Brain tumor localization and segmentation from magnetic resonance imaging (MRI) are hard and important tasks for several applications in the field of medical analysis. As each brain imaging modality gives unique and key details related to each part of the tumor, many recent approaches used four modalities T1, T1c, T2, and FLAIR. Although many of them obtained a promising segmentation result on the BRATS 2018 dataset, they suffer from a complex structure that needs more time to train and test. So, in this paper, to obtain a flexible and effective brain tumor segmentation system, first, we propose a preprocessing approach to work only on a small part of the image rather than the whole part of the image. This method leads to a decrease in computing time and overcomes the overfitting problems in a Cascade Deep Learning model. In the second step, as we are dealing with a smaller part of brain images in each slice, a simple and efficient Cascade Convolutional Neural Network (C-ConvNet/C-CNN) is proposed. This C-CNN model mines both local and global features in two different routes. Also, to improve the brain tumor segmentation accuracy compared with the state-of-the-art models, a novel Distance-Wise Attention (DWA) mechanism is introduced. The DWA mechanism considers the effect of the center location of the tumor and the brain inside the model. Comprehensive experiments are conducted on the BRATS 2018 dataset and show that the proposed model obtains competitive results: the proposed method achieves a mean whole tumor, enhancing tumor, and tumor core dice scores of 0.9203, 0.9113 and 0.8726 respectively. Other quantitative and qualitative assessments are presented and discussed.

The fourth industrial revolution heralds a paradigm shift in how people, processes, things, data and networks communicate and connect with each other. Conventional computing infrastructures are struggling to satisfy dramatic growth in demand from a deluge of connected heterogeneous end points located at the edge of networks while, at the same time, meeting quality of service levels. The complexity of computing at the edge makes it increasingly difficult for infrastructure providers to plan for and provision resources to meet this demand. While simulation frameworks are used extensively in the modelling of cloud computing environments in order to test and validate technical solutions, they are at a nascent stage of development and adoption for fog and edge computing. This paper provides an overview of challenges posed by fog and edge computing in relation to simulation.

The COVID-19 pandemic is a global, national, and local public health concern which has caused a significant outbreak in all countries and regions for both males and females around the world. Automated detection of lung infections and their boundaries from medical images offers a great potential to augment the patient treatment healthcare strategies for tackling COVID-19 and its impacts. Detecting this disease from lung CT scan images is perhaps one of the fastest ways to diagnose patients. However, finding the presence of infected tissues and segment them from CT slices faces numerous challenges, including similar adjacent tissues, vague boundary, and erratic infections. To eliminate these obstacles, we propose a two-route convolutional neural network (CNN) by extracting global and local features for detecting and classifying COVID-19 infection from CT images. Each pixel from the image is classified into the normal and infected tissues. For improving the classification accuracy, we used two different strategies including fuzzy c -means clustering and local directional pattern (LDN) encoding methods to represent the input image differently. This allows us to find more complex pattern from the image. To overcome the overfitting problems due to small samples, an augmentation approach is utilized. The results demonstrated that the proposed framework achieved precision 96%, recall 97%, F score, average surface distance (ASD) of 2.8 ± 0.3  mm, and volume overlap error (VOE) of 5.6 ± 1.2 % .

Clustering techniques are very attractive for identifying and extracting patterns of interests from datasets. However, their application to very large spatial datasets presents numerous challenges such as high-dimensionality, heterogeneity, and high complexity of some algorithms. Distributed clustering techniques constitute a very good alternative to the Big Data challenges (e.g., Volume, Variety, Veracity, and Velocity). In this paper, we developed and implemented a Dynamic Parallel and Distributed clustering (DPDC) approach that can analyse Big Data within a reasonable response time and produce accurate results, by using existing and current computing and storage infrastructure, such as cloud computing. The DPDC approach consists of two phases. The first phase is fully parallel and it generates local clusters and the second phase aggregates the local results to obtain global clusters. The aggregation phase is designed in such a way that the final clusters are compact and accurate while the overall process is efficient in time and memory allocation. DPDC was thoroughly tested and compared to wellknown clustering algorithms BIRCH and CURE. The results show that the approach not only produces high-quality results but also scales up very well by taking advantage of the Hadoop MapReduce paradigm or any distributed system.

In recent years, there has been significant advancement in resource management mechanisms for cloud computing infrastructure performance in terms of cost, quality of service (QoS) and energy consumption. The emergence of the Internet of Things has led to the development of infrastructure that extends beyond centralised data centers from the cloud to the edge, the so-called cloud-to-thing continuum (C2T). This infrastructure is characterised by extreme heterogeneity, geographic distribution, and complexity, where the key performance indicators (KPIs) for the traditional model of cloud computing may no longer apply in the same way. Existing resource management mechanisms may not be suitable for such complex environments and therefore require thorough testing, validation and evaluation before even being considered for live system implementation. Similarly, previously discounted resource management proposals may be more relevant and worthy of revisiting. Simulation is a widely used technique in the development and evaluation of resource management mechanisms for cloud computing but is a relatively nascent research area for new C2T computing paradigms such as fog and edge computing. We present a methodical literature analysis of C2T resource management research using simulation software tools to assist researchers in identifying suitable methods, algorithms, and simulation approaches for future research. We analyse 35 research articles from a total collection of 317 journal articles published from January 2009 to March 2019. We present our descriptive and synthetic analysis from a variety of perspectives including resource management, C2T layer, and simulation.