1st Workshop on Recent Advances in Deep Learning Methods and Evolutionary Computing for Health Care (RADLMECHC 2022)

Scope:

The 1st International Workshop on Recent advances in Deep Learning Methods and Evolutionary Computing for Health Care at World CIST 2022, aims to be a forum where researchers, practitioners and industry representatives have the opportunity to present and discuss ongoing work and latest research results of meaningful contributes and systems in health care using evolutionary computing approaches and deep learning techniques. With advancement in biomedical imaging, the amount of data generated is increasing in biomedical engineering. For example, data can be generated by multimodality image techniques, e.g., ranging from Computed Tomography (CT), Magnetic Resonance Imaging (MR), Ultrasound, Single Photon Emission Computed Tomography (SPECT), and Positron Emission Tomography (PET), to Magnetic Particle Imaging, EE/MEG, Optical Microscopy and Tomography, Photo acoustic Tomography, Electron Tomography, and Atomic Force Microscopy, etc. This poses a great challenge on how to develop new advanced imaging methods and computational models for efficient data processing, analysis and modeling in clinical applications and in understanding the underlying biological process. Evolutionary Computing and Deep learning are a rapidly advancing field in recent years, in terms of both methodological development and practical applications. It allows computational models of multiple processing layers to learn and represent data with multiple levels of abstraction. It is able to implicitly capture intricate structures of large-scale data and ideally suited to some of the hardware architectures that are currently available.
Evolutionary Computing covers several nature-inspired computational methodologies, mainly artificial neural networks (ANNs), fuzzy sets, genetic algorithms (GAs), Swarm Intelligence, and their hybridizations for addressing real-world problems to which conventional modeling can be useless due to several reasons such as complexity, existent of uncertainties, and the stochastic nature of the processes. Given the success of evolutionary computing methods and techniques in health care applications, it is expected that they can also be applied successfully to solve the medical issues. The focus of this workshop is to carry out the research article which could be more focused on to the in-health care applications, it is expected that they can also be applied successfully to solve the medical issues based on Evolutionary Computing and Deep Learning. In recent years, Evolutionary Computing and Deep Learning methods and its variants has been widely used by researchers. This Issue intends to bring new Evolutionary Computing and Deep Learning algorithm with some Innovative Ideas and find out the core problems in health care applications and can play a vital role in handling the different aspects of health care.


Topics of Interest:

Topics of interest include, but are not limited to, any of the followings:
•    Medical imaging
•    Medical text analysis
•    Clinical diagnosis and therapy
•    Clinical expert systems
•    Modeling and simulation of medical processes
•    Health Care Informatics
•    Biomedical imaging and image processing
•    Evolutionary Computing for recommendation in healthcare
•    Application of deep learning in biomedical engineering
•    Transfer learning and multi-task learning
•    Joint semantic segmentation, object detection on biomedical images
•    Improvising on the computation of a deep network; exploiting parallel computation techniques and GPU programming
•    New model of new structure of convolutional neural network
•    Visualization and explainable deep neural network


Organizing Committee:

•    Waqas Haider Bangyal, Senior Member IEE SMC, University of Gujrat, Gujrat, Pakistan, This email address is being protected from spambots. You need JavaScript enabled to view it.
•    Jamil Ahmad, Senior Member IEEE, Hazara University, KPK, Pakistan, This email address is being protected from spambots. You need JavaScript enabled to view it.
•    Kashif Nisar, Senior Member IEEE, University Malaysia Sabah, Malaysia, This email address is being protected from spambots. You need JavaScript enabled to view it.


Program Committee:

•    Hongcheng Liu, University of Florida, USA
•    Jingshan Li, University of Wisconsin, USA
•    Xiaohong Zhaang, Taiyuan University of Science and Technology, China
•    Fuyu Wang, Anhui University of Technology, China
•    Dzulkifli Mohamad, University Technology Malaysia ,Malaysia.
•    Nauman Malik, NUML, Islamabad
•    Muhammad Zubair, Gomal University, Pakistan
•    Saad Abdullah Bangyal, Abasyn University, Islamabad, Pakistan,
•    Shakir Ullah Shah, FAST, Peshawar, Paksitan

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