Hayoung Oh
Sungkyunkwan University · 情報科学
研究室紹介
Professor Hayoung Oh's research lab specializes in energy-efficient and intelligent systems for emerging ubiquitous environments, with a strong focus on wireless sensor networks, spam detection in online platforms, and automated analysis of user-generated content. The lab develops advanced routing and data transmission protocols to enhance energy efficiency and low-latency communication in resource-constrained networks, while also applying machine learning and clustering techniques to extract meaningful insights from large-scale review and comment data. Additionally, the lab explores seamless mobility solutions in IPv6-based mobile networks to improve handover performance and user experience. These interdisciplinary efforts bridge networking, data analytics, and real-world applications in healthcare and e-commerce.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15In the emerging ubiquitous home, sensors are placed everywhere in the house and collect various physical data such as temperature, humidity, and light to provide information to consumer electronics devices. The devices are then automatically activated if necessary. For example, the ventilator works when the air is foul and the heating system performs according to the weather and the existence of people in the house. Because sensors have limited battery power, energy-efficient routing is importan
This paper proposes a technique to detect spam comments on YouTube, which have recently seen tremendous growth. YouTube is running its own spam blocking system but continues to fail to block them properly. Therefore, we examined related studies on YouTube spam comment screening and conducted classification experiments with six different machine learning techniques (Decision tree, Logistic regression, Bernoulli Naïve Bayes, Random Forest, Support vector machine with linear kernel, Support vector
Energy efficiency, low latency, scalability are important requirements for wireless sensor networks. Specially, because sensor nodes are usually battery powered, and highly resource constrained, energy- efficient routing sensor routing scheme with low latency, scalability in wireless sensor networks is very important. In this paper, we present a sensor routing scheme, EESR (energy-efficient sensor routing) that provides energy-efficient data delivery from sensors to the base station. The propose
The feedback shared by consumers on e-commerce platforms holds immense value in marketing, as it offers insights into their opinions and preferences, which are readily accessible. However, analyzing a large volume of reviews manually is impractical. Therefore, automating the extraction of essential insights from these data can provide more comprehensive and efficient information. This research focuses on leveraging clustering algorithms to automate the extraction of consumer intentions, related
Data suggest that HCMV antigenemia titer can be used as a useful guide to preemptive treatment of HCMV infection after kidney transplantation in HCMV-positive donor and recipient.
With the rapid development of wireless technologies, the need to support moving hosts of IPv6-based mobile networks in ubiquitous is growing. Various well-known approaches to optimising the handover latency have been proposed in the literature: MIPv6, FMIPv6, HMIPv6 and F-HMIPv6. However, these approaches are inefficient in packet loss, out-of order problem, dependency of exact predictive information and a sudden disruption of the link. In this paper, we propose a seamless handover scheme with t
Detecting network intrusion has been not only important but also difficult in the network security research area. In Medical Sensor Network(MSN), network intrusion is critical because the data delivered through network is directly related to patients’ lives. Traditional supervised learning techniques are not appropriate to detect anomalous behaviors and new attacks because of temporal changes in network intrusion patterns and characteristics in MSN. Therefore, unsupervised learning techniques su
H. Y. Oh, M. S. Yang; Nucleotide Sequence of Genomic DNA Encoding the Potato [beta]-1,3-Glucanase, Plant Physiology, Volume 107, Issue 4, 1 April 1995, Pag
This paper introduces the Are u Depressed (AuD) model, which aims to detect depressive emotional intensity and classify detailed depressive symptoms expressed in user utterances. The study includes the creation of a BWS dataset using a tool for the Best-Worst Scaling annotation task and a DSM-5 dataset containing nine types of depression annotations based on major depressive disorder (MDD) episodes in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). The proposed model employs t
Chronic renal failure (CRF) is associated with a sundry of abnormalities in pancreatic islets including a rise in their cytosolic calcium, reduced ATP content, and impaired glucose-induced insulin secretion. The latter is also stimulated by amino acids (such as leucine), and the cellular processes involved in leucine-induced insulin secretion are different from those responsible for glucose-induced insulin release. The present study examined whether leucine-induced insulin secretion is also impa
Wireless Sensor Network (WSN) is a very attractive technique to support Ubiquitous Pervasive Computing(UPC) such as Medical Sensor Network (MSN) due to many merits such as compact form, low-power, and potential low cost. However, the more the sensor nodes exist, the more difficult the manager monitors sensor nodes individually, and faults of the sensing value are common due to the artificial attackers and the lack of physical protection. Therefore, we propose a key management and distributed abn
Detecting network intrusion has been not only critical but also difficult in the network security research area. Traditional supervised learning techniques are not appropriate to detect anomalous behaviors and new attacks because of temporal changes in network intrusion patterns and characteristics. Therefore, unsupervised learning techniques such as SOM (self-organizing map) are more appropriate for anomaly detection. In this paper, we proposed a real-time intrusion detection system based on SO
Due to the structural growth of e-commerce platforms, the frequency of exchange of opinions and the number of online reviews of platform participants related to products are increasing. However, given the growth of fake reviews, the corresponding growth in the quality of online reviews seems to be slow, at best. The number of cases of harm to retailers and customers caused by malicious false reviews is steadily increasing every year. In this context, it is becoming difficult for users to determi
With the advantage of practical way to experiment with new network protocols in realistic settings, NOX and OpenFlow switch networks are becoming extremely popular. However, because of basic characteristics of NOX and OpenFlow switch based on the separation between control and data plane, every OpenFlow switch faces a long transmission and retransmission delay when it fails to transmit its data. Until now, the virtualized programmable networks only consider how to achieve the throughput for the
Network coding is a promising technology that increases system throughput by reducing the number of packet transmissions from the source node to the destination node in a saturated traffic scenario. Nevertheless, some packets can suffer from end-to-end delay, because of a queuing delay in an intermediate node waiting for other packets to be encoded with exclusive or (XOR). In this paper, we analyze the delay according to packet arrival rate and propose two network coding schemes, iXOR (Intellige