No abstract

Aggressive driving emotions is indeed one of the major causes for traffic accidents throughout the world. Real-time classification in time series data of abnormal and normal driving is a keystone to avoiding road accidents. Existing work on driving behaviors in time series data have some limitations and discomforts for the users that need to be addressed. We proposed a multimodal based method to remotely detect driver aggressiveness in order to deal these issues. The proposed method is based on change in gaze and facial emotions of drivers while driving using near-infrared (NIR) camera sensors and an illuminator installed in vehicle. Driver's aggressive and normal time series data are collected while playing car racing and truck driving computer games, respectively, while using driving game simulator. Dlib program is used to obtain driver's image data to extract face, left and right eye images for finding change in gaze based on convolutional neural network (CNN). Similarly, facial emotions that are based on CNN are also obtained through lips, left and right eye images extracted from Dlib program. Finally, the score level fusion is applied to scores that were obtained from change in gaze and facial emotions to classify aggressive and normal driving. The proposed method accuracy is measured through experiments while using a self-constructed large-scale testing database that shows the classification accuracy of the driver's change in gaze and facial emotions for aggressive and normal driving is high, and the performance is superior to that of previous methods.Association Foundation for Traffic safety, published in 2009, that the aggressive behavior of driver causes 56% of traffic accidents [2]. Besides precious human lives, people, company, and government also lose billions of dollars due to road accidents. For this reason, aggressive driving behavior must be strongly discouraged that will result in reduction of the number of traffic accidents.The classification of aggressive and normal behavior is an important issue that can be used to increase awareness of driving habits of drivers as many drivers are over confident and are unaware of their bad driving habits [3]. If we can automatically identify the drivers driving behaviors, the drivers can be aware of their bad habits and assist them to avoid potential car accidents. Other than this if, monitoring results could be sent back to a security observing server of the local police station that could help to automatically detect aggressive drivers. The conventional method to keep a check on aggressive driving is by police patrolling, but, due to lack of police force, all roads cannot be simultaneously monitored and it also costs a lot [4]. The need of intelligent surveillance system is increasing with the increase in population. The advance driver assistance system (ADAS) that can monitor driver's attention and driving behavior can improve road safety, which will also enhance the effectiveness of the ADAS [5]. Many challenges are faced by these real time systems that ...

Multiple ocular region segmentation plays an important role in different applications such as biometrics, liveness detection, healthcare, and gaze estimation. Typically, segmentation techniques focus on a single region of the eye at a time. Despite the number of obvious advantages, very limited research has focused on multiple regions of the eye. Similarly, accurate segmentation of multiple eye regions is necessary in challenging scenarios involving blur, ghost effects low resolution, off-angles, and unusual glints. Currently, the available segmentation methods cannot address these constraints. In this paper, to address the accurate segmentation of multiple eye regions in unconstrainted scenarios, a lightweight outer residual encoder-decoder network suitable for various sensor images is proposed. The proposed method can determine the true boundaries of the eye regions from inferior-quality images using the high-frequency information flow from the outer residual encoder-decoder deep convolutional neural network (called ORED-Net). Moreover, the proposed ORED-Net model does not improve the performance based on the complexity, number of parameters or network depth. The proposed network is considerably lighter than previous state-of-theart models. Comprehensive experiments were performed, and optimal performance was achieved using SBVPI and UBIRIS.v2 datasets containing images of the eye region. The simulation results obtained using the proposed ORED-Net, with the mean intersection over union score (mIoU) of 89.25 and 85.12 on the challenging SBVPI and UBIRIS.v2 datasets, respectively.

SUMMARYSince the release of human genome sequences, one of the most important research issues is about indexing the genome sequences, and the suffix tree is most widely adopted for that purpose. The traditional suffix tree construction algorithms suffer from severe performance degradation due to the memory bottleneck problem. The recent disk-based algorithms also provide limited performance improvement due to random disk accesses. Moreover, they do not fully utilize the recent CPUs with multiple cores. In this paper, we propose a fast algorithm based on 'divideand-conquer' strategy for indexing the human genome sequences. Our algorithm nearly eliminates random disk accesses by accessing the disk in the unit of contiguous chunks. In addition, our algorithm fully utilizes the multi-core CPUs by dividing the genome sequences into multiple partitions and then assigning each partition to a different core for parallel processing. Experimental results show that our algorithm outperforms the previous fastest DIGEST algorithm by up to 10.5 times.

Brain tumors are a deadly disease with a high mortality rate. Early diagnosis of brain tumors improves treatment, which results in a better survival rate for patients. Artificial intelligence (AI) has recently emerged as an assistive technology for the early diagnosis of tumors, and AI is the primary focus of researchers in the diagnosis of brain tumors. This study provides an overview of recent research on the diagnosis of brain tumors using federated and deep learning methods. The primary objective is to explore the performance of deep and federated learning methods and evaluate their accuracy in the diagnosis process. A systematic literature review is provided, discussing the open issues and challenges, which are likely to guide future researchers working in the field of brain tumor diagnosis.