Search Results - (( parallel optimization method algorithm ) OR ( automatic classification mining algorithm ))
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1
Data mining of protein sequences with amino acid position-based feature encoding technique
Published 2014“…Biological data mining has been emerging as a new area of research by incorporating artificial intelligence and biology techniques for automatic analysis of biological sequence data. …”
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2
A hybrid approach for artificial immune recognition system / Mahmoud Reza Saybani
Published 2016“…The increasing size of data being stored have created the need for computer-based methods for automatic data analysis. Many researchers, who have developed methods and algorithms within the field of artificial intelligence, machine learning and data mining, have addressed extracting useful information from the data. …”
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3
An ensemble learning method for spam email detection system based on metaheuristic algorithms
Published 2015“…A well known task is classification that predicts the class of new instances using known features or attributes automatically. …”
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4
Voting algorithms for large scale fault-tolerant systems
Published 2011“…To solve this problem and gain benefits of this algorithm, we employed parallel algorithm technique and by using optimal number of processors, we could propose optimal algorithms known as Parallel Average Voting and Parallel Weighted Average Voting which both have optimal time complexity and less calculation cost. …”
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5
Topology-aware hypergraph based approach to optimize scheduling of parallel applications onto distributed parallel architectures
Published 2020“…Any optimization algorithm is suitable for only a specific domain of optimization problems. …”
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6
Classification Of Gender Using Global Level Features In Fingerprint For Malaysian Population
Published 2016“…Two classification approaches which are the descriptive statistical and data mining are used in order to examine the classification of the gender by using the five extracted features. …”
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7
Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms
Published 2015“…This was achieved by mitigating convergence in the genetic algorithms by employing a hierarchical parallel architecture. …”
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A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping
Published 2014“…An ensemble algorithm of data mining decision tree (DT)-based CHi-squared Automatic Interaction Detection (CHAID) is widely used for prediction analysis in variety of applications. …”
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Study Of Modified Training Algorithm For Optimized Convergence Speed Of Neural Network
Published 2016“…First proposed algorithm is the combination of momentum algorithm with adaptive learning rate (ALR) algorithm, and second proposed algorithm is the combination of momentum algorithm with automatic learning rate selection (ALRS) algorithm. …”
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10
Investigation on the dynamic of computation of semi autonomous evolutionary computation for syntactic optimization of a set of programming codes
Published 2007“…Genetic Algorithm as one of the Evolutionary Computation method improve the execution of parallel programming codes by optimizing the number of processors and the distribution of data. …”
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11
Improved black-winged kite algorithm and finite element analysis for robot parallel gripper design
Published 2024“…This paper presents a comprehensive study on the design optimization of a robotic gripper, focusing on both the gripper modeling and the optimization of its parallel mechanism structure. …”
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Sequential and parallel multiple tabu search algorithm for multiobjective urban transit scheduling problems
Published 2018“…Additionally, the MTS algorithm is also implemented in parallel computing to produce parallel MTS for generating comparable solutions in shorter computational times. …”
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13
Analysis of evolutionary computing performance via mapreduce parallel processing architecture / Ahmad Firdaus Ahmad Fadzil
Published 2014“…Examples of EC such as Genetic Algorithm (GA) and PSO (Particle Swarm Optimization) are prevalent due to their efficiency and effectiveness. …”
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14
Distributed parallel deep learning with a hybrid backpropagation-particle swarm optimization for community detection in large complex networks
Published 2022“…Next, the method is integrated with two optimization algorithms: (1) backpropagation (BP), which optimizes deep learning locally within each local chunk of the CN; (2) particle swarm optimization (PSO), which is used to improve the BP optimization involving all CN chunks. …”
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Integration of object-based image analysis and data mining techniques for detailes urban mapping using remote sensing
Published 2015“…This algorithm represents the decision tree knowledge model, enables fast classification of intra-urban classes, and disables subjectivities related to the interaction with analysts. …”
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16
PID Parameters Improvement for AGC in Three Parallel-Connected Power Systems
Published 2016“…The AGC loop is used to minimize the frequency deviation and control the power exchange in order to maintain them at their scheduled values due to the changes of the step-load disturbance. The optimal parameters of the PID scheme optimized by the proposed MS algorithm are compared with that one’s obtained by GA algorithm, and the proposed method has proven that its performance is more efficient and improved as well. …”
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17
Design and analysis of high performance matrix filling for DNA sequence alignment accelerator using asic design flow: article / Nurzaima Mahmod
“…The scope of this paper is to optimize the DNA sequences alignment on the matrix rilling module by implementing a parallel method of the Smith-Waterman algorithm. …”
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Design and analysis of high performance matrix filling for DNA sequence alignment accelerator using ASIC design flow / Nurzaima Mahmod
Published 2010“…The scope of this paper is to optimize the DNA sequences alignment on the matrix filling module by implementing a parallel method of the SmithWaterman algorithm. …”
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19
Application Of Genetic Algorithms For Robust Parameter Optimization
Published 2010“…Genetic algorithms (GA) are fairly recent in this respect but afford a novel method of parameter optimization. …”
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Towards a better feature subset selection approach
Published 2010“…The selection of the optimal features subset and the classification has become an important issue in the data mining field.We propose a feature selection scheme based on slicing technique which was originally proposed for programming languages.The proposed approach called Case Slicing Technique (CST).Slicing means that we are interested in automatically obtaining that portion 'features' of the case responsible for specific parts of the solution of the case at hand.We show that our goal should be to eliminate the number of features by removing irrelevant once.Choosing a subset of the features may increase accuracy and reduce complexity of the acquired knowledge.Our experimental results indicate that the performance of CST as a method of feature subset selection is better than the performance of the other approaches which are RELIEF with Base Learning Algorithm (C4.5), RELIEF with K-Nearest Neighbour (K-NN), RELIEF with Induction of Decision Tree Algorithm (ID3) and RELIEF with Naïve Bayes (NB), which are mostly used in the feature selection task.…”
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