Emerging Trends of Deep Learning in AI: Challenges and Methodologies

A special issue of AI (ISSN 2673-2688).

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 9118

Special Issue Editors


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Guest Editor
1. Aerospace Engineering Department, University of Michigan, Ann Arbor, MI 48109, USA
2. Deep Learning & Data Science Division, Capacloud AI, Kolkata 711103, India
Interests: computational material science; computational mechanics; condensed matter physics; computer vision; deep learning; generative adversarial networks; object detection; brain–machine interface

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Guest Editor
Deep Learning & Data Science Division, Capacloud AI, Kolkata 711103, India
Interests: deep learning; computer vision; artificial intelligence; brain–machine interface (BMI)

Special Issue Information

Dear Colleagues,

The last decade has seen the increasingly important, even dominant, application of deep learning (DL) in the field of various applications. Conventional machine learning methods have been the focus of intense investigations for years; however, they have limited capabilities, are biased to dataset selection, and are faced with an overwhelming challenge to integrate large, heterogeneous data sources. On the other hand, recent advancements in deep learning architectures, coupled with high-performance computing, have demonstrated significant breakthroughs in dealing with complexities by radically changing research methodologies toward a data-oriented approach.

This Special Issue encourages authors, from academia and industry, to submit new research results about positioning and navigation models based on machine learning for complex systems. The Special Issue topics include but are not limited to the following:

  • Artificial neural networks;
  • Convolutional neural networks;
  • Recurrent neural network;
  • Deep reinforcement learning;
  • Generative adversarial network;
  • Attention-based transformer;
  • Computer vision;
  • Vision transformer;
  • Object detection;
  • Image segmentation;
  • Brain–machine interface.

Dr. Arunabha Mohan Roy
Dr. Jayabrata Bhaduri
Guest Editors

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Keywords

  • deep learning
  • machine learning
  • artificial intelligence
  • big data
  • complex system

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Published Papers (1 paper)

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Research

14 pages, 2651 KiB  
Article
Distinguishing Malicious Drones Using Vision Transformer
by Sonain Jamil, Muhammad Sohail Abbas and Arunabha M. Roy
AI 2022, 3(2), 260-273; https://doi.org/10.3390/ai3020016 - 31 Mar 2022
Cited by 24 | Viewed by 5159
Abstract
Drones are commonly used in numerous applications, such as surveillance, navigation, spraying pesticides in autonomous agricultural systems, various military services, etc., due to their variable sizes and workloads. However, malicious drones that carry harmful objects are often adversely used to intrude restricted areas [...] Read more.
Drones are commonly used in numerous applications, such as surveillance, navigation, spraying pesticides in autonomous agricultural systems, various military services, etc., due to their variable sizes and workloads. However, malicious drones that carry harmful objects are often adversely used to intrude restricted areas and attack critical public places. Thus, the timely detection of malicious drones can prevent potential harm. This article proposes a vision transformer (ViT) based framework to distinguish between drones and malicious drones. In the proposed ViT based model, drone images are split into fixed-size patches; then, linearly embeddings and position embeddings are applied, and the resulting sequence of vectors is finally fed to a standard ViT encoder. During classification, an additional learnable classification token associated to the sequence is used. The proposed framework is compared with several handcrafted and deep convolutional neural networks (D-CNN), which reveal that the proposed model has achieved an accuracy of 98.3%, outperforming various handcrafted and D-CNNs models. Additionally, the superiority of the proposed model is illustrated by comparing it with the existing state-of-the-art drone-detection methods. Full article
(This article belongs to the Special Issue Emerging Trends of Deep Learning in AI: Challenges and Methodologies)
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