Adversarial machine learning: A multilayer review of the state-of-the-art and challenges for wireless and mobile systems

J Liu, M Nogueira, J Fernandes… - … Surveys & Tutorials, 2021 - ieeexplore.ieee.org
Machine Learning (ML) models are susceptible to adversarial samples that appear as
normal samples but have some imperceptible noise added to them with the intention of …

Stock price prediction using machine learning and LSTM-based deep learning models

S Mehtab, J Sen, A Dutta - … , SoMMA 2020, Chennai, India, October 14–17 …, 2021 - Springer
Prediction of stock prices has been an important area of research for a long time. While
supporters of the efficient market hypothesis believe that it is impossible to predict stock …

Stock price prediction using CNN and LSTM-based deep learning models

S Mehtab, J Sen - … Conference on Decision Aid Sciences and …, 2020 - ieeexplore.ieee.org
Designing robust and accurate predictive models for stock price prediction has been an
active area of research over a long time. While on one side, the supporters of the efficient …

Analysis of financial pressure impacts on the health care industry with an explainable machine learning method: China versus the USA

F Weng, J Zhu, C Yang, W Gao, H Zhang - Expert Systems with Applications, 2022 - Elsevier
This study analyzes the role of financial pressure in forecasting the volatility of health care
stock. The main finding shows that financial pressure helps to improve the volatility …

Long‐and‐Short‐Term Memory (LSTM) NetworksArchitectures and Applications in Stock Price Prediction

J Sen, S Mehtab - Emerging Computing Paradigms: Principles …, 2022 - Wiley Online Library
Although recurrent neural networks (RNNs) are effective in handling sequential data, they
are poor in capturing the long‐term dependencies in the data due to a problem known as …

Introductory chapter: machine learning in finance-emerging trends and challenges

J Sen, R Sen, A Dutta - Algorithms, Models and Applications, 2022 - books.google.com
The paradigm of machine learning and artificial intelligence has pervaded our everyday life
in such a way that it is no longer an area for esoteric academics and scientists putting their …

Machine learning models predicting returns: Why most popular performance metrics are misleading and proposal for an efficient metric

J Dessain - Expert Systems with Applications, 2022 - Elsevier
Numerous machine learning models have been developed to achieve the 'real-life'financial
objective of optimising the risk/return profile of investment strategies. In the current article:(a) …

Sustainable stock market prediction framework using machine learning models

FJG Peñalvo, T Maan, SK Singh, S Kumar… - International Journal of …, 2022 - igi-global.com
Prediction of stock prices is a challenging task owing to its volatile and constantly fluctuating
nature. Stock price prediction has sparked the interest of various investors, data analysists …

A study on the performance evaluation of equal-weight portfolio and optimum risk portfolio on the Indian stock market

A Sen, J Sen - International Journal of Business Forecasting …, 2025 - inderscienceonline.com
Designing an optimum portfolio for allocating suitable weights to its constituent assets so
that the return and risk associated with the portfolio are optimised is a computationally hard …

Stock portfolio optimization using a deep learning LSTM model

J Sen, A Dutta, S Mehtab - 2021 IEEE Mysore sub section …, 2021 - ieeexplore.ieee.org
Predicting future stock prices and their movement patterns is a complex problem. Hence,
building a portfolio of capital assets using the predicted prices to achieve the optimization …