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Pipeline scour rates prediction-based model utilizing a multilayer perceptron-colliding body algorithm
Published 2023“…Forecasting; Multilayers; Particle swarm optimization (PSO); Pipelines; Soft computing; Colliding bodies; MLP model; Multi layer perceptron; Optimization algorithms; Optimization modeling; Prediction model; Soft computing models; Wave characteristics; Scour; algorithm; hydrological modeling; model; optimization; pipeline; scour; Cetacea…”
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Inversion Of Surface Wave Phase Velocity Using New Genetic Algorithm Technique For Geotechnical Site Investigation
Published 2011“…Therefore the use of genetic algorithm (GA) optimization technique which is one of nonlinear optimization methods is an appropriate choice to solve surface wave inversion problem having high nonlinearity and multimodality. …”
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3
Improved whale optimization algorithm for feature selection in Arabic sentiment analysis
Published 2019“…In SA, feature selection phase is an important phase for machine learning classifiers specifically when the datasets used in training is huge. Whale Optimization Algorithm (WOA) is one of the recent metaheuristic optimization algorithm that mimics the whale hunting mechanism. …”
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Healthcare Data Analysis Using Water Wave Optimization-Based Diagnostic Model
Published 2021“…This paper presents a new diagnostic model for various diseases. In the proposed diagnostic model, a water wave optimization (WWO) algorithm was implemented for improving the diagnosis accuracy. …”
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Enhanced weight-optimized recurrent neural networks based on sine cosine algorithm for wave height prediction
Published 2021“…Therefore, the wind plays an essential role in the oceanic atmosphere and contributes to the formation of waves. This paper proposes an enhanced weight-optimized neural network based on Sine Cosine Algorithm (SCA) to accurately predict the wave height. …”
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Water wave optimization with deep learning driven smart grid stability prediction
Published 2022“…In this background, the current study introduces a novel Water Wave Optimization with Optimal Deep Learning Driven Smart Grid Stability Prediction (WWOODL-SGSP) model. …”
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Time series predictive analysis based on hybridization of meta-heuristic algorithms
Published 2018“…The identified meta-heuristic methods namely Moth-flame Optimization (MFO), Cuckoo Search algorithm (CSA), Artificial Bee Colony (ABC), Firefly Algorithm (FA) and Differential Evolution (DE) are individually hybridized with a well-known machine learning technique namely Least Squares Support Vector Machines (LS-SVM). …”
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Efficient Numerical Modelling of Extreme Wave
Published 2020“…The performance of the OceanWave 3D model under different wave cases had been identified to understand the efficiency of the model. …”
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Final Year Project -
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Efficient Numerical Modelling of Extreme Waves
Published 2020“…The performance of the OceanWave 3D model under different wave cases had been identified to understand the efficiency of the model. …”
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Rock brittleness prediction through two optimization algorithms namely particle swarm optimization and imperialism competitive algorithm
Published 2018“…The main goal of this research work is to propose the novel practical models to predict the BI through particle swarm optimization (PSO) and imperialism competitive algorithm (ICA). …”
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Classification with degree of importance of attributes for stock market data mining
Published 2004“…Alan Fan et aI., [2] use Support Vector Machine (SVM) to stock market prediction. The SVM is a training algorithm for learning classification and regression rules from data [7]. …”
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Time series predictive analysis based on hybridization of meta-heuristic algorithms
Published 2018“…The identified meta-heuristic methods namely Moth-flame Optimization (MFO), Cuckoo Search algorithm (CSA), Artificial Bee Colony (ABC), Firefly Algorithm (FA) and Differential Evolution (DE) are individually hybridized with a well-known machine learning technique namely Least Squares Support Vector Machines (LS-SVM). …”
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Gooseneck barnacle optimization algorithm: A novel nature inspired optimization theory and application
Published 2024“…In contrast to the previously published Barnacle Mating Optimizer (BMO) algorithm, GBO more accurately captures the unique static and dynamic mating behaviours specific to gooseneck barnacles. …”
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Optimization driven model-space approach for gas clouds using full waveform inversion
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Optimization of Amplitude Versus Offset Attributes for Lithology and Hydrocarbon Indicators Using Recurrent Neural Network
Published 2022“…This article demonstrates the implementation of recurrent neural network (RNN) model in optimizing amplitude versus offset (AVO) attributes for indicating lithology and hydrocarbon zone on seismic data. …”
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Selective opposition based constrained barnacle mating optimization: Theory and applications
Published 2024“…Mathematical models of Barnacle Mating Optimization (BMO) are based on observations of real-world barnacle mating behaviors such as sperm casting and self-fertilization. …”
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Deep Learning-Driven Mobility And Utility-Based Resource Management In Mm-Wave Enable Ultradense Heterogeneous Networks
Published 2025thesis::doctoral thesis -
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Iterative And Single-Step Solutions Of Two Dimensional Time-Domain Inverse Scattering Problem Featuring Ultra Wide Band Sensors
Published 2010“…First, the algorithms‘ performances were evaluated using numerical simulation employing two different scanning geometries: limited and full view scanning geometries, where the applicability of these algorithms was accessed by comparing the reconstructed images with actual model. …”
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Stock market turning points rule-based prediction / Lersak Photong … [et al.]
Published 2021“…Finally, rule-based optimisation techniques such as Particle Swarm Optimization (PSO), Differential Evolution (DE) and Grey Wolf Optimizer (GWO) were used to minimise the amount of time employed in the stock market turning points prediction. …”
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