Evolutionary algorithms have demonstrated superior global search capabilities, and have been applied to diverse real-life single, multi, and many-objective optimization problems. Examples include the use of evolutionary algorithms for optimal parameter identification and discriminative feature selection pertaining to diverse classification and regression models as well as hybrid evolutionary and clustering algorithms for image segmentation and visual saliency detection.
In parallel, deep learning models have demonstrated great success in dealing with complex computer vision tasks. Examples include the use of deep convolutional neural networks combined with recurrent models for image caption generation and visual question generation. Deep learning combined with transfer learning has also been employed to deal with various computer vision tasks. Nevertheless, the design of new and effective deep learning models and identification of the optimal hyper-parameters of the resulting models require profound domain knowledge, which may not always be available to researchers. In this regard, superior search capabilities of evolutionary algorithms can be exploited to tackle such optimization problems, e.g. to formulate evolving deep neural networks that fit the tasks at hand.
This special session aims to stimulate research pertaining to not only feature selection, optimal topology and hyper-parameter identification for clustering and classification systems but also evolving deep architecture generation through evolutionary algorithm and related paradigms.
It also aims to stimulate new developments to address research gaps such as deep network generation with residual and dense connectivity as well as hybrid cascaded architectures to tackle vanishing gradients and complex computer vision tasks such as object detection, image description and visual question generation.

• Image segmentation
• Data stream clustering
• Feature selection
• Object detection and recognition
• Image description generation
• Visual question generation
• Visual saliency detection
• Image classification
• Image retrieval
• Human or object attribute prediction
• Facial expression recognition and age estimation
• Human action recognition
• Bioinformatics (e.g. skin cancer, heart disease, and brain tumour classification etc.)
• Machine translation, language generation and speech recognition
• Evolving deep neural network generation for diverse computer vision, image processing and signal processing problems
• Hybrid clustering techniques (e.g. clustering models combined with evolutionary algorithms)
• Optimal topology and hyper-parameter identification for classification/regression and ensemble learning models

Authors must submit an electronic copy (in word or pdf) of their complete manuscript directly to the Session Organizer (li.zhang@rhul.ac.uk) before May 1, 2025

Professor Li Zhang
Royal Holloway, University of London, UK
li.zhang@rhul.ac.uk

In recent years, the development of soft computing technology has led to the advancement of healthcare and medical systems. In particular, technologies such as artificial intelligence (AI), fuzzy logic, neural networks, and genetic algorithms are being used in fields such as diagnostic support, personalized medicine, and remote monitoring. Due to the impact of the global pandemic and the progression of an aging society, there is an urgent need to build an efficient and flexible medical system.
The COVID-19 epidemic has accelerated the need for contactless medical treatment and remote medical care. Soft computing analyzes real-time data from patients and contributes to the early detection and prevention of diseases. For example, image diagnosis using deep learning supports radiologists in their diagnoses and contributes to improving accuracy. In addition, health management systems that combine wearable devices and machine learning enable risk assessment of chronic diseases such as heart disease and diabetes.
On the other hand, privacy and ethical issues have also emerged as challenges. Problems of diagnostic errors and bias caused by AI, and ensuring the security of patient data are required. In addition, differences in medical systems and regulations in each country complicate the introduction of the technology.
In the future, the medical application of soft computing will become increasingly important as data-driven medicine develops. Based on the above background, this session will discuss technologies related to realizing a sustainable and equitable healthcare system.

• Collection and analysis of human data using IoT and wearable devices
• Automatic diagnosis of X-ray, CT, and MRI using deep learning
• Personalized treatment using electronic medical records
• Recognition of body movements using machine learning
• AI diagnostic errors and bias issues

Authors must submit an electronic copy (in word or pdf) of their complete manuscript directly to the Session Organizer (nagamune@eng.u-hyogo.ac.jp) before May 1, 2024

Prof. Kouki Nagamune
University of Hyogo, Japan
nagamune@eng.u-hyogo.ac.jp