An Effective Deep Learning Model to Discriminate Coronavirus Disease From Typical Pneumonia

Current technological advances are paving the way for technologies based on deep learning to be utilized in the majority of life fields. The effectiveness of these technologies has led them to be utilized in the medical field to classify and detect different diseases. Recently, the pandemic of coronavirus disease (COVID-19) has imposed considerable press on the health infrastructures all over the world. The reliable and early diagnosis of COVID-19-infected patients is crucial to limit and prevent its outbreak. COVID-19 diagnosis is feasible by utilizing reverse transcript-polymerase chain reaction testing;however, diagnosis utilizing chest x-ray radiography is deemed safe, reliable, and precise in various cases. © 2022 IGI Global. All rights reserved.

Hybrid Protege Training Program in Corporate Setting: An Evaluative Case Study

This study evaluates a hybrid training program focusing on soft skills development for freshly graduated trainees in a multinational biomedical engineering company. The evaluation is conducted as a diagnostic tool to measure the performance of trainees that feeds the return- on- investment (ROI) of the assigned company. We implement an evaluative case study with both quantitative and qualitative methods;- learning analytics and semi-structured interviews. The quantitative data comes from the learning analytics of twenty-nine trainees, while qualitative data comes from five interviewees from the same cohort. We found that the trainees' negative learning experiences are traced to an inadequate time to participate online and a perception of irrelevant online training content. These resulted in lower performance scores and engagement during online sessions compared to face-to-face sessions. The respondents also stated that face-to-face sessions allow in-person interaction with trainers. This factor led to a positive learning experience and potentially heightened engagement that directed to a higher performance score. Instructional design considerations in planning effective hybrid training are deliberately discussed for future practitioners and researchers. © 2022 IEEE.

Intelligent OpenFlow Switch for SDN Networks Based on COVID-19’s High Network Traffic using Heuristic GA-Fuzzification Control Approach

The COVID-19 outbreak has impacted network operators and data centers in terms of congestion and high traffic that lead to outages and significant pressure on the network. The overhead traffic is generated from web, voice calls, and Internet activity. In this paper, we are investigating data center congestion control for Software Defined Networks (SDN) network data centers. A Software-Defined (SDN) data center is an emerging networking paradigm that simplifies the network architecture by decentralizing plane functionality into a single with centralized decision capabilities. Along with the SDN paradigm, there is a crucial part that is responsible for forwarding packet called OpenFlow switching engine. In a typical SDN environment, the rules are initiated by the SDN controller and pushed to the OpenFlow switches. The traditional OpenFlow switch has no forwarding decision and depends on the incoming policies from the controller’s southbound interface. Additionally, the flow of traffic is initiated from different sources that are assigned to a specific route. However, this significant flow of traffic due to COVID-19 can lead to congestion and degradation of network performance in terms of delay and interruption. To be precise, a single OpenFlow switch could receive a capacity of traffic that floods its forwarding table and lead to link flaps and outages. In order to optimize the OpenFlow switch with regards to how much traffic it can host and to adjust routing capabilities for dynamic changes in the network, we propose an optimized OpenFlow congestion control and fault prediction framework for inbound traffic to overcome the inefficient route planning in the network. The proposed developed optimization algorithm is based on Genetic Evolutionary Algorithm criteria and adds intelligence to the OpenFlow switch by the adoption of Fuzzy Logic prediction capabilities. The experimental evaluation shows that the proposed optimization method adds significant intelligence and optimization to OpenFlow operation. The testbed was implemented experimentally using Raspberry Pi (RPI)cluster with customized SDN and OpenFlow deployment. The probability of the best fitness was 14.11% for Gen 999. The proposed approach adds intelligence and prediction into the OpenFlow switch to overcome the unstable flows of traffic and to predict faults to enhance the traffic capacity levels and manage flows into an entirely uninterrupted production environment. © 2022 IEEE.

Covid-19 Detection from Chest X-Ray Images using Convolutional Neural Network

COVID-19 reported cases in Malaysia is increasing every day. The lab facilities for testing COVID-19 has almost reached their capacity. Traditionally, to detect the COVID-19, a swab test is used. This method will take some time and be costly. The swab test kits are very scarce, and the human resources to do this test are limited. Through modern technology, there is a probability of detecting positive COVID-19 using X-ray images with deep learning. In this paper, Convolutional Neural Network (CNN) approach is used to detect Covid-19 through digital X-ray images. The 2D convolution kernel layer consists of three layers. The first layer has a 3 × 3 kernel, the second part has a 5 × 5 kernel, and the third part has a 7 × 7 kernel. Then, the output will be combined into one layer. Afterwards, the concatenated layer continued with another sequential process consisting of two convolution processes, ReLU and max pooling. Next, the model is then flattened, dropout and dense. A total of 2100 positive Covid-19 and negative Covid-19 images from Github and Kaggle databases have been used in this research. Based on the experiment done, the accuracy was almost 96%. © 2021 IEEE.