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This article is devoted to the exact traveling wave solutions of the space-time fractional Gerdjikov–Ivanov equation by new generalized approach and improved Bernoulli sub-equation method. This equation is modified to include a fractional derivative. Obtained solutions are unique, because of the new conformal fractional derivative definition which is based on a limiting approach. For different types of nonlinear differential equations, the proposed schemes are effective and reliable. [ABSTRACT FROM AUTHOR]

Earth is often referred to as the "Blue Planet" because of much of its area covered by water (almost 71%). After air, water plays a fundamental role in human life. On Earth water is there on many different places and in many different forms. Compared to the availability of water for human consumption is limited to that of the amount of water exists. The majority of fresh water is actually found underground as soil moisture and in aquafiers. As population is growing at unprecedented rates globally, protecting groundwater is highest priority. Protecting groundwater directly protects surface water, rain water and all forms of water because water continues to cycle and recycle. For this the immediate action we can take right now is to improve water quality monitoring. Water quality describes the chemical, physical and biological characteristics, usually with respect to its suitability for a particular purpose. Water quality is classified into four main types: Potable Water, Palatable Water, Contam-inated Water and Infected Water. Traditional methods of water quality classification are cumbersome process and hence Machine Learning (ML) can be used as a catalyst for this. This study predicts ground water quality using ML techniques. Hence, the study is mainly useful for prediction of drinkable water. [ABSTRACT FROM AUTHOR]

Tourism is a multifaceted activity that provides a wonderful experience to the tourists. Today due to the fastest means of transportation and communication the world has shrunk into a Global village" and the age-old saying of "VasudhaivKutumbakam" (the whole earth is a family) can be easily be related to the current world scenario. Tourism brings economic prosperity and social understanding among nations. Tourism provides a thrust to the economy and provides long-term as well as short-term benefits to society. It generates revenue and employment and the multiplier effect of tourism further boosts the entire economy. Tourism not only generates financial benefits but also upgrades human skills. The infrastructure and the development created for tourism are used by all other sectors of the economy. The tourism industry, therefore, has much potential to offer. The cultural heritage of a place is continuously created and recreated by the people from time to time in response to the changing times. It is the cultural heritage of a person which gives him a sense of identity. People belonging to different castes, creeds, or races follow different traditions or practices. This is their intangible heritage. Anything or practice, we wish to preserve and want to pass it on to our next generation is known as heritage. Every person has a personal heritage which he or she keeps or preserves with pride like family pictures, music records, personal objects, souvenirs, a family house, and family traditions. This is the personal heritage that individuals conserve, recognize or preserve with care. Similarly, in a broader sense, every community possesses a collective heritage which it wants to preserve like buildings, parks traditions, skills, collections of objects, etc. [ABSTRACT FROM AUTHOR]

In most of the developed nations, there are markets where shares, securities or commodities are traded on daily basis, which generally reflects the growth of any country and the health of the company stocks which are traded.Stock market investment is considered to be one of the riskiest investments by investors all around the world, but if historical data is studied carefully then the gap between how the market behaves and what investors know, can be minimized.The data of opening and closing price, which is generated, is a times-series in nature. This time-series data of any index or stock attracts researchers to predict the next move or price of the commodity or index.There are lots of methods available to analyze the data like auto-regressive integrated moving average (ARIMA), regression based, neural network or moving averages methods.However, ARIMA is a type of model which is generally applied to the time-series data to gain insights into data,to understand what happened in the past and what is the next move to expect. In this chapter, an attempt has been made to use the time-series data of the past 5 years and based on that we can forecast the future direction of the indices. [ABSTRACT FROM AUTHOR]

The development of Optical Character Recognition (OCR) of the Indian text is an active area of research today. The presence of many characters in a set of alphabets, their intricate combinations, and intricate grapes are a challenge for the OCR designer. This paper focuses on improving the efficiency of the Tesseract OCR in the Hindi language. This paper introduces Google's Tesseract Optical Character Recognition software. An optical Character Recognition (OCR) is the process of identifying and converting the text into images in pixels in a computer-friendly presentation. The present work aims to improve the accuracy of the Tesseract 4.0 OCR engine. The OCR engine will extract the text from the image. Some information has in the extracted text is calculated to provide the decision to accept. The test results in Hindi, taken from a random sample of books, show the characters and the accuracy of the words. [ABSTRACT FROM AUTHOR]

Tracking accurate movement of stock market is a difficult job for investors in recent times due to the non- linear behavior and volatility of the stocks, which gets affected because of different reasons. Now, advancement in technology, introduction of artificial intelligence, improved computation power and programming methods have enabled researchers to forecast the market more efficiently. This paper is an attempt to create new variables with the help of artificial neural network (ANN) and random forests (RF) algorithm, to be used to forecast closing price of the next day. For this, open, high, closing and low price are used to create new variables, which can be treated as inputs to the model. The indicators used in this model are RMSE, MBE and MAPE and their low values are indicative of the fact that the proposed machine learning methods are suitable for the prediction of stock prices of the companies under consideration. [ABSTRACT FROM AUTHOR]