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Java Programming Source Code Projects
Java Programming Source Code Projects offers best projects in world 120+ countries. We provide projects and projects training on the following students: B.E, B.Tech, M.E, M.Tech, M.Phil, M.Sc, and MCA. Our young talented team have puts lot efforts and provide some projects in IEEE Standard. Over the next decade, Machine Learning will transform our world. There are plenty of researchers get started their research in Machine Learning. In this page, we provide a short guide to developing Machine Learning projects.
Let’s see some Top Machine Learning Libraries for Java,
- Weka
- MOA (Massive Online Analysis)
- Deeplearning4j
- GATE (General Architecture for Text Engineering)
- MALLET (Machine Learning for Language Toolkit)
- ELKI (Open Source Data Mining Library)
- Java-ML (Java Machine Learning Library)
- BIDMach, BIDMat, BIDParse (Big Data Libraries)
- BoofCV (Open Source Computer Vision Library)
- RankLib (Rank Algorithms Learning Library)
- N-Dimensional Arrays for Java (JVM Scientific Computing Libraries)
- Java (Java Port of the Numenta Platform)
Best Tools Started with Java Machine Learning:
- Neuroph (Object Oriented Neural Network)
- JSAT Tool (Java Statistical Analysis Tool)
- Apache Mahout (Hadoop Platform)
- OpenNLP (Apache OpenNLP Toolkit)
- LingPipe (Computational Linguistics Toolkit)
- SAMOA (Scalable Advanced Massive Online Analysis)
- MEKA (WEKA based Open Source Toolkit)
- Encog (Machine Learning Framework)
- Datumbox (Machine Learning Framework)
- Oryx2 (End-to-end Applications Development Tool)
- Stanford Classifier (Data mining based Machine Learning Tool)
- Apache Jena (Open Source Java Framework for Semantic Web)
- PowerLoom (Java API for knowledge based Applications)
Machine Learning Algorithms:
- Decision Tree
- 5
- Support Vector Machine
- Linear Regression
- Naïve Bayes Classifier
- Embedded Message Classifier
- Genetic Algorithms
- Viterbi Algorithm
- K-Nearest Neighbour Algorithm
- K-Means Clustering Algorithm
- Hierarchical Clustering Algorithm
- Artificial Neural Networks
- Bayesian Networks
- Genetic Programming
- Hidden Markov Chain Model
Java Machine Learning Environments:
- WEKA (Waikato Environment for Knowledge Analysis)
- RapidMiner/Yet Another Learning Environment (YALE)
- KNIME (Konstanz Information Miner)
- ELKI (Environment for Developing KDD Applications Supported by Index Structures)
Top Machine Learning Project Ideas:
- Implement algorithms for Regression, Clustering, Rule Extraction and Classification
- Sustainable strategy development for Customer Retention Prediction
- Machine Learning to Anomaly, Outlier, Fraud Detection
- Activity Recognition Model for eHealth Applications
- Deep learning Concepts, Algorithms and the Toolbox
- Creating own Neural Network
- Spam Detection, Knowledge Discovery in Texts
- Recommendation System and Knowledge Discovery in Texts
- Sentiment Analysis and Full Text Search
- Simple Machine Learning Examples:
-Recommendation system
-Digit recognition
-Shape detection
-Face detection
-Hand writing generation
-Symbolic integration
-Floor mopping robot system
/* Sample Java Code for Decision Tree Algorithm*/
BufferedReader datasetfile = readdatasetfile(“clouddataset.txt”);
Instances inputdata = new Instances(datasetfile);
inputdata.setClassIndex(inputdata.numAttributes() – 1);
Instances[][] split = crossValidationSplit(inputdata, 15);
// Separate split into training and testing arrays
Instances[] trainingSplits = split[1];
Instances[] testingSplits = split[2];
// Use a set of classifiers
Classifier[] MLmodels = {
new J48(), // a decision tree
new PART(),
new DecisionTable(),//decision table majority classifier
new DecisionStump() //one-level decision tree
};
// Run for each model
for (int j = 1; j <= MLmodels.length; j++) {
MLmodels
FastVector predictions = new FastVector();
// For each training-testing split pair, train and test the classifier
for (int i = 1; i <= trainingSplits.length; i++) {
Evaluation validation = classify(MLmodels[j], trainingSplits[i], testingSplits[i]);
predictions.appendElements(validation.predictions());
// Uncomment to see the MLmodels for each training-testing pair.
//System.out.println(MLmodels[j].toString());
}
// Calculate overall accuracy of current classifier on all splits
double accuracy = calculateAccuracy(predictions);
// Print current classifier’s name and accuracy in a complicated,
// but nice-looking way.
System.out.println(“Accuracy of ” + MLmodels[j].getClass().getSimpleName() + “: ”
+ String.format(“%.3f%%”, accuracy)
+ “\n———————————“);
Recent List of Java Programming Source Code Projects:
- A new source of UAV technology and machine learning techniques applied to the yield improvement in precision agriculture
- The new-fangled process of Dissolved Gas Analysis Interpretation and Intelligent Machine Learning Techniques
- The Design mechanism for Reliable SoCs With BIST Hardware and Machine Learning
- A fresh method for Photorealistic Monocular Gaze Redirection used by Machine Learning
- An effective performance for user-centric machine learning framework based on cyber security operations center
- The novel technique of Wavelet transform and unsupervised machine learning to detect insider threat on cloud file-sharing
- An innovative technology of Automation of a robotic cell using machine learning algorithms and internet of things
- A ground-breaking performance for Playback detection used by machine learning with spectrogram features approach
- An effectual Machine Learning performance for Decision-Support System Improves the Internet of Things’ Smart Meter Operations
- The new device for Stacking and rotation-based technique for machine learning classification with data reduction
