The conference features special sessions focusing on new research topic and innovative application. Papers in these session undergo a regular review process as those in general sessions. Special sessions has to contain at least four papers. Prospective organizers are invited to contact special session chairs to propose tracks with the title of the session, its scope, list of topics, and organizer name, by May 15, 2021. Each special session may have an invited talk as well. If the organizer(s) is(are) interested in and Invited talk.

 

List of Accepted Special Sessions

 

Session :

Artificial Neural Networks and Pattern Recognition in Geosciences

Organizers :
  1. Sid-Ali Ouadfeul. Algerian Petroleum Institute, IAP, Algeria
  2. Leila Aliouane. LABOPHYT, FHC, UMBB, Boumerdes, Algeria
Description : Artificial Neural Networks (ANNs) have become very useful tools in geosciences. For example, in petrophysics they are used for lithofacies recognition and reservoir characterization from well-logs data. In gravity and magnetism, the ANNs are used for contacts identification and causative sources characterization and in seismology; the ANNs are used for earthquake characterization. In seismic data processing, the artificial neural networks are used for waveform inversion, automated picking of seismic first-arrivals, and automatic interpretation of seismic horizons… etc. In electromagnetism, the ANNs are used for Interpretation of airborne data and in climatology; they are used for sunspot prediction and solar climate trends in climatology databases…etc.
In oceanography, the ANNs are used for segmentation and classification of topographic profiles of ridge-flank seafloor… etc.We would like to invite you to submit a contribution to the special session titled: Artificial Neural Networks and Pattern Recognition in Geosciences. It will be organized under the 23th International Conference on Neural Information Processing. It will be held in Bali, Indonesia from December 8-12, 2021.This session aims at bringing together scientists who are active in Artificial Neural Networks and Pattern Recognition in Geosciences and to show the utility of these tools in earth sciences.
Potential researchers are invited to submit papers related to Artificial Neural Networks and Pattern Recognition in Geosciences.
Scope : Artificial Neural Networks and Pattern Recognition in the following area and other relevant research:

  1. Petrophysics/Well-logs data.
  2. Seismic method.
  3. Gravity/Magnetic (Potential field) methods.
  4. Electromagnetism/Magneto-telluric.
  5. Geology/Geophysics.
  6. Climatology.
  7. Geography
  8. Geodesy/Topography.
  9. Oceanography.
  10. Hydrology/Hydrogeology.
  11. Fractal Analysis combined with Neural Network.
  12. Wavelet Transform approach combined with Neural Network.
  13. New Learning techniques in geosciences.
Important dates: :
  • June 30, 2021. Submission deadline for regular and Special Session papers
  • August 31, 2021. Notification of paper acceptance
  • September 30, 2021. Deadline for final camera-ready submission
Other Information : Potential authors should submit a paper describing their work in one of the areas described above. All accepted papers will be published in Lecture Notes in Computer Science (LNCS) and/or special issues of SCI journals.
Submission of a paper constitutes a commitment that, if accepted, one or more authors will attend the conference. On-line submission via EasyChair will be made available on this web-site timely.

Session :

Randomization-Based Deep and Shallow Learning Algorithms

Organizers :
  1. Dr. P. N. Suganthan. Nanyang Technological University, Singapore. epnsugan@ntu.edu.sg
  2. Dr. M. Tanveer. Indian Institute of Technology Indore, India. mtanveer@iiti.ac.in
Description : Randomization-based learning algorithms have received considerable attention from academics, researchers, and domain workers because randomization-based neural networks can be trained by non-iterative approaches possessing closed-form solutions. Those methods are in general computationally faster than iterative solutions and less sensitive to parameter settings. Even though randomization-based non-iterative methods have attracted much attention in recent years, their deep structures have not been sufficiently developed nor benchmarked. This special session aims to bridge this gap.

The first target of this special session is to present the recent advances of randomization- based learning methods. Randomization based neural networks usually offer non-iterative closed form solutions. Secondly, the focus is on promoting the concepts of non-iterative optimization with respect to counterparts, such as gradient-based methods and derivative-free iterative optimization techniques. Besides the dissemination of the latest research results on randomization-based and/or non-iterative algorithms, it is also expected that this special session will cover some practical applications, present some new ideas and identify directions for future studies.

Original contributions as well as comparative studies among randomization-based and non-randomized-based methods are welcome with unbiased literature review and comparative studies. Typical deep/shallow paradigms include (but not limited to) random vector functional link (RVFL), echo state networks (ESN), liquid state networks (LSN), kernel ridge regression (KRR) with randomization, extreme learning machines (ELM), random forests (RF), and so on.

Topics : Topics of the special session include (with randomization-based methods), but are not limited to:

  • Randomized convolutional neural networks
  • Randomized internal representation learning
  • Regression, classification and time series analysis by randomization-based methods
  • Kernel methods such as kernel ridge regression, kernel adaptive filters, etc. with randomization
  • Feedforward, recurrent, multilayer, deep and other structures with randomization
  • Ensemble learning with randomization l Moore-Penrose pseudo inverse, SVD and other solution procedures.
  • Gaussian process regression
  • Randomization-based methods for large-scale problems with and without kernels
  • Theoretical analysis of randomization-based methods
  • Comparative studies with competing methods without randomization
  • Applications of randomized methods in domains such as power systems, biomedical, finance, signal processing, big data and all other relevant areas
Important dates: :
  • 30th June 2021 – Paper submission deadline
  • 31st August 2021 – Paper acceptance notification
  • 8th – 12th December 2021 – Bali, Indonesia
Other Information : Papers submitted to this Special Session are reviewed according to the same rules as the submissions to the regular sessions of ICONIP 2021. Authors who submit papers to this session are invited to mention it in the form during the submission. Submissions to regular and special sessions follow identical format, instructions, deadlines and procedures of the other papers. Click here to information paper submission
Please, for further information and news refer to the ICONIP website: https://iconip2021.apnns.org

Session :

Advances in deep and shallow machine learning algorithms for biomedical data and imaging

Organizers :
  1. M. Tanveer. Indian Institute of Technology Indore, India. mtanveer@iiti.ac.in
  2. Chin-Teng Lin. University of Technology Sydney, Australia, Chin-Teng.Lin@uts.edu.au
  3. Kai-Lung Hua. NTUST, Taiwan, hua@mail.ntust.edu.tw
Description : Aim and Scope: Computational intelligence, particularly neural networks, plays a key role in the recent bloom of deep learning, which can be viewed as one of the most important revolutions in the field of artificial intelligence over the last decade. It has achieved great success in different tasks in computer vision, image processing, biomedical analysis and related fields. Researchers in deep and shallow machine learning including those working in the above fields, when tied with experienced clinicians, can play a significant role in understanding and working on complex medical data for improving patient care. To develop a novel deep or shallow machine learning algorithm specific to medical data is a challenge and need of the hour. Healthcare and biomedical sciences have become data-intensive fields, with a strong need for sophisticated data mining methods to extract the knowledge from the available information. Biomedical data contain several challenges in data analysis, including high dimensionality, class imbalance and low numbers of samples. Although the current research in this field has shown promising results, several research issues need to be explored. There is a need to explore novel feature selection methods to improve predictive performance along with interpretation, and to explore large scale data in biomedical sciences.

This special session aims to bring together the current research progress (from both academia and industry) on novel machine learning methods to address the challenges to biomedical complex data. Special attention will be devoted to handle feature selection, class imbalance, and data fusion in biomedical and machine learning applications. It will attract medical experts who have access to interesting sources of data but lack the expertise in using machine learning techniques effectively.

Topics : The topics relevant to the special issue include (but are not limited to) the following topics:

  • Computer aided detection and diagnosis
  • Machine learning methods applied to biomedical data
  • Computational intelligence techniques for neuroimaging data
  • Deep learning (DL) for neuroimaging
  • Applications of DL algorithms in biomedical data processing, pathological detection and diagnosis
  • Adversarial learning, meta-learning and few-shot learning in image segmentation
  • Biomedical image classification Evolutionary computing in bioinformatics
  • Pattern recognition for imaging and genomics
  • Computational intelligence for large scale imbalanced data
  • Improved algorithms for multimodality neuroimaging data fusion systems
  • Clustering and classification algorithms for Healthcare
  • Real-world applications of computational intelligence for biomedical data and imaging
Important dates: :
  • 30th June 2021 – Paper submission deadline
  • 31st August 2021 – Paper acceptance notification
  • 8th-12th December 2021 – Bali, Indonesia
Other Information : Papers submitted to this Special Session are reviewed according to the same rules as the
submissions to the regular sessions of ICONIP 2021. Authors who submit papers to this session are
invited to mention it in the form during the submission. Submissions to regular and special sessions
follow identical format, instructions, deadlines and procedures of the other papers. Click here to information paper submission Please, for further information and news refer to the ICONIP website: https://iconip2021.apnns.org