Social media has revolutionized the new-age customer's decision making through the myriads of sources available to them like online feedback or reviews, forum discussions, blogs and Twitter on the web. There is no need for them to depend on their peers any longer. When more convenient and efficient sources like user reviews are readily available to them over the internet. Vast and authentic information about all possible products ranges and services are at a click away. Even for commercial organization the task of gathering public opinion has been rendered tremendously easy, for the same reason that taking opinion polls and conducting surveys are now much simpler due to the abundance of information on the web. However, finding and monitoring opinion sites on the Web and filtering the information contained in them according to our need remains a difficult task because of the rapid increase in the number of distinct sites. Each site usually contains a huge volume of opinionated text which is difficult for any individual to go through. The average human reader will have difficulty identifying relevant sites and extracting and summarizing the opinions in them. Automated sentiment analysis systems are thus needed. This paper focuses on extracting the features from bank reviews taken from mouthshut.com and myBankTracker.com sites given by reviewers to state their opinions. This is done at aspect level of analysis using ontology. Then it determines whether they are positive or negative. Output of such analysis is then summarized.
Financial services have a ubiquitous need however the urban rich have easy and universal access with wider options, compared to the low-income group who are forced to accept informal, expensive and riskier means to fulfill their financial needs. The demand and supply of financial services for the poor is imbalanced, with supply being acutely constrained by lack of viability and sustainability of current business models. Technology and IT has a pivotal role in making financial inclusion a viable reality. Technology, including information technology can enable lowering costs by increasing automation, enhancing efficiency, enabling scaling up through uniformity, consistency and security. Multiple technology choices are available to financial service providers but few have been proven yet. This paper examines technology options at the front end and back-end in detail with a critique of alternatives available for financial inclusion in Indian context.
Financial services have a ubiquitous need however the urban rich have easy and universal access with wider options, compared to the low-income group who are forced to accept informal, expensive and riskier means to fulfill their financial needs. The demand and supply of financial services for the poor is imbalanced, with supply being acutely constrained by lack of viability and sustainability of current business models. Technology and IT has a pivotal role in making financial inclusion a viable reality. Technology, including information technology can enable lowering costs by increasing automation, enhancing efficiency, enabling scaling up through uniformity, consistency and security. Multiple technology choices are available to financial service providers but few have been proven yet. This chapter is based on available front end and back end technology options for financial inclusion. Further, it describe the role of front end and back end technology options in Indian context.
Soil is a critical part of successful agriculture and is the source of the nutrients that we use to grow crops. There are different types of soil and there are different properties of each soil. On these different properties, several types of crops grow. We need to know the properties and characteristics of various soil types to understand which crops sow in certain soil types. Machine Learning allows the user to feed a computer algorithm on an immense amount of data and have the computer analyze, make data-driven recommendations and decisions based to analyze the input data. Machine Learning techniques are used to model this process. Machine Learning has come into the picture with the big data technologies and high-performance computing that create new opportunities for data-intensive science in the multi-disciplinary agri-technology domain. In this paper, we have proposed a model that can find whether the soil is fertile or not, Sowing crop seed on fertile soil, and at last predicting the crop yield on different soil features. According to prediction, it can be suggested and recommended which crops grow more. Various Machine Learning algorithms such as Support Vector Machine (SVM), Random Forest, Naive Bayes, Linear Regression, Multilayer perceptron (MLP), and ANN are used for soil classification and crop yield. Test results show that the proposed ANN method follows a deep learning architecture which means it has several layers for input and output are connected to achieve better accuracy than numerous existing methods.
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