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An initial state of design and development of intelligent knowledge discovery system for stock exchange database
Published 2004“…We divide our problem in two modules.In first module we define Fuzzy Rule Base System to determined vague information in stock exchange databases.After normalizing massive amount of data we will apply our proposed approach, Mining Frequent Patterns with Neural Networks.Future prediction (e.g., political condition, corporation factors, macro economy factors, and psychological factors of investors) perform an important rule in Stock Exchange, so in our prediction model we will be able to predict results more precisely.In second module we will generate clustering algorithm. Generally our clustering algorithm consists of two steps including training and running steps.The training step is conducted for generating the neural network knowledge based on clustering.In running step, neural network knowledge based is used for supporting the Module in order to generate learned complete data, transformed data and interesting clusters that will help to generate interesting rules.…”
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Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm
Published 2025“…The Canopy clustering approach, enabled by distributed machine learning through Apache Mahout, is utilized as a preprocessing step within the K-means clustering technique. …”
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Wavelet network based online sequential extreme learning machine for dynamic system modeling
Published 2013“…The main advantage of OSELM over conventional algorithms is the ability of updating network weights sequentially through data sample-by-sample in a single learning step. …”
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Image classification of Aedes mosquitoes using transfer learning / Zetty Ilham Abdullah
Published 2021“…The advancement and rapid growth of machine learning field should not overlook this issue. Transfer learning concept in machine learning has been shown to improve learning of the targeted task by extending the original algorithm with knowledge gathered from the initial training to improve the performance of new model. …”
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Attacks detection in 6G wireless networks using machine learning
Published 2023“…The Attacks Detection in 6G (AD6Gs) wireless networks created by this research uses a Machine Learning (ML) algorithm. The pre-processing stage of the ML-AD6Gs process is the initial step. …”
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Detection on ambiguous software requirements specification written in malay using machine learning
Published 2017“…SRS is normally produced during the initial part of software development process and all subsequent steps in software development are influenced by the requirements. …”
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Data pre-processing of website browsing record: An initial step for web page classification
Published 2021“…A web page classification system is proposed to be constructed using a deep learning algorithm. The initial step for web page classification is data pre-processing. …”
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Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection
Published 2020“…The first improvement includes the use of Opposition Based Learning (OBL) at initialization phase of SSA to improve its population diversity in the search space. …”
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Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems
Published 2020“…A two-step enhanced non-dominated sorting HHMO (2SENDSHHMO) algorithm has been proposed to solve this problem. …”
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Nonstationary signal reconstruction from TVAR coefficients
Published 2018“…The proposed method consists of three steps, where in the first step, initial values for TVAR coefficients are estimated from synaptic weights of a three layer Artificial Neural Network (ANN) which is trained using Backpropagation (BP) learning algorithm. …”
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Adaptive model predictive control based on wavelet network and online sequential extreme learning machine for nonlinear systems
Published 2015“…Recently, an online sequential extreme learning machine (OSELM) algorithm has been introduced based on extreme learning machine (ELM) theories for single hidden layer feedforward neural networks (SLFN) and has been applied for different online applications. …”
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The new efficient and accurate attribute-oriented clustering algorithms for categorical data
Published 2012“…IG-ANMI algorithm improves G-ANMI by developing a new attribute-oriented initialization method in which part of initial chromosomes is generated by using the attributes partitions. …”
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Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images
Published 2024“…In the third classification algorithm, hybrid features are extracted using AlexNet and VGG-16 through a transfer learning approach where parameter manipulation is implemented to simplify the network. …”
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Converged Classification Network For Matching Cost Computation
Published 2020“…This paper focused on matching cost computation step as an initial step to produce the disparity or depth map. …”
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A new function of stereo matching algorithm based on hybrid convolutional neural network
Published 2022“…This paper proposes a new hybrid method between the learning-based and handcrafted methods for a stereo matching algorithm. …”
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A new variant of black hole algorithm based on multi population and levy flight for clustering problem
Published 2020“…Data clustering is one of the most popular branches in machine learning and data analysis. Partitioning-based type of clustering algorithms, such as K-means, is prone to the problem of producing a set of clusters that is far from perfect due to its probabilistic nature. …”
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RLMD-PA: A Reinforcement Learning-Based Myocarditis Diagnosis Combined with a Population-Based Algorithm for Pretraining Weights
Published 2024journal::journal article -
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