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 scales that make it impossible to quickly and directly measure the severity of depression. This study 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 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 the α and θ band, while the wavelet brainwave bands have a significant impact on the minimum value of approximated signals. This study 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 and early detection and treatment of depression. In addition, the individual abnormality is found in the low mood stage and appropriate relief methods are provided, potentially reducing the occurrence of depression.

Rapid breakthroughs in information technologies have driven substantial developments in artificial intelligence applications, particularly the widespread use of deep learning techniques in domains such as speech, image and text recognition. However, real world data distribution applications suffer from significant problems including data imbalance which can easily lead to machine learning biased towards the side with more data, resulting in inaccurate classification or prediction results. Therefore, effectively addressing data imbalance is a pressing research topic. Generative Adversarial Networks (GAN) addresses data imbalance, but is prone to vanishing gradients. Recent work has thus focused on improving the GAN architecture to resolve this problem. The present research extends these efforts, applying C4.5, Random Forest, Support Vector Machine, K-Nearest Neighbor and Naïve Bayes classification algorithms to a single imbalanced traffic collision dataset to identify methods for improving prediction results. Experimental results show that classification performance significantly improves after data augmentation using Synthetic Minority Oversampling Technique, GAN, Conditional GAN, and Gaussian Discriminant Analysis GAN as compared with the non-augmented dataset. In addition, the Gaussian Discriminant Analysis GAN with Naïve Bayes classifier produces a dataset that optimizes classification performance for traffic accident prediction at highway intersections.

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.

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.

MOTIVATION: Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be immediately fatal or cause irreparable damage to the heart sustained over long periods of time. Therefore, the ability to automatically identify arrhythmias from ECG recordings is important for clinical diagnosis and treatment. In this article, classification by using associative Petri net (APN) for personalized ECG-arrhythmia-pattern identification is proposed for the first time in literature. RESULTS: A rule-based classification model and reasoning algorithm of APN are created for ECG arrhythmias classification. The performance evaluation using MIT-BIH arrhythmia database shows that our approach compares well with other reported studies.

Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be immediately fatal or cause irreparable damage to the heart sustained over long periods of time. Therefore, the ability to automatically identify arrhythmias from ECG recordings is important for clinical diagnosis and treatment. In this study, classifier by using associative Petri net for personalized ECG arrhythmias pattern identification is proposed. Association production rules and reasoning algorithm of APN are created for ECG arrhythmias detection. The performance of our approach compares well with previously reported results and could be a part of monitoring system for the detection of ECG arrhythmias.

The growing popularity of mobile devices such as smartphones and handsets has made mobile devices a more attractive target for mobile malware. Thus, the role of an anti-malware detector for effectively detecting mobile malware is becoming extremely important. In our previous work, we focused on constructing an ontology-based behavioral analysis for mobile malware, which provides information about mobile malware for end users to help them use their mobile phones securely. In this paper, we extend our previous work by employing the proposed technique of ontology-based behavioral analysis to develop a detection method for smartphone malware. It is expected that this research will contribute to the development of detection methods for unknown smartphone malware in mobile environments.

With the increase in the number senior citizens and chronic diseases, the number of elderly patients who need constant assistance has increased. One key point of all critical care for elderly patient is the continuous monitoring of their vital signs. Among these, the ECG signal is used for noninvasive diagnosis of cardiovascular diseases. Also, there is a pressing need to have a proper system in place for patient identification. Errors in patient identification, and hence improper administration of medication can lead to disastrous results. This paper proposes a novel embedded mobile ECG reasoning system that integrates ECG signal reasoning and RF identification together to monitor an elderly patient. As a result, our proposed method has a good accuracy in heart beat recognition, and enables continuous monitoring and identification of the elderly patient when alone. Moreover, in order to examine and validate our proposed system, we propose a managerial research model to test whether it can be implemented in a medical organization. The results prove that the mobility, usability, and performance of our proposed system have impacts on the user's attitude, and there is a significant positive relation between the user's attitude and the intent to use our proposed system.

Although Bayesian networks (BNs) are increasingly being used to solve real-world risk problems, their use is still constrained by the difficulty of constructing the node probability tables (NPTs). A key challenge is to construct relevant NPTs using the minimal amount of expert elicitation, recognizing that it is rarely cost effective to elicit complete sets of probability values. We describe a simple approach to defining NPTs for a large class of commonly occurring nodes (called ranked nodes). The approach is based on the doubly truncated normal distribution with a central tendency that is invariably a type of weighted function of the parent nodes. In extensive real-world case studies, we have found that this approach is sufficient for generating the NPTs of a very large class of nodes. We describe one such case study for validation purposes. The approach has been fully automated in a commercial tool, called AgenaRisk, and is thus accessible to all types of domain experts. We believe that this work represents a useful contribution to the BN research and technology, since its application makes the difference between being able to build realistic BN models and not.