Search Results - (( bayesian classification tree algorithm ) OR ( java implementation during algorithm ))
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An experimental study of classification algorithms for crime prediction.
Published 2013“…This paper compares the two different classification algorithms namely, Naïve Bayesian and Decision Tree for predicting ‘Crime Category’ for different states in USA. …”
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Accuracy and performance analysis for classification algorithms based on biomedical datasets
Published 2021“…Trees based Decision Tree (ID3) algorithm, Bayesian Theorem based Hidden Naïve Bayes (HNB) algorithm. …”
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Predicting building damage grade by earthquake: a Bayesian Optimization-based comparative study of machine learning algorithms
Published 2024“…Using metrics, this study evaluates Random Forest, ElasticNet, and Decision Tree algorithms. This study showed damage level asymmetry. …”
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Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data
Published 2018“…Keyword: Random Forest, Bayesian Inference, Classification, Regression, Missing Data.…”
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Towards personalized intensive care decision support using a Bayesian network: A multicenter glycemic control study
Published 2023“…Benchmarking; Decision support systems; Hospital data processing; Intensive care units; Patient treatment; Trees (mathematics); Blood glucose measurements; Classification precision; Discretization algorithms; Discretizations; Glycemic control; Performance prediction; Structure-learning; Variable selection; Bayesian networks…”
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A study on classification learning algorithms to predict crime status.
Published 2013“…In this paper, we conducted an experiment to obtain better supervised classification learning algorithms to predict crime status by using two different feature selection methods tested on real dataset. …”
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Prediction of college student academic performance using data mining techniques.
Published 2013“…This study attempts to predict the success rate of students’ academic performance by analyzing their examination results to secure a place at college level for the subsequent semester. The classification algorithms used are the Decision Tree, Naïve Bayesian, and Multilayer Perception with the highest classification accuracy by the Naive Bayes algorithm with accuracy of 95.3%. …”
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A comparative study in classification techniques for unsupervised record linkage model
Published 2011“…Duplicate detection and classification of records in two real world datasets, namely Cora and Restaurant is experimented by Support Vector Machines, Naïve Bayes, Decision Tree and Bayesian Networks which are regarded as some prominent classification techniques. …”
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Optimizing the light gradient-boosting machine algorithm for an efficient early detection of coronary heart disease
Published 2024“…Borderline-SMOTE (Synthetic Minority Over-sampling Technique) was used on the training set to balance the dataset. The LightGBM algorithm was selected for its efficiency in classification tasks, and Bayesian Optimization with Tree-structured Parzen Estimator (TPE) was employed to fine-tune its hyperparameters. …”
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Pairwise testing tools based on hill climbing algorithm (PTCA)
Published 2014“…The actual implementation of the algorithm which is in Java programming language, the program is implemented on Net Bean 7.0.1. …”
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Machine Learning based Predictive Modelling of Cybersecurity Threats Utilising Behavioural Data
Published 2023“…Five supervised machine learning methods are being tested which are: Regression Logistics, K-Nearest Neighbour (KNN), Decision Tree (DT), Support Vector Machine (SVM), and Naïve Bayesian Classifier with the aid of a tool, RapidMiner. …”
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Optimisation of support vector machine hyperparameters using enhanced artificial bee colony variant to diagnose breast cancer
Published 2023“…The SVM-JAABC5ROC (SVM optimised by JAABC5ROC) was compared with three optimisers (Grid search, Random Search and Bayesian Optimisation) and compared with 5 classifiers (Artificial Neural Network (ANN), K-Nearest Neighbour (KNN), Naïve Bayes, Logistic Regression and Decision Tree). …”
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Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization
Published 2019“…The circuits have been designed by proteus, the microcontrollers have been programmed by micro C, and the Graphical User Interface (GUI) has been implemented in Java. Few by electronic components such as RFID, multiplexer, XBee, and servo motors have been used to realize the system. …”
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Talkout : Protecting mental health application with a lightweight message encryption
Published 2022“…The investigation of lightweight message encryption algorithms is conducted with systematic quantitative literature and experiment implementation in Java and Android running environment. …”
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Design & Development of a Robotic System Using LEGO Mindstorm
Published 2006“…The system is capable in operating an off-line programming method, starting from its programming sequences until robotic implementation of the program. During early stages, the research is emphasis more towards designing a robotic system using RoboLab software and C++ programming language. …”
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Data Hiding Techniques In Digital Images
Published 2003“…After all these studies, one of the algorithms in the masking technique is developed and implemented using JAVA program to embed message into true color image with a good quality and higher capacity. …”
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Multi-floor indoor location estimation system based on wireless local area network
Published 2007“…Before developing the system, study on characteristics of RSS is conducted to help the design, development and implementation of the proposed system. The proposed system is then designed and developed using Java programming language. …”
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Adaptive persistence layer for synchronous replication (PLSR) in heterogeneous system
Published 2011“…The PLSR architecture model, workflow and algorithms are described. The PLSR has been developed using Java Programming language. …”
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A malware analysis and detection system for mobile devices / Ali Feizollah
Published 2017“…We then used feature selection algorithms and deep learning algorithms to build a detection model. …”
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