Search Results - (( data classification learning algorithm ) OR ( parameter classification learning algorithm ))

Autism Spectrum Disorder Classification Using Deep Learning by Abdulrazak Yahya, Saleh, Lim Huey, Chern

“…The goal of this paper is to evaluate the deep learning algorithm for people placed in the Autism Spectrum Disorder (ASD) classification. …”
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Evaluation of the Transfer Learning Models in Wafer Defects Classification by Jessnor Arif, Mat Jizat, Anwar, P. P. Abdul Majeed, Ahmad Fakhri, Ab. Nasir, Zahari, Taha, Yuen, Edmund, Lim, Shi Xuen

“…In this paper, an evaluation for these transfer learning to be applied in wafer defect detection. The objective is to establish the best transfer learning algorithms with a known baseline parameter for Wafer Defect Detection. …”
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Development of an intelligent prediction tool for rice yield based on machine learning techniques by Md. Sap, Mohd. Noor, Awan, A. M.

“…Support vector machine algorithm is developed for classification of rice plantation data. …”
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Comparison of Logistic Regression, Random Forest, SVM, KNN Algorithm for Water Quality Classification Based on Contaminant Parameters by Teguh, Sutanto, Muhammad Rafli, Aditya, Haldi, Budiman, M.Rezqy, Noor Ridha, Usman, Syapotro, Noor, Azijah

“…This study compares four machine learning algorithms Logistic Regression, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) in water quality classification based on contaminant parameters. …”
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Classification of breast cancer disease using bagging fuzzy-id3 algorithm based on fuzzydbd by Nur Farahaina, Idris

“…The study verified that the FID3-DBD algorithm could classify the continuous data, and the BFID3-DBD algorithm overcame the overfitting issue, reduced high variance, and increased test data classification accuracy.…”
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Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification by Mohammed, Alhassan Afnan

“…However, finding the most appropriate deep learning algorithm for a medical classification problem along with its optimal parameters becomes a difficult task. …”
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Adaptive parameter control strategy for ant-miner classification algorithm by Al-Behadili, Hayder Naser Khraibet, Sagban, Rafid, Ku-Mahamud, Ku Ruhana

“…This criterion is responsible for adding only the important terms to each rule, thus discarding noisy data. The ACS algorithm is designed to optimize the IR parameter during the learning process of the Ant-Miner algorithm. …”
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Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System by Aljanabi, Mohammad, Mohd Arfian, Ismail, Mezhuyev, Vitaliy

“…The proposed method combined the improved teaching-learning-based optimisation (ITLBO) algorithm, improved parallel JAYA (IPJAYA) algorithm, and support vector machine. …”
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Classification of Planetary Nebulae through Deep Transfer Learning by Dayang N.F., Awang Iskandar, Zijlstra, Albert A., McDonald, Iain, Rosni, Abdullah, Fuller, Gary A., Ahmad H., Fauzi, Johari, Abdullah

“…It focusses on distinguishing PNe from other types of objects, as well as their morphological classification. We adopted the deep transfer learning approach using three ImageNet pre-trained algorithms. …”
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Improved intrusion detection algorithm based on TLBO and GA algorithms by Aljanabi, Mohammad, Mohd Arfian, Ismail

“…The proposed method combined the New Teaching-Learning-Based Optimization Algorithm (NTLBO), Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Logistic Regression (LR) (feature selection and weighting) NTLBO algorithm with supervised machine learning techniques for Feature Subset Selection (FSS). …”
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The formulation of a transfer learning pipeline for the classification of the wafer defects by Lim, Shi Xuen

“…Automated processes have been used commonly in recent years, with the judgement done by using conventional image processing algorithm. However, limitations such as robustness and difficulty in setting up the parameters required for image processing algorithm encourages the investigation in using Deep learning classification in detecting the wafer defects. …”
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A new model for iris data set classification based on linear support vector machine parameter's optimization by Faiz Hussain, Zahraa, Ibraheem, Hind Raad, Alsajri, Mohammad, Ali, Ahmed Hussein, Mohd Arfian, Ismail, Shahreen, Kasim, Sutikno, Tole

“…One of the outstanding classifications methods in data mining is support vector machine classification (SVM). …”
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Three-term backpropagation algorithm for classification problem by Saman, Fadhlina Izzah

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Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets by Saeed, Sana

“…Classification of imbalanced datasets remained a significant issue in data mining and machine learning (ML) fields. …”
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Gender classification on skeletal remains: efficiency of metaheuristic algorithm method and optimized back propagation neural network by Hairuddin, Nurul Liyana, Yusuf, Lizawati Mi, Othman, Mohd Shahizan

“…This was to obtain a good combination of parameters in order to produce a better gender classification. …”
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An improvement of back propagation algorithm using halley third order optimisation method for classification problems by Abdul Hamid, Norhamreeza

“…Back Propagation (BP) has proven to be a robust algorithm for different connectionist learning problems which commonly available for any functional induction that provides a computationally efficient method. …”
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Rao-SVM machine learning algorithm for intrusion detection system by Abd, Shamis N., Alsajri, Mohammad, Ibraheem, Hind Raad

“…Most of the intrusion detection systems are developed based on optimization algorithms as a result of the increase in audit data features; optimization algorithms are also considered for IDS due to the decline in the performance of the human-based methods in terms of their training time and classification accuracy. …”
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Hyper-heuristic framework for sequential semi-supervised classification based on core clustering by Adnan, Ahmed, Muhammed, Abdullah, Abd Ghani, Abdul Azim, Abdullah, Azizol, Huyop @ Ayop, Fahrul Hakim

“…Existing stream data learning models with limited labeling have many limitations, most importantly, algorithms that suffer from a limited capability to adapt to the evolving nature of data, which is called concept drift. …”
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Assessing the efficacy of machine learning algorithms for syncope classification: A systematic review by Goh, Choon-Hian, Ferdowsi, Mahbuba, Gan, Ming Hong, Kwan, Ban-How, Lim, Wei Yin, Tee, Yee Kai, Rosli, Roshaslina, Tan, Maw Pin *

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New learning algorithm based on Hidden Markov Model (HMM) as stochastic modelling for pattern calssification by Balakrishnan, Malarvili, Ting, Chee Ming, Shaikh Salleh, Sheikh Hussain

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