Search Results - (( data implication mining algorithm ) OR ( variable interactive learning algorithm ))
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Intelligent mining of large-scale bio-data: bioinformatics applications
Published 2017“…It also highlights some future perspectives of data mining in bioinformatics that can inspire further developments of data mining instruments. …”
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Evaluation and optimization of frequent, closed and maximal association rule based classification
Published 2014“…Real world applications of association rule mining have well-known problems of discovering a large number of rules, many of which are not interesting or useful for the application at hand.The algorithms for closed and maximal item sets mining significantly reduce the volume of rules discovered and complexity associated with the task, but the implications of their use and important differences with respect to the generalization power, precision and recall when used in the classification problem have not been examined.In this paper, we present a systematic evaluation of the association rules discovered from frequent, closed and maximal item set mining algorithms, combining common data mining and statistical interestingness measures, and outline an appropriate sequence of usage.The experiments are performed using a number of real-world datasets that represent diverse characteristics of data/items, and detailed evaluation of rule sets is provided as a whole and w.r.t individual classes. …”
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Organizational Culture Automated Audit System (OCAAS)
Published 2017“…Several state of the art technologies and techniques were used to design and developed OCAAS which include the use of machine learning and sentiment analysis based novel opinion mining algorithms for electronic opinion analysis and computerized statistics based mathematical algorithms for electronic data analysis as well as MySQL database integration for faster data processing and cognitive ergonomics system interface for user friendly interface navigation.…”
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Datasets Size: Effect on Clustering Results
Published 2013“…This gives a wider acceptance to data mining, being an interdisciplinary field that implements algorithm on stored data with a view to discovering hidden knowledge. …”
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Using predictive analytics to solve a newsvendor problem / S. Sarifah Radiah Shariff and Hady Hud
Published 2023“…The best algorithm will not be the same for all the data sets. …”
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Smart agriculture economics and engineering: Unveiling the innovation behind ai-enhanced rice farming
Published 2025“…This article introduced innovative Artificial Intelligence-based (AI-based) predictive algorithms for short-term rice production, utilizing the Cross Industry Standard Process for Data Mining (CRISP-DM) data science framework. …”
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Revolutionizing video analytics: a review of action recognition using 3D
Published 2024“…It also addresses the practicalities of implementing action recognition algorithms in real-world situations, which include tools like deep learning frameworks, pre-trained models, open-source libraries, cloud services, GPU acceleration, and evaluation metrics. …”
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Neural Network Multi Layer Perceptron Modeling For Surface Quality Prediction in Laser Machining
Published 2009“…One such method is machine learning, which involves using a computer algorithm to capture hidden knowledge from data. …”
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Short-term electricity price forecasting in deregulated electricity market based on enhanced artificial intelligence techniques / Alireza Pourdaryaei
Published 2020“…This merit is provided by balancing the exploitation of solution structure and exploration of its appropriate weighting factors through the use of Backtracking Search Algorithm (BSA) as an efficient optimization algorithm in learning process of ANFIS approach. …”
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Genetic ensemble biased ARTMAP method of ECG-Based emotion classification
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Support vector machine in precision agriculture: a review
Published 2021“…The Support Vector Machine (SVM) is a Machine Learning (ML) algorithm which may be used for acquiring solutions towards better crop management. …”
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Smart Agriculture Economics and Engineering: Unveiling the Innovation Behind AI-Enhanced Rice Farming
Published 2024“…To address these challenges, an innovative Artificial Intelligence-based (AI-based) predictive algorithm has been proposed, leveraging the Cross Industry Standard Process for Data Mining (CRISP-DM) data science framework. …”
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Application of artificial intelligence (AI) in islamic investments
Published 2020“…The technology helps investors to analyse their stocks in terms of price levels, the current stability of each stock and the future price forecasts based on current price and stock data. The study is a conceptual discussion on the application of AI in Islamic investment, which focuses on the discussion of Text Mining, Algorithmic Trading, Stock Pick and Robo in Investment, which include Robo Advisor, Robo Islamic Advisor (RIA) and Robo Financial Advisor (RFA) operating in Islamic investment system. …”
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Predicting sea levels using ML algorithms in selected locations along coastal Malaysia
Published 2024“…In consideration of the distinct behavior of machine learning (ML) algorithms, six well-defined ML used were carried out in this study for predicting sea level on a day-to-day basis. …”
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Predicting sea levels using ML algorithms in selected locations along coastal Malaysia
Published 2025“…In consideration of the distinct behavior of machine learning (ML) algorithms, six well-defined ML used were carried out in this study for predicting sea level on a day-to-day basis. …”
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The Effects Of Segmenting And Computational Thinking In Digital Video Courseware On Knowledge Achievement, Self-Efficacy And Motivation Among Students With Different Thinking Style...
Published 2023“…The researcher found significant main and interaction effects of the learner-paced predefined segment on all dependent variables. …”
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Analysis of daytime and nighttime ground level ozone concentrations using boosted regression tree technique
Published 2017“…Sensitivity testing of the BRT model was conducted to determine the best parameters and good explanatory variables. Using the number of trees between 2,500-3,500, learning rate of 0.01, and interaction depth of 5 were found to be the best setting for developing the ozone boosting model. …”
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