Sri Ramakrishna Engineering College

About: Sri Ramakrishna Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Control theory. The organization has 1030 authors who have published 843 publications receiving 3822 citations.

A novel method for Arduino based electric vehicle emulator

Abstract: This article presents the design of the test station, which is accessible for the testing of electric bicycles. In this process, a low-power Brushless Dc traction vehicle or a high-powered motor sc...

Green mediated synthesis of plasmonic nanoparticle (Ag) for antireflection coating in bare mono silicon solar cell

Abstract: Novelty of the present work is to investigate the efficiency of the antireflection property of green mediated synthesized plasmonic nanoparticle coated monocrystalline silicon (Si) solar cell. Green mediated synthesis of plasmonic silver (Ag) nanoparticles (NPs) was carried out using three different leaves extracts (Eucalyptus glopus, Azadirachta indica and Coriandrum sativum) and reported for the first time. The face centred cubic structure, crystalline nature and purity of the synthesized material were confirmed by powder X-ray diffraction studies. Fourier transform infrared spectrum shows the presence of various functional groups in the resultant material. The maximum absorbance observed around 400–460 nm reveal the characteristic absorbance of Ag NPs. The surface morphology of the synthesized Ag NPs was analyzed using atomic force microscopy. Ag NPs were coated as antireflective material on the monocrystalline Si solar cell surface and its performance was evaluated from the corresponding JV characteristics. The obtained photovoltaic performance was compared with bare Si for comparison purpose and it is observed that there is an enhanced performance in the photo-conversion efficiency of the Si solar cells.

Machine Learning based Rainfall Prediction

Abstract: Rainfall prediction is the one of the important technique to predict the climatic conditions in any country. This paper proposes a rainfall prediction model using Multiple Linear Regression (MLR) for Indian dataset. The input data is having multiple meteorological parameters and to predict the rainfall in more precise. The Mean Square Error (MSE), accuracy, correlation are the parameters used to validate the proposed model. From the results, the proposed machine learning model provides better results than the other algorithms in the literature.

Sustainable development in utilization of Tamarindus indica L. and its by-products in industries: A review

Abstract: In this review paper, the use of different parts of tamarind tree in industry for eco-friendly products and processes with sustainability has been explored. This review article presents the extraction methods adapted to modify the raw materials obtained from tamarind tree into useable form by industry. The review of published data reveals that, fruit shell can be efficiently used as toxic adsorbent, pulp is used in food industry as preservative, Tamarind seed polysaccharide (TSP) a biopolymer from tamarind seed is effectively used as additive in food industry, drug delivery applications and electrochemical industries. The tamarind seed oil is used as biofuel, tamarind leaf extract is employed in textile industry for developing antibacterial fabrics and fruit fiber is successfully used as reinforcement in composite material to replace synthetic fibers and finds application in automobile and construction industries. This study also exposes the fact that there is scope for further use of tamarind and its parts in several areas such targeted drug delivery applications, removal of toxins from industrial effluents, electrochemical industries, agricultural field and preparation of preserved foods which require further research.

Onto-based sentiment classification using machine learning techniques

Abstract: Sentiment analysis is a methodology used to analyse the emotion or view of an individual to a situation or topic In present scenario, Social media is the source for the collection of individual's feedbacks, user's emotions, reviews and personal experiences which lead to a need for efficient mining of the text to derive knowledge An optimal classification of text based on emotion is an unsolved problem in text mining To extract knowledge from text many machine learning tools and techniques were proposed An onto-based process is proposed to analyse the customer's emotion in this paper The input emotional text that needs to be classified is given as input to the NLP and processed and an emotional ontology is created for better understanding of the semantics and relationships When adding new instances, Ontology can be automatically classify them based on emotional relationship The Emowords from ontology can be further classified using any of the standard machine learning techniques which definitively gives a better performance This paper is a review of all the machine learning techniques that can be applied on the semantic analysis of sentiments