Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/592583
Title: A novel methodology for mining and analyzing heterogeneous big data towards precise prediction using deep neural networks
Researcher: Maria Michael Visuwasam L
Guide(s): Paulraj D
Keywords: Big Data
Data Mining.
Deep Neural Networks
University: Anna University
Completed Date: 2024
Abstract: The proliferation of digital technologies and big data has led to the newlinecreation of vast amounts of diverse and complex data. Extracting valuable newlineinsights and making precise predictions from this data presents significant newlinechallenges. This work proposes a novel approach for mining and analysing newlineheterogeneous big data using deep neural networks. Heterogeneity, which newlinerefers to the variety of data types, formats, structures, and features within the newlinedataset, is a primary limitation in big data mining. The integration and newlineharmonisation of data from various sources, including sensors, social media newlineplatforms, and transactional databases, is a significant challenge. Integrating newlineand effectively utilising unstructured data types (e.g., reviews, social media newlinesentiment) alongside structured data (e.g., booking records) can be technically newlinechallenging and resource-intensive. The quality of the data is also a newlinesignificant issue, with potential noise, missing values, outliers, or newlineinconsistencies. To address this, a combination of sophisticated data newlineintegration methods, feature engineering techniques, scalable algorithms, and newlineappropriate analytical models is required. The proposed approach achieves newlineprecise prediction with significantly higher accuracy than existing data newlinemining and predictive modelling techniques. This research investigates novel newlinearchitectures that enhance interpretability while maintaining predictive newlineperformance and develop scalable methodologies that can handle diverse data newlinetypes efficiently while focusing on creating robust preprocessing techniques newlineand addressing domain-specific challenges. This research has the potential to newlineempower various domains including tourism, healthcare, finance, and social newlinemedia by enabling them to leverage the full potential of heterogeneous big newlinedata for precise decision-making and forecasting. newline
Pagination: xiv,137p.
URI: http://hdl.handle.net/10603/592583
Appears in Departments:Faculty of Information and Communication Engineering

Files in This Item:
File Description SizeFormat 
01_title.pdfAttached File258.96 kBAdobe PDFView/Open
02_prelim_pages.pdf2.4 MBAdobe PDFView/Open
03_contents.pdf240.62 kBAdobe PDFView/Open
04_abstracts.pdf230.67 kBAdobe PDFView/Open
05_chapter1.pdf283.97 kBAdobe PDFView/Open
06_chapter2.pdf373.66 kBAdobe PDFView/Open
07_chapter3.pdf429.4 kBAdobe PDFView/Open
08_chapter4.pdf362.86 kBAdobe PDFView/Open
09_chapter5.pdf555.12 kBAdobe PDFView/Open
10_chapter6.pdf363.08 kBAdobe PDFView/Open
11_annexures.pdf138.58 kBAdobe PDFView/Open
80_recommendation.pdf174.65 kBAdobe PDFView/Open
Show full item record


Items in Shodhganga are licensed under Creative Commons Licence Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).

Altmetric Badge: