<span>According to recent statistics, drowsiness, rather than alcohol, is now responsible for one-quarter of all automobile accidents. As a result, many monitoring systems have been created to reduce and prevent such accidents. However, despite the huge amount of state-of-the-art drowsiness detection systems, it is not clear which one is the most appropriate. The following points will be discussed in this paper: Initial consideration should be given to the many sorts of existing supervised detecting techniques that are now in use and grouped into four types of categories (behavioral, physiological, automobile and hybrid), Second, the supervised machine learning classifiers that are used for drowsiness detection will be described, followed by a discussion of the advantages and disadvantages of each technique that has been evaluated, and lastly the recommendation of a new strategy for detecting drowsiness.</span>
A robust twofold zero-watermarking scheme for secret QR-Code (Quick Response Code) sharing is proposed in order to increase the security of commercial activities on the internet and media. In this paper we will present a twofold scheme for zerowatermarking to be used for copyright protection, implemented in discrete wavelet transform (DWT) as the first fold and discrete cosine transform (DCT) as a second fold for color images in which the visual secret sharing is used to generate unexpanded master and secret shares for the same QR-Code watermark. The experimental results indicate that the proposed scheme is highly robust and the QR-Code can be decodable even after different types of attack being applied.
There is a significant necessity to compress the medical images for the purposes of communication and storage. Most currently available compression techniques produce an extremely high compression ratio with a high-quality loss. In medical applications, the diagnostically significant regions (interest region) should have a high image quality. Therefore, it is preferable to compress the interest regions by utilizing the Lossless compression techniques, whilst the diagnostically lessersignificant regions (non-interest region) can be compressed by utilizing the Lossy compression techniques. In this paper, a hybrid technique of Set Partition in Hierarchical Tree (SPIHT) and Bat inspired algorithms have been utilized for Lossless compression the interest region, and the non-interest region is loosely compressed with the Discrete Cosine Transform (DCT) technique. The experimental results present that the proposed hybrid technique enhances the compression performance and ratio. Also, the utilization of DCT increases compression performance with low computational complexity.
Vegetable crops differ in size, shape, and color and which its suffer from this many leaf batches according to a particular reason. As a result of the plant, pathogens happen for Leaf batches. In agriculture whole fructification, it is essential to learn the origin of plant disease bundles early
<span lang="EN-US">In modern society, providing safe and collision-free travel is essential. Therefore, detecting the drowsiness state of the driver before its ability to drive is compromised. For this purpose, an automated hybrid sleepiness classification system that combines the artificial neural network and gray wolf optimizer is proposed to distinguish human Sleepiness and fatigue. The proposed system is tested on data collected from 15 drivers (male and female) in alert and sleep-deprived conditions where physiological signals are used as sleep markers. To evaluate the performance of the proposed algorithm, k-nearest neighbors (k-NN), support vector machines (SVM), and artificial neural networks (ANN) classifiers have been used. The results show that the proposed hybrid method provides 99.6% accuracy, while the SVM classifier provides 93.0% accuracy when the kernel is (RBF) and outlier (0.1). Furthermore, the k-NN classifier provides 96.7% accuracy, whereas the standalone ANN algorithm provides 97.7% accuracy.</span>
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