<?xml version="1.0" encoding="UTF-8"?>
<doi_batch version="4.3.0" xmlns="http://www.crossref.org/doi_resources_schema/4.3.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/doi_resources_schema/4.3.0 http://www.crossref.org/schema/deposit/doi_resources4.3.0.xsd">
<head>
<doi_batch_id>b70d723f-4b95-424c-bb20-85b293f66a05</doi_batch_id>
<depositor>
<name>beie</name>
<email_address>director@blueeyesintelligence.org</email_address>
</depositor>
</head>
<body>
<doi_citations>
<doi>10.35940/ijrte.B7808.0712223</doi>
<citation_list><citation key="ref0"><unstructured_citation>An Emotion and Attention Recognition System to Classify the Level of Engagement to a Video Conversation by Participants in Real Time Using Machine Learning Models and Utilizing a Neural Accelerator Chipby Janith Kodithuwakku ORCID,Dilki Dandeniya Arachchi and Jay Rajasekera.</unstructured_citation></citation><citation key="ref1"><doi>10.1109/JETCAS.2019.2951232</doi><unstructured_citation>W. -C. Fang, K. -Y. Wang, N. Fahier, Y. -L. Ho and Y. -D. Huang, &quot;Development and Validation of an EEG-Based Real-Time Emotion Recognition System Using Edge AI Computing Platform With Convolutional Neural Network System-on-Chip Design,&quot; in IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 9, no. 4, pp. 645-657, Dec. 2019, doi: 10.1109/JETCAS.2019.2951232. [CrossRef]</unstructured_citation></citation><citation key="ref2"><doi>10.3390/brainsci12080977</doi><unstructured_citation>Dai J, Xi X, Li G, Wang T. EEG-Based Emotion Classification Using Improved Cross-Connected Convolutional Neural Network. Brain Sci. 2022 Jul 24;12(8):977. doi: 10.3390/brainsci12080977. PMID: 35892418; PMCID: PMC9394254. [CrossRef]</unstructured_citation></citation><citation key="ref3"><doi>10.1109/SNPD.2016.7515888</doi><unstructured_citation>T. Kiran and T. Kushal, &quot;Facial expression classification using Support Vector Machine based on bidirectional Local Binary Pattern Histogram feature descriptor,&quot; 2016 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), Shanghai, China, 2016, pp. 115-120, doi: 10.1109/SNPD.2016.7515888. [CrossRef]</unstructured_citation></citation><citation key="ref4"><doi>10.1109/WICOM.2010.5600929</doi><unstructured_citation>M. Ye, T. Liu, Y. Ye, G. Xu and T. Xu, &quot;FPGA Implementation of CORDIC-Based Square Root Operation for Parameter Extraction of Digital Pre-Distortion for Power Amplifiers,&quot; 2010 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM), Chengdu, China, 2010, pp. 1-4, doi: 10.1109/WICOM.2010.5600929. [CrossRef]</unstructured_citation></citation><citation key="ref5"><doi>10.1109/CISP-BMEI.2017.8301923</doi><unstructured_citation>W. Swinkels, L. Claesen, F. Xiao and H. Shen, &quot;Real-time SVM-based emotion recognition algorithm,&quot; 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Shanghai, China, 2017, pp. 1-6, doi: 10.1109/CISP-BMEI.2017.8301923. [CrossRef]</unstructured_citation></citation><citation key="ref6"><doi>10.1109/ICCMC53470.2022.9753699</doi><unstructured_citation>K. N. V. Satyanarayana, T. Shankar, G. Poojita, G. Vinay, H. N. S. V. l. S. Amaranadh and A. G. Babu, &quot;An Approach to EEG based Emotion Identification by SVM classifier,&quot; 2022 6th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2022, pp. 650-654, doi: 10.1109/ICCMC53470.2022.9753699. [CrossRef]</unstructured_citation></citation><citation key="ref7"><doi>10.1109/BIBM.2018.8621562</doi><unstructured_citation>M. Healy, R. Donovan, P. Walsh and H. Zheng, &quot;A Machine Learning Emotion Detection Platform to Support Affective Well Being,&quot; 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain, 2018, pp. 2694-2700, doi: 10.1109/BIBM.2018.8621562. [CrossRef]</unstructured_citation></citation><citation key="ref8"><doi>10.1016/j.mejo.2021.105356</doi><unstructured_citation>Lichen Feng, Liying Yang, Shubin Liu, Chenxi Han, Yueqi Zhang, Zhangming Zhu, An efficient EEGNet processor design for portable EEG-Based BCIs, Microelectronics Journal, Volume 120, 2022, 105356, ISSN 0026-2692. [CrossRef]</unstructured_citation></citation><citation key="ref9"><doi>10.1109/VLSID51830.2021.00035</doi><unstructured_citation>B. S. Ajay and M. Rao, &quot;Binary neural network based real time emotion detection on an edge computing device to detect passenger anomaly,&quot; 2021 34th International Conference on VLSI Design and 2021 20th International Conference on Embedded Systems (VLSID), Guwahati, India, 2021, pp. 175-180, doi: 10.1109/VLSID51830.2021.00035. [CrossRef]</unstructured_citation></citation><citation key="ref10"><doi>10.1109/ICOM.2011.5937159</doi><unstructured_citation>E. M. Bouhabba, A. A. Shafie and R. Akmeliawati, &quot;Support vector machine for face emotion detection on real time basis,&quot; 2011 4th International Conference on Mechatronics (ICOM), Kuala Lumpur, Malaysia, 2011, pp. 1-6, doi: 10.1109/ICOM.2011.5937159. [CrossRef]</unstructured_citation></citation><citation key="ref11"><doi>10.1016/j.ifacol.2022.06.038</doi><unstructured_citation>Rijad Sarić, Nejra Beganović, Dejan Jokić, Edhem Čustović,Towards efficient implementation of MLP-ANN classifier on the FPGA-based embedded system,IFAC-PapersOnLine,Volume 55, Issue 4,2022,Pages 207-212,ISSN 2405-8963. [CrossRef]</unstructured_citation></citation></citation_list>
</doi_citations>
</body>
</doi_batch>
