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Big Data Analysis on Geographical Segmentations and Resource Constrained Scheduling of Production of Agricultural Commodities for Better Yield

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Procedia computer science 2016年 87卷 80-85页

作者： S. Senthilvadivu S. Vinu Kiran S. Prasanna Devi S. Manivannan Assistant Professor ArulmiguMeenakshi Amman College of Engineering Vadamanvandal Kanchipuram 604410 India Department of Computer Science & Engineering Apollo Engineering College Chennai 602105 India Deputy Dean Dr.MGR Educational & Research Institute University Chennai 600107 India

Agricultural geography is one of the subportion of human and our economic geography which examines the primary, secondary, tertiary and quaternary yield types activities that are carried out in agriculture. In this paper, we are examining the spatial distribution and concentration of crops and their yields along with their crop periods using Big data Analytics to find out the cropping patterns and combinations that varies in space and time. Our primary objective is to findout the crop associations and patterns under climatic influence for each geographical segmentation, to give better yeilds. The Big data analytic based association won’t to last as many of the farmers and scientists are rightly challenging over the agricultural sustainability. However, there is a strong possibility of the farmers to adopt a new combination in the coming decades as the Big data analytic based crop pattern decision facilitates the farmers, always try to optimize their agricultural re-turns and adopt new innovations which gives better yeilds since we are performing factual data centric analytics.

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Bigdata Analytics on Diabetic Retinopathy Study (DRS) on Real-time Data Set Identifying Survival Time and Length of Stay

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Procedia computer science 2016年 87卷 227-232页

作者： V. Sujatha S. Prasanna Devi S. Vinu Kiran S. Manivannan Phd Scholar MS University Assistant Professor Vellore Institute of TechnologyVellore Tamil Nadu 632014 India Department of Computer Science & Engineering Apollo Engineering College Chennai 602105 India Deputy Dean Dr.MGR Educational & Research Institute University Chennai 600107 India

In this paper, we had analyzed a large scale Diabetic data sets for several patients to find the length of time taken for treatment for each class of Diabetes and the risk of re-admission of diabetic patients performing Bigdata analytics, the type of diabetes and its outcome which acted as a high risk sample of patient data sets. We have collected and integrated different sources of diabetic information for several patients, from primary and secondary treatment information to administrative information, to analyze novel view of patient care processes such as type of treatments and every patient behaviors on which results multifaceted nature of chronic care that we take into our account to predict the survival factors and length of stay. Nowadays by using electronic medical equipments with high quality and high degree calibrations, we are able to gather large amounts of real-time diabetic data sets. The requires the usage of distributed platforms for making BigData analysis that results on making decisions based on available data and its trends. This type of Bigdata analysis allows geographical and environmental information of patients’ enables the capability of interpreting the ethnicity of data gathered and extract new analysis to identify survival options and treatment timelines (LOS) from them.

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Diabetic Retinopathy Detection Based on Eigenvalues of the Hessian Matrix

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Procedia computer science 2015年 47卷 311-318页

作者： S. Saranya Rubini A. Kunthavai Assistant Professor Department of Computer Science Engineering Coimbatore Institute of Technology Coimbatore India Assistant Professor (Senior Grade) Department of Computer Science Engineering Coimbatore Institute of Technology Coimbatore India

Diabetic Retinopathy (DR) is a medical condition caused by fluctuating insulin level in the blood which causes vision loss in case of severity. Timely treatment of such risks requires identification of the first clinical symptoms like microaneurysms (MAs) and hemorrhages (HMAs). The presence of those symptoms are visible in the digital color photographs of the retina and appear as round dark red spots in the image. In this paper, two approaches in the detection of MAs and HMAs are proposed. First, the semi automated approach applies semi automated hessian-based candidate selection algorithm (SHCS) followed by thresholding to detect true MAs and HMAs. The automated approach applies automated hessian-based candidate selection algorithm (AHCS) followed by feature extraction and SVM classification that uses twenty images for training manually annotated by medical domain experts. Implementations of both the approaches have been tested on real world images from retinal scan. From the results, the detection rate of automated algorithm when compared with that of the semi automated algorithm has been found to be significantly lesser with a probability p<0.005.

关键词： Diabetic retinopathy Microaneurysms Hemorrhages SVM SHCS AHCS

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Feature Selection of Gene Expression Data for Cancer Classification: A Review

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Procedia computer science 2015年 50卷 52-57页

作者： Rabindra Kumar Singh M. Sivabalakrishnan Assistant Professor School of Computer Science and Engineering VIT Univeristy Chennai Campus Associate Professor School of Computer Science and Engineering VIT Univeristy Chennai Campus

The DNA microarray technology has capability to determine the levels of thousands of gene simultaneously in a single experiment. Analysis of gene expression is important in many fields of biological research in order to retrieve the required information. As time progresses, the illness in general and cancer in particular have become more and more complex and complicated, in detecting, analyzing and curing. We know cancer is deadly disease. Cancer research is one of the major area of research in medical field. Predicting precisely of different tumor types is a great challenge and providing accurate prediction will have great value in providing better treatment to the patients. To achieve this, data mining algorithms are important tools and the most extensively used approach to achieve important feature of gene expression data and plays an important role for gene classification. One of major challenges is to discover how to extract useful information from huge datasets. This paper presents recent advances in the machine learning based gene expression data analysis with different feature selection algorithms. Gene expression profiles, which represent the state of a cell at a molecular level, have great potential as a medical diagnosis tool. But compared to the number of genes involved, available training data sets generally have a fairly small sample size for classification. These training data limitations constitute a challenge to certain classification methodologies. Feature selection techniques can be used to extract the marker genes which influence the classification accuracy effectively by eliminating the un wanted noisy and redundant genes This paper presents a review of feature selection techniques that have been employed in micro array data based cancer classification and also the predominant role of SVM for cancer classification.

关键词： Gene Expression Cancer Classification Feature selection

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Online Social Gaming and Social Networking Sites

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Procedia computer science 2016年 82卷 72-79页

作者： Linah Aburahmah Hajar AlRawi Yamamah Izz Liyakathunisa Syed Software Engineering student at Prince Sultan University – College of Women Riyadh Saudi Arabia Computer Science student at Prince Sultan University – College of Women Riyadh Saudi Arabia Information Systems student at Prince Sultan University – College of Women Riyadh Saudi Arabia Assistant Professor at Prince Sultan University – College of Women Riyadh Saudi Arabia

Online social games are becoming a significant component in today's social media sites. The social networking sites environment has provided a platform for online games to develop and expand in the virtual medium. Users are now able to play games online, compare scores, and challenge each other among many other things. Due to the diverse user demographics of social media sites, various motivations to playing social games emerge. The need for this present research was to answer the question whether the integration of social games within social networking sites and apps have increased the likelihood of playing those games. Therefore, the main objective of this research is to predict from the fact that will be used to decide whether to include games (Adventure, Fighting, Design\Art, Virtual Life…etc.) in the social media sites that still have not been implemented (such as Twitter, Tumblr, etc.). Also, we want to discover whether the inclusion of social games has improved the services offered by social sites and whether social gaming has effects on human behavior with regards to socializing and interacting with others. The aim of this study was to examine the relation between social media apps and games, and whether the former has increased users participation in online games. The primary setting for the quantitative method of research study was the online gamers regardless their age, gender, and interests. The data were collected by distributing an online survey, and were analyzed using WEKA data mining tool. Two popular classification algorithms were used to predict the answer of this research question. The resulting data were compared and tested for their accuracy using different metrics.

关键词： Online games Social sites Data Mining Random Forest Neural Networks

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Symptoms Based Treatment Based on Personal Health Record Using Cloud Computing

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Procedia computer science 2015年 47卷 22-29页

作者： P. Dhivya S. Roobini A. Sindhuja Assistant Professor Department of Computer Science and Engineering Coimbatore India Student Department of Computer Science and Engineering SNS College of Technology Coimbatore India

Cloud architecture is used for maintaining the personal health record and provide symptoms based treatment to the patients. The details of a patient need to be stored in a secured manner. It is to create a cloud storage server for long term storage over the internet. The storage server will act as a database server. Uploaded data stored in the cloud server through proxy re-encryption method. A secured threshold proxy re-encryption server and integrates it with a decentralized erasure code such that a secure distributed storage system. To generate proxy re-encryption key for one-time data access. A proxy server will be created virtually for one time data access. We can achieve the symptoms based treatment by secure Personal Health Record in cloud storage when applying the proposed encryption algorithm.

关键词： PersonalHealth Record(PHR) Encryption proxy treatments cloud computing

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SOA Based Secure Data Transmission over CMEA Protocol in MANET

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Procedia computer science 2015年 47卷 434-440页

作者： P. Dhivya S. Karthik T. Kalaikumaran Assistant Professor Department of Computer Science and Engineering SNS College of Technology Coimbatore India Professor & Dean Department of Computer Science and Engineering SNS College of Technology Coimbatore India Professor & HoD Department of Computer Science and Engineering SNS College of Technology Coimbatore India

Mobile Adhoc Networks is a self organizing network composed of mobile terminals connected by wireless links. Adhoc Networks are created dynamically without any preexisting network infrastructure Ad-hoc networks are very useful in situations like emergency search and reuse operations and meetings where people want to quickly share information. This network does not have any central administration, hence there are no designated routes, all the nodes can serve as routers for each other, and data packets are forwarded from node to node to node in multi hop fashion. Cluster based Mobility and Energy Aware (CMEA) routing protocols is used to find the reliable route in a cluster networks. The clustered network avoids path breaks and long path delay. When applying the energy and mobility aware metric over clustered network gives reliable route between Source and Destination. Service Oriented Architecture (SOA) is an evolution of past platforms, preserving successful characteristics of traditional architectures and bringing with it distinct principles that follow service orientation in support service oriented enterprise. It forces the network to improve the reliability, security, integrity etc. By applying the proposed XML technique for encrypting the secure data improves the content delivery over the CMEA network. Therefore SOA based secure data transmission over the reliable route improves the network performance.

关键词： Service Oriented Architecture (SOA) Mobile Adhoc Network (MANET) route discovery Cluster based Mobility and Energy Aware (CMEA) protocol Route Request (RREQ) Route Reply (RREP) Multi purpose Internet Mail Extension (MIME).

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Proposal to Provide Security in MANET's DSRRouting Protocol

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Procedia computer science 2015年 48卷 440-446页

作者： Milan Kumar Dholey G.P. Biswas Assistant Professor Computer Science & Engineering Hooghly Engineering & Technology College Hooghly 712103 India Computer Science & Engineering Indian School of Mines Dhanbad 826004 India

Mobile Ad-hoc network (MANET) is infrastructure less, self-organizing bandwidth constraint on demand wireless network. MANET is a multi-hop wireless network therefore, needs an efficient routing protocol to transmit data packet from source to destination. MANET routing protocol can be divided as proactive, reactive and hybrid. In an ad-hoc network a node cannot identify the nodes that are beyond its radio range. The nodes in MANET are movable so, a fixed path cannot be defined in between source to a destination. In MANET's reactive routing protocol, like Dynamic Source Routing (DSR), a source node locate the destination before data transmission take place by broadcasting/flooding route request (RREQ) control message and establishes the path after receiving route reply (RREP) control message. During route discovery using DSR routing protocol a malicious node may enter in between source-destination path as an intermediate node or eavesdrop to the source by sending RREP control message before original RREPs (multiple path exists between source to destination) reach to the source with unexpected path information. As a result, without knowing anything source may deliver data packet to a different destination. In this paper, we identified the issues and proposed a solution to prevent to enter the malicious node in between source-destination path. In our proposal, initially we formed a group by considering neighbour nodes of an individual node from the original network and presented the way of communication by the group member using Diffie-Hellman group key (GDH) management scheme in the network through our proposed algorithm. Our proposed algorithm will prevent a malicious node to enter in between source-destination path during route discovery using DSR routing protocol. Further our technique prevents to gainaccess to any node in the network except the group member hence malicious node cannot identify the source to sent RREP control message with wrong path information.

关键词： MANET Routing Protocol DSR GDH

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Frequent Subgraph Mining Algorithms – A Survey

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Procedia computer science 2015年 47卷 197-204页

作者： T. Ramraj R. Prabhakar Assistant Professor Department of Computer Science and Engineering Coimbatore Institute of Technology Coimbatore - 641014 India Emeritus Professor Department of Computer Science and Engineering Coimbatore Institute of Technology Coimbatore - 641014 India

Graphs are common data structures used to represent / model real-world systems. Graph Mining is one of the arms of Data mining in which voluminous complex data are represented in the form of graphs and mining is done to infer knowledge from them. Frequent sub graph mining is a sub section of graph mining domain which is extensively used for graph classification, building indices and graph clustering purposes. The frequent sub graph mining is addressed from various perspectives and viewed in different directions based upon the domain expectations. In this paper, a survey is done on the approaches in targeting frequent sub graphs and various scalable techniques to find them.

关键词： Candidate Graphs Support Apriori-based Pattern-growth.

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Study on online community user motif using web usage mining

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Journal of Physics: Conference Series 2016年第1期710卷

作者： Meera Alphy Ajay Sharma PhD Scholar Department of Computer Science and Engineering SRM University Haryana India Assistant Professor Department of Computer Science and Engineering SRM University Haryana India

The Web usage mining is the application of data mining, which is used to extract useful information from the online community. The World Wide Web contains at least 4.73 billion pages according to Indexed Web and it contains at least 228.52 million pages according Dutch Indexed web on 6th august 2015, Thursday. It's difficult to get needed data from these billions of web pages in World Wide Web. Here is the importance of web usage mining. Personalizing the search engine helps the web user to identify the most used data in an easy way. It reduces the time consumption; automatic site search and automatic restore the useful sites. This study represents the old techniques to latest techniques used in pattern discovery and analysis in web usage mining from 1996 to 2015. Analyzing user motif helps in the improvement of business, e-commerce, personalisation and improvement of websites.

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