Volume 9 Number 3 December 2019


Adaptive Optimization for Continuous Multi-Way Joins Using ACO System
G. Sakthivel, P. Madhubala

Abstract-The join operator is a core component of an ACO System. Query optimization, the process to generate an optimal execution plan for the posed query, is more challenging in such systems due to the huge search space of alternative plans incurred by distribution. . Due to the constantly updating nature of continuous queries, the query optimizer has to frequently change the optimal execution plan for a query. However, optimizing the join executing plan for every execution step might be prohibitively expensive; hence, dynamic optimization of continuous join operations is still a challenging problem so far. Therefore, this paper proposes the first adaptive optimization approach towards this problem in the ACO system. The approach comes with two dynamic cost-based optimization algorithms which use a light-weight process to search for the best execution plan for every execution step. The experimental results show that the proposed algorithm saves up to about 100% of optimization time with no significant difference in the quality of generated plans compared with the best existing genetic-based algorithm.


Comparative Study On Machine Learning Techniques ANN and SVM For Lymphoma Malady Precision and Hazard Factors
Sivaranjini, N,Gomathi, M.

Abstract- As indicated by the pervasiveness of coronary illness and the cost of conclusion and treatment strategies other than having such huge numbers of symptoms, presence of certain techniques, for example, information mining which makes doctors ready to have an exact expectation about the danger of an infection among patients with various characteristics is so significant, in light of the fact that it very well may be efficient, affordable and furthermore decline determination focuses clog. Another impact of applying these sorts of techniques is anticipation of patients from being affected by reactions brought about by some finding strategies, for example, angiography. Today information mining is being utilized in numerous fields of science including clinical science and one of its applications is infections expectation dependent on past encounters and datasets. Right now, are going to address a kind of coronary illness named Lymphoma Artery Disease (LAD) brought about by cholesterol sedimentation in primary veins and making a deterrent for blood development in those vessels. So as to address this issue, we initially have a concise presentation about LAD then we present a few patient order and malady expectation techniques. In the accompanying, we enroll a portion of these models to have an expectation on a dataset having a place with Cleveland which is a city in US and afterward we contrast these models with pick the most able one for forecast utilizing ROC bend and measurable techniques. At last, we figure their precision to pick the best model for this issue.


Novel Adaptive Detection Approach for Monitoring Drivers’ Eye Movement During Vehicle Movement
J. Mary Dallfin Bruxella,J.K.Kanimozhi

Abstract- The increase in vehicle accidents due to the driver drowsiness over the last years highlights the need for developing reliable drowsiness assistant systems by a reference drowsiness measure. Therefore, the paper at hand is aimed at classifying the driver vigilance state based on eye movements using electrooculography (EOG). In order to give an insight into the states of driving, which lead to critical safety situations, first, driver drowsiness, distraction and different terminologies in this context are described. Afterwards, countermeasures as techniques for keeping a driver awake and consequently preventing car crashes are reviewed. Since countermeasures do not have a long-lasting effect on the driver vigilance, intelligent driver drowsiness detection systems are needed. Driver state is quantifiable by objective and subjective measures. The objective measures monitor the driver either directly or indirectly. For indirect monitoring of the driver, one uses the driving performance measures such as the lane keeping behavior or steering wheel movements. On the contrary, direct monitoring mainly comprises the driver’s physiological measures such as the brain activities, heart rate and eye movements. In order to assess these objective measures, subjective measures such as self-rating scores are required. This study introduces these measures and discusses the concerns about their interpretation and reliability. EOG as a tool to measure the driver eye movements allows us to distinguish between drowsiness- or distraction-related and driving situation dependent eye movements. In order to cover all relevant eye movement patterns during awake and drowsy driving, different experiments are conducted in this work including daytime and nighttime experiments under real road and simulated driving conditions. Based on the measured signals in the experiments, investigate the conventional blink detection method based on the median filtering and show its drawback in detecting slow blinks and saccades. Afterwards, an adaptive detection approach is proposed based on the derivative of the EOG signal to simultaneously detect not only the eye blinks, but also other driving-relevant eye movements such as saccades and microsleep events. Finally, feature dimension reduction approaches to determine the applicability of extracted features for in-vehicle warning systems. On this account, filter and wrapper approaches are introduced and compared with each other. Our comparison results show that wrapper approaches outperform the filter-based methods.


Multi Node Data Transmission Analysis Based on Efficient Interruption Recognition Technique
A. Anthony Paul Raj, J. K. Kani Mozhi

Abstract- In the present paper describes the network node to node network security, the present scenario reports there are numerous algorithm and technique are developed and used by the peoples for the security of network but the drawback is the time complexity is high which helps for the peoples to crack the security of various nodes and it can`t identify the peoples .This describes paper the multi node traffic analysis and load on the various nodes connectivity by sending and receiving data frequency and in specific time period by calculating weight on every node the stack time and obtain critical path it is secure transmission if not then abnormality is detected efficiently. The proposed method provides the support to sort the lacuna of existing system in less time complexity and with accuracy


Big Data Security-A Review of Encryption Techniques in Big Data Technique
M. Geethanjali, P.Madhubala

Abstract- In recent years, big data have been hot research topic. The interesting amount of big data also increases the chance of breaching the privacy of individuals. Due to a rapid growth and spread of network services, mobile devices, and online users on the internet leading to a remarkable increase in the amount of data. However, it is not only very difficult to store and analyse them with traditional applications. But also it has challenging data privacy and security problems. This paper shows the fundamental concept of big data, concerns on big data, technologies used and presents comparative view of big data privacy and security approaches in literature.