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A Novel Aggregate Classification Technique Using Moment Invariants and Cascaded Multilayered Perceptron Network by Md Sani, Zamani

“…In the features selection stage, discriminant analysis is employed to select the optimum features for the aggregate shape classification. In the classification stage, a cascaded multilayered perceptron (c-MLP) network is proposed to categorize the aggregate into six shapes. …”
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Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad by Ahmad, Khairul Adilah

“…Therefore, this research has designed fuzzy learning algorithm that is able to classify fruits based on their shape and size features using Harumanis dataset. …”
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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

“…Beside that, classic bag of visual words algorithm (BoVW) is based on kmeans clustering and every SIFT feature belongs to one cluster and it leads to decreasing classification results. …”
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Automated plant classification system using a hybrid of shape and color features of the leaf by Hamid, Laith Emad

“…Automated plant leaf classification is a computerized approach that employs computer vision and machine learning algorithms to identify a plant based on the features of its leaf. …”
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Classification of brain tumors: using deep transfer learning by Husin, Nor Azura, Husam, Mohamed, Hussin, Masnida

“…Experimenters employed data augmentation and learning algorithms.…”
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A New Quadratic Binary Harris Hawk Optimization For Feature Selection by Abdullah, Abdul Rahim, Too, Jing Wei, Mohd Saad, Norhashimah

“…In this study, twenty-two datasets collected from the UCI machine learning repository are used to validate the performance of proposed algorithms. …”
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Building detection using object-based Image analysis (OBIA) and machine learning (ML) algorithms / Hanani Mohd Shahar by Mohd Shahar, Hanani

“…In order to obtain a good classification accuracy, the suitable segmentation parameters (scale, shape and compactness) and features selection have been determined and Machine learning (ML) algorithms, namely Support Vector Machine (SVM) and Decision Tree (DT) classifiers have been applied to categorized five different classes which are water, forest, green area, building, and road. …”
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A direct ensemble classifier for learning imbalanced multiclass data by Samry @ Mohd Shamrie Sainin

“…The learning framework consists of ensemble learning and decision combiner model with general supervised learning algorithms as base learner. …”
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Application Of Neural Network In Malaria Parasites Classification by Lim, Chia Li

“…Multilayer Perceptron (MLP) network and Radial Basis Function (RBF) network will be developed using MATLAB in which MLP network is trained with Back Propagation, Bayesian Rule and Levenberg-Marquardt learning algorithm and RBF network is trained with k-means clustering algorithm. …”
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Classification of Citrus (Rutaceae) by Using Image Processing by Najwa Bari'ah Mohd Tabri

“…A machine learning algorithms, SVM have been used to build species identification models. …”
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Online sequential extreme learning machine for classification of Mycobacterium tuberculosis in Ziehl-Neelsen stained tissue by Muhammad Khusairi, Osman, Mohd Yusoff, Mashor, Prof. Dr., Hasnan, Jaafar, Assoc. Prof. Dr.

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Detection of corneal arcus using rubber sheet and machine learning methods by Ramlee, Ridza Azri

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Robust Pornography Classification Solving the Image Size Variation Problem Based on Multi-Agent Learning by Zaidan, A.A., Karim, H.A., Ahmad, N.N., Zaidan, B.B., Mat Kiah, M.L.

“…This study proposed a pornography classifier using multi-agent learning as a combination of the Bayesian method using color features extracted from skin detection based on the YCbCr color space and the back-propagation neural network method using shape features also extracted from skin detection. …”
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RFE-based feature selection to improve classification accuracy for morphometric analysis of craniodental characters of house rats by Aneesha Balachandran Pillay, Dharini Pathmanathan, Arpah Abu, Hasmahzaiti Omar

“…We also performed a comparative study based on three machine learning algorithms such as Naïve Bayes, Random Forest, and Artificial Neural Network by using all features and the RFE-selected features to classify the R. rattus sample based on the age groups. …”
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STATISTICAL FEATURE LEARNING THROUGH ENHANCED DELAUNAY CLUSTERING AND ENSEMBLE CLASSIFIERS FOR SKIN LESION SEGMENTATION AND CLASSIFICATION by Adil H., Khan, Dayang Nurfatimah, Awang Iskandar, Jawad F., Al-Asad, SAMIR, EL-NAKLA, SADIQ A., ALHUWAIDI

“…Moreover, skin lesion images are clustered based on fused color, pattern and shape based features. A boost ensemble learning algorithm using Support Vector Machines (SVM) as initial classifiers and Artificial Neural Networks (ANN) as a final classifier is employed to learn the patterns of different skin lesion class features. …”
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FRUIT RECOGNITION APPLICATION USING MACHINE LEARNING by LOH, WENG KEONG

“…These features include color and shape features. In the classification phase, the system uses the extracted features to classify the fruits using a machine learning algorithm – a backpropagation neural network (BPNN). …”
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Data‑driven audiogram classifer using data normalization and multi‑stage feature selection by Abeer Elkhouly, Allan MelvinAndrew, HaslizaA Rahim, NidhalAbdulaziz, Mohd FareqAbd Malek, Shafquzzaman Siddique

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Classification of acute leukemia using image processing and machine learning techniques / Hayan Tareq Abdul Wahhab by Wahhab, Hayan Tareq Abdul

“…This combination resulted in a new algorithm we named CBCSA. Based on the Relative Ultimate Measurement Accuracy for Area, the proposed algorithm was able to achieve an accuracy of 96% and 94% in the extraction of the blast cell region and the nuclear region, respectively. …”
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Classification of metal screw defect detection using FOMO on edge impulse / Muhammad Imran Daing by Daing, Muhammad Imran

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Underwater Image Recognition using Machine Learning by Divya, N.K., Manjula, Sanjay Koti, Priyadarshini, S

“…A Convolutional Neural Network (CNN) is a type of a deep learned an algorithm that has been created for image processing when using convolutional layers to automatically and in a hierarchical way learn features from the input images. …”
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