Computer vision technology for detecting objects in a complex environment often includes other key technologies, including pattern recognition, artificial intelligence, and digital image processing. It has been shown that Fast Convolutional Neural Networks (CNNs) with You Only Look Once (YOLO) is optimal for differentiating similar objects, constant motion, and low image quality. The proposed study aims to resolve these issues by implementing three different object detection algorithms—You Only Look Once (YOLO), Single Stage Detector (SSD), and Faster Region-Based Convolutional Neural Networks (R-CNN). This paper compares three different deep-learning object detection methods to find the best possible combination of feature and accuracy. The R-CNN object detection techniques are performed better than single-stage detectors like Yolo (You Only Look Once) and Single Shot Detector (SSD) in term of accuracy, recall, precision and loss.

Information about healthcare is derived from healthcare data. Healthcare data sharing helps make healthcare systems more efficient as well as improving healthcare quality. Patients should own and control healthcare information, one of their most valuable assets, instead of letting data be spread out among health care providers differ. This protects data from being shared between healthcare systems and privacy. Public ledger accompanied by a decentralized network of peer's compromises patient has been demonstrated to be able to achieve trusted, auditable computing by blockchain. The use of access control and cryptographic primitives are insufficient in addressing modern cyber threats all privacy and security concerns associated with a cloud-based environment. In this paper, the authors proposed a lightweight blockchain technique based on privacy and security for healthcare data for the cloud system. The cost-effectiveness of our system's smart contracts is evaluated, as well as the procedures used for data processing in order to encrypt and pseudonymize patient data.

The concept of federated learning (FL) is becoming increasingly popular as a method for training collaborative models without loss the sensitive information. The term has become ubiquitous due to the extensive development of autonomous vehicles. Vehicular Networks and the Internet of Vehicles (IoV) enable cooperative learning through federated learning. It is still necessary to address several technical challenges. In recent years, Federated Learning (FL) has attracted significant interest in various sectors, including smart cities and transportation systems. FL-enabled attack detection for IoVs are still in its infancy. However, to determine the main challenges of deployment in real-world scenarios, there needs to be research efforts from various areas. Performance metrics are used to evaluate the effectiveness of the proposed FL framework. According to experiments, the proposed FL approach detected attacks in IOV networks with a maximum accuracy of 99.72%. In addition to precision, recall, and F1 scores, 99.70%, 99.20%, and 99.26% were achieved. A comparison of the proposed model with the existing model shows that the proposed model is more accurate.

The Internet of Vehicles (IoVs) has seen rapid development due to advances in advanced communication technologies. The 5-th Generation (5G) systems will be integrated into next-generation vehicles, enabling them to operate more efficiently by cooperating with the environment. The millimeter Wave (mmWave) technology is projected to provide a large bandwidth to meet future needs for more effective data rate communications. A viable approach for transferring raw sensor data among autonomous vehicles would be to use mmWave communication. This paper attracts various research interests in academic, indoor, and outdoor mmWave operations. This paper presents mmWave propagation measurements for indoor and outdoor at 66 GHz frequency for IoVs scenarios. The proposed model examines the equivalent path loss using Free-Space Path Loss (FSPL) based on the transmitter and receiver distances for indoor and outdoor communications of the vehicles. In the indoor scenario, path loss propagation has the lowest penetration loss, but it is ineffective in the outdoor scenario because distance increases as free space path loss increases. The probability of error is increased, concerning the transmitter and receiver distances due to propagation effect, packet collisions, busy receiver, and sensing threshold. The proposed methodology shows a higher packet delivery ratio and average throughput with less delay in the connection during transmission.

Intelligent transport systems (ITSs) aim to improve the performance of vehicular ad hoc networks (VANETs). It provides new opportunities for the Internet of Vehicle (IoVs) environment but poses some security concerns, especially concerning the Vehicle-to-Vehicle secure communication connection. The advent of the Next Generation technology for Internet of Vehicles (IOVs) secure communication has greatly benefited mobile networks. While 5G IoVs has many privacy and security challenges, several must be addressed. The confidentiality and privacy of data transmitted between vehicles, roadside units (RSU), and control rooms is a critical issue that must be addressed effectively. Blockchain technology presents a potential solution for securing the IOVs because of its decentralization, stability, and transaction tracking capabilities. To overcome these issues, this paper introduces BLAP-IOVs, a Blockchain-based Lightweight Authentication Protocol for generating Trustworthy IOVs communication. Comprehensive analysis and simulation results verify that the proposed BLAP-IOVs approach is efficient for secure information transmission in IOVs. Experimental results demonstrate that the proposed BLAP-IOVs approach achieves high success communication rates with acceptable vehicle-to-vehicle (V2V) loss and computation overhead. BLAP-IOVs suggest using Blockchain to improve Security, trust, and collaboration.