Article
Computer Science, Artificial Intelligence
S. Qasim Abbas, Lianhua Chi, Yi-Ping Phoebe Chen
Summary: Structural magnetic resonance imaging (sMRI) is a prevalent and potent imaging modality for the computer-aided diagnosis (CAD) of neurological diseases like dementia. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have shown promise in diagnosing Alzheimer's disease (AD) by learning the atrophy patterns in sMRIs. However, the current CNN-based approaches still need to improve their diagnostic performance. To address this issue, the proposed three-dimensional Jacobian domain convolutional neural network (JD-CNN) offers excellent classification performance without the need for landmark detection. The JD-CNN model is trained based on features transformed from the spatial domain to the Jacobian domain, and it surpasses previously reported state-of-the-art techniques in terms of classification performance.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Interdisciplinary Applications
Wenyong Zhu, Liang Sun, Jiashuang Huang, Liangxiu Han, Daoqiang Zhang
Summary: The DA-MIDL model utilizes Patch-Nets to extract discriminative features from sMRI patches, attention multi-instance learning pooling operation to balance contributions of each patch, and an attention-aware global classifier to learn integral features and make AD-related classification decisions. The model outperforms several state-of-the-art methods in identifying pathological locations and achieving classification accuracy and generalizability.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2021)
Article
Engineering, Multidisciplinary
Peng Chen, Yu Li, Kesheng Wang, Ming J. Zuo
Summary: Fault detection and diagnosis are crucial for planetary gearboxes in rotating machinery, but face challenges in non-stationary operating conditions. Signal processing methods are typically used, but current deep learning algorithms are limited to stationary conditions. To address this, an Automatic Speed Adaption Neural Network (ASANN) model with incorporating of instantaneous rotating speed is proposed in this paper, offering extraordinary capacity for planetary gearbox fault detection under varying operational scenarios.
Article
Engineering, Electrical & Electronic
Zuhao Liu, Huan Wang, Yibo Gao, Shunchen Shi
Summary: This article introduces a new attention learning method based on NAS for detecting cardiovascular diseases, which outperforms existing methods in experiments.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Computer Science, Artificial Intelligence
Zewei Ding, Wanqing Li, Jie Yang, Philip Ogunbona, Ling Qin
Summary: This paper proposes a fully automatic postural assessment method based on a convolutional neural network. The method learns to identify relevant regions and automatically extract features to estimate posture risk. Experimental results show that the proposed method achieves promising performance and is consistent with human assessment.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Interdisciplinary Applications
Hongling Zhu, Jinsheng Lai, Bingqiang Liu, Ziyuan Wen, Yulong Xiong, Honglin Li, Yuhua Zhou, Qiuyun Fu, Guoyi Yu, Xiaoxiang Yan, Xiaoyun Yang, Jianmin Zhang, Chao Wang, Hesong Zeng
Summary: A deep learning approach for the automated grading diagnosis of COVID-19 by pulmonary auscultation analysis was developed and showed promising performance in automatically diagnosing COVID-19 patients among different categories. The model is capable in identifying crackles, wheezes, and phlegm sounds in auscultation.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2022)
Article
Engineering, Biomedical
Debasis Maji, Souvik Maiti, Ashis Kumar Dhara, Gautam Sarkar
Summary: This paper presents a robust Convolutional Neural Network (CNN) architecture for grading diabetic retinopathy, which can effectively handle noise and variation in retinal fundus images. The proposed architecture outperforms standard architectures like ResNet50 and VGG16, and achieves promising results in grading the disease.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Computer Science, Artificial Intelligence
Togzhan Barakbayeva, Fatih M. Demirci
Summary: This paper proposes a framework for the automatic construction of CNN architectures, which improves the classification performance in image classification tasks without requiring manual interventions.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Fatima Hassan, Syed Fawad Hussain, Saeed Mian Qaisar
Summary: Schizophrenia, a severe mental disorder characterized by disorganized speech and delusions, can be identified using non-invasive and high temporal resolution EEG signals. In this study, a publicly available multi-channel EEG signals dataset is utilized to automatically identify Schizophrenia using a subset of data from selected channels. The combination of three specific channels achieved high accuracies of 90% and 98% on subject-based and non-subject based testing, respectively, using a fusion of CNN and LR.
INFORMATION FUSION
(2023)
Article
Robotics
Yu-Ming Hsieh, Tan-Ju Wang, Chin-Yi Lin, Li-Hsuan Peng, Fan-Tien Cheng, Sui-Yan Shang
Summary: Factories typically use sampling inspection rather than real-time comprehensive inspection, and AVM can address this issue. With the advancement of technology, the accuracy requirements for virtual metrology have increased, leading to the proposal of using CNN to improve the predictive accuracy of AVM on data, with experimental results showing that CNN can enhance the original AVM accuracy.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Plant Sciences
Ruoling Deng, Ming Tao, Hang Xing, Xiuli Yang, Chuang Liu, Kaifeng Liao, Long Qi
Summary: A new automatic diagnosis method based on deep learning was developed to diagnose six types of rice diseases, achieving an overall accuracy of 91% by integrating the three best submodels into an Ensemble Model, which effectively minimized confusion among different diseases and improved disease recognition accuracy in a smartphone app setting.
FRONTIERS IN PLANT SCIENCE
(2021)
Article
Biology
Fei Liu, Huabin Wang, Shiuan-Ni Liang, Zhe Jin, Shicheng Wei, Xuejun Li
Summary: Structural magnetic resonance imaging (sMRI) is widely used in the diagnosis of Alzheimer's disease (AD), but only a few specific atrophy areas in sMRI scans are strongly associated with AD. The challenge lies in identifying discriminating atrophy features between patients, as the degree of atrophy and lesion areas differ among individuals. To address this, we propose a MPS-FFA model that combines multiplane and multiscale feature-level fusion attention.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Immunology
Rhiannon McNeill, Christopher Kehrwald, Murielle Brum, Katrin Knopf, Nathalie Brunkhorst-Kanaan, Semra Etyemez, Carolin Koreny, Robert A. Bittner, Florian Freudenberg, Sabine Herterich, Andreas Reif, Sarah Kittel-Schneider
Summary: Nitric oxide (NO) signaling has been studied in relation to mental illnesses, but its specific contribution remains unclear. This study investigated the association between peripheral NO concentration, specific diagnoses, and genetic variation in NO synthase (NOS) genes. The results showed that patients with schizophrenia had significantly higher peripheral NO metabolite concentrations compared to other groups. Additionally, carriers of the NOS1 VNTR1 short allele had increased NO concentrations, which remained significant even at discharge. The data also suggested that patients with unresolved depressive symptoms had higher NO concentrations, with a positive correlation between symptom severity and NO concentration.
BRAIN BEHAVIOR AND IMMUNITY
(2022)
Article
Automation & Control Systems
Fei Wang, Ruonan Liu, Qinghua Hu, Xuefeng Chen
Summary: A cascade CNN (C-CNN) with progressive optimization is proposed for motor fault diagnosis in nonstationary conditions, addressing the limitations of traditional CNNs. Through physical characteristics of nonstationary vibration signals, the C-CNN achieves better performance in both constant and variable speed scenarios compared to existing methods.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Telecommunications
Xue Fu, Guan Gui, Yu Wang, Tomoaki Ohtsuki, Bamidele Adebisi, Haris Gacanin, Fumiyuki Adachi
Summary: This paper proposes a decentralized learning AMC method using model consolidation and lightweight design, which reduces the storage and computational capacity requirements, improves the training efficiency, and lowers the communication overhead.
IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING
(2022)
Article
Computer Science, Hardware & Architecture
Xingzhi Chang, Wei Liu, Chuan Zhu, Xiaohua Zou, Guan Gui
Summary: In this paper, a bilayer Markov random field (BMRF) method is proposed to address the issue of false detections caused by the lack of edge information in patterned fabric defect detection. The proposed method utilizes a constraint layer and a data layer, along with a new potential function and parameter estimation method, to achieve high recall rates on star-, box-, and dot-patterned fabrics.
JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS
(2022)
Review
Chemistry, Analytical
Anand Kumar, Sudhan Majhi, Guan Gui, Hsiao-Chun Wu, Chau Yuen
Summary: This paper discusses the importance and applications of blind modulation classification in future wireless communications, and provides a comprehensive overview of various statistical and machine learning techniques. The advantages and limitations of these methods are emphasized, and comparisons are made through simulations and experiments. Future research directions in blind MC are also discussed.
Article
Computer Science, Information Systems
Pengyu Wang, Yufan Cheng, Binhong Dong, Guan Gui
Summary: This letter proposes optimized binarized neural networks (BNNs) for wireless interference identification (WII) by constraining weights and activations to binary values, achieving extreme quantization. A novel approximation method is introduced to overcome the difficulty in propagating gradients during back-propagation. Additionally, two techniques are proposed to minimize quantization noise and create multiple routes for parameter updates, resulting in improved performance.
IEEE WIRELESS COMMUNICATIONS LETTERS
(2022)
Article
Engineering, Civil
Yuchao Chen, Jinlong Sun, Yun Lin, Guan Gui, Hikmet Sari
Summary: This paper presents a novel aircraft coordinate prediction hybrid model based on deep learning, which combines inception modules and LSTM modules to extract spatial and temporal features of dataset. The model uses ADS-B signal strength instead of specific information to obtain aircraft coordinates, sacrificing precision for reliability. Experimental results show that the proposed 2-Inception-LSTM model is optimal for positioning reliability, suitable for scenarios where high accuracy of aircraft coordinates is not required.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Environmental Sciences
Wenmei Li, Huaihuai Chen, Qing Liu, Haiyan Liu, Yu Wang, Guan Gui
Summary: This article introduces a solution to the classification of hyperspectral remote sensing images by introducing an attention mechanism and depthwise separable convolution to a three-dimensional convolutional neural network. The proposed models, 3DCNN-AM and 3DCNN-AM-DSC, have been shown to improve classification accuracy and reduce computing time.
Article
Engineering, Electrical & Electronic
Yiyang Ni, Xiaoqing Li, Haitao Zhao, Jie Yang, Wenchao Xia, Guan Gui
Summary: The study introduces an effective hybrid V2V/V2I transmission method based on a neural network to minimize transmission latency by predicting vehicle arrival rate and constructing an objective function, leading to significantly lower overall transmission latency compared to pure V2I transmission methods.
PHYSICAL COMMUNICATION
(2022)
Article
Computer Science, Information Systems
Segun Popoola, Ruth Ande, Bamidele Adebisi, Guan Gui, Mohammad Hammoudeh, Olamide Jogunola
Summary: This article proposes a federated deep learning method for zero-day botnet attack detection in IoT-edge devices. The method generates a global model by coordinating the training of independent models in multiple IoT-edge devices, achieving high-performance classification detection of zero-day botnet attacks and ensuring data privacy and security.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Cheng Cheng, Liang Guo, Tong Wu, Jinlong Sun, Guan Gui, Bamidele Adebisi, Haris Gacanin, Hikmet Sari
Summary: This article introduces a conflict detection algorithm based on ADS-B technology for aerial vehicles and further improves flight safety and conflict detection by predicting the trajectories of aerial vehicles.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Yang Peng, Pengfei Liu, Yu Wang, Guan Gui, Bamidele Adebisi, Haris Gacanin
Summary: This paper proposes a novel RFF identification method based on HCTF and SIC, achieving high accuracy by eliminating manual feature extraction and automatically extracting more features.
IEEE WIRELESS COMMUNICATIONS LETTERS
(2022)
Article
Telecommunications
Xue Fu, Guan Gui, Yu Wang, Tomoaki Ohtsuki, Bamidele Adebisi, Haris Gacanin, Fumiyuki Adachi
Summary: This paper proposes a decentralized learning AMC method using model consolidation and lightweight design, which reduces the storage and computational capacity requirements, improves the training efficiency, and lowers the communication overhead.
IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING
(2022)
Article
Telecommunications
Yu Wang, Guan Gui, Haris Gacanin, Bamidele Adebisi, Hikmet Sari, Fumiyuki Adachi
Summary: Automatic modulation classification is a promising technology for identifying modulation types, and deep learning-based methods have shown advanced performance. The introduction of federated learning aims to address the issues of data leakage risk and performance loss in centralized solutions.
IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING
(2022)
Article
Computer Science, Information Systems
Ruijie Zhao, Guan Gui, Zhi Xue, Jie Yin, Tomoaki Ohtsuki, Bamidele Adebisi, Haris Gacanin
Summary: This article proposes a lightweight deep neural network (LNN) based NID method for IoT, which achieves excellent classification performance and is suitable for classifying IoT traffic through feature dimensionality reduction and low computational cost feature extraction.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Telecommunications
Guanghui Fan, Jinlong Sun, Guan Gui, Haris Gacanin, Bamidele Adebisi, Tomoaki Ohtsuki
Summary: Due to the lack of channel reciprocity in FDD massive MIMO systems, downlink CSI needs to be continuously fed back to the base station from the user equipment, consuming bandwidth resources. This paper proposes a fully convolutional neural network for compressing and decompressing the downlink CSI. Experimental results demonstrate that the proposed method outperforms the baseline in terms of reconstruction performance and reduction of storage and computational overhead, and is robust to quantization error in real feedback scenarios.
IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING
(2022)
Article
Engineering, Electrical & Electronic
Tiantian Tang, Tao Chen, Guan Gui
Summary: Satellite precipitation products (SPPs) are significant data sources in hydrometeorology, especially for ungauged or sparsely gauged basins. However, these products have varying degrees of uncertainty and their applicability may differ in different regions. This study performs statistical evaluations and improves the accuracy of five SPPs using a merging model. The utility of the precipitation sets is investigated using a hydrological model, and the results show significant improvements in two basins.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
Proceedings Paper
Computer Science, Theory & Methods
Ziteng Wang, Wenmei Li, Guan Gui
Summary: Explored an image classification method for high spatial resolution remote sensing images. Utilized transfer learning to improve performance with a small sample size. Experimental results demonstrated that increasing the sample size can stabilize the classification performance of the model.
ADVANCED HYBRID INFORMATION PROCESSING, PT I
(2022)