artificial Neural Network performance in image coding
Published 2007“…The essence of this study is to investigate the most efficient and effective training methods for use in image compression and its subsequent applications. The obtained results show that the Quasi-Newton based algorithm has better performance as compared to the other two algorithms.…”
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Machine learning: tasks, modern day applications and challenges
Published 2019“…Machine learning algorithms learned from available data. Further, this learning laid the foundation to develop AI for the various systems around us. …”
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Optimal parameters of an ELM-based interval type 2 fuzzy logic system: a hybrid learning algorithm
Published 2018“…Type 2 fuzzy logic system has more parameters than the type 1 fuzzy logic system and is therefore much more complex than its counterpart. This paper proposes optimal parameters for an extreme learning machine-based interval type 2 fuzzy logic system to learn its best configuration. …”
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