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The driving behaviors of electric vehicle (EV) and hybrid electric vehicle (HEV) drivers have received considerable attention in the literature. The use of image recognition in combination with GPS and driving data has emerged as a popular approach to improving driver safety. However, such methods often generate sensitive personal information, including driver images, names, and GPS locations, which may risk the safety of the driver’s privacy. To address this issue, a privacy-preserving approach for identifying driver behavior characteristics is necessary. To achieve this, we utilize on-board diagnostic (OBD) interface vehicle-mounted devices to collect and analyze data from an electronic control unit (ECU), thereby collecting only onboard data for data processing and analysis. In this work, we propose deep learning models such as long short-term memory (LSTM) networks and gated recurrent units (GRUs), which enable the learning of specific behavior patterns and represent the state-of-the-art model to classify and predict driving behavior that potentially leads to dangerous accidents. The predictions were then converted into alarm signals and transmitted to the dashboard of vehicle. Our experiments showed that our proposed system model achieved excellent results with an excellent kappa score of 96.5%, demonstrating that it can accurately identify unique driving behaviors in a privacy-preserving manner.

To achieve successful investments, in addition to financial expertise and knowledge of market information, a further critical factor is an individual’s personality. Decisive people tend to be able to quickly judge when to invest, while calm people can analyze the current situation more carefully and make appropriate decisions. Therefore, in this study, we developed a multimodal personality-recognition system to understand investors’ personality traits. The system analyzes the personality traits of investors when they share their investment experiences and plans, allowing them to understand their own personality traits before investing. To perform system functions, we collected digital human behavior data through video-recording devices and extracted human behavior features using video, speech, and text data. We then used data fusion to fuse human behavior features from heterogeneous data to address the problem of learning only one-sided information from a single modality. Through several experiments, we demonstrated that multimodal (i.e., three different signal inputs) personality trait analysis is more accurate than unimodal models. We also used statistical methods and questionnaires to evaluate the correlation between the investor’s personality traits and risk tolerance. It was found that investors with higher openness, extraversion, and lower neuroticism personality traits took higher risks, which is similar to research findings in the field of behavioral finance. Experimental results show that, in a case study, our multimodal personality prediction system exhibits high performance with highly accurate prediction scores in various metrics.

Aroma and taste have long been considered important indicators of quality coffee. Specialty coffee, that is, coffee from a single estate, farm, or village in a coffee-growing region, in particular, has a unique aroma that reflects the coffee-producing region. In order to enable the traceability of coffee origin, in this study we have developed an e-nose system to discriminate the aroma of freshly roasted coffee in different production regions. In the case study, we employed the e-nose system to experiment with various machine learning models for recognizing several collected coffee beans such as coffees from Yirgacheffe and Kona. Additionally, our contribution also includes the development of a method to create an aromatic digital fingerprint of a specific coffee bean to identify its origin. The experimental results show that the developed e-nose system achieves good recognition performance for coffee aroma recognition. The extracted digital fingerprints have great potential to be stored in an extensible coffee aroma database similar to a comprehensive library of specific coffee bean aroma characteristics, for traceability and reconfirmation of their origin.

Recently, an emerging application field through Twitter messages and algorithmic computation to detect real-time world events has become a new paradigm in the field of data science applications. During a high-impact event, people may want to know the latest information about the development of the event because they want to better understand the situation and possible trends of the event for making decisions. However, often in emergencies, the government or enterprises are usually unable to notify people in time for early warning and avoiding risks. A sensible solution is to integrate real-time event monitoring and intelligence gathering functions into their decision support system. Such a system can provide real-time event summaries, which are updated whenever important new events are detected. Therefore, in this work, we combine a developed Twitter-based real-time event detection algorithm with pre-trained language models for summarizing emergent events. We used an online text-stream clustering algorithm and self-adaptive method developed to gather the Twitter data for detection of emerging events. Subsequently we used the Xsum data set with a pre-trained language model, namely T5 model, to train the summarization model. The Rouge metrics were used to compare the summary performance of various models. Subsequently, we started to use the trained model to summarize the incoming Twitter data set for experimentation. In particular, in this work, we provide a real-world case study, namely the COVID-19 pandemic event, to verify the applicability of the proposed method. Finally, we conducted a survey on the example resulting summaries with human judges for quality assessment of generated summaries. From the case study and experimental results, we have demonstrated that our summarization method provides users with a feasible method to quickly understand the updates in the specific event intelligence based on the real-time summary of the event story.