Search Results - (( framework implementation using algorithm ) OR ( leaf optimization method algorithm ))
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Automated plant classification system using a hybrid of shape and color features of the leaf
Published 2016“…Automated plant leaf classification is a computerized approach that employs computer vision and machine learning algorithms to identify a plant based on the features of its leaf. …”
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Leaf condition analysis using convolutional neural network and vision transformer
Published 2024“…Besides, existing leaf disease detection programs do not provide an optimized user’s experience. …”
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Plant leaf recognition algorithm using ant colony-based feature extraction technique
Published 2013“…Then, based on the characteristics of each species, decision making is done by means of ant colony optimisation as a search algorithm to return the optimal subset of features regarding the related species. …”
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4
Using GA and KMP algorithm to implement an approach to learning through intelligent framework documentation
Published 2023“…The main objective of this paper is to propose and implement an intelligent framework documentation approach that integrates case-based learning (CBL) with genetic algorithm (GA) and Knuth-Morris-Pratt (KMP) pattern matching algorithm with the intention of making learning a framework more effective. …”
Conference paper -
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Quantum Processing Framework And Hybrid Algorithms For Routing Problems
Published 2010“…The framework is used to increase the implementation performance of quantum algorithms. …”
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Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning
Published 2016“…Moreover, instead of concatenating feature vectors together and send to classifier, sparse coding and dictionary learning methods are used and instead of considering all features as one view (visual feature), K-SVD algorithm that is one of the famous algorithms for sparse representation is optimized and developed to multi-view model.The experimental results prove that the proposed methods has improved accuracy by 53.77% compared to concatenating features and classic K-SVD dictionary learning model as well.…”
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7
Low-level hybridization scripting language with dynamic parameterization in PSO-GA / Suraya Masrom
Published 2015“…Evaluations of four different sets of applications that used the proposed implementation frameworks with dynamic parameterization have indicated the effectiveness of each tested algorithm in comparison to the single PSO and constant parameterization. …”
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8
Implementing case-based reasoning approach to framework documentation
Published 2023“…In CBR, reasoning is based on remembering past cases. Genetic algorithm (GA) is used in implementing the CBR's "retrieve", "reuse", and "revise" steps. …”
Conference paper -
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Design and Implementation of Intelligent Interoperability Framework for Heterogeneous Subsystems in Smart Home Environment
Published 2011“…The first algorithm, named as Framework Initialization algorithm, is developed to initialize the framework and discovering the subsystems configured in order to derive the interoperation tasks. …”
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10
Deep plant: A deep learning approach for plant classification / Lee Sue Han
Published 2018“…They look for the procedures or algorithms that maximize the use of leaf databases for plant predictive modelling, but this results in leaf features which are liable to change with different leaf data and feature extraction techniques. …”
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11
Implementation of perez-dumortier calibration algorithm
Published 2014“…The implementation of the filtration procedure in step-by-step is discussed to render better framework of the proposed calibration algorithm. …”
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Chapter In Book -
12
A Telemedicine Tool Framework For Lung Sounds Classification Using Ensemble Classifier Algorithms
Published 2020“…The overall classification accuracy for the Improved Random Forest algorithm has 99.04%. The telemedicine framework was implemented with the Improved Random Forest algorithm. …”
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Genetic algorithm based ensemble framework for sentiment analysis
Published 2018“…Finally, the last experiment involves creating the complete optimized multilayered ensemble framework and implementation of the framework to find the suitable combination of methods in each layer to produce satisfactory sentiment analysis accuracy. …”
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15
Enhancing battery state of charge estimation through hybrid integration of barnacles mating optimizer with deep learning
“…In response to the growing importance of SoC estimation, this study introduces a hybrid approach called the Barnacles Mating Optimizer with Deep Learning (BMO-DL) for SoC of Nissan Leaf batteries. …”
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Edge assisted crime prediction and evaluation framework for machine learning algorithms
Published 2022“…In particular, this study proposes a crime prediction and evaluation framework for machine learning algorithms of the network edge. …”
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Conference or Workshop Item -
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An intelligent framework for modelling and active vibration control of flexible structures
Published 2004“…Performance of the AVC algorithm is assessed based on parametric design techniques, using RLS and GAS, and non-parametric design techniques, using MLP-NN and ANFIS in the suppression of vibration of the flexible structures. …”
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Immune-based technique for undergraduate programmes recommendation / Muhammad Azrill Mohd Zamri
Published 2017“…Based on the framework, a prototype was implemented to evaluate the accuracy of the proposed technique. 52% is the highest accuracy obtained from the technique by using ten-fold cross validation. …”
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Real-time anomaly detection using clustering in big data technologies / Riyaz Ahamed Ariyaluran Habeeb
Published 2019“…Based on the outcome of the analysis, this research proposed a novel framework namely real-time anomaly detection based on big data technologies (RTADBDT), along with supporting implementation algorithms. …”
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Rapid software framework for the implementation of machine learning classification models
Published 2021“…The machine learning model in the two platforms were tested on breast cancer and tax avoidance datasets with Decision Tree algorithm. The results indicated that although the software framework is easier than the programming platform for implementing the machine learning model, the results from the software framework were highly accurate and reliable. …”
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