Search Results - (( developing learning housing algorithm ) OR ( java application mining algorithm ))
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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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Citation Index Journal -
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Analysis on The Prediction of Homeless using Machine Learning Algorithm
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Final Year Project -
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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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Particle Swarm Optimization in Machine Learning Prediction of Airbnb Hospitality Price Prediction
Published 2022“…While a bigger impact has been given by the Airbnb community to the local economic development of each country, there has been very little effort that investigates on Airbnb pricing issue with machine learning techniques. …”
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Green building valuation based on machine learning algorithms / Thuraiya Mohd ... [et al.]
Published 2021“…To our knowledge, there is still no implementation of machine learning models on green building valuation features for building price prediction compared to conventional building development. …”
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Home intruder detection system using machine learning and IoT
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Predicting real estate prices with AI: a comparative study of machine learning models
Published 2025“…The Random Forest model was the best-performing predictor, with the lowest error values and highest R2 score compared to other tested algorithms. This research offers practical insights into key price-influencing features and highlights the efficacy of machine learning for robust and interpretable house price prediction in the Malaysian real estate market.…”
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Green building factor in machine learning based condominium price prediction
Published 2022“…As research on green building with machine learning techniques is rarely reported in the literature, this paper presents the fundamental design and the comparison results of three machine learning algorithms namely deep learning (DL), decision tree (DT), and random forest (RF). …”
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Individual And Ensemble Pattern Classification Models Using Enhanced Fuzzy Min-Max Neural Networks
Published 2014“…Firstly is by enhancing the learning algorithm of a neural-fuzzy network; and secondly by devising an ensemble model to combine the predictions from multiple neural-fuzzy networks using an agent-based framework. …”
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Thesis -
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Vehicle Detection in Deep Learning
Published 2019“…The objective of this project is vehicle detection with deep learning, so, vehicles data set from highway, urban road and housing area had been collected and applied to the deep learning and computer vision algorithms. …”
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Final Year Project / Dissertation / Thesis -
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Evaluations of oil palm fresh fruit bunches maturity degree using multiband spectrometer
Published 2017“…Furthermore, the Lazy-IBK algorithm have been validated to produce the best classifier model, with the machine learning algorithm performance of 65.26%, recall of 65.3%, and 65.4% F-measured as compared to other evaluated machine learning classifier algorithms proposed within the WEKA data mining algorithm. …”
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Household overspending model amongst B40, M40 and T20 using classification algorithm
Published 2020“…The model development employs five machine learning algorithms namely decision tree, Naive Bayes, Neural network, Support Vector Machines, Nearest Neighbour. …”
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Feature recognition and classification of shanxi cave dwellings based on deep learning
Published 2023“…Through detailed investigation and research on specific examples in Shanxi, the results are as follows: (1) We identify and classify the architectural features of Shanxi cave dwellings based on deep learning algorithm so that we can better understand and explore Shanxi cave dwellings. (2) By analyzing the present situation of examples, it is concluded that most villagers affirm the living comfort and regional adaptability of cave buildings, and the identification and classification of cave building features in Shanxi based on deep learning can provide effective reference, in order to better complete the renovation and development of cave buildings in Shanxi.…”
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