Medical Image Contrast Enhancement using Tuned Fuzzy Logic Intensification for COVID-19 Detection Applications

Recently, COVID-19 is spreading rapidly and fast detection of COVID-19 can save millions of lives. Further, the COVID-19 can be detected easily from computed tomography (CT) images using artificial intelligence methods. However, the performance of these application and methods are reduced due to noises presented in the CT images, which degrading the performance of overall systems. Therefore, this article is focused on implementation of an innovative method for quickly processing CT images of low quality, which enhances the contrast using fuzzy logic. This method makes use of tuned fuzzy intensification operators and is intended to speed up the processing time. Extensive experiments were carried out to test the processing capacity of the method that was proposed, and the results obtained demonstrated that it was capable of filtering a variety of images that had become degraded. © 2022 IEEE.

Performance evaluation of a fast and efficient intrusion detection framework for advanced persistent threat-based cyberattacks

After the COVID-19 pandemic, cyberattacks are increasing as non-face-to-face environments such as telecommuting and telemedicine proliferate. Cyberattackers exploit vulnerabilities in remote systems and endpoint devices in major enterprises and infrastructures. To counter these attacks, fast detection and response are essential because advanced persistent threat (APT) attacks intelligently infiltrate endpoint devices for long periods and spread to large-scale environments. However, because conventional security systems are signature-based, fast detection of APT attacks is challenging, and it is difficult to respond flexibly to the environment. In this study, we propose an APT fast detection and response technique using open-source tools that improves the efficiency of existing endpoint information protection systems and swiftly detects the APT attack process. Performance test results based on realistic scenarios using the open-source APT attack library and MITER ATT&CK indicated that fast detection was possible with higher accuracy for the early stages of APT attacks in scenarios where endpoint attack detectors are interworking environments. © 2022 The Authors

Fuzzy Logic and Deep learning Techniques for Covid-19 Detection

With the development of ICT and its adoption in various domains, it gained remarkable intention in the healthcare sector which introduce the telemedicine term. The coronavirus pandemic has created several challenges for researchers to develop an accurate and fast detection system. In this paper, we present a new telemedicine application to predict Covid-19 using CNN and Fuzzy set techniques. The evaluation of the system indicates high performance with a 98% F1 score, 99% of recall, 98% for precision, and 97% of accuracy. © 2022 IEEE.

Design of a simple SARS-CoV-2 (COVID-19) detector for fast detection

This work introduces a simple detector for SARS-CoV-2 (COVID-19) virus. The detector operates in a very simple mechanism. Peripheral circuits to represent the testing result are also simulated. The system can be designed and fabricated in a single integrated circuit (IC) chip. The response time analysis of the device shows the speed of detection of this device. This detector will be highly effective to detect the SARS-CoV-2 virus in the future. © 2022 IEEE.

Facial mask detection system based on YOLOv4 algorithm

This paper mainly addresses the detection of facial mask wear under the new COVID-19. To meet this demand, this paper performs facial mask wear detection on specific targets through a model trained based on the YOLOv4 algorithm. It has the characteristics of fast detection and light weight, and the application of this system to daily mask wear detection requires high real-time system performance. YOLOv4 meets this requirement, so the system designed based on this model has practical significance. This paper further demonstrates that the facial mask detection system designed based on the YOLOv4 algorithm is capable of working in multiple scenes of daily life, successfully detecting whether the target is wearing a mask in many scenes such as routine, multi-person and occlusion environment. © 2022 IEEE.

ANDTi Virtual Assistant

Due of the current COVID-19 pandemic crises, there is a worldwide need for quick medical findings. Furthermore, due to a lack of medical facilities and medical practitioners' hectic schedules, several examinations must now be performed by the general public. Also because of the high rate of transmissibility of COVID-19, even asymptomatic patients can readily transfer the virus to others, faster detection is critical during the initial phase of COVID-19, which is early identification. The earlier a patient is detected;the better the virus's spread may be stopped and the patient can undergo proper treatment. As the nationwide vaccination process is in its later part, it is obvious that the government will uplift its regulations and the employees will have to return to their workplaces or offices. As a solution to this upcoming urgency the authors would like to propose a solution to identify COVID-19 patients in advance at corporate level. As an IoT based solution a device is supposed to be setup on top of each employee's desk, which in return will be used to monitor the oxygen level, temperature, and heartbeat rate of the employees. © 2022 IEEE.

Realtime Face Mask Detection Using TINY-YOLO V4

In the time of the Covid-19 pandemic there is a need to maintain social distancing and prioritize personal hygiene by the use of face masks and proper sanitary precautions. This although is hard to be monitored and controlled accurately and efficiently, can be done through the use of object detection using convolutional neural networks. This can be done in a way using Tiny-YOLOv4 which is an object detection algorithm that provides lightning-fast detection for many classes of objects without the use of such hardware resources. This project aims to train and test a custom data set using this algorithm to create a highly efficient and accurate face mask detection system that can be easily customized to add additional features such as warning systems, etc. It aims to be a system that can prove to be useful once the pandemic is over as it provides crucial data for the prevention and control of any other possible pandemics that may occur in the future. © 2021 IEEE.

Nanomaterials for Viral Detection

The mass testing tactic is among the main strategies to fight a virus pandemic. It allows for an early diagnosis in the initial phase of the disease and reduces disease transmission. In this sense, there is a growing interest in developing devices with high sensitivity, selectivity, and fast detections. With this purpose, nanobiosensors are presented as a promising alternative, produced from nanomaterials with different structures and properties. On biosensing, NMs comprise transduction elements (transducers) associated with biomarkers to recognize and amplify different signals when interacting with biological material. The primary transducers involve optical and electrochemical methods. Gold nanoparticles (AuNPs) and carbon-based, such as graphene, graphene oxide, and carbon nanotube (CNT), make up most NMs used in biosensing. For such application, the use of magnetic nanoparticles (MNPs) and quantum dots (QDs) of different compositions, such as the basis of cadmium and tellurium (CdTe QDs), are also widely studied. In addition to applications in biosensing, nanomaterials can be applied in biomarker immobilization and extraction procedure in standard tests such as RT-PCR and LFIA (ELISA). NMs allow for the improvement of different techniques used in viral detection, presenting diverse and unique solutions for health crisis moments, including for Covid-19. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PRNet: Progressive Resolution based Network for Radiograph based disease classification

COVID-19 and Pneumonia have impacted human life significantly. The number of infected people and deaths are increasing every day due to COVID. Rapid COVID detection is important to control and stop the spread of the disease. Considering that AI can play a significant role in accurate and fast detection of such diseases, EE-RDS conducted a multi-class classification challenge by providing chest X-rays of pneumonia, COVID-19 and normal patients. We proposed PRNet, a novel deep learning pipeline and achieved 96.3% accuracy winning the 2nd position on the test set Leaderboard. © 2021 IEEE.

Deep Learning to Improve COVID-19 Detection: Using CNN-Based Transfer Learning from Chest X-Ray Images

Novel coronavirus (COVID-19) is a contagious disease that has already caused millions of deaths and infected millions of people worldwide. Thus, all technological gadgets that allow the fast detection of COVID-19 infection with high accuracy can offer help to healthcare professionals. Artificial intelligence techniques regarding image classification can assist in the early diagnosis of the disease. In this context, deep learning algorithms can be used to improve radiologists' COVID diagnosis. Specifically, these algorithms have recently demonstrated their potential for screening and detecting COVID-19 in CT and x-ray images. In this paper was explored the effectiveness of Convolutional Neural Networks with transfer learning in the rapid and reliable detection of COVID-19-induced and pneumonia based on chest X-ray imaging. In this study, reliable pre-trained deep learning algorithms were applied using dataset involved three classes of chest X-ray images: 415 images with confirmed Covid-19, 1,666 normal cases and 166 pneumonia. Those were collected from the available X-ray images on public medical repositories Github and Kaggle. The study revealed the superiority of Model DenseNet121 over Vgg16 model where the model performed best in terms of overall scores and based-class scores, where DenseNet121 pretrained model achieved overall accuracy of 94% and by VGG16 pretrained model only 89% is achieved. According to the results, deep learning with X-ray imaging is conducive for the physicians to make a diagnosis of this infectious disease and DenseNet121 can be used effectively for detecting COVID-19 from chest radiology images. © 2021 ACM.