Search Results - (( data visualization _ algorithm ) OR ( data visualization learning algorithm ))
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1
Performance of correlational filtering and deep learning based single target tracking algorithms / ZhongMing Liao and Azlan Ismail
Published 2023“…Therefore, reviewing the visual target tracking algorithms based on deep learning from different perspectives is important. …”
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Article -
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Interpretation of machine learning model using medical record visual analytics
Published 2021“…This paper analyzed several visual analytics ap- proach instantiated in machine learning algorithm for medical record analytics. …”
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Proceeding Paper -
3
Algorithm visualization design guidelines for mobile leaning
Published 2018“…This study is about the algorithm visualization design guidelines for mobile platform, to teach students data structures and algorithm (DSA) subject effectively. …”
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Designing algorithm visualization on mobile platform: The proposed guidelines
Published 2017“…This paper entails an ongoing study about the design guidelines of algorithm visualization (AV) on mobile platform, helping students learning data structures and algorithm (DSA) subject effectively.Our previous review indicated that design guidelines of AV on mobile platform are still few.Mostly, previous guidelines of AV are developed for AV on desktop and website platform. …”
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Interpretation of machine learning model using medical record visual analytics
Published 2022“…This paper analyzed several visual analytics approaches instantiated in machine learning algorithm for medical record analytics. …”
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Proceeding Paper -
6
A hybrid spiking neural network model for multivariate data classification and visualization.
Published 2011“…This study proposes a hybrid model of Self-Organizing Map with modified adaptive coordinates (SOM-AC) and Spiking Neural Network (SNN) for multivariate spatial and temporal data visualization and classification. SOM is one of the most prominent unsupervised learning algorithms. …”
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Proceeding -
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Dimensional Reduction and Data Visualization Using Hybrid Artificial Neural Networks
Published 2015“…Data with dimension higher than three is not possible to be visualized directly. …”
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Article -
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A review on monocular tracking and mapping: from model-based to data-driven methods
Published 2022“…This article starts by introducing the basic sensor systems and analyzing the evolution of monocular tracking and mapping algorithms through bibliometric data. Then, it covers the overview of filtering and bundle adjustment methods, followed by recent advancements in methods using deep learning with the mathematical constraints applied on the networks. …”
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Visual analysis to investigate the capability of ANFIS in modelling hydrological relationship using synthetic dataset
Published 2018“…In hydrology especially, every process is unique and dependent on large number of natural factors hence modelling using machine learning algorithm without considering hydrological insight is very dangerous. …”
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Virtual reality in algorithm programming course: practicality and implications for college students
Published 2024“…The analysis of learning problems shows the unavailability of interactive learning media that can support various learning styles of students in programming algorithm materials. …”
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Sentiment Analysis of Sexual Harassment in Malaysia on Twitter Using Machine Learning Algorithms
Published 2023“…The transformed data is then modelled using machine learning algorithms such as Naïve Bayes classifier and Support Vector Machine to predict the overall sentiment of tweets, in which the finding depicted an overall positive sentiment surrounding the issue. …”
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Final Year Project Report / IMRAD -
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Dimensional reduction and data visualization using hybrid artificial neural networks.
Published 2015“…Data with dimension higher than three is not possible to be visualized directly. …”
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Dimensional reduction and data visualization using hybrid artificial neural networks.
Published 2015“…Data with dimension higher than three is not possible to be visualized directly. …”
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14
Exploring employee working productivity: initial insights from machine learning predictive analytics and visualization / Mohd Norhisham Razali ... [et al.]
Published 2023“…Future research can explore more advanced machine learning algorithms, incorporate time-series analysis for temporal dependencies, and expand data collection from diverse organizational settings to improve the generalizability of predictive models.…”
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15
Exploration of COVID‑19 data in Malaysia through mapper graph
Published 2024“…To keep with the expanding quantity and complexity of data while employing minimal assumptions, a topological data analysis tool known as the Mapper algorithm is used to explore Malaysia’s daily confirmed cases, deaths, and vaccination data from the onset of the pandemic to June 2022 via data visualization and clustering. …”
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Adopting machine learning to automatically identify a suitable surgery type for refractive error patients
Published 2024“…ML is dedicated to the advancement and use of algorithms that possess the capacity to acquire knowledge from data and enhance their predictive capabilities without explicit programming. …”
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Intelligent agent for e-commerce using genetic algorithm / Kok Sun Sun
Published 2000“…This system is achieved using the Genetic Algorithm which is capable of performing information retrieval and learning algorithm. …”
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Thesis -
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Development of Machine Learning Algorithm for Acquiring Machining Data in Turning Process
Published 2004“…The design network is trained by presenting several target machining data that the network must learn according to a learning rule (algorithm). …”
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Thesis -
19
Improving F-Score of the imbalance visualized pattern dataset for yield prediction robustness
Published 2008“…In a non closed loop manufacturing process, a prediction model of the yield outcome can be achieved by visualizing the temporal historical data pattern generated from the inspection machine, discretize to visualized data patterns, and map them into machine learning algorithm.Our previous study shows that combination of under-sampling and over sampling techniques unabel wider range of data sets where SMOTE+VDM and random under-sampling produced robust classifier performance of handling better with different batches of prediction test data.In this paper, the integration of K* entropy base similarity distance function with SMOTE, CNN+Tomek Links and the introduction of SMOTE and SMaRT (Synthetic Majority Replacement Technique)combination, has improved the classifiers F-Score robustness.…”
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Conference or Workshop Item -
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An artificial neural network model for multi dimension reduction and data structure exploration, Proceedings of International Conference of Soft Computing and Pattern Recognition (...
Published 2009“…This paper proposes an hybrid artificial neural network (ANN) with self-organizing map (SOM) and modified adaptive coordinates (AC) for multivariate dimension reduction and data structures exploration. SOM, being a prominent unsupervised learning algorithm, is often used for multivariate data visualization. …”
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Proceeding
