Search alternatives:
coding algorithm » cosine algorithm (Expand Search), colony algorithm (Expand Search), scheduling algorithm (Expand Search)
model algorithm » mould algorithm (Expand Search)
data algorithm » jaya algorithm (Expand Search), deer algorithm (Expand Search)
element » elements (Expand Search)
coding algorithm » cosine algorithm (Expand Search), colony algorithm (Expand Search), scheduling algorithm (Expand Search)
model algorithm » mould algorithm (Expand Search)
data algorithm » jaya algorithm (Expand Search), deer algorithm (Expand Search)
element » elements (Expand Search)
-
121
Eye-Clustering: An Enhanced Centroids Prediction for K-means Algorithm
Published 2024“…Hundreds of such labeled graphs were used to train the model to predict the location of centroids. The objective is to produce a model capable of predicting centroids with greater accuracy than the traditional random initialization used in K-means. …”
Get full text
Get full text
Get full text
masterThesis -
122
Parallel physical optimization algorithms for allocating data to multicomputer nodes
Published 1994“…The parallel genetic algorithm (PGA) is based on a natural model of evolution. …”
Get full text
Get full text
Get full text
article -
123
Economic load dispatch using memetic sine cosine algorithm
Published 2022“…SCA is a recent population based optimizer turned towards the optimal solution using a mathematical-based model based on sine and cosine trigonometric functions. …”
Get full text
-
124
Physical optimization algorithms for mapping data to distributed-memory multiprocessors
Published 1992“…PGA has excellent speed-ups by virtue of the natural evolution model on which it is based. PSA and PNN include communication schemes adapted to the properties of the mapping problem and of the algorithms themselves for reducing the communication overhead. …”
Get full text
Get full text
Get full text
masterThesis -
125
-
126
Type 2 Diabetes Mellitus Automated Risk Detection Based on UAE National Health Survey Data: A Framework for the Construction and Optimization of Binary Classification Machine Learn...
Published 2020“…The first is a comprehensive ML framework for the construction of diagnostic binary classification high accuracy models to predict T2DM in the United Arab Emirates based on STEPS style National Health Survey. …”
Get full text
-
127
-
128
A stochastic iterative learning control algorithm with application to an induction motor
Published 2004“…A recursive optimal algorithm, based on minimizing the input error covariance matrix, is derived to generate the learning gain matrix of a P-type ILC for linear discrete-time varying systems with arbitrary relative degree. …”
Get full text
Get full text
Get full text
Get full text
article -
129
Estimating Construction Project Duration Using a Machine Learning Algorithm
Published 2024Get full text
Get full text
Get full text
masterThesis -
130
Metaheuristic algorithm for testing web 2.0 applications. (c2012)
Published 2012Get full text
Get full text
masterThesis -
131
Wild Blueberry Harvesting Losses Predicted with Selective Machine Learning Algorithms
Published 2022“…Statistical parameters i.e., mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R<sup>2</sup>), were used to assess the prediction accuracy of the models. The results of the correlation matrices showed that the blueberry yield and losses (leaf loss, blower loss) had medium to strong correlations accessed based on the correlation coefficient (r) range 0.37–0.79. …”
-
132
Approximation and heuristic algorithms for computing backbones in asymmetric ad-hoc networks
Published 2018“…We consider the problem of dominating set-based virtual backbone used for routing in asymmetric wireless ad-hoc networks. …”
Get full text
Get full text
Get full text
Get full text
article -
133
Correlation Clustering via s-Club Cluster Edge Deletion
Published 2023“…In certain situations, the requirement for clusters to be cliques was deemed excessively stringent, leading to the proposal of alternative relaxed clique models for dense subgraphs, such as s-club. In this work, we implement three approaches to tackle the 2-club clustering via edge deletion: a heuristic approach based on the influence of the edge to resolve maximum conflicts, a parameterized algorithm in which by deleting a maximum of k edges, the graph can be transformed into a 2-club cluster based on a branching algorithm, and the approach in Integer Linear Programming to find the optimized solution in an integer formulation. …”
Get full text
Get full text
Get full text
masterThesis -
134
-
135
Enhancing Breast Cancer Diagnosis With Bidirectional Recurrent Neural Networks: A Novel Approach for Histopathological Image Multi-Classification
Published 2025“…The BRNN model, refined using the Adagrad optimization algorithm, efficiently integrates the learned features from both branches. …”
-
136
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. …”
Get full text
Get full text
Get full text
Get full text
conferenceObject -
137
Spectral energy balancing system with massive MIMO based hybrid beam forming for wireless 6G communication using dual deep learning model
Published 2024“…In this paper, a Dual-Deep-Network technique is described for the extraction of statistical structures from a hybrid beam forming model based on mmWave logics, as well as training logic for the network map functions. …”
-
138
A new family of multi-step quasi-Newton algorithms for unconstrained optimization
Published 1999“…It concentrates on deriving a variable-metric family of minimum curvature algorithms for unconstrained optimization. The derivation is based on considering a rational model, with a certain tuning parameter, where the aim is to develop a general framework that encompasses all possible two-step minimum curvature algorithms generated by appropriate parameter choices. …”
Get full text
Get full text
article -
139
-
140