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Suspicious Activity is predicting the part or joint locations of someone from a picture or a video. Human suspicious Activity is one amongst the key issues in laptop vision that has been studied for over fifteen years. it's necessary due to the sheer variety of applications which mightlike Activity detection. for instance, human cause estimation is employed in applications as well as video police investigation, animal following and behavior understanding, language detection, advanced human-computer interaction, and marker less motion capturing. Low price depth sensors have limitations like restricted to indoor use, and their low resolution and yelling depth info build it troublesome to estimate human poses from depth pictures. Hence, we have a tendency to attempt to use neural networks to over- return these issues. Suspicious act recognition from police investigation video is an energetic analysis space of image process and laptop vision. Through the visual police investigation, human activities may be monitored in sensitive and public areas like busstations, railway stations, airports, banks, searching malls, faculty and faculties, parking tons, roads, etc. to stop act of terrorism, theft, accidents and ill-gotten parking, vandalism, fighting, chain snatching, crime and different suspicious activities.

DMSO-assisted approach for the synthesis of N-alkylated quinazolinones from readily available 2-aminobenzamide and aryl methyl ketones has been developed. DMSO played a dual role, acting as a solvent as well as a dual carbon synthon, making this process an environmentally benign approach.

Summary In the current scenario of the pandemic, social distancing can act as a remedy to the widely spreading COVID-19, as it can slow down the spread of the virus. This is the reason why government officials are imposing lots of social distancing norms in the current scenario. But maintaining social distancing by the physical appointment of officials in the particular area may risk the life of that official. In the era of technology, finding a technical solution to this problem may not be a difficult task. With the advancement in deep learning algorithms, object detections are quite an easy job. The proposed manuscript initially tries to build a relationship between social distancing and an increase in new cases of COVID-19 and then targets build a social distancing analyzer using deep learning approaches. This social distancing analyzer may be utilized to remotely monitor the area where social distancing norms may breach by the general public or to monitor social events. Various deep learning and machine learning algorithms are employed to build to the model.

This manuscript presents a geospatial and temporal analysis of the COVID'19 along with its mortality rate worldwide and an empirical evaluation of social distance policies on economic activities. Stock Market Indices, Purchasing Manager Index (PMI), and Stringency Index values are evaluated with respect to rising COVID-19 cases based on the collected data from Jan 2020 to June 2021. The findings for the stock market index reveal the highest negative correlation coefficient value, i.e., -0.2, for the Shanghai index, representing a negative relation on stock markets, whereas the value of the correlation coefficient is minimum for Indian markets, i.e., 0.3, indicating the most impact by COVID-19 spread. Further, the results concerning PMI show that the highest value of the correlation coefficient is for the China i.e., -0.52, points to the sharpest pace of contraction. This reflects the lower value of the correlation indicating that the economy is on the way of growth, which can be seen from the PMI value of the various countries. The manuscript presents a novel geospatial model by empirically evaluating the correlation coefficient of COVID-19 with stock market index, PMI, and stringency index to understand the effect of COVID-19 on the global economy.

The whole world is going through an unprecedented outbreak of COVID-19 leading to huge economy disruption across the globe. Moreover, the alarming pace of COVID-19 is quite upsetting to the leading financial stakeholders as it has resulted in the fleeing away of the customers, investors, and foreign trading partners. Resultantly, global markets succumbed leading to erosion of more than the US $6 trillion within just one week in February 2020. During the same week, alone S&P 500 index also experienced a loss of more than $5 trillion value in the US. This manuscript attempts to perform multivariate analysis of the global economy during the COVID-19 period. An empirical evaluation of the effect of containment policies on financial activity, stock market indices, purchasing manager index and commodity prices are also carried out. The obtained results reveal that the number of lockdown days, overseas travel ban and fiscal stimulus significantly influence the economic activity.

During this era of the energy crisis, when the non-renewable sources are rapidly diminishing, efforts are being taken to utilize renewable sources predominantly. This manuscript presents a hybrid deep learning framework using long short term memory (LSTM) Layer with vanishing time series gradient and maximal overlap discrete wavelet transform (MODWT) model for photovoltaic (PV) power forecasting through time series decomposition. The proposed framework is implemented on the dataset collected from Yulara Solar System, Australia. During the experimental evaluation, obtained results demonstrate short term temporal dependence of PV power forecasting on solar power magnitudes as well as weather conditions. Moreover, the proposed model outperforms existing state-of-the-art models in terms of mean average percentage error (MAPE) by 14.17%, 3.01%, and 16.49% for 1 day, 10 days, and 1 month, respectively, establishing its efficacy even for longer intervals.

In the present era, stock market has become the storyteller of all the financial activities of any country. Therefore, stock market has become the place of high risks, but even then it is attracting the mass because of its high return value. Stock market tells about the economy of any country and has become one of the biggest investment places for the general public. In this manuscript, we present the various forecasting approaches and linear regression algorithm to successfully predict the Bombay Stock Exchange (BSE) SENSEX value with high accuracy. Depending upon the analysis performed, it can be said successfully that linear regression in combination with different mathematical functions produces the best results. This model gives the best output with BSE SENSEX values and gross domestic product (GDP) values as it shows the least p-value as 5.382e−10 when compared with other model’s p-values.

Experimental investigations are reported for air–water two-phase flow through a 2.25 mm circular minichannel U-bend. Influence of radius of curvature is established by developing two minichannel U-bends with radius of curvature as 6 mm and 12 mm. T-junction is used as a mixing geometry for air and water. The superficial gas and liquid velocities are set in the range of 0.0419–0.4192 m/s. Total five different flow patterns such as plug, slug, bubbly, bubbly–plug and deformed plug are observed. Flow pattern transition maps are developed as a function of radius of curvature. The positional shift of gas bubbles within the curved portion of minichannel U-bend minichannel for bubbly, plug and deformed plug flow is identified. The influence of radius of curvature on hydrodynamics of Taylor bubble flow such as void fraction, liquid holdup, Taylor bubble velocity, Taylor bubble and liquid slug length is investigated. The radius of curvature with other flow parameters is found to have significant effect on two-phase flow patterns, its transition boundaries and hydrodynamics of Taylor bubble flow.

In the present era of the world, Stock market has become the place of high risks, but even then it is attracting the mass because of its high return value. Stock market tells about the economy of any country. Today, Stock market has become one of the biggest investment place for general public. In this manuscript we put forward the various forecasting approaches for predicting the BSE SENSEX using various forecasting models like ARIMA, BoxCox, Exponential Smoothing, Mean Forecasting, Naive, Seasonal Naive, Neural Network, and then comparing their mean error for deducing the best suitable model. The analysis is done on the Bombay Stock Exchange(BSE) SENSEX. Results of this analysis shows that, the Exponential smoothing and Neural network gives the best results if we compare the mean error of the both models with the other models.

In the present era, Stock Market has become the storyteller of all the financial activity of any country. Therefore, stock market has become the place of high risks, but even then it is attracting the mass because of its high return value. Stock market tells about the economy of any country and has become one of the biggest investment place for the general public. In this manuscript, we present the various forecasting approaches and linear regression algorithm to successfully predict the Bombay Stock Exchange (BSE) SENSEX value with high accuracy. Depending upon the analysis performed, it can be said successfully that Linear Regression in combination with different mathematical functions prepares the best model. This model gives the best output with BSE SENSEX values and Gross Domestic Product (GDP) values as it shows the least p-value as 5.382e−10 when compared with other model’s p-values.

Nowadays due to the technological advancement in collecting, processing and producing the information available, it is quite challenging to obtain the relevant information out from various sources of data. According to earlier research in homeopathy, a pattern system of treatment is being followed, which ultimately helps in finding the cure for the disease. One of the main goals of this system of treatment is to individualize the model in a manner that patient-specific prediction can be fixated with the help of pattern identification. By adopting the context of homeopathy system, the aim of this paper focuses on identifying appropriate parameters from a large quantity of raw clinical data, which are responsible for developing celiac disease in the human body. The result is based on the extensive quantitative research of a clinical database, shows that one parameter is correlating with another and such kind of pattern identification will be useful in the curing of celiac disease.