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R. Sandhiya

Bio: R. Sandhiya is an academic researcher. The author has contributed to research in topics: Computer science & Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The author has an hindex of 1, co-authored 1 publications receiving 7 citations.

Papers
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Journal ArticleDOI
TL;DR: The results show that the VGG16 architecture gives better accuracy compared to other architectures for COVID-19, a novel pandemic that has emerged as a pandemic in recent years.
Abstract: SARS-CoV-2 is a novel virus, responsible for causing the COVID-19 pandemic that has emerged as a pandemic in recent years. Humans are becoming infected with the virus. In 2019, the city of Wuhan reported the first-ever incidence of COVID-19. COVID-19 infected people have symptoms that are related to pneumonia, and the virus affects the body's respiratory organs, making breathing difficult. A real-time reverse transcriptase-polymerase chain reaction (RT-PCR) kit is used to diagnose the disease. Due to a shortage of kits, suspected patients cannot be treated promptly, resulting in disease spread. To develop an alternative, radiologists looked at the changes in radiological imaging, like CT scans, that produce comprehensive pictures of the body of excellent quality. The suspected patient's computed tomography (CT) scan is used to distinguish between a healthy individual and a COVID-19 patient using deep learning algorithms. A lot of deep learning methods have been proposed for COVID-19. The proposed work utilizes CNN architectures like VGG16, DeseNet121, MobileNet, NASNet, Xception, and EfficientNet. The dataset contains 3873 total CT scan images with “COVID” and “Non-COVID.” The dataset is divided into train, test, and validation. Accuracies obtained for VGG16 are 97.68%, DenseNet121 is 97.53%, MobileNet is 96.38%, NASNet is 89.51%, Xception is 92.47%, and EfficientNet is 80.19%, respectively. From the obtained analysis, the results show that the VGG16 architecture gives better accuracy compared to other architectures.

73 citations

Proceedings ArticleDOI
22 Jun 2022
TL;DR: In this paper , the authors proposed a framework to create solid chickens and decrease the death pace of chickens to further develop efficiency via computerizing the course of observed and control utilizing the Internet of Things (IoT).
Abstract: Poultry farms in India are mostly monitored and controlled manually. The most important and basic factors that need to be monitored and controlled in poultry farms are hotness, moisture, air value point, illumination, freshening, door magnetic sensor, light-dependent resistor, egg count, and food feeding. The point is to work on the creation of solid chickens and decrease the death pace of chickens to further develop efficiency via computerizing the course of observed and control utilizing the Internet of Things (IoT). When the above parameters exceed the edge esteems, the cycles are started naturally that can assist with decreasing the death pace of chickens on the ranch. The framework likewise sends a programmed ready warning to the client through message, E-mail, and online message App. A Web point of interaction is likewise made to screen and show these boundaries.

14 citations

Journal Article
TL;DR: Evaluating the efficacy of epidural butophanol tartrate in postoperative analgesia and monitoring its side effects concluded that sedation may be beneficial to patients in the immediate postoperative period.
Abstract: Objectives: The objective of this study was to evaluate the efficacy of epidural butophanol tartrate in postoperative analgesia and to monitor its side effects. Methods: 80 patients of ASA 1 and 2 scheduled for elective abdominal and gynaecological procedure were chosen for the study. At the end of surgery, study group received 2mg of butorphanol in 10 ml normal saline through an epidural catheter and the control group received 10 ml of normal saline. Postoperatively vitals, VAPS, sedation score and side effects were pointed. Patients received rescue analgesic when VAPS was greater than 6. Results: Epidural butorphanol produced duration of analgesia of 7.46 ± 1.35 hours. The quality of analgesia was excellent in 75% of patients and good in 25% of patients. The two main adverse effects observed were sedation and vomiting. Sedation may be beneficial to patients in the immediate postoperative period. Conclusion: Epidural butorphanol produces long lasting, good quality analgesia with minimal side effects.

7 citations

Journal ArticleDOI
TL;DR: A deep neural network (DNN) for forecasting the intra-day solar irradiance, photovoltaic PV plants, regardless of whether or not they have energy storage, can benefit from the work being done here.
Abstract: In this paper, we introduce a deep neural network (DNN) for forecasting the intra-day solar irradiance, photovoltaic PV plants, regardless of whether or not they have energy storage, can benefit from the work being done here. The proposed DNN utilises a number of different methodologies, two of which are cloud motion analysis and machine learning, in order to make forecasts regarding the climatological conditions of the future. In addition to this, the accuracy of the model was evaluated in light of the data sources that were easily accessible. In general, four different cases have been investigated. According to the findings, the DNN is capable of making more accurate and reliable predictions of the incoming solar irradiance than the persistent algorithm. This is the case across the board. Even without any actual data, the proposed model is considered to be state-of-the-art because it outperforms the current NWP forecasts for the same time horizon as those forecasts. When making predictions for the short term, using actual data to reduce the margin of error can be helpful. When making predictions for the long term, however, weather information can be beneficial.

1 citations

Proceedings ArticleDOI
10 Nov 2022
TL;DR: In this article , the authors proposed a smart poultry system that tries to provide the solution for all the issues in managing a poultry farm, which is labour intensive due to the need for constant surveillance and control over a wide range of environmental factors.
Abstract: According to studies, there are approximately 850 million poultry birds across India, with an average of 30 million farmers working in the sector. In other words, a poultry farm is a trustworthy and long-term way to make money in India. However, managing a poultry farm is labour intensive due to the need for constant surveillance and control over a wide range of environmental factors. The actual implementation of this is significantly more complicated, expensive, and time-consuming. The paper suggested a smart poultry system that tries to provide the solution for all the issues. The health of poultry birds heavily relies on environmental parameters, so variables like temperature and humidity are measured and monitored continuously. The website was made so that poultry keepers may get reliable information about their birds’ health and use that information to take the appropriate measures. Moreover, in the event of a crisis, such as a fire or the illness of a single bird, the owner will receive a notification. It is also possible to gather information about the poultry in the specified timespan. The Firebase cloud is used for wireless monitoring and managing the poultry system. The suggested automatic smart poultry system will make the birds healthy and it indirectly helps the owners to increase their profit with minimal human effort.

1 citations


Cited by
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Journal ArticleDOI
TL;DR: The ultrasound-guided TAP block provides good postoperative analgesia, reduces analgesic requirements, and provides good VAS scores with fewer complications following inguinal hernia surgery.
Abstract: Background and aim Transversus abdominis plane block (TAP block) is a novel procedure to provide postoperative analgesia following inguinal hernia surgery. The utilization of ultrasound has greatly augmented the success rate of this block and additionally avoiding complications. The aim of our study was to gauge the analgesic efficacy of ultrasound-guided TAP block in patients undergoing unilateral inguinal hernia repair.

31 citations

Journal ArticleDOI
TL;DR: Transfer learning of pretrained convolutional neural network is examined and then transfer it to detect retinal problems from Optical Coherence Tomography (OCT) images and offers a good level of accuracy in classifying retinal diseases compared to other approaches, which only detect a small number ofretinal diseases.
Abstract: Retinal abnormalities have emerged as a serious public health concern in recent years and can manifest gradually and without warning. These diseases can affect any part of the retina, causing vision impairment and indeed blindness in extreme cases. This necessitates the development of automated approaches to detect retinal diseases more precisely and, preferably, earlier. In this paper, we examine transfer learning of pretrained convolutional neural network (CNN) and then transfer it to detect retinal problems from Optical Coherence Tomography (OCT) images. In this study, pretrained CNN models, namely, VGG16, DenseNet201, InceptionV3, and Xception, are used to classify seven different retinal diseases from a dataset of images with and without retinal diseases. In addition, to choose optimum values for hyperparameters, Bayesian optimization is applied, and image augmentation is used to increase the generalization capabilities of the developed models. This research also provides a comparison of the proposed models as well as an analysis of them. The accuracy achieved using DenseNet201 on the Retinal OCT Image dataset is more than 99% and offers a good level of accuracy in classifying retinal diseases compared to other approaches, which only detect a small number of retinal diseases.

20 citations

Journal ArticleDOI
TL;DR: Epidural buprenorphine significantly reduced pain and increased the quality of analgesia with a longer duration of action and was a better alternative to butorphanol for postoperative pain relief.
Abstract: Background: The purpose of this study was to compare the safety and efficacy of postoperative analgesia with epidural buprenorphine and butorphanol tartrate. Methods: Sixty patients who were scheduled for elective laparoscopic hysterectomies were randomly enrolled in the study. At the end of the surgery, in study Group A 1 ml (0.3 mg) of buprenorphine and in Group B 1 ml (1 mg) of butorphanol tartrate both diluted to 10 ml with normal saline was injected through the epidural catheter. Visual analog pain scales (VAPSs) were assessed every hour till the 6th h, then 2nd hourly till the 12th h. To assess sedation, Ramsay sedation score was used. The total duration of postoperative analgesia was taken as the period from the time of giving epidural drug until the patients first complain of pain and the VAPS is more than 6. Patients were observed for any side effects such as respiratory depression, nausea, vomiting, hypotension, bradycardia, pruritus, and headache. Results: Buprenorphine had a longer duration of analgesia when compared to butorphanol tartrate (586.17 ± 73.64 vs. 342.53 ± 47.42 [P Conclusion: Epidural buprenorphine significantly reduced pain and increased the quality of analgesia with a longer duration of action and was a better alternative to butorphanol for postoperative pain relief.

11 citations

Journal ArticleDOI
TL;DR: This research adopts the transfer learning method to identify the COVID-19 patients from normal individuals when there is an inadequacy of medical image data to save time by generating reliable results promptly and could be a great way which will help to classify COVID -19 patients quickly and prevent the viral transmission in the community.
Abstract: COVID-19 has caused over 528 million infected cases and over 6.25 million deaths since its outbreak in 2019. The uncontrolled transmission of the SARS-CoV-2 virus has caused human suffering and the death of uncountable people. Despite the continuous effort by the researchers and laboratories, it has been difficult to develop reliable efficient and stable vaccines to fight against the rapidly evolving virus strains. Therefore, effectively preventing the transmission in the community and globally has remained an urgent task since its outbreak. To avoid the rapid spread of infection, we first need to identify the infected individuals and isolate them. Therefore, screening computed tomography (CT scan) and X-ray can better separate the COVID-19 infected patients from others. However, one of the main challenges is to accurately identify infection from a medical image. Even experienced radiologists often have failed to do it accurately. On the other hand, deep learning algorithms can tackle this task much easier, faster, and more accurately. In this research, we adopt the transfer learning method to identify the COVID-19 patients from normal individuals when there is an inadequacy of medical image data to save time by generating reliable results promptly. Furthermore, our model can perform both X-rays and CT scan. The experimental results found that the introduced model can achieve 99.59% accuracy for X-rays and 99.95% for CT scan images. In summary, the proposed method can effectively identify COVID-19 infected patients, could be a great way which will help to classify COVID-19 patients quickly and prevent the viral transmission in the community.

11 citations