Integrating Machine Learning and Neural Approaches for Predictive and Analytical Tasks: A Comprehensive Review

Hadia Khalid; Bushra Khan; Mahnoor Tunio; Sobia Soomro

Authors

Hadia Khalid Department of Computer Science, Quaid-e-Awam University of Engineering Science & Technology, Nawabshah, Sindh
Bushra Khan Department of Computer Science, Quaid-e-Awam University of Engineering Science & Technology, Nawabshah, Sindh
Mahnoor Tunio Department of Computer Science, Quaid-e-Awam University of Engineering Science & Technology, Nawabshah, Sindh
Sobia Soomro Department of Computer Science, Quaid-e-Awam University of Engineering Science & Technology, Nawabshah, Sindh

Keywords:

Machine Learning (ML), Deep Learning, Natural Language Processing, Artificial Neural Network (ANN), Predictive Analytics, Comparative Analysis

Abstract

This study aims to review and analyze recent research on Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and Artificial Neural Networks (ANN) for predictive and analytical tasks across domains such as education, healthcare, software engineering, and materials science. The paper examines the methodologies, tools, and performance metrics of these techniques by analyzing seven recent studies. A comparative analysis is conducted using key evaluation parameters, including accuracy, precision, recall, F1-score, and efficiency. The findings indicate that Deep Learning and ANN models demonstrate higher predictive accuracy in complex analytical problems, particularly in image-based and engineering applications. NLP methods show strong performance in text processing and language-related tasks, while ML approaches are effective for structured data analysis and decision-making applications. The review highlights the strengths and limitations of each approach and identifies potential directions for future research in integrating these techniques for improved predictive performance.

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