{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T06:20:04Z","timestamp":1768458004893,"version":"3.49.0"},"reference-count":90,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T00:00:00Z","timestamp":1694736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>In the realm of foreign exchange (Forex) market predictions, Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been commonly employed. However, these models often exhibit instability due to vulnerability to data perturbations attributed to their monolithic architecture. Hence, this study proposes a novel neuroscience-informed modular network that harnesses closing prices and sentiments from Yahoo Finance and Twitter APIs. Compared to monolithic methods, the objective is to advance the effectiveness of predicting price fluctuations in Euro to British Pound Sterling (EUR\/GBP). The proposed model offers a unique methodology based on a reinvigorated modular CNN, replacing pooling layers with orthogonal kernel initialisation RNNs coupled with Monte Carlo Dropout (MCoRNNMCD). It integrates two pivotal modules: a convolutional simple RNN and a convolutional Gated Recurrent Unit (GRU). These modules incorporate orthogonal kernel initialisation and Monte Carlo Dropout techniques to mitigate overfitting, assessing each module\u2019s uncertainty. The synthesis of these parallel feature extraction modules culminates in a three-layer Artificial Neural Network (ANN) decision-making module. Established on objective metrics like the Mean Square Error (MSE), rigorous evaluation underscores the proposed MCoRNNMCD\u2013ANN\u2019s exceptional performance. MCoRNNMCD\u2013ANN surpasses single CNNs, LSTMs, GRUs, and the state-of-the-art hybrid BiCuDNNLSTM, CLSTM, CNN\u2013LSTM, and LSTM\u2013GRU in predicting hourly EUR\/GBP closing price fluctuations.<\/jats:p>","DOI":"10.3390\/bdcc7030152","type":"journal-article","created":{"date-parts":[[2023,9,17]],"date-time":"2023-09-17T06:36:45Z","timestamp":1694932605000},"page":"152","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Predicting Forex Currency Fluctuations Using a Novel Bio-Inspired Modular Neural Network"],"prefix":"10.3390","volume":"7","author":[{"given":"Christos","family":"Bormpotsis","sequence":"first","affiliation":[{"name":"Department of Computer Science, School of Digital Technologies and Arts, Staffordshire University, College Road, Stoke-on-Trent ST4 2DE, UK"},{"name":"CBS International Business School, Hardefuststrasse 1, 50677 Cologne, Germany"}]},{"given":"Mohamed","family":"Sedky","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Digital Technologies and Arts, Staffordshire University, College Road, Stoke-on-Trent ST4 2DE, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1636-5955","authenticated-orcid":false,"given":"Asma","family":"Patel","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Digital Technologies and Arts, Staffordshire University, College Road, Stoke-on-Trent ST4 2DE, UK"},{"name":"Department of The Operations and Information Management, Aston Business School, Aston University, Birmingham B4 7ET, UK"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.physa.2017.09.068","article-title":"Currency Co-Movement and Network Correlation Structure of Foreign Exchange Market","volume":"492","author":"Mai","year":"2018","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hayward, R. (2018). Foreign Exchange Speculation: An Event Study. Int. J. Financ. Stud., 6.","DOI":"10.3390\/ijfs6010022"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ray, R., Khandelwal, P., and Baranidharan, B. (2018, January 13\u201314). A Survey on Stock Market Prediction Using Artificial Intelligence Techniques. Proceedings of the 2018 International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India.","DOI":"10.1109\/ICSSIT.2018.8748680"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Berradi, Z., Lazaar, M., Mahboub, O., and Omara, H. (2021, January 5\u201312). A Comprehensive Review of Artificial Intelligence Techniques in Financial Market. Proceedings of the 2020 6th IEEE Congress on Information Science and Technology (CiSt), Agadir\u2013Essaouira, Morocco.","DOI":"10.1109\/CiSt49399.2021.9357175"},{"key":"ref_5","first-page":"603","article-title":"Deep learning needs a prefrontal cortex","volume":"107","author":"Russin","year":"2020","journal-title":"Work Bridg. AI Cogn. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.neuropsychologia.2015.10.026","article-title":"Ventromedial Prefrontal Cortex Damage Alters Relative Risk Tolerance for Prospective Gains and Losses","volume":"79","author":"Pujara","year":"2015","journal-title":"Neuropsychologia"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1515\/auseb-2018-0003","article-title":"Theoretical and Conceptual Framework of Access to Financial Services by Farmers in Emerging Economies: Implication for Empirical Analysis","volume":"6","year":"2018","journal-title":"Acta Univ. Sapientiae Econ. Bus."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"113063","DOI":"10.1016\/j.dss.2019.05.003","article-title":"Behavioral Economics for Decision Support Systems Researchers","volume":"122","author":"Arnott","year":"2019","journal-title":"Decis. Support Syst."},{"key":"ref_9","unstructured":"Buskens, V. (2015). International Encyclopedia of the Social & Behavioral Sciences, Elsevier."},{"key":"ref_10","unstructured":"Zey, M.A. (2015). International Encyclopedia of the Social & Behavioral Sciences, Elsevier."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1146\/annurev-psych-010213-115043","article-title":"Emotion and Decision Making","volume":"66","author":"Lerner","year":"2015","journal-title":"Annu. Rev. Psychol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1146\/annurev.psych.121208.131647","article-title":"The Neuroscience of Social Decision-Making","volume":"62","author":"Rilling","year":"2011","journal-title":"Annu. Rev. Psychol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1007\/s00429-010-0251-3","article-title":"The Role of Anterior Insular Cortex in Social Emotions","volume":"214","author":"Lamm","year":"2010","journal-title":"Brain Struct. Funct."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.neuron.2004.08.028","article-title":"Hippocampus","volume":"44","author":"Eichenbaum","year":"2004","journal-title":"Neuron"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1038\/nrn1825","article-title":"Cognitive Neuroscience of Emotional Memory","volume":"7","author":"LaBar","year":"2006","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"146","DOI":"10.3389\/fnhum.2012.00146","article-title":"The Hippocampus Supports Multiple Cognitive Processes through Relational Binding and Comparison","volume":"6","author":"Olsen","year":"2012","journal-title":"Front. Hum. Neurosci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.neuron.2005.09.025","article-title":"Contributions of the Amygdala to Emotion Processing: From Animal Models to Human Behavior","volume":"48","author":"Phelps","year":"2005","journal-title":"Neuron"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1038\/nrn2651","article-title":"Stress, Memory and the Amygdala","volume":"10","author":"Roozendaal","year":"2009","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1038\/s41467-019-08665-5","article-title":"Deep Brain Activities Can Be Detected with Magnetoencephalography","volume":"10","author":"Pizzo","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"340","DOI":"10.3389\/fnhum.2013.00340","article-title":"The Role of Medial Prefrontal Cortex in Early Social Cognition","volume":"7","author":"Grossmann","year":"2013","journal-title":"Front. Hum. Neurosci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.1038\/nn.4086","article-title":"Mechanisms of Stress in the Brain","volume":"18","author":"McEwen","year":"2015","journal-title":"Nat. Neurosci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1038\/npp.2009.104","article-title":"Neurocircuitry of Mood Disorders","volume":"35","author":"Price","year":"2010","journal-title":"Neuropsychopharmacology"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1093\/scan\/nss025","article-title":"Insular and Hippocampal Contributions to Remembering People with an Impression of Bad Personality","volume":"8","author":"Tsukiura","year":"2013","journal-title":"Soc. Cogn. Affect. Neurosci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"116549","DOI":"10.1016\/j.neuroimage.2020.116549","article-title":"Anterior Insula Reflects Surprise in Value-Based Decision-Making and Perception","volume":"210","author":"Pfeuffer","year":"2020","journal-title":"NeuroImage"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6711","DOI":"10.1038\/s41598-018-24617-3","article-title":"Being Right, but Losing Money: The Role of Striatum in Joint Decision Making","volume":"8","author":"Ruissen","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e00938","DOI":"10.1016\/j.heliyon.2018.e00938","article-title":"State-of-the-Art in Artificial Neural Network Applications: A Survey","volume":"4","author":"Abiodun","year":"2018","journal-title":"Heliyon"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"220226","DOI":"10.1098\/rsos.220226","article-title":"An Insula Hierarchical Network Architecture for Active Interoceptive Inference","volume":"9","author":"Fermin","year":"2022","journal-title":"R. Soc. Open Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"115019","DOI":"10.1016\/j.eswa.2021.115019","article-title":"A Hybrid Model Integrating Deep Learning with Investor Sentiment Analysis for Stock Price Prediction","volume":"178","author":"Jing","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"103143","DOI":"10.1016\/j.frl.2022.103143","article-title":"Aggregate Investor Attention and Bitcoin Return: The Long Short-Term Memory Networks Perspective","volume":"49","author":"Wang","year":"2022","journal-title":"Financ. Res. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"106285","DOI":"10.1016\/j.eneco.2022.106285","article-title":"Renewable Energy Stocks Forecast Using Twitter Investor Sentiment and Deep Learning","volume":"114","author":"Herrera","year":"2022","journal-title":"Energy Econ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4324878","DOI":"10.1155\/2019\/4324878","article-title":"Is Deep Learning for Image Recognition Applicable to Stock Market Prediction?","volume":"2019","author":"Sim","year":"2019","journal-title":"Complexity"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1016\/j.procs.2020.07.087","article-title":"Stock Market Prediction on High Frequency Data Using Long-Short Term Memory","volume":"175","author":"Lanbouri","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1007\/s10462-019-09706-7","article-title":"A Review of Modularization Techniques in Artificial Neural Networks","volume":"52","author":"Amer","year":"2019","journal-title":"Artif. Intell. Rev."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2979","DOI":"10.1007\/s00521-017-3246-7","article-title":"Artificial Neural Network Based Screening of Cervical Cancer Using a Hierarchical Modular Neural Network Architecture (HMNNA) and Novel Benchmark Uterine Cervix Cancer Database","volume":"31","author":"Ali","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1016\/j.procs.2018.01.085","article-title":"Time Series Analysis Based on Modular Architectures of Neural Networks","volume":"123","author":"Yarushev","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"114800","DOI":"10.1016\/j.eswa.2021.114800","article-title":"A Comprehensive Survey on Deep Neural Networks for Stock Market: The Need, Challenges, and Future Directions","volume":"177","author":"Thakkar","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"105596","DOI":"10.1016\/j.knosys.2020.105596","article-title":"A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends","volume":"194","author":"Sengupta","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wong, G., Greenhalgh, T., Westhorp, G., Buckingham, J., and Pawson, R. (2013). RAMESES Publication Standards: Meta-Narrative Reviews. BMC Med., 11.","DOI":"10.1186\/1741-7015-11-20"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.jbusres.2019.07.039","article-title":"Literature Review as a Research Methodology: An Overview and Guidelines","volume":"104","author":"Snyder","year":"2019","journal-title":"J. Bus. Res."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Chaddad, A., Li, J., Lu, Q., Li, Y., Okuwobi, I.P., Tanougast, C., Desrosiers, C., and Niazi, T. (2021). Can Autism Be Diagnosed with Artificial Intelligence? A Narrative Review. Diagnostics, 11.","DOI":"10.3390\/diagnostics11112032"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1038\/s41746-022-00589-7","article-title":"Natural Language Processing Applied to Mental Illness Detection: A Narrative Review","volume":"5","author":"Zhang","year":"2022","journal-title":"NPJ Digit. Med."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1111\/ijmr.12071","article-title":"How Organizational Cognitive Neuroscience Can Deepen Understanding of Managerial Decision-making: A Review of the Recent Literature and Future Directions","volume":"18","author":"Butler","year":"2016","journal-title":"Int. J. Manag. Rev."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1016\/S1364-6613(99)01399-6","article-title":"Social Cognition and the Human Brain","volume":"3","author":"Adolphs","year":"1999","journal-title":"Trends Cogn. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"12574","DOI":"10.1523\/JNEUROSCI.2614-09.2009","article-title":"Neural Correlates of Value, Risk, and Risk Aversion Contributing to Decision Making under Risk","volume":"29","author":"Christopoulos","year":"2009","journal-title":"J. Neurosci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"6613","DOI":"10.1523\/JNEUROSCI.0003-10.2010","article-title":"Neural Processing of Risk","volume":"30","author":"Mohr","year":"2010","journal-title":"J. Neurosci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.bbr.2010.12.009","article-title":"The Role of the Ventromedial Prefrontal Cortex in Memory Consolidation","volume":"218","author":"Nieuwenhuis","year":"2011","journal-title":"Behav. Brain Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.neuron.2018.06.038","article-title":"Economic Choice as an Untangling of Options into Actions","volume":"99","author":"Yoo","year":"2018","journal-title":"Neuron"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1146\/annurev-neuro-061010-113648","article-title":"Neurobiology of Economic Choice: A Good-Based Model","volume":"34","year":"2011","journal-title":"Annu. Rev. Neurosci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.conb.2010.03.001","article-title":"Neural Computations Associated with Goal-Directed Choice","volume":"20","author":"Rangel","year":"2010","journal-title":"Curr. Opin. Neurobiol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3664","DOI":"10.1038\/s41467-019-11537-7","article-title":"Challenging the Point Neuron Dogma: FS Basket Cells as 2-Stage Nonlinear Integrators","volume":"10","author":"Tzilivaki","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Yao, H., Zhang, X., Zhou, X., and Liu, S. (2019). Parallel Structure Deep Neural Network Using CNN and RNN with an Attention Mechanism for Breast Cancer Histology Image Classification. Cancers, 11.","DOI":"10.3390\/cancers11121901"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"119005","DOI":"10.1016\/j.neuroimage.2022.119005","article-title":"Deep Neural Networks Reveal Topic-Level Representations of Sentences in Medial Prefrontal Cortex, Lateral Anterior Temporal Lobe, Precuneus, and Angular Gyrus","volume":"251","author":"Acunzo","year":"2022","journal-title":"NeuroImage"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"115111","DOI":"10.1016\/j.eswa.2021.115111","article-title":"Discrepancy Detection between Actual User Reviews and Numeric Ratings of Google App Store Using Deep Learning","volume":"181","author":"Sadiq","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1258","DOI":"10.1016\/j.neuron.2022.01.005","article-title":"Orthogonal Representations for Robust Context-Dependent Task Performance in Brains and Neural Networks","volume":"110","author":"Flesch","year":"2022","journal-title":"Neuron"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1016\/j.eswa.2018.07.019","article-title":"ModAugNet: A New Forecasting Framework for Stock Market Index Value with an Overfitting Prevention LSTM Module and a Prediction LSTM Module","volume":"113","author":"Baek","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"113704","DOI":"10.1016\/j.eswa.2020.113704","article-title":"Stock Market Forecasting with Super-High Dimensional Time-Series Data Using ConvLSTM, Trend Sampling, and Specialized Data Augmentation","volume":"161","author":"Lee","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1002\/isaf.1404","article-title":"Deep Networks for Predicting Direction of Change in Foreign Exchange Rates","volume":"24","author":"Galeshchuk","year":"2017","journal-title":"Intell. Syst. Account. Financ. Manag."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Shiao, Y.C., Chakraborty, G., Chen, S.F., Hua Li, L., and Chen, R.C. (2019, January 23\u201325). Modeling and Prediction of Time-Series-A Case Study with Forex Data. Proceedings of the 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST), Morioka, Japan.","DOI":"10.1109\/ICAwST.2019.8923188"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"7887","DOI":"10.1007\/s00500-021-05830-1","article-title":"Forecasting Foreign Exchange Markets: Further Evidence Using Machine Learning Models","volume":"25","author":"Maneejuk","year":"2021","journal-title":"Soft Comput."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Hossain, M.A., Karim, R., Thulasiram, R., Bruce, N.D.B., and Wang, Y. (2018, January 18\u201321). Hybrid Deep Learning Model for Stock Price Prediction. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India.","DOI":"10.1109\/SSCI.2018.8628641"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Althelaya, K.A., El-Alfy, E.-S.M., and Mohammed, S. (2018, January 3\u20135). Evaluation of Bidirectional LSTM for Short-and Long-Term Stock Market Prediction. Proceedings of the 2018 9th International Conference on Information and Communication Systems (ICICS), Irbid, Jordan.","DOI":"10.1109\/IACS.2018.8355458"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"6622927","DOI":"10.1155\/2020\/6622927","article-title":"A CNN-LSTM-Based Model to Forecast Stock Prices","volume":"2020","author":"Lu","year":"2020","journal-title":"Complexity"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"113250","DOI":"10.1016\/j.eswa.2020.113250","article-title":"Convolution on Neural Networks for High-Frequency Trend Prediction of Cryptocurrency Exchange Rates Using Technical Indicators","volume":"149","author":"Cervantes","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"117123","DOI":"10.1016\/j.eswa.2022.117123","article-title":"BiCuDNNLSTM-1dCNN\u2014A Hybrid Deep Learning-Based Predictive Model for Stock Price Prediction","volume":"202","author":"Kanwal","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_65","first-page":"100385","article-title":"Predicting NEPSE Index Price Using Deep Learning Models","volume":"9","author":"Pokhrel","year":"2022","journal-title":"Mach. Learn. Appl."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Islam, M.S., Hossain, E., Rahman, A., Hossain, M.S., and Andersson, K. (2020). A Review on Recent Advancements in FOREX Currency Prediction. Algorithms, 13.","DOI":"10.3390\/a13080186"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"106181","DOI":"10.1016\/j.asoc.2020.106181","article-title":"Financial Time Series Forecasting with Deep Learning: A Systematic Literature Review: 2005\u20132019","volume":"90","author":"Sezer","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Ellefsen, K.O., Mouret, J.-B., and Clune, J. (2015). Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills. PLoS Comput. Biol., 11.","DOI":"10.1371\/journal.pcbi.1004128"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2413","DOI":"10.1109\/TNNLS.2015.2479117","article-title":"Echo State Networks with Orthogonal Pigeon-Inspired Optimization for Image Restoration","volume":"27","author":"Duan","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1007\/978-3-030-31372-2_24","article-title":"Prediction Uncertainty Estimation for Hate Speech Classification","volume":"Volume 11816","author":"Purver","year":"2019","journal-title":"Statistical Language and Speech Processing"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1017\/S1474747219000052","article-title":"Unbanked Status and Use of Alternative Financial Services among Minority Populations","volume":"20","author":"Barcellos","year":"2021","journal-title":"J. Pension Econ. Financ."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Nobata, C., Tetreault, J., Thomas, A., Mehdad, Y., and Chang, Y. (2016, January 11\u201315). Abusive Language Detection in Online User Content. Proceedings of the 25th International Conference on World Wide Web, Montr\u00e9al, QC, Canada.","DOI":"10.1145\/2872427.2883062"},{"key":"ref_73","unstructured":"Yang, J., and Counts, S. (2010, January 23\u201326). Predicting the Speed, Scale, and Range of Information Diffusion in Twitter. Proceedings of the International AAAI Conference on Web and Social Media, Washington, DC, USA."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Chen, C.-C., Kuo, C., Kuo, S.-Y., and Chou, Y.-H. (2015, January 9\u201312). Dynamic Normalization BPN for Stock Price Forecasting. Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, Kowloon Tong, Hong Kong.","DOI":"10.1109\/SMC.2015.497"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Almasri, E., and Arslan, E. (2018, January 25\u201327). Predicting Cryptocurrencies Prices with Neural Networks. Proceedings of the 2018 6th International Conference on Control Engineering & Information Technology (CEIT), Istanbul, Turkey.","DOI":"10.1109\/CEIT.2018.8751939"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1080\/07421222.2001.11045659","article-title":"An Empirical Analysis of Data Requirements for Financial Forecasting with Neural Networks","volume":"17","author":"Walczak","year":"2001","journal-title":"J. Manag. Inf. Syst."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Hutto, C., and Gilbert, E. (2014, January 1\u20134). VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. Proceedings of the International AAAI Conference on Web and Social Media, Ann Arbor, MI, USA.","DOI":"10.1609\/icwsm.v8i1.14550"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of Deep Learning: Concepts, CNN Architectures, Challenges, Applications, Future Directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. Big Data"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Aryal, S., Nadarajah, D., Kasthurirathna, D., Rupasinghe, L., and Jayawardena, C. (2019, January 5\u20136). Comparative Analysis of the Application of Deep Learning Techniques for Forex Rate Prediction. Proceedings of the 2019 International Conference on Advancements in Computing (ICAC), Malabe, Sri Lanka.","DOI":"10.1109\/ICAC49085.2019.9103428"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"2879","DOI":"10.1109\/TNNLS.2019.2934110","article-title":"Nonpooling Convolutional Neural Network Forecasting for Seasonal Time Series With Trends","volume":"31","author":"Liu","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_81","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified Linear Units Improve Restricted Boltzmann Machines. Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Golmohammadi, M., Ziyabari, S., Shah, V., Von Weltin, E., Campbell, C., Obeid, I., and Picone, J. (2017, January 2). Gated Recurrent Networks for Seizure Detection. Proceedings of the 2017 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), Philadelphia, PA, USA.","DOI":"10.1109\/SPMB.2017.8257020"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1016\/j.asoc.2017.12.022","article-title":"Research and Application of Local Perceptron Neural Network in Highway Rectifier for Time Series Forecasting","volume":"64","author":"Dong","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_84","unstructured":"Gal, Y., and Ghahramani, Z. (2015, January 20\u201322). Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. Proceedings of the 33rd International Conference on International Conference on Machine Learning, New York, NY, USA."},{"key":"ref_85","unstructured":"Ramachandran, P., Zoph, B., and Le, Q.V. (2017). Searching for Activation Functions. arXiv."},{"key":"ref_86","unstructured":"Courbariaux, M., Bengio, Y., and David, J.-P. (2015, January 7\u201312). BinaryConnect: Training Deep Neural Networks with Binary Weights during Propagations. Proceedings of the 28th International Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_87","unstructured":"Kingma, D.P., Salimans, T., and Welling, M. (2015). Variational Dropout and the Local Reparameterization Trick. arXiv."},{"key":"ref_88","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_89","unstructured":"Laves, M.-H., Ihler, S., Fast, J.F., Kahrs, L.A., and Ortmaier, T. (2020, January 6\u20138). Well-calibrated regression uncertainty in medical imaging with deep learning. Proceedings of the Third Conference on Medical Imaging with Deep Learning, PMLR, Montreal, QC, Canada."},{"key":"ref_90","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/7\/3\/152\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:51:50Z","timestamp":1760129510000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/7\/3\/152"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,15]]},"references-count":90,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["bdcc7030152"],"URL":"https:\/\/doi.org\/10.3390\/bdcc7030152","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,15]]}}}