New Machine Learning Research Has Been Reported by Researchers at Pak-Austria Fa chhochschule: Institute of Applied Sciences and Technology (XEmoAccent: Embracin g Diversity in Cross-Accent Emotion Recognition Using Deep Learning)

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New Machine Learning Research Has Been Reported by Researchers at Pak-Austria Fa chhochschule: Institute of Applied Sciences and Technology (XEmoAccent: Embracin g Diversity in Cross-Accent Emotion Recognition Using Deep Learning)

机译：巴基斯坦-奥地利应用科学与技术研究所的研究人员报告了新的机器学习研究（XEmoAccent：使用深度学习在跨口音情感识别中拥抱多样性）

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By a News Reporter-Staff News Editor at Robotics Machine Learning DailyNews Daily News – Investigators publish new report on ar tificial intelligence. According to news reportingoriginating from Haripur, Pak istan, by NewsRx correspondents, research stated, “Speech is a powerfulmeans to expressing thoughts, emotions, and perspectives. However, accurately determinin g the emotionsconveyed through speech remains a challenging task.”

机译：由一位新闻记者-机器人与机器学习的新闻编辑每日新闻 - 调查人员发布关于智能的新报告。根据 NewsRx 记者来自巴基斯坦哈里普尔的新闻报道，研究表明，“言语是表达思想、情感和观点的有力手段。然而，准确确定通过语音传达的情感仍然是一项具有挑战性的任务。

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《Robotics & Machine Learning Daily News》 |2024年第9期|89-89|共1页
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中图分类自动化技术及设备;
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Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology; Haripur; Pakistan; Cyborgs; Emerging Technol ogies; Machine Learning;

机译：巴基斯坦-奥地利法赫霍赫科勒：应用科学与技术学院;哈里普尔;巴基斯坦;半机械人;新兴科技;机器学习;

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4. Investigating Variational Phase-Amplitude Coupling in EEG-based Emotion Recognition**This study is supported by the National Natural Science Foundation of China (Grant No. 62001026), and the Beijing Institute of Technology Research Fund Program for Young Scholars (Grant No. 3050012222022). [C] . Chuting Zhang, Wenbin Shi, Chien-Hung Yeh International IEEE/EMBS Conference on Neural Engineering . 2021

机译：调查基于EEG的情感识别中的变分相位振幅耦合**本研究得到了中国国家自然科学基金（Grant No.62001026）的支持，以及北京技术研究基金计划的青年学者（Grant No.305001222022 ）。
5. Medical Signal Recognition System Based on Machine Learning, Deep Learning, and Internet of Things [D] . Taqi, Arwa Mohammed . 2021

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6. Emotion and Stress Recognition Related Sensors and Machine Learning Technologies [O] . Kyandoghere Kyamakya, Fadi Al-Machot, Ahmad Haj Mosa, 2021

机译：情感和应力识别相关传感器和机器学习技术
7. EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed TarekShahriar Abstract—An EMD-chaos based approach is proposed todiscriminate EEG signals corresponding to healthy persons,and epileptic patients during seizure-free intervals and seizureattacks. An electroencephalogram (EEG) is first empiricallydecomposed to intrinsic mode functions (IMFs). The nonlineardynamics of these IMFs are quantified in terms of the largestLyapunov exponent (LLE) and correlation dimension (CD).This chaotic analysis in EMD domain is applied to a large groupof EEG signals corresponding to healthy persons as well asepileptic patients (both with and without seizure attacks). It isshown that the values of the obtained LLE and CD exhibitfeatures by which EEG for seizure attacks can be clearlydistinguished from other EEG signals in the EMD domain.Thus, the proposed approach may aid researchers in developingeffective techniques to predict seizure activities. Index Terms—Electroencephalogram (EEG), empiricalmode decomposition (EMD), largest Lyapunov exponent (LLE),correlation dimension (CD), epileptic seizures. The Authors are with the Electrical and Electronic EngineeringDepartment, Bangladesh University of Engineering and Technology,Dhaka-1000, Bangladesh (e-mail: imamul@eee.buet.ac.bd) PDF Cite: S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed Tarek Shahriar, "EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain," International Journal of Computer and Electrical Engineering vol. 4, no. 3, pp. 326-330, 2012. PREVIOUS PAPER Perception of Emotions Using Constructive Learningthrough Speech NEXT PAPER Physical Layer Impairments Aware OVPN Connection Selection Mechanisms Copyright © 2008-2013. International Association of Computer Science and Information Technology Press (IACSIT Press) [O] . S. M. Shafiul Alam, Syed TarekShahriar 2012

机译：EEG信号在EMD域S. S. Shafiul Alam，S中的非线性动力学使用非线性动力学。 M. Shafiul Alam，Aurangozeb和Syed Tarekshahriar摘要 - 基于EMD Chaos的方法，提出了对应于健康人的EEG信号，癫痫发作期间的癫痫患者和Seizureattacks。脑电图（EEG）首先被凭经上分解为内在模式功能（IMF）。这些IMF的非线性动力学在最大范围的指数（LLE）和相关尺寸（CD）方面是量化的。本域中的混沌分析应用于与健康人相对应的大型脑电图（Asepileptic患者）（两者都有癫痫发作）。因此，所获得的LLE和CD表展的价值可以从EMD领域的其他EEG信号中清晰地区分脑电图的表达展示。本拟议的方法可以帮助研究人员以预测癫痫发作的癫痫发作技术。索引术语 - 脑电图（EEG），仿真态分解（EMD），最大的Lyapunov指数（LLE），相关维度（CD），癫痫发作。作者与电气电子和电子工程公司，孟加拉国工程和技术大学，孟加拉国达卡 - 1000（电子邮件：imamul@eee.buet.ac.bd）pdf cite：s. m. shafiul Alam，s。 M. Shafiul Alam，Aurangozeb和Syed Tarek Shahriar，“EEG信号歧视在EMD领域的非线性动态，”计算机电气工程卷国际杂志。 4，不。 3，pp。326-330,2012，上一篇论文对情绪的看法，使用建设性的学习言论下一篇论文物理层障碍意识到OVPN连接选择机制版权所有©2008-2013。国际计算机科学与信息技术协会出版社（IACSIT Press）
8. Machine Learning Technologies and Their Applications for Science and Engineering Domains Workshop -- Summary Report. [R] . Ambur, M., Schwartz, K. G., Mavris, D. N. 2016

机译：机器学习技术及其在科学和工程领域的应用研讨会 - 总结报告。

New Machine Learning Research Has Been Reported by Researchers at Pak-Austria Fa chhochschule: Institute of Applied Sciences and Technology (XEmoAccent: Embracin g Diversity in Cross-Accent Emotion Recognition Using Deep Learning)

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