Real-Time Multi-Class Infection Classification for Respiratory Diseases

Real-time disease prediction has emerged as the main focus of study in the field of computerized medicine.Intelligent disease identification framework can assist medical practitioners in diagnosing disease in a way that is reliable,consistent,and timely,successfully lowering mortality rates,particularly during endemics and pandemics.To prevent this pandemic’s rapid and widespread,it is vital to quickly identify,confine,and treat affected individuals.The need for auxiliary computer-aided diagnostic(CAD)systems has grown.Numerous recent studies have indicated that radiological pictures contained critical information regarding the COVID-19 virus.Utilizing advanced convolutional neural network(CNN)architectures in conjunction with radiological imaging makes it possible to provide rapid,accurate,and extremely useful susceptible classifications.This research work proposes a methodology for real-time detection of COVID-19 infections caused by the Corona Virus.The purpose of this study is to offer a two-way COVID-19(2WCD)diagnosis prediction deep learning system that is built on Transfer Learning Methodologies(TLM)and features customized fine-tuning on top of fully connected layered pre-trained CNN architectures.2WCD has applied modifications to pre-trained models for better performance.It is designed and implemented to improve the generalization ability of the classifier for binary and multi-class models.Along with the ability to differentiate COVID-19 and No-Patient in the binary class model and COVID-19,No-Patient,and Pneumonia in the multi-class model,our framework is augmented with a critical add-on for visually demonstrating infection in any tested radiological image by highlighting the affected region in the patient’s lung in a recognizable color pattern.The proposed system is shown to be extremely robust and reliable for real-time COVID-19 diagnostic prediction.It can also be used to forecast other lung-related disorders.As the system can assist medical practitioners in diagnosing the greatest number of patients in the shortestamount of time, radiologists can also be used or published online to assistany less-experienced individual in obtaining an accurate immediate screeningfor their radiological images.

Intelligent Approach for Traffic Orchestration in SDVN Based on CMPR

The vehicle ad hoc network that has emerged in recent years was originally a branch of the mobile ad hoc network.With the drafting and gradual establishment of standards such as IEEE802.11p and IEEE1609,the vehicle ad hoc network has gradually become independent of the mobile ad hoc network.The Internet of Vehicles(Vehicular Ad Hoc Network,VANET)is a vehicle-mounted network that comprises vehicles and roadside basic units.This multi-hop hybrid wireless network is based on a vehicle-mounted self-organizing network.As compared to other wireless networks,such as mobile ad hoc networks,wireless sensor networks,wireless mesh networks,etc.,the Internet of Vehicles offers benefits such as a large network scale,limited network topology,and predictability of node movement.The paper elaborates on the Traffic Orchestration(TO)problems in the Software-Defined Vehicular Networks(SDVN).A succinct examination of the Software-defined networks(SDN)is provided along with the growing relevance of TO in SDVN.Considering the technology features of SDN,a modified TO method is proposed,which makes it possible to reduce time complexity in terms of a group of path creation while simultaneously reducing the time needed for path reconfiguration.A criterion for path choosing is proposed and justified,which makes it possible to optimize the load of transport network channels.Summing up,this paper justifies using multipath routing for TO.