Topic Editors

Department of Computer Science & Engineering, Guru Nanak University, Ibrahimpatnam, Hyderabad 501506, India
Department of Electronic System Engineering, Malaysia-Japan International Institute Of Technology, University Teknologi Malaysia (UTM), Jalan Sultan Yahya Petra, Kuala Lumpur 54100, Malaysia
Department of Software Engineering, Nişantasi University, Istanbul 34398, Turkey
Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA

Recent Advances in Computational Optimization Techniques and Their Modern Applications for Smart HealthCare

Abstract submission deadline
closed (15 May 2023)
Manuscript submission deadline
20 August 2023
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3610

Topic Information

Dear Colleagues,

Medical and healthcare-related research is moving to new fronts in accuracy due to the growing number of data being acquired in healthcare systems and the convergence of diverse areas of knowledge in the health sector. Along with providing a rare chance and solid assurance for tackling various pressing issues, this trend also has technical applications of artificial intelligence (AI) and operations research (OR). Recently, AI techniques have been researched and used as potentially useful tools for the creation and implementation of intelligent systems inside the healthcare system. By taking into account the undeniable uncertainty of health data and procedures, AI-based systems can often learn from data and adapt according to real-time changes and fluctuations. There have been numerous attempts to date using a variety of techniques, including machine learning (ML), neural networks, optimization, computational intelligence, and human-machine interaction. This Topic Issue invites papers discussing recent advances in the development and application of Optimization, Machine Learning, and Artificial Intelligence in Healthcare.

Dr. Mohd Dilshad Ansari
Prof. Dr. Mohd Fauzi Bin Othman
Dr. Jawad Rasheed
Dr. Mazdak Zamani
Topic Editors

Keywords

  • optimization methods
  • computational approaches
  • machine learning
  • artificial intelligence
  • smart healthcare system
  • heuristic search methods
  • automation by AI, ML and DL in healthcare etc.

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Mathematics
mathematics
2.592 3.5 2013 16.8 Days 2100 CHF Submit
Applied Sciences
applsci
2.838 4.5 2011 14.9 Days 2300 CHF Submit
Electronics
electronics
2.690 4.7 2012 14.4 Days 2000 CHF Submit
Healthcare
healthcare
3.160 2.7 2013 19.1 Days 2000 CHF Submit
Symmetry
symmetry
2.940 4.9 2009 14.2 Days 2000 CHF Submit

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Published Papers (2 papers)

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Article
Advanced Deep Learning Approaches for Accurate Brain Tumor Classification in Medical Imaging
Symmetry 2023, 15(3), 571; https://doi.org/10.3390/sym15030571 - 22 Feb 2023
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Abstract
A brain tumor can have an impact on the symmetry of a person’s face or head, depending on its location and size. If a brain tumor is located in an area that affects the muscles responsible for facial symmetry, it can cause asymmetry. [...] Read more.
A brain tumor can have an impact on the symmetry of a person’s face or head, depending on its location and size. If a brain tumor is located in an area that affects the muscles responsible for facial symmetry, it can cause asymmetry. However, not all brain tumors cause asymmetry. Some tumors may be located in areas that do not affect facial symmetry or head shape. Additionally, the asymmetry caused by a brain tumor may be subtle and not easily noticeable, especially in the early stages of the condition. Brain tumor classification using deep learning involves using artificial neural networks to analyze medical images of the brain and classify them as either benign (not cancerous) or malignant (cancerous). In the field of medical imaging, Convolutional Neural Networks (CNN) have been used for tasks such as the classification of brain tumors. These models can then be used to assist in the diagnosis of brain tumors in new cases. Brain tissues can be analyzed using magnetic resonance imaging (MRI). By misdiagnosing forms of brain tumors, patients’ chances of survival will be significantly lowered. Checking the patient’s MRI scans is a common way to detect existing brain tumors. This approach takes a long time and is prone to human mistakes when dealing with large amounts of data and various kinds of brain tumors. In our proposed research, Convolutional Neural Network (CNN) models were trained to detect the three most prevalent forms of brain tumors, i.e., Glioma, Meningioma, and Pituitary; they were optimized using Aquila Optimizer (AQO), which was used for the initial population generation and modification for the selected dataset, dividing it into 80% for the training set and 20% for the testing set. We used the VGG-16, VGG-19, and Inception-V3 architectures with AQO optimizer for the training and validation of the brain tumor dataset and to obtain the best accuracy of 98.95% for the VGG-19 model. Full article
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Article
Sppn-Rn101: Spatial Pyramid Pooling Network with Resnet101-Based Foreign Object Debris Detection in Airports
Mathematics 2023, 11(4), 841; https://doi.org/10.3390/math11040841 - 07 Feb 2023
Viewed by 748
Abstract
Over the past few years, aviation security has turned into a vital domain as foreign object debris (FOD) on the airport paved path possesses an enormous possible threat to airplanes at the time of takeoff and landing. Hence, FOD’s precise identification remains significant [...] Read more.
Over the past few years, aviation security has turned into a vital domain as foreign object debris (FOD) on the airport paved path possesses an enormous possible threat to airplanes at the time of takeoff and landing. Hence, FOD’s precise identification remains significant for assuring airplane flight security. The material features of FOD remain the very critical criteria for comprehending the destruction rate endured by an airplane. Nevertheless, the most frequent identification systems miss an efficient methodology for automated material identification. This study proffers a new FOD technique centered on transfer learning and also a mainstream deep convolutional neural network. For object detection (OD), this embraces the spatial pyramid pooling network with ResNet101 (SPPN-RN101), which assists in concatenating the local features upon disparate scales within a similar convolution layer with fewer position errors while identifying little objects. Additionally, Softmax with Adam Optimizer in CNN enhances the training speed with greater identification accuracy. This study presents FOD’s image dataset called FOD in Airports (FODA). In addition to the bounding boxes’ principal annotations for OD, FODA gives labeled environmental scenarios. Consequently, every annotation instance has been additionally classified into three light-level classes (bright, dim, and dark) and two weather classes (dry and wet). The proffered SPPN-ResNet101 paradigm is correlated to the former methodologies, and the simulation outcomes exhibit that the proffered study executes an AP medium of 0.55 for the COCO metric, 0.97 AP for the pascal metric, and 0.83 MAP of pascal metric. Full article
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