S. Pallavi ¹, M. Lakshmi ¹

Affiliations
1 Department of Computer Science and Engineering, Sathyabama University, Rajiv Gandhi Salai, Jeppiaar Nagar, Chennai, Tamilnadu, 600119, IN

Abstract

Optical Burst Switching (OBS) is a switching paradigm that offers very high throughput with reasonable delay. In OBS, data is transported in the form of the optical burst of unknown length. Till date, the size of the burst can't be estimated in advance. Hence, in OBS deflection routing, contending burst is deflected to some other node and after some more slot, it will re-appear on the same node. This mechanism is known as deflection of burst. In our previous work, an estimation of burst is done and optical node architecture is used to store optical burst. The buffering of burst will reduce average latency as well as improve Burst Error Probability (BER). In this paper, the performance evaluation of the node architecture is presented under various conditions and it is shown that deflection routing along with buffering of contending burst provide very effective solution.

Keywords

Burst Loss Probability, Switch Architecture, OBS.

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M. Lakshmi ¹, K. Sowmya ¹

Affiliations
1 Department of Computer Science and Engineering, Sathyabama University, Rajiv Gandhi Salai, Chennai, Tamil Nadu, IN

Abstract

India is the largest producer of Rice and Wheat which has gained the attention of small and large scale agricultural farmers, but storing these crops without wastage is a long time problem being faced by Food Corporation of India and by small farmers. The main cause of grain damage is due to our country's climatic condition. Prevention of grain damage due to climatic issue is a process. A Systematic approach of identifying suitable dates for storing different grains in a different specific location is been analyzed automatically using Big Data. Also the system will generate an automated alert of any unsafe climatic conditions in the grainstorage. Pattern evaluation and Handling huge volume of data are the two most important issues of this analysis, which has created a wide view for the research and analysis. Since each hourly climatic parameters needs to be analyzed for about 5 years, this analysis shows lots of Big Data advantages such as scalability, reliability, robustness volume management etc. An outline of Insect Infestation Detection Algorithm for safe grain storage report has been generated in this paper. A generic report on safe, unsafe and temporary unsafe dates are being generated for five years of climatic data. Using the report our algorithm generates alert signals if same such sequence of climatic pattern occurs in the upcoming years. Generating such signals will enable the Food Corporation of India to take necessary measure before grain damage.

Keywords

Big Data, Data Warehousing, Insect Infestation Detection Algorithm, Volume Management.

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Anjana Joseph Joseph ¹, M. Lakshmi ²

Affiliations
1 Department of Computer Science and Engineering, Sathyabama University,Chennai-600119, Tamil Nadu, IN
2 Faculty of Computing, Sathyabama University, Chennai-600119,Tamil Nadu, IN

Abstract

Objectives: The main objective of this paper is to propose a methodology to develop a Storm analysis model from Raw Rainfall dataset using techniques such as Artificial Neural Network and Min-Max Algorithm. Storm analysis model aims at predicting the occurrence and strength of a storm by analyzing the rainfall data of that region. Methods: In the proposed methodology the raw rainfall dataset is being trained by Artificial Neural network based on the three layers -Input, Hidden, and Output layers. The trained dataset is then summarized into a model which performs the prediction of storm centric characteristics. Neural network training is implemented in Hadoop framework. We obtain a considerable improvement in the total performance of the system by employing Artificial Neural Network. Min-Max algorithm is also used in the system for predicting the intensity of storm. The dataset used for training and prediction consists of daily rainfall data of Cherrapunjee area collected by The Meteorological Department of India. Findings: In the existing system, the raw rainfall dataset is collected and stored in a relational database and then map-reduce based techniques are applied for storm analysis. The major disadvantages associated with this technique are the performance and accuracy rate get reduced with increase in data size. In the proposed methodology as the raw rainfall dataset is being trained by Artificial Neural network the performance and accuracy rate got improved. Also, the training process is done on multi-node hadoop cluster by considering large raw rainfall dataset. With multi-node hadoop cluster there was a large reduction in the total training time. Storm depth of a particular region is calculated by applying MIN-MAX algorithm. This improved the total efficiency of the storm intensity prediction. Applications/Improvement: The performance of the system can be further improved by reducing the training time by adding more nodes while implementing the process in multi node hadoop cluster. Also higher prediction accuracy can be obtained by combining various suitable fuzzy inference models5 with the proposed neural network mode.

Keywords

Artificial Neural Network, Hadoop, MIN-MAX Algorithm, Rainfall Data, Storm Analysis

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Neenu Rachel ¹, M. Lakshmi ²

Affiliations
1 Department of Computer Science and Engineering, Sathyabama University, Chennai - 600119, Tamil Nadu, IN
2 Faculty of Computing, Sathyabama University, Chennai - 600119, Tamil Nadu, IN

Abstract

Objective: The paper aims in presenting a prediction model by using Support Vector Machine (SVM) technique which is meant to possess a strong capability to predict landslides by forecasting rainfall dataset using BigData concept. Methods: The dataset has been taken for the Cherapunjee region which receives the highest intensity of rainfall in India. The aim is to predict the landslide occurrence and classify the risk level associated with the landslide. To improve the reliability in landslide prediction, the proposed model uses pre-processing for removing null values in the dataset. After getting the pre-processed dataset, it will apply normalization, then SVM training and finally the Testing process. Thus the Support Vector Machine concept proved to exhibit a large degree of flexibility in handling tasks of varied complexities because of the non-linear boundary functions. Findings: The study concludes that SVM proved to be an efficient technique to forecast the landslides by predicting the rainfall in advance. The comparative results of SVM in regard with Artificial Neural Networks were proven. The study has been done specifically for Cherrapunjee region and can be implemented for any landslide prone area. Novelty/Improvement: Researchers worldwide are having a great pace to develop early prediction mechanisms for natural hazards. The study uses Radial Basis Function as an initial parameter for predicting the risk level classification of landslide. The novelty is in providing an initial selection of the kernel parameter in order to save the time on finding the best parameters.

Keywords

BigData, Hadoop, Rainfall Data, SVM.

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