The COVID-19 pandemic profoundly affected global patterns, and the period of the declared virus pandemic has had a negative influence on all aspects of life. This research focuses on categorizing and empirically investigating the role of digital platforms in learning and business processes during the COVID-19 pandemic outbreak. The main purpose of this paper is to investigate to what extent the use of electronic learning (EL) has been boosted by COVID-19’s spread, and EL’s effectiveness on the sustainable development of electronic commerce due to the demand for a variety of electronic devices. For this purpose, the information has been collected through an online questionnaire applied to 430 participants from the Kurdistan Region of Iraq (KRI). The results indicate that participant usage and skills with electronic devices and online software programs are increasing, as the ratio indicated a level of 68% for both genders. Thus, the significance of EL concerning electronic commercial enterprises has been openly acknowledged and influenced by numerous factors. In addition, several suggestions and steps to be undertaken by the government are highlighted. Finally, this research mentions the current limitations of EL and suggests future works to build sustainable online experiences.

The manipulation of digital images has become a popular trend. Due to the development of image processing tools and visualization techniques integrating deep learning and artificial intelligence (AI) algorithms (in particular generative adversarial networks–GAN), this can pose serious threats to privacy and security. In recent years, Deepfake algorithms have been designed to exchange faces or modify facial features, potentially leading to more severe problems in this context. In this manuscript, we provide a comprehensive review of the two most important facial image processing technologies: (i) deepfake face manipulation; and (ii) face manipulation detection techniques. Furthermore, we explore the state-of-the-art of popular GAN techniques. In particular, three types of Deepfake face detection technologies are reviewed: (i) hand-crafted features (ii) artifacts; and (iii) learning-based features, while highlighting related improvements and challenges. Furthermore, this article discusses potential challenges and promising research directions for future investigation. We believe that this review has been organized to provide a structured analysis of important research papers and to discuss each study’s main findings and conclusions. Our investigation reveals shortcomings in manipulation detection benchmarks due to real-world scenario variations and biased dataset comparisons. Current research priorities revolve around enhancing GAN training stability, resolution, and manipulable facial features. Moreover, GANs have shown superior results in identifying fake images; however, their reliance prompts a systematic approach to detecting fakes. This dependency raises questions about detecting fake images with or without manipulated GAN architecture, urging the need for novel computational techniques to identify manipulations without GAN assistance.

Blockchain, a breakthrough technology, offers solutions to several healthcare challenges, includes Managing and sharing data securely, Making different systems work together seamlessly, Increasing transparency in the supply chain, Giving patients more control over their health information, and Adhering to regulations and guidelines. This paper offers an in-depth study of current literature on blockchain in healthcare. It covers areas that have been investigated, studied and implemented by previous research, provides valuable insights, and introduces practical suggestions. Research findings indicate that blockchain has the capacity to improve data security, privacy, and immutability. It can also facilitate effortless data sharing among different parties involved, enhance transparency in pharmaceutical supply chains, empower patients in managing their health information, and address regulatory concerns. The results of this study recommends Healthcare organizations to consider implementing secure transaction protocols to ensure the confidentiality and integrity of their data. Also to explore the use of Hyperledger Fabric architecture, a blockchain framework specifically designed for enterprise applications, Implementation of Secure Transaction Protocols, Utilization of Blockchain for Data Sharing, Adoption of Lightweight Consensus Protocols. By leveraging blockchain technology, healthcare businesses may securely share data with other trusted parties, improving interoperability and data exchange. Additionally, adopting lightweight consensus methods can help streamline decision-making processes within the organization. Future research shall involve the integration of blockchain technology with artificial intelligence and data analytics, the establishment of decentralized parallel healthcare organizations, the integration of blockchain with cloud computing, and the resolution of regulatory and standards difficulties

Driver drowsiness is a severe problem that usually causes traffic accidents, classified as more dangerous. The record of the National Safety Council reported that drowsy driving is caused by 9.5 of all crashes (100,000 cases). Therefore, preventing and minimizing driver fatigue is a significant research area. This study aims to design a nonintrusive real-time drowsiness system based on image processing and fuzzy logic techniques. It is an enhanced approach for ViolaJones to examine different visual signs to detect the driver's drowsiness level. It extracted eye blink duration and mouth features to detect driver drowsiness based on the desired facial feature image in a specific driver video frame. The size and orientation of the captured features were tracked and handled for determining image features such as brightness, shadows, and clearness. Lastly, the fuzzy control system provides different alert sounds based on the tracked information from the face, eyes, and mouth in separate cases, such as race, wearing glasses or not, gender, and various illumination backgrounds. The experiments results show that the proposed approach achieved high accuracy of 94.5 in detecting driver status compared with other studies. Also, the fuzzy logic controller efficiently issued the required alert signal of the drowsy driver status that helps to save the driver's life.

The Internet of Things (IoT) encompasses a network of different devices, both stationary and mobile, that may interact with the physical world. Ensuring the security of the Internet of Things (IoT) is of utmost importance in a world where devices are interconnected at various levels, including wearables, home automation, smart cities, industrial sectors, and more. Ensuring the security of this interconnected network of "things" and devices is imperative, leaving no space for mistakes or inadequacies. The purpose of this article is to present an approach that utilizes Digital Object Architecture (DOA) to identify Internet of Things (IoT) devices and applications within communication networks. This study investigates various methodologies for incorporating the DOA identifier into Internet of Things (IoT) devices that are equipped with a range of wireless data transmission modules. Furthermore, this study presents a security model that aims to strengthen the resolution system based on data transmission security. The objective is to reduce the number of service messages exchanged between internetwork parts and decrease network latency. The essay finishes by examining some strategies for modernizing DOA in order to improve the quality of service.

Objective: Segmentation of liver and liver tumors from 3D medical images is a challenging and computationally expensive task. Organs that are in close proximity may have similar shape, texture, and intensity, which makes it difficult for accurate segmentation. Accurate segmentation of liver tumors is important for diagnosis and treatment planning of liver cancer. Methods: A hybrid model with a U-Net based structure and the Whale Optimization Algorithm (WOA) was proposed. WOA was used to optimize the hyperparameters of the conventional LiTS-Res-UNet to obtain the best segmentation performance of the deep learning model. Results: The LiTS-Res-Unet + WOA hybrid model achieved a performance of 99.54% for accuracy, with a Dice coefficient of 92.38% and a Jaccard index of 86.73% on the benchmark dataset, outperforming state-of-the-art methods. Conclusions: The WOA-based adaptive search space was able to obtain an optimal set of hyperparameters for deep learning model convergence while increasing the accuracy of the model in the proposed hybrid model. The robust performance and clinical applicability of the model in liver tumor segmentation were demonstrated.