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A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification by Quteishat, A., Lim, C.P., Tan, K.S.

“…The first stage consists of a modified fuzzy min-max (FMM) neural-network-based pattern classifier, while the second stage consists of a genetic-algorithm (GA)-based rule extractor. …”
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Individual And Ensemble Pattern Classification Models Using Enhanced Fuzzy Min-Max Neural Networks by F. M., Mohammed

“…Owing to a number of salient features which include the ability of learning incrementally and establishing nonlinear decision boundary with hyperboxes, the Fuzzy Min-Max (FMM) network is selected as the backbone for designing useful and usable pattern classification models in this research. …”
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Modern fuzzy min max neural networks for pattern classification by Al Sayaydeh, Osama Nayel Ahmad

“…Among these algorithms, Fuzzy Min Max (FMM) neural network algorithm has been proven to be one of the premier neural networks for undertaking the pattern classification problems. …”
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A Comparative Study of Z-Score and Min-Max Normalization for Rainfall Classification in Pekanbaru by Rahmad Ramadhan, Laska, Anne Mudya, Yolanda

“…Data preprocessing plays a crucial role in enhancing the performance of machine learning algorithms for classification tasks. Among the essential preprocessing stages is data normalization, which aims to standardize data into a comparable range of values. …”
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Integrated combined layer algorithm of jamming detection and classification in manet / Ahmad Yusri Dak by Dak, Ahmad Yusri

“…The fourth stage is to design evaluation methodology of Max-Min Rule-Based Classification Algorithm using classifier model. …”
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Kernel and multi-class classifiers for multi-floor wlan localisation by Abd Rahman, Mohd Amiruddin

“…Unlike the classical kNN algorithm which is a regression type algorithm, the proposed localisation algorithms utilise machine learning classification for both linear and kernel types. …”
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CNN architectures for road surface wetness classification from acoustic signals by Bahrami, Siavash, Doraisamy, Shyamala, Azman, Azreen, Nasharuddin, Nurul Amelina, Yue, Shigang

“…Although machine learning algorithms such as recurrent neural networks (RNN), support vector machines (SVM), artificial neural networks (ANN) and convolutional neural networks (CNN) have been studied for road surface wetness classification, the improvement of classification performances are still widely being investigated whilst keeping network and computational complexity low. …”
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Statistical data preprocessing methods in distance functions to enhance k-means clustering algorithm by Dalatu, Paul Inuwa

“…The suggested approaches are called new approach to min-max (NAMM) and decimal scaling (NADS). The Hybrid mean algorithms which are based on spherical clusters is also proposed to remedy the most significant limitation of the K-Means and K-Midranges algorithms. …”
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Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition by Al-Qammaz, Abdullah Yousef, Ahmad, Farzana Kabir, Yusof, Yuhanis

“…Whereby, the max accuracy obtained is 66.66% and 70.83%, the mean accuracy obtained is 55.51±7.17 and 60.97±8.38 for 3-level of valence emotions and 3-level of arousal emotions classification respectively.…”
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The effect of data preprocessing on the performance of artificial neural networks techniques for classification problems by Atomi, Walid Hasen

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The effect of pre-processing techniques and optimal parameters on BPNN for data classification by HUSSEIN, AMEER SALEH

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Brain Machine Interface Controlled Robot Chair by Hema Chengalvarayan, Radhakrishnamurthy

“…Signals collected from 10 trained subjects are used in the analysis of synchronous and asynchronous BMI designs. A max-one algorithm for translation of the hand motor imagery signals into robot chair movements is presented. …”
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Dense-cluster based voting approach for license plate identification by Asadzadehkaljahi, Maryam, Shivakumara, Palaiahnakote, Roy, Sangheeta, Olatunde, Mojeed Salmon, Anisi, Mohammad Hossein, Lu, Tong, Pal, Umapada

“…Furthermore, the recognition results from classification show that recognition rate improves significantly compared to prior classification.…”
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Automated plaque classification using computed tomography angiography and Gabor transformations by Acharya, U. Rajendra, Meiburger, Kristen Mariko, Wei Koh, Joel En, Vicnesh, Jahmunah, Ciaccio, Edward J., Shu Lih, Oh, Tan, Sock Keow, Raja Aman, Raja Rizal Azman, Molinari, Filippo, Ng, Kwan Hoong

“…The features were then ordered based on the F-value and input to numerous classification methods to achieve the best classification accuracy with the least number of features. …”
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Classification of compressive strength grades for lightweight aggregate concrete with palm oil fuel ash (POFA) using k-Nearest Neighbour (k-NN) by Mohamad Hushnie, Haron, Nur Azzimah, Zamri, Khairunisa, Muthusamy

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ANALYSIS OF BIOSENSOR PHYSIOLOGICAL SIGNALS FOR ASSESSMENT OF NEUROLOGICAL STATUS by QIAN XIN, SOONG

“…The extracted features are then fed into the Support Vector Machines (SVM) as well as the Ensemble Classifier which is a supervised learning model with associated learning algorithm that helps us to analyze the data for classification of neurological status of the subjects. …”
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Enhancing obfuscation technique for protecting source code against software reverse engineering by Mahfoudh, Asma

“…The proposed technique can be enhanced in the future to protect games applications and mobile applications that are developed by java; it can improve the software development industry. …”
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Predicting STEM academic performance in secondary schools: data mining approach by Termedi @Termiji, Mohammad Izzuan, Ab. Jalil, Habibah

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Assessment of forest aboveground biomass estimation from superview-1 satellite image using machine learning approaches / Azinuddin Mohd Asri by Mohd Asri, Azinuddin

“…The Random Forest algorithm was the best algorithm compared to the artificial neural network, which produced the highest R2 (0.998) and lowered RSME (55.067). …”
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Classification of compressive strength grades for lightweight aggregate concrete with palm oil fuel ash (POFA) using kNearest Neighbour (k-NN) by Mohamad Hushnie, Haron, Nur Azzimah, Zamri, Khairunisa, Muthusamy

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