Summary With the ever‐increasing popularity of resource‐intensive mobile applications, today, Fog‐to‐Cloud (F2C) computing system becomes a prominent technology for the next generation wireless networks. Despite the benefits of fog computing regarding localized storage and processing, it suffers from restricted power allowance and computational capability of the edge nodes. User nodes also may suffer from extensive delay, especially in offloading periods. Therefore, it is essential to develop a distributed mechanism for users' computation in offloading periods. According to this mechanism, not only the computational servers are exploited at their best capacity but also the users' latency constraints fulfilled. Consequently, this paper develops automated distributed fog computing for computational offloading using the theory of minority game. The proposed scheme achieves user satisfaction latency deadline as well as Quality‐of‐Experience. Moreover, it guarantees an adaptive equilibrium level of F2C computing system, which is suitable for heterogeneous wireless networks.

No abstract

Detection and classification of brain tumors are of formidable importance in neuroscience. Deep learning (DL), specifically convolution neural networks (CNN), has demonstrated breakthroughs <br />in the field of brain image analysis and brain tumors classification. This work proposes a novel CNN based model for brain tumor classification. Our pipeline starts with prepossessing and data augmentation techniques. Then, a CNN classification step is developed and utilizes ResNet50 architecture as its core. Particularly, our design modified the ResNet50 output with a global average pooling (GAP) layer to avoid over-fitting. The proposed model is trained and tested using different optimization algorithms. The final classification is achieved using a sigmoid layer. We tested the proposed structure on T1 weighted contrast-enhanced magnetic resonance images (T1-w MRI) that are collected from three datasets. A total of 3586 images containing two classes (i.e., bengin, and malignant) were used in our experiments. The proposed model reach highest accuracy 99.8%, and optimal error 0.005 using Adam when compared with other six well-known CNN architectures.

Accurate detection of COVID-19 is of immense importance to help physicians intervene with appropriate treatments. Although RT-PCR is routinely used for COVID-19 detection, it is expensive, takes a long time, and is prone to inaccurate results. Currently, medical imaging-based detection systems have been explored as an alternative for more accurate diagnosis. In this work, we propose a multi-level diagnostic framework for the accurate detection of COVID-19 using X-ray scans based on transfer learning. The developed framework consists of three stages, beginning with a pre-processing step to remove noise effects and image resizing followed by a deep learning architecture utilizing an Xception pre-trained model for feature extraction from the pre-processed image. Our design utilizes a global average pooling (GAP) layer for avoiding over-fitting, and an activation layer is added in order to reduce the losses. Final classification is achieved using a softmax layer. The system is evaluated using different activation functions and thresholds with different optimizers. We used a benchmark dataset from the kaggle website. The proposed model has been evaluated on 7395 images that consist of 3 classes (COVID-19, normal and pneumonia). Additionally, we compared our framework with the traditional pre-trained deep learning models and with other literature studies. Our evaluation using various metrics showed that our framework achieved a high test accuracy of 99.3% with a minimum loss of 0.02 using the LeakyReLU activation function at a threshold equal to 0.1 with the RMSprop optimizer. Additionally, we achieved a sensitivity and specificity of 99 and F1-Score of 99.3% with only 10 epochs and a 10−4 learning rate.

The photonics world is becoming increasingly interested in plasmonic photodetectors. The field of plasmonics allows light to be dedicated into small spaces in metallic structures, and this property has the ability to simulate extra improvements in performance of photodetectors. In this work it is presented gold (Au) and Aluminum (Al) surface plasmon polariton (SPP) GaAs PIN photodetector achieved higher internal quantum efficiency (IQE), as the IQE with Au and Al SPP rectangle grating is 95.01%, while the IQE with Au SPP rectangle grating is 85%, which indicates an improvement of 10.01%. It is also presented nanograting various shapes such as triangular, ellipse, bowtie and circle, and investigate its impact on improving the performance of the proposed photodetector. The internal quantum efficiency is improved to 98.48% with a total enhancement IQE of 13.48%. Moreover, output current and responsivity are improved. This remarkable enhancement is achieved with bowtie grating as compared to other shapes and previous published papers.