Search Results - (( developing function new algorithm ) OR ( learning application optimization algorithm ))

A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption by Nurnajmin Qasrina Ann, Ayop Azmi

“…The first research objective is to develop a new deep learning algorithm by a hybrid of DNN and K-Means Clustering algorithms for estimating the Lorenz chaotic system. …”
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Graph-Based Algorithm With Self-Weighted And Adaptive Neighbours Learning For Multi-View Clustering by He, Yanfang

“…To address this issue, this study incorporated joint graph learning from the gmc algorithm into swmcan, creating a new algorithm called swmcan-jg. …”
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Adaptive mechanism for enhanced performance of shark smell optimization / Nur Atharah Kamarzaman, Shahril Irwan Sulaiman and Intan Rahayu Ibrahim by Kamarzaman, Nur Atharah, Sulaiman, Shahril Irwan, Ibrahim, Intan Rahayu

“…Shark Smell Optimization (SSO) algorithm has been proven to have high efficiency in many optimization applications. …”
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Hybridization of metaheuristic algorithm in training radial basis function with dynamic decay adjustment for condition monitoring / Chong Hue Yee by Chong , Hue Yee

“…In this research work, the motivation is to develop an autonomous learning model based on the hybridization of an adaptive ANN and a metaheuristic algorithm for optimizing ANN parameters so that the network could perform learning and adaptation in a more flexible way and handle condition classification tasks more accurately in industries, such as in power systems. …”
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PROPOSED METHODOLOGY FOR OPTIMIZING THE TRAINING PARAMETERS OF A MULTILAYER FEED-FORWARD ARTIFICIAL NEURAL NETWORKS USING A GENETIC ALGORITHM by ABDALLA, OSMAN AHMED

“…In this thesis, the approach has been analyzed and algorithms that simulate the new approach have been mapped out.…”
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Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm by Manoharan P., Ravichandran S., Kavitha S., Tengku Hashim T.J., Alsoud A.R., Sin T.C.

“…The orthogonal learning mechanism improves the performance of the original GOOSE algorithm. …”

Chaos search in fourire amplitude sensitivity test by Koda, Masato

“…This paper explores the characterization of learning functions involved in FAST and derives the underlying dynamical relationships with chaos search, which can provide new learning algorithms. …”
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Chaos Search in Fourier Amplitude Sensitivity Test by Koda, Masato

“…This paper explores the characterization of learning functions involved in FAST and derives the underlying dynamical relationships with chaos search, which can provide new learning algorithms. …”
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Quasi oppositional—Manta ray foraging optimization and its application to pid control of a pendulum system by Ahmad Azwan, Abdul Razak, Ahmad Nor Kasruddin, Nasir, Nurul Amira, Mhd Rizal, Nor Maniha, Abd Ghani, Mohd Falfazli, Mat Jusof, Mohd Ashraf, Ahmad

“…From the study, MRFO is a relatively new developed algorithm and has low convergence rate. …”
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Deep Learning-Driven Mobility And Utility-Based Resource Management In Mm-Wave Enable Ultradense Heterogeneous Networks by Ukasyah bin Mahamod

An evaluation of Monte Carlo-based hyper-heuristic for interaction testing of industrial embedded software applications. by S. Ahmed, Bestoun, Enoiu, Eduard, Afzal, Wasif, Kamal Z., Zamli

“…Hyper-heuristic is a new methodology for the adaptive hybridization of meta-heuristic algorithms to derive a general algorithm for solving optimization problems. …”
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Synergizing intelligence and knowledge discovery: Hybrid black hole algorithm for optimizing discrete Hopfield neural network with negative based systematic satisfiability by Rusdi, Nur ‘Afifah, Zamri, Nur Ezlin, Kasihmuddin, Mohd Shareduwan Mohd, Romli, Nurul Atiqah, Manoharam, Gaeithry, Abdeen, Suad, Mansor, Mohd. Asyraf

“…Based on the findings, the development of the new systematic SAT and the implementation of the Hybrid Black Hole algorithm to optimize the retrieval capabilities of DHNN to achieve multi-objective functions result in updated final neuron states with high diversity, high attainment of global minima solutions, and produces states with a low similarity index. …”
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Modeling flood occurences using soft computing technique in southern strip of Caspian Sea Watershed by Borujeni, Sattar Chavoshi

“…Among the available learning algorithms in the Neural Network Toolbox of MATLAB, three algorithms, gradient descent back propagation (TRAINGD), gradient descent with adaptive learning rule back propagation (TRAINGDA) and the Levenberg-Marquardt (TRAINLM) were studied. …”
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Adaptive genetic algorithm to improve negotiation process by agents e-commerce by Ebadi, Sahar

“…The proposed negotiation algorithm employs Bayesian learning and similarity functions in order to predict opponent agent’s type and preferences. …”
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Enhancing reservoir simulation models with genetic algorithm optimized neural networks across diverse climatic zones / Saad Mawlood Saab by Saad Mawlood , Saab

“…Also, the study introduces a new procedure for reservoir simulation. The current research presents three different AI approaches: i) Multi-Layer Perceptron Neural Network (MLP-NN), ii) Radial Basis Function Neural Network (RBF-NN), and iii) Deep Learning Neural Network (DLNN). …”
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Application of deep learning technique to predict downhole pressure differential in eccentric annulus of ultra-deep well by Krishna, S., Ridha, S., Ilyas, S.U., Campbell, S., Bhan, U., Bataee, M.

“…The data generated from this model, field data, and experimental data are used to train and test the FFBP-DNN networks. The network is developed used Kerasâ��s deep learning framework. After testing the models, the most optimal arrangement of FFBP-DNN is the ReLU algorithm as an activation function, 4-hidden layers, the learning rate of 0.003, and 2300 of training numbers. …”
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A Modified Conjugate Gradient Method With Taylor Approximation: Applications In Electric Circuits And Image Restoration by Ahmad Alhawarat, Sultanah Masmali, Ibtisam Masmali, Mehiddin Al-Baali, Shahrina Ismail

Implementation of machine learning techniques with big data and IoT to create effective prediction models for health informatics by Zamani, Abu Sarwar, Hassan Abdalla Hashim, Aisha, Shatat, Abdallah Saleh Ali, Akhtar, Md. Mobin, Rizwanullah, Mohammed, Mohamed, Sara Saadeldeen Ibrahim

“…In the reduction phase, the optimal features are selected with theaid of the developed Hybrid Flower Pollination Bumblebees Optimization Algorithm (HFPBOA). …”
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Modular deep neural network in reducing overfitting to enhance generalization / Mohd Razif Shamsuddin by Shamsuddin, Mohd Razif

“…Most of those research results varies as it uses different data, different network design, different parameters and optimizing algorithm. This research aims to experiment a new DNN model that functions modularly by looking into several features that will affect the NN training dynamics. …”
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An optimized ensemble for predicting reservoir rock properties in petroleum industry by Kenari, Seyed Ali Jafari

“…The first method isbased on fuzzy genetic algorithm to overcome the premature convergence. The second method is based on two other functions instead of traditional fitness function in genetic algorithmnamely MSE to determine the individual's weight in an ensemble.This approach is based on Huber and Bisquare functions which are meant to avoid the influence of outliers that can be found in many real data such as geosciences data. …”
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