Vehicular fog computing enabled by the Fifth Generation (5G) has been on the rise recently, providing real-time services among automobiles in the field of smart transportation by improving road traffic safety and enhancing driver comfort. Due to the public nature of wireless communication channels, in which communications are conveyed in plain text, protecting the privacy and security of 5G-enabled vehicular fog computing is of the utmost importance. Several existing works have proposed an anonymous authentication technique to address this issue. However, these techniques have massive performance efficiency issues with authenticating and validating the exchanged messages. To face this problem, we propose a novel anonymous authentication scheme named ANAA-Fog for 5G-enabled vehicular fog computing. Each participating vehicle’s temporary secret key for verifying digital signatures is generated by a fog server under the proposed ANAA-Fog scheme. The signing step of the ANAA-Fog scheme is analyzed and proven secure with the use of the ProfVerif simulator. This research also satisfies privacy and security criteria, such as conditional privacy preservation, unlinkability, traceability, revocability, and resistance to security threats, as well as others (e.g., modify attacks, forgery attacks, replay attacks, and man-in-the-middle attacks). Finally, the result of the proposed ANAA-Fog scheme in terms of communication cost and single signature verification is 108 bytes and 2.0185 ms, respectively. Hence, the assessment metrics section demonstrates that our work incurs a little more cost in terms of communication and computing performance when compared to similar studies.

Brain tumours are considered one of the deadliest tumours in humans and have a low survival rate due to their heterogeneous nature. Several types of benign and malignant brain tumours need to be diagnosed early to administer appropriate treatment. Magnetic resonance (MR) images provide details of the brain’s internal structure, which allow radiologists and doctors to diagnose brain tumours. However, MR images contain complex details that require highly qualified experts and a long time to analyse. Artificial intelligence techniques solve these challenges. This paper presents four proposed systems, each with more than one technology. These techniques vary between machine, deep and hybrid learning. The first system comprises artificial neural network (ANN) and feedforward neural network (FFNN) algorithms based on the hybrid features between local binary pattern (LBP), grey-level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT) algorithms. The second system comprises pre-trained GoogLeNet and ResNet-50 models for dataset classification. The two models achieved superior results in distinguishing between the types of brain tumours. The third system is a hybrid technique between convolutional neural network and support vector machine. This system also achieved superior results in distinguishing brain tumours. The fourth proposed system is a hybrid of the features of GoogLeNet and ResNet-50 with the LBP, GLCM and DWT algorithms (handcrafted features) to obtain representative features and classify them using the ANN and FFNN. This method achieved superior results in distinguishing between brain tumours and performed better than the other methods. With the hybrid features of GoogLeNet and hand-crafted features, FFNN achieved an accuracy of 99.9%, a precision of 99.84%, a sensitivity of 99.95%, a specificity of 99.85% and an AUC of 99.9%.

Cervical cancer is a global health problem that threatens the lives of women. Liquid-based cytology (LBC) is one of the most used techniques for diagnosing cervical cancer; converting from vitreous slides to whole-slide images (WSIs) allows images to be evaluated by artificial intelligence techniques. Because of the lack of cytologists and cytology devices, it is major to promote automated systems that receive and diagnose huge amounts of images quickly and accurately, which are useful in hospitals and clinical laboratories. This study aims to extract features in a hybrid method to obtain representative features to achieve promising results. Three proposed approaches have been applied with different methods and materials as follows: The first approach is a hybrid method called VGG-16 with SVM and GoogLeNet with SVM. The second approach is to classify the cervical abnormal cell images by ANN classifier with hybrid features extracted by the VGG-16 and GoogLeNet. A third approach is to classify the images of abnormal cervical cells by an ANN classifier with features extracted by the VGG-16 and GoogLeNet and combine them with hand-crafted features, which are extracted using Fuzzy Color Histogram (FCH), Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) algorithms. Based on the mixed features of CNN with features of FCH, GLCM, and LBP (hand-crafted), the ANN classifier reached the best results for diagnosing abnormal cells of the cervix. The ANN network achieved with the hybrid features of VGG-16 and hand-crafted an accuracy of 99.4%, specificity of 100%, sensitivity of 99.35%, AUC of 99.89% and precision of 99.42%.

Vehicular fog systems that are enabled by fifth-generation (5G) networks can significantly improve traffic safety and efficiency through vehicle-to-vehicle communication. Vehicles must have their legal status verified before they can join the network, as vehicular systems depend on open communication. Consequently, several anonymous authentication schemes have been created to withstand malicious vehicles, with the foundations being bilinear pairing or elliptic curve cryptography. But quantum attacks can easily defeat these schemes. To address this problem, this study introduces a 5G-supported vehicular fog system anonymous authentication strategy that makes use of lattice-based cryptography. The suggested system communicates with trusted authorities (TAs) and automobiles over the 5G-base station’s communication range, and it employs fog servers to authenticate vehicles and distribute anonymous data. By recognizing the cars and the users they are affiliated with, the TA in the suggested method can keep tabs on everyone working on a specific task. With the use of ProVerif, a protocol verification tool, we were able to determine that our authentication mechanism effectively hides user, vehicle, and fog server data. After calculating the associated computation and transmission costs, we integrated the suggested technique with state-of-the-art anonymous authentication utilizing lattice-based encryption to ward off quantum attacks. The computational costs of the proposal technique for message signing and batch/single verification are 0.3149 ms, 0.0724 ms, and 0.0724 n ms, respectively. Whereas, 18448 bits is the communication cost of the suggested method. Thus, the results show that our solution has a reduced processing cost for creating and verifying message signatures, but a much higher transmission cost.

The fast development of vehicle technology and the appearance of 5G networks have brought new opportunities as well as challenges for vehicular fog computing. On the one hand, these advances would bring a safer, more efficient, and comfortable car onto our roads; but on the other, they open up huge security and privacy vulnerabilities. Current solutions typically offer inadequate security coverage, rendering vehicular networks susceptible to diverse attacks. We present a first holistic solution that combines entity authentication with privacy preservation in 5G-enabled vehicular fog computing. A novel feature obfuscation involves the vehicle request messages at an early stage and employs advanced cryptographic methods, rendering any potential attacks much more challenging while preventing even a Trusted Authority (TA) from inferring their underlying purpose. This scheme not only maximizes request communication, reduces interaction and computational overhead but also lessens the dependency on TA thus making it more scalable as well as resilient. Theoretical and simulation results show the merits of this protocol in reducing verification latency, and packet loss rate along with computation overheads as well as communication overhead. Experimental results demonstrate the effectiveness of our proposed protocol against existing approaches to provide privacy and security for vehicular communications. The work thus adds to the body of knowledge taking vehicular communication in 5G capable networks a step forward enhancing secured and efficient operation ensuring more secure roads that deliver their contextual alerts setting new standards for safety levels within any existing vehicular network as well.

Arabic natural language processing (NLP) has garnered significant attention in recent years due to the growing demand for automated text and Arabic-based intelligent systems, in addition to digital transformation in the Arab world. However, the unique linguistic characteristics of Arabic, including its rich morphology, diverse dialects, and complex syntax, pose significant challenges to NLP researchers. This paper provides a comprehensive review of the main linguistic challenges inherent in Arabic NLP, such as morphological complexity, diacritics and orthography issues, ambiguity, and dataset limitations. Furthermore, it surveys the major computational techniques employed in tokenisation and normalisation, named entity recognition, part-of-speech tagging, sentiment analysis, text classification, summarisation, question answering, and machine translation. In addition, it discusses the rapid rise of large language models and their transformative impact on Arabic NLP.