COVID-19 is now becoming a global issue and declared as pandemic by world health organization. This virus spread out from China to entire world. This paper performed a descriptive analysis of COVID-19 in special reference of India. Different attributes such as age, gender, travel history, communication type and current status are analyzed. Till now it can be stated from the study that age is not a significant factor that affect a person to be captured by this disease, also age attribute is normally distributed in current dataset. A significant relationship is found between gender (male and female) and Transmission Type (imported from other country or communicated from local) of the patients.

Security plays a major role in an individual’s life to win this world with highly secure and authentic lifestyle with the digital equipment’s. The paper proposed an encryption based secure face detection and recognition model which can be implemented in daily life to generate a more robust and efficient security bubble around the world. The most crucial problem encountered during face recognition is due to the variation in face direction of an individual, the model solves the mentioned pose variation problem. The proposed model takes the help of face recognition library to recognize the face and use HOG (Histogram of Oriented Gradients) & SVM for checking the face authentication by performing an image match, the model also applies the concept of HOG to generate the encoded features from the image. The system is divided into two modules first is to detect a face and then match the detected face from the authentic persons dataset available. The system uses the concept of OpenCV library for giving a support system for the real time image. For data encryption, proposed model used the concept of DES3 and RSA algorithm. The proposed model gets 83.33% accuracy while tested for three different image types and states that the RSA algorithm performs encryption in less computational time.

In this era of globalization, thinking styles of the professionals hold significant prominence in the leadership development. Cognitive aspects of professionals including thinking, attitudes, and motivation are great concerns of managerial psychology, which escalate organizational productivity as, different occupations reinforce different thinking, cognitive and metacognitive styles. This paper proposes to conduct the research gap of studying the effect of cognitive styles and metacognition styles on thinking styles in order to find out the preference of different thinking styles in professionals at executive positions. Smart PLS 3.3.2 software has been used to execute partial least squares structural equation modeling (PLS-SEM) for the research framework on a sample of 453 top-executive professionals working in different public and private firms in northern India. Six hypotheses were framed in the study. PLS-SEM supported five out of six hypotheses as per their respective models. Results indicate that professionals’ cognitive and metacognition skills influence their thinking styles. This study encourages the task-oriented workplace deployment as a solution of evolving organizational productivity. When practiced in organizations, it boosts the workplace expectations and organizational requirements giving birth to greater creativity. Implications, limitations, and suggestions for future research are also added.

Machine learning is one of the active fields and technologies to realize artificial intelligence (AI). The complexity of machine learning algorithms creates problems to predict the best algorithm. There are many complex algorithms in machine learning (ML) to determine the appropriate method for finding regression trends, thereby establishing the correlation association in the middle of variables is very difficult, we are going to review different types of regressions used in Machine Learning. There are mainly six types of regression model Linear, Logistic, Polynomial, Ridge, Bayesian Linear and Lasso. This paper overview the above-mentioned regression model and will try to find the comparison and suitability for Machine Learning. A data analysis prerequisite to launch an association amongst the innumerable considerations in a data set, association is essential for forecast and exploration of data. Regression Analysis is such a procedure to establish association among the datasets. The effort on this paper predominantly emphases on the diverse regression analysis model, how they binning to custom in context of different data sets in machine learning. Selection the accurate model for exploration is the most challenging assignment and hence, these models considered thoroughly in this study. In machine learning by these models in the perfect way and thru accurate data set, data exploration and forecast can provide the maximum exact outcomes.