Special Sessions

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Submission is via Easychair - see the submission page - and then choose an appropriate Special Session. Deadline for these Special Sessions has bene extended to 15th August 2022

Scope  

CO2 emissions have been identified as one of the significant causes of climate change. These emissions are mainly produced by non-renewable energy production systems and non-sustainable transport means, still widely used nowadays. As a result, there is a widespread consensus that renewable energy sources such as wind, marine, hydro and solar as well as green transport systems must be considered to mitigate climate change and reduce air pollution. Consequently, research on renewable energies and green transport, particularly, on control and efficiency is encouraged to contribute to this sustainable trend. Expert systems, fuzzy control, neural networks, genetic algorithms, artificial immune networks, swarming particle techniques, ACO, reinforcement learning, and other intelligent techniques have been shown to be effective in many different fields. They can be applied to tackle complex problems where conventional methods are less efficient or unsuccessful. The goal of this special session is to provide a platform for researchers, engineers, and industrial practitioners from different fields to share and exchange their ideas, research results, and experiences in the field of intelligent techniques applied to renewable energy and green transport. Contributions to this special session are welcome to present and discuss novel methods, algorithms, frameworks, architectures, platforms, and applications.  

Topics of interest include but are not limited to:   

• Intelligent control: fuzzy control, neuro-control, neuro-fuzzy, intelligent-PID control, …  

• Optimization by heuristic techniques in system engineering and control  

• Modelling and identification by automated learning  

• Identification and control by hybrid intelligent strategies  

• Real-world applications on wind, marine, and hydro renewable energy   

• Real-world applications on transport and smart industry: AGVs and autonomous vehicles    

Organisers/chairs 

• J. Enrique Sierra García, University of Burgos (Spain)

• Matilde Santos Peñas, Complutense University of Madrid (Spain)

• Fares M’zoughi, University of the Basque Country, Bilbao (Spain)

• Payam Aboutalebi, University of the Basque Country, Bilbao (Spain)

Scope and topics  

Data selection focuses on reducing the training time and, at the same time, taking advantage to do better predictions. Too much information is not handy at all since uninformative samples or features may be learnt and consequently the ability to generalize could be hindered. Addressing any problem may mean not having prior knowledge and even to become able, through data selection and even transformation measure, to learn the important data for the forthcoming prediction on unseen data. Depending on the followed methodology to conduct the process model for data mining, the data selection may be named with different names although the core is the same. Tools based on graphical user interfaces are of particular interest in the sense that may make easier the procedure to refine the raw data and eventually to get the ready data to face the mining phase. Data pre-processing deals with many tasks such as data cleansing, attribute selection, instance selection, noise reduction and detecting wrong or distorted labels. Visual data analytics is on the rise especially in multi-dimensional business applications. It is not uncommon to require any data imputation task prior to the application of the data selection stage. We encourage to submit very recent applications and if possible unprecedented. Additionally, new theoretical or empirical approaches are welcome. 

Topics of interest for this session include but are not limited to:   

• Data selection

• Data pre-processing

• Data cleansing 

• Data engineering

• Attribute selection

• Instance selection  

• Data fusing

• Data mining  

• Text mining   

• Speech mining   

• Signal mining

• Stream mining   

• Motif mining

• Itemset mining   

• Sequential pattern mining         

• Frequent pattern mining   

• Infrequent pattern mining    

• Rare pattern mining       

Organisers/chairs 

• Ireneusz Czarnowski, Gdynia Maritime University (Poland)

• Antonio J. Tallón-Ballesteros, University of Huelva (Spain)

Scope and topics  

Imbalanced classification is one of the most important tasks in machine learning, which

has attracted much attention from both academic and industrial communities. Imbalanced

classification has a very wide range of real-world applications, most of which are closely

related to our daily life, such as medical diagnosis, intrusion detection, anomaly detection,

and credit card fraud detection. Imbalanced data exhibits a skewed distribution between

its classes. If the class imbalance issue is not well-addressed, classifier are likely to ignore

the class of interest which is constituted by a few instances. Computational intelligence

is a subfield of artificial intelligence, covering three research branches, including evolutionary computation, fuzzy sets, and artificial neural networks. Computational intelligence

techniques have been applied and achieved great contributions to imbalanced classification .

In the big data era, the amount of data is growing very rapidly, either increasing in a number of features or instances. This brings further difficulty in constructing effective classifiers

when learning from imbalanced data but in return enriches new opportunities.

The aim of this special session is to join the contemporary use of computational intelligence techniques for imbalanced classification . This special session attempts to bring

together some of leading experts or researchers from different branches in computational intelligence and offers a forum for them to present their latest research, discuss open questions

as well as current advances in imbalanced classification . The session welcomes studies and

contributions that introduce novel methods based on different computational intelligence

paradigms to imbalanced classification and its applications. 

Topics of interest for this session include but are not limited to:   

• Evolutionary computation (e.g. Genetic Algorithms, Genetic Programming and

Particle Swarm Optimization, etc.) for imbalanced classification 

• Evolutionary computation with sampling methods (including undersampling,

oversampling and hybrid sampling) for imbalanced classification 

• Evolutionary computation with cost-sensitive learning for imbalanced classification  

• Fuzzy sets, Rough sets, Granular computing for imbalanced classification 

• Fuzzy rule-based classification systems for imbalanced classification 

• Instance selection  

• Neural networks for imbalanced classification 

• Deep learning for imbalanced classification 

• Sampling methods for imbalanced classification 

• Instance selection for large-scale imbalanced data   

• Cost-sensitive learning for imbalanced classification 

• Active learning for imbalanced classification 

• Explainable Artificial Intelligence (XAI) for imbalanced classification 

• Feature selection/construction/extraction/ranking/analysis for imbalanced

classification with high-dimensional data   

• Real-world applications of imbalanced classification , e.g., medical data analysis, fault

detection, anomaly detection, software defect prediction, and text mining         

Organisers/chairs 

• Wenbin Pei, Dalian University of Technology (China)

• Bing Xue, Victoria University of Wellington (New Zealand)

• Antonio J. Tallón-Ballesteros, University of Huelva (Spain)

Scope and topics  

This special session aims to cover the latest innovations on intelligent Video, Image, Speech and Text Analysis (VISTA). All works tackling theoretical issues and applications, from industry or academia, embracing one or several modalities are welcome and encouraged.

Computer Vision (CV) and image processing is a big research field that studies the use of computers to process, extract, analyse and understand information from digital images and videos as the human vision system does. Typical tasks related to computer vision and image processing include image classification, image segmentation, object detection and/or recognition, scene and motion analysis or image restoration and enhancement.

Natural Language Processing (NLP) and Understanding is another big field of study that aims to use computers to process, extract, analyse and understand information from speech and texts. Typical tasks include text classification and summarization, question answering, machine translation, sentiment analysis and speech recognition.

Thanks to the recent advances of deep learning, CV and NLP applications have received an unprecedented boost in performance, generating growing interest from the machine learning community. This special session is devoted to novel research about popular and fashionable applications of machine learning and deep learning solutions to CV and NLP tasks.

The aim of this special session is to join the contemporary use of computational intelligence techniques for imbalanced classification . This special session attempts to bring

together some of leading experts or researchers from different branches in computational intelligence and offers a forum for them to present their latest research, discuss open questions

as well as current advances in imbalanced classification . The session welcomes studies and

contributions that introduce novel methods based on different computational intelligence

paradigms to imbalanced classification and its applications. 

Topics of interest for this session include but are not limited to:   

• Supervised learning

• Semi-supervised learning 

• Unsupervised and weakly supervised methods 

• Transfer learning  

• Shallow and Deep Learning   

• Neural network Architectures

and their applications to

• Image processing, classification, segmentation, enhancement, restoration, retrieval
• Object detection, recognition, tracking
• Scene analysis and understanding
• Face recognition, facial expression and emotion analysis
• Medical image analysis
• Remote sensing image analysis
• Big data in computer vision and image analysis
• Other computer vision and image analysis applications
• Natural language generation
• Information extraction, retrieval and text mining
• Machine translation
• Question answering and summarization
• Syntax and semantics (sentence and document levels, textual Inference, etc)
• Sentiment analysis, stylistic analysis, and argument mining
• Language modelling and analysis of language models
• Speech comprehension, translation and synthesis
• Speaker and language identification
• Speech-to-text, text-to-speech

Organisers/chairs 

• Miguel Guevera, Polytechnic Institute of Setúbal (Portugal)

• Teresa Gonçalves, University of Évora (Portugal )

• Vítor Nogueira, University of Évora (Portugal)

Scope and topics  

Diabetes is a chronic disease that affects individuals all over the world, and it is predicted that 700–800 million people will be diagnosed with diabetes by 2030, which is around 10–11% of the world population. Over 4.5 million people have been diagnosed with diabetes in the UK, and the current cost of this service is over £1 billion, which is equal to 10% of the NHS budget. Diabetes Neuropathy (DN) is also one of the critical diseases which cause damages to human’s lower body nerves in particular legs or feet. As result of raise in the rate of limb amputation or foot ulceration around world, healthcare professionals and podiatrists have recommended the implementation of an early detection and prediction of developing the risk of Neuropathy Diabetes. As solution, AI-based techniques can be used effectively for an early identification of developing the risks of DN and prevent patient from limb amputation or foot ulceration among other diabetes causes. 

The goal of this special session is to provide a platform for researchers, data scientists or data engineers, and industrial and healthcare professionals from different fields to share and exchange their ideas, research results, and experiences in the field of intelligent techniques applied to detect and predict patients from developing ND risk levels or other related diabetes health issues such as amputations of inferior limbs, foot ulceration, diabetic eye visions, to name a few. Contributions to this special session are welcome to present and discuss novel methods, algorithms, frameworks, architectures, platforms, and applications.  

Topics of interest for this session include but are not limited to:   

• Big Data methods for processing and analysis high volumes of heterogeneous diabetes dataset  

• Machine Learning models for predicting higher risk of foot ulceration or limbs amputation 

• Deep Learning for diabetic foot thermograms detection 

• Fuzzy rule-based classification for imbalanced diabetes dataset classification task

• Detecting anomalies from diabetes clinical assessment   

• Automated machine learning (AutoML) and real-world application on diabetes prediction and detection

• AI-based techniques and applications for an early detection of diabetic retinopathy, nephropathy and neuropathy

• Computer vision and deep learning to determine the risk of foot ulcer 

• Knowledge-based system and application for an early identification of diabetes

• Explainable AI (XAI) methods and applications for automatic diabetes diagnosis

Organisers/chairs 

• Bakhtiar Amen, University of Huddersfield (UK)

• Tianhua Chen, University of Huddersfield (UK)

Scope and topics  

This special session aims at encompassing the lastest innovations about data mining in the context of economy and finance. Therefore, the presentation of works tackling theoretical issues and applications, from industry or academia, on data mining is welcome. Economy as well as finance may be focused on short, medium or long-term period; all of them have room in this session. The problem complexity is increasing due to the huge amount of transactions that are happening at a specific instant all over the world and is not easy to detect the causality among all of them independtly of the very distant point wherever the operations could take place. Nowadways, the labour market is also becoming of paramount importance in the variation of the (monthly and yearly) indicators of the economy of any country.

Topics of interest for this session include but are not limited to:   

• Economy

• Finance

• Data mining

• Economical prediction

• Stock-market analysis

• Markets on the rise

• Reputation on finance

• Economist intelligence

• Money laundering avoidance

• Early detection of tax evasion

• Technological issues affecting the economy of a company

• Big numbers in the economy of a country

• Contemporary trade

• Business intelligence

• Social economy finance

• Circular economy

• Economic forecasting

• Labour market

Organisers/chairs 

• Carlos Usabiaga, Pablo de Olavide University, Seville (Spain)

• Fernando Núñez Hernández, University of Seville (Spain)

• Antonio J. Tallón-Ballesteros, University of Huelva (Spain)

Scope and topics  

Security is one of the major concerns while the data is being shared through a network or stored or processed on various platforms like cloud, fog, edge etc. As per the recent McAfee Cloud Adoption and security risk report in only 8% of the total cloud service providers meet the data protection standards. Data engineering for security issues in clouds’ environments is a crucial processing task. Cloud provides services like SaaS, PaaS, IaaS and each service has different users and so are the different levels of security issues. SaaS cloud security issues are centred on data and access because most shared security responsibility models leave those two as the sole responsibility for users or customers using SaaS. It is every organization’s responsibility to understand what data they put in the cloud, who can access it, and what level of protection they (and the cloud provider) have applied. Protecting data is critical in IaaS. As customer accountability extends to applications, network traffic, and operating systems, additional threats are introduced. New systems technologies capable of providing both security of data as well as intelligent data processing in order to do accurate predictions are the need of hour.

The aim of the session is to provide a platform for both academic and industrial communities to share the recent research advancements in the field of cloud security. Topics of Interest include but are not limited to:   

• Bio-Computing Algorithms

• Cloud Misbehaviour

• Cyber security through AI and ML

• Data engineering in cloud systems’ security

• Encryption

• Mimetic Algorithms & Security

• Security & Trust management

• Web Applications & Security

• IoT

• Intelligence-based Network Security System

• Real time secure data processing

• Intelligent data Analytics in the field of security of health care systems, threat prediction

• Intelligent document processing

Organisers/chairs 

• Halima Sadia, Department of Computer Science & Engineering, Integral University, Lucknow (India)

• Ankita Srivastava, Department of Computer Science & Engineering, Integral University, Lucknow (India)