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1.
Studies in Computational Intelligence ; 942:323-345, 2023.
Article in English | Scopus | ID: covidwho-2128355

ABSTRACT

Covid-19 pandemic is of major concern that largely impacts the human and growth of respective countries. Countries like India also tried their best to manage this Covid outbreak situation through lockdown and handle its growth through strict relaxation using zonal distribution strategy. An urge of proper estimation for this outbreak is required, which can be beneficial in arrangement of proper healthcare facilities in different states of the country. India has wide diversity between its states. The effect of temperature and dense population have been two key parameters that have been poorly studied with respect to each state. In this paper, we tried to forecast the number of Covid-19 cases (8 Jan 2020 to 25 April 2020) using Kalman filter at state and national levels to generate various trends and patterns. Our analysis has been evaluated on four classification of states: most affected, moderate affected, least affected and pandemic free states. The results have been collected on vulnerable temperature parameters (historical and forecast data) of each state. The national level estimates are further compared with other countries like United States of America, Spain, France, Italy and Germany through confirmed, recovered and death cases. In the current lockdown situation our estimation shows that India should expect as many as 60,140 cases by May 24, 2020. The trends achieved shows that India has been found to be one of the beneficiaries of lockdown decisions but failed at some places in its regions due to social activities, huge dense population and temperature variation. This study will be beneficial for different state level bodies to manage various health care resources between its states or can support intra-state and can start their administrative functionality accordingly. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2.
7th EAI International Conference on Science and Technologies for Smart Cities, SmartCity360° 2021 ; 442 LNICST:422-433, 2022.
Article in English | Scopus | ID: covidwho-1930337

ABSTRACT

The transportation problem is a very applicable and relevant logistic problem. In this paper, to test meta-heuristics on the transportation problem and also improve initial feasible solutions in few number of iterations, four recent and effective meta-heuristic algorithms are used to solve transportation problems. Laying Chicken Algorithm (LCA), Volcano Eruption Algorithm (VEA), COVID-19 Optimizer Algorithm (CVA), and Multiverse Algorithm (MVA) are implemented to solve different sizes of the transportation problem. Computational results show that CVA is the most efficient optimizer for large size cases and LCA is the best algorithm for the others. Finally, convergence of algorithms will be discussed and rate of convergence will be compared. The advantage of these heuristics are that they can be easily adapted to more challenging versions of the transportation problem which are not solveable by the Simplex method. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

3.
Studies in Big Data ; 80:91-105, 2020.
Article in English | Scopus | ID: covidwho-1503512

ABSTRACT

In this paper, we performed a comparative analysis using machine learning algorithms named support vector machine (SVM), decision tree (DT), k-nearest neighbor (kNN), and convolution neural network (CNN) to classify pneumonia level (mild, progressive, and severe stage) of the COVID-19 confirmed patients. More precisely, the proposed model consists of two phases: first, the model computes the volume and density of lesions and opacities of the CT images using morphological approaches. In the second phase, we use machine learning algorithms to classify the pneumonia level of the confirmed COVID-19 patient. Extensive experiments have been carried out and the results show the accuracy of 91.304%, 91.4%, 87.5%, 95.622% for kNN, SVM, DT, and CNN, respectively. © Springer International Publishing AG 2018.

4.
Ieee Consumer Electronics Magazine ; 10(4):18-27, 2021.
Article in English | Web of Science | ID: covidwho-1307643

ABSTRACT

Without an effective vaccine, treatment, or therapy, the Coronavirus Disease 2019 (COVID-19) is spreading like fire and claiming lives. Countries began to adopt various strategies such as lockdown, mass testing, tracing, quarantine, sanitization, isolation, and treatment to contain COVID-19. However, it was soon realized that we need to take the help of powerful technologies to combat the spread of deadly COVID-19 until a vaccine or a drug is discovered. In this article, we discuss how the use of cutting edge technologies such as the Internet of Things (IoT), Big data, artificial intelligence (AI), unmanned aerial vehicles (UAVs)/drones, blockchain, robotics, autonomous ground vehicles, communication technologies in screening, testing, contact tracing, spread analysis, sanitization, and protocol enforcements can help prevent the COVID-19 spread.

5.
Multimodal Image Exploitation and Learning 2021 ; 11734, 2021.
Article in English | Scopus | ID: covidwho-1295153

ABSTRACT

The novel coronavirus 2019 (COVID-19) first appeared in Wuhan province of China and spread quickly around the globe and became a pandemic. The gold standard for confirming COVID-19 infection is through Reverse Transcription-Polymerase Chain Reaction (RT-PCR) assay. The lack of sufficient RT-PCR testing capacity, false negative results of RT-PCR, time to get back the results and other logistical constraints enabled the epidemic to continue to spread albeit interventions like regional or complete country lockdowns. Therefore, chest radiographs such as CT and X-ray can be used to supplement PCR in combating the virus from spreading. In this work, we focus on proposing a deep learning tool that can be used by radiologists or healthcare professionals to diagnose COVID-19 cases in a quick and accurate manner. However, the lack of a publicly available dataset of X-ray and CT images makes the design of such AI tools a challenging task. To this end, this study aims to build a comprehensive dataset of X-rays and CT scan images from multiple sources as well as provides a simple but an effective COVID-19 detection technique using deep learning and transfer learning algorithms. In this vein, a simple convolution neural network (CNN) and modified pre-trained AlexNet model are applied on the prepared X-rays and CT scan images. The result of the experiments shows that the utilized models can provide accuracy up to 98% via pre-trained network and 94.1% accuracy by using the modified CNN. © 2021 SPIE.

6.
21st IEEE International Conference on Information Reuse and Integration for Data Science, IRI 2020 ; : 180-187, 2020.
Article in English | Scopus | ID: covidwho-860074

ABSTRACT

Coronaviruses are a famous family of viruses that cause illness in both humans and animals. The new type of coronavirus COVID-19 was firstly discovered in Wuhan, China. However, recently, the virus has widely spread in most of the world and causing a pandemic according to the World Health Organization (WHO). Further, nowadays, all the world countries are striving to control the COVID-19. There are many mechanisms to detect coronavirus including clinical analysis of chest CT scan images and blood test results. The confirmed COVID-19 patient manifests as fever, tiredness, and dry cough. Particularly, several techniques can be used to detect the initial results of the virus such as medical detection Kits. However, such devices are incurring huge cost, taking time to install them and use. Therefore, in this paper, a new framework is proposed to detect COVID-19 using built-in smartphone sensors. The proposal provides a low-cost solution, since most of radiologists have already held smartphones for different daily-purposes. Not only that but also ordinary people can use the framework on their smartphones for the virus detection purposes. Today's smartphones are powerful with existing computation-rich processors, memory space, and large number of sensors including cameras, microphone, temperature sensor, inertial sensors, proximity, colour-sensor, humidity-sensor, and wireless chipsets/sensors. The designed Artificial Intelligence (AI) enabled framework reads the smartphone sensors' signal measurements to predict the grade of severity of the pneumonia as well as predicting the result of the disease. © 2020 IEEE.

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