Arun Agarwal

Bio: Arun Agarwal is an academic researcher from University UCINF. The author has contributed to research in topics: Image segmentation & Computer science. The author has an hindex of 14, co-authored 68 publications receiving 1001 citations. Previous affiliations of Arun Agarwal include Massachusetts Institute of Technology & University of Hyderabad.

Recurrent neural network and a hybrid model for prediction of stock returns

Abstract: A novel hybrid model is proposed for prediction of stocks returns.The proposed model is hybrid of two linear models and a non-linear model.An optimization model is introduced which generates weights for proposed model.Proposed model is able to capture non-linear patterns of stock data very well. In this paper, we propose a robust and novel hybrid model for prediction of stock returns. The proposed model is constituted of two linear models: autoregressive moving average model, exponential smoothing model and a non-linear model: recurrent neural network. Training data for recurrent neural network is generated by a new regression model. Recurrent neural network produces satisfactory predictions as compared to linear models. With the goal to further improve the accuracy of predictions, the proposed hybrid prediction model merges predictions obtained from these three prediction based models. An optimization model is introduced which generates optimal weights for proposed model; the model is solved using genetic algorithms. The results confirm about the accuracy of the prediction performance of recurrent neural network. As expected, an outstanding prediction performance has been obtained from proposed hybrid prediction model as it outperforms recurrent neural network. The proposed model is certainly expected to be a promising approach in the field of prediction based models where data is non-linear, whose patterns are difficult to be captured by traditional models.

The Anatomy of Big Data Computing

Abstract: Advances in information technology and its widespread growth in several areas of business, engineering, medical and scientific studies are resulting in information/data explosion. Knowledge discovery and decision making from such rapidly growing voluminous data is a challenging task in terms of data organization and processing, which is an emerging trend known as Big Data Computing; a new paradigm which combines large scale compute, new data intensive techniques and mathematical models to build data analytics. Big Data computing demands a huge storage and computing for data curation and processing that could be delivered from on-premise or clouds infrastructures. This paper discusses the evolution of Big Data computing, differences between traditional data warehousing and Big Data, taxonomy of Big Data computing and underpinning technologies, integrated platform of Big Data and Clouds known as Big Data Clouds, layered architecture and components of Big Data Cloud and finally discusses open technical challenges and future directions.

The anatomy of big data computing

Abstract: Advances in information technology and its widespread growth in several areas of business, engineering, medical, and scientific studies are resulting in information/data explosion. Knowledge discovery and decision-making from such rapidly growing voluminous data are a challenging task in terms of data organization and processing, which is an emerging trend known as big data computing, a new paradigm that combines large-scale compute, new data-intensive techniques, and mathematical models to build data analytics. Big data computing demands a huge storage and computing for data curation and processing that could be delivered from on-premise or clouds infrastructures. This paper discusses the evolution of big data computing, differences between traditional data warehousing and big data, taxonomy of big data computing and underpinning technologies, integrated platform of big data and clouds known as big data clouds, layered architecture and components of big data cloud, and finally open-technical challenges and future directions. Copyright © 2015 John Wiley & Sons, Ltd.

Stock market prediction and Portfolio selection models: a survey

Abstract: Stock data is known to be chaotic in nature and it is a challenging task to predict the non-linear patterns of such data. Forming an optimal portfolio of stocks is yet another challenging task and limitations do exist in every portfolio model in some form or the other. In order to resolve such problems, many artificial intelligence models have appeared in literature which are also known as intelligent models. Prediction of stocks as well as investing in appropriate stocks has remained in focus among investors, industrialists as well as among academicians. This paper surveys important published articles in the related area available in literature. This survey highlights traditional mathematical models available in articles which have appeared decades back till artificial intelligence based models available in recent articles.

Robot vision

Abstract: A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

Algorithms for image processing and computer vision

Abstract: A cookbook of algorithms for common image processing applicationsThanks to advances in computer hardware and software, algorithms have been developed that support sophisticated image processing without requiring an extensive background in mathematics This bestselling book has been fully updated with the newest of these, including 2D vision methods in content-based searches and the use of graphics cards as image processing computational aids Its an ideal reference for software engineers and developers, advanced programmers, graphics programmers, scientists, and other specialists who require highly specialized image processingAlgorithms now exist for a wide variety of sophisticated image processing applications required by software engineers and developers, advanced programmers, graphics programmers, scientists, and related specialistsThis bestselling book has been completely updated to include the latest algorithms, including 2D vision methods in content-based searches, details on modern classifier methods, and graphics cards used as image processing computational aidsSaves hours of mathematical calculating by using distributed processing and GPU programming, and gives non-mathematicians the shortcuts needed to program relatively sophisticated applicationsAlgorithms for Image Processing and Computer Vision, 2nd Edition provides the tools to speed development of image processing applications

Subset Selection in Regression

Abstract: 8. Subset Selection in Regression (Monographs on Statistics and Applied Probability, no. 40). By A. J. Miller. ISBN 0 412 35380 6. Chapman and Hall, London, 1990. 240 pp. £25.00.