Review
Computer Science, Artificial Intelligence
T. O. Kehinde, Felix T. S. Chan, S. H. Chung
Summary: This paper presents a scientometric review in stock market forecasting, analyzing 220 reputable articles to identify trends and patterns in the field. It introduces a less computational approach that shows promise for improving accuracy in stock market prediction, which has not been thoroughly explored by previous researchers. The paper provides valuable insights for early stage researchers, governments, funding bodies, managers, analysts, financial enthusiasts, practitioners, and investors.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Environmental Studies
Guangqiang Liu, Xiaozhu Guo
Summary: This paper finds that incorporating commodity futures volatility information with model shrinkage methods is an effective way to predict US stock market volatility, with clearer predictability during high volatility periods. The robust results of the study have practical implications.
Article
Computer Science, Information Systems
Mohd Asyraf Zulkifley, Ali Fayyaz Munir, Mohd Edil Abd Sukor, Muhammad Hakimi Mohd Shafiai
Summary: This paper aims to provide a comprehensive review of automated methods for detecting stock market manipulation cases, including a concise definition of manipulation taxonomy and some outputs of early experimental research.
CMC-COMPUTERS MATERIALS & CONTINUA
(2023)
Article
Computer Science, Artificial Intelligence
Akshat Chauhan, S. J. Shivaprakash, H. Sabireen, Md Abdul Quadir, Neelanarayanan Venkataraman
Summary: The stock market is a platform for buying and selling publicly listed company stocks. This paper proposes a method that uses deep learning techniques and metaheuristic algorithms to predict stock price movements, and demonstrates the superior performance of the proposed ensemble model through experimentation.
APPLIED SOFT COMPUTING
(2023)
Review
Computer Science, Artificial Intelligence
Mahinda Mailagaha Kumbure, Christoph Lohrmann, Pasi Luukka, Jari Porras
Summary: This literature review explores the application of machine learning techniques in stock market prediction. It focuses on the stock markets investigated in the literature and the types of variables used as input in machine learning techniques for predicting these markets. The review includes an examination of 138 journal articles published between 2000 and 2019 and provides extensive insights into the data and machine learning techniques used for stock market prediction.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Editorial Material
Engineering, Electrical & Electronic
Hung-Jen Chen, Hong-Han Shuai, Wen-Huang Cheng
Summary: The fashion industry is undergoing an unprecedented change, thanks to advancements in machine learning, computer vision, and artificial intelligence. This article provides an overview of three major aspects of fashion research: fashion analysis, fashion recommendation, and fashion synthesis. Each aspect is explored with problem formulations, method comparisons, and evaluation metrics, and future research directions are outlined to inspire further advancements in the field.
IEEE SIGNAL PROCESSING MAGAZINE
(2023)
Article
Business, Finance
Yan Li, Chao Liang, Toan Luu Duc Huynh
Summary: This study proposes a method to measure the aggressive stock-selection opportunity and examines its role in predicting stock market returns. The results show that the change of aggressive stock-selection opportunity has a significant impact on future market returns, improving return forecasting performance and increasing investors' economic values.
FINANCE RESEARCH LETTERS
(2022)
Article
Computer Science, Artificial Intelligence
Md Abdul Quadir, Sanjit Kapoor, A. V. Chris Junni, Arun Kumar Sivaraman, Kong Fah Tee, H. Sabireen, N. Janakiraman
Summary: Stock markets are volatile and predicting stock prices accurately is a complex task. This study proposes an optimization approach for stock price prediction using a Multi-Layer Sequential Long Short Term Memory (MLS LSTM) model with the adam optimizer. The results show high prediction accuracy and outperformance compared to other machine learning and deep learning algorithms.
APPLIED SOFT COMPUTING
(2023)
Article
Operations Research & Management Science
Erdinc Akyildirim, Aurelio F. Bariviera, Duc Khuong Nguyen, Ahmet Sensoy
Summary: The study compared the performance of various advanced forecasting techniques for predicting stock price movements based on past prices, and found that random forest and support vector machine were able to capture future price directions and percentage changes satisfactorily. The consistent ranking of the methodologies across different time frequencies and train/test set partitions further proved the robustness of the empirical findings.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Multidisciplinary Sciences
Rohitash Chandra, Yixuan He
Summary: This study focuses on the application of Bayesian neural networks in stock price prediction, providing a method to quantify uncertainty in predictions. With the impact of the COVID-19 pandemic, researching the performance of stock price prediction models during this period has become increasingly important. The results indicate that despite the high volatility during COVID-19, Bayesian neural networks are able to provide reasonable predictions with uncertainty quantification.
Article
Computer Science, Artificial Intelligence
Alexandru Topirceanu
Summary: The study introduces a novel temporal attenuation (TA) model that combines micro-scale opinion dynamics and temporal epidemics to enhance forecasting accuracy in elections. The TA model outperforms traditional statistical methods and pollsters in predicting US presidential elections, improving forecasting performance by 23-37% compared to the state of the art.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Business, Finance
Xiaojun Chu, Jianying Qiu
Summary: The study reveals that the first half-hour order imbalance (FOIB) has a stronger predictive power on stock returns in the Chinese market compared to daily order imbalance, both statistically and economically. The evidence suggests that the predictive power of FOIB is primarily driven by large trade size.
INTERNATIONAL JOURNAL OF FINANCE & ECONOMICS
(2021)
Article
Computer Science, Information Systems
Gerardo Alfonso, A. Daniel Carnerero, Daniel R. Ramirez, Teodoro Alamo
Summary: Stock price forecasting is a challenging and relevant issue that has attracted the interest of engineers and scientists. This paper discusses two techniques for stock price and price intervals forecasting, both of which are based on local data extraction from a database. These techniques are data-driven, adaptable to market changes, and highly parallelizable.
Article
Business
Yongsheng Yi, Yaojie Zhang, Jihong Xiao, Xunxiao Wang
Summary: In this study, the authors forecast the volatility of the Chinese stock market using the HAR model and various extended models. They employ the SPCA approach to extract volatility information from the G7 stock markets and find that this combined information significantly predicts Chinese stock market volatility. The results suggest that the HAR-SPCA model outperforms other competing models in terms of accuracy and stability under various evaluation criteria.
EMERGING MARKETS FINANCE AND TRADE
(2022)
Article
Business, Finance
Jun Gao, Xiang Gao, Chen Gu
Summary: This study examines the lead-lag relationships of volatility among European stock markets. It finds that the UK market portfolio has a long-run leading role in predicting volatilities in non-UK countries. Volatility shocks in the UK gradually affect market fluctuations across Europe with varying delays. The findings support the limited attention explanation for the volatility predictability of the lagged UK equity index.
INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS
(2023)
Article
Automation & Control Systems
Rahul Kumar, Uday Pratap Singh, Arun Bali, Kuldip Raj
Summary: An adaptive hybrid neural control scheme is proposed for uncertain non-linear discrete-time systems with non-symmetric dead-zone input and unknown disturbances. The scheme overcomes the complexity of controlling such systems by introducing an adaptive compensative term for the non-symmetric dead-zone and constructing a hybrid neural network controller. Simulation examples validate the effectiveness of the proposed scheme, including an example inspired by a real-world system called continuous stirred tank reactor.
INTERNATIONAL JOURNAL OF CONTROL
(2023)
Review
Pathology
Neha Kumari, Ritu Verma, Vinita Agrawal, Uday Pratap Singh
Summary: This study retrospectively analyzed the clinical features, morphological characteristics, and outcome of renal neuroendocrine tumors. Six patients who underwent radical nephrectomy were included in the study. The results showed that these tumors were mostly well-differentiated and had a low risk of metastasis or relapse.
INTERNATIONAL JOURNAL OF SURGICAL PATHOLOGY
(2023)
Correction
Computer Science, Artificial Intelligence
Sumarga Kumar Sah Tyagi, Elias Pimenidis, Sanjeev Jain, Will Serrano
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Automation & Control Systems
Arun Bali, Uday Pratap Singh, Rahul Kumar, Sanjeev Jain
Summary: This work focuses on the design of a hybrid neural tracking controller for a class of uncertain switched nonlinear systems. The controller combines the backstepping approach and neural network approximation to construct a tracking control model. The stability of the model is ensured through complexity analysis. Numerical examples and a real-life example demonstrate the effectiveness of the proposed hybrid controller.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Computer Science, Information Systems
Jasvinder Pal Singh, Uday Pratap Singh, Sanjeev Jain
Summary: This paper focuses on recognizing people in a multi-gait scenario. It presents a model-based approach to reconstruct occluded regions and extract linear kinematic features. A hybrid classifier is proposed for multi-gait identification and experimental results demonstrate its superiority over existing methods.
MULTIMEDIA SYSTEMS
(2023)
Article
Automation & Control Systems
Arun Bali, Uday Pratap Singh, Rahul Kumar
Summary: This work focuses on the hybrid neural network control problem for a certain class of switched uncertain nonlinear systems in strict-feedback form. An improved whale optimization algorithm (IWOA)-based hybrid neural network controller is introduced to approximate unknown nonlinear functions. The stability analysis is conducted using the backstepping technique and common Lyapunov function (CLF) based on the hybrid neural network approximation ability. The proposed method guarantees the semi-global uniform ultimate boundedness (SGUUB) of all signals in the closed-loop system and convergence of the tracking error to a small neighborhood of zero. The effectiveness of the proposed scheme is verified through application to a ship maneuvering system and a numerical example.
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
(2023)
Article
Computer Science, Information Systems
Shubhangi Solanki, Uday Pratap Singh, Siddharth Singh Chouhan, Sanjeev Jain
Summary: A brain tumor is caused by rapid and uncontrolled cell growth, which can be fatal if not treated early. Despite significant efforts, accurate segmentation and classification of brain tumors remain challenging due to variations in location, structure, and proportions. This study aims to provide researchers with comprehensive literature on the ability of Magnetic Resonance imaging to identify brain tumors. Using computational intelligence and statistical image processing techniques, the research proposes several methods for detecting brain cancer and tumors. The study also includes an evaluation matrix for specific systems and dataset types, as well as discussions on tumor morphology, available datasets, augmentation methods, and categorization of Deep Learning, Transfer Learning, and Machine Learning models. Additionally, the research compiles relevant information on tumor identification, including benefits, drawbacks, advancements, and future trends.
Article
Automation & Control Systems
Arun Bali, Uday Pratap Singh, Rahul Kumar, Sanjeev Jain
Summary: An adaptive finite-time fault-tolerant control technique is proposed for a class of switched nonlinear systems in strict-feedback form. The system is affected by actuator fault, input dead-zone, and external disturbances. Radial basis function neural networks (RBFNNs) are used to approximate unknown functions and minimize the negative effect of faults. The proposed control method, based on neural networks and the backstepping method, ensures both transient and steady-state control performance and has been shown to be effective in simulation examples, including a continuous stirred tank reactor (CSTR) application. (c) 2023 European Control Association. Published by Elsevier Ltd. All rights reserved.
EUROPEAN JOURNAL OF CONTROL
(2023)
Article
Computer Science, Information Systems
Misbah Shafi, Rakesh Kumar Jha, Sanjeev Jain
Summary: The advancement in wireless communication technologies necessitates the need for improved security measures, particularly in detecting attacks such as spoofing and signal strength attacks. This paper proposes an Intrusion Detection System based on graph theory to identify attacked nodes in the communication network. The algorithm analyzes the network layer by layer to extract vulnerable nodes and determine the attacked node(s). The IDS strategy is based on energy efficiency and secrecy rate analysis, detecting nodes with values beyond specified thresholds. The proposed approach outperforms conventional intrusion detection methods in terms of performance, computation time, and complexity.
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
(2023)
Article
Computer Science, Cybernetics
Misbah Shafi, Rakesh Kumar Jha, Sanjeev Jain
Summary: The extensive use of applications on smartphones has led to various security threats, such as permission control attacks, phishing attacks, and spyware attacks. This article proposes an application-based attack modeling and detection method to identify vulnerabilities based on the behavior of applications.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Meeting Abstract
Urology & Nephrology
Sanjoy Kumar Sureka, Sumit Mandal, Uday Pratap Singh
JOURNAL OF UROLOGY
(2023)
Article
Urology & Nephrology
Sanjoy Kumar Sureka, Ankit Misra, Himanshu Raj, Anupam Shukla, Uday Pratap Singh
Summary: The efficacy of 2-core prostate biopsy in advanced prostate cancer patients was assessed. A retrospective analysis of 12-core prostate biopsies and a prospective validation were conducted to determine if a reduced number of cores are sufficient for histopathological diagnosis.
INTERNATIONAL UROLOGY AND NEPHROLOGY
(2023)
