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Scene classification for aerial images based on CNN using sparse coding technique by Qayyum, A., Malik, A.S., Saad, N.M., Iqbal, M., Faris Abdullah, M., Rasheed, W., Rashid Abdullah, T.A., Bin Jafaar, M.Y.

“…Recent developments include several approaches and numerous algorithms address the task. This article proposes a convolutional neural network (CNN) approach that utilizes sparse coding for scene classification applicable for HRRS unmanned aerial vehicle (UAV) and satellite imagery. …”
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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

“…Through statistical analysis, important features were extracted and a multi-class classification model using geomagnetic data was created. …”
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Scene classification for aerial images based on CNN using sparse coding technique by Qayyum A., Malik A.S., Saad N.M., Iqbal M., Faris Abdullah M., Rasheed W., Rashid Abdullah T.A., Bin Jafaar M.Y.

Improving hand written digit recognition using hybrid feature selection algorithm by Wong, Khye Mun

“…While mRMR was capable of identifying a subset of features that were highly relevant to the targeted classification variable, it still carry the weakness of capturing redundant features along with the algorithm. …”
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Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning by Safa, Soodabeh

“…Moreover, instead of concatenating feature vectors together and send to classifier, sparse coding and dictionary learning methods are used and instead of considering all features as one view (visual feature), K-SVD algorithm that is one of the famous algorithms for sparse representation is optimized and developed to multi-view model.The experimental results prove that the proposed methods has improved accuracy by 53.77% compared to concatenating features and classic K-SVD dictionary learning model as well.…”
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Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms by Choong, Chun Sern

“…It was shown that up to 95.02% of the trained Random Forest Model could be classified, indicating that the established framework is viable for pallet classification. …”
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A multilayered convolutional sparse coding framework for modeling of pooling operation of convolution neural networks by , Abdul Wahid, Khan, Adnan Umar, , Mukhtarullah, Khan, Sheroz, Shah, Jawad

“…The multilayered version of CSC(ML-CSC) is shown to be connected to forward pass of CNNs and dictionary learning and sparse coding algorithms of this model are analyzed for solving classification and inverse problems in image processing. …”
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Dynamic android malware category classification using semi-supervised deep learning by Mahdavifar, Samaneh, Kadir, Andi Fitriah Abdul, Fatemi, Rasool, Alhadidi, Dima, Ghorbani, Ali A

“…Despite the academic and industrial attempts, devising a robust and efficient solution for Android malware detection and category classification is still an open problem. Supervised machine learning has been used to solve this issue. …”
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XAIRF-WFP: a novel XAI-based random forest classifier for advanced email spam detection by Bouke, Mohamed Aly, Alramli, Omar Imhemed, Abdullah, Azizol

“…This paper presents a novel approach to spam detection using a Random Forest (RF) Classifier model enhanced by a meticulously designed methodology. …”
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Using genetic algorithms to optimise land use suitability by Pormanafi, Saeid

“…In this study, under environmentfriendliness objective, based on multi-agent genetic algorithms, was developed a geospatial model for the land use allocation. …”
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Object-Oriented Programming semantics representation utilizing agents by Mohd Aris, Teh Noranis

“…Novices tend to refer to source codes examples and adapt the source codes to the problem given in their assignments. …”
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Maldroid- attribute selection analysis for malware classification by Rahiwan Nazar, Romli, Mohamad Fadli, Zolkipli, Mohd Zamri, Osman

“…Hence, the objective of this paper is to find the most effective and efficient attribute selection and classification algorithm in malware detection. Moreover, in order to get the best combination between attribute selection and classification algorithm, eight attributes selection and seven categories machine learning algorithm are applied in this study. …”
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A novel hybrid classification model of genetic algorithms, modified k-Nearest Neighbor and developed backpropagation neural network by Salari, Nader, Shohaimi, Shamarina, Najafi, Farid, Nallappan, Meenakshii, Karishnarajah, Isthrinayagy

“…In the present study, the results of the implementation of a novel hybrid feature selection-classification model using the above mentioned methods are presented. …”
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Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model by Sulaiman, Md. Nasir, Mohamed, Raihani, Mustapha, Norwati, Zainudin, Muhammad Noorazlan Shah

“…EM and K-means clustering algorithms are used to cluster the multi-class classification attribute according to its relevance criteria and afterward, the clustered attributes are classified using an ensemble random forest classifier model. …”
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An adaptive ant colony optimization algorithm for rule-based classification by Al-Behadili, Hayder Naser Khraibet

“…Various classification algorithms have been developed to produce classification models with high accuracy. …”
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Predicting breast cancer using ant colony optimisation / Siti Sarah Aqilah Che Ani by Che Ani, Siti Sarah Aqilah

“…This study implements a machine learning algorithm called Ant Colony Optimization (ACO) algorithm to develop an accurate classification model for predicting breast cancer cells. …”
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Formulating new enhanced pattern classification algorithms based on ACO-SVM by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…ACO originally deals with discrete optimization problem.In applying ACO for solving SVM model selection problem which are continuous variables, there is a need to discretize the continuously value into discrete values.This discretization process would result in loss of some information and hence affects the classification accuracy and seeking time.In this algorithm we propose to solve SVM model selection problem using IACOR without the need to discretize continuous value for SVM.The second algorithm aims to simultaneously solve SVM model selection problem and selects a small number of features.SVM model selection and selection of suitable and small number of feature subsets must occur simultaneously because error produced from the feature subset selection phase will affect the values of SVM model selection and result in low classification accuracy.In this second algorithm we propose the use of IACOMV to simultaneously solve SVM model selection problem and features subset selection.Ten benchmark datasets were used to evaluate the proposed algorithms.Results showed that the proposed algorithms can enhance the classification accuracy with small size of features subset.…”
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Classification model for water quality using machine learning techniques by Azilawati, Rozaimee, Azrul Amri, Jamal, Azwa, Abdul Aziz

“…In assessing the result, the Lazy model using K Star algorithm was the best classification model among the five models had the most outstanding accuracy of 86.67%. …”
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Whale Optimisation Freeman Chain Code (WO-FCC) extraction algorithm for handwritten character recognition by Muhammad Arif, Mohamad, Jamaludin, Sallim, Kohbalan, Moorthy

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

“…Whereas for the German Frankfurt dataset, best DM classification model was found using Random Forest algorithm with an accuracy of 98.77%.…”
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