Publications
1. Unsupervised Sentiment Analysis using small recurrent language models
Abstract
We explore the possibility of unsupervised byte-level sentiment learning of a sentence in the English language using small recurrent language models. Long Short-Term Memory (LSTM) network is a simple and effective network to use while working with sequential data like text or audio. As LSTM processes the data it learns all the information regarding the given input in the context of all the inputs before that. A. Radford et al [1] provided the evidence that a multiplicative LSTM (mLSTM) [9] is able to learn the concept of sentiment in a manipulable way, but they were able to achieve this result due to the huge amount of data samples used for training. This paper tries to investigate the neuron or neurons responsible for sentiment analysis inside a Long Short-Term Memory (LSTM) network when there is a limited amount of training samples available.
2. Predicting Biological Oxygen Demand and pH of Water with Multiple Regression Methods
Abstract
This paper compares the learning curve generated by different machine learning models by predicting Biological Oxygen Demand (BOD) and pH given Chemical Oxygen Demand in the water sample. The continuing advancements in the field of sensor interface development are to calibrate and correct the inherent non-idealities present in transducers forced us to work in this area. Machine Learning algorithms have made a profound impact in the field of Science and Engineering in the past few decades. The purpose of this paper is to propose an approach which is more users friendly and fast in operation by modelling and optimization of sensor used for dissolved oxygen measurement. This is to overcome several drawbacks generally found in the previous work like complex designing, nonlinearity and long computation time. It is found that there is a possibility to replace hardware sensor technology by Machine Learning and Artificial Intelligence provided appropriate and sufficient data.