Plants leaves recognition is an important scientific field that is concerned of recognizing leaves using image processing techniques. Several methods are presented using different algorithms to achieve the highest possible accuracy. This paper provides an analytical survey of various methods used in image processing for the recognition of plants through their leaves. These methods help in extracting useful information for botanists to utilize the medicinal properties of these leaves, or for any other agricultural and environmental purposes. We also provide insights and a complete review of different techniques used by researchers that consider different features and classifiers. These features and classifiers are studied in term of their capabilities in enhancing the accuracy ratios of the classification methods. Our analysis shows that both of the Support Victor Machines (SVM) and the Convolutional Neural Network (CNN) are positively dominant among other methods in term of accuracy.

<p class="0abstract">The novel coronavirus (COVID-19) has become widespread around the world. It started in Wuhan, China, and has since spread rapidly among people living in other countries. Hence, the World Health Organization has considered COVID-19 as a pandemic that threatens millions of people’s lives. Due to the high number of infected people, many hospitals have been facing critical issues in providing the required medical services. For instance, some clinical centers have been unable to provide one of the most important medical services, namely blood tests to determine whether an individual is infected with COVID-19. Therefore, it is important to present an alternative diagnosis option to prevent the further spread of COVID-19. In this paper, a proposed intelligent detection communication system (IDCS) is configured for distributed mobile clinical centers to control the pandemic. In addition, the intelligent system is integrated with the Zigbee communication protocol to build a mobile COVID-19 detection system. The proposed system was trained on X-ray COVID-19 lung images used to identify infected people. The Zigbee protocol and decision tree algorithm were used to design the IDCS. The results of the proposed system show high accuracy 94.69% and accept results according to the performance measurements.</p>

Self-driving and semi-self-driving cars play an important role in our daily lives. The effectiveness of these cars is based heavily on the use of their surrounding areas to collect sensitive and vital information. However, external infrastructures also play significant roles in the transmission and reception of control data, cooperative awareness messages, and caution notifications. In this case, roadside units are considered one of the most important communication peripherals. Random distribution of these infrastructures will overburden the spread of self-driving vehicles in terms of cost, bandwidth, connectivity, and radio coverage area. In this paper, a new distributed roadside unit is proposed to enhance the performance and connectivity of these cars. Therefore, this approach is based primarily on k-means to find the optimal location of each roadside unit. In addition, this approach supports dynamic mobility with a long period of connectivity for each car. Further, this system can adapt to various locations (e.g., highways, rural areas, urban environments). The simulation results of the proposed system are reflected in its efficiency and effectively. Thus, the system can achieve a high connectivity rate with a low error rate while reducing costs.

Embedded systems are increasing in many areas, some examples of an embedded system are mobile phones, video game consoles, and industrial monitoring. Currently, interest with embedded systems is concerned with the issues of the providing safety, privacy and security. Security techniques rely on keeping the keys hidden, system can be exposed when the keys are described. We propose a system based on ICMetric that exploits the features of an embedded device to produce the identification of device. This paper proves that unique device features can be utilized to present an identity to a device which can be consumed for the providing security. The ICMetric technology has the facility to protect the systems by exploiting features of device. The proposed system uses the embedded MEMS magnetometer, gyroscope and accelerometer in the myAHRS_plus sensor to create a device ICMetric. An intelligent wheelchair is required which is equipped with the MEMS sensors. The proposed system is built on the features which have been produced by bias values of magnetometer, gyroscope and accelerometer. Reading generated from sensors are analyzed statistically to generate a triple ICMetric numbers used for device identification. The ICMetric number is not stored on the system and can be reproduced when necessary. If the system is attacked, there will be no theft because the ICMetric number is non-store. The proposal system proves that using MEMS sensors to generate an ICMetric number which using for device identification increase authentication and security of embedded devices.

In the past decade, traditional networks have been utilized to transfer data between more than one node. The primary problem related to formal networks is their stable essence, which makes them incapable of meeting the requirements of nodes recently inserted into the network. Thus, formal networks are substituted by a Software Defined Network (SDN). The latter can be utilized to construct a structure for intensive data applications like big data. In this paper, a comparative investigation of Deep Neural Network (DNN) and Machine Learning (ML) techniques that uses various feature selection techniques is undertaken. The ML techniques employed in this approach are decision tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM). The proposed approach is tested experimentally and evaluated using an available NSL–KDD dataset. This dataset includes 41 features and 148,517 samples. To evaluate the techniques, several estimation measurements are calculated. The results prove that DT is the most accurate and effective approach. Furthermore, the evaluation measurements indicate the efficacy of the presented approach compared to earlier studies.