The classification and recommendation system for identifying social networking site (SNS) users’ interests plays a critical role in various industries, particularly advertising. Personalized advertisements help brands stand out from the clutter of online advertisements while enhancing relevance to consumers to generate favorable responses. Although most user interest classification studies have focused on textual data, the combined analysis of images and texts on user-generated posts can more precisely predict a consumer’s interests. Therefore, this research classifies SNS users’ interests by utilizing both texts and images. Consumers’ interests were defined using the Curlie directory, and various convolutional neural network (CNN)-based models and recurrent neural network (RNN)-based models were tested for our user interest classification system. In our hybrid neural network (NN) model, CNN-based classification models were used to classify images from users’ SNS postings while RNN-based classification models were used to classify textual data. The results of our extensive experiments show that the classification of users’ interests performed best when using texts and images together, at 96.55%, versus texts only, 41.38%, or images only, 93.1%. Our proposed system provides insights into personalized SNS advertising research and informs marketers on making (1) interest-based recommendations, (2) ranked-order recommendations, and (3) real-time recommendations.

Driver-centered infotainment and telematics services are provided for intelligent vehicles that improve driver convenience. Driver-centered services are performed after identification, and a biometrics system using bio-signals is applied. The electrocardiogram (ECG) signal acquired in the driving environment needs to be normalized because the intensity of noise is strong because the driver’s motion artifact is included. Existing time, frequency, and phase normalization methods have a problem of distorting P, QRS Complexes, and T waves, which are morphological features of an ECG, or normalizing to signals containing noise. In this paper, we propose an adaptive threshold filter-based driver identification system to solve the problem of distortion of the ECG morphological features when normalized and the motion artifact noise of the ECG that causes the identification performance deterioration in the driving environment. The experimental results show that the proposed method improved the average similarity compared to the results without normalization. The identification performance was also improved compared to the results before normalization.

As autonomous driving technologies develop, biometrics technology using driver's bio-signals provides various driver-customized infotainment services. Many studies on driver identification are underway to improve the feature extraction and classification steps to identify drivers with high accuracy. The existing identification systems have used a driver's single biological signal to identify the driver without considering the driver's driving state. The identification error rate was high because electromyography (EMG) is included according to motion artifacts while driving in the electrocardiogram (ECG) acquired by the driver's behavioral characteristics. In this letter, the identification accuracy was improved by using the EMG acquired by the driver's motion while driving and the ECG acquired by the behavioral characteristics simultaneously. The proposed identification system converts ECG and EMG into 2D constant Q transform (CQT) images and classifies drivers by multi-stream convolutional neural network (CNN). Our extensive experiments show that a single ECG can achieve 98.1% of the identification accuracy, a single EMG can achieve 84.4% of the identification accuracy, and the multi-signal can achieve 98.9% of the identification accuracy with 2D CQT.

The security in vehicular ad hoc networks (VANETs) has become a large consideration in safeguarding growing applications and intelligent transport systems. A group signature, a popular authentication approach for VANETs, can be implemented to protect vehicular communications against malicious users. However, the issue of securely distributing group keys to fast-moving vehicular nodes arises. The growing size of the certificate revocation list (CRL) has provided the corresponding complication in its management and distribution in VANETs. In this paper, an efficient key management protocol for group signature based authentication is proposed. A group is extended to a domain with various roadside units forming a hierarchical topology. Our proposed scheme provides a secure method to deliver group keys to vehicular nodes, ensuring the security requirements. Similarly, through utilizing the two Bloom filters in our hierarchical topology, an efficient and scalable vehicle revocation mechanism can be achieved that can minimize the CRL size. Our experiment results demonstrate a scalable, efficient, and secure key distribution scheme in vehicular networking. Moreover, an effective CRL management mechanism can be accomplished using the hierarchical topology.