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Direct approach for mining association rules from structured XML data
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Study and Implementation of Data Mining in Urban Gardening
Published 2019“…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
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Mining Sequential Patterns Using I-PrefixSpan
Published 2007“…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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A web-based implementation of k-means algorithms
Published 2022“…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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Mining Sequential Patterns using I-PrefixSpan
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Machine learning: tasks, modern day applications and challenges
Published 2019“…Over the last decade, we are able to develop algorithms which can produce better accuracies so better decision making can be achieved. …”
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Predictive modelling of nanofluids thermophysical properties using machine learning
Published 2021“…This thesis aimed to develop machine learning algorithms to estimate the thermophysical properties of commonly used nanofluids. …”
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Machine learning for data classification in construction project planning
Published 2023“…The concept of the Machine Learning is the ability of the machine able to learn the situation with algorithms rules and make a predictions or decision. …”
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Final Year Project / Dissertation / Thesis -
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Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction
Published 2014“…Over the past decades, the Least Squares Support Vector Machines (LSSVM) has been widely utilized in prediction task of various application domains. …”
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Phylogenetic tree classification system using machine learning algorithm
Published 2015“…A study is conducted to develop an automated phylogenetic tree image classification system by using machine learning algorithm. …”
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Final Year Project Report / IMRAD -
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Impact learning: A learning method from feature's impact and competition
Published 2023“…We, moreover, manifest the prevalence of impact learning over the conventional machine learning algorithm.…”
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Comparison of supervised machine learning algorithms for malware detection / Mohd Faris Mohd Fuzi ... [et al.]
Published 2023“…Malware has also evolved significantly over the past few years. With the advancement of malware analysis, Machine Learning (ML) is increasingly being used to detect malware. …”
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Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. The proposed model will be tested and evaluated on both NSL-KDD and KDD-CUP 99 using several performance metrics.…”
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Impact learning : A learning method from feature’s impact and competition
Published 2023“…We, moreover, manifest the prevalence of impact learning over the conventional machine learning algorithm.…”
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Flexible job shop scheduling using priority heuristics and genetic algorithm
Published 2010“…In the next method, a genetic algorithm has been developed. It has been shown that proposed genetic algorithm with a reinforced initial population (GA2) has better efficiency compared to a proposed genetic algorithm with fully random initial population (GA0). …”
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The influence of machine learning on the predictive performance of cross-project defect prediction: empirical analysis
Published 2024“…This empirical investigation delves into the influence of machine learning (ML) algorithms in the realm of cross-project defect prediction, employing the AEEEEM dataset as a foundation. …”
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Impact learning: A learning method from feature’s impact and competition
Published 2023“…We, moreover, manifest the prevalence of impact learning over the conventional machine learning algorithm.…”
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Hybrid dynamic scheduling model for flexible manufacturing system with machine availability and new job arrivals
Published 2015“…The BBO-VNS match-up algorithm manipulates the idle times on machines within the time horizon for assigning the affected operations by breakdown and/or newly arrived orders. …”
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