AI-Based Techniques For Data Processing

Authors: Akmal Yusof

Abstract: Artificial intelligence (AI)-based techniques for data processing have emerged as a transformative approach for handling large-scale, complex, and heterogeneous datasets in modern computing environments. With the exponential growth of data generated from sources such as social media, IoT devices, enterprise systems, and cloud platforms, traditional data processing methods are often insufficient in terms of scalability, speed, and accuracy. This paper explores the role of AI techniques, including machine learning, deep learning, natural language processing, and intelligent data mining, in enhancing data preprocessing, transformation, analysis, and decision-making. It highlights how AI-driven methods improve data quality through noise reduction, missing value handling, feature extraction, and anomaly detection. The study also examines the integration of AI with cloud computing and big data frameworks to enable real-time and distributed data processing. Furthermore, it discusses applications across various domains such as healthcare, finance, cybersecurity, and smart systems. Key challenges including data privacy, computational complexity, and model interpretability are also addressed along with emerging solutions like federated learning and edge AI. The findings emphasize that AI-based data processing significantly enhances efficiency, accuracy, and scalability in modern data-driven systems.

DOI: https://doi.org/10.5281/zenodo.20283200