Search Results - (( code classifications learning algorithm ) OR ( _ classification techniques algorithm ))
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Android Malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization
Published 2014“…Several machine learning techniques based on supervised learning have been adopted in the classification of malware. …”
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Proceeding Paper -
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Static code analysis of permission-based features for android malware classification using apriori algorithm with particle swarm optimization
Published 2015“…However, supervised learning technique has limitations for malware classification task. …”
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Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach
Published 2023“…Aerial photography; Aircraft detection; Antennas; Codes (symbols); Discrete cosine transforms; Discrete wavelet transforms; Glossaries; Image classification; Image coding; Image enhancement; Learning algorithms; Learning systems; Object recognition; Remote sensing; Satellite imagery; Satellites; Unmanned aerial vehicles (UAV); Discrete tchebichef transforms; Discriminative features; Finite Ridgelet Transform; Histogram of oriented gradients; Image processing and computer vision; Scale invariant feature transforms; SIFT; Sparse coding; Classification (of information)…”
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Genetic algorithm fuzzy logic for medical knowledge-based pattern classification
Published 2018“…The proposed algorithm was applied and tested in four critical illness datasets in medical knowledge pattern classification. …”
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Systematic review for phonocardiography classification based on machine learning
Published 2023“…This systematic review aims to examine the existing literature on phonocardiography classification based on machine learning, focusing on algorithms, datasets, feature extraction methods, and classification models utilized. …”
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Malware Classification and Detection using Variations of Machine Learning Algorithm Models
Published 2025“…Attack data was obtained from the ClaMP dataset, which has an unbalanced data set, and has very high noise, so it is necessary to analyze data packets in network logs and optimize feature extraction which is then analyzed statistically with machine learning algorithms. The purpose of the study is to detect, classify malware attacks using a variety of ML Algorithm models such as SVM, KNN and Neural Network and testing detection performance. …”
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Multi-class classification automated machine learning for predicting earthquakes using global geomagnetic field data
Published 2025“…The extracted features were the input for AutoML, an automatic algorithm selection that was measured by Bayesian Optimization algorithm to select the best performance model. …”
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Genetic algorithm fuzzy logic for medical knowledge-based pattern classification
Published 2023“…The proposed algorithm was applied and tested in four critical illness datasets in medical knowledge pattern classification. …”
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Classifying good and bad websites
Published 2015“…These web pages often contain semi-structured content which make the classification process challenging. This paper addresses the use of machine learning techniques to classify good and bad websites. …”
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Final Year Project Report / IMRAD -
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Cross-project software defect prediction
Published 2022“…Through this work, it was discovered the majority of the selected studies used machine learning techniques as classification algorithms, and 64% of the studies used the combination of Object-Oriented (OO) and Line of Code (LOC) metrics. …”
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Evaluations of oil palm fresh fruit bunches maturity degree using multiband spectrometer
Published 2017“…Furthermore, the Lazy-IBK algorithm have been validated to produce the best classifier model, with the machine learning algorithm performance of 65.26%, recall of 65.3%, and 65.4% F-measured as compared to other evaluated machine learning classifier algorithms proposed within the WEKA data mining algorithm. …”
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Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms
Published 2020“…The ensemble learning technique, changes of activation function in Neural Network as well as the unsupervised learning (k-means clustering algorithm and Friis Transmission Equation) was also applied to classify the multiclass classification in pallet-level. …”
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Blood cell classification using deep learning
Published 2022“…The advancement of Artificial Intelligence (AI) has introduced complex methods such as deep learning that would automate the classification of blood cells in a fast and accurate manner Thus, the study of White Blood Cells (WBCs) classification using deep learning techniques is proposed in this research. …”
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Final Year Project / Dissertation / Thesis -
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Learner’s emotion prediction using production rules classification algorithm through brain computer interface tool
Published 2018“…No EEG studies in Malaysia has been done on school children to study their emotional behaviour while learning. Classification and prediction are the functions provided by the data mining techniques that suit in EEG signal processing. …”
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Dynamic android malware category classification using semi-supervised deep learning
Published 2020“…We evaluate our proposed model on CICMalDroid2020 and conduct a comparison with Label Propagation (LP), a well-known semi-supervised machine learning technique, and other common machine learning algorithms. …”
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Proceeding Paper -
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A Systematic Review of Metaheuristic Algorithms in Human Activity Recognition : Applications, Trends, and Challenges
Published 2025“…Metaheuristic algorithms have emerged as promising techniques for optimizing human activity recognition (HAR) systems. …”
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Enhancement Of Static Code Analysis Malware Detection Framework For Android Category-Based Application
Published 2021“…Four types of machine learning algorithm were tested with four different kind of splitting dataset techniques separately. …”
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Improving brain tumor segmentation in MRI images through enhanced convolutional neural networks
Published 2023“…The accuracy of 2D tumor detection and segmentation are increased, enabling more 3D detection, and achieving a mean classification accuracy of 98 across system records. Finally, a hybrid approach of GoogLeNet deep learning algorithm and Convolution Neural Network- Support Vector Machines (CNN-SVM) deep learning is performed to increase the accuracy of tumor classification. …”
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Enhancement of feature sets for subjectivity analysis on Malay-English code-switching text
Published 2023“…The algorithm predicts the type of each word in a code-switching sentence according to the language of the word. …”
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Thesis
