Driver drowsiness detection is a significant element of Advanced Driver-Assistance Systems (ADASs), which utilize deep learning (DL) methods to improve road safety. A driver drowsiness detection system can trigger timely alerts like auditory or visual warnings, thereby stimulating drivers to take corrective measures and ultimately avoiding possible accidents caused by impaired driving. This study presents a Deep Learning-based Intelligent Driver Drowsiness Detection for Advanced Driver-Assistance Systems (DLID3-ADAS) technique. The DLID3-ADAS technique aims to enhance road safety via the detection of drowsiness among drivers. Using the DLID3-ADAS technique, complex features from images are derived through the use of the ShuffleNet approach. Moreover, the Northern Goshawk Optimization (NGO) algorithm is exploited for the selection of optimum hyperparameters for the ShuffleNet model. Lastly, an extreme learning machine (ELM) model is used to properly detect and classify the drowsiness states of drivers. The extensive set of experiments conducted based on the Yawdd driver database showed that the DLID3-ADAS technique achieves a higher performance compared to existing models, with a maximum accuracy of 97.05% and minimum computational time of 0.60 s.

Due to contemporary communication trends, the amount of multimedia data created and transferred in 5G networks has reached record levels. Multimedia applications communicate an enormous quantity of images containing private data that tend to be attacked by cyber-criminals and later used for illegal reasons. Security must consider and adopt the new and unique features of 5G/6G platforms. Cryptographic procedures, especially secret sharing (SS), with some extraordinary qualities and capacities, can be conceived to handle confidential data. This paper has developed a secured (k, k) multiple secret sharing (SKMSS) scheme with Hybrid Optimal SIMON ciphers. The proposed SKMSS method constructs a set of noised components generated securely based on performing hash and block ciphers over the secret image itself. The shares are created and safely sent after encrypting them through the Hybrid Optimal SIMON ciphers based on the noised images. This is a lightweight cryptography method and helps reduce computation complexity. The hybrid Particle Swarm Optimization-based Cuckoo Search Optimization Algorithm generates the keys based on the analysis of the peak signal to noise ratio value of the recovered secret images. In this way, the quality of the secret image is also preserved even after performing more computations upon securing the images.

A one-time password (OTP) is a password that is valid for only one login session or transaction, in IT systems or digital devices. This is one of the human-centered security services and is commonly used for multi-factor authentication. This is very similar to generating pseudo-random bit streams in cryptography. However, it is only part of what is used as OTP in the bit stream. Therefore, the OTP mechanism requires an algorithm to extract portions. It is also necessary to convert hexadecimal to decimal so that the values of the bit strings are familiar to human. In this paper, we classify three algorithms for extracting the final data from the pseudo random bit sequence. We also analyze the fact that a vulnerability occurs during the extraction process, resulting in a high frequency of certain numbers; even if cryptographically secure generation algorithms are used.

The expanding utilization of edge consumer electronic (ECE) components and other innovations allows medical devices to communicate with one another to distribute sensitive clinical information. This information is used by health care authorities, specialists and emergency clinics to offer enhanced medication and help. The security of client data is a major concern, since modification of data by hackers can be life-threatening. Therefore, we have developed a privacy preservation approach to protect the wearable sensor data gathered from wearable medical devices by means of an anomaly detection strategy using artificial intelligence combined with a novel dynamic attribute-based re-encryption (DABRE) method. Anomaly detection is accomplished through a modified artificial neural network (MANN) based on a gray wolf optimization (GWO) technique, where the training speed and classification accuracy are improved. Once the anomaly data are removed, the data are stored in the cloud, secured through the proposed DABRE approach for future use by doctors. Furthermore, in the proposed DABRE method, the biometric attributes, chosen dynamically, are considered for encryption. Moreover, if the user wishes, the data can be modified to be unrecoverable by re-encryption with the true attributes in the cloud. A detailed experimental analysis takes place to verify the superior performance of the proposed method. From the experimental results, it is evident that the proposed GWO–MANN model attained a maximum average detection rate (DR) of 95.818% and an accuracy of 95.092%. In addition, the DABRE method required a minimum average encryption time of 95.63 s and a decryption time of 108.7 s, respectively.