Mu-hong Min

Gesture recognition in dynamic images is challenging in computer vision, automation and medical field. Hand gesture tracking and recognition between both human and computer must have symmetry in real world. With advances in sensor technology, numerous researchers have recently proposed RGB gesture recognition techniques. In our research paper, we introduce a reliable hand gesture tracking and recognition model that is accurate despite any complex environment, it can track and recognise RGB dynam

Autonomous vehicle detection and tracking are crucial for intelligent transportation management and control systems. Although many techniques are used to develop smart traffic systems, this article discusses vehicle detection and tracking using pixel-labeling and real-time tracking. We propose a novel smart traffic control system that segments the image using an Extreme Gradient Boost (XGBoost) classifier to extract the foreground objects. The proposed model is divided into the following steps:

Regulatory compliance in the pharmaceutical industry involves navigating complex and voluminous guidelines, often requiring significant amounts of human resources. Recent advancements in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) methods provide promising enhancements to data processing and knowledge management, potentially easing these burdens. However, despite these advancements, conventional Retrieval-Augmented Generation (RAG) methods fall short in this domain due

Despite the extensive ramifications of illegal online gambling (IOG) services, actions taken by government authorities have had little effect in halting these operations. In order to reduce the prevalence of IOG, the ability to detect malicious uniform resource locators (URLs) is crucial. Text mining and binary classification have been widely adopted to detect and prevent spam short message services (SMSs), but government authorities and various task forces that monitor and regulate gambling als

The emergence of illegal online gambling (IOG) has led to an increase in gambling addiction and threats to cyber security. Since IOG is advertised through short message service (SMS), we propose a novel system to detect and extract uniform resource locator (URL) information from SMS spam. For majority of the cases, these URLs are not directly linked to a real website address, which causes difficulties for detection systems. In order to address this problem, we utilized a readable transformation

During the past decade, the technological advancement have allowed the gambling industry worldwide to deploy various platforms such as the web and mobile applications. Government agencies and local authorities have placed strict regulations regarding the location and amount allowed for gambling. These efforts are made to prevent gambling addictions and monitor fraudulent activities. The revenue earned from gambling provides a considerable amount of tax revenue. The inception of internet gambling

Nowadays, social network services, such as Twitter and Facebook, are widely being exploited. Many people have participated in social network services to enjoy the communication with their friends and to extend personal social networks. Since it is easy to extend their relations to others, people usually have a large acquaintance list in social network services. In the lists, various users, not only friends but also unfamiliar users such as celebrities, news media, or even unknown users, can be i

With the development of Web search engines, it is considered as an important task to provide retrieved documents in a proper manner. Many search engines have used various document ranking algorithms to provide their retrieved documents in a more efficient way for users. However, even though a good algorithm is used, there are some limitations if they do not consider the characteristic of queries which is diverse depending on user intention or interest. Even if a user searches documents with the

We propose a Long Short-Term Memory(LSTM)-based autoencoder model for multivariate time series anomaly detection that incorporates contrastive learning tailored to time-series characteristics. By leveraging contrastive representation learning, the model effectively pulls normal data closer to the original representation while pushing anomalous data further away, enhancing detection performance. To generate positive and negative pairs, the model applies time series-specific augmentations by sampl