Ali Safaa Sadiq

Anuradha Banerjee, Abu Sufian, Ali Safaa Sadiq et al.

Routing in Software-Defined Wireless sensor networks (SD-WSNs) can be either single or multi-hop, whereas the network is either static or dynamic. In static SD-WSN, the selection of the optimum route from source to destination is accomplished by the SDN controller(s). On the other hand, if moving sensors are there, then SDN controllers of zones cannot handle route discovery sessions by themselves; they can only store information about the most recent zone state. Moving sensors find lots of robotics applications where robots continue to move from one room to another to sensing the environment. A huge amount of energy can be saved in these networks if transmission range control is applied. Multiple power levels exist in each node, and each of these levels takes possible actions after a potential sender node decides to transmit/forward a message. Based on each such activity, the next states of the concerned sender node and the communication session are re-determined while the router receives a reward. The Epsilon-greedy algorithm is applied in this study to decide the optimum power level in the next iteration. It is determined anew depending upon the present network scenario. Simulation results show that our proposed work leads the network to equilibrium by reducing energy consumption and maintaining network throughput.

Halgurd S. Maghdid, Aras T. Asaad, Kayhan Zrar Ghafoor et al.

COVID-19 (also known as 2019 Novel Coronavirus) first emerged in Wuhan, China and spread across the globe with unprecedented effect and has now become the greatest crisis of the modern era. The COVID-19 has proved much more pervasive demands for diagnosis that has driven researchers to develop more intelligent, highly responsive and efficient detection methods. In this work, we focus on proposing AI tools that can be used by radiologists or healthcare professionals to diagnose COVID-19 cases in a quick and accurate manner. However, the lack of a publicly available dataset of X-ray and CT images makes the design of such AI tools a challenging task. To this end, this study aims to build a comprehensive dataset of X-rays and CT scan images from multiple sources as well as provides a simple but an effective COVID-19 detection technique using deep learning and transfer learning algorithms. In this vein, a simple convolution neural network (CNN) and modified pre-trained AlexNet model are applied on the prepared X-rays and CT scan images dataset. The result of the experiments shows that the utilized models can provide accuracy up to 98 % via pre-trained network and 94.1 % accuracy by using the modified CNN.

Halgurd S. Maghdid, Kayhan Zrar Ghafoor, Ali Safaa Sadiq et al.

Coronaviruses are a famous family of viruses that cause illness in both humans and animals. The new type of coronavirus COVID-19 was firstly discovered in Wuhan, China. However, recently, the virus has widely spread in most of the world and causing a pandemic according to the World Health Organization (WHO). Further, nowadays, all the world countries are striving to control the COVID-19. There are many mechanisms to detect coronavirus including clinical analysis of chest CT scan images and blood test results. The confirmed COVID-19 patient manifests as fever, tiredness, and dry cough. Particularly, several techniques can be used to detect the initial results of the virus such as medical detection Kits. However, such devices are incurring huge cost, taking time to install them and use. Therefore, in this paper, a new framework is proposed to detect COVID-19 using built-in smartphone sensors. The proposal provides a low-cost solution, since most of radiologists have already held smartphones for different daily-purposes. Not only that but also ordinary people can use the framework on their smartphones for the virus detection purposes. Nowadays Smartphones are powerful with existing computation-rich processors, memory space, and large number of sensors including cameras, microphone, temperature sensor, inertial sensors, proximity, colour-sensor, humidity-sensor, and wireless chipsets/sensors. The designed Artificial Intelligence (AI) enabled framework reads the smartphone sensors signal measurements to predict the grade of severity of the pneumonia as well as predicting the result of the disease.