Synergistic News Aggregation Paradigm Utilizing Autonomous Web Extraction and Computational Intelligence:

Pratheeve R. Babin Hindusthan College of Arts and Science, India A. V. Senthil Kumar Hindusthan College of Arts and Science, India https://orcid.org/0000-0002-8587-7017 Ismail Bin Musirin Universiti Teknologi Mara, Malaysia Anto Lourdu Xavier Raj Arockia Selvarathinam Grand Valley State University, USA https://orcid.org/0009-0007-3389-031X R. V. Suganya Vels Institute of Science, Technology, and Advanced Studies, India Adnan Ibrahim Alrabea Al-Balqa Applied University, Al-Salt, Jordan Sreelakshmi Kata Madanapalle Institute of Technology and Science, India https://orcid.org/0000-0001-7164-5955 Kalpana Sukumar Saveetha Engineering College, India Neeru Sidana Amity University, India Aparna Kumari Nirma University, India https://orcid.org/0000-0001-5991-6193 Bhavanam S. Nagakishore Mangalayatan University, India https://orcid.org/0000-0003-3798-6945 R. Harsha RNS Institute of Technology, India https://orcid.org/0009-0009-0956-3014 Synergistic News Aggregation Paradigm Utilizing Autonomous Web Extraction and Computational Intelligence

The exponential growth of digital news sources has revolutionized the way information is consumed, but it has also introduced significant challenges, such as information overload, data redundancy, misinformation, and biased reporting. To address these issues, this chapter proposes a Synergistic News Aggregation Paradigm Utilizing Autonomous Web Extraction and Computational Intelligence. This system leverages advanced technologies like artificial intelligence (AI), natural language processing (NLP), and machine learning (ML) to autonomously extract, filter, and deliver personalized, real-time news content from diverse online sources. The proposed framework integrates autonomous web extraction techniques for real-time data collection with computational intelligence models for content analysis, sentiment detection, and context-based classification. It ensures the credibility and relevance of aggregated news while eliminating data redundancy through sophisticated clustering and summarization methods.
chapter 7 8 15 2025 177 212 10.4018/979-8-3373-2352-7.ch007 20251114042748 https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/979-8-3373-2352-7.ch007 https://www.igi-global.com/viewtitle.aspx?TitleId=388639