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  1. 1
    Ant colony optimization algorithm for rule based classification: Issues and potential

    Ant colony optimization algorithm for rule based classification: Issues and potential by Al-Behadili, Hayder Naser Khraibet, Ku-Mahamud, Ku Ruhana, Sagban, Rafid

    Published 2018
    “…This paper presents a review of related work of ACO rule classification which emphasizes the types of ACO algorithms and issues. …”
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  2. 2
    A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification

    A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification by Talpur, N., Abdulkadir, S.J., Hasan, M.H., Alhussian, H., Alwadain, A.

    Published 2023
    “…The performance of the proposed SCSO algorithm was compared with six state-of-the-art and recent wrapper-based optimization algorithms using the validation metrics of classification accuracy, optimum feature size, and computational cost in seconds. …”
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  3. 3
    An enhanced soft set data reduction using decision partition order technique

    An enhanced soft set data reduction using decision partition order technique by Mohammed, Mohammed Adam Taheir

    Published 2017
    “…Also, the accuracy of original soft-set optimal and sub-optimal results have been improved using an intelligent SSR-BPSO-BBO algorithm. …”
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  4. 4
    Hybrid performance measures and mixed evaluation method for data classification problems

    Hybrid performance measures and mixed evaluation method for data classification problems by Hossin, Mohammad

    Published 2012
    “…This study investigates two different issues of performance measure in data classification problem. …”
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  5. 5
    An improved method using fuzzy system based on hybrid boahs for phishing attack detection

    An improved method using fuzzy system based on hybrid boahs for phishing attack detection by Noor Syahirah, Nordin

    Published 2022
    “…The algorithms involved were Genetic Algorithm, Differential Evolution Algorithm, Particle Swarm Optimization, Butterfly Optimization Algorithm, Teaching-Learning-Based Optimization Algorithm, Harmony Search Algorithm and Gravitational Search Algorithm. …”
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  6. 6
    Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi

    Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi by Atefi, Kayvan

    Published 2019
    “…Despite attempts to solve the data clustering issues, there are also many variants of modified algorithms in traditional information clustering that attempt to solve issues such as clustering algorithms based on condensation. …”
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  7. 7
    Intrusion Detection Systems, Issues, Challenges, and Needs

    Intrusion Detection Systems, Issues, Challenges, and Needs by Aljanabi, Mohammad, Mohd Arfian, Ismail, Ali, Ahmed Hussein

    Published 2021
    “…However, these algorithms suffer from many lacks especially when apply to detect new type of attacks, and need for new algorithms such as JAYA algorithm, teaching learning-based optimization algorithm (TLBO) algorithm is arise. …”
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  8. 8
    A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem

    A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem by Mohd Pozi, Muhammad Syafiq

    Published 2016
    “…There are two methods in dealing with imbalanced classification problem, which are based on data or algorithmic level. …”
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  9. 9
    Optimizing in-car-abandoned children’s sounds detection using deep learning algorithms / Nur Atiqah Izzati Md Fisol

    Optimizing in-car-abandoned children’s sounds detection using deep learning algorithms / Nur Atiqah Izzati Md Fisol by Md Fisol, Nur Atiqah Izzati

    Published 2023
    “…To address this problem, an optimized in-car-abandoned children's sounds detection model using deep learning algorithms is proposed. …”
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  10. 10
    An integrated model to email spam classification using an enhanced grasshopper optimization algorithm to train a multilayer perceptron neural network

    An integrated model to email spam classification using an enhanced grasshopper optimization algorithm to train a multilayer perceptron neural network by Mohamad, M., Abdullah, E.F.H.S., Ghaleb, S.A.A., Ghanem, W.A.H.M.

    Published 2021
    “…This study introduces an email detection model that is designed based on use of an improved version of the grasshopper optimization algorithm to train a Multilayer Perceptron in classifying emails as ham and spam. …”
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  11. 11
    Real-time classification improvement of Indonesian sign system letters (SIBI) using K-Nearest Neighbor algorithm

    Real-time classification improvement of Indonesian sign system letters (SIBI) using K-Nearest Neighbor algorithm by Dhewa, Oktaf Agni, Utama, Safitri Yuliana, Nasuha, Aris, Gunawan, Teddy Surya, Pratama, Gilang Nugraha Putu

    Published 2024
    “…A novel approach is introduced to enhance SIBI character predictions using the K-Nearest Neighbor (K-NN) algorithm. The K-NN algorithm is employed to predict the most suitable SIBI character based on the similarity of linguistic features between input speech and existing data. …”
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  12. 12
    Balancing data utility versus information loss in data-privacy protection using k-Anonymity

    Balancing data utility versus information loss in data-privacy protection using k-Anonymity by Esmeel, Thamer Khalil, Hasan, Md Munirul, Kabir, Muhammad Nomani, Ahmad, Firdaus

    “…Based on the classification accuracy, the optimal values of k and c are obtained, and thus, the optimal k and c can be used for kanonymity algorithm to anonymize optimal number of columns of the dataset.…”
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  13. 13
    Feature fusion using a modified genetic algorithm for face and signature recognition system

    Feature fusion using a modified genetic algorithm for face and signature recognition system by Suryanti, Awang

    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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  14. 14
    Feature Subset Selection in Intrusion Detection Using Soft Computing Techniques

    Feature Subset Selection in Intrusion Detection Using Soft Computing Techniques by AHMAD, IFTIKHAR

    Published 2011
    “…Based on the selected features, the classification is performed. …”
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  15. 15
    The classification of wink-based eeg signals by means of transfer learning models

    The classification of wink-based eeg signals by means of transfer learning models by Jothi Letchumy, Mahendra Kumar

    Published 2021
    “…Whilst it was observed that the optimized k-NN model based on the aforesaid pipeline could achieve a classification accuracy of 100% for the training, validation, and tes t data. …”
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  16. 16
    Hyperparameter Optimization of Evolving Spiking Neural Network for Time-Series Classification

    Hyperparameter Optimization of Evolving Spiking Neural Network for Time-Series Classification by Ibad, T., Abdulkadir, S.J., Aziz, N., Ragab, M.G., Al-Tashi, Q.

    Published 2022
    “…Therefore, this research presents an optimizer-based eSNN architecture, intended to solve the issue regarding optimum hyperparametersâ�� valuesâ�� selection of eSNN. …”
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  17. 17
    Feature Subset Selection in Intrusion Detection Using Soft Computing Techniques

    Feature Subset Selection in Intrusion Detection Using Soft Computing Techniques by Iftikhar , Ahmad, Azween, Abdullah

    Published 2011
    “…Based on the selected features, the classification is performed. …”
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  18. 18
    Towards a better feature subset selection approach

    Towards a better feature subset selection approach by Shiba, Omar A. A.

    Published 2010
    “…The selection of the optimal features subset and the classification has become an important issue in the data mining field.We propose a feature selection scheme based on slicing technique which was originally proposed for programming languages.The proposed approach called Case Slicing Technique (CST).Slicing means that we are interested in automatically obtaining that portion 'features' of the case responsible for specific parts of the solution of the case at hand.We show that our goal should be to eliminate the number of features by removing irrelevant once.Choosing a subset of the features may increase accuracy and reduce complexity of the acquired knowledge.Our experimental results indicate that the performance of CST as a method of feature subset selection is better than the performance of the other approaches which are RELIEF with Base Learning Algorithm (C4.5), RELIEF with K-Nearest Neighbour (K-NN), RELIEF with Induction of Decision Tree Algorithm (ID3) and RELIEF with Naïve Bayes (NB), which are mostly used in the feature selection task.…”
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  19. 19
    An efficient anomaly intrusion detection method with evolutionary neural network

    An efficient anomaly intrusion detection method with evolutionary neural network by Sarvari, Samira

    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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  20. 20
    Integrated artificial intelligence-based classification approach for prediction of acute coronary syndrome

    Integrated artificial intelligence-based classification approach for prediction of acute coronary syndrome by Salari, Nader

    Published 2014
    “…The classification performance of K1-K2-NN model was benchmarked against 13 commonly used classification models using repeated random sub-sampling crossvalidation on ACSEKI data set. …”
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