Search Results - (( java implementation tree algorithm ) OR ( using continuous training algorithm ))
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Study and Implementation of Data Mining in Urban Gardening
Published 2019“…Using the J48 tree algorithm implemented through WEKA API on a Java Servlet, data provided is processed to derive a health index of the plant, with the possible outcomes set to “Good,” “Okay”, or “Bad”. …”
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Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde
Published 2022“…The method was implemented using Java and the results of the simulation were evaluated using five standard performance metrics: accuracy, AUC, precision, recall, and f-Measure. …”
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Thesis -
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Optimization and discretization of dragonfly algorithm for solving continuous and discrete optimization problems
Published 2024“…The ANNs trained by the optimized DA also achieve higher accuracy than those trained by some other swarm intelligence algorithms. …”
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Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization
Published 2019“…This study proposes a car parking management system which applies Dijkstra’s algorithm, Ant Colony Optimization (ACO) and Binary Search Tree (BST) in structuring a guidance system for indoor parking. …”
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Malay continuous speech recognition using continuous density hidden Markov model
Published 2007“…This thesis describes the investigation of the use of Continuous Density Hidden Markov Model (CDHMM) for Malay Automatic Speech Recognition (ASR). …”
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Development of real-time embedded system with speech recognition for smart house
Published 2007“…In SI task, 40 speakers, different from the training speakers, with each recorded 2 tokens are used for testing. …”
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Abnormal Pattern Detection In Ppg Signals Using Time Series Analysis
Published 2022“…The abnormal PPG pattern detection using rule-based algorithm has produced accuracy of 87.30% in training process and 87.18% in testing process with coverage of rule for training and testing, 89.26% and 87.33%. …”
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Undergraduates Project Papers -
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Tracing the real power transfer of individual generators to loads using least squares support vector machine with continuous genetic algorithm
Published 2011“…This paper attempts to trace the real power transfer of individual generators to loads in pool based power system by incorporating the hybridization of Least Squares Support Vector Machine (LS-SVM) with Continuous Genetic Algorithm (CGA)- CGA-LSSVM. The idea is to use CGA to find the optimal values of regularization parameter, γ and Kernel RBF parameter, σ 2 , and adapt a supervised learning approach to train the LS-SVM model. …”
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Conference or Workshop Item -
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An integrated model to email spam classification using an enhanced grasshopper optimization algorithm to train a multilayer perceptron neural network
Published 2021“…This study introduces an email detection model that is designed based on use of an improved version of the grasshopper optimization algorithm to train a Multilayer Perceptron in classifying emails as ham and spam. …”
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Conference or Workshop Item -
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Intelligent Sensor Data Pre-processing Using Continuous Restricted Boltzmann Machine
Published 2007“…The noise inputs cause neurons to have continuous-valued, probabilistic outputs. The use ofstochastic neurons in CRBMgives it modelingflexibility that is useful with real data. …”
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Final Year Project -
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Parallel implementation on improved error signal of backpropagation algorithm
Published 2001“…The BP algorithm uses the data partitioning method with columnwise block striped and the batch mode weight updating strategy. …”
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Text Categorization Using Naive Bayes Algorithm
Published 2005“…This paper presents text categorization system based on naive Bayes algorithm. This algorithm has long been used for text categorization tasks. …”
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A simplified PID-like ANFIS controller trained by genetic algorithm to control nonlinear systems
Published 2010“…In addition, the real-coded Genetic Algorithm (GA) has been utilized to train this ANFIS controller, instead of the hybrid learning methods that are widely used in the literature, and hence, the necessity for the teaching signal required by other techniques has been eliminated. …”
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Intelligent grading of agarwood essential oil quality using artificial neural network (ANN) / Noratikah Zawani Mahabob
Published 2022“…The result revealed that the ANN performance had the highest accuracy when training using the LM training algorithm. The ANN with the seven inputs (seven significant compounds from Z-score pre-processing technique) trained by one hidden neuron of LM algorithm provided the best performance with 100% for accuracy, specificity, sensitivity and precision as well as minimum convergence epoch.…”
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A comparison study of classifier algorithms for mobile-phone’s accelerometer based activity recognition
Published 2012“…Our earlier research has shown that data from mobile-phone embedded accelerometer can be used for activity recognition purpose [1]. As a continuation of the research towards the search for a suitable and reliable algorithm for real-time activity recognition using mobile phone, an evaluation and comparison study of the performance of seven different categories of classifier algorithms in classifying user activities were conducted. …”
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Detection of the spread of Covid-19 in Indonesia using K-Means Clustering Algorithm / Mohammad Yazdi Pusadan ... [et al.]
Published 2023“…The data source used as training data comes from the official Kaggle website, the data used in this study is data on the spread of the coronavirus collected from 2020 to 2021 with a total of 20,816 training data. …”
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Book Section -
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River segmentation using satellite image contextual information and Bayesian classifier
Published 2016“…The use of a training map from erosion morphology helps to extract the non-predictive river’s curves in the image. …”
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Prediction of football club winning rate using Bayesian model algorithm / Adam Khairul Anuar
Published 2023“…The evaluation involves K-fold cross-validation, indicating superior performance compared to Single Train-Test Split. Recommendations include continuous model training, detailed features, collaboration with experts, and exploring alternative algorithms. …”
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