Elucidation of infection asperity of CT scan images of COVID-19 positive cases: A Machine Learning perspective.

Owing to the profoundly irresistible nature of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, an enormous number of individuals halt in the line for Computed Tomography (CT) scan assessment, which overburdens the medical practitioners, radiologists, and adversely influences the patient's remedy, diagnosis, as well as restraint the epidemic. Medical facilities like intensive care systems and mechanical ventilators are restrained due to highly infectious diseases. It turns out to be very imperative to characterize the patients as per their asperity levels. This article exhibited a novel execution of a threshold-based image segmentation technique and random forest classifier for COVID-19 contamination asperity identification. With the help of the image segmentation model and machine learning classifier, we can identify and classify COVID-19 individuals into three asperity classes such as early, progressive, and advanced, with an accuracy of 95.5% using chest CT scan image database. Experimental outcomes on an adequately enormous number of CT scan images exhibit the adequacy of the machine learning mechanism developed and recommended to identify coronavirus severity.

COVID-19-The Role of Artificial Intelligence, Machine Learning, and Deep Learning: A Newfangled.

The absolute previously infected novel coronavirus (COVID-19) was found in Wuhan, China, in December 2019. The COVID-19 epidemic has spread to more than 220 nations and territories globally and has altogether influenced each part of our day-to-day lives. As of 9th March 2022, a total aggregate of 44,78,82,185 (60,07,317) contaminated (dead) COVID-19 cases were accounted for all over the world. The quantities of contaminated cases passing despite everything increment essentially and do not indicate a controlled circumstance. The scope of this paper is to address this issue by presenting a comprehensive and comparative analysis of the existing Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI) based approaches used in significance in reacting to the COVID-19 epidemic and diagnosing the severe impacts. The paper provides, firstly, an overview of COVID-19 infection and highlights of this article; Secondly, an overview of exploring various executive innovations by utilizing different resources to stop the spread of COVID-19; Thirdly, a comparison of existing predicting methods of COVID-19 in the literature, with focus on ML, DL and AI-driven techniques with performance metrics; and finally, a discussion on the results of the work as well as future scope.

Fully automated unified prognosis of Covid-19 chest X-ray/CT scan images using Deep Covix-Net model.

SARS-COV2 (Covid-19) prevails in the form of multiple mutant variants causing pandemic situations around the world. Thus, medical diagnosis is not accurate. Although several clinical diagnostic methodologies have been introduced hitherto, chest X-ray and computed tomography (CT) imaging techniques complement the analytical methods (for instance, RT-PCR) to a certain extent. In this context, we demonstrate a novel framework by employing various image segmentation models to leverage the available image databases (9000 chest X-ray images and 6000 CT scan images). The proposed methodology is expected to assist in the prognosis of Covid-19-infected individuals through examination of chest X-rays and CT scans of images using the Deep Covix-Net model for identifying novel coronavirus-infected patients effectively and efficiently. The slice of the precision score is analysed in terms of performance metrics such as accuracy, the confusion matrix, and the receiver operating characteristic curve. The result leans on the database obtainable in the GitHub and Kaggle repository, conforming to their endorsed chest X-ray and CT images. The classification performances of various algorithms were examined for a test set with 1800 images. The proposed model achieved a 96.8% multiple-classification accuracy among Covid-19, normal, and pneumonia chest X-ray databases. Moreover, it attained a 97% accuracy among Covid-19 and normal CT scan images. Thus, the proposed mechanism achieves the rigorousness associated with the machine learning technique, providing rapid outcomes for both training and testing datasets.

Data science and the role of Artificial Intelligence in achieving the fast diagnosis of Covid-19.

The rapid spread of novel coronavirus (namely Covid-19) worldwide has alarmed a pandemic since its outbreak in the city of Wuhan, China in December 2019. While the world still tries to wrap its head around as to how to contain the rapid spread of the novel coronavirus, the pandemic has already claimed several thousand lives throughout the world. Yet, the diagnosis of virus spread in humans has proven complexity. A blend of computed tomography imaging, entire genome sequencing, and electron microscopy have been at first adapted to screen and distinguish SARS-CoV-2, the viral etiology of Covid-19. There are a less number of Covid-19 test kits accessible in hospitals because of the expanding cases every day. Accordingly, it is required to utensil a self-exposure framework as a fast substitute analysis to contain Covid-19 spreading among individuals considering the world at large. In the present work, we have elaborated a prudent methodology that helps identify Covid-19 infected people among the normal individuals by utilizing CT scan and chest x-ray images using Artificial Intelligence (AI). The strategy works with a dataset of Covid-19 and normal chest x-ray images. The image diagnosis tool utilizes decision tree classifier for finding novel corona virus infected person. The percentage accuracy of an image is analyzed in terms of precision, recall score and F1 score. The outcome depends on the information accessible in the store of Kaggle and Open-I according to their approved chest X-ray and CT scan images. Interestingly, the test methodology demonstrates that the intended algorithm is robust, accurate and precise. Our technique accomplishes the exactness focused on the AI innovation which provides faster results during both training and inference.

Facile Electrochemical Activity of Monoclinic Li2MnSiO4 as Potential Cathode for Li-Ion Batteries.

Synthesis of pure single-phase Li2MnSiO4 is challenging because of its rich polymorphism. Here, we demonstrate our success in preparing crystalline pure, battery-grade monoclinic phase Li2MnSiO4 (LMS) employing the temperature-programmed reaction technique. Systematic analysis of the electrochemical behavior of Li2MnSiO4 reveals its excellent battery activity in the monoclinic phase, with an initial discharge capacity of ∼250 mAh g-1 associated with the reversible intercalation of more than one Li+. The extraction of Li+ ions from Li2MnSiO4 corresponding to the oxidation of Mn2+ to Mn3+ then to Mn4+ appears as single oxidation/reduction peaks at 4.3/3.9 V in the first charge/discharge sweep of cyclic voltammogram within the potential window of 3.0-4.4 V. However, an extension of cathodic sweep to 2.5 V results in the appearance of an additional redox peak at 2.7/3.1 V vs Li+/Lio due to the reversible phase transition of monoclinic phase into battery-active orthorhombic phase induced by Jahn-Teller-active Mn3+ as evident from ex situ X-ray diffractograms. Indeed, the reversible intercalation of Li+ into the newly formed phase accounts for the high specific capacity of LMS within the potential window of 2.5-4.4 V. The capacity loss in the repeated cycles of monoclinic Li2MnSiO4 is explained by the formation of Mn2O3 owing to the dissolution of Mn3+.

Nano-Ionic Solid State Resistive Memories (Re-RAM): A Review.

Nano-ionic devices based on modest to fast ion conductors as active materials intrigued a revolution in the field of nano solid state resistive memories (the so-called Re-RAM) ever since HP labs unveiled the first solid state memristor device based on titanium dioxide (TiO2). This has brought impetus to the practical implementation of fourth missing element called "Memristor" correlating charge (q) and flux (φ) based on the conceptual thought by Chua in 1971 completing a missing gap between the passive electronic components (R, C and L). It depicts various functional features as memory element in terms of ionic charge transport in solid state by virtue of external electric flux variations. Consequently, a new avenue has been found by manipulating the ionic charge carriers creating a fast switching resistive random access memory (Re-RAM) or the so-called Memristors. The recent research has led to low power, faster switching speed, high endurance and high retention time devices that can be scaled down the order of few nanometers dimension and the 3D stacking is employed that significantly reduces the die area. This review is organized to provide the progress hitherto accomplished in the materials arena to make memristor devices with respect to current research attempts, different stack structures of ReRAM cells using various materials as well as the application of memristive system. Different synthesis approaches to make nano-ionic conducting metal oxides, the fabrication methods for ReRAM cells and its memory performance are reviewed comprehensively.