Currently, the number of surveillance cameras is rapidly increasing responding to security issues. But constructing an intelligent detection system is not easy because it needs high computing performance. This study aims to construct a real-world video surveillance system that can effectively detect moving person using limited resources. To this end, we propose a simple framework to detect and recognize moving objects using outdoor CCTV video footages by combining background subtraction and Convolutional Neural Networks (CNNs). A background subtraction algorithm is first applied to each video frame to find the regions of interest (ROIs). A CNN classification is then carried out to classify the obtained ROIs into one of the predefined classes. Our approach much reduces the computation complexity in comparison to other object detection algorithms. For the experiments, new datasets are constructed by filming alleys and playgrounds, places where crimes are likely to occur. Different image sizes and experimental settings are tested to construct the best classifier for detecting people. The best classification accuracy of 0.85 was obtained for a test set from the same camera with training set and 0.82 with different cameras.

Background: Accurate medical advice is paramount in ensuring optimal patient care, and misinformation can lead to misguided decisions with potentially detrimental health outcomes. The emergence of large language models (LLMs) such as OpenAI’s GPT-4 has spurred interest in their potential health care applications, particularly in automated medical consultation. Yet, rigorous investigations comparing their performance to human experts remain sparse. Objective: This study aims to compare the medical accuracy of GPT-4 with human experts in providing medical advice using real-world user-generated queries, with a specific focus on cardiology. It also sought to analyze the performance of GPT-4 and human experts in specific question categories, including drug or medication information and preliminary diagnoses. Methods: We collected 251 pairs of cardiology-specific questions from general users and answers from human experts via an internet portal. GPT-4 was tasked with generating responses to the same questions. Three independent cardiologists (SL, JHK, and JJC) evaluated the answers provided by both human experts and GPT-4. Using a computer interface, each evaluator compared the pairs and determined which answer was superior, and they quantitatively measured the clarity and complexity of the questions as well as the accuracy and appropriateness of the responses, applying a 3-tiered grading scale (low, medium, and high). Furthermore, a linguistic analysis was conducted to compare the length and vocabulary diversity of the responses using word count and type-token ratio. Results: GPT-4 and human experts displayed comparable efficacy in medical accuracy (“GPT-4 is better” at 132/251, 52.6% vs “Human expert is better” at 119/251, 47.4%). In accuracy level categorization, humans had more high-accuracy responses than GPT-4 (50/237, 21.1% vs 30/238, 12.6%) but also a greater proportion of low-accuracy responses (11/237, 4.6% vs 1/238, 0.4%; P=.001). GPT-4 responses were generally longer and used a less diverse vocabulary than those of human experts, potentially enhancing their comprehensibility for general users (sentence count: mean 10.9, SD 4.2 vs mean 5.9, SD 3.7; P<.001; type-token ratio: mean 0.69, SD 0.07 vs mean 0.79, SD 0.09; P<.001). Nevertheless, human experts outperformed GPT-4 in specific question categories, notably those related to drug or medication information and preliminary diagnoses. These findings highlight the limitations of GPT-4 in providing advice based on clinical experience. Conclusions: GPT-4 has shown promising potential in automated medical consultation, with comparable medical accuracy to human experts. However, challenges remain particularly in the realm of nuanced clinical judgment. Future improvements in LLMs may require the integration of specific clinical reasoning pathways and regulatory oversight for safe use. Further research is needed to understand the full potential of LLMs across various medical specialties and conditions.

Background: While clinical entity recognition mostly aims at electronic health records (EHRs), there are also the demands of dealing with the other type of text data. Automatic medical diagnosis is an example of new applications using a different data source. In this work, we are interested in extracting Korean clinical entities from a new medical dataset, which is completely different from EHRs. The dataset is collected from an online QA site for medical diagnosis. Bidirectional Encoder Representations from Transformers (BERT), which is one of the best language representation models, is used to extract the entities.Results: A slightly modified version of BERT labeling strategy replaces the original labeling to enhance the separation of postpositions in Korean. A new clinical entity recognition dataset that we construct, as well as a standard NER dataset, have been used for the experiments. A pre-trained multilingual BERT model is used for the initialization of the entity recognition model. BERT significantly outperforms a character-level bidirectional LSTM-CRF, a benchmark model, in terms of all metrics. The micro-averaged precision, recall, and f1 of BERT are 0.83, 0.85 and 0.84, whereas that of bi-LSTM-CRF are 0.82, 0.79 and 0.81 respectively. The recall values of BERT are especially better than that of the other model. It can be interpreted that the trained BERT model could detect out of vocabulary (OOV) words better than bi-LSTM-CRF.Conclusions: The recently developed BERT and its WordPiece tokenization are effective for the Korean clinical entity recognition. The experiments using a new dataset constructed for the purpose and a standard NER dataset show the superiority of BERT compared to a state-of-the-art method. To the best of our knowledge, this work is one of the first studies dealing with clinical entity extraction from non-EHR data.

The present work proposes a holonic-based mechanism for self-learning factories based on a hybrid learning approach. The self-learning factory is a manufacturing system that gains predictive capability by machine self-learning, and thus automatically anticipates the performance results during the process planning phase through learning from past experience. The system mechanism, including a modeling method, architecture, and operational procedure, is structured to agentize machines and manufacturing objects under the paradigm of Holonic Manufacturing Systems. This mechanism allows machines and manufacturing objects to acquire their data and model interconnection and to perform model-driven autonomous and collaborative behaviors. The hybrid learning approach is designed to obtain predictive modeling ability in both data-existent and even data-absent environments via accommodating machine learning (which extracts knowledge from data) and transfer learning (which extracts knowledge from existing knowledge). The present work also implements a prototype system to demonstrate automatic predictive modeling and autonomous process planning for energy reduction in milling processes. The prototype generates energy-predictive models via hybrid learning and seeks the minimum energy-using machine tool through the contract net protocol combined with energy prediction. As a result, the prototype could achieve a reduction of 9.70% with respect to energy consumption as compared with the maximum energy-using machine tool.

Deep neural networks have become a standard framework for image analytics. Besides the traditional applications, such as object classification and detection, the latest studies have started to expand the scope of the applications to include artworks. However, popular art forms, such as comics, have been ignored in this trend. This study investigates visual features for comic classification using deep neural networks. An effective input format for comic classification is first defined, and a convolutional neural network is used to classify comic images into eight different artist categories. Using a publicly available dataset, the trained model obtains a mean F1 score of 84% for the classification. A feature visualization technique is also applied to the trained classifier, to verify the internal visual characteristics that succeed in classification. The experimental result shows that the visualized features are significantly different from those of general object classification. This work represents one of the first attempts to examine the visual characteristics of comics using feature visualization, in terms of comic author classification with deep neural networks.