2023-2024 Java Projects for Engineering Students

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2023-2024 Java Projects for Engineering Students of Computer Science

Java uses an automatic garbage collector to manage memory in the object lifecycle. The programmer determines when objects are created, and the Java runtime is responsible for recovering the memory once objects are no longer in use. Once no references to an object remain, the unreachable memory becomes eligible to be freed automatically by the garbage collector. Something similar to a memory leak may still occur if a programmer's code holds a reference to an object that is no longer needed, typically when objects that are no longer needed are stored in containers that are still in use. If methods for a nonexistent object are called, a "null pointer exception" is thrown.One of the ideas behind Java's automatic memory management model is that programmers can be spared the burden of having to perform manual memory management. In some languages, memory for the creation of objects is implicitly allocated on the stack or explicitly allocated and deallocated from the heap. In the latter case, the responsibility of managing memory resides with the programmer. If the program does not deallocate an object, a memory leak occurs. If the program attempts to access or deallocate memory that has already been deallocated, the result is undefined and difficult to predict, and the program is likely to become unstable or crash. This can be partially remedied by the use of smart pointers, but these add overhead and complexity. Note that garbage collection does not prevent "logical" memory leaks, i.e., those where the memory is still referenced but never used. Garbage collection may happen at any time. Ideally, it will occur when a program is idle. It is guaranteed to be triggered if there is insufficient free memory on the heap to allocate a new object; this can cause a program to stall momentarily. Explicit memory management is not possible in Java. Java does not support C/C++ style pointer arithmetic, where object addresses and unsigned integers (usually long integers) can be used interchangeably. This allows the garbage collector to relocate referenced objects and ensures type safety and security. As in C++ and some other object-oriented languages, variables of Java's primitive data types are either stored directly in fields (for objects) or on the stack (for methods) rather than on the heap, as is commonly true for non-primitive data types (but see escape analysis). This was a conscious decision by Java's designers for performance reasons.
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2023-2024 BCA Projects for 6th Sem

2023-2024 Java Projects for IT Engineering Students


  1. About Java: Before taking your step, the most important thing to do is to get the answer of all WHYs. Here it refers to the questions like WHAT IS JAVA, WHY IT IS POPULAR, WHAT ARE ITS FEATURES, etc etc. By digging into the mentioned article, you will not only learn the important things about Java but also you will understand how to start learning it.

    Learn about Java here: Java Tutorial on How to start learning Java

  2. Java Environment: To work on any programming language, one first needs to know about its environment. Environment refers to the circumstances where a programming language works and how that program works. Java runs on a JVM environment. Click on the mentioned article to know more about JVM, its architecture and how it works.

    Learn about JVM here: Java Tutorial on JVM

  3. Java Programming Basics: To become proficient in any programming language, one Firstly needs to understand the basics of that language. Therefore, this article will give you in-depth knowledge of the basics of Java in a very simple format.

    By reading this article, you will get the to topics from how to set up the Java Environment to the details about its coding.

2023-2024 Java Projects for IT Engineering Students

1. A Correlation-based Feature Weighting Filter for Naive Bayes

2. A Two-Phase Algorithm for Differentially Private Frequent Subgraph Mining

3. A Two-stage Biomedical Event Trigger Detection Method Integrating Feature Selection and Word Embeddings

4. A Weighted Frequent Itemset Mining Algorithm for Intelligent Decision in Smart Systems

5. An Efficient Method for High Quality and Cohesive Topical Phrase Mining

6. Automated Phrase Mining from Massive Text Corpora

7. Bayesian Nonparametric Learning for Hierarchical and Sparse Topics

8. Characterizing and Predicting Early Reviewers for Effective Product Marketing on E-Commerce Websites

9. CoDetect: Financial Fraud Detection With Anomaly Feature Detection

10. Collective List-Only Entity Linking: A Graph-Based Approach


11. Comments Mining With TF-IDF: The Inherent Bias and Its Removal

12. Complementary Aspect-based Opinion Mining

13. Discovering Canonical Correlations between Topical and Topological Information in Document Networks

14. Document Summarization for Answering Non-Factoid Queries

15. Efficient Vertical Mining of High Average-Utility Itemsets based on Novel Upper-Bounds

16. Emotion Recognition on Twitter: Comparative Study and Training a Unison Model

17. Entity Linking: A Problem to Extract Corresponding Entity with Knowledge Base

18. Fast Cosine Similarity Search in Binary Space with Angular Multi-index Hashing

19. Frequent Itemsets Mining with Differential Privacy over Large-scale Data

20. Fuzzy Bag-of-Words Model for Document Representation


21. Health Monitoring on Social Media over Time

22. Highlighter: automatic highlighting of electronic learning documents

23. l-Injection: Toward Effective Collaborative Filtering Using Uninteresting Items

24. On Generalizing Collective Spatial Keyword Queries

25. Online Product Quantization

26. Predicting Contextual Informativeness for Vocabulary Learning

27. Principal Component Analysis Based Filtering for Scalable, High Precision k-NN Search

28. Privacy Characterization and Quantification in Data Publishing

29. Range-based Nearest Neighbor Queries with Complex-shaped Obstacles

30. Reverse k Nearest Neighbor Search over Trajectories

31. Road Traffic Speed Prediction: A Probabilistic Model Fusing Multi-Source Data

32. Scalable Content-Aware Collaborative Filtering for Location Recommendation

33. SDE: A Novel Clustering Framework Based on Sparsity-Density Entropy

34. Search Result Diversity Evaluation based on Intent Hierarchies

35. Selective Database Projections Based Approach for Mining High-Utility Itemsets

36. Supervised Topic Modeling using Hierarchical Dirichlet Process-based Inverse Regression: Experiments on E-Commerce Applications

37. Web Media and Stock Markets : A Survey and Future Directions from a Big Data Perspective

38. When to Make a Topic Popular Again? A Temporal Model for Topic Re-hotting Prediction in Online Social Networks

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2023-2024 IEEE Projects for CSE Final Year Contact: 9591912372