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Found 44 papers, 2 papers with code
Boosting Digital Safeguards: Blending Cryptography and Steganography
In today's digital age, the internet is essential for communication and the sharing of information, creating a critical need for sophisticated data security measures to prevent unauthorized access and exploitation.
Semantic Stealth: Adversarial Text Attacks on NLP Using Several Methods
In various real-world applications such as machine translation, sentiment analysis, and question answering, a pivotal role is played by NLP models, facilitating efficient communication and decision-making processes in domains ranging from healthcare to finance.
Evaluating Adversarial Robustness: A Comparison Of FGSM, Carlini-Wagner Attacks, And The Role of Distillation as Defense Mechanism
Furthermore, the study proposes the robustness of defensive distillation as a defense mechanism to counter FGSM and CW attacks.
Generative AI-Based Text Generation Methods Using Pre-Trained GPT-2 Model
This work delved into the realm of automatic text generation, exploring a variety of techniques ranging from traditional deterministic approaches to more modern stochastic methods.
Information Security and Privacy in the Digital World: Some Selected Topics
In the era of generative artificial intelligence and the Internet of Things, while there is explosive growth in the volume of data and the associated need for processing, analysis, and storage, several new challenges are faced in identifying spurious and fake information and protecting the privacy of sensitive data.
A Modified Word Saliency-Based Adversarial Attack on Text Classification Models
This paper introduces a novel adversarial attack method targeting text classification models, termed the Modified Word Saliency-based Adversarial At-tack (MWSAA).
Adversarial Attacks on Image Classification Models: Analysis and Defense
The notion of adversarial attacks on image classification models based on convolutional neural networks (CNN) is introduced in this work.
A Comparative Study of Portfolio Optimization Methods for the Indian Stock Market
The top stocks of each cluster are identified based on their free-float market capitalization from the report of the NSE published on July 1, 2022 (NSE Website).
A Portfolio Rebalancing Approach for the Indian Stock Market
The portfolios are designed based on the training data from January 4, 2021 to June 30, 2022.
Performance Evaluation of Equal-Weight Portfolio and Optimum Risk Portfolio on Indian Stocks
Three portfolios are designed following the above approaches choosing the top ten stocks from each sector based on their free-float market capitalization.
Portfolio Optimization: A Comparative Study
These three approaches to portfolio design are applied to the historical prices of stocks chosen from ten thematic sectors listed on the National Stock Exchange (NSE) of India.
Adversarial Attacks on Image Classification Models: FGSM and Patch Attacks and their Impact
However, very powerful and pre-trained CNN models working very accurately on image datasets for image classification tasks may perform disastrously when the networks are under adversarial attacks.
A Comparative Analysis of Portfolio Optimization Using Mean-Variance, Hierarchical Risk Parity, and Reinforcement Learning Approaches on the Indian Stock Market
Three approaches of portfolio optimization that are considered in this work are the mean-variance portfolio (MVP), hierarchical risk parity (HRP) portfolio, and reinforcement learning-based portfolio.
Data Privacy Preservation on the Internet of Things
Recent developments in hardware and information technology have enabled the emergence of billions of connected, intelligent devices around the world exchanging information with minimal human involvement.
A Framework of Customer Review Analysis Using the Aspect-Based Opinion Mining Approach
Opinion mining is the branch of computation that deals with opinions, appraisals, attitudes, and emotions of people and their different aspects.
Designing Efficient Pair-Trading Strategies Using Cointegration for the Indian Stock Market
This paper presents a cointegration-based approach that identifies stocks listed in the five sectors of the National Stock Exchange (NSE) of India for designing efficient pair-trading portfolios.
Design and Analysis of Optimized Portfolios for Selected Sectors of the Indian Stock Market
The evaluation of the portfolios is done based on their cumulative returns over the test period from Jan 1, 2021, to Dec 31, 2021.
Stock Volatility Prediction using Time Series and Deep Learning Approach
It has been observed the LSTM performed better in predicting volatility in pharma over banking and IT sectors.
A Comparative Study of Hierarchical Risk Parity Portfolio and Eigen Portfolio on the NIFTY 50 Stocks
The portfolios are built following the two approaches to historical stock prices from Jan 1, 2016, to Dec 31, 2020.
Stock Performance Evaluation for Portfolio Design from Different Sectors of the Indian Stock Market
We have built a minimum variance portfolio and optimal risk portfolio for all the six chosen sectors by using the daily stock prices over the past five years as training data and have also conducted back testing to check the performance of the portfolio.
Robust Portfolio Design and Stock Price Prediction Using an Optimized LSTM Model
Six months after the construction of the portfolios, i. e., on Jul 1, 2021, the actual returns and the LSTM-predicted returns for the portfolios are computed.
Precise Stock Price Prediction for Optimized Portfolio Design Using an LSTM Model
Optimum portfolios are designed on the selected seven sectors.
Hierarchical Risk Parity and Minimum Variance Portfolio Design on NIFTY 50 Stocks
Portfolio design and optimization have been always an area of research that has attracted a lot of attention from researchers from the finance domain.
Portfolio Optimization on NIFTY Thematic Sector Stocks Using an LSTM Model
Optimum risk and eigen portfolios for each sector are designed based on ten critical stocks from the sector.
Precise Stock Price Prediction for Robust Portfolio Design from Selected Sectors of the Indian Stock Market
In this project, we have built an efficient portfolio and to predict the future asset value by means of individual stock price prediction of the stocks in the portfolio.
Machine Learning: Algorithms, Models, and Applications
The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed.
Comprehensive Movie Recommendation System
A recommender system, also known as a recommendation system, is a type of information filtering system that attempts to forecast a user's rating or preference for an item.
Analysis of Sectoral Profitability of the Indian Stock Market Using an LSTM Regression Model
This paper presents an optimized predictive model built on long-and-short-term memory (LSTM) architecture for automatically extracting past stock prices from the web over a specified time interval and predicting their future prices for a specified forecast horizon, and forecasts the future stock prices.
Stock Portfolio Optimization Using a Deep Learning LSTM Model
The predicted and the actual returns of each portfolio are found to be high, indicating the high precision of the LSTM model.
Stock Price Prediction Using Time Series, Econometric, Machine Learning, and Deep Learning Models
This paper demonstrates a set of time series, econometric, and various learning-based models for stock price prediction.
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 effort to solve a challenging research problem.
Optimum Risk Portfolio and Eigen Portfolio: A Comparative Analysis Using Selected Stocks from the Indian Stock Market
Three portfolios are built for each of the seven sectors chosen for this study, and the portfolios are analyzed on the training data based on several metrics such as annualized return and risk, weights assigned to the constituent stocks, the correlation heatmaps, and the principal components of the Eigen portfolios.
Design and Analysis of Robust Deep Learning Models for Stock Price Prediction
Building predictive models for robust and accurate prediction of stock prices and stock price movement is a challenging research problem to solve.
Volatility Modeling of Stocks from Selected Sectors of the Indian Economy Using GARCH
Volatility clustering is an important characteristic that has a significant effect on the behavior of stock markets.
An Algorithm for Recommending Groceries Based on an Item Ranking Method
The algorithm is based on the perspective that, since the grocery items are usually bought in bulk, a grocery recommender system should be capable of recommending the items in bulk.
Profitability Analysis in Stock Investment Using an LSTM-Based Deep Learning Model
Even more challenging is to build a system for constructing an optimum portfolio of stocks based on the forecasted future stock prices.
Accurate Stock Price Forecasting Using Robust and Optimized Deep Learning Models
Designing robust frameworks for precise prediction of future prices of stocks has always been considered a very challenging research problem.
Robust Analysis of Stock Price Time Series Using CNN and LSTM-Based Deep Learning Models
Prediction of stock price and stock price movement patterns has always been a critical area of research.
Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models
In this approach, the open values of the NIFTY 50 index are predicted on a time horizon of one week, and once a week is over, the actual index values are included in the training set before the model is trained again, and the forecasts for the next week are made.
Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models
In this work, we propose an approach of hybrid modeling for stock price prediction building different machine learning and deep learning-based models.
Machine Learning Applications in Misuse and Anomaly Detection
Machine learning and data mining algorithms play important roles in designing intrusion detection systems.
A Time Series Analysis-Based Stock Price Prediction Using Machine Learning and Deep Learning Models
We contend that the agglomerative approach of model building that uses a combination of statistical, machine learning, and deep learning approaches, can very effectively learn from the volatile and random movement patterns in a stock price data.
Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Timeseries
Based on the NIFTY data during the said period, we build various predictive models using machine learning approaches, and then use those models to predict the Close value of NIFTY 50 for the year 2019, with a forecast horizon of one week.
A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing
Based on the data of 2015 to 2017, we build various predictive models using machine learning, and then use those models to predict the closing value of NIFTY 50 for the period January 2018 till June 2019 with a prediction horizon of one week.
