مقاله انگلیسی رایگان در مورد بهینه سازی اصلاح شده کرم خاکی با تشخیص احساسات به کمک یادگیری عمیق برای رابط کامپیوتر انسانی – IEEE 2023

مقاله انگلیسی رایگان در مورد بهینه سازی اصلاح شده کرم خاکی با تشخیص احساسات به کمک یادگیری عمیق برای رابط کامپیوتر انسانی – IEEE 2023

 

مشخصات مقاله
ترجمه عنوان مقاله بهینه سازی اصلاح شده کرم خاکی با تشخیص احساسات به کمک یادگیری عمیق برای رابط کامپیوتر انسانی
عنوان انگلیسی مقاله Modified Earthworm Optimization With Deep Learning Assisted Emotion Recognition for Human Computer Interface
نشریه آی تریپل ای – IEEE
سال انتشار ۲۰۲۳
تعداد صفحات مقاله انگلیسی  ۸ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
نوع نگارش مقاله
مقاله پژوهشی (Research article)
مقاله بیس این مقاله بیس میباشد
نمایه (index) Scopus – Master Journal List – JCR – DOAJ
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۴٫۸۲۵ در سال ۲۰۲۲
شاخص H_index ۲۰۴ در سال ۲۰۲۳
شاخص SJR ۰٫۹۲۶ در سال ۲۰۲۲
شناسه ISSN ۲۱۶۹-۳۵۳۶
شاخص Quartile (چارک) Q1 در سال ۲۰۲۲
فرضیه ندارد
مدل مفهومی دارد
پرسشنامه ندارد
متغیر دارد
رفرنس دارد
رشته های مرتبط مهندسی کامپیوتر
گرایش های مرتبط هوش مصنوعی – مهندسی الگوریتم و محاسبات
نوع ارائه مقاله
ژورنال
مجله / کنفرانس دسترسی آی تریپل ای – IEEE Access
دانشگاه Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Saudi Arabia
کلمات کلیدی تعامل انسان و کامپیوتر – هوش مصنوعی – یادگیری عمیق – تشخیص احساسات – الگوریتم بهینه سازی کرم خاکی
کلمات کلیدی انگلیسی Human-computer interaction – artificial intelligence – deep learning – emotion recognition – earthworm optimization algorithm
شناسه دیجیتال – doi
https://doi.org/10.1109/ACCESS.2023.3264260
لینک سایت مرجع
https://ieeexplore.ieee.org/document/10091537
کد محصول e17470
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
Abstract
I Introduction
II Related Works
III The Proposed Model
IV Experimental Validation
V Conclusion
References

 

بخشی از متن مقاله:

Abstract

Among the most prominent field in the human-computer interface (HCI) is emotion recognition using facial expressions. Posed variations, facial accessories, and non-uniform illuminations are some of the difficulties in the emotion recognition field. Emotion detection with the help of traditional methods has the shortcoming of mutual optimization of feature extraction and classification. Computer vision (CV) technology improves HCI by visualizing the natural world in a digital platform like the human brain. In CV technique, advances in machine learning and artificial intelligence result in further enhancements and changes, which ensures an improved and more stable visualization. This study develops a new Modified Earthworm Optimization with Deep Learning Assisted Emotion Recognition (MEWODL-ER) for HCI applications. The presented MEWODL-ER technique intends to categorize different kinds of emotions that exist in the HCI applications. To do so, the presented MEWODL-ER technique employs the GoogleNet model to extract feature vectors and the hyperparameter tuning process is performed via the MEWO algorithm. The design of automated hyperparameter adjustment using the MEWO algorithm helps in attaining an improved emotion recognition process. Finally, the quantum autoencoder (QAE) model is implemented for the identification and classification of emotions related to the HCI applications. To exhibit the enhanced recognition results of the MEWODL-ER approach, a wide-ranging simulation analysis is performed. The experimental values indicated that the MEWODL-ER technique accomplishes promising performance over other models with maximum accuracy of 98.91%.

Introduction

Since people have become more informed, they need a high level of computer intelligence [1], [2]. Human-computer interaction (HCI) is not limited to original hardware-related communication. Certain smarter communication techniques are appearing gradually in the life of people like a sequence of more intellectual techniques relevant to voice recognition [3], face recognition, and gesture recognition. Intellectual mechanisms can help establish interactions between computers and humans [4], [5]. The advent of more convenient communication techniques is becoming a major advancement trend in the current domain of HCI. The objective of HCI development was naturally to make computers adapt and serve the requirements of individuals [6].

People-centred instead of compelling persons to adapt to computers. Emotions had a main role during the interaction. Detection of facial emotions will be helpful in several tasks like social robots, criminal justice systems, security monitoring, customer satisfaction identification, smart card applications, e-learning, etc [7], [8]. The core blocks in the conventional emotion recognition mechanism were classifying the emotions, detecting faces, and extracting the features [9].

Conclusion

In this study, a novel MEWODL-ER method was introduced for emotion classification in the HCI applications. The presented MEWODL-ER algorithm is intended for the identification of various types of emotions that exist in the HCI applications. The proposed model follows a three stage process namely GoogleNet feature extraction, MEWO based hyperparameter tuning, and QAE classification. The design of automated hyperparameter adjustment using the MEWO algorithm helps in attaining an improved emotion recognition process. To show the enhanced recognition results of the MEWODL-ER approach, a wide-ranging simulation analysis is done. The experimental values indicated that the MEWODL-ER technique accomplishes promising performance over other models with higher accuracy of 98.91%. In the future, the efficiency of the MEWODL-ER algorithm will be boosted by hybrid DL classifiers.

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