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Electronics
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Recent Trends in Explainable Artificial Intelligence (XAI) for Computer Vision
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Recent Trends in Explainable Artificial Intelligence (XAI) for Computer Vision

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electronic Multimedia".

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 8508

Special Issue Editors

Dr. Mohit Mittal

E-Mail Website
Guest Editor
Knowtion GmbH, Amalienbadstraße 41, Bau, 76227 Karlsruhe, Germany
Interests: computer vision; machine learning; deep learning; wireless sensor networks; IoT
Special Issues, Collections and Topics in MDPI journals
Dr. Rocío Pérez de Prado

E-Mail Website
Guest Editor
Department of Telecommunication Engineering, University of Jaén, 23071 Jaén, Spain
Interests: consumption; data centers; scientific workflows; machine learning; soft computing; artificial intelligence; optical communications; cloud computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Valentina E. Balas

E-Mail Website
Guest Editor
Department of Automatics and Applied Software, Faculty of Engineering, Aurel Vlaicu University of Arad, Ro-310025 Arad, Romania
Interests: intelligent systems; soft computing; fuzzy control; modeling and simulation; nonlinear systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Explainable artificial intelligence (XAI) refers to a set of algorithms, processes and methods that enable human users to understand and appropriately trust the predictive results or outputs evaluated via machine learning algorithms. Explainable AI has been found to solve complex black-box problems to interpret the system processes, and helps to rectify the bugs. The insight into complex problems is given by machine learning and other statistical algorithms to help characterize models in terms of accuracy, fairness and transparency. AI-powered decision making is an incredible tool to solve the multimodal structure models that consist of various data types that directly belong to computer vision. Computer vision is one of most popular emerging trends in which images, video and textual data analysis is involved to efficiently predict and evaluate systems. XAI is used to envision the concept of why a system has generated the wrong results or identify the major reasons for incorrect predictions. Deep neural networks (DNNs) are associated with multimodal data analytics in computer vision, and offer the best solutions in various domains such as facial cues detection, biometrics, healthcare, manufacturing, smart homes, smart agriculture, and cloud computing, to name a few. This Special Issue on explainable AI aims to publish papers addressing various current research areas, such as semantic segmentation, object detection, tracking, reconstruction, synthesis, prediction, perception, and classification.

Dr. Mohit Mittal 
Dr. Rocío Pérez de Prado 
Prof. Dr. Valentina E. Balas
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.

Keywords

  • XAI for computer vision
  • XAI for object detection
  • XAI for prediction
  • facial cues and applications
  • object detection methods and applications
  • sensor-based XAI
  • sensor-based E-learning
  • E-Learning concepts, methods and applications
  • prediction and classification
  • deep-neural-network-based concepts, trends and applications
  • machine learning and statistical analysis on various data

Published Papers (2 papers)

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14 pages, 431 KiB  
Article
Multi-View Projection Learning via Adaptive Graph Embedding for Dimensionality Reduction
by Haohao Li, Mingliang Gao, Huibing Wang and Gwanggil Jeon
Electronics 2023, 12(13), 2934; https://doi.org/10.3390/electronics12132934 - 03 Jul 2023
Viewed by 834
Abstract
In order to explore complex structures and relationships hidden in data, plenty of graph-based dimensionality reduction methods have been widely investigated and extended to the multi-view learning field. For multi-view dimensionality reduction, the key point is extracting the complementary and compatible multi-view information [...] Read more.
In order to explore complex structures and relationships hidden in data, plenty of graph-based dimensionality reduction methods have been widely investigated and extended to the multi-view learning field. For multi-view dimensionality reduction, the key point is extracting the complementary and compatible multi-view information to analyze the complex underlying structure of the samples, which is still a challenging task. We propose a novel multi-view dimensionality reduction algorithm that integrates underlying structure learning and dimensionality reduction for each view into one framework. Because the prespecified graph derived from original noisy high-dimensional data is usually low-quality, the subspace constructed based on such a graph is also low-quality. To obtain the optimal graph for dimensionality reduction, we propose a framework that learns the affinity based on the low-dimensional representation of all views and performs the dimensionality reduction based on it jointly. Although original data is noisy, the local structure information of them is also valuable. Therefore, in the graph learning process, we also introduce the information of predefined graphs based on each view feature into the optimal graph. Moreover, assigning the weight to each view based on its importance is essential in multi-view learning, the proposed GoMPL automatically allocates an appropriate weight to each view in the graph learning process. The obtained optimal graph is then adopted to learn the projection matrix for each individual view by graph embedding. We provide an effective alternate update method for learning the optimal graph and optimal subspace jointly for each view. We conduct many experiments on various benchmark datasets to evaluate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Recent Trends in Explainable Artificial Intelligence (XAI) for Computer Vision)
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Review

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40 pages, 2594 KiB  
Review
A Survey of Explainable Artificial Intelligence for Smart Cities
by Abdul Rehman Javed, Waqas Ahmed, Sharnil Pandya, Praveen Kumar Reddy Maddikunta, Mamoun Alazab and Thippa Reddy Gadekallu
Electronics 2023, 12(4), 1020; https://doi.org/10.3390/electronics12041020 - 18 Feb 2023
Cited by 37 | Viewed by 6988
Abstract
The emergence of Explainable Artificial Intelligence (XAI) has enhanced the lives of humans and envisioned the concept of smart cities using informed actions, enhanced user interpretations and explanations, and firm decision-making processes. The XAI systems can unbox the potential of black-box AI models [...] Read more.
The emergence of Explainable Artificial Intelligence (XAI) has enhanced the lives of humans and envisioned the concept of smart cities using informed actions, enhanced user interpretations and explanations, and firm decision-making processes. The XAI systems can unbox the potential of black-box AI models and describe them explicitly. The study comprehensively surveys the current and future developments in XAI technologies for smart cities. It also highlights the societal, industrial, and technological trends that initiate the drive towards XAI for smart cities. It presents the key to enabling XAI technologies for smart cities in detail. The paper also discusses the concept of XAI for smart cities, various XAI technology use cases, challenges, applications, possible alternative solutions, and current and future research enhancements. Research projects and activities, including standardization efforts toward developing XAI for smart cities, are outlined in detail. The lessons learned from state-of-the-art research are summarized, and various technical challenges are discussed to shed new light on future research possibilities. The presented study on XAI for smart cities is a first-of-its-kind, rigorous, and detailed study to assist future researchers in implementing XAI-driven systems, architectures, and applications for smart cities. Full article
(This article belongs to the Special Issue Recent Trends in Explainable Artificial Intelligence (XAI) for Computer Vision)
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