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Prediction of device performance in SnO2 based inverted organic solar cells using machine learning framework
Published 2024“…In this project, the machine learning (ML) framework with different algorithm models and kernel functions was employed to predict the device performance of solution-processed SnO2-based organic solar cells. …”
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Corrosion Inhibition Study Of Carboxymethyl Celluloseionic Liquid Via Electrochemical And Machine Learning Technique
Published 2024journal::journal article -
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Spear- phishing attack detection using artificial intelligence
Published 2024“…The goal is to mitigate phishing and spam threats by using advanced algorithms to detect malicious URLs and classify messages effectively. …”
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Final Year Project / Dissertation / Thesis -
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An automated pipeline for constructing 3D models of monogenean hardparts using machine learning technique for landmarks detection / Teo Bee Guan
Published 2019“…Researchers have been looking at alternative methods to assist them in their investigation on how monogeneans function. 3D models may aid researchers in studying morphology and function by rotating the 3D models in 360 degrees for 3D visualization. …”
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Artificial intelligence in sustainability reporting / Prof. Dr Corina Joseph
Published 2023“…The rapid progress of AI has been facilitated by the extensive use of other technologies like machine learning methods for structured data, modern deep learning, and natural language processing for unstructured data. …”
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Identifying suicidal ideation through twitter sentiment analysis using Naïve Bayes / Annasuha Atie Atirah Alias
Published 2023“…Thus, this project aims to design, develop, and evaluate web-based application utilizing sentiment analysis, specifically employing the Naïve Bayes algorithm, to identify and analyze suicidal ideation within Twitter posts. …”
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Thesis -
7
Optimization of Lipase Catalysed Synthesis of Sugar Alcohol Esters Using Taguchi Method and Neural Network Analysis
Published 2011“…Various feedforward neural networks were performed using different learning algorithms. The best algorithm was found to be Levenberg–Marquardt (LM) for a network composed of two hidden layers with six and seven neurons in the first and second layers, respectively for xylitol stearate and xylitol palmitate and also seven and five neurons in the first and second layers for xylitol caprate, with hyperbolic tangent sigmoid transfer function. …”
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8
Machine learning classifications of multiple organ failures in a malaysian intensive care unit
Published 2024“…This study aims to perform the classification of multiple organ failures using machine learning algorithms based on SOFA score. …”
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Machine Learning Classifications of Multiple Organ Failures in a Malaysian Intensive Care Unit
Published 2025“…This study aims to perform the classification of multiple organ failures using machine learning algorithms based on SOFA score. …”
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A comparative sales forecast study between supervised and unsupervised learning algorithm on restaurant / Azhar Tamby
Published 2006“…In this research, there are two algorithm of neural network will be used. It is coming from supervised and unsupervised learning algorithm. …”
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Student Project -
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Semiparametric inference procedure for the accelarated failure time model with interval-censored data
Published 2019“…The rank-based methods, estimating algorithms, and resampling techniques that are developed do not involve the difficulties of the existing estimating procedures. …”
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Thesis -
12
Phishing attack detection using machine learning method
Published 2019“…The Naïve Bayes algorithm, Decision Tree algorithm and Support Vector Machine algorithm has been used to prevent the spam attack, as well as the phishing attack. …”
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Undergraduates Project Papers -
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Development and applications of metaheuristic algorithms in engineering design and structural optimization / Ali Sadollah
Published 2013“…In addition, two novel optimization methods are developed and presented which are named the mine blast algorithm (MBA) and the water cycle algorithm (WCA). …”
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14
The use of SOM for fingerprint classification
Published 2023“…This paper introduces an approach to fingerprint classification by using Self-Organizing Maps (SOM). In order to be able to deal with fingerprint images having distorted regions, the SOM learning and classification algorithms are modified. …”
Conference paper -
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Octane number prediction for gasoline blends using convolution neural network / Zhu Yue
Published 2021“…At present, there are many prediction algorithms based on machine learning. According to the "80/20 rule" for building machine learning model, 80% of the time is spent of finding, cleaning, and organizing data, while the remaining 20% for training of the machine learning model. …”
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16
A Novel Discrete Filled Function Algorithm in Solving Discrete Optimization Problems (S/O: 12408)
Published 2016“…We focus our study on a recently developed method known as discrete filled function method. …”
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Monograph -
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An improved self organizing map using jaccard new measure for textual bugs data clustering
Published 2018“…Considering the unsupervised learning algorithms, Self-Organizing Map (SOM) considers the equally compatible algorithm for clustering, as both algorithms are closely related but different in way they were used in data mining. …”
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An improved self organizing map using jaccard new measure for textual bugs data clustering
Published 2018“…Considering the unsupervised learning algorithms, SelfOrganizing Map (SOM) considers the equally compatible algorithm for clustering, as both algorithms are closely related but different in way they were used in data mining. …”
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Thesis -
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Economic dispatch with piecewise linear function and line loss / Amir Ashraf Mohd Johari
Published 2014“…A system of three generation units are tested with developed algorithm. The results obtained show that the proposed method is able to provide the solution of economic dispatch with piecewise linear incremental function and line losses.…”
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Thesis -
20
Underwater Image Recognition using Machine Learning
Published 2024“…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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