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Vol. 2, Issue 2

July-December 2024

Volume 2 · 2024

AI Tech International Journal (AITIJ) (ISSN: 3079-4749) invites researchers, scholars, industry experts, and practitioners worldwide to submit original research papers, reviews, and case studies for its upcoming issue, July-December, 2024. As a peer-reviewed, open-access publication based in Hong Kong, AITIJ provides a global platform to showcase high-quality theoretical advancements and real-world applications in artificial intelligence. We welcome multidisciplinary submissions across machine learning, natural language processing, computer vision, robotics, AI ethics, IoT, and big data analytics. All accepted articles receive a Crossref DOI, indexed listing in Google Scholar, and are published on a continuous online basis following a rigorous double-blind peer-review process.

Table of Contents

Article Pages Full text
The Future of Speech Recognition: Trends and Innovations
Dr. Clara Johnson
DOI: https://doi.org/10.64180/oct.techai.240201
1-7 Download PDF
Enhancing Network Security with AI-Based Threat Hunting Algorithms
Dr. Bilal Abbas
DOI: https://doi.org/10.64180/oct.techai.240202
8-15 Download PDF
Exploring the Synergy between Blockchain and AI for Supply Chain Transparency
Dr. Elena Petrova
DOI: https://doi.org/10.64180/oct.techai.240203
16-22 Download PDF
AI-Enabled Multi-Cloud Strategies: Balancing Costs and Performance
Dr. Diego López
DOI: https://doi.org/10.64180/oct.techai.240204
23-30 Download PDF
AI-Enhanced Cyber security: Proactive Threat Detection Using Deep Learning
Dr. Claire Davidson
DOI: https://doi.org/10.64180/oct.techai.240205
31-41 Download PDF
From Infodemic to Informed Choice: An International, Human-Governed and AI-Enabled Framework for Health Information Verification on Social Media
Dr. Kuhoo Gaur
DOI: https://doi.org/10.64180/oct.techai.240206
42-46 Download PDF
Retrieval-Augmented Generation in Enterprise Knowledge Systems
Bhaskar Babu Narasimhaiah
DOI: https://doi.org/10.64180/oct.techai.240207
47-58 Download PDF
AIOps-Driven Incident Response: Using LLMs for Root Cause & Runbook Discovery
Naveen Anne
DOI: https://doi.org/10.64180/oct.techai.240208
59-70 Download PDF
Digital Twin Models for Simulating and Optimizing Enterprise Data Pipeline Performance
Sarvesh Kumar Gupta
DOI: https://doi.org/10.64180/oct.techai.240209
71-82 Download PDF