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  1. 1
    Aco-based feature selection algorithm for classification

    Aco-based feature selection algorithm for classification by Al-mazini, Hassan Fouad Abbas

    Published 2022
    “…The modified graph clustering ant colony optimisation (MGCACO) algorithm is an effective FS method that was developed based on grouping the highly correlated features. …”
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  2. 2
    Feature extraction and selection algorithm based on self adaptive ant colony system for sky image classification

    Feature extraction and selection algorithm based on self adaptive ant colony system for sky image classification by Petwan, Montha

    Published 2023
    “…The ant colony optimisation (ACO) algorithm have been used to select feature subset. …”
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  3. 3
    Classification of Diabetes Mellitus (DM) using Machine Learning Algorithms

    Classification of Diabetes Mellitus (DM) using Machine Learning Algorithms by Sirajun Noor, Noor Azmiya

    Published 2021
    “…Apart from that, ensemble methods such as bagging, adaptive boosting using AdaBoostM1, hybrid classifier using combinations of Random Forest with various base classifiers and ensemble algorithm which is the Random Forest has also been studied. …”
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  4. 4
    Improvement of fuzzy neural network using mine blast algorithm for classification of Malaysian Small Medium Enterprises based on strength

    Improvement of fuzzy neural network using mine blast algorithm for classification of Malaysian Small Medium Enterprises based on strength by Hussain Talpur, Kashif

    Published 2015
    “…Many researchers have trained ANFIS parameters using metaheuristic algorithms but very few have considered optimizing the ANFIS rule-base. …”
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  5. 5
    Activity recognition using one-versus-all strategy with relief-f and self-adaptive algorithm

    Activity recognition using one-versus-all strategy with relief-f and self-adaptive algorithm by Zainudin, Muhammad Noorazlan Shah, Sulaiman, Md. Nasir, Mustapha, Norwati, Perumal, Thinagaran

    Published 2018
    “…Relief-f used to rank the features and prune insignificant features, self-adaptive algorithm selects the relevant ones, and OVA transform features into a series of two-class classification problems, and later recognized by based classifier. …”
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  6. 6
    Utilisation of Exponential-Based Resource Allocation and Competition in Artificial Immune Recognition System

    Utilisation of Exponential-Based Resource Allocation and Competition in Artificial Immune Recognition System by Hormozi, Shahram Golzari

    Published 2011
    “…Artificial Immune Recognition System is one of the several immune inspired algorithms that can be used to perform classification, a data mining task. …”
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  7. 7
    Fair bandwidth distribution marking and scheduling algorithm in network traffic classification

    Fair bandwidth distribution marking and scheduling algorithm in network traffic classification by Al-Kharasani, Ameen Mohammed Abdulkarem

    Published 2019
    “…Thus, proposing the method of reestimating the dropping functions in the RED algorithm. …”
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  8. 8
    Mussels wandering optimization algorithmn based training of artificial neural networks for pattern classification

    Mussels wandering optimization algorithmn based training of artificial neural networks for pattern classification by Abusnaina, Ahmed A., Abdullah, Rosni

    Published 2013
    “…Traditional training algorithms have some drawbacks such as local minima and its slowness.Therefore, evolutionary algorithms are utilized to train neural networks to overcome these issues.This research tackles the ANN training by adapting Mussels Wandering Optimization (MWO) algorithm.The proposed method tested and verified by training an ANN with well-known benchmarking problems.Two criteria used to evaluate the proposed method were overall training time and classification accuracy.The obtained results indicate that MWO algorithm is on par or better in terms of classification accuracy and convergence training time.…”
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  9. 9
    Logistic regression methods for classification of imbalanced data sets

    Logistic regression methods for classification of imbalanced data sets by Santi Puteri Rahayu, -

    Published 2012
    “…These results can be seen as further explanation on the success of Truncated Newton method in TR-KLR and TR Iteratively Re-weighted Least Square (TR-IRLS) algorithm respectively, because of the equivalence of iterative method used by these algorithms. …”
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  10. 10
    Comparison on performance of adaptive algorithms for eye blinks removal in electroencephalogram

    Comparison on performance of adaptive algorithms for eye blinks removal in electroencephalogram by Abd Rahman, Faridah, Othman, Mohd Fauzi, Hamzah, Mohd Ilham Rusydan Hamzah

    Published 2018
    “…To evaluate the results, the SNR, MSE and correlation coefficient values are calculated based on the results obtained by using one of the widely used methods for blinks removal, independent component analysis (ICA). …”
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  11. 11
    Eye closure and open detection using Adaptive Thresholding Histogram Enhancement (ATHE) technique and connected components utilisation

    Eye closure and open detection using Adaptive Thresholding Histogram Enhancement (ATHE) technique and connected components utilisation by Mat Ibrahim, Masrullizam, Awang Md Isa, Azmi, Darsono, Abd Majid

    Published 2014
    “…This paper introduces a method to detect eye closure using Adaptive Thresholding Histogram Enhancement (ATHE) technique and connected component utilisation. …”
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  12. 12
    Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh

    Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh by Rawaa Dawoud Hassan, Al-Dabbagh

    Published 2015
    “…ARDE algorithm makes use of JADE strategy and the MDE_pBX parameters adaptive schemes as frameworks. …”
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  13. 13
    Adaptive method to improve web recommendation system for anonymous users

    Adaptive method to improve web recommendation system for anonymous users by Almurtadha, Yahya Mohammed

    Published 2011
    “…This research focuses on improving the prediction of the next visited web pages and introduces them to current anonymous user. An enhanced classification algorithm is used to assign the current anonymous user to the best web navigation profile. …”
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  14. 14
    An intra-severity classification and adaptation technique to improve dysarthric speech recognition accuracy / Bassam Ali Qasem Al-Qatab

    An intra-severity classification and adaptation technique to improve dysarthric speech recognition accuracy / Bassam Ali Qasem Al-Qatab by Bassam Ali Qasem, Al-Qatab

    Published 2020
    “…Secondly, the identified severity level of a particular dysarthric speaker in the first stage is applied to the corresponding intra-severity adaptation of dysarthric speech. For the classification part, there are six algorithms used to classify the intra-severity of dysarthric speakers. …”
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  15. 15
    Multi label ranking based on positive pairwise correlations among labels

    Multi label ranking based on positive pairwise correlations among labels by Alazaidah, Raed, Ahmad, Farzana Kabir, Mohsin, Mohamad

    Published 2020
    “…Two common approaches are being used to solve the problem of MLC: Problem Transformation Methods (PTMs) and Algorithm Adaptation Methods (AAMs). …”
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  16. 16
    Computational Technique for an Efficient Classification of Protein Sequences With Distance-Based Sequence Encoding Algorithm

    Computational Technique for an Efficient Classification of Protein Sequences With Distance-Based Sequence Encoding Algorithm by Iqbal, M.J., Faye, I., Said, A.M.D., Samir, B.B.

    Published 2017
    “…The major problems in classifying protein sequences into existing families/superfamilies are the following: the selection of a suitable sequence encoding method, the extraction of an optimized subset of features that possesses significant discriminatory information, and the adaptation of an appropriate learning algorithm that classifies protein sequences with higher classification accuracy. …”
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  17. 17
    An efficient intrusion detection model based on hybridization of artificial bee colony and dragonfly algorithms for training multilayer perceptrons

    An efficient intrusion detection model based on hybridization of artificial bee colony and dragonfly algorithms for training multilayer perceptrons by Ghanem, Waheed Ali H. M., Aman, Jantan, Ahmed Ghaleb, Sanaa Abduljabbar, Naseer, Abdullah B.

    Published 2020
    “…This study proposes a new binary classification model for intrusion detection, based on hybridization of Artificial Bee Colony algorithm (ABC) and Dragonfly algorithm (DA) for training an artificial neural network (ANN) in order to increase the classification accuracy rate for malicious and non-malicious traffic in networks. …”
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  18. 18
    Optimization and discretization of dragonfly algorithm for solving continuous and discrete optimization problems

    Optimization and discretization of dragonfly algorithm for solving continuous and discrete optimization problems by Bibi Amirah Shafaa, Emambocus

    Published 2024
    “…The steepest-ascent hill climbing algorithm is used as a local search technique to improve the exploitation of the adapted discrete DA. …”
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  19. 19
    Swarm negative selection algorithm for electroencephalogram signals classification

    Swarm negative selection algorithm for electroencephalogram signals classification by Sahel Ba-Karait, Nasser Omer, Shamsuddin, Siti Mariyam, Sudirman, Rubita

    Published 2009
    “…The SNS classification model use negative selection and PSO algorithms to form a set of memory Artificial Lymphocytes (ALCs) that have the ability to distinguish between normal and epileptic EEG patterns. …”
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  20. 20
    A new LoRa based positioning algorithm utilizing sequence based deep learning technique

    A new LoRa based positioning algorithm utilizing sequence based deep learning technique by Suseenthiran, Kavetha

    Published 2023
    “…Next, RSSI and Signal-to-Noise Ratio (SNR) that is measured is being classified whether it is LoS or NLoS environment based on the sequence-based Bi-LSTM model. Furthermore, an analysis of classification using different sequence length is done. …”
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