Mohammed MubarkootTechnology Management, Economics and Policy, Seoul National University, Seoul, Korea
Understanding and interpreting user behavior while interacting with e-services is fundamental to improving service design, delivery efficiency, and customer experience. Traditional web analytics systems provide descriptive insights but often fail to capture the dynamic and context-specific patterns of user interaction. This study proposes a conceptual framework that combines machine learning (ML) with web analytics to transform behavioral data into actionable intelligence for optimizing e-service delivery. The proposed model enables user clustering, anomaly detection, and adaptive content localization through continuous learning mechanisms. By leveraging ML, organizations can move beyond static dashboards toward intelligent, predictive analytics that inform content strategy, enhance personalization, and optimize computing resources. The paper contributes to both academic research and managerial practice by articulating how ML-enhanced web analytics can bridge the gap between data abundance and strategic action in digital ecosystems.
Web Analytics, Machine Learning, E-Service Delivery, User Behavior Analysis, Intelligent Content, Service Optimization
Shiquan Piao,
Division of AI, Big data and Block chain, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Korea
Aerial acoustic communication is a low-rate data exchange technology that transmits and receives information using audible or inaudible acoustic waves. Although Near Field Communication (NFC) has received considerable attention in recent years, its actual adoption among mobile users remains relatively low due to hardware availability and platform restrictions. In contrast, aerial acoustic communication can operate on virtually all smart devices, allowing phones to exchange information through acoustic waves without relying on specialized short-range communication hardware. This makes it an attractive complementary technology to existing wireless communication methods. A major advantage of this approach is that it requires no modification to existing hardware, since standard microphones and speakers can be used for both transmission and reception. However, conventional devices primarily support the audible frequency range, and most of this range overlaps with human speech and ambient sounds, which can cause interference. As a result, only a narrow frequency band can be reliably used for communication. To overcome this limitation, the proposed method employs a frequency band that is supported by common smart devices yet lies in a region minimally affected by human speech and everyday acoustic environments, thereby improving communication stability. To further enhance robustness and frequency resolution in this constrained spectrum, this paper introduces a narrow-band aerial acoustic communication technique based on Zoom FFT. By leveraging its high- resolution spectral analysis capabilities, the system can accurately extract communication signals even in noisy indoor environments, making it suitable for practical short-range data exchange applications.
Acoustic Communication, FFT, Zoom FFT, NFC
