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 …
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
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 …
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 …
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
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 …
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 …
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
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 …
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) …
objective of optimising the risk/return profile of investment strategies. In the current article:(a) …
Sustainable stock market prediction framework using machine learning models
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 …
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
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 …
that the return and risk associated with the portfolio are optimised is a computationally hard …
Stock portfolio optimization using a deep learning LSTM model
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 …
building a portfolio of capital assets using the predicted prices to achieve the optimization …