Advanced and Intelligent Computations in Diagnosis and Control

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11th International Conference on Advanced Computational Intelligence

About this book This book is devoted to the demands of research and industrial centers for diagnostics, monitoring and decision making systems that result from the increasing complexity of automation and systems, the need to ensure the highest level of reliability and safety, and continuing research and the development of innovative approaches to fault diagnosis. Show all. Show next xx. Recommended for you.


TPK4155 - Applied Computational Intelligence in Intelligent Manufacturing

The last chapter considers the problem of diagnosing large scale complex systems using local agents which, in particular, can be implemented using computational intelligence based fault diagnosis techniques. Several case studies are used including applying computational intelligence techniques to aero-engines fault diagnosis and diagnosing faults to various industrial devices, such as a flow control valve and a hydraulic installation in a rolling mill plant. Skip to main content Skip to table of contents. Advertisement Hide. Driven by these mega trends, Health Engineering as a new interdisciplinary field of research and development is emerging, focusing on the applications of engineering principles and efficient and economical approaches to solve problems in healthcare and well-being.

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Health Engineering will lead to a revolutionized healthcare system that enables the participation of all people for the early prediction and prevention of diseases so that preemptive and pro-active treatment can be delivered to realize personalized, precision, pervasive, and patient-centralized healthcare.

This special issue seeks to present the technological advancements of the enabling technologies in Health Engineering for the new revolution of Healthcare 4. Computational Pathology. The huge amount of information and data available in multi-gigapixel histopathology images makes digital pathology the perfect use case for advanced image analysis techniques.

For this reason, deep learning and artificial intelligence have successfully powered computational pathology research in recent years.

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The goal of this special issue is to attract and highlight the latest developments in computational pathology, and feature papers proposing state-of-the-art solutions in the field of digital pathology using advanced image analysis and artificial intelligence. Deep Learning in Ultrasound Imaging. Submission Deadline: 31 May Among different imaging modalities, ultrasound is the most widespread modality for visualizing human tissue, because of its advantages compared to others: cheap, harmless no ionizing radiations , allowing real-time feedback, convenient to operate, and well established technology present in all place.

Special Issues Now Accepting Submissions

This book is devoted to the demands of research and industrial centers for diagnostics, monitoring and decision making systems that result from the increasing. of the second International Workshop on Advanced Computational Intelligence Save 40% on select Business & Management books + FREE shipping or.

Also because of these benefits, tons of medical images are being generated from ultrasound devices. On the other hand, ultrasound images suffer from the disadvantage of being user dependent and noisy which makes the interpretation of US images is sometimes difficult. This special issue seeks to present and highlight the latest development on applying advanced deep learning techniques in ultrasound imaging.

About this book

Predictive Intelligence in Biomedical and Health Informatics. Despite the terrific progress that analytical methods have made in the last twenty years in medical image segmentation, registration or other related applications, efficient predictive intelligent models are somewhat lagging behind.

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The goal of this Special Issue is to publish original manuscripts and the latest research advancements in different aspects of biomedical, health informatics, and medical image analysis, where predictive methods in artificial intelligence, deep learning, and computer vision intersect with healthcare and life sciences. Information Fusion for Medical Data: early, late and deep fusion methods for multimodal data. Another typical source of variability is the existence of data from different time points, such as pre and post treatment, for instance.

This high and diverse amount of information needs to be organized and mined in an appropriate way so that meaningful information can be extracted. Several questions, however, arise when dealing with these situations. Should different types of information be treated differently?

Should a common framework be derived? Are new analytic approaches needed? It is our hope that these and other questions will be addressed by this special issue. In this call, we thus focus on sharing recent advances in algorithms and applications that involve combining multiple sources of medical information. Topics appropriate for this special issue include novel supervised, unsupervised, semi-supervised and reinforcement algorithms, new architectures, new formulations, and applications related to medical information fusion. Submission Deadline: 31 December Mental health is one of the major global health issues affecting substantially more people than other non-communicable diseases.

Recent advances in imaging and sensing have facilitated the acquisition of detailed neurological signals and imaging techniques for better understanding of the disorder.

Black Box Optimization Benchmarking (BBOB)

Personal tools Log in. It may also be necessary to employ a hybrid system combining techniques to solve a problem. AlShahrani AM et al Automated system for crops recognition and classification. JavaScript is currently disabled, this site works much better if you enable JavaScript in your browser. All of these suggest the pressing need of new methodologies designed for dealing with MaOPs, new performance metrics and test functions tailored for experimental and comparative studies of evolutionary many-objective optimization EMaO algorithms.

In addition, new wearable technologies have enabled continuous sensing of neurological, physiological, and behavioural information of the users. These technologies have led to new insights into mental illnesses providing the needed data to improve the diagnosis, identify triggers of episodes, and enable preventative interventions with diverse machine learning approaches. This special issue is dedicated to cover the related topics on technological advancements for mental health care and diagnosis with focus on pervasive sensing and machine learning.

Interactive Virtual Environments for Neuroscience. Submission Deadline EXTENDED: 30 June Virtual and augmented reality are computational technologies that provide artificial sensory feedback, allowing a subject to experiment activities and events similar to those that can be found in real life and to develop motor and cognitive abilities in immersive three-dimensional environments that resemble the real world, besides being economically viable.

Computational Intelligence

This special issue is based on the technological advances considered in the process of neurorehabilitation using virtual environments, serious games among other technologies for a playful, non-invasive treatment and that has shown to be quite efficient and effective in improving the clinical condition of the patients and their re insertion into society.

Furthermore, it aims to introduce the recent progress of virtual environments in Neuroscience and addresses the challenges in developing dedicated systems for various clinical applications, while proposing new ideas and directions for future development. Biomedical Informatics across the Cancer Continuum. Submission Deadline EXTENDED: 30 June, A pre-requisite for achieving the vision for more precise and personalized diagnostics and treatment and high-quality cancer care concerns the development of learning health information management systems that enable real-time analysis of data from cancer patients in a variety of care settings.

The most often cited challenges are related to the intrinsic complexity of the underlying biomedical and clinical data and the fact that information exists in both structured and unstructured formats. In addition, as cancer is more and more changing to a chronic disease, tools that would empower cancer patients in self-management are clearly needed. Also, the issue will seek contributions presenting current approaches for the development of oncology decision-support solutions that offer seamless data integration across specialties and locations, data-driven decision making, and tools for proactive patient involvement.

Together, these trends have influenced connected-health informatic systems, which comprise various processes for sensing, data transfer, storage and analytics to improve overall health and wellbeing. Increasingly, each of these processes are being infused with artificial intelligence AI , leading to unprecedented advances in how automated care is being delivered. This automation has helped engineers shift focus from mundane issues like feature optimization to productive ones like understanding clinical relevance and evaluating strategies for responsive health care.

This special issue aims to bring the spotlight on AI techniques that have helped advance connected-health informatics.