Kongu Engineering College

About: Kongu Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Cluster analysis. The organization has 2001 authors who have published 1978 publications receiving 16923 citations.

State estimation in power system using weighted least squares method

Abstract: State estimation is a key of Energy Management System (EMS) function, used for estimating the state of the power system Power system may be a quasi-static system and thus changes slowly with time Since state estimation is computationally valuable, it's difficult to execute it repetitively at short intervals to understand the real time monitoring of such a dynamic system The state of an electrical power system is characterized by the voltage magnitudes and angles in the system buses In this state estimation, the states are estimated by weighted least square technique and further the errors due to because of imperfect measurements should be reduced Static state estimation is performed on the data accessible through the SCADA system In this estimation the data is acquired through Newton Raphson Load flow analysis Weighted least square technique evaluates the state of the power system supported the load given to every measurement and it is done for IEEE 14 bus, 30 bus and the results are taken

Latex reinforced waste buffing dust-jeans cotton composites and its characterization

Abstract: This study aims to fabricate flexible composite sheets from waste buffing dust and post-consumer cotton waste through a simple solution casting technique. Natural rubber latex (NRL) was used as a binder material in different mixing ratios. To validate the chemical bonding between buffing dust and cotton fiber, FTIR was performed. Thermal stability of as-prepared composites was confirmed through TGA and DTA studies., The surface tropology of fabricated composites, were examined by FESEM analysis. From the results of oxygen gas transmittance rates, it was found that prepared composites possess enhanced gas barrier properties as compared to pure buffing dust. The physical and mechanical properties such as tensile strength, elongation, hardness, and density of prepared composites with optimum NRL content were augmented by 58, 48, 35, and 21%, respectively, as compared to pure buffing dust sheets. Thereby, these simple, low cost and flexible composite sheets would be a promising material for packaging as well as interior decoration industries.

Aiding the Digital Mammogram for Detecting the Breast Cancer Using Shearlet Transform and Neural Network

Abstract: Objective: Breast Cancer is the most invasive disease and fatal disease next to lung cancer in human. Early detection of breast cancer is accomplished by X-ray mammography. Mammography is the most effective and efficient technique used for detection of breast cancer in women and also to improve the breast cancer prognosis. The numbers of images need to be examined by the radiologists, the resulting may be misdiagnosis due to human errors by visual Fatigue. In order to avoid human errors, Computer Aided Diagnosis is implemented. In Computer Aided Diagnosis system, number of processing and analysis of an image is done by the suitable algorithm. Methods: This paper proposed a technique to aid radiologist to diagnosis breast cancer using Shearlet transform image enhancement method. Similar to wavelet filter, Shearlet coefficients are more directional sensitive than wavelet filters which helps detecting the cancer cells particularly for small contours. After enhancement of an image, segmentation algorithm is applied to identify the suspicious region. Result: Many features are extracted and utilized to classify the mammographic images into harmful or harmless tissues using neural network classifier. Conclusions: Multi-scale Shearlet transform because more details on data phase, directionality and shift invariance than wavelet based transforms. The proposed Shearlet transform gives multi resolution result and generate malign and benign classification more accurate up to 93.45% utilizing DDSM database.

Detecting and filtering rumor in social media using news media event

Abstract: In social media platform, many users post messages to express their interests and preferences daily. Because of its fast and easy access abundant people follow news events and there is a possibility for spreading rumor or fake news. This fake news is unverified at the time of posting. Therefore, it is necessary to detect and remove the fake news before it spread widely. Rumors or fake news are created illegally for the purpose of popularity, hike in their business or financial, and so forth, and these rumors need to be detected as easily as possible. Our proposed rumor detection method compares the social media content with news media and applies the support vector machine (SVM) as binary classification technique. Our experiments results revealed that the proposed method attained considerable improvement when compared to the existing machine learning techniques. The proposed SVM rumor detection approach attained better results of 89% precision, 64% recall, and 85% F‐measure. The experimental results proved that the proposed method will be useful for avoiding social damages caused by rumors in social media.

Predicting calorific value for mixed food using image processing

Abstract: India is the second largest country within the world with 2 thirds of the population in their youth With economic development and adoption of western lifestyle, a large number of people in India are affected by obesity The obesity is the major cause due to the intake of junk and processed foods As a result, people likely to suffer severe health issues such as high cholesterol, hypertension, heart attack, type II diabetes, breast and colon cancer, and breathing disorders To avoid the cause of obesity, food calorie and nutrition measurement system is to monitor the intake food Further, the obese people are suffering from severe health conditions such as type II diabetes, hypertension, heart attack, high cholesterol, breast and colon cancer and breathing disorders To reduce the wearable diseases a mobile application is made to live the worth of calorie for mixed food The objective of this paper is to predict and to fix diet control for various diseases by measuring the calorific value to help the patients and nutritionists The image captured through a mobile phone/tablet camera will provide information concerning the calorie rate of the food The captured image undergoes various processes like fuzzy c-means clustering for segmentation and Morphological operation for extracting image components From the processed image, the calorie value of the food may be obtained