Highlights From the pandemic scenario of COVID-19 disease cases in all over the world, the outbreak prediction becomes very complex for the emerging scientifically research. Several epidemiological mathematical models of spread are increasing day by day to forecast correctly. Here, the classical SIR modelling approach is carried out to study the different parameters of this model in case of India county. This type of approach analyzed by considering different governmental lock down measures in India. The outcome results showed the extreme interventions should be taken to tackle this type of pandemic situation in near future.

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An optimization concept of the various machining parameters for the plasma arc cutting procedures on AISI 316 stainless steel conducting a hybrid optimization method has been carried out. A new composition of response surface methodology and grey relational analysis coupled with principal component analysis has been proposed to evaluate and estimate the effect of machining parameters on the responses. The major responses selected for these analyses are kerf, chamfer, dross, surface roughness and material removal rate, and the corresponding machining parameters concentrated for this study are feed rate, current, voltage and torch height. Thirty experiments were conducted on AISI 316 stainless steel workpiece materials based on a facecentered central composite design. The experimental results obtained are applied in grey relational analysis, and the weights of the responses were evaluated by the principal component analysis and further evaluated using response surface method. The results show that the grey relational grade was significantly affected by the machining parameters directly as well as with some interactions. This method is straightforward with easy operability, and the results have also been established by running confirmation tests. The premise attributes beneficial knowledge for managing the machining parameters to enhance the preciseness of machined parts by plasma arc cutting.

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Background The coronavirus disease 2019 (COVID-19) is reported in Algeria on February 25th, 2020. Since then, the number is still increasing leading to a total number of 36,699 cases and 1333 deaths on August 12th, 2020. Thus, comprehension of the epidemic curve is very important to predict its evolution and subsequently adapt the best prevention strategies. In this way, the current study was conducted to estimate the parameters of the classical SIR model and to predict the peak of the COVID-19 epidemic in Algeria using data from February 25th, 2020 to August 12th, 2020. Results Results showed that the peak of the epidemic will be reached on September 8th, 2021 and the total infected persons will exceed 800,000 cases at the end of the epidemic. Also, more than 15 million persons will be susceptible. The reproduction number (R0) is estimated at 1.23254. Conclusion These results may be helpful for the Algerian authorities to adapt their strategies and may be taken into consideration in the future phase of discontainment.

Deep learning has surged in popularity in recent years, notably in the domains of medical image processing, medical image analysis, and bioinformatics. In this study, we offer a completely autonomous brain tumour segmentation approach based on deep neural networks (DNNs). We describe a unique CNN architecture which varies from those usually used in computer vision. The classification of tumour cells is very difficult due to their heterogeneous nature. From a visual learning and brain tumour recognition point of view, a convolutional neural network (CNN) is the most extensively used machine learning algorithm. This paper presents a CNN model along with parametric optimization approaches for analysing brain tumour magnetic resonance images. The accuracy percentage in the simulation of the above-mentioned model is exactly 100% throughout the nine runs, i.e., Taguchi’s L9 design of experiment. This comparative analysis of all three algorithms will pique the interest of readers who are interested in applying these techniques to a variety of technical and medical challenges. In this work, the authors have tuned the parameters of the convolutional neural network approach, which is applied to the dataset of Brain MRIs to detect any portion of a tumour, through new advanced optimization techniques, i.e., SFOA, FBIA and MGA.

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