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Research Paper

Big data: An analytic architecture and prediction using spark for E-agriculture

Nowadays Big Data has a key role in E-Agriculture. Previous technologies have some limitations so Big Data is very useful for E-Agriculture because agriculture has very large structure and unstructured data. Moreover, big data analytics can be used to increase and improve the productivity of agricultural. The main aim of this paper is to propose an open source, economical, ideal and flexible big data analytics architecture for E-Agriculture. In the implementation, an analytic framework for big data application development is built and implemented. Also, a prototype application prediction base for agriculture in spark framework. Based on the agriculture prediction model various recommendations can be provided to agro users.

Published by: Patel Jaydeep Pravinbhai, Ashutosh Abhangi

Author: Patel Jaydeep Pravinbhai

Paper ID: V4I3-1183

Paper Status: published

Published: May 5, 2018

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Research Paper

Time and attribute factors combined access control on time-sensitive data in public cloud

Security of data stored in the cloud is an important issue nowadays. We are using different types of technologies to protect this information. Here we are using an advanced technology, by using encryption and decryption mechanism. The data owner can store the encrypted data in the cloud. Then the owner can issue the decryption keys to the authorized users. Based on this scheme data owners can easily share the data with intended users. An extensive number of users is trying to access data stored in the cloud simultaneously; it leads to new challenges mainly on confidentiality and integrity of data stored in the cloud. This paper mainly addresses these issues and implements strategies for solving this, by combining CP-ABE (Ciphertext-Policy Attribute-Based Encryption) and TRE (Timed Released Encryption) commonly known as Time and Attribute Factors Combined Access control for Time-Sensitive data in Public Cloud (TAFC).

Published by: Dhanya. K, Preethi. S

Author: Dhanya. K

Paper ID: V4I3-1238

Paper Status: published

Published: May 5, 2018

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Research Paper

A new method of finding solutions of a solvable standard quadratic congruence of comparatively large prime modulus

In this paper, a new method of finding solutions of a solvable standard quadratic congruence of comparatively large prime modulus is described. A comparative study was made by solving numerical problems using the existed method and the proposed method. The merits and demerits of both the methods are also discussed.

Published by: B. M. Roy

Author: B. M. Roy

Paper ID: V4I3-1247

Paper Status: published

Published: May 5, 2018

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Research Paper

Nomograms for isotropically reinforced polygonal slab

Slabs comprise the maximum area of the building which comes up to 20 to 25 percent of total cost. Thus the economical design of the building can be achieved to great extent by designing the slab economically. The yield line method is a method which allows the redistribution of force that will take place after the yielding of slab reinforcement. Using this method, slabs that are easy and quick to design and to construct can be generated. The resulting slab is thin and has the very low amount of reinforcement in regular arrangements. This makes it easy to detail. Above all, this design generates concrete slab which is very economical, because features at the ultimate limit state are considered. The aim of this study is to apply the yield line theory for polygonal slab of N equal sides by using the virtual work principle and obtain a general relationship between the ultimate load and desired arrangement of reinforcement in a slab for any given radius of circle in which the slab is inscribed and to generate nomograms for frequently used slabs.

Published by: Pavithra Reddy S J, Ravindranatha, Premanand Shenoy

Author: Pavithra Reddy S J

Paper ID: V4I3-1249

Paper Status: published

Published: May 5, 2018

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Review Paper

Review on big data: Prediction techniques and analytics architecture for E-agriculture

Big data is a very new and important trend in agriculture domain. People now realize the importance of Big Data in E-Agriculture. Big data analytics is a very tough thing in the agriculture field. However, how to use big data analytics in agriculture field to improve the productivity in practices. Purpose of this E-Agriculture to reduce technological gap between rural communities and share information via recommendations and decision support system. Apache Spark is a distributed memory-based computing framework which is naturally suitable for machine learning. Hadoop, the spark has the better way of functionality and ability of computing learning. In this paper, analyze spark framework with basic concept means spark’s primary framework this paper proposes an architecture for managing big data in the agriculture area. The main advantage of this method is managing massive dataset which is already existing. This technique is faster than any other traditional one.

Published by: Patel Jaydeep Pravinbhai, Ashutosh Abhangi

Author: Patel Jaydeep Pravinbhai

Paper ID: V4I3-1251

Paper Status: published

Published: May 5, 2018

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Research Paper

Prediction model of crop yield for food crop grown above ground level through big data analytics

Agriculture is believed to be as the backbone of Indian economic system. For the past few decades, agriculture field has seen lots of technological changes to improve better productivity. Day by day the population is increasing leading to increasing demand for resources but the amount of resources required has been reducing and falling down. Therefore, there has been extensive endeavors to create imaginative and technological advances methodologies for manageable harvest generation. Using prediction methods, farmers can enhance the productivity of crops. These strategies are utilized to find the required number of crops, seeds, moistness, water level and other supplements. Since prediction refers to a statement about an uncertain event, hence modeling the prediction would a good solution to adopt. Predictive modeling uses statistics to predict outcomes. Quantifying the yield is essential to optimize policies to ensure food security. This paper aims at providing a new method to predict the crop yield of food crops grown above the ground level based on big-data analysis technology, which differs with traditional methods in the structure of handling data and in the means of modeling. Firstly, the method can make full use of the existing massive agriculture relevant datasets and can be still utilized with the volume of data growing rapidly, due to big-data friendly processing structure. Secondly, the "nearest neighbors"modeling, which employs results gained from the former data processing structure.

Published by: Varisha Ashraf, Ankit Jain, Manjunath C. R, Sahana Shetty

Author: Varisha Ashraf

Paper ID: V4I3-1263

Paper Status: published

Published: May 5, 2018

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