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Vol. 1 No. 1 (2024): Volume 1 Issue 1 (Inaugural Issue)

Vol. 1 No. 1 (2024): Volume 1 Issue 1 (Inaugural Issue)
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Welcome to the inaugural issue of AI Journal of Business, a platform dedicated to exploring the dynamic intersection of artificial intelligence (AI) and business. As the Founding President of AI Business Academy, the institution behind this journal, I am honored to present our first edition and share the vision that guides this publication.

We are living in an era where artificial intelligence is reshaping industries, redefining business models, and transforming decision-making processes. No longer a distant concept, AI has rapidly evolved into a vital force driving innovation across the global economy. From data-driven insights to automation, from personalized customer experiences to intelligent supply chains, the applications of AI are vast and continue to grow at an unprecedented pace.

However, amid this transformation, it has become clear that there is a need for a dedicated forum where business leaders, academics, and researchers can come together to share knowledge, ideas, and innovations that will drive the future of business. The AI Journal of Business was born from this need—a commitment to providing a premier platform for the exchange of groundbreaking research, case studies, and critical discussions around the role of AI in business.

Published: 2024-11-15
Articles
A Study on Methods for Summarizing Information from YouTube Channels
Hyoung-sik Park
This study explored the possibility of summarizing information from YouTube channels. To achieve this, the research compared and analyzed speech-to-text (STT) data from top-viewed videos and comment data from recent videos. The keyword network...
YouTube Channels Information Summarization STT (Speech-to-Text) Keyword Network Similarity Analysis Text Analysis Comment Analysis Influencer Marketing
5-13
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AI-Driven Stock Prediction and Investment Strategy Analysis for the Magnificent 7 Tech Giants
Jumin Lee
This study aims to predict stock prices and develop optimized investment strategies for the "Magnificent 7" companies using AI. By employing Linear Regression, Random Forest, XGBoost, and LSTM models, the study analyzed stock price data for...
AI Machine Learning Stock Prediction Investment Strategy Magnificent 7 LSTM XGBoost Random Forest Linear Regression
14-25
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Study on the Effect of Data Augmentation on Image Learning
Tae-hwan Choi
T his paper aims to demonstrate the effective use of data in AI research by examining the impact of data augmentation on AI learning. This study utilized a subset of the AI Hub’s Ocean Deposition Garbage Image dataset. The accuracy of image...
AI Data Augmentation Image CNN VGG
26-37
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The Prioritization of Evaluation Indications for Artificial Intelligence (AI) Business Capabilities in the Financial Sector: Focusing on the Global AI Index: Focusing on the Global AI Index
Suho Ahn; Tae Yeon Oh; Yongkyu Kim
The financial sector is actively considering the adoption of artificial intelligence (AI) in various areas. AI technologies are being introduced not only in key areas of finance, such as marketing, product development, credit evaluation, and...
Artificial Intelligent Financial IT Investment AHP Competency Evaluation index Global AI Index
38-51
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Reducing Training Time and Enhancing Efficiency in Large Language Models: Ternary Output Quantization(TOQ)
Do-hoon Lee
This study examines the background of recent advancements in artificial intelligence technology and the need for large-scale language models (LLM) and multimodal models (LMM), proposing the Ternary Output Quantization (TOQ) technique to reduce...
AI Ternary Output Quantization LLM Learning Efficiency Data Processing Minimization
52-59
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A Study on the LLM-Based Software Architecture for Multi Robot Control
Sangho Choi
This paper examines the latest developments in the convergence of robotics and AI, with a particular focus on analyzing key cases of robot control research using large language models (LLMs). Recent AI technologies such as computer vision,...
AI Large Language Models robot control sofrtware architectdure few-shot learning prompt
60-70
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