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 th...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.展开更多
There are many advantages of using Computer Aided Design (CAD) Systems in a shipbuilding environment: ease of design, speed of construction, use and reuse of information, etc. It is expected that in future CAD tools w...There are many advantages of using Computer Aided Design (CAD) Systems in a shipbuilding environment: ease of design, speed of construction, use and reuse of information, etc. It is expected that in future CAD tools will advance further and allow greater information management and virtual access through smart devices. The authors of this paper talk about a new concept in shipbuilding, the Internet-of-Ships (IoS) which would have a deep impact on the ship design and production, with a huge diversity of present and potential applications.展开更多
In this paper, a simple method for merging of Bezier curves is presented by using constrained optimization method. The use of the “discrete” coefficient norm in L2 sense greatly simplifies the merging process. Furth...In this paper, a simple method for merging of Bezier curves is presented by using constrained optimization method. The use of the “discrete” coefficient norm in L2 sense greatly simplifies the merging process. Furthermore, continuity at the endpoint of curves are considered in the merging process.展开更多
基金This work was funded by the Researchers Supporting Project Number(RSP-2021/300),King Saud University,Riyadh,Saudi Arabia.
文摘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.
文摘There are many advantages of using Computer Aided Design (CAD) Systems in a shipbuilding environment: ease of design, speed of construction, use and reuse of information, etc. It is expected that in future CAD tools will advance further and allow greater information management and virtual access through smart devices. The authors of this paper talk about a new concept in shipbuilding, the Internet-of-Ships (IoS) which would have a deep impact on the ship design and production, with a huge diversity of present and potential applications.
文摘In this paper, a simple method for merging of Bezier curves is presented by using constrained optimization method. The use of the “discrete” coefficient norm in L2 sense greatly simplifies the merging process. Furthermore, continuity at the endpoint of curves are considered in the merging process.
