Search Results - (( using evolutionary detection algorithm ) OR ( java adaptation optimization algorithm ))
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Adaptive glioblastoma detection using evolutionary-based algorithm / Nurul Amira Mohd Ali
Published 2020“…The objectives of the project are to design and develop a prototype of adaptive Glioblastoma detection using Evolutionary-based algorithm to assist in detecting brain tumor and also to test the prototype’s functionality and detection accuracy. …”
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Parallel distributed genetic algorithm development based on microcontrollers framework
Published 2023Conference paper -
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An efficient anomaly intrusion detection method with evolutionary kernel neural network random weights
Published 2020“…Considering that using a combination of ANN and EA can produce an advanced technique to develop an efficient anomaly detection approach for IDS, several types of research have used ENN algorithms to detect the attacks. …”
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Fault Detection Relevant, Neural Network and Evolutionary Algorithm based Model for a Single-shaft Industrial Gas Turbine
Published 2009“…In this paper the result of an attempt to develop a substitute nonlinear model based on multilayer neural network (MLNN) and evolutionary algorithm (EA) for a single-shaft gas turbine having IGVs and VSVs is presented. …”
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Evolutionary cost cognizant regression test prioritization for object-oriented programs based on fault dependency
Published 2018“…Furthermore, they did not consider incorporating evolution process such as applying genetic algorithms to their technique. In this work, we proposed an evolutionary cost-cognizant regression testing approach that prioritizes test case according to the rate of severity detection of test cases connected with dependent faults using genetic algorithms. …”
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An efficient anomaly intrusion detection method with evolutionary neural network
Published 2020“…The third proposed method is a new Evolutionary Neural Network (ENN) algorithm with a combination of Genetic Algorithm and Multiverse Optimizer (GAMVO) as a training part of ANN to create efficient anomaly-based detection with low false alarm rate. …”
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An efficient anomaly intrusion detection method with feature selection and evolutionary neural network
Published 2020“…A proposed model has been practically used to the problem of intrusion detection as well as been validated using the NSL-KDD dataset. …”
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Backpropagation neural network based on local search strategy and enhanced multi-objective evolutionary algorithm for breast cancer diagnosis
Published 2019“…First, we enhance the famous multiobjective evolutionary algorithms, which is a non-dominated sorting genetic algorithm (NSGA-II). …”
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Computational intellegence approach for overcurrent relay coordination
Published 2024text::Final Year Project -
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Semi-automatic cortical boundary detection / Noor Elaiza Abdul Khalid.
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Enhanced feature selections of Adaboost training for face detection using genetic algorithm (GABoost)
Published 2007“…Generally, a large number of features are required to be selected for training purposes of face detection system. Often some of these features are irrelevant and does not contribute directly to the face detection algorithm. …”
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Enhanced feature selections of Adaboost training for face detection using genetic algorithm
Published 2007“…With these new feature types and larger search space, Genetic Algorithm (GA) is used within the Adaboost framework, to find sets of features which can provide a better cascade of boosted classifiers with a shorter training time. …”
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A Recent Research on Malware Detection Using Machine Learning Algorithm: Current Challenges and Future Works
Published 2023Conference Paper -
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An optimal mesh algorithm for remote protein homology detection
Published 2011“…Remote protein homology detection is a problem of detecting evolutionary relationship between proteins at low sequence similarity level. …”
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Evolutionary cost-cognizant regression test case prioritization for object-oriented programs
Published 2019“…Afterward evolutionary algorithm (EA) was employed to prioritize test cases based on the rate severity of fault detection per unit test cost. …”
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