Epilepsy is a common neurological disease that can cause seizures and loss of consciousness and can have a severe negative impact on long-term cognitive function. Reducing the severity of impact requires early diagnosis and treatment. Epilepsy is traditionally diagnosed using electroencephalography (EEG) performed by trained physicians or technicians but this process is time-consuming and prone to interference, which can negatively impact accuracy. This paper develops a model for epilepsy diagnosis using discrete wavelet transform to analyze sub-bands within the EEG parameter and select EEG characteristics for epilepsy detection. The minimize entropy principle approach is used to build fuzzy membership functions of the characteristics of each brain wave and are then used as the basis for the construction of an associative Petri net model. Using our APN model, the associative Petri net approach provides diagnosis accuracy rates of 93.8%, outperforming similar approaches using decision tree, support vector machine, neural network, Bayes net, naïve Bayes, and tree augmented naïve Bayes. Thus, the proposed approach shows promise for fast, accurate, and objective diagnosis of epilepsy in clinical settings.

In this study, a high accident risk prediction model is developed to analyze traffic accident data, and identify priority intersections for improvement. A database of the traffic accidents was organized and analyzed, and an intersection accident risk prediction model based on different mechanical learning methods was created to estimate the possible high accident risk locations for traffic management departments to use in planning countermeasures to reduce accident risk. Using Bayes’ theorem to identify environmental variables at intersections that affect accident risk levels, this study found that road width, speed limit and roadside markings are the significant risk factors for traffic accidents. Meanwhile, Naïve Bayes, Decision tree C4.5, Bayesian Network, Multilayer perceptron (MLP), Deep Neural Networks (DNN), Deep Belief Network (DBN) and Convolutional Neural Network (CNN) were used to develop an accident risk prediction model. This model can also identify the key factors that affect the occurrence of high-risk intersections, and provide traffic management departments with a better basis for decision-making for intersection improvement. Using the same environmental characteristics as high-risk intersections for model inputs to estimate the degree of risk that may occur in the future, which can be used to prevent traffic accidents in the future. Moreover, it also can be used as a reference for future intersection design and environmental improvements.

Drivers and traffic system planners require accurate forecasting of future travel times. To alleviate traffic congestion on Taiwan’s Provincial Highway No. 61, this study considers temporal (weekdays, weekends or continuous holidays) and spatial characteristics (road types), and establishes short-term and long-term travel time prediction models. Data pre-processing is accomplished using the Google Maps API and floating car method to verify travel time comparisons, finding that post-processing travel times are reliable, credible and reasonable. This study develops a collaborative intelligent transportation system (CITS) based on 9 different algorithms for the prediction of current and future travel-time. The results show that $k$ NN-R provides the most accurate short-term predictions, with average prediction error within 20 seconds per kilometer. For long-term forecasting, SARIMAX and fbProphet provide the most accurate results for weekday and continuous holiday modes. Travel time prediction can assist traffic management agencies in the timely implementation of appropriate traffic management measures. CITS also provides real-time traffic query and travel time prediction functions to help drivers avoid traffic congestion.

Students often face challenges while learning computer programming because programming languages’ logic and visual presentations differ from human thought processes. If the course content does not closely match learners’ skill level, the learner cannot follow the learning process, resulting in frustration, low learning motivation, or abandonment. This research proposes a web programming learning recommendation system to provide students with personalized guidance and step-by-step learning planning. The system contains front-end and back-end web development instructions. It can create personalized learning paths to help learners achieve a sense of accomplishment. The system can help learners build self-confidence and improve learning effectiveness. In study 1, the recommendation system was developed based on the personal data and feedback of 41 professional web design engineers. The system uses C4.5 decision tree methods to develop a programming learning recommendation model to provide appropriate learning recommendations and establish personalized learning paths. The test group included 13 beginner programmers. After 4 weeks’ programming instructions in front-end and back-end web development, the learners were interviewed to understand their preferences and learning effectiveness. The results show that the effectiveness of the recommendation system is acceptable. In study 2, online real-time feedback and adaptive instruction platform is developed, which is different from the past adaptive curriculums mainly using the Internet platform and only the submitted assignments to determine the newly recommended learning process for students. The study found that the students’ learning performance in the adaptive instruction group is better than those in the fixed instruction group.

Long-term depression and negative emotional cycles affect life quality and work productivity. However, depression is not easy to detect, with current methods mostly relying on the use of scales that make it impossible to quickly and directly measure the severity of depression. This research seeks to empirically identify brainwave stimulation feedback electrode points and brain regions related to potential depression. Using brainwave data collected by mood-induction procedures, the front and occipital lobes as found to have the greatest role in the operation of depressive emotions, especially the Fp1 and Fp2 positions and the O1 and O2 positions. The Fourier brainwave bands are mainly affected in α and θ band, while the wavelet brainwave bands have a significant impact on the minimum value of approximated signals. This research uses two signal processing methods, combined with deep neural network techniques (Multilayer perceptron, Deep neural network, Deep belief network, and Long Short-Term Memory) to develop 8 potential depression assessment models, with models constructed using deep neural networks providing the best and most stable performance. Therefore, this model can be developed as an auxiliary system for rapid and objective assessment of underlying depression, thereby assisting in the autonomous management of emotions, as well as the early detection and treatment of depression. In addition, in the low mood stage, individual abnormality is found, and appropriate relief methods are provided, potentially reducing the occurrence of depression.