مقاله انگلیسی رایگان در مورد کمک یادگیری عمیق به اینترنت اشیا برای ارزیابی خسارت بلایا – تیلور و فرانسیس ۲۰۲۲

taylorandfrancis

 

مشخصات مقاله
ترجمه عنوان مقاله همکاری یادگیری عمیق با موتور جستجوی اینترنت اشیا برای ارزیابی خسارت بلایا
عنوان انگلیسی مقاله Deep Learning Assist IoT Search Engine for Disaster Damage Assessment
انتشار  مقاله سال ۲۰۲۲
تعداد صفحات مقاله انگلیسی  ۲۵ صفحه
هزینه  دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده  نشریه تیلور و فرانسیس – Taylor & Francis
نوع نگارش مقاله مقاله پژوهشی (Research article)
مقاله بیس این مقاله بیس میباشد
نمایه (index) Scopus
نوع مقاله
ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۱٫۸۲۶ در سال ۲۰۲۰
شاخص H_index ۱۱ در سال ۲۰۲۲
شاخص SJR ۰٫۳۸۲ در سال ۲۰۲۰
شناسه ISSN ۲۳۳۳-۵۷۸۵
شاخص Quartile (چارک) Q2 در سال ۲۰۲۰
فرضیه ندارد
مدل مفهومی دارد
پرسشنامه ندارد
متغیر ندارد
رفرنس دارد
رشته های مرتبط مهندسی کامپیوتر – مهندسی فناوری اطلاعات
گرایش های مرتبط هوش مصنوعی – مهندسی نرم افزار – اینترنت و شبکه های گسترده
نوع ارائه مقاله
ژورنال
مجله / کنفرانس سیستم های فیزیکی-سایبری – Cyber-Physical Systems
دانشگاه Department of Computer and Information Sciences, Towson University, USA
کلمات کلیدی اینترنت اشیا (IoT) – موتور جستجوی اینترنت اشیا (IoTSE) – یادگیری ماشینی (ML) – یادگیری عمیق (DL)
کلمات کلیدی انگلیسی  Internet of things (IoT) – IoT search engine (IoTSE) – machine learning (ML) – deep learning (DL)
شناسه دیجیتال – doi https://doi.org/10.1080/23335777.2022.2051210
کد محصول e16614
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
دانلود رایگان مقاله دانلود رایگان مقاله انگلیسی
سفارش ترجمه این مقاله سفارش ترجمه این مقاله

 

فهرست مطالب مقاله:

Abstract

۱٫ Introduction

۲٫ Related works

۳٫ Preliminaries

۴٫ Our approach

۵٫ Performance evaluation

۶٫ Discussion

۷٫ Final remarks

Acknowledgments

References

 

بخشی از متن مقاله:

Abstract

     In this paper, we address the issue of disaster damage assessments using deep learning (DL) techniques. Specifically, we propose integrating DL techniques into the Internet of Things Search Engine (IoTSE) system to carry out disaster damage assessment. Our approach is to design two scenarios, Single and Complex Event Settings, to complete performance validation using four Convolutional Neural Network (CNN) models. These two scenarios are designed with three possible network services. Our experimental results confirm that all four CNN models can learn each label during the single event setting well. Whereas, with complex event settings, the CNN models have learning difficulty because multiple events have closely related labels.

Introduction

     As technology advances, so does our ability to provide data and deep learning (DL)-driven analytics and predictions. ML techniques, especially DL, have received growing attention and applied to numerous areas, including image and video classification, natural language processing, robotics, networking, mobile computing and cybersecurity, among others [1–۹]. DL is prominent in our daily lives; at home with our digital assistants (Apple’s Siri, Amazon’s Alexa, Google Home), within our businesses that forecast finance models, among social interactions affording collaboration, and around emerging in autonomous vehicles and smart homes with a variety of smart devices (NEST thermostats, Ring cameras) [3,10]. DL techniques work to secure the network with anomaly and intrusion detection systems using supervised and unsupervised techniques, provide the capability of identifying security breaches that can occur on a specific computer or on the network [11–۱۵], and balance privacy, utility, safety and reliability [16,17].

Discussion

     Potential future research directions for DL-based IoTSE for damage assessment concern performance and security.

     Performance issue: After running through the performance evaluation, we have determined that all of these models are less likely to recognise the same label in a different but same-topic event. There are two potential ways to improve this. One is utilising DL models that will be able to understand the same or similar context from different datasets. The other is designing a new data pre-processing scheme, which focuses on the similar topic dataset, making the same label data easier for the model to know whether they are the same thing. With respect to the network performance issue, since the whole IoTSE system may be operated over a constrained network environment, the more queries sent to IoTSE, more overhead occurs. To deal with a large number of queries (the cost for responding some queries could be high), we can not only develop the multi-class scheduling algorithms to handle queries with different priorities but also enhance the design of IoTSE architecture to reduce the time taken for processing queries [40 41]. In addition to use the traditional scheduling and optimisation techniques to efficiently manage resources in IoTSE, the new data-oriented network architectures such as Named Data Network (NDN) [64] could be considered.

ارسال دیدگاه

نشانی ایمیل شما منتشر نخواهد شد.