123 20 16481 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Algorithmic Trading: Financial Markets Using Artificial Intelligence Subject The topic of the resource Artificial Intelligence; Financial Market; Machine Learning; Market Trends; Stock Marketing Description An account of the resource This research study gives an in - depth view of the recent developments in the fields of Machine Learning (ML) and Reinforced Learning (RL) techniques as they are related to various models for forecasting and systems for financial trading. The practical usage of deep learning models, that incorporates Neural Networks such as Recurrent, Convolutional along with hybrid models integrating genetic algorithms with LSTM networks, for forecasting the stock market patterns as well as bank failures, and fluctuations in exchange rate which is addressed in this study in an in - depth review analysis of the latest literature. In addition to this it also investigates how trading algorithm performance as well as risk management can be enhanced by applying techniques of deep reinforcement learning. This study also demonstrates the enhanced, efficacy, precision and the profitability achieved by using these artificial intelligence methods as compared with conventional economic modelling and detailed technical study models by analysing a number of stock markets and different kinds of assets. 2024 IEEE. Creator An entity primarily responsible for making the resource Chanda S.; Chib S.; Bose S.S.; Lakshmi S.R.; Zabiullah B.I.; Samal A. Source A related resource from which the described resource is derived Proceedings of International Conference on Communication, Computer Sciences and Engineering, IC3SE 2024, pp. 806-809. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/IC3SE62002.2024.10593357" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IC3SE62002.2024.10593357</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200691250&doi=10.1109%2FIC3SE62002.2024.10593357&partnerID=40&md5=58fa3246a697084186f9bb16aa915bac" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200691250&doi=10.1109%2fIC3SE62002.2024.10593357&partnerID=40&md5=58fa3246a697084186f9bb16aa915bac</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835036684-6 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chanda S., Datta Meghe Institute Of Management Studies, Madhya Pradesh, Nagpur, India; Chib S., Datta Meghe Institute Of Management Studies, Madhya Pradesh, Nagpur, India; Bose S.S., Amity Business School, Amity University, Chhattisgarh, Raipur, India; Lakshmi S.R., Business & Management, Christ University, Nagasandra, Karnataka, Bangalore, India; Zabiullah B.I., School Of Management, Cmr University, Karnataka, Bangalore, India; Samal A., Sri Venkateshwara College Of Engineering, Department Of Mba, Karnataka, Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Chanda, Neelatphal; Gupta, Ishayu Title A name given to the resource Ethical AI in Humanitarian Contexts: Challenges, Transparency, and Safety Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived AI for Humanitarianism: Fostering Social Change Through Emerging Technologies;pp.155-166 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003479109-12" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003479109-12</a> <a href="https://www.scopus.com/pages/publications/85216653361?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85216653361?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chanda N., Department of Media Studies, Christ University, Bangalore, India; Gupta I., Department of Media Studies, Christ University, Bangalore, India Description An account of the resource This chapter elaborates on how emerging technologies for artificial intelligence (AI) can help create social change and solve worldwide problems. The chapter brings to light the issue of ethical matters and responsible AI practices that should be considered to avoid technology usage by the vulnerable population to harden already present inequalities. This chapter also examines the role of AI in ensuring that quality education is accessible to all, in addressing poverty through innovative approaches, and in the amplification quest of human rights advocacy by marginalized groups. This chapter presents a complete picture of the impact of AI on humanitarianism, exemplifying the devices of new horizons and emphasizing the necessity of responsible and inclusive applications. This chapter provides findings and advice for researchers, practitioners, policymakers, and all interested parties who are involved in using the new technologies to make their world fairer and well-sustained. The chapter aims to comprehend the AI-humanitarianism nexus and simultaneously proclaim safety measures and transparency for the sake of social upheaval. 2025 selection and editorial matter, Adeyemi Abel Ajibesin and Narasimha Rao Vajjhala; individual chapters, the contributors. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104025787-6; 978-103276571-6; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Letter Faculty Publications-Letters Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Chanda, Neelatphal; Gupta, Ishayu; Kanjilal, Manas Kumar Title A name given to the resource Plugged in, tuned out: How earbuds on roads are becoming a silent menace Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Traffic Injury Prevention; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1080/15389588.2025.2578410" target="_blank" rel="noreferrer noopener">https://doi.org/10.1080/15389588.2025.2578410</a> <a href="https://www.scopus.com/pages/publications/105022685018?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105022685018?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chanda N., Department of Media Studies, Christ University, Karnataka, Bengaluru, India; Gupta I., School of Media and Communication, University of Leeds, Leeds, United Kingdom; Kanjilal M.K., School of Media and Communication, University of Leeds, Leeds, United Kingdom Description An account of the resource [No abstract available] Publisher An entity responsible for making the resource available Taylor and Francis Ltd. Relation A related resource ISSN: 15389588; CODEN: TIPRC Language A language of the resource English Type The nature or genre of the resource Letter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Chandan, N.; Manimekala, B.; Siva Balan, R.V. Title A name given to the resource Enhanced Image Classification using Transfer Learning with ResNet50-V2: A Case Study on Wildlife Recognition Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025 - Proceedings;pp.1565-1570 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICMCSI64620.2025.10883359" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICMCSI64620.2025.10883359</a> <a href="https://www.scopus.com/pages/publications/105000007871?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105000007871?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chandan N., Department of Computer Science, CHRIST (Deemed to be University), Yeshwanthpur Campus, Karnataka, Bangalore, 560073, India; Manimekala B., Department of Computer Science, CHRIST (Deemed to be University), Yeshwanthpur Campus, Karnataka, Bangalore, 560073, India; Siva Balan R.V., Department of Computer Science, CHRIST (Deemed to be University), Yeshwanthpur Campus, Karnataka, Bangalore, 560073, India Description An account of the resource This study explores the application of transfer learning using the ResNet50-V2 architecture for accurate classification of Arctic wildlife species, including Arctic foxes, polar bears, and walruses. Transfer learning leverages pretrained networks to enhance performance in new tasks with limited labeled data, reducing the need for extensive data collection and computational resources. In this work, we utilized a dataset of 1000 labeled images across the three species and applied ResNet50-V2, pre-trained on ImageNet, as a feature extractor. The model achieved high accuracy, with training and validation accuracies nearing 99% and 95-97%, respectively, though minor overfitting was observed. This indicates the model's strong ability to generalize across the dataset while benefiting from pre-trained weights on diverse, non-related images. Additionally it compares with models like SSD and CycleGAN, emphasizing its capability to generalize well, handle small datasets, and mitigate overfitting. We discuss model architecture, data preprocessing, and the experimental results, focusing on improvements achievable through regularization techniques to counteract overfitting. This study demonstrates the effectiveness of transfer learning for wildlife classification, providing insights into optimizing CNNs for ecological and conservation applications. 2025 IEEE. Subject The topic of the resource CNN; CycleGAN; Model Selection; ResNet50-V2; Transfer Learning; wildlife classification Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152266-7; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Analysing Employee Management Using Machine Learning Techniques and Solutions in Human Resource Management Subject The topic of the resource Human Resource management; Machine Learning Description An account of the resource In the contemporary landscape of Human Resource Management (HRM), organizations are increasingly turning to advanced technologies to streamline employee management processes. This study explores the integration of machine learning (ML) techniques as a transformative solution for optimizing HRM practices, with a specific focus on employee management. By leveraging the power of ML algorithms, this research aims to enhance decision-making, efficiency, and overall effectiveness in HRM. The study encompasses a comprehensive analysis of existing HRM challenges, such as talent acquisition, performance evaluation, and employee retention, and proposes ML-based solutions to address these issues. By applying natural language processing, pattern identification, and predictive analytics, businesses may learn a great deal about employee behavior, performance patterns, and possible areas for development. HR professionals are more equipped to make well-informed choices, customize employee experiences, and put proactive talent development initiatives into action thanks to this data-driven approach. Additionally, the study examines the moral issues and difficulties surrounding the use of ML in HRM, stressing the significance of openness, justice, and privacy. By understanding and mitigating these concerns, organizations can successfully harness the transformative potential of ML in employee management, fostering a more dynamic and adaptive HRM framework. The study's conclusions add to the growing body of knowledge on the relationship between technology and HRM and offer useful advice to businesses looking to use cutting-edge approaches to improve labor management procedures. 2024 IEEE. Creator An entity primarily responsible for making the resource Chandana C.; Sarkar A.; Deshmukh K.; Kulkarni P.; Bikash Acharjee P.; Lourens M. Source A related resource from which the described resource is derived 4th International Conference on Innovative Practices in Technology and Management 2024, ICIPTM 2024 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICIPTM59628.2024.10563736" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICIPTM59628.2024.10563736</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197736135&doi=10.1109%2FICIPTM59628.2024.10563736&partnerID=40&md5=e42fbd3b725a8066fff56cd3582705c4" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197736135&doi=10.1109%2fICIPTM59628.2024.10563736&partnerID=40&md5=e42fbd3b725a8066fff56cd3582705c4</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835030775-7 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chandana C., Kg Reddy College of Engineering & Technology, India; Sarkar A., Delhi Technical Campus, Department of Management Studies, Greater Noida, India, Guru Gobind Singh Indraprastha University, Delhi, India; Deshmukh K., Indira School of Business Studies, Pune, India; Kulkarni P., Lotus Business School, Pune, India; Bikash Acharjee P., Christ University, Computer Science, Pune, India; Lourens M., Durban University of Technology, Faculty of Management Sciences, South Africa Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Chandar S, Kumar Title A name given to the resource Artificial Intelligence Driven Air Quality Prediction for Sustainable Goa Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of 3rd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2025;pp.1181-1192 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICAISS61471.2025.11041723" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICAISS61471.2025.11041723</a> <a href="https://www.scopus.com/pages/publications/105010755728?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010755728?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chandar S K., Christ University, School of Business and Management, India Description An account of the resource Clean Air is essential for the health and survival of both humans and wildlife. Air pollution has been linked to various serious diseases, including cancer. Rapid industrial growth and increasing population have contributed to rising pollution from transportation, industries, and agriculture. As a result, air pollution has become a major issue, particularly in developing countries like India. To ensure good air quality, accurate and reliable monitoring and prediction are required. Machine Learning (ML) models have shown promise in predicting Air Quality Index (AQI) over traditional methods. This research aims to propose a AQI prediction model using Attention based Bi-directional Long Short-Term Memory (ABiLSTM) to predict AQI in various cities across Goa, India. Data processing methods are used to manage date before providing it into the ABiLSTM model. Daily AQI series from 2022 to 2024 for six cities in Goa- Panaji, Pond, Assanora, Codli, Tilamol, and Tuem are collected and utilized to verify the proposed model. Two models are tested, including BiLSTM and ABiLSTM. Experimental results showed that the ABiLSTM model outperformed BiLSTM model in all cities, reporting lower error values and higher R2 scores. A comprehensive analysis with a set of evaluation indices confirmed that the proposed ABiLSTM model effectively captures the characteristics of the original AQI series and achieves a higher accuracy in AQI prediction. 2025 IEEE. Subject The topic of the resource Air Quality Index Prediction; Attention Based bidirectional Short-Term Memory; Deep Learning; Machine Learning Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833150724-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Deep learning framework for stock price prediction using long short-term memory Subject The topic of the resource Artificial intelligence; Deep learning neural network; Long short-term memory; Mean absolute percentage error; Stock prediction; Technical indicators Description An account of the resource Forecasting stock prices is always considered as complicated process due to the dynamic and noisy characteristics of stock data influenced by external factors. For predicting the stock market, several approaches have been put forward. Many academics have successfully forecasted stock prices using soft computing models. Recently, there has been growing interest in applying deep learning techniques in combination with technical indicators to forecast stock prices, attracting attention from both investors and researchers. This paper focuses on developing a reliable model for anticipating future stock prices in one day advance using Long Short-Term Memory (LSTM). Three steps make up the suggested model. The approach begins with ten technical indicators computed from previous data as feature vectors. The second phase involves data normalization to scale the feature vectors. Finally, in the third phase, the LSTM model analyzes the closing price for the next day using the normalized characteristics as input. Two stock markets, NASDAQ and NYSE are chosen to evaluate the efficacy of the developed model. To demonstrate how effective the new model is in making predictions, its performance is compared to earlier models. Comparing the suggested model to other models, the findings revealed that it had a high level of prediction accuracy. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Creator An entity primarily responsible for making the resource Chandar S.K. Source A related resource from which the described resource is derived Soft Computing, Vol-28, No. 17-18, pp. 10557-10567. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s00500-024-09836-3" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s00500-024-09836-3</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198502005&doi=10.1007%2Fs00500-024-09836-3&partnerID=40&md5=102b635ab0ef7ec3725454c066bb2eb3" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198502005&doi=10.1007%2fs00500-024-09836-3&partnerID=40&md5=102b635ab0ef7ec3725454c066bb2eb3</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 14327643 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chandar S.K., School of Business and Management, CHRIST (Deemed to be University), Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Convolutional neural network for stock trading using technical indicators Subject The topic of the resource Artificial neural network; Convolutional neural network; Deep learning; Gramian angular field; Stock trading and technical indicators Description An account of the resource Stock market prediction is a very hot topic in financial world. Successful prediction of stock market movement may promise high profits. However, an accurate prediction of stock movement is a highly complicated and very difficult task because there are many factors that may affect the stock price such as global economy, politics, investor expectation and others. Several non-linear models such as Artificial Neural Network, fuzzy systems and hybrid models are being used for forecasting stock market. These models have limitations like slow convergence and overfitting problem. To solve the aforementioned issues, this paper intends to develop a robust stock trading model using deep learning network. In this paper, a stock trading model by integrating Technical Indicators and Convolutional Neural Network (TI-CNN) is developed and implemented. The stock data investigated in this work were collected from publicly available sources. Ten technical indicators are extracted from the historical data and taken as feature vectors. Subsequently, feature vectors are converted into an image using Gramian Angular Field and fed as an input to the CNN. Closing price of stock data are manually labelled as sell, buy, and hold points by determining the top and bottom points in a sliding window. The duration considered over a period from January 2009 to December 2018. Prediction ability of the developed TI-CNN model is tested on NASDAQ and NYSE data. Performance indicators such as accuracy and F1 score are calculated and compared to prove effectiveness of the proposed stock trading model. Experimental results demonstrate that the proposed TI-CNN achieves high prediction accuracy than that of the earlier models considered for comparison. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. Creator An entity primarily responsible for making the resource Chandar S.K. Source A related resource from which the described resource is derived Automated Software Engineering, Vol-29, No. 1 Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s10515-021-00303-z" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s10515-021-00303-z</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122152758&doi=10.1007%2Fs10515-021-00303-z&partnerID=40&md5=b2ba6f6eba75f6c099414c109c676a25" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122152758&doi=10.1007%2fs10515-021-00303-z&partnerID=40&md5=b2ba6f6eba75f6c099414c109c676a25</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 9288910; CODEN: ASOEE Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chandar S.K., School of Business & Management, CHRIST (Deemed To Be University), Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Forecasting intraday stock price using ANFIS and bio-inspired algorithms Subject The topic of the resource Adaptive neuro-fuzzy inference system; ANFIS; Bio-inspired algorithm; Genetic algorithm; Intraday day; Minute price; Particle swarm optimisation; PSO Description An account of the resource The main focus of this study is to explore the predictability of stock price with variants of adaptive neuro-fuzzy inference system (ANFIS) and suggests a hybrid model to enhance the prediction accuracy. Two variants of ANFIS model are designed which includes genetic algorithm-ANFIS (GA-ANFIS) and particle swarm optimisation-ANFIS (PSO-ANFIS) to forecast stock price more accurately. The standard ANFIS is tuned employing GA and PSO algorithm. The experimental data used in this investigation are stocks traded per minute price of four companies from NSE. Sixteen technical indicators are calculated from the historical prices and used as inputs to the developed models. Prediction ability of the developed models is analysed by varying number of input samples. Numerical results obtained from the simulation confirmed that the PSO-ANFIS model has the potential to predict the future stock price more precisely than GA-ANFIS as well as other earlier methods. Copyright 2021 Inderscience Enterprises Ltd. Creator An entity primarily responsible for making the resource Chandar S.K. Source A related resource from which the described resource is derived International Journal of Networking and Virtual Organisations, Vol-25, No. 1, pp. 29-47. Publisher An entity responsible for making the resource available Inderscience Publishers Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1504/IJNVO.2021.117754" target="_blank" rel="noreferrer noopener">https://doi.org/10.1504/IJNVO.2021.117754</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116032336&doi=10.1504%2FIJNVO.2021.117754&partnerID=40&md5=53f2d695cabbbb83cb85efa610a25d16" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116032336&doi=10.1504%2fIJNVO.2021.117754&partnerID=40&md5=53f2d695cabbbb83cb85efa610a25d16</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 14709503 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chandar S.K., School of Business and Management, CHRIST (Deemed to be University), Bengaluru, 560029, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Stock market prediction using subtractive clustering for a neuro fuzzy hybrid approach Subject The topic of the resource ANFIS training method; Neuro fuzzy approach; Stock market prediction; Technical indicators Description An account of the resource Stock market prediction is the challenging area for the investors to yield profits in the financial markets. The investors need to understand the financial markets which are more volatile and affected by many external factors. This paper proposes a subtractive clustering based adaptive neuro fuzzy approach for predicting apple stock data prices. The research data used in this study is from 3rd Jan 2005 to 30th Jan 2015. Four technical indicators are proposed in this study. They are Simple moving average for 1week, simple moving average for 2weeks, 14days Disparity and Larry Williams R%. These variables are used as inputs to the neuro fuzzy system to predict the daily apple stock prices. Also, this study compares the proposed work with the ANFIS training method and subtractive clustering method etc. The performance of all these models is analyzed. The measures like training error, testing error, number of rules and number of parameters are calculated and compared for analysis. From the simulation results, the average performance of subtractive clustering based neuro fuzzy approach was found considerably better than the other networks. 2017, Springer Science+Business Media, LLC. Creator An entity primarily responsible for making the resource Chandar S.K. Source A related resource from which the described resource is derived Cluster Computing, Vol-22, pp. 13159-13166. Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2019-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s10586-017-1321-6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s10586-017-1321-6</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033572347&doi=10.1007%2Fs10586-017-1321-6&partnerID=40&md5=62b0aca48d64ccd9964a4345b43a2fb0" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033572347&doi=10.1007%2fs10586-017-1321-6&partnerID=40&md5=62b0aca48d64ccd9964a4345b43a2fb0</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 13867857 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chandar S.K., Department of Management Studies, Christ University, Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Forecasting gold prices based on extreme learning machine Subject The topic of the resource Extreme learning machine; Feed forward neural networks; Gold price forecasting Description An account of the resource In recent years, the investors pay major attention to invest in gold market because of huge profits in the future. Gold is the only commodity which maintains its value even in the economic and financial crisis. Also, the gold prices are closely related with other commodities. The future gold price prediction becomes the warning system for the investors due to unforeseen risk in the market. Hence, an accurate gold price forecasting is required to foresee the business trends. This paper concentrates on forecasting the future gold prices from four commodities like historical data's of gold prices, silver prices, Crude oil prices, Standard and Poor's 500 stock index (S & P500) index and foreign exchange rate. The period used for the study is from 1st January 2000 to 31st April 2014. In this paper, a learning algorithm for single hidden layered Feed forward neural networks called Extreme Learning Machine (ELM) is used which has good learning ability. Also, this study compares the five models namely Feed forward networks without feedback, Feed forward back propagation networks, Radial basis function, ELMAN networks and ELM learning model. The results prove that the ELM learning performs better than the other methods. 2006-2016 by CCC Publications. Creator An entity primarily responsible for making the resource Chandar S.K.; Sumathi M.; Sivanadam S.N. Source A related resource from which the described resource is derived International Journal of Computers, Communications and Control, Vol-11, No. 3, pp. 372-380. Publisher An entity responsible for making the resource available Agora University Date A point or period of time associated with an event in the lifecycle of the resource 2016-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.15837/ijccc.2016.3.2009" target="_blank" rel="noreferrer noopener">https://doi.org/10.15837/ijccc.2016.3.2009</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962439513&doi=10.15837%2Fijccc.2016.3.2009&partnerID=40&md5=fa9701d9ed74c52a195f9c916daddd6e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962439513&doi=10.15837%2fijccc.2016.3.2009&partnerID=40&md5=fa9701d9ed74c52a195f9c916daddd6e</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 18419836 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chandar S.K., Christ University, Bangalore, India; Sumathi M., Sri Meenakshi Government College For Arts For Women (Autonomous), Madurai, India; Sivanadam S.N., Karpagam College Of Engineering, Coimbatore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Prediction of stock market price using hybrid of wavelet transform and artificial neural network Subject The topic of the resource Artificial neural network; Discrete Wavelet Transform (DWT); Time series and stock market prediction Description An account of the resource Background/Objectives: Accurate prediction of stock market is highly challenging. This paper presents a forecasting model based on Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) for predicting financial time series. Methods/Statistical analysis: The idea of forecasting stock market prices with discrete wavelet transform is the central element of this paper. The proposed forecasting model uses the Discrete Wavelet Transform to decompose the financial time series data. The obtained approximation and detail coefficients after decomposition of the original time series data are used as input variables of back propagation neural network to forecast future stock prices. Approximation coefficients can characterize the coarse structure of the data and detail coefficients capture ruptures, discontinuities and singularities in the original data, to recognize the long-term trends in the original data. Findings: The proposed model was applied to five datasets. For all of the datasets, accuracy measures showed that the presented model outperforms a conventional model. It also proved that the hybrid forecasting technique has achieved better results compared with the approach which is not using the wavelet transform. Applications/Improvements: The accuracy of the proposed hybrid method can also be improved by developing a model using artificial neural network with Adaptive Neuro Fuzzy Interference System. Creator An entity primarily responsible for making the resource Chandar S.K.; Sumathi M.; Sivanandam S.N. Source A related resource from which the described resource is derived Indian Journal of Science and Technology, Vol-9, No. 8 Publisher An entity responsible for making the resource available Indian Society for Education and Environment Date A point or period of time associated with an event in the lifecycle of the resource 2016-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.17485/ijst/2016/v9i8/87905" target="_blank" rel="noreferrer noopener">https://doi.org/10.17485/ijst/2016/v9i8/87905</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961990702&doi=10.17485%2Fijst%2F2016%2Fv9i8%2F87905&partnerID=40&md5=cc3ed64f826a1bf9ad0e40616099e3dd" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961990702&doi=10.17485%2fijst%2f2016%2fv9i8%2f87905&partnerID=40&md5=cc3ed64f826a1bf9ad0e40616099e3dd</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 9746846 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chandar S.K., Christ University, Bangalore, Karnataka, 560029, India; Sumathi M., Sri Meenakshi Government College for Arts for Women (Autonomous), Madurai, Tamil Nadu, 625002, India; Sivanandam S.N., Karpagam College of Engineering, Coimbatore, Tamil Nadu, 641032, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Foreign exchange rate forecasting using Levenberg-Marquardt learning algorithm Subject The topic of the resource Exchange rate; Forecasting error; Mean absolute error; Network topology and levenberg - Marquardt learning algorithm Description An account of the resource Background/Objectives: Foreign currency Exchange (FOREX) plays a vital role for currency trading in the international market. Accurate prediction of foreign currency exchange rate is a challenging task. The paper investigates the FOREX prediction using feed forward neural network. Methods/Statistical analysis: This paper employs artificial neural network to forecast foreign currency exchange rate in India during 2010-2015.The exchange rates considered between Indian Rupee and four major currencies Euro, Japanese Yen, Pound Sterling and US Dollar. The network developed consists of an input layer, hidden layer and output layer. The neural network was trained with Levenberg-Marquardt (LM) learning algorithm. Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Forecasting Error (FE) are used as indicators for the performance of the networks. Findings: Simulation results are presented to show the performance of the proposed system. The paper also aims to suggest about network topology that must be chosen in order to fit time series kind of complicated data to a neural network model. The proposed technique gives the evidence that there is possibility of extracting information hidden in the foreign exchange rate and predicting into the future. Applications/Improvements: Finally, this paper presents the best network topology for FOREX prediction by comparing the effectiveness of various hidden layer performance algorithm using MATLAB neural network software as a tool. Creator An entity primarily responsible for making the resource Chandar S.K.; Sumathi M.; Sivanandam S.N. Source A related resource from which the described resource is derived Indian Journal of Science and Technology, Vol-9, No. 8 Publisher An entity responsible for making the resource available Indian Society for Education and Environment Date A point or period of time associated with an event in the lifecycle of the resource 2016-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.17485/ijst/2016/v9i8/87904" target="_blank" rel="noreferrer noopener">https://doi.org/10.17485/ijst/2016/v9i8/87904</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962040427&doi=10.17485%2Fijst%2F2016%2Fv9i8%2F87904&partnerID=40&md5=7f867347a5f03a1a11617aedf10b53ae" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962040427&doi=10.17485%2fijst%2f2016%2fv9i8%2f87904&partnerID=40&md5=7f867347a5f03a1a11617aedf10b53ae</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 9746846 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chandar S.K., Christ University, Bangalore, Karnataka, 560029, India; Sumathi M., Sri Meenakshi Government College for Arts for Women (Autonomous), Madurai, Tamil Nadu, 625002, India; Sivanandam S.N., Karpagam College of Engineering, Coimbatore, Tamil Nadu, 641032, India https://archives.christuniversity.in/files/original/9d3a8ab54b55589f0d4abe0833bbf0f0.jpg af90d8c6421ec8d2b146d4bd69f99480 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Business intelligence techniques for customer relationship management in the banking sector / Subject The topic of the resource Engineering Description An account of the resource International journal Of Applied Engineering Research, Vol.10, Issue 79, pp.835-838, ISSN No: 0973-4562. Creator An entity primarily responsible for making the resource Chandar, Dr. S Kumar. Source A related resource from which the described resource is derived <a href="https://www.ripublication.com/ijaerspl3/ijaerv10n79spl_169.pdf" target="_blank" title="Business intelligence techniques for customer relationship management in the banking sector" rel="noreferrer noopener">https://www.ripublication.com/ijaerspl3/ijaerv10n79spl_169.pdf</a> Publisher An entity responsible for making the resource available Research India publication Date A point or period of time associated with an event in the lifecycle of the resource 2015-02-10 Language A language of the resource English Type The nature or genre of the resource Journal Article https://archives.christuniversity.in/files/original/3c60877723b9ae965ddb8a9f35f7699e.jpg af90d8c6421ec8d2b146d4bd69f99480 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Radon transform based image steganography in frequency domain / Subject The topic of the resource Engineering Description An account of the resource International journal Of Applied Engineering Research, Vol.10, Issue 70, pp.830-834, ISSN No: 0973-4562. Creator An entity primarily responsible for making the resource Chandar, Dr. S Kumar. Source A related resource from which the described resource is derived <a href="https://www.ripublication.com/ijaerspl3/ijaerv10n79spl_168.pdf" target="_blank" title="Radon transform based image steganography in frequency domain" rel="noreferrer noopener">https://www.ripublication.com/ijaerspl3/ijaerv10n79spl_168.pdf</a> Publisher An entity responsible for making the resource available Research India publication Date A point or period of time associated with an event in the lifecycle of the resource 2015-02-10 Language A language of the resource English Type The nature or genre of the resource Journal Article https://archives.christuniversity.in/files/original/a49ba2830ab7ee1ab996fdeb985dd84a.jpg 8180a41ff194c26c6d215b57d6168bfb Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Predicting the stock price index of yahoo data using elman network / Subject The topic of the resource Yahoo stock data Description An account of the resource International Journal of Control Theory and Applications, Vol.10, Issue 10, pp.481-497, ISSN: 0974-5572. Creator An entity primarily responsible for making the resource Chandar, S Kumar. Source A related resource from which the described resource is derived <a href="https://serialsjournals.com/abstract/36038_50.pdf" target="_blank" title="Predicting the stock price index of yahoo data using elman network" rel="noreferrer noopener">https://serialsjournals.com/abstract/36038_50.pdf</a> Publisher An entity responsible for making the resource available International Science Press Date A point or period of time associated with an event in the lifecycle of the resource 2017-02-16 Language A language of the resource English Type The nature or genre of the resource Journal Article https://archives.christuniversity.in/files/original/1283fd1b0bbec398688056b7694c1b5a.jpg c12d8cd52781b538234a968684454ef5 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Stock market prediction using subtractive clustering for a neuro fuzzy hybrid approach / Subject The topic of the resource Management Description An account of the resource Cluster Computing (The Journal Of Networks, Software Tools And Applications), Vol.22, pp.13159–13166. Creator An entity primarily responsible for making the resource Chandar, S Kumar. Source A related resource from which the described resource is derived <a href="https://link.springer.com/article/10.1007/s10586-017-1321-6" target="_blank" title="Stock market prediction using subtractive clustering for a neuro fuzzy hybrid approach" rel="noreferrer noopener">https://link.springer.com/article/10.1007/s10586-017-1321-6</a> Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2017-03-07 Language A language of the resource English Type The nature or genre of the resource Journal Article https://archives.christuniversity.in/files/original/3d6c6131b4c4499a7c7b0819321704ac.png dff3d3bdddff12ea6b1cef24016a12b1 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Forecasting gold prices based on extreme learning machine / Subject The topic of the resource Extreme Learning Machine Description An account of the resource International Journal Of Computers Communications & Control, Vol.11, Issue 3, pp.372-380, ISSN: 1841-9836. Creator An entity primarily responsible for making the resource Chandar, S Kumar., Sumathi, M., Sivanadam, S N. Source A related resource from which the described resource is derived <a href="https://univagora.ro/jour/index.php/ijccc/article/view/2009" target="_blank" title="Forecasting gold prices based on extreme learning machine" rel="noreferrer noopener">https://univagora.ro/jour/index.php/ijccc/article/view/2009</a> Publisher An entity responsible for making the resource available CCC Publications Date A point or period of time associated with an event in the lifecycle of the resource 2016-02-14 Language A language of the resource English Type The nature or genre of the resource Journal Article https://archives.christuniversity.in/files/original/b817de489ce6c881c6355815d1af57c9.jpg aef8f902d41826f50c5dcaac97c0760b Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Prediction of stock market price using hybrid of wavelet transform and artificial neural network / Subject The topic of the resource Stock Market Prediction Description An account of the resource Indian Journal of Science and Technology, Vol.9, Issue 8, pp.1-5, ISSN: 0974-5645 (Online) 0974-6846 (Print). Creator An entity primarily responsible for making the resource Chandar, S Kumar., Sumathi, M., Sivanandam S N. Source A related resource from which the described resource is derived <a href="https://indjst.org/articles/prediction-of-stock-market-price-using-hybrid-of-wavelet-transform-and-artificial-neural-network" target="_blank" title="Prediction of stock market price using hybrid of wavelet transform and artificial neural network" rel="noreferrer noopener">https://indjst.org/articles/prediction-of-stock-market-price-using-hybrid-of-wavelet-transform-and-artificial-neural-network</a> Publisher An entity responsible for making the resource available INDJST Date A point or period of time associated with an event in the lifecycle of the resource 2016-02-09 Language A language of the resource English Type The nature or genre of the resource Journal Article Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Chandar, S. Kumar; Vijayadurai, J.; Palanivel Rajan, M. Title A name given to the resource Consumer Decision Recognition Based on EEG Signals for Neuromarketing Applications Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Journal of Information Technology and Decision Making;Volume;24;Issue;6;pp.1825-1847 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1142/S0219622025500245" target="_blank" rel="noreferrer noopener">https://doi.org/10.1142/S0219622025500245</a> <a href="https://www.scopus.com/pages/publications/105001338618?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105001338618?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chandar S.K., School of Business & Management, CHRIST University, Madurai Kamaraj University, Karnataka, India; Vijayadurai J., Department of Management Studies, Madurai Kamaraj University, Tamil Nadu, Madurai, India; Palanivel Rajan M., Department of Management Studies, Madurai Kamaraj University, Tamil Nadu, Madurai, India Description An account of the resource Neuromarketing is a blooming interdisciplinary field that tries to understand the biology of consumer behavior by combining neuroscience with marketing. This technique can be used to grasp consumers hidden choices, intentions and decisions by analyzing their physiological and brain signals. Electroencephalography (EEG) is one of the popular neuroimaging techniques to capture and record the neural activity of the brain. Numerous research projections have been made in this field to achieve better results. Earlier approaches did not prioritize effective EEG signal preprocessing and classification methods. This paper presents a model to recognize consumer preferences by analyzing and classifying EEG signals. In this model, EEG signals are decomposed into many subbands using wavelet transform. An enhanced wavelet thresholding method is proposed to eliminate noise from subbands. Several wavelet features are computed from each subband and then fed as input to classifiers. Finally, three different machine learning classifiers are used to classify the signal between like and dislike. The classifiers are K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP) and Support Vector Machine (SVM). EEG signals from 25 people are collected to verify the developed systems performance. The effectiveness of the developed method with different classifiers is validated by varying brain lobe features and band features. In comparison to other classifiers like KNN and MLP, the designed system with the SVM classifier performs better and achieves an accuracy of 98.21%. The experimental findings for the developed system suggest that research in this area has the potential to alter and enhance marketing tactics for the benefit of both manufacturers and consumers, ultimately leading to a mutually beneficial outcome. World Scientific Publishing Company. Subject The topic of the resource Classification; consumer choice; EEG; machine learning; neuromarketing Publisher An entity responsible for making the resource available World Scientific Relation A related resource ISSN: 2196220; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online