Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.9
Journals
Electronics
Special Issues
New Standards, Technologies and Communication Systems for Artificial Intelligence of...
share announcement

New Standards, Technologies and Communication Systems for Artificial Intelligence of Things (AIoT) Networks

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 2442

Special Issue Editors

Dr. Muzaffar Rao

E-Mail Website
Guest Editor
Department of Electronic & Computer Engineering, University of Limerick, V94 T9PX Limerick, Ireland
Interests: artificial intelligence (AI); Internet of Things (IoTs); Internet of Everything (IoE); Industrial Internet of Things (IIoTs); wireless sensor networks; cyber-physical systems; cybersecurity
Dr. Kashif Naseer Qureshi

E-Mail Website
Guest Editor
Department of Electronic & Computer Engineering, University of Limerick, V94 T9PX Limerick, Ireland
Interests: wireless sensor networks (WSNs); routing; security; energy efficiency; Internet of Things (IoT); Internet of Everything (IoE)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is devoted to the new standards, technologies, and communication systems for artificial intelligence of things (AIoT) networks. Smart and intelligent communication networks have gained significant attention due to the combination of AI and IoT networks to improve human and machine interfaces and enhance data processing and services. AIoT networks involve the collection of data from billions of devices and sensor nodes from the environment. AI can enhance these networks to make them faster, greener, smarter, and safer. Computer vision, language processing, and speech recognition are some examples of AIoT networks. Due to the large number of devices in today’s world, efficient and intelligent data processing is essential for problem solving and decision making. AI multiplies the value of these networks and promotes intelligence and learning capabilities, especially in homes, offices, and cities. However, several challenges have been observed in deploying AIoT networks, such as scalability, complexity, accuracy, and robustness. In addition, these networks are integrated with cloud, 5G networks, and blockchain methods for service provision. Many different solutions have been proposed to address issues related to machine and deep learning methods, ontology-based approaches, genetic algorithms, fuzzy-based systems, and quantum computing. This Special Issue aims to contribute to the state of the art and present current standards, technologies, and approaches for AIoT networks. This Special Issue focuses on existing issues in AIoT network technologies and applications. The Guest Editors invite papers on topics including, but not limited to:

  • AIoT interoperability and mobility;
  • AIoT for smart healthcare networks;
  • AIoT for smart home appliances;
  • AIoT solutions for smart city networks;
  • AIoT smart industrial networks;
  • Advanced AIoT design and architecture;
  • Machine and deep learning with IoT networks and systems;
  • Quantum computing for AIoT;
  • Security solutions for AIoT networks;
  • Trust models for AIoT networks;
  • Routing solutions for AIoT networks;
  • Blockchain solution for AIoT;
  • Green AIoT networks;
  • Data analytics for AIoT;
  • Cyber-physical systems.

Dr. Muzaffar Rao
Dr. Kashif Naseer Qureshi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

23 pages, 4931 KiB  
Article
Interactive Effect of Learning Rate and Batch Size to Implement Transfer Learning for Brain Tumor Classification
by Irfan Ahmed Usmani, Muhammad Tahir Qadri, Razia Zia, Fatma S. Alrayes, Oumaima Saidani and Kia Dashtipour
Electronics 2023, 12(4), 964; https://doi.org/10.3390/electronics12040964 - 15 Feb 2023
Cited by 8 | Viewed by 1902
Abstract
For classifying brain tumors with small datasets, the knowledge-based transfer learning (KBTL) approach has performed very well in attaining an optimized classification model. However, its successful implementation is typically affected by different hyperparameters, specifically the learning rate (LR), batch size (BS), and their [...] Read more.
For classifying brain tumors with small datasets, the knowledge-based transfer learning (KBTL) approach has performed very well in attaining an optimized classification model. However, its successful implementation is typically affected by different hyperparameters, specifically the learning rate (LR), batch size (BS), and their joint influence. In general, most of the existing research could not achieve the desired performance because the work addressed only one hyperparameter tuning. This study adopted a Cartesian product matrix-based approach, to interpret the effect of both hyperparameters and their interaction on the performance of models. To evaluate their impact, 56 two-tuple hyperparameters from the Cartesian product matrix were used as inputs to perform an extensive exercise, comprising 504 simulations for three cutting-edge architecture-based pre-trained Deep Learning (DL) models, ResNet18, ResNet50, and ResNet101. Additionally, the impact was also assessed by using three well-known optimizers (solvers): SGDM, Adam, and RMSProp. The performance assessment showed that the framework is an efficient framework to attain optimal values of two important hyperparameters (LR and BS) and consequently an optimized model with an accuracy of 99.56%. Further, our results showed that both hyperparameters have a significant impact individually as well as interactively, with a trade-off in between. Further, the evaluation space was extended by using the statistical ANOVA analysis to validate the main findings. F-test returned with p < 0.05, confirming that both hyperparameters not only have a significant impact on the model performance independently, but that there exists an interaction between the hyperparameters for a combination of their levels. Full article
(This article belongs to the Special Issue New Standards, Technologies and Communication Systems for Artificial Intelligence of Things (AIoT) Networks)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop