Social Economic Impacts for Covid-19 Pandemics Using Machine Learning Based Optimization Algorithm

As the number of COVID-19 patients grows exponentially, not all cases are likely dealt with by doctors and medical professionals. Researchers will add to the fight against COVID-19 by developing smarter strategies to achieve accelerated control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus that causes disease. Proposed method suggests best ways to optimize protection and avoid COVID-19 spread. Big benefit of the hybrid algorithm is that COVID-19 is diagnosed and treated more rapidly. Pandemic diseases possibilities are handling with help of Computational Intelligence, using cases and applications from current COVID-19 pandemic. This work discusses data that can be analyzed based on optimization algorithm which provides betterCOVID-19 detection and diagnosis. This algorithm uses a machine learning model to decide how the hazard function changes concerning characteristics of potential methods to find parameters in optimization of machine learning model, which has in many cases been shown to be accurate for actual clinical datasets. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Computational Intelligence Against Covid-19 for Diagnosis Using Image Processing Techniques in Healthcare Sector

Coronavirus 2019 (COVID-19) medical images detection and classification are used in artificial intelligence (AI) techniques. Few months back, from the observation it is witnessed that there is a rapid increase in using AI techniques for diagnosing COVID-19 with chest computed tomography (CT) images. AI more accurately detects COVID-19;moreover efficiently differentiates this from other lung infection and pneumonia. AI is very useful and has been broadly accepted in medical applications as its accuracy and prediction rates are high. This paper is developed and aims to fight against corona through AI using computational intelligence in detecting and classifying COVID-19 using Densnet-121 architecture on chest CT images from a global diverse multi-institution dataset. Furthermore, data from clinics and images from medical applications improve the performance of the proposed approach and provide better response with practical applications. Classification performance was evaluated by confusion matrices followed by overall accuracy, precision, recall and specificity for precisely classifying COVID-19 against any condition. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

A Lightweight Depthwise Separable Convolution Neural Network for Screening Covid-19 Infection from Chest CT and X-ray Images

Because Covid-19 spreads swiftly in the community, an automatic detection system is required to prevent Covid-19 from spreading among humans as a rapid diagnostic tool. In this paper, we propose to employ Convolution Neural Networks to detect coronavirus-infected patients using Computed Tomography and X-ray images. In addition, we look into the transfer learning of a deep CNN model, DenseNet201 for detecting infection from CT and X-ray scans. Grid Search optimization is utilized to select ideal values for hyperparameters, while image augmentation is employed to increase the model's capacity to generalize. We further modify DenseNet architecture to incorporate a depthwise separable convolution for detecting coronavirus-infected patients utilizing CT and Xray images. Interestingly, all of the proposed models scored greater than 94% accuracy, which is equivalent to or higher than the accuracy of earlier deep learning models. Further, we demonstrate that depthwise separable convolution reduces the training time and computation complexity.

AI Powered COVID-19 Detection System using Non -Contact Sensing Technology and Deep Learning Techniques

The SARS-CoV-2 virus causes coronary artery disease (COVID-19). The majority of persons who are infected with the virus will have mild to severe respiratory illness and recover without the need for therapy. Some, on the other hand, will become critically unwell and require medical assistance. People over the age of 65, as well as those with underlying medical diseases such as cardiovascular disease, diabetes, chronic respiratory disease, or cancer, are at a higher risk of developing serious illness. Being thoroughly informed on the disease and how it spreads is the best strategy to avoid and slow down transmission. Stay at least 1 metre apart from other people to avoid infection. In this research work, we focus on how non-contact sensing technology and deep learning technique are being used to detect COVID-19 and assist healthcare workers in caring for COVID-19 patients. The proposed system captures images from the patient using non -contact sensing technologies and feeds the data into deep learning convolutional neural network architectures such as VGG16, VGG19, ResNet101, NASNet, DenseNet121, MobileNet, Xception, EfficientNet, and InceptionV3. In comparison to other architectures, the VGG16 architecture delivers superior accuracy.

A study on Histopathological changes and neonatal outcomes in the placenta of covid-19 positive mothers: A prospective study

CONTEXT It has been estimated that are about 168 million lab confirmed COVID 19 cases worldwide as of 28th may, 2021. Due to the high prevalence of this disease, it is of utmost importance to study its effect on the vulnerable population of pregnant women. aim: Aim of the study are 1: the histopathological changes in placenta of COVID19 mothers. 2: To correlate the histopathological changes with the fetal outcome in COVID 19 positive mothers. MATERIALS & METHOD: Twenty five placentas of Covid 19 positive mothers were received in formalin with proper clinical history including age of the mother, gestational age, mode of delivery, complications during pregnancy and during labor, baby weight and APGAR score of the baby. The specimen were allowed to fix in neutral buffered formalin for a period of 48 hrs. The placentas were then grossly and histopathologically examined. RESULTS: Out of 25 placentas, some showed features of maternal vascular malperfusion (MVM), particularly villous infarct, villous agglutination and intervillous and perivillous fibrin deposits. Some showed fetal vascular malperfusion features like avascular villi, stem vessel obliteration were also present in a few of the cases. Out of the 25 pregnancies, 21 babies were delivered live with normal birth weight. There were 4 spontaneous abortions ranging from 14 - 22 weeks. CONCLUSION As the placenta acts as a bridge between the mother and the developing baby, any insult to the placenta in the form of maternal or fetal vascular malperfusion may result in an adverse perinatal outcome. © 2022. RED FLOWER PUBLICATIONS PVT LTD. All Rights Reserved.

A Detailed Exploration of Artificial Intelligence and Digital Education and Its Sustainable Impact on the Youth of Society

Artificial intelligence is a software-based modern technology that helps to improve the communication system. Therefore, by using artificial intelligence some digital applications are launched for education digitally. Thus, the use of artificial intelligence and digital applications for education has increased during the COVID-19 pandemic situation. As all the institutions, colleges, and schools were closed for a certain time due to the pandemic situation;the stoppage of studies was impacting the productivity of the students. Therefore, the digital applications and artificial intelligence help the students to continue their education through those applications. Apart from that, these applications and systems for digital education impact the youth of the society largely. The students have to stay in their homes and they can use these digital applications to continue their education and this impacts their communication skills. Along with that, there are a lot of beneficial sites of artificial intelligence and sustainable impacts on the youth of the society. On the other hand, the purpose of this particular research study is to analyse the sustainable impacts of artificial intelligence and digital applications of education on the youth of the society. Artificial intelligence has a great impact on the education system. Therefore, the advantages and disadvantages of the uses of artificial intelligence in education are analysed in this particular research study. Furthermore, the researcher has used several types of methods and techniques for collecting and analysing more data and information about the particular research topic. Thereafter the researcher has used the secondary methods and sources for collecting data about artificial intelligence and its impacts on youth of the society. Apart from that, the research has used the qualitative techniques for analysing all the collected data in this particular research study in a proper way. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Effective Cyber Security Using IoT to Prevent E-Threats and Hacking During Covid-19

This research work is conducted to make the analysis of digital technology is one of the most admired and effective technologies that has been applied in the global context for faster data management. Starting from business management to connectivity, everywhere the application of IoT and digital technology is undeniable. Besides the advancement of the data management, cyber security is also important to prevent the data stealing or accessing from the unauthorized data. In this context the IoT security technology focusing on the safeguarding the IoT devices connected with internet. Different technologies are taken under the consideration for developing the IoT based cyber security such as Device authentication, Secure on boarding, data encryption and creation of the bootstrap server. All of these technologies are effective to its ground for protecting the digital data. In order to prevent cyber threats and hacking activities like SQL injection, Phishing, and DoS, this research paper has proposed a newer technique of the encryption process by using the python codes and also shown the difference between typical conventional system and proposed system for understanding both the system in a better way. © 2022 by Dr. Santosh Kumar, Dr. Rajeev Yadav, Dr. Priyanka Kaushik, S B G Tilak Babu, Dr. Rajesh Kumar Dubey and Dr. Muthukumar Subramanian.

Screening Covid-19 Infection from Chest CT Images using Deep Learning Models based on Transfer Learning

As the global epidemic of Covid19 progresses, accurate diagnosis of Covid19 patients becomes important. The biggest problem in diagnosing test-positive people is the lack or lack of test kits due to the rapid spread of Covid19 in the community. As an alternative rapid diagnostic method, an automated detection system is needed to prevent Covid 19 from spreading to humans. This article proposes to use a convolutional neural network (CNN) to detect patients infected with coronavirus using computer tomography (CT) images. In addition, the transfer learning of the deep CNN model VGG16 is investigated to detect infections on CT scans. The pretrained VGG16 classifier is used as a classifier, feature extractor, and fine tuner in three different sets of tests. Image augmentation is used to boost the model's generalization capacity, while Bayesian optimization is used to pick optimum values for hyperparameters. In order to fine-tune the models and reduce training time, transfer learning is being researched. Surprisingly, all of the proposed models scored greater than 93% accuracy, which is on par with or better than previous deep learning models. The results show that optimization improved generalization in all models and highlight the efficacy of the proposed strategies. © 2022 IEEE.

Real-time monitoring of social distancing with person marking and tracking system using YOLO V3 model

The global economy has been affected enormously due to the spread of coronavirus (COVID-19). Even though, there is the availability of vaccines, social distancing in public places is one of the viable solutions to reduce the spreading of COVID-19 suggested by the World Health Organization (WHO) for fighting against the pandemic. This paper presents a YOLO v3 object detection model to automate the monitoring of social distancing among persons through a CCTV surveillance camera. Furthermore, this research work used to detect and track the person, measure the inter-person distance in the crowd under a challenging environment which includes partial visibility, lighting variations, and person occlusion. Moreover, the YOLO V3 model experiments with Darknet53 and ShuffleNetV2 backbone architecture. Compared with Darknet53 architecture, ShuffleNetV2 achieves better detection accuracy tested on Custom Video Footage Dataset (CVFD), Oxford Town Centre Dataset (OTCD), and Custom Personal Image Dataset (CPID) datasets.

Patient satisfaction with ophthalmic telemedicine visits during the COVID-19 pandemic

Purpose : To compare patient satisfaction for telemedicine visits to traditional in-person clinical visits during the COVID-19 pandemic in the Ophthalmology Department at Boston Medical Center (BMC), the largest academic safety-net hospital in New England. Methods : Patient satisfaction surveys using the NRC Health platform were sent to all patients in their preferred language following eye clinic visits at BMC from June to October 2020. Three visit types were studied: 1) virtual visits via telephone or video conferencing, 2) hybrid visits with protocol-driven set of undilated imaging (e.g. OCT, fundus photos, visual fields), visual acuity, and intraocular pressure obtained by a trained technician, followed by a virtual visit with the physician within 1-2 weeks, and 3) traditional in-person visits. Twotailed Student's t-test was used to compare survey responses of telemedicine to traditional visits in 4 questions: 1) trust in provider (4-point scale, trust), 2) felt provider listened (4- point scale, listened), 3) satisfied with amount of time spent with provider (4-point scale, time), and 4) recommend provider to other patients (10-point scale, recommend). Additionally, responses between English and non-English speakers, requiring trained interpreter services, were compared. Results : A total of 793 visits were included (44 virtual, 56 hybrid, 693 traditional). The majority of telemedicine visits were from the retina and optometry services (Figure 1). There was no statistically significant difference in trust, listened, time, or recommend when comparing virtual or hybrid visits to traditional visits (Table 1a). NonEnglish speakers had statistically significant lower scores in trust, listened, and time with no difference in recommend when compared to English speakers (Table 1b). When stratified by visit type, non-English speakers had a trend towards a lower score in trust for both virtual and hybrid groups. Conclusions : Telemedicine provides patients access to clinical care with decreased risk of infection during the COVID-19 pandemic. Non-English speakers tended to have less trust in the physician for all visit types, which should be considered when communicating with patients. Overall, we found that patients were equally satisfied with telemedicine visits as with traditional in-person visits in a hospital-based academic eye clinic.

A prediction of prostate cancer deathsspiking by SARS-CoV-2 infection

COVID-19 is a global issue, with over 6.25 million cases in 213 countries and territories on June 1, 2020. Althoughthis virus infects all groups, data indicate that the risk for severe disease and death is much higher in older men, which coincides with the same group of patients at risk for prostate cancer. A recent Italian study investigated theprevalence and severity of COVID-19 in men with prostate cancer. This study indicated that of a total of 4,532 men with COVID-19, from the Veneto region of Italy, 9.5% (n=430) had cancer and out of those around 30% (n=118) hadprostate cancer. Data also indicated that male cancer patients had a 1.8-fold increased risk of COVID-19 infectionand developed a more severe disease. Interestingly, they observed that the prostate cancer patients (n=4) treated with androgen-deprivation therapy (ADT) were less likely to develop COVID-19, and in those who were infected, thedisease was less severe. In this current study, we focused on determining the genetic basis of the higher COVID-19prevalence and severity in male patients and particularly for prostate cancer patients. Researchers found two genesthat are essential for severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). ACE2 is a SARS-CoV-2receptor, whereas the serine protease, TMPRSS2, primes the virus for cell entry through cleavage of the viral spikeprotein (S). The expression of TMPRSS2 is significantly high in normal prostate tissue and is regulated in large partby an androgen response element in the promoter region. Therefore, we decided to investigate the status of thesetwo genes in various tumors from The Cancer Genome Atlas (TCGA) database using the cBioportal platform. Weanalyzed over 46,000 tumor samples from 176 studies and found that aggressive metastatic prostate cancer, including neuroendocrine prostate cancer (NEPC), has significantly higher amplification (copy number alteration) ofthe ACE2 and TMPRSS2 genes compared to other cancers. Next, we focused on drugs that could simultaneouslytarget ACE2 or TMPRSS2 and oncogenic pathways and would be beneficial for prostate cancer patients infected with SARS-CoV-2. Although several inhibitors are validated in literature for both ACE2 and TMPRSS2, very limitedstudies were performed to see the effect on cancer cells. Therefore, we analyzed a cytotoxic effect database of over130,000 drugs on NCI-60 cell lines with COMPARE algorithm and found two relevant compounds, NSC-148958 (FT-701) and NSC-280594 (triciribine phosphate), which target ACE2 and TMPRSS2, respectively. Computational dataare currently validating different prostate cancer cell-lines and their response to these drugs. In summary, ourfindings provide the premise that men who are at risk for or diagnosed with prostate cancer may be moresusceptible to severe infection and death in response to SARS-CoV-2 due to the high expression of ACE2 andTMPRSS2, and triciribine phosphate and FT-701 could be a therapeutic intervention to target co-occurrence ofCOVID-19 and prostate cancer.