Byoungchol Chang 교수

COVID-19 detection and classification using chest X-ray images is a current hot research topic based on the important application known as medical image analysis. To halt the spread of COVID-19, it is critical to identify the infection as soon as possible. Due to time constraints and the expertise of radiologists, manually diagnosing this infection from chest X-ray images is a difficult and time-consuming process. Artificial intelligence techniques have had a significant impact on medical image

In the last few years, artificial intelligence has shown a lot of promise in the medical domain for the diagnosis and classification of human infections. Several computerized techniques based on artificial intelligence (AI) have been introduced in the literature for gastrointestinal (GIT) diseases such as ulcer, bleeding, polyp, and a few others. Manual diagnosis of these infections is time consuming, expensive, and always requires an expert. As a result, computerized methods that can assist doc

Tumor detection has been an active research topic in recent years due to the high mortality rate. Computer vision (CV) and image processing techniques have recently become popular for detecting tumors in MRI images. The automated detection process is simpler and takes less time than manual processing. In addition, the difference in the expanding shape of brain tumor tissues complicates and complicates tumor detection for clinicians. We proposed a new framework for tumor detection as well as tumo

In this paper, a novel multistep ahead predictor based upon a fusion of kernel recursive least square (KRLS) and Gaussian process regression (GPR) is proposed for the accurate prediction of the state of health (SoH) and remaining useful life (RUL) of lithium-ion batteries. The empirical mode decomposition is utilized to divide the battery capacity into local regeneration (intrinsic mode functions) and global degradation (residual). The KRLS and GPR submodels are employed to track the residual an

Identifying fruit disease manually is time-consuming, expert-required, and expensive; thus, a computer-based automated system is widely required. Fruit diseases affect not only the quality but also the quantity. As a result, it is possible to detect the disease early on and cure the fruits using computer-based techniques. However, computer-based methods face several challenges, including low contrast, a lack of dataset for training a model, and inappropriate feature extraction for final classifi

This paper describe the case study of developing and providing the ontology-based educational information search service to overcome the limitations of the existing keyword-based search service. We used the OWL-DL 1) to build the core ontology DB from the existing metadata DB represented by the LOM, 2) to build the domain ontology DB of middle school Mathematics contents DB, 3) to merge the core ontology DB and the domain ontology DB. And both the OWL-DL and the SWRL are used to support the educ

Deep learning has significantly contributed to medical imaging and computer-aided diagnosis (CAD), providing accurate disease classification and diagnosis. However, challenges such as inter- and intra-class similarities, class imbalance, and computational inefficiencies due to numerous hyperparameters persist. This study aims to address these challenges by presenting a novel deep-learning framework for classifying and localizing gastrointestinal (GI) diseases from wireless capsule endoscopy (WCE

Breast cancer (BC) is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year. The BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6% of total cases. Early diagnosis and detection of breast cancer disease with proper treatment may reduce the number of deaths. The gold standard for BC detection is biopsy analysis which needs an expert for corre

In this paper we present a methodology for designing competency based Web search framework using ontology to acquire the learning contents that meets the individualspsila competency. We used ontology with OWL-DL to built competency ontology, content ontology and domain ontology to implement competency based contents search system. The basic ontology-based search system, however, lacks the method of ranking the search results. To present more appropriate learning contents for the learner, we appl

For industry 5.0, constructing new large scale systems by using existing and new technologies is the latest research trend; being a combination of hardware and software technologies, these are referred to as hybrid systems. For constructing large and complex systems, the process of incremental and iterative development is popular for many decades. In the component based software development community, using EX-MAN component model (EX-MAN) a new incremental development process is proposed in this

Rapid advancement information and communication technologies has introduced various dimension of e-Learning environment such as u-learning(ubiquitous learning), m-learning(mobile learning) and t-learning(television learning). These technologies enabled learners to access learning contents through variety of devices with more flexibility and consistency. In order to implement learning through these multiple environments, basically it is necessary to acquire and process the platform information th