Swarm Intelligence is a metaheuristic optimization approach that has become very predominant over the last few decades. These algorithms are inspired by animals' physical behaviors and their evolutionary perceptions. The simplicity of these algorithms allows researchers to simulate different natural phenomena to solve various real-world problems. This paper suggests a novel algorithm called Donkey and Smuggler Optimization Algorithm (DSO). The DSO is inspired by the searching behavior of donkeys. The algorithm imitates transportation behavior such as searching and selecting routes for movement by donkeys in the actual world. Two modes are established for implementing the search behavior and route-selection in this algorithm. These are the Smuggler and Donkeys. In the Smuggler mode, all the possible paths are discovered and the shortest path is then found. In the Donkeys mode, several donkey behaviors are utilized such as Run, Face & Suicide, and Face & Support. Real world data and applications are used to test the algorithm. The experimental results consisted of two parts, firstly, we used the standard benchmark test functions to evaluate the performance of the algorithm in respect to the most popular and the state of the art algorithms. Secondly, the DSO is adapted and implemented on three real-world applications namely; traveling salesman problem, packet routing, and ambulance routing. The experimental results of DSO on these real-world problems are very promising. The results exhibit that the suggested DSO is appropriate to tackle other unfamiliar search spaces and complex problems.Highlights Donkey and Smuggler Optimization Algorithm (DSO) is a novel optimization algorithm. The DSO is inspired by Donkeys' behaviors. It has been evaluated against other algorithms. DSO solves different problems such as TSP, packet routing, and ambulance routing. The outcomes on the tests exhibit a greater manipulation of DSO. The outcomes prove that DSO has a greater searching ability.

At present, deep learning is widely used in a broad range of arenas. A convolutional neural networks (CNN) is becoming the star of deep learning as it gives the best and most precise results when cracking real-world problems. In this work, a brief description of the applications of CNNs in two areas will be presented: First, in computer vision, generally, that is, scene labeling, face recognition, action recognition, and image classification; Second, in natural language processing, that is, the fields of speech recognition and text classification.

For the recent few years, Smart Wearable Systems (SWS) have been one of the most productive areas that can be considered for clinical utilization. They are an end-to-end connected source and sink (aggregator) nodes implemented to be in communication together either locally or remotely. However, a number of these sensors can be integrated on the Wireless Body Area Networks (WBAN), which is a new method for health monitoring. In particular, technology has become a trend that affect the patients' needs for requesting an innovation, low cost and flexible system in order to be aware in the change of their physiological and non-physiological body parameters. To provide this demand, HealthMate system is proposed as a solution for real-time health monitoring system that is based on Smart Wearable System for Health Monitoring (SWSHM). This paper is aimed at providing a health monitoring system for patients to measure their body parameters through e-Health sensors via smart phone and enabling doctors to check the health status of their patients remotely. Our solution allows patients to measure their body physiological and non-physiological parameters using body temperature, electrocardiogram, pulse and oxygen in blood, airflow and body position sensors. In addition, by using internet technologies, XBee wireless (802.15.4), and SMS to transmit measured patients body parameters to either mobile application or database on a cloud server, where artificial intelligence is accomplished to examine the data in order to notify health professions in emergency case using SMS services or e-mail.