CLOUD-BASED DATA ANALYTICS FRAMEWORK FOR MOBILE APP EVENT ANALYSIS

Mobile analytics studies the behavior of end users of mobile applications and the mobile application itself. These mobile applications, being an important part of the various businesses products, need to be monitored and the usage patterns are to be analyzed. The data collected from these apps can help to drive important business strategies by identifying the usage patterns. Enriching the data with information available from other sources, like sales/service information, provides holistic view about the solution. Thus, here we aim at exploring some set of tools that give capabilities as event trailing with higher extraction of its linguistics. If the application is used worldwide, the data generated out of it is Big Data, which traditional systems cannot handle. We therefore propose a special framework for efficient data collection, storage and processing at Big Data scale on cloud platform. Â

MEDIA MIX MODELING COMPARISON OF INTERACTION MODEL TO SIMPLE LOG-LINEAR MODEL

The objective of current study is to compare a new model for media mix problem with popular model named as simple log linear model. A modified approach proposed to improve the results of media mix model from simple log linear method includes the simultaneous effect of different media variables on sales. The combined effect caused by various media variables shows a synergy in the curve for sales and hence considering it makes the model much effective and accurate

SURVEY ON ADVISOR INTELLIGENCE THROUGH PURCHASE PATTERNS AND SALES ANALYTICS

In mutual fund, an individual or a firm that is in the business of giving advice about securities to clients is an investment advisor. Investment advisers are individuals or firms that receive compensation for giving advice on investing in stocks, bonds, mutual funds, or exchange-traded funds. Investment advisors manage portfolios of securities. Advisors can use new cognitive and analytics capabilities to better understand their clients and needs and have a stronger ability to deepen relationships with a better portfolio. In this paper, we analyze data points foreach advisor, and distinguish the best prospects, obtain insight into their experience and credentials, and learn about their portfolio, in other words, to recognize the pattern of portfolio of the advisors. Such analysis helps the sales people to sell the fund company products to the suitable advisors based on the nature of the product they want to sell. This is done by investigating what kind of products advisors have been buying, and what kind of products they might be looking for. This helps to increase the sales of the products as sales people will be reaching the appropriate advisors

A Study on Fish Classification Techniques using Convolutional Neural Networks on Highly Challenged Underwater Images

Underwater Fish Species Recognition (UFSR) has attained significance because of evolving research in underwater life. Manual techniques to distinguish fish can be tricky and tedious. They might require enormous inspecting endeavours, but they can be costly. It results in limited data and a lack of human resources, which may cause incorrect object identification. Automating the fish species detection and recognition utilizing technology would assist sea life science to evolve further. UFSR in wild natural habitats is difficult because the images open natural habitat, complex background, and low luminance. Species Visualization can assist us with deep knowledge of the movements of the species underwater. Automation systems can help to classify the fish accurately and consistently. Image classification has been emerging research with the advancement of deep learning systems. The reason is that the convolutional neural networks (CNNs) don't require explicit feature extraction methods. The vast majority of the current object detection and recognition mechanisms are based on images in the outdoor environment. This paper mainly reviews the strategies proposed in the past years for underwater fish detection and classification. Further, the paper also presents the classification of three different underwater datasets using CNN with evaluation metrics

Influence of potassium iodate and chitosan iodate complex on growth, yield, quality and iodine uptake in ‘shivam’ hybrid of tomato (Solanum lycopersicum L.)

An iodine biofortification experiment was conducted by applying potassium iodate fertilizer in soil and foliar form and chitosan complex forms to investigate the growth, yield, quality and uptake of iodine in shivam hybrid of tomato in Palaviduthi soil series of Coimbatore region. Soil fertilization alone resulted in lower uptake of iodine in fruits because the iodine is susceptible to high volatilization and less phytoavailability and also resulted in less yield and poor quality of fruits. When the chitosan and potassium iodate were applied in combination through foliar form, the quality of the fruits was found to be superior (carotene-1.24 mg 100gm-1 ascorbic acid- 3.56 mg 100gm-1, titrable acidity-0.96%), with higher fruit yield (94.81 t ha-1) and uptake of iodine in fruits (0.99ppm). Potassium iodate alone, either in the form of soil or foliar application, increased the quality of fruits, but it did not prevent the loss of various pigments and acids during ripening and also the loss of iodine through volatilization. But chitosan conserved the losses by reducing the respiration rate and oxygen permeability. Further, chitosan formed an electrostatic interaction with potassium iodate, preventing volatilisation and gradually increasing the bioavailability of iodine from soil to fruits. Hence biofortifying iodine in the form of potassium iodate chitosan complex was preferred for enhancing yield, improving quality and increasing the iodine content in fruits