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  1. 1
    Dengue classification system using clonal selection algorithm / Karimah Mohd

    Dengue classification system using clonal selection algorithm / Karimah Mohd by Mohd, Karimah

    Published 2012
    “…This project can be improved by making a comparative study on Artificial Immune System and other techniques or algorithms used to solve dengue classification problems.…”
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  2. 2
    Trademark image classification approaches using neural network and rough set theory

    Trademark image classification approaches using neural network and rough set theory by Saad, Puteh

    Published 2003
    “…The approaches contain five major stages, namely: image acquisition, image preprocessing, feature extraction, data transformation and classification. Feature normalization and data discretization techniques are utilized to perform the data transformation phase. …”
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  3. 3
    Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition

    Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition by Nur Farahaina, Idris, Mohd Arfian, Ismail

    Published 2021
    “…FUZZYDBD method, an automatic fuzzy database definition method, would be used to design the fuzzy database for fuzzification of data in the FID3 algorithm. …”
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  4. 4
    Text Extraction Algorithm for Web Text Classification

    Text Extraction Algorithm for Web Text Classification by Theab, Mustafa Muwafak

    Published 2010
    “…This study provides a text extraction algorithm for web text classification. The extraction algorithm consists of three phases namely web page extraction, rule formulation, and algorithm validation. …”
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  5. 5
    Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal

    Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal by Fadzal, Ahmad Nazmi

    Published 2017
    “…ACO classification accuracy is compared to Genetic Algorithm classifier which also a wrapper method. …”
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  6. 6
    Breast cancer disease classification using fuzzy-ID3 algorithm based on association function

    Breast cancer disease classification using fuzzy-ID3 algorithm based on association function by Nur Farahaina, Idris, Mohd Arfian, Ismail, Mohd Saberi, Mohamad, Shahreen, Kasim, Zalmiyah, Zakaria, Sutikno, Tole

    Published 2022
    “…The fuzzy-ID3 algorithm with association function implementation (FID3-AF) is proposed as a classification technique for breast cancer detection. …”
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  7. 7
    Evaluation and Comparative Analysis of Feature Extraction Methods on Image Data to increase the Accuracy of Classification Algorithms

    Evaluation and Comparative Analysis of Feature Extraction Methods on Image Data to increase the Accuracy of Classification Algorithms by Rachmad, Iqbal, Tri Basuki, Kurniawan, Misinem, ., Edi Surya, Negara, Tata, Sutabri

    Published 2024
    “…The classification algorithm used in this research is the Convolutional Neural Network (CNN) algorithm. …”
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  8. 8
    A comparative study in classification techniques for unsupervised record linkage model

    A comparative study in classification techniques for unsupervised record linkage model by Ektefa, Mohammadreza, Sidi, Fatimah, Ibrahim, Hamidah, A. Jabar, Marzanah, Memar, Sara

    Published 2011
    “…In order to utilize the supervised classification algorithms without consuming a lot of time for labeling data manually, a two step method which selects the training data automatically has been proposed in previous studies. …”
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  9. 9
    A hybrid-based modified adaptive fuzzy inference engine for pattern classification

    A hybrid-based modified adaptive fuzzy inference engine for pattern classification by Sayeed, Md. Shohel, Ramli, Abdul Rahman, Hossen, Md. Jakir, Samsudin, Khairulmizam, Rokhani, Fakhrul Zaman

    Published 2011
    “…A modified Apriori algorithm technique is utilized to reduce a minimal set of decision rules based on input output data set. …”
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  10. 10
    An ensemble learning method for spam email detection system based on metaheuristic algorithms

    An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

    Published 2015
    “…A well known task is classification that predicts the class of new instances using known features or attributes automatically. …”
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  11. 11
    A hybrid approach for artificial immune recognition system / Mahmoud Reza Saybani

    A hybrid approach for artificial immune recognition system / Mahmoud Reza Saybani by Mahmoud Reza, Saybani

    Published 2016
    “…Among all these techniques, classification is one of the most commonly used tasks in data mining, which is used by many researchers to classify instances into two or more pre-determined classes. …”
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  12. 12
    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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  13. 13
    Performance comparison of classification algorithms for EEG-based remote epileptic seizure detection in wireless sensor networks

    Performance comparison of classification algorithms for EEG-based remote epileptic seizure detection in wireless sensor networks by Abualsaud, Khalid, Mahmuddin, Massudi, Saleh, Mohammad, Mohamed, Amr

    Published 2014
    “…Identification of epileptic seizure remotely by analyzing the electroencephalography (EEG) signal is very important for scalable sensor-based health systems.Classification is the most important technique for wide-ranging applications to categorize the items according to its features with respect to predefined set of classes.In this paper, we conduct a performance evaluation based on the noiseless and noisy EEG-based epileptic seizure data using various classification algorithms including BayesNet, DecisionTable, IBK, J48/C4.5, and VFI.The reconstructed and noisy EEG data are decomposed with discrete cosine transform into several sub-bands.In addition, some of statistical features are extracted from the wavelet coefficients to represent the whole EEG data inputs into the classifiers.Benchmark on widely used dataset is utilized for automatic epileptic seizure detection including both normal and epileptic EEG datasets.The classification accuracy results confirm that the selected classifiers have greater potentiality to identify the noisy epileptic disorders.…”
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  14. 14
    Data mining of protein sequences with amino acid position-based feature encoding technique

    Data mining of protein sequences with amino acid position-based feature encoding technique by Iqbal, M.J., Faye, I., Md Said, A., Samir, B.B.

    Published 2014
    “…The classification results indicate that the proposed encoding technique with a decision tree classification algorithm has achieved 85.9 classification accuracy over the Yeast protein sequence dataset. …”
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  15. 15
    AN ACCURATE MODULATION CLASSIFICATION ALGORITHM FOR MULTIPLE-INPUT MULTIPLE-OUTPUT WIRELESS SYSTEMS

    AN ACCURATE MODULATION CLASSIFICATION ALGORITHM FOR MULTIPLE-INPUT MULTIPLE-OUTPUT WIRELESS SYSTEMS by RIDA , MOHAMMAD BAHLOUL

    Published 2015
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  16. 16
    Use of hybrid classification algorithm for land use and land cover analysis in data scarce environment

    Use of hybrid classification algorithm for land use and land cover analysis in data scarce environment by Al-Doski, Jwan M. Mohammed

    Published 2013
    “…In conclusion, hybrid classification as a combination of k-means and support vector machine algorithms and post-classification comparison change detection technique can be used to monitor land cover changes in Halabja city, Iraq. …”
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  17. 17
    The Perspective Classification of Balanced Scorecard with Ontology Technique

    The Perspective Classification of Balanced Scorecard with Ontology Technique by Kaewninprasert, Kittisak, Chai-Arayalert, Supaporn, Yamaqupta, Narueban

    Published 2024
    “…This paper aims to present the Perspective Classification of Balanced Scorecard with Ontology (PCBSC-Onto) framework for automatically classifying performance indicators into BSC’s four perspectives using an ontological approach to support the dynamic capability of the management system’s performance. …”
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  18. 18
    Multi-class classification automated machine learning for predicting earthquakes using global geomagnetic field data

    Multi-class classification automated machine learning for predicting earthquakes using global geomagnetic field data by Qaedi, Kasyful, Abdullah, Mardina, Yusof, Khairul Adib, Hayakawa, Masashi, Zulhamidi, Nur Fatin Irdina

    Published 2025
    “…Through statistical analysis, important features were extracted and a multi-class classification model using geomagnetic data was created. …”
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  19. 19
    Sentiment analysis for malay newspaper (SAMNews) using negative selection algorithm / Nur Amalina Redzuan

    Sentiment analysis for malay newspaper (SAMNews) using negative selection algorithm / Nur Amalina Redzuan by Redzuan, Nur Amalina

    Published 2013
    “…In future, a comparative study on Artificial Immune System and other techniques or algorithms can be carried out to enhance the performance of the classification model.…”
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  20. 20
    Advanced neural networking and classification techniques for human brain tissues diagnoses: segmenting healthy, cancer affected and edema brain tissues

    Advanced neural networking and classification techniques for human brain tissues diagnoses: segmenting healthy, cancer affected and edema brain tissues by Falah Ali, Wadah

    Published 2019
    “…Especially, in this work MRI images are used to diagnose tumor in the brain. However, the huge amount of data generated by MRI scan thwarts manual classification of tumor vs non-tumor in a particular time. …”
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