There has been a great deal of research into the considerable challenge of managing of traffic at road junctions; its application to vehicular ad hoc network (VANET) has proved to be of great interest in the developed world. Dynamic topology is one of the vital challenges facing VANET; as a result, routing of packets to their destination successfully and efficiently is a non-simplistic undertaking. This paper presents a MDORA, an efficient and uncomplicated algorithm enabling intelligent wireless vehicular communications. MDORA is a robust routing algorithm that facilitates reliable routing through communication between vehicles. As a position-based routing technique, the MDORA algorithm, vehicles' precise locations are used to establish th

Image pattern classification is considered a significant step for image and video processing. Although various image pattern algorithms have been proposed so far that achieved adequate classification, achieving higher accuracy while reducing the computation time remains challenging to date. A robust image pattern classification method is essential to obtain the desired accuracy. This method can be accurately classify image blocks into plain, edge, and texture (PET) using an efficient feature extraction mechanism. Moreover, to date, most of the existing studies are focused on evaluating their methods based on specific orthogonal moments, which limits the understanding of their potential application to various Discrete Orthogonal Moments (DOM

Hand gestures are currently considered one of the most accurate ways to communicate in many applications, such as sign language, controlling robots, the virtual world, smart homes, and the field of video games. Several techniques are used to detect and classify hand gestures, for instance using gloves that contain several sensors or depending on computer vision. In this work, computer vision is utilized instead of using gloves to control the robot's movement. That is because gloves need complicated electrical connections that limit user mobility, sensors may be costly to replace, and gloves can spread skin illnesses between users. Based on computer vision, the MediaPipe (MP) method is used. This method is a modern method that is discover

Antibiotic resistance has been a growing worldwide public health issue. The World Health Organization (WHO) has stated that the search for new antibiotics is slow, while antibiotic resistance is growing. WHO has also declared that antibiotic resistance is one of the top 10 global public health threats facing humanity in the 21st century. Therefore, this review discusses the potential of metal-based drugs as antibacterial agents from the period of the early 2000s to date. The review reveals that a lot of preliminary work has been done to assess these as potential drugs. However, their mode of action is faintly described. Furthermore, a few examples of metal-based drugs assessed for their modes of action are described. These compounds are ide

Due to the importance of nanotechnology because of its features and applications in various fields, it has become the focus of attention of the world and researchers. In this study, the concept of nanotechnology and nanomaterials was identified, the most important methods of preparing them, as well as the preparation techniques and the most important devices used in their characterization.

A new  light-weight nanocarbon prepared by spray-drying method  to obtain  particle size is 21.7 nm based of polylactic acid biodegradable film in antistatic packaging .Bio carbon (biochar) is  obtained from  plants and soils to naturally absorb and store carbon dioxide from the atmosphere . Therefor it has been used to support biodegradable polylactic acid (PLA) with to obtain 100% recyclable material.

Using plasticizer thymol of polylactic acid and biochar (bio carbon) as composites were prepared by a solution casting method with (0.5-10)wt% biochar. The composites  characterized by  FTIR, electrical conductivity, mechanical properties , contact angle and Colar and Brightness . Results show th

Face recognition and identity verification are now critical components of current security and verification technology. The main objective of this review is to identify the most important deep learning techniques that have contributed to the improvement in the accuracy and reliability of facial recognition systems, as well as highlighting existing problems and potential future research areas. An extensive literature review was conducted with the assistance of leading scientific databases such as IEEE Xplore, ScienceDirect, and SpringerLink and covered studies from the period 2015 to 2024. The studies of interest were related to the application of deep neural networks, i.e., CNN, Siamese, and Transformer-based models, in face recogni