This paper introduces a multilayer traffic network model and traffic network clustering method for solving the route selection problem (RSP) in car navigation system (CNS). The purpose of the proposed method is to reduce the computation time of route selection substantially with acceptable loss of accuracy by preprocessing the large size traffic network into new network form. The proposed approach further preprocesses the traffic network than the traditional hierarchical network method by clustering method. The traffic network clustering considers two criteria. We specify a genetic clustering algorithm for traffic network clustering and use NSGA-II for calculating the multiple objective Pareto optimal set. The proposed method can overcome the size limitations when solving route selection in CNS. Solutions provided by the proposed algorithm are compared with the optimal solutions to analyze and quantify the loss of accuracy.

Object detection is one of the most important aspects of computer vision, and the use of CNNs for object detection has yielded substantial results in a variety of fields. However, due to the fixed sampling in standard convolution layers, it restricts receptive fields to fixed locations and limits CNNs in geometric transformations. This leads to poor performance of CNNs for slender object detection. In order to achieve better slender object detection accuracy and efficiency, this proposed detector DFAM-DETR not only can adjust the sampling points adaptively, but also enhance the ability to focus on slender object features and extract essential information from global to local on the image through an attention mechanism. This study uses slender objects images from MS-COCO dataset. The experimental results show that DFAM-DETR achieves excellent detection performance on slender objects compared to CNN and transformer-based detectors.

Most existing truth discovery approaches are designed for structured data, and cannot meet the strong need to extract trustworthy information from raw text data for its unique characteristics such as multifactorial property of text answers (i.e., an answer may contain multiple key factors) and the diversity of word usages (i.e., different words may have the same semantic meaning). As for text answers, there are no absolute correctness or errors, most answers may be partially correct, which is quite different from the situation of traditional truth discovery. To solve these challenges, we propose an optimization-based text truth discovery model which jointly groups keywords extracted from the answers of the specific question into a set of multiple factors. Then, we select the subset of multiple factors as identified truth set for each question by parallel ant colony synchronization optimization algorithm. After that, the answers to each question can be ranked based on the similarities between factors answer provided and identified truth factors. The experiment results on real dataset show that though text data structures are complex, our model can still find reliable answers compared with retrieval-based and state-of-the-art approaches.

The multiobjective route selection problem (m-RSP) is a key research topic in the car navigation system (CNS) for ITS (Intelligent Transportation System). In this paper, we propose an interactive multistage weight-based Dijkstra genetic algorithm (mwD-GA) to solve it. The purpose of the proposed approach is to create enough Pareto-optimal routes with good distribution for the car driver depending on his/her preference. At the same time, the routes can be recalculated according to the driver's preferences by the multistage framework proposed. In the solution approach proposed, the accurate route searching ability of the Dijkstra algorithm and the exploration ability of the Genetic algorithm (GA) are effectively combined together for solving the m-RSP problems. Solutions provided by the proposed approach are compared with the current research to show the effectiveness and practicability of the solution approach proposed.

Route guidance system is one of the essential components of a vehicle navigation system in ITS. In this paper, a centrally determined route guidance system is established to solve congestion problems. The Sarsa learning method is used to guide vehicles, and global and local parameter strategy is proposed to adjust the vehicle guidance by considering the whole traffic system and local traffic environment, respectively. The proposed method can save the average driving time and relieve traffic congestion. The evaluation was done using two cases on different road networks. The experimental results show the efficiency and effectiveness of the proposed algorithm.

In medical education, many of computerized Problem-Based Learning (PBL) systems are used into their training curricula. But these systems do not truly reflect the situations which practitioners may actually encounter in a real medical environment, and hence their effectiveness as learning tools is somewhat limited. Therefore, the present study analyzes the computerized PBL teaching case, and considers how a clinical teaching case can best be presented to the student. Specifically, this paper attempts to develop a web-based PBL system which emulates the real clinical situation by introducing the concept of a "time sequence" within each teaching case. The proposed system has been installed in the medical center of National Cheng Kung University in Taiwan for testing purposes. The participants in this study were 50 of 5th grade (equivalent to 1st grade students in a medical school of the American medical education system) students for the evaluation process. Some experiments are conducted to verify the advantages of designing teaching cases with the concept of the "time sequence."