Search Results - (( process classification learning algorithm ) OR ( parameter optimization model algorithm ))
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Optimization of support vector machine parameters in modeling of Iju deposit mineralization and alteration zones using particle swarm optimization algorithm and grid search method
Published 2023“…Copper deposits; Deposits; Geology; Learning algorithms; Mineralogy; Static Var compensators; Support vector machines; Three dimensional computer graphics; Alteration zones; Grid search; Grid-search method; Mineralization zone; Model Selection; Particle swarm optimization algorithm; Penalty parameters; Performance; Support vector classifiers; Support vectors machine; Particle swarm optimization (PSO); accuracy assessment; algorithm; classification; computer simulation; copper; geological survey; mineral alteration; mineralization; numerical model; ore deposit; parameterization; performance assessment; porphyry; resource assessment; support vector machine; three-dimensional modeling; Iran…”
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Hybridization of metaheuristic algorithm in training radial basis function with dynamic decay adjustment for condition monitoring / Chong Hue Yee
Published 2023“…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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Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification
Published 2022“…However, finding the most appropriate deep learning algorithm for a medical classification problem along with its optimal parameters becomes a difficult task. …”
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A new model for iris data set classification based on linear support vector machine parameter's optimization
Published 2020“…As a process of knowledge discovery. Classification is a data analysis that extracts a model which describes an important data classes. …”
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Evaluating Adan vs. Adam: an analysis of optimizer performance in deep learning
Published 2025“…Optimizers play an essential role in adjusting the model’s parameters to minimize errors, assisting the learning process during the model development. …”
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Classification of diabetic retinopathy clinical features using image enhancement technique and convolutional neural network / Abdul Hafiz Abu Samah
Published 2021“…Based on investigation different architecture and parameter, the suitable deep learning model has been presented to get optimize best result and testing time. …”
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Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images
Published 2024“…This proposed classifier achieved 97.9% classification accuracy on the ISIC dataset. 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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A hybridization of butterfly optimization algorithm and harmony search for fuzzy modelling in phishing attack detection
Published 2023“…In this paper, BOA was improvised by combining this algorithm with Harmony Search (HS) in order to achieve optimal results in fuzzy modelling. …”
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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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The formulation of a transfer learning pipeline for the classification of the wafer defects
Published 2023“…However, limitations such as robustness and difficulty in setting up the parameters required for image processing algorithm encourages the investigation in using Deep learning classification in detecting the wafer defects. …”
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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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Modified word representation vector based scalar weight for contextual text classification
Published 2024“…For this experiment, the modified word vectors serve as input to train a Machine Learning (ML) model for the text classification process, aiming for the developed ML model to have a significantly smaller parameter count. …”
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Improved personalised data modelling using parameter independent fuzzy weighted k-nearest neighbour for spatio/spectro-temporal data
Published 2021“…Additionally, it does not consider the class-specific fuzzy weight information during the classification process. Therefore, a data modelling mechanism which implements PIfwkNN classifier algorithm for improving the overall classification accuracy of the NeuCube architecture has been proposed. …”
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Fractional Stochastic Gradient Descent Based Learning Algorithm For Multi-layer Perceptron Neural Networks
Published 2021“…The performance is highly subjective to the optimization of learning parameters. In this study, we propose a learning algorithm for the training of MLP models. …”
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Robust tweets classification using arithmetic optimization with deep learning for sustainable urban living
Published 2024“…In this view, this research develops an arithmetic optimization algorithm with deep learning based tweets classification (AOADL-TC) approach for sustainable living. …”
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Detection of corneal arcus using rubber sheet and machine learning methods
Published 2019“…The benchmark of the classification algorithm for CA is needed to analyze the optimal output of the algorithm. …”
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Water Quality Evaluation and Analysis by Integrating Statistical and Machine Learning Approaches
Published 2026journal::journal article -
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Optimized techniques for landslide detection and characteristics using LiDAR data
Published 2018“…These results indicated that the proposed models with optimized hyper-parameters produced the accurate classification results. …”
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