SDM College of Engineering and Technology

About: SDM College of Engineering and Technology is a based out in . It is known for research contribution in the topics: Diesel fuel & Combustion. The organization has 350 authors who have published 351 publications receiving 2399 citations.

Vegetation type classification system using pseudo zernike moments and ELM

Abstract: Vegetation classification system is one of the evergreen applications of remote sensing technology. The objective of this classification system is to boost up the agricultural yield by removing the weedy plants. Understanding the benefits of vegetation classification system, this work proposes a vegetation classification system that can distinguish between tree, shrub and grasslands. The goal of the work is achieved by segregating the entire work into three important phases, which are image pre-processing, feature extraction and classification. The satellite images are pre-processed by guided filter. In this work, both global and local features are extracted by pseudo zernike moments and first order features respectively. Finally, Extreme Learning Machine (ELM) is employed as the classifier to differentiate between trees, shrubs and grasslands. The performance of the proposed approach is satisfactory in terms of classification accuracy, sensitivity and specificity.

Performance improvement of RMSA using different substrates

Abstract: To Study the behavioral characteristics of the microstrip patch antennas with different dielectric substrates such as Glass epoxy, FR-4 and RT-Duroid. The paper gives the comparative study of Rectangular Microstrip Patch Antenna's of different dielectric substrates for design parameters like Return loss, Bandwidth, Gain, Directivity and Efficiency at 2.5GHz. Different dielectric materials have different relative permittivity and this varies the radiating capability of antennas.

Sentiment Analysis using Deep Belief Network for User Rating Classification

Abstract: People’s attitudes, opinions, feelings and sentiments which are usually expressed in the written languages are studied by using a well known concept called the sentiment analysis. The emotions are expressed at various different levels like document, sentence and phrase level are studied by using the sentiment analysis approach. The sentiment analysis combined with the Deep learning methodologies achieves the greater classification in a larger dataset. The proposed approach and methods are Sentiment Analysis and deep belief networks, these are used to process the user reviews and to give rise to a possible classification for recommendations system for the user. The user assessment classification can be progressed by applying noise reduction or pre-processing to the system dataset. Further by the input nodes the system uses an exploration of user’s sentiments to build a feature vector. Finally, the data learning is achieved for the suggestions; by using deep belief network. The prototypical achieves superior precision and accuracy when compared with the LSTM and SVM algorithms.

Customised Compressed Air Energy Storage Model for Rural Household Application

Abstract: Energy storage systems are gaining increased attention from the concerned stakeholders due to the technological advancements, affordable cost, modularities and the availability of abundant input from renewables However, in the rural sectors the effective implementation of energy storage system is very essential for the overall growth of a country like India, where the rural ecosystem is predominant The reliable and continuous supply of energy in rural India can make the contribution from rural India significantly to the overall GDP of the nation The energy storage system should be conducive to the rural ecosystem with ease of operation, nontoxic and manageable by rural skills In this paper, a model designed based on the Compressed Air Energy Storage (CAES) for a typical rural household application with customized parameters is presented The economics of the proposed model reveal that the system can be a very effective model in the long run