Annamalai University

About: Annamalai University is a education organization based out in Chidambaram, Tamil Nadu, India. It is known for research contribution in the topics: Lipid peroxidation & Antioxidant. The organization has 8098 authors who have published 10758 publications receiving 203872 citations.

Review on phase change material based free cooling of buildings—The way toward sustainability

Abstract: The present world energy scenario signifies the importance of renewable energy utilization and paves the pathway towards green and net zero energy building concepts for a sustainable future. In the recent years, substantial energy is spent in building space heating/cooling applications to meet the human comfort requirements. In order to reduce the unnecessary losses associated with the buildings, several advancements toward energy efficient concepts are also being proposed and implemented in many buildings. Free cooling is one such novel concept through which building cooling demands can be met without compromising the indoor air quality. Free cooling concept stores the abundant atmospheric night cool energy in phase change materials (PCM) and uses the stored energy during the day hours to achieve the desired room comfort conditions. This review article aims to update and consolidate the substantial work carried out in the recent years by various researchers on free cooling technology using PCMs in latent heat thermal energy storage (LHTES) systems. In addition, future potential of free cooling technologies, scope for further improvement, policies that needs be promoted by the government toward its sustainability to ensure market penetration of free cooling technologies are also discussed in detail.

On normal numbers

Abstract: By elaborating the argument for Theorem I, we can prove Theorems II, III, IV, V in my paper referred to. Theorem VI is also true. I do not find any necessity to modify the conjectures given there.

A comparative performance evaluation of neural network based approach for sentiment classification of online reviews

Abstract: The aim of sentiment classification is to efficiently identify the emotions expressed in the form of text messages. Machine learning methods for sentiment classification have been extensively studied, due to their predominant classification performance. Recent studies suggest that ensemble based machine learning methods provide better performance in classification. Artificial neural networks (ANNs) are rarely being investigated in the literature of sentiment classification. This paper compares neural network based sentiment classification methods (back propagation neural network (BPN), probabilistic neural network (PNN) & homogeneous ensemble of PNN (HEN)) using varying levels of word granularity as features for feature level sentiment classification. They are validated using a dataset of product reviews collected from the Amazon reviews website. An empirical analysis is done to compare results of ANN based methods with two statistical individual methods. The methods are evaluated using five different quality measures and results show that the homogeneous ensemble of the neural network method provides better performance. Among the two neural network approaches used, probabilistic neural networks (PNNs) outperform in classifying the sentiment of the product reviews. The integration of neural network based sentiment classification methods with principal component analysis (PCA) as a feature reduction technique provides superior performance in terms of training time also.