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1
Mutable Composite Firefly Algorithm for Microarray-Based Cancer Classification
Published 2024“…Recently, the swarm-based hybrid algorithms have given significant performance in cancer classification. …”
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2
Breast cancer disease classification using fuzzy-ID3 algorithm based on association function
Published 2022“…The FID3-AF algorithm is a hybridisation of the fuzzy system, the iterative dichotomizer 3 (ID3) algorithm, and the association function. …”
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3
A novel nonlinear time‑varying sigmoid transfer function in binary whale optimization algorithm for descriptors selection in drug classifcation
Published 2022“…The proficiency of the proposed BWOA-3 and the comparative optimization algorithms are measured based on convergence speed, the length of the selected feature subset, and classification performance (accuracy, specificity, sensitivity, and f-measure). …”
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4
Fair bandwidth distribution marking and scheduling algorithm in network traffic classification
Published 2019“…Second, propose an Optimized Time Sliding Window based Three Colour Marker. Finally, propose a new method of obtaining optimal parameters dropping functions for Random Early Detection (RED) algorithm. …”
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5
Feature fusion using a modified genetic algorithm for face and signature recognition system
Published 2015“…Balance of the combined features has not been assessed whereas it is essential to ensure one of the biometrics does not dominate accuracy performance. To overcome the issue of incompatible features to be combined, Wrapper Genetic Algorithm (GA) was implemented as the feature selection algorithm due to its ability to evaluate the features irrespective of which domain by masking the features with bit number. …”
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6
Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad
Published 2018“…Therefore, this research has designed fuzzy learning algorithm that is able to classify fruits based on their shape and size features using Harumanis dataset. …”
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7
ESS-IoT: The Smart Waste Management System for General Household
Published 2024“…On the other hand, the waste classification is implemented using two classification algorithms: Random Forest (RF) prediction model and Convolutional Neural Network (CNN) prediction model. …”
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Genetic algorithms-based quality of service service selection in cloud computing using multilayer perceptron
Published 2014“…The attribute selection method is based genetic algorithms (GA) and is designed to rank the cloud services before the attributes are being fed into a multi-layer perceptron (MLP) classification system. …”
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A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem
Published 2016“…There are two methods in dealing with imbalanced classification problem, which are based on data or algorithmic level. …”
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10
Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy
Published 2018“…The performance of most metaheuristic algorithms depends on parameters whose settings essentially serve as a key function in determining the quality of the solution and the efficiency of the search. …”
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11
Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.
Published 2005“…Based on the experiment results, the classification method using the TIP approach has successfully performed rules generation and classification tasks as required during a classification operation. …”
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12
Differential evolution for neural networks learning enhancement
Published 2008“…These algorithms can be used successfully in many applications requiring the optimization of a certain multi-dimensional function. …”
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13
An efficient anomaly intrusion detection method with evolutionary neural network
Published 2020“…The second anomaly detection method is the Evolutionary Kernel Neural Network Random Weights (EKNNRW) in order to increase the accuracy of classification. 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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14
The classification of wink-based eeg signals by means of transfer learning models
Published 2021“…The implementation of pre-processing algorithms has been demonstrated to be able to mitigate the signal noises that arises from the winking signals without the need for the use signal filtering algorithms. …”
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15
Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms
Published 2020“…The ensemble learning technique, changes of activation function in Neural Network as well as the unsupervised learning (k-means clustering algorithm and Friis Transmission Equation) was also applied to classify the multiclass classification in pallet-level. …”
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16
Discretization of integrated moment invariants for writer identification
Published 2008“…The results disclose that the proposed discretized invariants reveal 99% accuracy of classification by using 3520 training data and 880 testing data.…”
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Smart fall detection by enhanced SVM with fuzzy logic membership function
Published 2023“…In addition, they use thresholds to identify falls based on artificial experiences or machine learning (ML) algorithms. …”
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18
Deep learning-based classification of breast tumors in ultrasound images / Ayub Ahmed Omar
Published 2022“…In this research project, recent breast tumor classification model algorithms are investigated and analyzed, and then the limitations and gaps in previous techniques are highlighted. …”
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19
A framework of modified adaptive neuro-fuzzy inference engine
Published 2012“…One of the vital significant issues for constructing a high quality neuro-fuzzy system is the creation of the knowledge base, which mainly consists of membership functions and fuzzy rules. …”
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20
Discrete wavelet packet transform for electroencephalogram based valence-arousal emotion recognition
Published 2015“…However, the challenging issues regarding EEG-based emotional state recognition is that it requires well-designed methods and algorithms to extract necessary features from the complex, chaotic, and multichannel EEG signal in order to achieve optimum classification performance. …”
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