V. Janhavi’s scientific contributions

Agriculture is associated with the production of essential food crops for decades and is the one which is playing an important role in the economy of a country as well as in life of an individual. Due to various uncertain variations in the climatic conditions such as rain and other affecting factors, crop prices vary in an unusual pattern. This variation of prices without the knowledge of the farmer may lead to losses in the economy of the individual who is involved in agriculture. In this paper, we have discussed a well-designed system which accurately predicts the crop prices of future months. We have used a Supervised Machine Learning algorithm that is Decision Tree Regression technique for the design of the prediction model as the data is of continuous form. The parameters, which are considered in the dataset, include crop name, month, year, rainfall and wholesale price index (WPI). We have considered the data of 22 crops in total with 4 parameters. We have developed a user-friendly user interface consisting of 22 crop profiles with the predicted prices. Our results show that the regression model achieved an accuracy of 97.32% which will help the farmer on decision of future crop selection for the growth and also hyperinflation can be avoided.

Busy lifestyle, modernization, increasing pollution and unhealthy diet have led to problems which people are neglecting. Not drinking enough water, stress and hormonal changes are causing problems to skin. Causes may be situational or genetic. Few skin conditions are minor while others can be life-threatening. The skin is the largest organ of the body and is composed of water, proteins, fats and minerals. Problems appear on outer layer of the skin that is epidermis. Skin diseases are considered to be the fourth most common cause of human illness. Skin diseases are observed to increase with age and were seen frequently in both men and women. Skin disorders can be temporary or permanent. Skin diseases have an impact on individual, family and social life caused by inadequate self-treatment which may also induce psychological problems. In recent years, use of computer technologies is becoming practically universal for both personal and professional issues. Facial skin problem identification and recognition has evolved to a great extent over the years. Detection of skin diseases is done using Convolution Neural Network (CNN) and image processing methods. CNN yields better performance in terms of accuracy, precision and results than the existing conventional methods. Image processing uses digital computer to process the images through an algorithm. We focus on features like skin tone, skin texture and color. We present a brief review about various facial skin problems providing more insight about the effective models and algorithms used.

In the modern era where the internet is found everywhere and there is rapid adoption of social media which has led to the spread of information that was never seen within human history before. This is due to the usage of social media platforms where consumers are creating and sharing more information where most of them are misleading with no relevance with reality. Classifying the text article automatically as misinformation is a bit challenging task. This development addresses how automated classification of text articles can be done. We use a machine learning approach for the classification of news articles. Our study involves exploring different textual properties that may be often used to distinguish fake contents from real ones. By using those properties, can train the model with different machine learning algorithms and evaluate their performances. The classifier with the best performance is used to build the classification model which predicts the reliability of the news articles present in the dataset.

Knowledge is prodigious, and learning has no boundary. The curiosity of the one to learn, discover and invent decides the future of the world. Educational Institutions’ main objective is to provide qualitative knowledge to the students and supporting tools used for learning play a major role. The conventional education system is arduous to be used in the current situation of the global pandemic of COVID-19. New methods and tools are required to make learning and imparting knowledge more effective. Applications like Google Meet, Zoom, Cisco WebEx are being used in schools and colleges. When it comes to simulations in technical education, for instance, to develop any electrical circuits, robots, buildings, etc., software like MATLAB, 3Ds MAX, GNU Octave is being used. These methods neither are interactive nor provide an immersive experience to the user. To subdue this problem, Mixed Reality (MR) technology can be utilized as a boon by developing an application where students can have interactive classes, submerging themselves, and gaining the required knowledge. Also, the technical students can simulate their experiments onto the real world, providing an idea of how the world may look when new things are adopted and can undergo a walk-through experience in the MR world.

Coronavirus (COVID-19), detected around the world, has led to confusion and massive casualties. The world needs an anonymous system that can aid in learning and addressing concerns, questions, and doubts. Such a system would help individuals to overcome present taboos in their society and encourage people to take proper action [1]. A system, which would maintain the integrity of patient data, can play a significant role in solving such problems.