Computational and Mathematical Methods in Medicine

Computational Intelligence for Health Care


Publishing date
01 Jul 2021
Status
Closed
Submission deadline
26 Feb 2021

Lead Editor

1Huazhong University of Science and Technology (HUST), Wuhan, China

2University of Gujrat, Gujrat, Pakistan

3Hazara University Manshera, Manshera, Pakistan

This issue is now closed for submissions.
More articles will be published in the near future.

Computational Intelligence for Health Care

This issue is now closed for submissions.
More articles will be published in the near future.

Description

Computational Intelligence covers a number of nature-inspired computational methodologies, mainly artificial neural networks (ANNs), fuzzy sets, genetic algorithms (GAs), swarm intelligence, and their hybridisation for addressing real-world problems to which conventional modelling cannot be used due to reasons such as complexity, existence of uncertainties, and the stochastic nature of the processes. Computational intelligence is a powerful methodology for a wide range of pattern recognition and data analysis problems such as financial forecasting, as well as industrial, scientific, and social media applications. The recent advances in computational intelligence have shown very promising results in industry, business, and social media studies. These techniques have been particularly successful in the fields of pattern recognition and data analytics.

Given the success of computational intelligence methods and techniques in big data analysis applications, it is expected that they can also be applied successfully in pattern recognition. Recent improvements in artificial intelligence, big data, and machine learning have enhanced the importance of biomedical signal and image processing research. Biomedical image processing is similar in concept to biomedical signal processing in multiple dimensions. It includes the analysis, enhancement, and presentation of images captured via X-Ray, ultrasound, MRI, nuclear medicine, and visual imaging technologies. Machine Learning is now quickly extending in all science and engineering research fields, including biomedical sciences. In this context, computational intelligence paradigms comprising of numerous branches including neural networks, swarm intelligence, expert systems, evolutionary computing, fuzzy systems, and artificial immune systems can play a vital role in handling the different aspects of pattern recognition and data analytics.

The aim of this Special Issue is to gather and present recent work where computational intelligence algorithms are specifically designed for, or applied to, solving complex real-world problems in data analytics and pattern recognition, by means of state-of-the-art methods having general applicability, domain-specific solutions, or hybrid algorithms that integrate computational intelligence with traditional numerical and mathematical methods. Review articles discussing the current state of the art are also welcomed.

Potential topics include but are not limited to the following:

  • Pattern recognition/data mining/optimisation methods
  • Biomedical engineering
  • Soft computing approaches
  • Health care informatics
  • Artificial intelligence techniques
  • Medical imaging and pattern recognition
  • Biomedical imaging and image processing
  • Pattern mining algorithms on biological problems
  • Computational intelligence for data analysis and pattern recognition
  • Artificial intelligence and pattern recognition technologies for recommendation in healthcare
  • Deep learning and machine learning algorithms for efficient indexing and retrieval in medical imaging

Articles

  • Special Issue
  • - Volume 2021
  • - Article ID 5545297
  • - Research Article

A Real-Time Clinical Decision Support System, for Mild Cognitive Impairment Detection, Based on a Hybrid Neural Architecture

Carmen Paz Suárez-Araujo | Patricio García Báez | ... | for the Alzheimer’s Disease Neuroimaging Initiative
  • Special Issue
  • - Volume 2021
  • - Article ID 5588385
  • - Research Article

Feature Selection on Elite Hybrid Binary Cuckoo Search in Binary Label Classification

Maoxian Zhao | Yue Qin
  • Special Issue
  • - Volume 2021
  • - Article ID 5527271
  • - Research Article

Radiologists versus Deep Convolutional Neural Networks: A Comparative Study for Diagnosing COVID-19

Abdulkader Helwan | Mohammad Khaleel Sallam Ma’aitah | ... | Ozum Tuncyurek
  • Special Issue
  • - Volume 2021
  • - Article ID 5512241
  • - Research Article

Detection and Classification of Psychopathic Personality Trait from Social Media Text Using Deep Learning Model

Junaid Asghar | Saima Akbar | ... | Abdu Gumaei
  • Special Issue
  • - Volume 2021
  • - Article ID 5514224
  • - Research Article

A Framework for Automatic Burn Image Segmentation and Burn Depth Diagnosis Using Deep Learning

Hao Liu | Keqiang Yue | ... | Zhihui Fu
  • Special Issue
  • - Volume 2021
  • - Article ID 5588241
  • - Research Article

Patient Mix Optimization in Admission Planning under Multitype Patients and Priority Constraints

Jialing Li | Li Luo | Guiju Zhu
  • Special Issue
  • - Volume 2021
  • - Article ID 5585238
  • - Research Article

Anomalous Behavior Detection Framework Using HTM-Based Semantic Folding Technique

Hamid Masood Khan | Fazal Masud Khan | ... | Daniyal M. Alghazzawi
  • Special Issue
  • - Volume 2021
  • - Article ID 6662779
  • - Review Article

Comparison of Diagnosis Accuracy between a Backpropagation Artificial Neural Network Model and Linear Regression in Digestive Disease Patients: an Empirical Research

Wei Wei | Xu Yang
  • Special Issue
  • - Volume 2021
  • - Article ID 6691096
  • - Research Article

Integrated Learning: Screening Optimal Biomarkers for Identifying Preeclampsia in Placental mRNA Samples

Rong Guo | Zhixia Teng | ... | Dan Liu
  • Special Issue
  • - Volume 2021
  • - Article ID 6657119
  • - Review Article

Reinforcement Learning in Neurocritical and Neurosurgical Care: Principles and Possible Applications

Ying Liu | Nidan Qiao | Yuksel Altinel
Computational and Mathematical Methods in Medicine
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