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Boundary extraction and corner point detection for map of kariah Kg. Bukit Kapar / 'Afina AmirHussin by AmirHussin, 'Afina

“…Traditional learning-based boundary extraction algorithms classify each pixel edge separately and then get boundaries from the local decisions of a classifier. …”
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Predicting hearing loss symptoms from Audiometry data using FP-Growth Algorithm and Bayesian Classifier by G. Noma, Nasir, Mohd Khanapi, Abd Ghani, Mohamad Khir , Abdullah, Noorizan , Yahya

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Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad by Ahmad, Khairul Adilah

“…This learning algorithm represents an automatic generation of membership functions and rules from the data. …”
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Comprehensive Review of Machine Learning (ML) in Image Defogging: Taxonomy of Concepts, Scenes, Feature Extraction, and Classification techniques by Arif Z.H., Mahmoud M.A., Abdulkareem K.H., Mohammed M.A., Al-Mhiqani M.N., Mutlag A.A., Dama�evi?ius R.

Recognition of isolated elements picture using backpropagation neural network / Melati Sabtu by Sabtu, Melati

“…The purpose of the project is basically to extract and identify each object elements in an image picture. …”
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A novel quasi-oppositional modified Jaya algorithm for multi-objective optimal power flow solution by Warid, Warid, Hizam, Hashim, Mariun, Norman, Abdul Wahab, Noor Izzri

“…Significant modifications to the basic Jaya algorithm are done to create a modified Jaya (MJaya) algorithm that can handle the MOOPF problem. …”
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Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition by Ali Adlan, Hanan Hassan

“…The RNN was used to detect patterns present in satellite image. A novel feature extraction algorithm was developed to extract the feature vectors. …”
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Raspberry Pi-Based Finger Vein Recognition System Using PCANet by Quek, Ee Wen

“…For classification, k-Nearest Neighbours (kNN) with Euclidean distance algorithm is implemented. An enhancement version for kNN algorithm, k-General Nearest Neighbours (kGNN) have been proposed at initial stage. …”
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Review of deep convolution neural network in image classification by Al-Saffar, Ahmed Ali Mohammed, Tao, Hai, Mohammed, Ahmed Talab

“…This paper first introduces the rise and development of deep learning and convolution neural network, and summarizes the basic model structure, convolution feature extraction and pooling operation of convolution neural network. …”
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i-ManGoeS by Ahmad, Khairul Adilah, Abdul Malik, Anis Faradella

“…Then, the features are extracted from the object. Finally fuzzy learning algorithm will formulate the object features to classify the children activity and satisfaction.…”
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DSC766: Text Analytics / College of Computing, Informatics and Mathematics by UiTM, College of Computing, Informatics and Mathematics

“…However, a number of statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. Student will learn the basic concepts, principles, and major algorithms in text mining and their potential applications.…”
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Simulation on Emotion Recognition for Autism Therapy by Adzmi, Muhammad Azrin

“…This paper mainly focusing on the simulation of emotion recognition software based on the Local Binary Pattern (LBP) algorithm to extract the features from the image. The program will be used by the therapist during therapy session with the autism child in order to create more exciting environment for them to learn about the classification of basic human emotions with the help of human-computer interaction. …”
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A novel neuroscience-inspired architecture: for computer vision applications by Hassan, Marwa Yousif, Khalifa, Othman Omran, Abu Talib, Azhar, Olanrewaju, Rashidah Funke, Hassan Abdalla Hashim, Aisha

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Systematic review and meta-analysis on the classification metrics of machine learning algorithm based radiomics in hepatocellular carcinoma diagnosis by Mohd Haniff, Nurin Syazwina, Ng, Kwan Hoong, Kamal, Izdihar, Mohd Zain, Norhayati, Abdul Karim, Muhammad Khalis

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Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining. by Saeed, Walid

“…The accuracy for rules and classification resulted from the TIP method are compared with other methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) from Rough Set, Genetic Algorithm (GA), Johnson reducer, HoltelR method, Multiple Regression (MR), Neural Network (NN), Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5); all other classifiers that are mostly used in the classification tasks. …”
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Deep learning-based breast cancer detection and classification using histopathology images / Ghulam Murtaza by Ghulam , Murtaza

“…The trained EBrC-Net is used to extract discriminative features. The extracted features are evaluated through six machine learning (ML) classifiers namely softmax, k-nearest neighbor (kNN), support vector machine, linear discriminant analysis, decision tree, and naive Bayes. …”
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Development of durian ontology from unstructured text and external knowledge source / Khairul Nurmazianna Ismail by Ismail, Khairul Nurmazianna

“…Thirdly, ontology elements from both ontology methods are extracted by using RDF Query Language. Ontology element extraction of manual ontology development consists of three processes namely ontology element selection, query construction and ontology element extracted. …”
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Recent Advances in Classification of Brain Tumor from MR Images – State of the Art Review from 2017 to 2021 by Ghazanfar, Latif, Faisal Yousif, Al Anezi, Dayang Nurfatimah, Awang Iskandar, Abul, Bashar, Jaafar, Alghazo

“…In addition, the review paper will facilitate researchers who are new to machine learning algorithms for brain tumor recognition to understand the basics of the field and pave the way for them to be able to contribute to this vital field of medical research. …”
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Image Splicing Detection With Constrained Convolutional Neural Network by Lee, Yang Yang

“…Nowadays there are many related efforts in detecting spliced images, but most of them are either feature-specific or complicated algorithms. Constrained CNN is basically a Deep Learning CNN model with its first layer weights being constrained so that it only extracts splicing manipulation features instead of object features. …”
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A deep learning approach: The impact of sentiment analysis of Bangladeshi workers over the world by Tomal, Md Raihanul Islam, Kader, Tanveer, Kohbalan, Moorthy, Mazlina, Abdul Majid

“…TF-IDF vectorization was used for feature extraction, followed by basic machine learning algorithms such as Decision Tree, Support Vector Machine, and Naive Bayes. …”
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