Perbandingan Metode CBR dan Dempster-Shafer pada Sistem Pakar Terintegrasi Layanan Kesehatan

Infectious disease is a very dangerous disease with a high mortality rate. Delays in handling the spread of an infectious disease can be minimized using an expert system. This study uses an expert system as a disease consulting service that is integrated with the health care system. Integration with the health care system is used for the knowledge acquisition process. The knowledge base on the expert system uses patient medical record data obtained through the health care system. The expert system can diagnose infectious diseases of sore throat (Pharyngitis), diphtheria, dengue fever, Typhoid fever, tuberculosis, and leprosy. The knowledge acquisition process produces 43 symptoms. These symptoms are used to diagnose new cases using Case-Based Reasoning (CBR) and Dempster-Shafer methods. In the CBR method, the similarity measurement process is determined by comparing the K-Nearest Neighbor, Minkowski Distance, and 3W-Jaccard similarity measurement methods. The expert system obtains accuracy values ​​for the CBR K-Nearest Neighbor, CBR Minkowski Distance, and CBR 3W-Jaccard methods at a threshold of 70%, respectively 65.71%, 80%, and 85.71%. The average length of retrieve time required for each similarity method is 0.083s, 0.107s, and 6.325s, respectively. While the diagnosis of disease with Dempster-Shafer gets an accuracy value of 88.57%.

Measuring Patient Similarity on Multiple Diseases by Joint Learning via a Convolutional Neural Network

Patient similarity research is one of the most fundamental tasks in healthcare, helping to make decisions without incurring additional time and costs in clinical practices. Patient similarity can also apply to various medical fields, such as cohort analysis and personalized treatment recommendations. Because of this importance, patient similarity measurement studies are actively being conducted. However, medical data have complex, irregular, and sequential characteristics, making it challenging to measure similarity. Therefore, measuring accurate similarity is a significant problem. Existing similarity measurement studies use supervised learning to calculate the similarity between patients, with similarity measurement studies conducted only on one specific disease. However, it is not realistic to consider only one kind of disease, because other conditions usually accompany it; a study to measure similarity with multiple diseases is needed. This research proposes a convolution neural network-based model that jointly combines feature learning and similarity learning to define similarity in patients with multiple diseases. We used the cohort data from the National Health Insurance Sharing Service of Korea for the experiment. Experimental results verify that the proposed model has outstanding performance when compared to other existing models for measuring multiple-disease patient similarity.

IMAGE RETRIEVAL BASED ON DISCRETE CURVELET TRANSFORM

Content-Based Image Retrieval (CBIR) is a process of searching for an image according to the content or feature that is within it. Nowadays, most image retrieval applications have been developed to meet these needs, so this application will provide comfort in introducing and searching for an image. This paper proposed a standard structured framework with three stages: Preprocessing is the first step, in which noise from images is removed using various filters. The filters' results are compared to determine the best and most appropriate filter for the images. Feature Extraction of images using Curvelet Transform is the second stage. The third stage includes similarity measurement between query image features to database image features and extracting the identical image from the image dataset. The system was performed using Matlab 2017b, GUI and, with ten different classes of 1000 images using a coral database. The results show improved performance of precision and recall when higher decomposition levels are used.

EVALUATION METHODOLOGY ON TRAJECTORY OF INBOUND SINGLE SHIP USING SIMILARITY MEASUREMENT BETWEEN PLANAR CLOUDS

In order to evaluate the ship trajectory more reasonable based on the quantitative information. This paper presents a new approach to evaluate the inward-port single ship trajectory quantitatively based on ship-handling simulator. First, a ship tracking points generating algorithm is proposed to generate sufficient tracking points in order to address the issue that the sample information is not enough on the ship simulator. Second, three reference tracking belts are established based on the sample data and cloud drop contribution degrees for the scenario that the collected samples information are enough. Finally, a quantitative score evaluation method that combines the qualitative information and the quantitative information is proposed, the similarity measurement results verify that the MES algorithm is more reasonable, the evaluation results of inward-port single ship trajectory illustrative that the proposed method is effective when applied to quantitative evaluation problems.

Knowledge Service Technology for Supporting Intelligent Product Design

In this paper, a knowledge service method that supports the intelligent design of products is investigated. The proposed method provides the solutions to computational problems and reasoning and decision-making problems in the field of intelligent design. The requirement analysis of a knowledge-based intelligent design system integrates design knowledge into case-based reasoning activities through scheme analysis, scheme evaluation, and scheme adjustment, thus achieving knowledge-based intelligent reasoning and decision-making. During the similarity matching, a new hybrid similarity measurement method is proposed to calculate the similarity of crisp and fuzzy sets. This method integrates the fuzzy set similarity theory based on the traditional similarity measurement method. A method of attribute level classification is proposed to assign weight coefficients. The attributes are divided into the primary matching and auxiliary matching levels according to the decisiveness of case matching, and the set of weight coefficients is continuously and dynamically updated through case-based reasoning learning. Then, the weighted global similarity measure is used to obtain the set of similar cases from the case database. Finally, a design example of a computer numerical control tool holder product is studied to present the practicability and effectiveness of the proposed method.