Call for Papers - New Advances in Explainable Artificial Intelligence for Image Understanding and Signal Recovery from Uncertain Data



Constantly there is a growth in data concerning its variety, uncertainty, velocity, and volume. In this big data era, data veracity or uncertainty is considered a defining characteristic of any dataset. The abundance of these uncertain data in both unstructured and structured data sets potentially deviates the interpretations of the data. As an emerging problem in database systems, these uncertain data also lead to the imprecise nature of various applications. More specifically, the process of understanding the digital images and recovering their signal from uncertain data sets seems to be a complex task. However, these uncertainties could be effectively removed by leveraging the power of disruptive technologies. Accordingly, Artificial Intelligence (AI), Big Data Analytics, Cloud/Fog/Edge Computing, the Internet of Things (IoT), Machine/Deep Learning, Data Mining, etc are some technologies that could be involved in removing the uncertainties in the database. Explicitly, with the capability to comprehend and build trust with the outputs and the results, Explainable Artificial Intelligence (XAI) could enhance fairness with an uncertain database. With the greatest progress over the past decades, transparency and accuracy in AI-empowered decision-making could be effectively obtained. Also, this explainability stays as an advantageous factor that regulates the understanding of AI-enabled systems. All these possibilities led this intelligent system to effectively track the uncertain datasets for signal recovery and image understanding.

 Utilization of this XAI-enabled platform for image understanding could pragmatically support a wide range of applications. For instance, smart healthcare services, intelligent transportation systems, e-business ecosystems, digital learning environments, smart manufacturing systems, etc are a few areas that could be benefitted from this technology. Despite having so many beneficial factors there are still certain difficulties that are persisting in this technological framework. In the first place, there is an increasing concern about security and privacy issues, since these technologies are more vulnerable to unauthenticated access to data. Secondly, the pooling of a large amount of data requires huge data storage facilities that are lacking in the system. Thirdly, despite having enhanced transparency, this system performs in a biased circumstance, as this intelligence does not possess any creative sense like humans. Moreover, the inherent difficulties in understanding the explanation given by this system and other such challenges are seen as a potential drawback that hinders the performance of the system. These issues have created a need for the development of innovative approaches that are in trend. Also, this special issue allows the upcoming researchers to present their latest achievements for the betterment of this domain.

Topics of interest:

  • Trends in XAI for multimedia image processing in IoT applications
  • Big data and AI-based image analysis for smart healthcare services
  • Emerging XAI methodologies for context prediction in IoMT
  • Novel XAI with deep learning approaches for image understanding from uncertain data
  • Understanding digital images by using XAI with machine learning algorithms
  • Interpreting complex images from uncertain data using data mining and XAI technologies
  • Deep learning-based XAI for intelligent medical image analysis
  • Introducing XAI with cloud computing for optimizing uncertain data for image analysis
  • Perils and pitfalls of XAI with machine learning for image understanding from uncertain datasets
  • Real-time image processing for smart transportation using XAI and fog computing technologies


Suggested Timeline for SI:
Manuscript submissions due: March 27, 2023
First round of reviews completed: June 25,2023
Revised manuscripts due: (date)
Second round of reviews completed: August 28, 2023
Final manuscripts due: November 26, 2023

Guest Editors:
Mahaveerakannan R., Saveetha University, Chennai, Tamil Nadu, India
Seifedine Kadry, Department of Applied Data Science, Noroff University College, Norway
Soheil Salahshour, Faculty of Engineering and Natural Sciences, Bahcesehir University, Istanbul, Turkey