Evaluating the Preparedness of Indian States against COVID-19 Pandemic Risk: A Fuzzy Multi-criteria Decision-Making Approach.

The preparedness of Indian states and union territories (UTs) against the COVID-19 pandemic has been evaluated. Ten parameters related to demographic, socioeconomic, and healthcare aspects have been considered and the performances of 27 states and three UTs have been evaluated applying the Fuzzy Analytic Hierarchy Process. Opinions of medical experts have been considered to ascertain the relative importance of decision criteria as well as subcriteria. The scores of various states and UTs in each of the decision subcriteria have been calculated by using the secondary data collected from authentic sources. It is found that Kerala and Bihar are the best prepared and worst prepared states, respectively, to combat COVID-19 pandemic. Karnataka, Goa, and Tamil Nadu have very good preparedness whereas Chhattisgarh, Jharkhand, and Bihar have very poor preparedness. Maharashtra, the most affected state in India, has average preparedness. As around 650 million people are vulnerable due to the poor and very poor preparedness of their states, the country needs to make region specific mitigation strategies to combat the COVID-19 pandemic and the preparedness map will be helpful in that direction.

An improved symbiotic organisms search algorithm for higher dimensional optimization problems

The symbiotic organisms search (SOS) algorithm was introduced by considering the relationships among the creatures in a natural ecosystem. Despite the superior efficiency of SOS, it has been observed that fixing benefit factors of mutualism phase at 1 or 2;the algorithm obstructs itself from an extensive and diverse search of the search region. Moreover, alteration of random dimensions in the parasitism phase increases the computational burden of the algorithm. Considering these limitations, a modified SOS algorithm, namely nwSOS, has been proposed in this study to solve higher dimensional optimization problems. In the suggested nwSOS, the benefit factors are calculated by a non-linear approach. The mutual vector is modified, and weights of both benefit factors are utilized to effectively explore and exploit the search region. Moreover, the parasitism phase is tailored to lessen the computational overhead. The modified method is then used to evaluate twenty basic benchmark functions using 100 and 500 dimensions. Results are compared with six state-of-the-art algorithms. Evaluated and compared the results of 100 dimensions with SOS and its five modified variants. Four designing issues from both unconstrained and constrained classifications are solved utilizing nwSOS. Complexity analysis, statistical analysis, and convergence analysis are executed to measure the algorithm’s effectiveness from different aspects. Moreover, the proposed algorithm has been used for segmenting COVID-19 chest X-ray images with the help of multi-level thresholding approach using different thresholds. All the results confirmed the enhancement of the proposed algorithm.

COVID-19 X-ray image segmentation by modified whale optimization algorithm with population reduction.

Coronavirus disease 2019 (COVID-19) has caused a massive disaster in every human life field, including health, education, economics, and tourism, over the last year and a half. Rapid interpretation of COVID-19 patients' X-ray images is critical for diagnosis and, consequently, treatment of the disease. The major goal of this research is to develop a computational tool that can quickly and accurately determine the severity of an illness using COVID-19 chest X-ray pictures and improve the degree of diagnosis using a modified whale optimization method (WOA). To improve the WOA, a random initialization of the population is integrated during the global search phase. The parameters, coefficient vector (A) and constant value (b), are changed so that the algorithm can explore in the early stages while also exploiting the search space extensively in the latter stages. The efficiency of the proposed modified whale optimization algorithm with population reduction (mWOAPR) method is assessed by using it to segment six benchmark images using multilevel thresholding approach and Kapur's entropy-based fitness function calculated from the 2D histogram of greyscale images. By gathering three distinct COVID-19 chest X-ray images, the projected algorithm (mWOAPR) is utilized to segment the COVID-19 chest X-ray images. In both benchmark pictures and COVID-19 chest X-ray images, comparisons of the evaluated findings with basic and modified forms of metaheuristic algorithms supported the suggested mWOAPR's improved performance.