Aadesh Mallya - Academia.edu (original) (raw)
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Papers by Aadesh Mallya
International Journal of Advanced Trends in Computer Science and Engineering, Feb 15, 2021
Monocular depth estimation has been a challenging topic in the field on computer vision. There ha... more Monocular depth estimation has been a challenging topic in the field on computer vision. There have been multiple approaches based on stereo and geometrical concepts to try and estimate depth of objects in a two-dimensional field such as that of a plain photograph. While stereo and lidar based approaches have their own merits, there is one issue that seems recurrent in them, the vanishing point problem. An improvised approach to solve this issue involves using deep neural networks to train a model to estimate depth. Even this solution has multiple approaches to it. The general supervised approach, an unsupervised approach (using autoencoders) and a semisupervised approach (using the concept of transfer learning). This paper presents a comparative account of the three different learning models and their performance evaluation.
International Journal of Engineering Applied Sciences and Technology
The housing market is a standout amongst the most engaged with respect to estimating the price an... more The housing market is a standout amongst the most engaged with respect to estimating the price and continues to vary. Individuals are cautious when they are endeavoring to purchase another house with their financial plan and market strategies. Consequently, making the housing market one of the incredible fields to apply the ideas of machine learning on how to enhance and anticipate the house prices with precision. The objective of the paper is the prediction of the market value of a real estate property and present a performance comparison between various regression models applied. Nine algorithms were selected to predict the dependent variable in our dataset and then their performance was compared using R2 score, mean absolute error, mean squared error and root mean squared error. Moreover, this study attempts to analyze the correlation between variables to determine the most important factors that are bound to affect the prices of house.
2021 International Conference on Emerging Smart Computing and Informatics (ESCI)
Stock market is essentially a chaotic and a highly unstable time-series. People have used approac... more Stock market is essentially a chaotic and a highly unstable time-series. People have used approaches such as clipping gradient i.e., stopping the gradient from getting too small or too high, via gated recurrent units and LSTM units, to predict the stock prices. However, information is still lost and hence is not an ideal approach. This paper aims to compare the efficiency of standard approaches of stock prediction namely, regression, recurrent neural networks using long short-term memory units, with the echo state network (ESN) which uses reservoir computing to model chaotic non-linear systems such as daily closing stock prices in a stock market. My experiments on the NY Stocks dataset demonstrates as to how the novel echo state network outperforms the conventional regression and neural network approach.
International Journal of Advanced Trends in Computer Science and Engineering , 2021
Monocular depth estimation has been a challenging topic in the field on computer vision. There ha... more Monocular depth estimation has been a challenging topic in the field on computer vision. There have been multiple approaches based on stereo and geometrical concepts to try and estimate depth of objects in a two-dimensional field such as that of a plain photograph. While stereo and lidar based approaches have their own merits, there is one issue that seems recurrent in them, the vanishing point problem. An improvised approach to solve this issue involves using deep neural networks to train a model to estimate depth. Even this solution has multiple approaches to it. The general supervised approach, an unsupervised approach (using autoencoders) and a semi-supervised approach (using the concept of transfer learning). This paper presents a comparative account of the three different learning models and their performance evaluation.
International Journal of Advanced Trends in Computer Science and Engineering, Feb 15, 2021
Monocular depth estimation has been a challenging topic in the field on computer vision. There ha... more Monocular depth estimation has been a challenging topic in the field on computer vision. There have been multiple approaches based on stereo and geometrical concepts to try and estimate depth of objects in a two-dimensional field such as that of a plain photograph. While stereo and lidar based approaches have their own merits, there is one issue that seems recurrent in them, the vanishing point problem. An improvised approach to solve this issue involves using deep neural networks to train a model to estimate depth. Even this solution has multiple approaches to it. The general supervised approach, an unsupervised approach (using autoencoders) and a semisupervised approach (using the concept of transfer learning). This paper presents a comparative account of the three different learning models and their performance evaluation.
International Journal of Engineering Applied Sciences and Technology
The housing market is a standout amongst the most engaged with respect to estimating the price an... more The housing market is a standout amongst the most engaged with respect to estimating the price and continues to vary. Individuals are cautious when they are endeavoring to purchase another house with their financial plan and market strategies. Consequently, making the housing market one of the incredible fields to apply the ideas of machine learning on how to enhance and anticipate the house prices with precision. The objective of the paper is the prediction of the market value of a real estate property and present a performance comparison between various regression models applied. Nine algorithms were selected to predict the dependent variable in our dataset and then their performance was compared using R2 score, mean absolute error, mean squared error and root mean squared error. Moreover, this study attempts to analyze the correlation between variables to determine the most important factors that are bound to affect the prices of house.
2021 International Conference on Emerging Smart Computing and Informatics (ESCI)
Stock market is essentially a chaotic and a highly unstable time-series. People have used approac... more Stock market is essentially a chaotic and a highly unstable time-series. People have used approaches such as clipping gradient i.e., stopping the gradient from getting too small or too high, via gated recurrent units and LSTM units, to predict the stock prices. However, information is still lost and hence is not an ideal approach. This paper aims to compare the efficiency of standard approaches of stock prediction namely, regression, recurrent neural networks using long short-term memory units, with the echo state network (ESN) which uses reservoir computing to model chaotic non-linear systems such as daily closing stock prices in a stock market. My experiments on the NY Stocks dataset demonstrates as to how the novel echo state network outperforms the conventional regression and neural network approach.
International Journal of Advanced Trends in Computer Science and Engineering , 2021
Monocular depth estimation has been a challenging topic in the field on computer vision. There ha... more Monocular depth estimation has been a challenging topic in the field on computer vision. There have been multiple approaches based on stereo and geometrical concepts to try and estimate depth of objects in a two-dimensional field such as that of a plain photograph. While stereo and lidar based approaches have their own merits, there is one issue that seems recurrent in them, the vanishing point problem. An improvised approach to solve this issue involves using deep neural networks to train a model to estimate depth. Even this solution has multiple approaches to it. The general supervised approach, an unsupervised approach (using autoencoders) and a semi-supervised approach (using the concept of transfer learning). This paper presents a comparative account of the three different learning models and their performance evaluation.