بهینه‌سازی ابرپارامترهای مدل‌های ترکیبی یادگیری عمیق با رویکرد تشخیص آپنه خواب با استفاده از الگوریتم‌های هوش جمعی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار دانشکده فنی و مهندسی، گروه مهندسی کامپیوتر و فناوری اطلاعات، دانشگاه قم ، قم، ایران

2 دانشجوی کارشناسی ارشد دانشکده فنی و مهندسی، گروه مهندسی کامپیوتر و فناوری اطلاعات، دانشگاه قم ، قم، ایران

10.22034/abmir.2025.22637.1087

چکیده

این مقاله به بررسی کارایی طبقه‌بندهای ترکیبی CNN-DRNN در شناسایی آپنه خواب با استفاده از سیگنال الکتروکاردیوگرام قلب (ECG) پرداخته است. در این مطالعه، مدل‌های مختلف شبکه‌های عصبی کانولوشنی ازجمله AlexNet، VGG16، VGG19 و ZFNet در ترکیب با مدل‌های شبکه عصبی بازگشتی عمیق شامل LSTM، GRU و BiLSTM مورد ارزیابی قرارگرفته است. این مدل‌ها با و بدون استفاده از بهینه‌سازهای هوش جمعی گورکن عسل و گرگ خاکستری برای تعیین مقادیر بهینه ابرپارامترها مقایسه شده‌اند. نتایج نشان می‌دهد که مدل ترکیبی AlexNet-GRU پس از اعمال هر دو بهینه‌ساز، بهترین عملکرد را با دقت ۹۵٪، نرخ تشخیص 61/97٪ و F-Score 37/93٪ ارائه کرده است. در این پژوهش، چالش بهینه‌سازی ابرپارامترها در مدل‌های یادگیری عمیق با استفاده از دو بهینه‌ساز گورکن عسل و گرگ خاکستری بررسی‌شده است. این بهینه‌سازها با الهام از رفتارهای طبیعت، تعامل غیرمستقیم میان عامل‌ها و توزیع هوشمند به حل این چالش کمک می‌کنند. البته، بهینه‌ساز گورکن عسل در مقایسه با گرگ خاکستری در انتخاب مقادیر بهینه ابرپارامترها عملکرد بهتری از خود نشان داده است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Optimization of Hyperparameters in Hybrid Deep Learning Models for Sleep Apnea Detection Using Swarm Intelligence Algorithms

نویسندگان [English]

  • Faranak Fotouhi-Ghazvini 1
  • Maryam Badiee 2
1 Assistant Professor Department of Computer Engineering and IT, University of Qom, Qom, Iran
2 M.Sc Student Department of Computer Engineering and IT, University of Qom, Qom, Iran
چکیده [English]

This study investigates the efficiency of CNN-DRNN hybrid classifiers in detecting sleep apnea using electrocardiogram (ECG) signals. Various CNN models were evaluated, including AlexNet, VGG16, VGG19, and ZFNet, along with DRNN models such as LSTM, GRU, and BiLSTM. These models were compared with and without the application of swarm intelligence optimizers, namely the Honey Badger Algorithm (HBA) and Grey Wolf Optimizer (GWO), for optimizing hyperparameter values. The results demonstrated that the AlexNet-GRU hybrid model achieved the best performance after applying both optimizers, with an accuracy of 95%, a detection rate of 97.61%, and an F-Score of 93.37%.This research also explores the challenges of hyperparameter optimization in deep learning models using swarm intelligence-based optimizers. These optimizers, inspired by natural behaviors, facilitate problem-solving through intelligent distribution, indirect interactions among agents, and simplification of complex processes. Additionally, the findings revealed that HBA outperformed GWO in determining optimal hyperparameter values, leading to enhanced model performance. Overall, the study highlights the potential of integrating deep learning models with swarm intelligence optimizers to improve sleep apnea detection.

کلیدواژه‌ها [English]

  • Sleep Apnea
  • HoneyBadger Optimizer
  • GreyWolf Optimizer
  • Convolutional Neural Network
  • Deep Recurrent Neural Network

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پژوهش های نظری و کاربردی هوش ماشینی
دوره 2، شماره 2
اسفند 1403
صفحه 31-48

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