Java Learning Projects

    Java Learning Projects is the best solution to stop talking about the problems related to selecting a topic, completed within allotted time and quality. We provide project training for students to familiarize with the project development process. Java is the most wanted language that used for desktop apps, all type of android apps and backend systems. It provides various features for Java programmers like scalability, versatility, ease of learning and rich set of APIs.  Our Java Learning Projects providing the list of project topics, which helpful for students to do final year projects. Depending on the nature of the project, we allocated our entire research team. We have 150+ world class experts who specialized for Java programming language and have 10+ years of experience in all research domains.

Java Learning Projects

    Java Learning Projects is one of our great service that is created for students around the world. In today’s world, Java is the most dominant language due to its multiprogramming support. Let’s get started with the Java Learning Projects. In this page, we explains three terms: Artificial Intelligence, Machine Learning and Deep Learning

Highlighted Features

  • Scalable on Hadoop Framework
  • GPU support for AWS scaling
  • Adapted on micro-service architecture
  • Support Spark MLib
  • Using GPU as a Service (GaaS)
  • Support Matlab Functions

Major Research Areas and Methods in each of field:

Artificial Intelligence

  • 5D Robotics
  • Machine learning
  • Natural language processing
  • Language synthesis
  • Computer vision
  • Sensor analysis
  • Simulation and optimization

Machine Learning

  • Support Vector Machines
  • Deep Learning
  • Decision Trees
  • K-means Clustering
  • Regression
  • Association Rule Learning
  • Bayes Learning

Deep Learning

  • Artificial Neural Networks
  • Convolutional Neural Networks
  • Recursive Neural Networks
  • Deep Belief Networks
  • Recursive Neural Networks
  • Machine Vision
  • Text Processing
  • Generative Networks
  • Automatic Text Generation
  • Drug Discovery
  • Image Classification
  • Biological Analogs

Major Applications of Three Terms

  • Satellite Images Analysis
  • Power Image Recognition and Tagging
  • Fraud Detection
  • Financial Marketing
  • Stock Market prediction
  • Customer Recommendations
  • Handwriting Recognition
  • Traffic Sign Recognition
  • Asian Handwriting Recognition
  • Volumetric Brain Image Segmentation
  • Human Action Recognition
  • Scene Parsing from Depth Images
  • Breast Cancer Cell Mitosis Detection

Major Supported Java Libraries

  • Arbiter (Tool for Machine Learning Algorithms Evaluation)
  • DataVec (Machine Learning Tool for ETL Operations)
  • Deeplearning4J (Neural Net Library)
  • JavaCPP (Bridge between Native C++ and Java)
  • ND4J (JVM Numpy)
  • RL4J (Deep Reinforcement Library for the JVM)
  • Java-ML (Open Source Machine Learning Library)
  • Weka (Machine Learning Algorithms Platform)
  • RapidMiner (Similar to Weka Tool)
  • Jenetics (Genetic Algorithm in Java)
  • Encong (Machine Learning Framework Supported in Java)
  • ECJ 23 (Java based Research Framework)
  • Watchmaker Framework (GA Framework in Java)
  • JGAP (Java Genetic Algorithm Package)
  • D3Web (Reasoning Engine for Expert Systems)
  • Apache Jena (Open Source Java Framework)

      Now let’s discuss the one example about Brain Tumor Classification and Clustering using MIPAV (Medical Image Processing, Analysis and Visualization). MIPAV is the standard user-interface tool for analysing various images such as MRI, PET, CT/Microscopy, etc.


  • Java 1.6 or above
  • JOCL 0.1.7 or above
  • Jogamp (JOGL and JOCL Libraries)
  • JMF 2.1.1.e or above
  • Java 3D 5.2 or above

//Source Code…….

import java.awt.image.BufferedImage;

import java.awt.image.Raster;

import java.awt.image.WritableRaster;


import javax.imageio.ImageIO;

public class Bicluster {

public static final String IMG = “C:/Users/pragati/Desktop/project/bicluster/glioma.jpg”;

public static BufferedImage getImageFromArray(int[] pixels, int width, int height) {

BufferedImage image = new BufferedImage(width, height, BufferedImage.TYPE_BYTE_GRAY);

WritableRaster raster = image.getRaster();


System.out.println(“Writing Image”);

File output=new File(“C:/Users/pragati/Desktop/project/bicluster/gliomabi.jpg”);

try {



catch (Exception e)




return image;


public static void main(String[] args) throws Exception   {

int i,j,fill=0;

// File input = new File (“C:/Users/Harsha/Pictures/t1.jpg”);

BufferedImage img;


img = File(IMG));

//BufferedImage img = new BufferedImage(img1.getWidth(), img1.getHeight(), img1.TYPE_BYTE_BINARY);

int[][] array1=new int[img.getWidth()][img.getHeight()];

int [] arr =new int[img.getWidth()*img.getHeight()];

int [] a =new int[img.getWidth()*img.getHeight()];

int l=0;

Raster raster = img.getData();

//  System.out.println(“Binary Image”);

for ( j = 0; j < img.getWidth(); j++) {

for (int k = 0; k < img.getHeight(); k++) {

array1[j][k] = raster.getSample(j, k, 0);



// System.out.print(array1[j][k]+” “);



int y=255/2;

int maxn=img.getWidth()*img.getHeight();












System.out.println(“New Image”);


System.out.print(a[i]+ ” “);

Bicluster ip =new Bicluster();

BufferedImage im =ip.getImageFromArray(a, img.getHeight(),img.getWidth() );



      Here is more sample topics provided for undergraduate (B.E/B.Tech) and postgraduate (M.E/M.Tech/M.Phil) students.

Artificial Intelligence

  • A novel integrated approach of DBLearn based in Adaptive e-learning for practical database course
  • On the use of Enhancing the accuracy of firefly algorithm by reproduction mechanism
  • An innovative topic for word embeddings based in Clustering search engine suggests by an integrating model
  • The new process of an improved ant colony optimization for green multi-depot vehicle routing problem with time windows
  • A new effective process of passenger hotspots searching algorithm for taxis in urban area

Machine Learning

  • An inventive Paradigm for Dynamic Runtime Exploitation of Various Knowledge Sources and Machine Learning system
  • A development of framework with High Performance Machine Learning (HPML) for Support to DDDAS Decision Support Systems
  • An innovative development for Support Vector Machine Meets Software Defined Networking based by IDS Domain
  • The new process of Integrating Short History for Improving Clustering Based on Network Traffic in Anomaly Detection
  • On the use of Time-Growing Neural Network development in DML Method by Classifying to Cyclic Time Series for Biological Signals

Deep Learning

  • On the exploit of Functional and Structural MRI process for 3D CNN based in Automatic Diagnosis of Attention Deficit Hyperactivity Disorder
  • A new source of Deep Residual Networks with Dynamically Weighted Wavelet Coefficients for Fault Diagnosis based in Planetary Gearboxes
  • On the development of Recurrent Neural Networks in Deep Learning Approach for Intrusion Detection
  • A fresh mechanism for Deep Bimodal Regression of Apparent Personality Traits from Short Video Sequences