2. Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/1/7
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Item Measuring the influence of moods on stock market using Twitter analysis(2019) Cowlessur, S.K.; Annappa, B.; Sree, B.K.; Gupta, S.; Velaga, C.It is a well-known fact that sentiments play a vital role and is an incredibly influential tool in several aspects of human life. Sentiments also drive proactive business solutions. Studies have shown that the more appropriate data is gathered and analyzed at the right time, the higher the success of sentiment analysis. This paper analyses the correlation between the public mood and the variation in stock prices towards companies in different domains. For each tweet, scores are assigned to eight predefined moods namely �Joy�, �Sadness�, �Fear�, �Anger�, �Trust�, �Disgust�, �Surprise� and �Anticipation�. A regression model is applied to the mood scores and the stock prices dataset to obtain the R-squared score, which is a metric used to evaluate the model. The paper aims to find the moods that best reflect the stock values of the respective companies. From the results, it is observed that there is a definite correlation between public mood and stock market. � Springer Nature Singapore Pte Ltd. 2019.Item Recommending an alternative path of execution using an online decision support system(2017) Thomas, L.; Manoj, Kumar, M.V.; Annappa, B.Prediction of disease severity is highly essential for understanding the progression of disease and initiating an alternative path of execution, which is priceless in treatment planning. An online decision support system (ODeSS) is proposed here for stratification of the patients who may need Endoscopic Retrograde CholangioPancreatography (ERCP) and recommend an alternate path of execution. By this an immediate intervention can be avoided. In this study gallstone disease (GSD) whose prevalence is increasing in India is considered. ODeSS is a versatile non-linear information model which clustered the traces based on the duration of its completion. This is a Retrospective analyses of 575 traces. ODeSS applied the technique of longest common subsequence for identifying the sequence of an online execution and discovering to which cluster of variants it may belong. This discovery assist in taking appropriate clinical decision by recommending an alternative path of execution for such cases which may need emergency interventions. ODeSS performance was evaluated using area under receiver operating characteristic curve (area under ROC curve). This showed an accuracy of 0.9653 in prediction. The proposed model was validated using ROC curve in k-fold cross validation. Hence the proposed ODeSS can be used to conduct a non-linear statistical analysis since, the relationships between the predictive variables are not linear. It can be used as a clinical practice to recommend the path of execution. This would assist in better treatment planning, avoiding future complications. � 2017 ACM.Item Real time big data analytics in smart city applications(2019) Manjunatha; Annappa, B.Technological revolution in the recent past has enabled the concept of Smart City for urban development. Smart City concept is conceived with the objectives of providing better services to the citizens and improves the quality of life. Information and Communication Technology (ICT) and Internet of Things (IoT) made smart city applications as much simpler and effective. Big data technologies play a major role in smart city applications. This paper gives an overview of the role of big data in building smart city applications and proposes a framework for real time big data analytics. Real-time big data analytics help in making better decisions and more accurate predictions at right time to offer better services to the citizens. Here, we discussed some of important solutions and services for the smart city where the real-time big data analytics helps in improving the quality of services in smart city applications. � 2018 IEEE.Item Profile Matching of Online Social Network with Aadhaar Unique Identification Number(2017) Gautam, B.; Jain, V.; Jain, S.; Annappa, B.Matching user's profile over multiple Online Social Network (OSN) brings many new insights. Considering the fact of user's disambiguation over different OSN, Existing user profile matching are highly computationally expensive. In this paper, we propose a novel e-identity Architecture using Infrastructure as a service (IaaS) of Cloud to reduce the computational cost of the profile matching algorithm. Profile equivalent through proposed architecture are based on the public attributes available in overall social network and lastly, profiles are mapped to the Aadhaar Unique Identification Number (UID). � 2016 IEEE.Item Privacy and trust in cloud database using threshold-based secret sharing(2013) Dutta, R.; Annappa, B.In today's cloud computing scenario, privacy of data and trust on the service provider have become a major issue and concern. Achieving trust and preserving the privacy of data stored in third-party cloud databases has emerged as a key research area. To achieve this, several different techniques have been proposed based on cryptography, auditing by a third party, etc. Secret sharing schemes have also been considered to address these issues of trust and privacy in databases by various researchers. In this paper, we propose a technique of using a well-known threshold-based visual secret sharing scheme to address the issue of privacy and trust in cloud databases and database-as-a-service offerings. We consider data records with at least one prime attribute and propose an indexing technique for the secret shares of records in a large database based on some properties of the secret sharing technique. Our technique is aimed at minimizing storage overhead of secret shares as well as high speed upload and retrieval of data. We discuss the results obtained from our implementation. Our implementation using Hadoop Distributed File System (HDFS) with Matlab shows that this technique minimizes storage overhead due to secret shares and ensures high speed data upload and retrieval. � 2013 IEEE.Item Prediction of gallstone disease progression using modified cascade neural network(2018) Thomas, L.; Manoj, Kumar, M.V.; Annappa, B.; Arun, S.; Mubin, A.Prediction of disease severity is highly essential for understanding the progression of disease and initiating an early diagnosis, which is priceless in treatment planning. A Modified Cascade Neural Network (ModCNN) is proposed for stratification of the patients who may need Endoscopic Retrograde Cholangiopancreatography (ERCP). In this study, gallstone disease (GSD) whose prevalence is increasing in India is considered. A retrospective analysis of 100 patients was conducted and their case history was recorded along with the routine investigations. Using ModCNN, the associated risk factors were extracted for the prediction of disease progression toward severe complication. The proposed model outperformed showing better accuracy with an area under receiver operating characteristic curve (area under ROC curve) of 0.9793, 0.9643, 0.9869, and 0.9768 for choledocholithiasis, pancreatitis, cholecystitis, and cholangitis, respectively, when compared with Artificial Neural Network (ANN) showing an accuracy of 0.884. Hence, the proposed technique can be used to conduct a nonlinear statistical analysis for the better prediction of disease progression and assist in better treatment planning, avoiding future complications. � 2018, Springer Nature Singapore Pte Ltd.Item Prediction based dynamic resource provisioning in virtualized environments(2017) Raghunath, B.R.; Annappa, B.Dynamic provisioning to virtual machines (VMs) is one of the important requirements in the virtualized data centers to make effective utilization of resources. This can be achieved by vertical scaling or horizontal scaling of attached resources. Live virtual machine migration of virtual machines across physical machines is a vertical scaling technique which facilitates resource hotspot mitigation, server consolidation, load balancing and system level maintenance. As live migration is triggered during heavy workload (hotspot) and its procedure takes significant amount of resources to iteratively copy memory pages from source to destination, it affects the performance of other running VMs hosted on the source as well as destination physical machine (PM). Hence to avoid such performance interference effects it is necessary to trigger the migration procedure at such a point where sufficient amount of resources will be available to all the running VMs and to the migrating procedure. It is also important to select such a VM which will produce less performance interference at the source and destination. This paper presents an intelligent decision maker to trigger the migration in such a way that it avoids the said performance interference effects. It predicts the future workload for early detection of overloads and accordingly triggers the migration procedure. It also models the migration procedure to calculate performance parameters and interference parameters which are used in the decision of selection of a VM. Experimental results show that it is able to increase the performance by 45%-50% for network intensive workloads and 25%-30% for CPU, memory intensive workloads when compared with traditional method. It improves the performance by 35%-40% for network intensive workloads and 15%-20% for CPU, memory intensive workloads when compared with Sandpiper method. � 2017 IEEE.Item Phenomenon of concept drift from process mining insight(2014) Manoj, Kumar, M.V.; Thomas, L.; Annappa, B.Process mining is originated form the fact that the modern information systems systematically record and maintain history of the process which they monitor and support. Systematic study of the recorded information in process centric manner will help to understand the process in a better way. Process mining acts as enabling technology by facilitating process centric analysis of data, which other available data science like data mining etc. fails to provide. Process mining algorithms are able to provide excellent insights on the process which they analyze, but they fail to handle the change in the process. Concept drift is a phenomenon of change in the process while it is being analyzed and it is a non-stationary learning problem. As the process changes while it is being analyzed, end result of the analysis becomes obsolete. Process mining algorithms are static biased, they assume that process at the beginning of analysis period will remain as same at the end of analysis period. There is at most requirement to effectively deal with the change in process to conduct optimal analysis. The main focus of this paper is to identify different factors to be considered while designing the solution for the problem of concept drift and explain each of the identified factors briefly. As the phenomenon of concept drift is extensively under consideration for research in other scientific research disciplines, this article considers restricting the content strictly concerning to the context of process mining. � 2014 IEEE.Item Performance evaluation of CoDel for active queue management in wired-cum-wireless networks(2014) Jain, T.; Annappa, B.; Tahiliani, M.P.Internet is the major source of information today and its usage is increasing at an alarming rate. A wide variety of data travels over the Internet to cater the needs of end users. This has eventually led to heavy congestion in the network which in turn, worsens the user perceived latency. Internet routers are the main agents that detect congestion prior to end hosts. Traditional router incorporates Passive Queue Management (PQM) strategies which fail to control congestion. Moreover, PQM has several drawbacks which drew the attention of researchers towards the evolution of Active Queue Management (AQM). AQMs are designed to effectively avoid congestion at network routers. AQM apparently became very popular for wired networks, but there are very few researches to find their effectiveness over wireless networks. In this paper we evaluate the effectiveness of a recently proposed AQM mechanism called Controlled Delay (CoDel) in wired-cum-wireless networks. Simulations are carried out by using ns-2 and CoDel's performance is compared with that of Random Early Detection (RED) and Droptail. � 2014 IEEE.Item Performance analysis of graph based iterative algorithms on MapReduce framework(2014) Debbarma, A.; Annappa, B.; Mude, R.G.In the recent few years, there has been an enormous growth in the amount of digital data that is being produced. Numerous attempts are being made to process this large amount of data in a fast and effective manner. Hadoop MapReduce is one such software framework that has gained popularity in the last few years for distributed computation of Big Data. It provides a scalable, economical and easier way to process massive amounts of data in-parallel on large computing cluster preserving the properties of fault tolerance in a transparent manner. However, Hadoop always stores intermediate results to the local disk for running iterative jobs. As a result, Hadoop usually suffers from long execution runtimes for iterative jobs as it typically pays a high I/O cost, wasting CPU cycles and network bandwidth. This paper analyses the problems of existing Hadoop and compare its performance against iMapReduce and HaLoop for graph based iterative algorithms. HaLoop offers better performance as it stores intermediate results in cache and reuses those data on the next successive iteration. For using cache invariant data (inter-iteration locality) it schedules the tasks onto the same node that might occur in different iterations. � 2014 IEEE.