Article
Biochemical Research Methods
Xinshan Zhu, Jiayu Wang, Biao Sun, Chao Ren, Ting Yang, Jie Ding
Summary: By using ensemble learning, this study combines multiple single imputation methods to improve imputation performance, allowing for more efficient utilization of known data information for missing data imputation.
BMC BIOINFORMATICS
(2021)
Article
Computer Science, Information Systems
Jae-Min Lee, Min-Seok Seo, Dae-Han Kim, Sang-Woo Lee, Jong-Chan Park, Dong-Geol Choi
Summary: In this work, the authors propose a Split-and-Share Module (SSM), which splits given features into parts and shares them among multiple sub-classifiers, in order to improve the performance of image classification tasks and identify structural characteristics within the features. SSM can be easily integrated into various architectures and has been validated to show significant improvements over baseline architectures.
Article
Mathematics
Zeeshan Hameed, Waheed Ur Rehman, Wakeel Khan, Nasim Ullah, Fahad R. Albogamy
Summary: A weighted hybrid feature reduction algorithm was proposed to address the curse of dimensionality and noise in PD speech data, showing the highest accuracy and stability for PD classification. It also effectively deals with imbalanced data and achieves the highest AUC in most cases.
Article
Engineering, Multidisciplinary
Fatih Aydin, Zafer Aslan
Summary: Parkinson's disease is a common neurodegenerative disorder with motor symptoms. This paper utilizes advanced algorithms and techniques to identify gait patterns in PD patients, aiming to improve diagnostic accuracy and stability.
ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH
(2021)
Article
Computer Science, Artificial Intelligence
Peng Zhou, Xia Wang, Liang Du
Summary: Unsupervised feature selection is an important task in machine learning but suffers from stability and robustness issues due to the absence of labels. This paper proposes a novel bi-level feature selection ensemble method that not only ensembles at the feature level but also learns a consensus clustering result to guide the feature selection, outperforming other state-of-the-art methods.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Mir Moynuddin Ahmed Shibly, Tahmina Akter Tisha, Tanzina Akter Tani, Shamim Ripon
Summary: This study aims to classify handwritten Bangla characters through three phases, utilizing convolutional neural networks and ensemble methods to achieve higher performance, ultimately achieving accuracy, precision, and recall of 98.68%, 98.69%, and 98.68% respectively.
PEERJ COMPUTER SCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Javier Huertas-Tato, Alejandro Martin, Julian Fierrez, David Camacho
Summary: This paper proposes an ensemble method for accurate image classification, which combines automatically detected features and statistical indicators to achieve better performance. Testing on various datasets shows that including additional indicators and using an ensemble classification approach can improve performance.
INFORMATION FUSION
(2022)
Article
Biology
Mate Hires, Matej Gazda, Peter Drotar, Nemuel Daniel Pah, Mohammod Abdul Motin, Dinesh Kant Kumar
Summary: The computerized detection of Parkinson's disease (PD) using an ensemble of convolutional neural networks (CNNs) achieved excellent results in distinguishing the voices of people with PD and those of healthy people for all vowels. This method has the potential for use in clinical practice for the screening, diagnosis, and monitoring of PD.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Information Systems
De-He Yang, Xin Zhou, Xiu-Ying Wang, Jian-Ping Huang
Summary: The discrimination of source depth in micro-earthquake monitoring is crucial, and this study explores the use of machine learning techniques, showing that deep learning outperforms traditional classification methods in distinguishing source depth.
INFORMATION SCIENCES
(2021)
Article
Medicine, General & Internal
Abdullah Marish Ali, Farsana Salim, Faisal Saeed
Summary: This study investigates the impact of filter feature selection, followed by ensemble learning methods and genetic selection, on the detection of Parkinson's disease patients based on attributes extracted from voice clips. Two distinct datasets were used, and various classification models were tested on the filtered data. Decision tree, random forest, and XGBoost classifiers achieved remarkable results, especially on Dataset 1 with 100% accuracy by decision tree and random forest. Ensemble learning methods and genetic selection further improved the precision of the predictions.
Article
Computer Science, Artificial Intelligence
Yongquan Yang, Haijun Lv, Ning Chen, Yang Wu, Jiayi Zheng, Zhongxi Zheng
Summary: Ensembles of deep CNNs play a crucial role in ensemble learning for artificial intelligence applications, but the increasing complexity of deep CNN architectures and large data dimensionality have made their usage costly. A new approach is proposed to find multiple models converging to local minima in the subparameter space of deep CNNs, which can improve generalization while being more affordable during training and testing stages.
PATTERN RECOGNITION
(2021)
Article
Medicine, General & Internal
Mingyao Yang, Jie Ma, Pin Wang, Zhiyong Huang, Yongming Li, He Liu, Zeeshan Hameed
Summary: A novel Hierarchical Boosting Dual-Stage Feature Reduction Ensemble Model (HBD-SFREM) is proposed in this paper to improve the accuracy of Parkinson's disease (PD) speech recognition significantly. By incorporating iterative deep extraction and feature reduction methods, HBD-SFREM achieves higher quality features and is not affected by small sample datasets.
Article
Computer Science, Theory & Methods
Chuanchang Liu, Jianyun Lu, Wendi Feng, Enbo Du, Luyang Di, Zhen Song
Summary: This paper presents MOBIPCR, an efficient mobile-oriented malware detection system that integrates a cloud-based architecture, machine learning model, and detection process to protect personal data.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Engineering, Electrical & Electronic
Chaorong Li
Summary: In this paper, a multi-feature DCNN ensemble learning method based on machine learning is proposed for facial recognition and verification. By expanding the feature dimension of the DCNN model and utilizing machine learning methods for secondary learning, more discriminative features are extracted, leading to improved recognition and validation accuracy of existing DCNN models.
SIGNAL IMAGE AND VIDEO PROCESSING
(2023)
Article
Computer Science, Information Systems
Mazin Abed Mohammed, Mohamed Elhoseny, Karrar Hameed Abdulkareem, Salama A. Mostafa, Mashael S. Maashi
Summary: This study focuses on Parkinson's disease (PD) diagnosis through voice data features. A new multi-agent feature filter (MAFT) algorithm is proposed to select the best features from the voice dataset. By integrating MAFT with multiple machine learning methods, a powerful voice-based PD diagnosis model is established, which shows significant improvements in diagnosis accuracy.
ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
(2021)
Article
Clinical Neurology
Jaiver Macea, Miguel Bhagubai, Victoria Broux, Maarten De Vos, Wim Van Paesschen
Summary: The performance of an EEG seizure-detector algorithm was evaluated in patients with refractory epilepsy using a wearable device. The sensitivity of the device was found to be 52% in inpatients and 23% in outpatients, with high false alarm rates and low performance scores. Although well-received by patients, the device had side effects and its implementation in clinical practice is currently limited.
Letter
Respiratory System
Paul Desbordes, Maarten De Vos, Julie Maes, Frans de Jongh, Karl Sylvester, Claus Franz Vogelmeier, Anh Tuan Dinh-Xuan, Jann Mortensen, Wim Janssens, Marko Topalovic
EUROPEAN RESPIRATORY JOURNAL
(2023)
Article
Engineering, Biomedical
Tim Hermans, Laura Smets, Katrien Lemmens, Anneleen Dereymaeker, Katrien Jansen, Gunnar Naulaers, Filippo Zappasodi, Sabine Van Huffel, Silvia Comani, Maarten De Vos
Summary: This paper proposes a semi-supervised deep learning approach for artefact detection in neonatal EEG. The proposed method outperforms existing state-of-the-art models and achieves good performance on two separate datasets. The results demonstrate the effectiveness of the semi-supervised multi-task training strategy and the relevance of artefact detection for automated EEG analysis.
JOURNAL OF NEURAL ENGINEERING
(2023)
Article
Respiratory System
Kenneth Verstraete, Iwein Gyselinck, Helene Huts, Nilakash Das, Marko Topalovic, Maarten De Vos, Wim Janssens
Summary: This study developed machine learning models to estimate and predict individual treatment effects of interventions in patients with chronic obstructive pulmonary disease (COPD). The results showed that poor lung function and blood eosinophils were the strongest predictors of individual treatment effects. The findings suggest that machine learning models can be used to guide personalized treatment decisions in COPD.
Article
Biotechnology & Applied Microbiology
Miguel Bhagubai, Kaat Vandecasteele, Lauren Swinnen, Jaiver Macea, Christos Chatzichristos, Maarten De Vos, Wim Van Paesschen
Summary: This study evaluated a semi-automated multimodal wearable seizure detection framework using bte-EEG and ECG data. The results showed that combining ECG with bte-EEG can improve the accuracy of seizure detection and reduce false alarm rates, while also saving time for both clinicians and patients.
BIOENGINEERING-BASEL
(2023)
Article
Clinical Neurology
Tim Hermans, Mohammad Khazaei, Khadijeh Raeisi, Pierpaolo Croce, Gabriella Tamburro, Anneleen Dereymaeker, Maarten De Vos, Filippo Zappasodi, Silvia Comani
Summary: This study aims to explore the use of microstate (MS) analysis to evaluate global brain dynamics changes during maturation in preterm neonates with normal neurodevelopmental outcome. The study found that MS duration decreased and occurrence increased with age in preterm neonates, and significant changes in MS topographies and transitions occurred when neonates reached 37 weeks. Additionally, the Hurst exponent of the individual MS sequence decreased with age.
Meeting Abstract
Neurosciences
Laure Sillisa, Cleo Vandegoor, Cato Vercaeren, Karel Allegaert, Annick Bogaerts, Maarten De Vos, Titia Hompes, Anne Smits, Kristel Van Calsteren, Jan Y. Verbakel, Veerle Foulon, Michael Ceulemans
NEUROTOXICOLOGY AND TERATOLOGY
(2023)
Article
Computer Science, Information Systems
Oliver Y. Chen, Florian Lipsmeier, Huy Phan, Frank Dondelinger, Andrew Creagh, Christian Gossens, Michael Lindemann, Maarten de Vos
Summary: Personalized longitudinal disease assessment is crucial for MS diagnosis, management, and therapeutic adaptation. We propose a novel model that utilizes smartphone sensor data to map individual disease trajectories in an automated way, even with missing values. The model incorporates sensor-based assessments and imputation for missing data, and identifies potential markers of MS through a generalized estimation equation. The results demonstrate the potential of this model for personalized MS assessment, suggesting that digitally collected features related to gait, balance, and upper extremity function can serve as useful markers for predicting MS over time.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Cardiac & Cardiovascular Systems
John Prince, John Maidens, Spencer Kieu, Caroline Currie, Daniel Barbosa, Cody Hitchcock, Adam Saltman, Kambiz Norozi, Philipp Wiesner, Nicholas Slamon, Erica Del Grippo, Deepak Padmanabhan, Anand Subramanian, Cholenahalli Manjunath, John Chorba, Subramaniam Venkatraman
Summary: This study evaluated a set of machine learning algorithms applied to cardiac auscultation, which outperformed clinicians in accuracy. The results suggest that adopting machine learning algorithms could improve the detection of structural heart disease.
JOURNAL OF THE AMERICAN HEART ASSOCIATION
(2023)
Article
Neurosciences
Gabrielle Cretot-Richert, Maarten De Vos, Stefan Debener, Martin G. Bleichner, Jeremie Voix
Summary: This study investigates the potential of using EEG recorded inside and around the human ear to determine levels of attention and focus. The results suggest that neural oscillations recorded with ear-EEG can differentiate between levels of cognitive workload and working memory when multi-channel recordings are available.
FRONTIERS IN NEUROSCIENCE
(2023)
Article
Respiratory System
Katleen Swinnen, Kenneth Verstraete, Claudia Baratto, Laura Hardy, Maarten De Vos, Marko Topalovic, Guido Claessen, Rozenn Quarck, Catharina Belge, Jean-Luc Vachiery, Wim Janssens, Marion Delcroix
Summary: This study developed and validated a machine learning model to improve the prediction accuracy of PH-LHD in a population of PAH and PH-LHD patients. The model significantly improved the sensitivity of PH-LHD prediction at 100% specificity, and may substantially reduce the number of patients referred for invasive diagnostics without missing PAH diagnoses.
Meeting Abstract
Critical Care Medicine
M. Topalovic, M. De Vos, J. Maes, J. Kaspers, N. Stachowicz, P. Desbordes
AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE
(2023)
Meeting Abstract
Transplantation
Amelie Dendooven, Aristotelis Styanidis, Louis Raes, Amaryllis Van Craenenbroeck, Matthias Maeyens, Konstantinos Kotras, Maarten De Vos
NEPHROLOGY DIALYSIS TRANSPLANTATION
(2023)
Article
Respiratory System
Kenneth Verstraete, Nilakash Das, Iwein Gyselinck, Marko Topalovic, Thierry Troosters, James D. Crapo, Edwin K. Silverman, Barry J. Make, Elizabeth A. Regan, Robert Jensen, Maarten De Vos, Wim Janssens
Summary: The shape of MEFVC is associated with CT parameters of emphysema, small airways disease (SAD), and bronchial wall thickening (BWT) in COPD. It is a valuable predictor for emphysema and SAD in moderate-severe COPD, but not a suitable screening tool for early disease phenotypes identified by CT scan.
RESPIRATORY RESEARCH
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Oliver Y. Chen, Vu Duy Thanh, Gilbert Greub, Hengyi Cao, Xingru He, Yannick Muller, Constantinos Petrovas, Haochang Shou, Viet-Dung Nguyen, Bangdong Zhi, Laurent Perez, Jean-Louis Raisaro, Guy Nagels, Maarten de Vos, Wei He, Gottardo Palie Smart, Marcus Munafo, Giuseppe Pantaleo
Summary: This article presents a systematic approach to studying varying brain. It discusses different types of brain variability and provides examples for each. It explores classical analysis of covariance as well as advanced residual analysis methods that aim to decompose the total variance of brain or behavior data. The article also considers innate and acquired brain variability, the neural law of large numbers for big brain data, and the gut-brain axis as an important source of brain variability.
2023 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP, SSP
(2023)