159 research outputs found
Data for: 3514038
No full textPrediction of future movement of stock prices
has been a subject matter of many research work. There is a
gamut of literature of technical analysis of stock prices where
the objective is to identify patterns in stock price movements
and derive profit from it. Improving the prediction accuracy
remains the single most challenge in this area of research. We
propose a hybrid approach for stock price movement
prediction using machine learning, deep learning, and natural
language processing. We select the NIFTY 50 index values of
the National Stock Exchange (NSE) of India, and collect its
daily price movement over a period of three years (2015 –
2017). Based on the data of 2015 – 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. For predicting the price movement patterns, we use a
number of classification techniques, while for predicting the
actual closing price of the stock, various regression models
have been used. We also build a Long and Short-Term Memory
(LSTM)-based deep learning network for predicting the
closing price of the stocks and compare the prediction
accuracies of the machine learning models with the LSTM
model. We further augment the predictive model by
integrating a sentiment analysis module on Twitter data to
correlate the public sentiment of stock prices with the market
sentiment. This has been done using Twitter sentiment and
previous week closing values to predict stock price movement
for the next week. We tested our proposed scheme using a
cross validation method based on Self Organizing Fuzzy Neural
Networks (SOFNN) and found extremely interesting results
Privacy in Federated Learning
Get PDFFederated Learning (FL) represents a significant advancement in distributed
machine learning, enabling multiple participants to collaboratively train
models without sharing raw data. This decentralized approach enhances privacy
by keeping data on local devices. However, FL introduces new privacy
challenges, as model updates shared during training can inadvertently leak
sensitive information. This chapter delves into the core privacy concerns
within FL, including the risks of data reconstruction, model inversion attacks,
and membership inference. It explores various privacy-preserving techniques,
such as Differential Privacy (DP) and Secure Multi-Party Computation (SMPC),
which are designed to mitigate these risks. The chapter also examines the
trade-offs between model accuracy and privacy, emphasizing the importance of
balancing these factors in practical implementations. Furthermore, it discusses
the role of regulatory frameworks, such as GDPR, in shaping the privacy
standards for FL. By providing a comprehensive overview of the current state of
privacy in FL, this chapter aims to equip researchers and practitioners with
the knowledge necessary to navigate the complexities of secure federated
learning environments. The discussion highlights both the potential and
limitations of existing privacy-enhancing techniques, offering insights into
future research directions and the development of more robust solutions.Comment: This is the accepted version of the book chapter that has been
accepted for inclusion in the book titled "Data Privacy: Techniques,
Applications, and Standards. Editor: Jaydip Sen, IntechOpen Publishers,
London, UK. ISBN: 978-1-83769-675-8. The chapter is 29 pages lon
A Comparative Study of Portfolio Optimization Methods for the Indian Stock Market
Get PDFThis chapter presents a comparative study of the three portfolio optimization
methods, MVP, HRP, and HERC, on the Indian stock market, particularly focusing
on the stocks chosen from 15 sectors listed on the National Stock Exchange of
India. 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). For each sector, three portfolios are designed on stock prices from
July 1, 2019, to June 30, 2022, following three portfolio optimization
approaches. The portfolios are tested over the period from July 1, 2022, to
June 30, 2023. For the evaluation of the performances of the portfolios, three
metrics are used. These three metrics are cumulative returns, annual
volatilities, and Sharpe ratios. For each sector, the portfolios that yield the
highest cumulative return, the lowest volatility, and the maximum Sharpe Ratio
over the training and the test periods are identified.Comment: This is the draft version of the chapter that has been accepted for
publication in the edited volume titled "Data Science: Theory and Practice".
The volume is edited by Jaydip Sen and Sayantani Roy Choudury and will be
published by IntechOpen, London, UK. The chapter is 74 pages long and it
contains 32 tables and 62 figure
Applied Cryptography and Network Security
Get PDFCryptography will continue to play important roles in developing of new security solutions which will be in great demand with the advent of high-speed next-generation communication systems and networks. This book discusses some of the critical security challenges faced by today's computing world and provides insights to possible mechanisms to defend against these attacks. The book contains sixteen chapters which deal with security and privacy issues in computing and communication networks, quantum cryptography and the evolutionary concepts of cryptography and their applications like chaos-based cryptography and DNA cryptography. It will be useful for researchers, engineers, graduate and doctoral students working in cryptography and security related areas. It will also be useful for faculty members of graduate schools and universities
Computer and Network Security
Get PDFIn the era of Internet of Things (IoT), and with the explosive worldwide growth of electronic data volume and the associated needs of processing, analyzing, and storing this data, several new challenges have emerged. Particularly, there is a need for novel schemes of secure authentication, integrity protection, encryption, and non-repudiation to protect the privacy of sensitive data and to secure systems. Lightweight symmetric key cryptography and adaptive network security algorithms are in demand for mitigating these challenges. This book presents state-of-the-art research in the fields of cryptography and security in computing and communications. It covers a wide range of topics such as machine learning, intrusion detection, steganography, multi-factor authentication, and more. It is a valuable reference for researchers, engineers, practitioners, and graduate and doctoral students working in the fields of cryptography, network security, IoT, and machine learning
Applied Cryptography and Network Security
No full textCryptography will continue to play important roles in developing of new security solutions which will be in great demand with the advent of high-speed next-generation communication systems and networks. This book discusses some of the critical security challenges faced by today's computing world and provides insights to possible mechanisms to defend against these attacks. The book contains sixteen chapters which deal with security and privacy issues in computing and communication networks, quantum cryptography and the evolutionary concepts of cryptography and their applications like chaos-based cryptography and DNA cryptography. It will be useful for researchers, engineers, graduate and doctoral students working in cryptography and security related areas. It will also be useful for faculty members of graduate schools and universities
Theory and Practice of Cryptography and Network Security Protocols and Technologies
Get PDFIn an age of explosive worldwide growth of electronic data storage and communications, effective protection of information has become a critical requirement. When used in coordination with other tools for ensuring information security, cryptography in all of its applications, including data confidentiality, data integrity, and user authentication, is a most powerful tool for protecting information. This book presents a collection of research work in the field of cryptography. It discusses some of the critical challenges that are being faced by the current computing world and also describes some mechanisms to defend against these challenges. It is a valuable source of knowledge for researchers, engineers, graduate and doctoral students working in the field of cryptography. It will also be useful for faculty members of graduate schools and universities
Machine Learning
No full textRecent times are witnessing rapid development in machine learning algorithm systems, especially in reinforcement learning, natural language processing, computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of machine learning models, algorithms, and their applications, and with the emergence of more innovative uses–cases of deep learning and artificial intelligence, the current volume presents a few innovative research works and their applications in real-world, such as stock trading, medical and healthcare systems, and software automation. The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers, professionals, and consultants working on the broad areas of machine learning, deep learning, and artificial intelligence
Advances in Security in Computing and Communications
No full textIn the era of Internet of Things (IoT) and with the explosive worldwide growth of electronic data volume, and associated need of processing, analysis, and storage of such humongous volume of data, several new challenges are faced in protecting privacy of sensitive data and securing systems by designing novel schemes for secure authentication, integrity protection, encryption, and non-repudiation. Lightweight symmetric key cryptography and adaptive network security algorithms are in demand for mitigating these challenges. This book presents some of the state-of-the-art research work in the field of cryptography and security in computing and communications. It is a valuable source of knowledge for researchers, engineers, practitioners, graduates, and doctoral students who are working in the field of cryptography, network security, and security and privacy issues in the Internet of Things (IoT). It will also be useful for faculty members of graduate schools and universities
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