بدائل البحث:
using function » using fusion (توسيع البحث)
algorithm i » algorithm _ (توسيع البحث), algorithm a (توسيع البحث), algorithm its (توسيع البحث)
i function » _ functional (توسيع البحث)
using function » using fusion (توسيع البحث)
algorithm i » algorithm _ (توسيع البحث), algorithm a (توسيع البحث), algorithm its (توسيع البحث)
i function » _ functional (توسيع البحث)
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161
A combined resource allocation framework for PEVs charging stations, renewable energy resources and distributed energy storage systems
منشور في 2017"…The formulation employs a general objective function that optimizes the total Annual Cost of Energy (ACOE). …"
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article -
162
The Use of Enumerative Techniques in Topological Optimization of Computer Networks Subject to Fault Tolerance and Reliability
منشور في 2003"…Experimental results obtained from a set of randomly generated networks using the proposed algorithms are presented and compared to those obtained using the existing techniques [1], [2]. …"
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article -
163
Joint distributed synchronization and positioning in UWB ad hoc networks using TOA
منشور في 2006"…Finally, the proposed distributed maximum log-likelihood algorithm proves to preserve a reasonable level of complexity in each node by approximating asynchronously the positive gradient direction of the log-likelihood function. …"
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article -
164
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165
Modelling Exchange Rates during Currency Crisis using Neural Networks
منشور في 2006"…This paper presents an artificial neural network (ANN) approach to the forecasting of exchange rate movements during periods of currency crises characterized by excessive volatility. The models are built using the feedforward ANN structure trained by the backpropagation algorithm. …"
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conferenceObject -
166
Parameter Identification of Flexible Drive Systems using Particle Swarm Optimization
منشور في 2023احصل على النص الكامل
doctoralThesis -
167
Intelligent Bilateral Client Selection in Federated Learning Using Game Theory
منشور في 2022"…To overcome this problem, we present in this paper FedMint, an intelligent client selection approach for federated learning on IoT devices using game theory and bootstrapping mechanism. Our solution involves designing (1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several factors such as accuracy and price, (2) intelligent matching algorithms that take into account the preferences of both parties in their design, and (3) bootstrapping technique that capitalizes on the collaboration of multiple federated servers in order to assign initial accuracy value for the new connected IoT devices. …"
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masterThesis -
168
Distinguishing Between Fake and Real Smiles Using EEG Signals and Deep Learning
منشور في 2020احصل على النص الكامل
doctoralThesis -
169
A Hybrid Transfer Learning Approach to Teeth Diagnosis Using Orthopantomogram Radiographs
منشور في 2024"…Fortunately, the availability of modern computing devices has made the automated diagnosis of teeth readily possible using deep learning. Despite this, concerns about the accuracy and function of automated diagnosis remain among patients. …"
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article -
170
Crashworthiness optimization of composite hexagonal ring system using random forest classification and artificial neural network
منشور في 2024"…At the same time, the mean square error value serves as the loss function for the ANN model (i.e., the loss function values were 2.84 × 10<sup>−7</sup> and 6.40 × 10<sup>−7</sup>, respectively, for X1 and X2 loading conditions at 45° angle). …"
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171
A Quasi-Oppositional Method for Output Tracking Control by Swarm-Based MPID Controller on AC/HVDC Interconnected Systems With Virtual Inertia Emulation
منشور في 2021"…The role of the proposed quasi oppositional based SMPID controller is to modify the tracking strategy on AC/HVDC interconnected systems while reducing the related cost function. The proposed analysis is established considering the most highly cited, well-known tested and newly expanded swarm-based optimization algorithms (SBOAs), such as Grasshopper Optimization Algorithm (GOA), Grey Wolf Optimization (GWO), Artificial Fish Swarm Algorithm (AFSA), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). …"
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172
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173
Modeling and Identification of Nonlinear DC Motor Drive Systems Using Recurrent Wavelet Networks
منشور في 2013احصل على النص الكامل
doctoralThesis -
174
Vibration suppression in a cantilever beam using a string-type vibration absorber
منشور في 2017"…In the first, the spring stiffness, the position of the second attachment point of the string and a preliminary damping constant are calculated using a genetic algorithm approach where the objective function is the maximum displacement on the beam. …"
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conferenceObject -
175
Isolating Physical Replacement of Identical IoT Devices Using Machine and Deep Learning Approaches
منشور في 2021احصل على النص الكامل
doctoralThesis -
176
StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features
منشور في 2024"…Additionally, we applied the local interpretable model-agnostic explanations (LIME) algorithm to understand the contribution of selected features to the overall prediction. …"
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177
Prediction of biogas production from chemically treated co-digested agricultural waste using artificial neural network
منشور في 2020"…An ANN model consists of three layers, 15 neutrons and 260 <i>epochs</i> accurately predict the CMP with 99.1% of data within ±10% deviation of the mean experimental value. …"
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178
Fleet sizing of trucks for an inter-facility material handling system using closed queueing networks
منشور في 2022"…Analytical model results are validated using the simulation results, which are proved to be very accurate, with deviations ranges within ±7%.…"
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179
Autism Detection of MRI Brain Images Using Hybrid Deep CNN With DM-Resnet Classifier
منشور في 2023"…The hyper parameters are optimized with DM optimization algorithm which improves the accuracy of classifier. …"
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180
Energy utilization assessment of a semi-closed greenhouse using data-driven model predictive control
منشور في 2021"…The proposed method consists of a multilayer perceptron model representing the greenhouse system integrated with an objective function and an optimization algorithm. The multilayer perceptron model is trained using historical data from the greenhouse with solar radiation, outside temperature, humidity difference, fan speed, HVAC control as the input parameters to predict the temperature. …"