33 research outputs found
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. This
field has attracted substantial research interest in recent years. Aspect-level
(called aspect-based opinion mining) is often desired in practical applications
as it provides detailed opinions or sentiments about different aspects of
entities and entities themselves, which are usually required for action. Aspect
extraction and entity extraction are thus two core tasks of aspect-based
opinion mining. his paper has presented a framework of aspect-based opinion
mining based on the concept of transfer learning. on real-world customer
reviews available on the Amazon website. The model has yielded quite
satisfactory results in its task of aspect-based opinion mining.Comment: This is the accepted version of the paper that has been presented and
published in the 20th IEEE Conference, OCIT'22. The final published version
is copyright-protected by the IEEE. The paper consists of 5 pages, and it
includes 5 figures and 1 tabl
Portfolio Optimization: A Comparative Study
Portfolio optimization has been an area that has attracted considerable
attention from the financial research community. Designing a profitable
portfolio is a challenging task involving precise forecasting of future stock
returns and risks. This chapter presents a comparative study of three portfolio
design approaches, the mean-variance portfolio (MVP), hierarchical risk parity
(HRP)-based portfolio, and autoencoder-based portfolio. 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. The
portfolios are designed using the stock price data from January 1, 2018, to
December 31, 2021, and their performances are tested on the out-of-sample data
from January 1, 2022, to December 31, 2022. Extensive results are analyzed on
the performance of the portfolios. It is observed that the performance of the
MVP portfolio is the best on the out-of-sample data for the risk-adjusted
returns. However, the autoencoder portfolios outperformed their counterparts on
annual returns.Comment: This is the preprint of the book chapter accepted for publication in
the book titled "Deep Learning - Recent Finding and Researches" edited by
Manuel Dom\'inguez-Morales. The book is scheduled to be be published by
IntechOpen, London, UK in January 2024. This is not the final version of the
chapte
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. While the well-known efficient market hypothesis
rules out any possibility of accurate prediction of stock prices, there are
formal propositions in the literature demonstrating accurate modeling of the
predictive systems that can enable us to predict stock prices with a very high
level of accuracy. In this paper, we present a suite of deep learning-based
regression models that yields a very high level of accuracy in stock price
prediction. To build our predictive models, we use the historical stock price
data of a well-known company listed in the National Stock Exchange (NSE) of
India during the period December 31, 2012 to January 9, 2015. The stock prices
are recorded at five minutes intervals of time during each working day in a
week. Using these extremely granular stock price data, we build four
convolutional neural network (CNN) and five long- and short-term memory
(LSTM)-based deep learning models for accurate forecasting of the future stock
prices. We provide detailed results on the forecasting accuracies of all our
proposed models based on their execution time and their root mean square error
(RMSE) values.Comment: The paper is the accepted version of our work in the 4th IEEE
International Conference on Electronics, Communication, and Aerospace
Technology (ICECA'20), November 5 - 7, 2020, Coimbatore, INDIA, The paper
consists of 10 pages. It contains 12 figures and 8 table
Processing Analytical Queries in the AWESOME Polystore [Information Systems Architectures]
Modern big data applications usually involve heterogeneous data sources and
analytical functions, leading to increasing demand for polystore systems,
especially analytical polystore systems. This paper presents AWESOME system
along with a domain-specific language ADIL. ADIL is a powerful language which
supports 1) native heterogeneous data models such as Corpus, Graph, and
Relation; 2) a rich set of analytical functions; and 3) clear and rigorous
semantics. AWESOME is an efficient tri-store middle-ware which 1) is built on
the top of three heterogeneous DBMSs (Postgres, Solr, and Neo4j) and is easy to
be extended to incorporate other systems; 2) supports the in-memory query
engines and is equipped with analytical capability; 3) applies a cost model to
efficiently execute workloads written in ADIL; 4) fully exploits machine
resources to improve scalability. A set of experiments on real workloads
demonstrate the capability, efficiency, and scalability of AWESOME
Adversarial Attacks on Image Classification Models: FGSM and Patch Attacks and Their Impact
This chapter introduces the concept of adversarial attacks on image classification models built on convolutional neural networks (CNN). CNNs are very popular deep-learning models which are used in image classification tasks. 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. In this work, two very well-known adversarial attacks are discussed and their impact on the performance of image classifiers is analyzed. These two adversarial attacks are the fast gradient sign method (FGSM) and adversarial patch attack. These attacks are launched on three powerful pre-trained image classifier architectures, ResNet-34, GoogleNet, and DenseNet-161. The classification accuracy of the models in the absence and presence of the two attacks are computed on images from the publicly accessible ImageNet dataset. The results are analyzed to evaluate the impact of the attacks on the image classification task
Portfolio Optimization: A Comparative Study
Portfolio optimization has been an area that has attracted considerable attention from the financial research community. Designing a profitable portfolio is a challenging task involving precise forecasting of future stock returns and risks. This chapter presents a comparative study of three portfolio design approaches, the mean-variance portfolio (MVP), hierarchical risk parity (HRP)-based portfolio, and autoencoder-based portfolio. 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. The portfolios are designed using the stock price data from January 1, 2018, to December 31, 2021, and their performances are tested on the out-of-sample data from January 1, 2022, to December 31, 2022. Extensive results are analyzed on the performance of the portfolios. It is observed that the performance of the MVP portfolio is the best on the out-of-sample data for the risk-adjusted returns. However, the autoencoder portfolios outperformed their counterparts on annual returns