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1
Optimized clustering with modified K-means algorithm
Published 2021“…Generally, the proposed modified k-means algorithm is able to determine the optimum number of clusters for huge data.…”
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2
Fuzzy clustering method and evaluation based on multi criteria decision making technique
Published 2018“…The new proposed method (MBPSO+MKN+GK) Gustafson- Kessel algorithm (GK)integrated with modified of Kohonen Network algorithm (MKN)and modified binary particle swarm optimization (MBPSO) was used to classify the credit scoring data. …”
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3
Facial features point localization using modified SIFT scale space / Zulfikri Paidi
Published 2020“…Three tests were used on the original SIFT algorithm and modified SIFT. The first test is to evaluate the accuracy of the built-in vector feature. …”
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4
New random approaches of modified adaptive bats sonar algorithm for reservoir operation optimization problems
Published 2024“…In the fourth phase, the newly developed algorithm undergoes testing on the formulated ROOPs and compared to several contemporary optimizer algorithms. …”
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5
CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING
Published 2017“…In this research work, a modified backpropagation neural network is combined with a modified chaos-search genetic algorithm for STLF of one day and a week ahead. …”
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6
Stochastic And Modified Sequent Peak Algorithm For Reservoir Planning Analysis Considering Performance Indices
Published 2016“…In the next stage, the modified Sequent Peak Algorithm (SPA) is employed for the Storage-yield planning analysis of reservoir systems at different demands, reliability and vulnerability performance metrics employing the synthetic streamflow data. …”
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7
Modified damage location indices in beam-like structure: Analytical study
Published 2011“…It is also concluded that the modified algorithms have the sensitivity to detect the damage regardless of its severity. …”
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8
The Bacterial Foraging Optimisation Algorithm using Prototype Selection and Prototype Generation for Data Classification
Published 2020“…There are two modified instance-based classifiers of BFOA were developed in this study. …”
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9
Analysis and Modification of Grain - 128 Stream Cipher Algorithm
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Modified Data Encryption Standard Algorithm for Textbased Applications
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11
M-Factors Fuzzy Time Series for Forecasting Moving Holiday Electricity Load Demand in Malaysia (S/O 14589)
“…The modified algorithm, Weighted Subsethood Segmented Fuzzy Time Series (WeSuSFTS) consists of four main phases; data pre-processing, model development, model implementation and model evaluation. …”
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12
An ensemble of neural network and modified grey wolf optimizer for stock prediction
Published 2019“…Grey Wolf Optimizer (GWO) is a recently developed meta-heuristic algorithm which is appealing to researcher owing to its demonstrated performance as cited in the scientific literature. …”
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13
Analyzing enrolment patterns: modified stacked ensemble statistical learning based approach to educational decision-making
Published 2024“…The study’s primary objectives were to identify the determinants that impacted urban upper-secondary students' enrolment in Additional Mathematics within the Kuantan District, Pahang, Malaysia, and to develop a novel modified stacked ensemble statistical learning-based algorithm based on potential determinants, following the Cross Industry Standard Process for Data Mining (CRISP-DM) data science methodology. …”
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14
A novel hybrid classification model of genetic algorithms, modified k-Nearest Neighbor and developed backpropagation neural network
Published 2014“…Second, the obtained results that included arrays of the top-ranked features were used as the initial population of a genetic algorithm to produce optimum arrays of features. Third, using a modified k-Nearest Neighbor method as well as an improved method of backpropagation neural networks, the classification process was advanced based on optimum arrays of the features selected by genetic algorithms. …”
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15
Robust diagnostics and variable selection procedure based on modified reweighted fast consistent and high breakdown estimator for high dimensional data
Published 2022“…In addressing this problem, some robust procedures for high dimensional dataset via the RFCH algorithm are developed. A modified reweighted fast consistent and high breakdown (MRFCH) estimator in high dimensional data based on the diagonal elements of the scatter matrix instead of its entire elements in the computation of robust Mahalanobis distance within the RFCH algorithm is developed. …”
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16
Feature fusion using a modified genetic algorithm for face and signature recognition system
Published 2015“…To overcome the issue of incompatible features to be combined, Wrapper Genetic Algorithm (GA) was implemented as the feature selection algorithm due to its ability to evaluate the features irrespective of which domain by masking the features with bit number. …”
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17
Stroke-to-stroke matching in on-line signature verification
Published 2010“…This project suggesting a modified DTW algorithm with the proposed Missed Nodes Recovery Algorithm aims to improve the mapping performance, hence the development of stroke to stroke signature comparison is possible. …”
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18
Application of Bat Algorithm and Its Modified Form Trained with ANN in Channel Equalization
Published 2022“…An alternative approach to training neural network-based equalizers is to use metaheuristic algorithms. Here, in this work, to develop the symmetry-based efficient channel equalization in wireless communication, this paper proposes a modified form of bat algorithm trained with ANN for channel equalization. …”
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19
Modified word representation vector based scalar weight for contextual text classification
Published 2024“…To validate this algorithm, the modified word vectors are compared with original LLM-generated word vectors to evaluate their reflection of the intended context. …”
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20
Weighted subsethood and reasoning based fuzzy time series for moving holiday electricity load demand forecasting
Published 2021“…The modified algorithm, Weighted Subsethood Segmented Fuzzy Time Series (WeSuSFTS) consists of four main phases; data pre-processing, model development, model implementation and model evaluation. …”
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