Search Results - (( data machine learning algorithm ) OR ( java application based algorithm ))
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1
Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde
Published 2022“…This thesis is based on the application of sentiment classification algorithm to tweet data with the goal of classifying messages based on the polarity of sentiment towards a particular topic (or subject matter). …”
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AI powered asthma prediction towards treatment formulation: an android app approach
Published 2022“…Besides, an android application is being cre-ated to give therapy based on machine learning predictions. …”
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AI powered asthma prediction towards treatment formulation : An android app approach
Published 2022“…Besides, an android application is being cre-ated to give therapy based on machine learning predictions. …”
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Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly
Published 2019“…Iris data set has been taken from UCO machine learning to assist in testing phase. …”
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5
Mathematical simulation for 3-dimensional temperature visualization on open source-based grid computing platform
Published 2009“…The development of this architecture is based on several programming language as it involves algorithm implementation on C, parallelization using Parallel Virtual Machine (PVM) and Java for web services development. …”
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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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7
The implications for ahybrid detection technique against malicious sqlattacks on web applications
Published 2025“…The outcome of this study will add to the body of knowledge the most important and recent proposed solutions to mitigate SQL injection attack, in particular those based on machine learning algorithm…”
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Comparative performance of deep learning and machine learning algorithms on imbalanced handwritten data
Published 2018“…Imbalanced data is one of the challenges in a classification task in machine learning. …”
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Mapping the distribution of oil palm using Landsat 8 data by comparing machine learning and non-machine learning algorithms
Published 2019“…Hence, the mapping of oil palm distributions via machine learning algorithm was better than that via non-machine learning algorithm.…”
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Impact learning: A learning method from feature's impact and competition
Published 2023“…Machine learning is the study of computer algorithms that can automatically improve based on data and experience. …”
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Machine learning model for performance prediction in mobile network management / Muhammad Hazim Wahid
Published 2022“…The methodology includes drive test measurement for data collection, exploratory data analysis, data preparation, and applying machine learning algorithms to predict mobile network performance. …”
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Impact learning : A learning method from feature’s impact and competition
Published 2023“…Machine learning is the study of computer algorithms that can automatically improve based on data and experience. …”
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Comparison of malware detection model using supervised machine learning algorithms / Syamir Mohd Shahirudin
Published 2022“…Then, the outcomes demonstrated that the best classifier for categorizing our data with 0.96% accuracy is the Decision Tree machine learning algorithm. …”
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E4ML: Educational Tool for Machine Learning
Published 2003“…There are various types of machine learning algorithms with certain processes taken by the algorithm.In teaching of the machine learning algorithms, such processes need to be explained especially to the beginner in introductory level.This paper discusses the development the tool that addresses the process by certain algorithm to produce a hypothesis or output based on given data.This tool can also be used in teaching and learning purposes.The explanation of processes by the algorithms is demonstrated through simple simulation.The source of the algorithms was adapted from Mitchell book [1] that cover popular algorithms in machine learning for teaching and learning such as Concept Learning, Decision Tree, Bayesian Learning, Neural Networks, and Instance based Learning.The tool also used several classes of Weka (Waikato Environment for Knowledge Analysis) as a basis for the design and implementation of the new tool that focuses on explaining the processes taken by certain algorithm.…”
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15
Performance comparison of different machine learning algorithms on a time-series of covid-19 data: A case study for Saudi Arabia
Published 2021“…In this study we have applied several machine learning algorithms to analyse time-series data related to COVID-19 in Saudi Arabia. …”
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Performance comparison of different machine learning algorithms on a time-series of covid-19 data: A case study for Saudi Arabia
Published 2021“…In this study we have applied several machine learning algorithms to analyse time-series data related to COVID-19 in Saudi Arabia. …”
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An optimized variant of machine learning algorithm for datadriven electrical energy efficiency management (D2EEM)
Published 2024“…The scope of this study is tri folded, First, an exhaustive and parametric comparative study on a wide variety of machine learning algorithms is presented to evaluate the performance of machine learning algorithms in energy load prediction. …”
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Machine learning: tasks, modern day applications and challenges
Published 2019“…These machine learning algorithms are a collection of complex mathematical models and human intuitions. …”
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Impact learning: A learning method from feature’s impact and competition
Published 2023“…Machine learning is the study of computer algorithms that can automatically improve based on data and experience. …”
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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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