Advances in Computational Research

Number 1, 2011

25-30
A HYBRID STACKING ENSEMBLE FRAMEWORK FOR EMPLOYMENT PREDICTION PROBLEMS

Authors: SUDHEEP ELAYIDOM, SUMAM MARY IDIKKULA, JOSEPH ALEXANDER

Abstract

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Number of views: 593

In this paper we put forward a hybrid stacking ensemble approach for classifiers which is found to be a better choice than selecting the best base level classifier. This paper also describes and compares various data mining methodologies for the domain called employment prediction. The proposed application helps the prospective students to make wise career decisions. A student enters his Entrance Rank, Gender (M/F), Sector (rural/urban) and Reservation category. Based on the entered information the data mining model will return which branch of study is Excellent, Good, Average or poor for him/her. Various data mining models are prepared, compared and analyzed.

31-36
LINEAR DISCRIMINANT ANALYSIS BASED SENSING CHARACTERISTICS STUDY OF ZNO THICK FILM GAS SENSOR

Authors: SAROJ KUMAR LENKA, AMBARISH G. MOHAPATRA

Abstract

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Number of views: 672

Driving under the influence (DUI) is a synonymous term that represents the criminal offense of operating a motor vehicle while being under the influence of alcohol. Semiconductor oxides such as SnO2, TiO2, and ZnO have been more successfully employed as sensing materials compare to organic semiconductors for the detection of ethanol gas concentration. This work investigates the fabrication and characterization of the thick-film gas sensor based on Zinc-oxide on an Alumina substrate. The sensor exhibits excellent ethanol sensing characteristics at temperatures between 175Â°C to 300Â°C and the characteristics of the sensor for different concentration of the ethanol gas has been successfully studied using Linear Discriminant Analysis (LDA). This system can be used to detect alcoholic driving for automotive safety applications. The system is designed to detect the exact concentration of ethanol gas using virtual instrumentation software and data acquisition hardware. This work presents the data analysis results of 230 samples of each concentration levels of ethanol gas using Linear Discriminant Analysis (LDA) technique. The overall system can be tested with the engine ignition system for automotive safety and warning applications in the prevention of a crash during drunken driving.

37-41
INFORMATION HIDING TECHNOLOGY- A WATERMARKING

Authors: MEENA V. KAMBLE,

Abstract

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Number of views: 622

In this Paper, we are discussing about a security which is essential for today world .we briefly discuss the Digital watermarking and historical development of watermarking which is use as a one of the tool for hiding data/ information . We also introduce various data hiding terminologies used in current literature and attempt has clear distinction of them. Digital watermarking is the process of embedding information into a digital signal in a way that is difficult to remove. The signal may be audio, pictures or video. In visible watermarking, the information is visible in the picture or video. Typically, the information is text or a logo which identifies the owner of the media. The image on the right has a visible watermark. In invisible watermarking, information is added as digital data to audio, picture or video, but it cannot be perceived as such (although it may be possible to detect that some amount of information is hidden) The watermark may be intended for widespread use and is thus made easy to retrieve or it may be a form of Steganography where a party communicates a secret message embedded in the digital signal.

42-49
MOTION ARTIFACT CANCELLATION IN AMBULATORY ECG MEASUREMENT SYSTEM FOR THE DETECTION OF CARDIAC DISEASES

Authors: AMBARISH G. MOHAPATRA, SAROJ KUMAR LENKA

Abstract

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Number of views: 584

In this work, a simple and efficient artifact cancellation in ambulatory ECG using adaptive filter is designed for the detection of different cardiac diseases like bradycardia, tachycardia, left ventricular hypertrophy and right ventricular hypertrophy. Our work is focused on extraction of noise free ECG signal and the real-time implementation of artifacts removal techniques. As ECG signal is very sensitive in nature, and even if small noise mixed with original signal the various characteristics of the signal changes, data corrupted with noise must either filtered or discarded, filtering is important issue for design consideration of real-time ECG measurement systems. Here we have implemented different adaptive filtering algorithms (LMS-Least Mean Square, RLS-Recursive Least Squares) using virtual instrumentation technique to minimize the noisy components and to analyze different cardiac diseases like bradycardia, tachycardia, left ventricular hypertrophy and right ventricular hypertrophy. Finally the overall performance of LMS and RLS algorithm is also compared according to the error signal generated by the techniques.