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Data Analytics for COVID-19
Published in Chhabi Rani Panigrahi, Bibudhendu Pati, Mamata Rath, Rajkumar Buyya, Computational Modeling and Data Analysis in COVID-19 Research, 2021
This chapter will analyze various means of a faster method of testing using chest X-ray and CT scan images on deep neural networks for predicting whether the person is infected or not. Using these methods in conjunction with patient characteristics or pre-existing conditions can improve the performance and reliability of the models. This chapter makes use of various machine learning algorithms to provide future forecasts about the spread of the infection. These are usually regression algorithms which use data from previous months and predict future numbers, and their correctness can be verified using the actual data. Data analysis helps to understand the exposure of various age groups to infection when the factors of lockdown and social distancing are taken into account. Classical time series modeling can be used for important short-term predictions. Various results have been proposed which provide short-term real-time forecasts and risk assessment. This chapter will provide results and comparisons made between various predictive models such as Auto-Regressive Integrated Moving Average (ARIMA), Prophet, and LSTM (Long Short Term Memory).
Digital Design with Programmable Logic Devices
Published in Suman Lata Tripathi, Sobhit Saxena, Sushanta Kumar Mohapatra, Advanced VLSI Design and Testability Issues, 2020
M. Panigrahy, S. Jena, R. L. Pradhan
Medical imaging equipment such as computerized tomography (CT) scanner, positron emission tomography, or magnetic resonance imaging (MRI) plays an important role to diagnose, analyze, and predict health condition of a patient. Accurate detection and diagnosis needs extraction of minute details of the image and demands high resolution. However, for analyzing and processing a high-resolution image at a fast rate, complex DSP algorithms are requires. Again, telemedicine systems require real-time data analysis and monitoring. This necessitates dedicated hardware for parallel computations, on-chip memory. Additionally, desirable features such as portability, low cost, low power, and long life medical instruments with ability to handle and update complex algorithms drive the use of FPGA. Nowadays, FPGAs are used in most of the medical instruments such as CT scan, MRI, and 3D ultrasound. FPGAs are used for measurement of body temperature, blood pressure, and respiratory rate; analysis of electrocardiogram, electroencephalography, and electromyography; monitoring blood oxygen; etc. Apart from these, FPGA finds its applications in wireless body sensor network, ultrasonography, diabetic monitoring, prediction of ventricular arrhythmia, detection of cardiac dysrythmia, and breast and brain cancer. Additionally, further advancement of FPGA technology allows MRI filtering and tumor characterization, medical surgery using FPGA robotics, and DNA sequence analysis operations.
X-ray Vision: Diagnostic X-rays and CT Scans
Published in Suzanne Amador Kane, Boris A. Gelman, Introduction to Physics in Modern Medicine, 2020
Suzanne Amador Kane, Boris A. Gelman
Radiography – the science of medical x-ray images – was revolutionized in the 1970s by the widespread introduction of computed tomography (CT), a computerized technique that permits the reconstruction of three-dimensional images using x-rays. With the invention of the CT scanner, x-ray imaging has become an even more sophisticated and sensitive probe of anatomy and function. Using a combination of CT and magnetic resonance imaging (MRI) (Chapter 8), physicians can noninvasively analyze the anatomy of any part of the body, including the brain. The wide utility of CT has led to an explosive growth in its use, with an estimated 80 million or more CT scans per year estimated to occur in the US alone. Increasingly, CT is being combined with other methods of imaging, such as positron emission tomography (PET) (Chapter 6), to provide complementary imaging of body anatomy and function or pathology.
Investigation on the discoloration of freeze-dried carrots and the color protection by microwave combined with coating pretreatment
Published in Drying Technology, 2022
Ju Shen, Min Zhang, Arun S. Mujumdar, Yuchuan Wang, Kai Chen, Jingjing Chen
Approximately 3.5 g rehydrated and fresh carrot slices were analyzed by low-field nuclear magnetic resonance (LF-NMR) technique (Niumag Co., Ltd., Shanghai, China) to obtain the water status.[13] Each peak corresponded to the corresponding relaxation time and the peak area was obtained by integrating each peak. The values of peak areas and amplitudes in Table 1 and Figure 2A were obtained from the corresponding sample. The main experimental parameters were as following: spectrometer frequency (SF) was 20 MHz, 90° pulse time (P1) was 6.52 μs, 180° pulse time (P2) was set as 12.48 μs, spectral width (SW) was 200 kHz, number of echoes (NECH) was 10000, time waiting (TW) was 2500 ms, number of scans (NS) was 4. The time echo (TE) was 0.25 ms. False color images were obtained by magnetic resonance imaging (MRI) to analyze the water distribution situation of the samples, which is shown in Figure 2B.[14]
Semi-analytical and numerical explorations on entropy optimization of EMHD in Casson hybrid nanofluid with radiation slip and convective boundary conditions
Published in Waves in Random and Complex Media, 2022
Gunisetty Ramasekhar, P. Bala Anki Reddy
Many scientists analyze the influence of electric and magnetic fields, which have incredible applications like medicine and engineering, such as purification of liquid metals, iron varnish, skin disorders, magnetic resonance imaging equipment, cooling nuclear reactors, and malaria treatment [18]. A porous medium is one with pores or empty spaces that enable fluids to move through them. Porous media include natural materials such as rocks, soil, biological tissues, sand, and wooden structures. The porosity qualities of this medium are often used to customize it. The wide range of applications of porous media in applied science and engineering, such as geothermal systems, thermal insulation, biomedical applications and electronic cooling, such as tissue replacement and drug delivery, has fixed the interest of scientists and researchers to conduct more research and solve real-world problems involving this medium [19–23].
Identification of Severity of Infection for COVID-19 Affected Lungs Images using Elephant Swarm Water Search Algorithm
Published in International Journal of Modelling and Simulation, 2022
Most of the researchers have used multilevel thresholding [9,10] for segmentation of images to extract the different pixel information from the CT scan images. However, a suitable optimization [11] technique is always required for multilevel thresholding to identify the optimal threshold levels of image segmentation. Li et al. [12] used CT scan images to classify the images in either COVID-19 or normal, and they also calculated the severity of infection. Hassanien et al. [13] used support vector machine and multilevel thresholding technique to classify the X-ray images of lungs. On the hand, Sing et al. [14] used multiobjective differential evolution-based convolutional neural networks to classify CT scan images of chest affected by COVID-19. Yan et al. [15] also used image processing tools to identify the severity of infection for COVID-19. Rajinikanth et al. [16] used Harmony Search (HS) algorithm along with multilevel thresholding to identify the infection severity in lung CT scan images that were created due to COVID-19. Bhandary et al. [17] used deep learning method to analyze the CT scan images of lungs.