http://www.digital.lib.esn.ac.lk//handle/123456789/3849 Tue, 07 Apr 2026 13:03:11 GMT 2026-04-07T13:03:11Z http://www.digital.lib.esn.ac.lk//handle/123456789/3852 Effect of Microwave Heating on the Quality and Shelf Life of Whole Grain Wheat Flour under Air-Conditioned Storage A. Inthuja, V. Anujaa; T. Mahendran, M. R. Roshana A study was conducted to evaluate the quality changes that could be occurred in the whole grain wheat flour during the storage and also to study the effect of microwave heating in the quality of the wheat flour. Whole grain wheat flour was packed in low density polyethylene bags separately as microwaved and nonmicrowaved and the quality changes were assessed for 12 weeks under airconditioned storage temperature of 27°C. The quality characteristics such as smell, sieve test, long moisture, moisture NIR, protein NIR, ash NIR, wet-gluten, fat acidity, colour and weevil count were evaluated in 2 weeks interval. Fat and fiber content were evaluated in 4 weeks interval. Healthy cake was prepared from microwaved and non-microwaved flour and the sensory characteristics were evaluated using seven point hedonic scale method at the end of 4 weeks. The results of the study revealed that there were no significant differences (p>0.05) in the quality parameters of whole grain wheat flour in protein, ash, smell, fat and fiber whereas moisture, wet gluten, colour and the fat acidity changed significantly during the 12 weeks of storage. There was no any weevil infestation found in both flours. Sensory characteristics of the healthy cake were not affected by storage in both microwaved and non-microwaved flours. It was found from this study that, whole grain wheat flour can be stored under air-conditioned temperature of 27°C for 12 weeks from the date of manufacture. Mon, 01 Jan 2018 00:00:00 GMT http://www.digital.lib.esn.ac.lk//handle/123456789/3852 2018-01-01T00:00:00Z http://www.digital.lib.esn.ac.lk//handle/123456789/3851 A MARKOV MODEL FOR INVESTIGATING THE STOCK MARKET VOLUME BEHAVIOR Tharshan, Arivalzahan, S R In recent decades, the stock market prediction has become a high research area due to its immense importance not only for every profitable industry, but also for shareholders and investors to hug out a self-assured decision for a good investment into the stock market. This paper provides a discrete time stochastic model for the behavior analysis of stock market volume, applying the Markov model. The proposed model is validated in terms of model assumptions to predict the stock market behavior. An illustration, the top ten largest global banks’ stock market behaviors through the steady-state distributions and expected number of transitions are discussed. Wherein the secondary datasets for 505 days of volumes from 1st of January 2014 to 31st of December 2015, 2 year duration are used in each bank. Mon, 01 Jan 2018 00:00:00 GMT http://www.digital.lib.esn.ac.lk//handle/123456789/3851 2018-01-01T00:00:00Z http://www.digital.lib.esn.ac.lk//handle/123456789/3850 REVIEW ON SENTIMENT ANALYSIS IN TAMIL TEXTS Sajeetha Thavareesan, Sinnathamby Mahesan Sentiment Analysis (SA) is an application of Natural Language Processing (NLP) to analyse the sentiments expressed in the text. It classifies into categories of qualities and opinions such as good, bad, positive, negative, neutral, etc. It employs machine learning techniques and lexicons for the classification. Nowadays, people share their opinions or feelings about movies, products, services, etc. through social media and online review sites. Analysing their opinions is beneficial to the public, business organisations, film producers and others to make decisions and improvements. SA is mostly employed in English language but rare for Indian languages including Tamil. This review paper aims to critically analyse the recent literature in the field of SA with Tamil text. Objectives, Methodologies and success rates are taken in consideration for the review. We shall conclude from the review that SVM and RNN classifiers taking TF-IDF and Word2vec features of Tamil text give better performance than grammar rules based classifications and other classifiers with presence of words, TF and BoW as features. Mon, 01 Jan 2018 00:00:00 GMT http://www.digital.lib.esn.ac.lk//handle/123456789/3850 2018-01-01T00:00:00Z