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Article
Computer Science, Artificial Intelligence
Leila Boussaad et al.
Summary: The paper aims to study face recognition across different ages. The author demonstrates the effectiveness of using the feature extraction layer of the pre-trained AlexNet model and the extreme learning machine classifier in age-invariant face recognition.
INTERNATIONAL JOURNAL OF APPLIED METAHEURISTIC COMPUTING
(2022)
Article
Biotechnology & Applied Microbiology
Maha M. Althobaiti et al.
Summary: The rapid development of technologies in biomedical research has led to the design of more advanced medical equipment, with photoacoustic multimodal imaging playing a crucial role in breast cancer detection. The advancements of deep learning models have opened up new possibilities for using biomedical images for breast cancer detection and classification.
BIOMED RESEARCH INTERNATIONAL
(2022)
Article
Chemistry, Multidisciplinary
Xinyi Hu et al.
Summary: This paper proposes a fault diagnosis method based on the BCOA algorithm to optimize the KELM classifier. By selecting features and automatically adjusting the network hyperparameters, the performance of the classifier is improved. Experiments demonstrate that this method outperforms other methods in terms of classification accuracy.
APPLIED SCIENCES-BASEL
(2022)
Article
Chemistry, Multidisciplinary
Jesse Miettinen et al.
Summary: Intelligent fault diagnosis models have the potential to automate machine condition monitoring systems and improve diagnosis accuracy. Recent research suggests that using convolutional layers and normalization techniques can extract features from vibration data and further enhance the performance of fault diagnosis models.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Environmental
Alireza Mohaghegh et al.
Summary: In this study, a new framework is developed to model missing groundwater table data in Damghan plain using geographical data, data mining techniques, and hesitant fuzzy-multicriteria decision-making methods. Different data mining techniques are employed to establish a relationship between geographical data and groundwater table, and hesitant fuzzy-multicriteria decision-making methods are used to select the best technique. M5 and LSSVM show more accurate results compared to other techniques, while ELM has the worst performance. The results also indicate that M5 and RF have the best and worst performance in terms of computation time. Additionally, LSSVM has the least uncertainty, while ELM has the highest uncertainty. The results of the hesitant fuzzy-multicriteria decision-making method rank M5 and LSSVM as the top two techniques. The MARS algorithm ranks third with a slight difference from the top two techniques.
GROUNDWATER FOR SUSTAINABLE DEVELOPMENT
(2022)
Article
Engineering, Biomedical
Mehmet Akif Ozdemir et al.
Summary: This paper presents a comparative hand gesture classification approach using time-frequency (TF) images of sEMG signals and transfer learning. The results show that the HHT method utilizing IMFs obtained by EMD provided improved TF resolution and achieved better classification performance than STFT and CWT methods. The best accuracies were obtained by the HHT method with the pre-trained ResNet-50 model.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Biology
Betul Ay et al.
Summary: This paper introduces the characteristics and harm of nasal polyps and proposes a reliable rhinology assistance system for recognizing them. The authors design a new dataset including 80 participants and conduct experiments using machine learning and deep learning algorithms. They find that deep learning algorithms achieve high accuracy in identifying nasal polyps. The research results are significant for supporting clinical decision systems.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Paul Gavrikov et al.
Summary: This paper proposes a method to study the changes in trained convolutional neural network (CNN) models' learned weights and provides a dataset with over 1.4 billion filters for analysis. The results show that model pre-training can be successful on arbitrary datasets if size and variance conditions are met. However, many pre-trained models contain degenerated filters that make them less robust and less suitable for fine-tuning on target applications.
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022)
(2022)
Article
Engineering, Environmental
Yaoke Shi et al.
Summary: This study proposes a membrane module fault diagnosis method based on attention mechanism and convolutional neural network (ECA-CNN), which can improve diagnosis accuracy, reduce energy consumption, and provide a theoretical foundation for practical production.
JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING
(2022)
Article
Engineering, Biomedical
J. Zuluaga-Gomez et al.
Summary: This study introduces a computer-aided diagnosis system for breast cancer based on thermal images and convolutional neural networks (CNN), showing the superior performance of CNNs in breast cancer diagnosis. Through research, it was found that implementing data augmentation techniques in CAD systems resulted in excellent accuracy and F1 scores. The study also highlights the significant impact of data augmentation and database size on breast cancer diagnosis using CNNs.
COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION
(2021)
Article
Medicine, General & Internal
Tahir Mahmood et al.
JOURNAL OF CLINICAL MEDICINE
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Karan Gupta et al.
INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND DATA SCIENCE
(2020)
Article
Computer Science, Information Systems
Zhiqiong Wang et al.
Article
Engineering, Biomedical
Fei Gao et al.
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
(2018)
Article
Computer Science, Hardware & Architecture
Alex Krizhevsky et al.
COMMUNICATIONS OF THE ACM
(2017)
Article
Computer Science, Artificial Intelligence
Rahimeh Rouhi et al.
EXPERT SYSTEMS WITH APPLICATIONS
(2015)
Article
Computer Science, Information Systems
Zhang YuDong et al.
SCIENCE IN CHINA SERIES F-INFORMATION SCIENCES
(2009)
Article
Computer Science, Information Systems
Zhang YuDong et al.
SCIENCE IN CHINA SERIES F-INFORMATION SCIENCES
(2008)
Article
Computer Science, Artificial Intelligence
Guang-Bin Huang et al.
