Search Results - (( java implementation a algorithm ) OR ( using classifier learning algorithm ))
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1
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 -
2
Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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3
RSA Encryption & Decryption using JAVA
Published 2006“…The key is the trigger mechanism to the algorithm. Until the advent of the Internet, encryption was rarely used by the public, but was largely a military tool. …”
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Final Year Project -
4
Direct approach for mining association rules from structured XML data
Published 2012“…The thesis also provides a two different implementation of the modified FLEX algorithm using a java based parsers and XQuery implementation. …”
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5
Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm
Published 2008“…The prototype system, known as Java Plagiarism Detection System (JPDS) implements the Greedy-String-Tiling algorithm to detect similarities among tokens in a Java source code files. …”
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6
Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions
Published 2017“…A 5-fold cross-validation testing was implemented for classification results analysis. …”
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7
Provider independent cryptographic tools
Published 2003“…The library is implemented by using Java cryptographic service provider framework that conforms to Java Cryptographic Architecture (JCA) and Java Cryptographic Extension (JCE). …”
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Monograph -
8
An ensemble learning method for spam email detection system based on metaheuristic algorithms
Published 2015“…In the second phase, a classifier ensemble learning model is proposed consisting of separate outputs: (i) To select a relevant subset of original features based on Binary Quantum Gravitational Search Algorithm (QBGSA), (ii) To mine data streams using various data chunks and overcome a failure of single classifiers based on SVM, MLP and K-NN algorithms. …”
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9
Performance evaluation of real-time multiprocessor scheduling algorithms
Published 2016“…We hyave used the CPU profiler of Oracle JavaTM VisualVM to monitor the execution of LRE-TL as well as USG algorithms. …”
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Conference or Workshop Item -
10
Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model
Published 2018“…EM and K-means clustering algorithms are used to cluster the multi-class classification attribute according to its relevance criteria and afterward, the clustered attributes are classified using an ensemble random forest classifier model. …”
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11
Sentiment analysis for airline services on Twitter using deep learning with word embedding / Mawada Mohamed Nour El Daim El Khalifa
Published 2020“…The role of Sentiment Analysis (SA) is to classify people's opinions into different categories, such as positive and negative from text, using existing algorithms. …”
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12
Comparative study of machine learning algorithms in data classification
Published 2025“…This research conducts a comparative study of various machine learning algorithms for dataset classification to identify the most accurate and reliable classifier. …”
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Final Year Project / Dissertation / Thesis -
13
A direct ensemble classifier for imbalanced multiclass learning
Published 2012“…Researchers have shown that although traditional direct classifier algorithm can be easily applied to multiclass classification, the performance of a single classifier is decreased with the existence of imbalance data in multiclass classification tasks.Thus, ensemble of classifiers has emerged as one of the hot topics in multiclass classification tasks for imbalance problem for data mining and machine learning domain.Ensemble learning is an effective technique that has increasingly been adopted to combine multiple learning algorithms to improve overall prediction accuraciesand may outperform any single sophisticated classifiers.In this paper, an ensemble learner called a Direct Ensemble Classifier for Imbalanced Multiclass Learning (DECIML) that combines simple nearest neighbour and Naive Bayes algorithms is proposed. …”
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14
Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition
Published 2017“…Phase 1 is mainly to evaluate the performance of clustering algorithm (K-Means and FCM). Phase 2 is to study the performance of proposed integration system which using the data clustered to be used as train data for Naïve Bayes classifier. …”
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15
Classification of Diabetes Mellitus (DM) using Machine Learning Algorithms
Published 2021“…The objective of this study is to perform DM classification using various machine learning algorithms using Weka as a tool. …”
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Final Year Project -
16
Automatic Number Plate Recognition on android platform: With some Java code excerpts
Published 2016“…The main challenges of implementing ANPR algorithm on mobile phone are how to produce a higher coding efficiency, lower computational complexity, and higher scalability. …”
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Book -
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Autism Spectrum Disorder Classification Using Deep Learning
Published 2021“…Several methods have been used to classify the ASD from non-ASD people. However, there is a need to explore more algorithms that can yield better classification performance. …”
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Article -
18
The efficacy of deep learning algorithm in classifying chilli plant growth stages
Published 2021“…The demand in this field has created various opportunities, especially for automatic classification using deep learning methods. In this paper, the efficiency of deep learning algorithms in classifying the growth stage of chili plants is studied. …”
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19
Cyberbullying detection: a machine learning approach
Published 2022“…Bag of Words model was used to convert text into numerical inputs. The machine learning algorithm, Support Vector Machine was chosen after comparing it with other algorithms such as Multinomial Naïve Bayes, Decision Tree Classifier, and Random Forest Classifier. …”
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Final Year Project / Dissertation / Thesis -
20
Evaluating the performance of machine learning techniques in the classification of Wisconsin Breast Cancer
Published 2018“…Numerous research works have tried to apply the algorithms of machine learning for classifying breast cancer and it was proven by many researchers that machine learning algorithms act preferable in the diagnosing process. …”
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