Data Analysis Research Topics

Data Analysis research topics are utilized for data augmentation and preprocessing the data. It is utilized in our research to improve the dataset quality. Here we provide some information or details related to this research.

  1. Define data pre-processing and augmentation

At the beginning of this research we first see the definition, data preprocessing is the initial procedure in data analysis that contains transforming, cleaning and organizing raw data in an appropriate structure for further examination.

  1. What is data pre-processing and augmentation?

After the definition we see the in-depth explanation for data preprocessing and augmentation. Data pre-processing is the procedure of structuring and cleaning of raw data to generate it appropriate for machine learning or examining. Whereas, Data augmentation consists of making extra training data by implementing different transformations to enhance the size of the dataset and strength of the model frequently utilized in NLP and computer vision. Both are important for enhancing the quality of data and the achievement of the model.

  1. Where data pre-processing and augmentation used?

Next to the in-depth explanation we examine where to utilize this data pre-processing and augmentation technique. This process is used in Natural Language Processing (NLP), Signal Processing, Machine Learning, Computer Vision and Data Analysis are the methods that use our proposed technique.

  1. Why data pre-processing and augmentation technology proposed? , Previous technology issues

In this research the data Pre-processing and augmentation technologies are proposed to overcome the difficulties on working over the raw and frequent inadequate data. Pre-processing assists in transforming, normalizing, and cleaning data, and creating it appropriate for analysis, whereas augmentation enhances the dataset size and diversity, improving the strength and inference of the machine learning methods. These methods are significant for enhancing the consistency and accuracy of the data-driven applications among different fields, like Natural Language Processing, Machine learning and Computer vision. Some of the previous technology issues that it overcomes are Augmentation obstacles, Optimization challenges and preprocessing difficulties.

  1. Algorithms / protocols

Genetic Algorithm XGBoost and LightGBM (GA–XGBoost and LightGBM), Gaussian Symmetric Markov Random Field with Bilateral Filter, and Generative Adversarial Network with Moth Flame Optimization (GAN – MFO) are the methods or algorithms to be used in this research.

  1. Comparative study / Analysis

For comparative analysis we compare the methods to improve the findings of this research. Here we compare the methods are as follows

  • The Gaussian Symmetric Markov Random Field with Bilateral Filter method is used to reduce the noise, which removes the sounds (i.e. inadequate data) from the relevant dataset. By employing this filter we remove the noise and unnecessary data.
  • For data augmentation we utilize the technique Generative Adversarial Network with Moth Flame Optimization (GAN-MFO).
  • The combined technique of Genetic Algorithm (GA) with XGBoost and LightGBM is employed for classification and this method enhances the forecasting of diabetes and classification accuracy.
  1. Simulation results / Parameters

Now our proposed technology is compared with various performance metrics or parameters to get the appropriate findings. The metrics that we compared are Missing Data Ratio with the Mean Square Error and Number of Samples with Obtained Metrics and the other metrics like F1-score, Precision, Accuracy and Recall are the metrics that we compared for this research.

  1. Dataset LINKS / Important URL

Here, we offer the dataset link to be used for this research; we go to this link by getting the information related to the dataset:

  1. Data pre-processing and augmentation Applications

The proposed technique is now widely used in many applications. Some of the applications to be used are Finance, Machine Learning, Healthcare, Computer Vision, Natural Language Processing, Manufacturing and IoT and Social Media Analysis are the most common applications that utilize this technique.

  1. Topology for data pre-processing and augmentation

Topology that uses data pre-processing and augmentation techniques are Topological Data Analysis (TDA), Electrical Engineering – Circuit Topology, Network Topology and Mathematics – General Topology.

  1. Environment in data pre-processing and augmentation

Let’s discuss the environment that uses our proposed methodology. Technological Environment, Ecological Environment, Built Environment, Natural Environment, Political Environment, Economic Environment and Social and Cultural Environment are the environments that use the data augmentation and pre-processing methods.

  1. Simulation Tools

Now we can see the software requirements that are employed for this research. The tool that is required for this research is Python 3.11.4, this tool is used to implement our proposed research. Then the research is operated by employing the operating system namely Windows – 10 (64- bit).

  1. Results:

Data analysis is the method, which is proposed in this research, it overcomes few previous technology issues. It is used for structuring, cleaning and normalizing the data. For this research we analyze the different performance metrics to get the possible findings and are widely used in many applications.

Data Analysis Research Ideas:

Below, we provide the topics that are related to the data analysis method that includes data augmentation and data pre-processing technique. We utilize these topics when the doubts or clarifications arise among us:

  1. Application of Data Mining Technology in Financial Data Analysis Methods Under the Background of Big Data
  2. A High Performance Computing Platform for Big Biological Data Analysis
  3. Domain-Oriented Transformation Method for Big Data Analysis Process Model
  4. Data Governance Based on Full-Service Data Analysis Domain of Power Grid
  5. scGCC: Graph Contrastive Clustering With Neighborhood Augmentations for scRNA-Seq Data Analysis
  6. Research on Accurate Portrait Construction of Online Platform Learners Based on Data Analysis
  7. Research on the Impact of Big Data Analysis and Integration Capability on Enterprise Innovation Performance—The Intermediary Effect of Supply Chain Collaborative Innovation
  8. An Exploratory Data Analysis and Visualizations of Underprivileged Communities Diabetes Dataset for Public Good
  9. Gang Theft Crime Behavior and Prevention Control System Based on Computer Data Analysis
  10. Research on the Application of Relationship Graphs in Data Analysis Algorithm Design
  11. Persistence Landscape-based Topological Data Analysis for Personalized Arrhythmia Classification
  12. Traffic Data Analysis and Forecasting
  13. Design of Distributed Timing Job Scheduling System for Data Analysis Platform
  14. Computer-Assisted Qualitative Data Analysis in the Healthcare Cold Chain
  15. Economic data analysis and intelligent prediction based on intelligent matching
  16. Data Analysis for Machine Sound Detection: Challenges, Methods, and Future Trends
  17. Development of Data Analysis and Dump System for Harmonious High-power Diesel Locomotive
  18. Distributed Data Multi-Level Storage Encryption Method Based on Full-Flow Big Data Analysis
  19. Exploratory Data Analysis: An Analysis on Geotagged Twitter COVID Data
  20. A Robust Warranty Data Analysis Method Using Data Science Techniques
  21. VALS: Supporting Visual Data Analysis in Longitudinal Clinical Studies
  22. VALS: Supporting Visual Data Analysis in Longitudinal Clinical Studies
  23. Study on Exploratory Data Analysis Applied to Education
  24. Design and Implementation of Neurology Medical Data Analysis System
  25. News Data Analysis System Based on UML and Computer Aided Technology
  26. A Novel Method for Multi-subject fMRI Data Analysis: Independent Component Analysis with Clustering Embedded (ICA-CE)
  27. Research on ship data analysis based on Spark platform
  28. A Predictive Model for Road Traffic Data Analysis and Visualization to Detect Accident Zones
  29. Preprocessing Network Traffic using Topological Data Analysis for Data Poisoning Detection
  30. Exploratory Data Analysis in Wind Energy Datasets
  31. Exploratory Data Analysis of WhatsApp group chat
  32. Cyber Threat Analysis Using Pearson and Spearman Correlation Via Exploratory Data Analysis
  33. A K-Means Clustering Algorithm for Data Analysis of Wearable Equipment of Construction Personnel
  34. A Personalized Low-Rank Subspace Clustering Method Based on Locality and Similarity Constraints for scRNA-seq Data Analysis
  35. Application of Intelligent Algorithms in Data Analysis of Financial Sharing System
  36. Research on Ship AIS Data Analysis Based on Stream Computing and Virtual Fence
  37. Student Data Analysis using Hadoop
  38. Technical Briefing on Socio-Technical Grounded Theory for Qualitative Data Analysis
  39. Accounting Resource Sharing Management System Based on Data Analysis Algorithms
  40. The Influence of Visual Provenance Representations on Strategies in a Collaborative Hand-off Data Analysis Scenario
  41. A Statistical Assessment of Zener Diode Behavior Using Functional Data Analysis
  42. The Role of Exploratory Data Analysis and Pre-processing in the Machine Learning Predictive Model for Heart Disease
  43. IPL Data Analysis and Visualization for Team Selection and Profit Strategy
  44. Modeling and Classification of EV Charging Profiles Utilizing Topological Data Analysis
  45. Quality Anomaly Detection Using Predictive Techniques: An Extensive Big Data Quality Framework for Reliable Data Analysis
  46. Intelligent Scheduling Algorithm of Enterprise Human Resources Based on Data Analysis
  47. Multi-viewpoints based Visual methods for Efficient Exploratory Data Analysis of Current Events and Trends
  48. Learning from Product Warranty Field Data Analysis
  49. Polyphony: an Interactive Transfer Learning Framework for Single-Cell Data Analysis
  50. An Algorithm Based on Topological Data Analysis for Solving Unsupervised Machine Learning Problems