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Feasibility analysis for predicting the compressive and tensile strength of concrete using machine learning algorithms
Published 2024“…The model had an impressive performance during the training phase, with a R2 of 0.98, RMSE of 2.412 MPa, and MAE of 1.6249 MPa when using 8 input variables to predict the compressive strength of concrete. …”
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Enhancing obfuscation technique for protecting source code against software reverse engineering
Published 2019“…The proposed technique can be enhanced in the future to protect games applications and mobile applications that are developed by java; it can improve the software development industry. …”
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Improvement of land cover mapping using Sentinel 2 and Landsat 8 imageries via non-parametric classification
Published 2020“…The results indicated that good classification performance depends on these factors. All algorithms showed more stability and accuracy when training size applied is more than 6% by the Equal Sample Rate (ESR) method with six variables. …”
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Predicting sea levels using ML algorithms in selected locations along coastal Malaysia
Published 2025“…The study emphasizes the importance of thorough training and testing of ML to aid decision-makers in developing mitigation actions for the climate change phenomena of sea level rise through reliable ML. © 2023…”
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Dynamics and control of underactuated systems with applications in robotics / Ahmad Azlan Mat Isa … [et al.]
Published 2011“…The generality and efficiency of the proposed algorithm are demonstrated through simulations of an under-actuated robot manipulator, finally the obtained results were verified experimentally.…”
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Research Reports -
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An ensemble of neural network and modified grey wolf optimizer for stock prediction
Published 2019“…Additionally, the research restricts the number of variables through feature selection to enhance the performance of the algorithm. …”
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Robust Data Fusion Techniques Integrated Machine Learning Models For Estimating Reference Evapotranspiration
Published 2022“…Nonetheless, based on the literature review performed, machine learning models are data-hungry in nature, which increases the difficulty of training a model from scratch. The data hunger of machine learning models can be classified into two categories, namely the qualitative hunger (where machine learning models need for various features for training) and quantitative hunger (need for a vast amount of historical data for training). …”
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Final Year Project / Dissertation / Thesis -
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The Impact of Starting Positions and Breathing Rhythms on Cardiopulmonary Stress and Post-Exercise Oxygen Consumption after High-Intensity Metabolic Training: A Randomized Crossove...
Published 2024“…A two-way ANOVA, multi-variable Cox regression, and random survival forest machine learning algorithm were used to conduct the statistical analysis. …”
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Artificial Intelligence (AI) to predict dental student academic performance based on pre university results
Published 2021“…Exploratory Data Analysis will be performed with training and testing data. For modeling, several prediction models will be trained using neural networks. …”
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Proceeding Paper -
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Predictions of isolate and normal pentene of debutanizer catalytic reforming unit by using artificial neural network
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Conference or Workshop Item -
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Predicting sea levels using ML algorithms in selected locations along coastal Malaysia
Published 2024“…The study emphasizes the importance of thorough training and testing of ML to aid decision-makers in developing mitigation actions for the climate change phenomena of sea level rise through reliable ML. � 2023…”
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Depression prediction system from Twitter’s tweet by using sentiment analysis / Nur Amalina Kamaruddin
Published 2020“…The classification model was built using Naïve Bayes algorithm. The number of data used in this project is 15952 with 1 independent variable and 1 dependent variables. …”
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Self-calibration algorithm for a pressure sensor with a real-time approach based on an artificial neural network
Published 2018“…The traditional calibration process for this kind of sensor is a time-consuming task because it is usually done through manual and repetitive identification. Furthermore, a traditional computational method is inadequate for solving the problem since it is extremely difficult to resolve the mathematical formula among multiple confounding pressure variables. …”
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Sleep as a predictor of depression level using Naïve Bayes / Nur Syakinah Md Roduan
Published 2017“…Future work on this subject should improve the findings by modifying the variables used and/or by using other methods in term of data collection or the algorithm itself.…”
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Assessment of predictive models for chlorophyll-a concentration of a tropical lake.
Published 2011“…FL and RANN models were developed using parameters selected through sensitivity analysis. The selected variables were water temperature, pH, dissolved oxygen, ammonia nitrogen, nitrate nitrogen and Secchi depth. …”
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Assessment of predictive models for chlorophyll-a concentration of a tropical lake.
Published 2011“…FL and RANN models were developed using parameters selected through sensitivity analysis. The selected variables were water temperature, pH, dissolved oxygen, ammonia nitrogen, nitrate nitrogen and Secchi depth. …”
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Predicting Cheaters in PlayerUnknown’s Battlegrounds (PUBG) using Random Forest Algorithm
Published 2023“…Patterns and relationships between input variables and cheating behaviours are analysed through the application of supervised learning techniques, specifically a classification model. …”
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Final Year Project Report / IMRAD -
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A new descriptor for smile classification based on cascade classifier in unconstrained scenarios
Published 2021“…Through accumulating positive and negative images of a single object, this algorithm can build a complete classifier capable of classifying different smiles in a limited amount of time (near real time) and with a high level of precision (92.2–98.8%) as opposed to other algorithms by large margins (5% compared with traditional neural network and 2% compared with Deep Neural Network based methods).…”
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Cross-domain analysis of YOLOv8 and faster R-CNN models for enhanced precision in maritime object detection
Published 2025“…Recent advancements in machine vision, particularly through Convolutional Neural Networks (CNNs), have significantly improved object detection in maritime environments. …”
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