End-to-end security in embedded system for modern mobile communication technologies

Modern mobile electronic devices such as smartphones or cell phones can now be used for distant devices such as technical systems to monitor and control. While surveillance systems do not require high standards navigating about the time of transfer of the displayed data. More real-time data are needed for a remote mobile robot transfer. Therefore, it has investigated and measured not only the possibilities of employing mobile devices. But also, the supported data transmission channels, such as UMTS, GSM, Wireless LAN, and Bluetooth. The remotecontrol system is used in many applications such as smart homes, cities, smart hospitals, etc., but it must be today updated to ensure fast-changing technology. Extensive coverage, remote control, and reliable operation in realtime in the deployment of wireless security knowledge. The home automation control system delivers significant features together with a user-friendly interface. A secure remote-based end-to-end security system NTMobile, a technique that enables NAT to provide transverse and encrypted communication from end to end. This confirmed that evaluating the performance of the system in the ECHONET lite compatible smartphone ecosystem. This gives flexibility in configuring time-sensitive industrial networks and enables them to be secured. A safe and reliable remote-control system is also conceivable under the privacy of the user

Removal notice to “Machine learning in health condition check-up: An approach using Breiman's random forest algorithm”

This article has been removed: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/policies/article-withdrawal).This article has been removed at the request of the Authors, due to incomplete authorisation for the publication of the article from one of the author's institutions. The authors sincerely apologize for the inconvenience

Leaf disease identification and classification using optimized deep learning

Diseases that affect plant leaves stop the growth of their individual species. Early and accurate diagnosis of plant diseases may reduce the likelihood that the plant will suffer further harm. The intriguing approach needed more time, exclusivity, and skill. Images of leaves are used to identify plant leaf diseases. Research on deep learning (DL) appears to have a lot of potential for improved accuracy. The substantial advancements and expansions in deep learning have created the opportunity to improve the coordination and accuracy of the system for identifying and appreciating plant leaf diseases. This study presents an innovative deep learning technique for disease detection and classification named Ant Colony Optimization with Convolution Neural Network (ACO-CNN).The effectiveness of disease diagnosis in plant leaves was investigated using ant colony optimization (ACO). Geometries of colour, texture, and plant leaf arrangement are subtracted from the provided images using the CNN classifier. A few of the effectiveness metrics used for analysis and proposing a suggested method prove that the proposed approach performs better than existing techniques with an accuracy rate concert measures are utilized for the execution of these approaches. These steps are used in the phases of disease detection: picture acquisition, image separation, nose removal, and classification