Search Results - (( data integration learning algorithm ) OR ( data optimization method algorithm ))
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Evaluating integrated weight linear method to class imbalanced learning in video data
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Modeling time series data using Genetic Algorithm based on Backpropagation Neural network
Published 2018“…The first objective is to study the effects of varying the architecture designs and parameter values of the backpropagation neural network (BPNN) learning algorithm. The second objective is to compare the performances of machine learning (ML) techniques (e.g., BPNN and GA) with the statistical techniques (e.g., autoregressive integrated moving average (ARIMA)) in learning time series data. …”
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A new variant of black hole algorithm based on multi population and levy flight for clustering problem
Published 2020“…The first modification is the integration of BH algorithm and levy flight, which result in data clustering method, namely “Levy Flight Black Hole (LBH)”. …”
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Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms
Published 2015“…The methodology was benchmarked using popular data sets from UCI machine learning repository.…”
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A case study of microarray breast cancer classification using machine learning algorithms with grid search cross validation
Published 2023“…In this work, simple machine learning methods are used to classify breast cancer microarray data to normal and relapse. …”
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Enhanced computational methods for detection and interpretation of heart disease based on ensemble learning and autoencoder framework / Abdallah Osama Hamdan Abdellatif
Published 2024“…This thesis presents two innovative methods that holistically address these challenges at algorithmic and data levels to enhance heart disease detection. …”
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Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm
Published 2025“…The safe experimentation dynamics algorithm (SEDA) is one such method that optimizes controller parameters using data-driven techniques. …”
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Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm
Published 2025“…The safe experimentation dynamics algorithm (SEDA) is one such method that optimizes controller parameters using data-driven techniques. …”
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Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli
Published 2018“…To assess the applicability and accuracy of the proposed method for long-term electrical energy consumption, its estimates are compared with those obtained from artificial neural network (ANN), support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), rule-based data mining algorithm, GEP, linear, quadratic and exponential models optimized by particle swarm optimization (PSO), cuckoo search algorithm (CSA), artificial cooperative search (ACS) algorithm and backtracking search algorithm (BSA). …”
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11
Feature and Instances Selection for Nearest Neighbor Classification via Cooperative PSO
Published 2014“…Therefore, it can be solved by using population-based techniques such as Genetic Algorithm and Particle Swarm Optimization. This paper proposes the integration of feature reduction and data reduction for fuzzy modeling using Cooperative Binary Particle Swarm Optimization (CBPSO). …”
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Sauvola Segmentation and Support Vector Machine-Salp Swarm Algorithm Approach for Identifying Nutrient Deficiencies in Citrus Reticulata Leaves
Published 2024“…The proposed method integrates colour and texture feature-based image analysis with machine learning algorithms for classification. …”
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Integration of machine learning and remote sensing for above ground biomass estimation through Landsat-9 and field data in temperate forests of the Himalayan region
Published 2024“…Through the utilization of openly accessible fine-resolution data and employing the RF algorithm, the research demonstrated promising outcomes in the identification of optimal predictor-algorithm combinations for forest AGB mapping. …”
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Gravitational Search Algorithm Based LSTM Deep Neural Network for Battery Capacity and Remaining Useful Life Prediction With Uncertainty
Published 2025“…The proposed method is assessed using aging data from the NASA battery dataset. …”
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CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING
Published 2017“…In the hybrid scheme, the initial parameters of the modified BP neural network are optimized by using the global search ability of genetic algorithm, improved by cat chaotic mapping to enrich its optimization capability. …”
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Hybrid OCSSA-VMD and optimized deep learning networks for runoff forecasting
Published 2025“…To improve accuracy and address the non-linearity and non-stationarity in monthly runoff forecasting, this paper proposes a method that integrates intelligent optimization techniques with Deep Learning (DL) network. …”
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Heart disease prediction using artificial neural network with ADAM optimization and harmony search algorithm
Published 2025“…Drawing from an extensive review of existing predictive models and cardiovascular health risk factors, this research proposes an enhanced ADAM optimization algorithm, integrated with advanced data processing and feature selection methodologies, to identify and refine key predictors for improved model performance. …”
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Enhancing project completion date prediction using a hybrid model: rule-based algorithm and machine learning algorithm
Published 2025“…The study employs a hybrid predictive model that combines Big Data technologies, Extract Load Transfer (ELT) processes, rule-based algorithms (RBA), machine learning (ML), and Power BI visualizations. …”
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Development of a scaled conjugate gradient algorithm for significant RF neural signal processing
Published 2025“…Scale Conjugate Gradient (SCG) algorithm is an efficient training method for ANN that accelerates the learning process and improves output accuracy. …”
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Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms
Published 2018“…In the OGC framework, the exhibited explorative search behavior of the Gravitational Clustering (GC) algorithm has been addressed by (i) eliminating the agent velocity accumulation, and (ii) integrating an initialization method of agents using variance and median to subrogate the exploration process. …”
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