Article
Chemistry, Analytical
Gang Hu, Kejun Wang, Liangliang Liu
Summary: A new deep neural network model for underwater target recognition is proposed in this paper, which can automatically extract features from the raw data of ship-radiated noise. Evaluation using measured data shows that the features extracted from the model have good characteristics of intra-class aggregation and inter-class separation. Verification with cross-folding model enhances the generalization ability of the model.
Article
Automation & Control Systems
Fan-Hsun Tseng, Kuo-Hui Yeh, Fan-Yi Kao, Chi -Yuan Chen
Summary: This study proposes a new artificial intelligence model, MiniNet, which reduces computations and shortens training time under limited hardware resources. MiniNet utilizes depthwise and pointwise convolutions and incorporates dense connection technique and Squeeze-and-Excitation operations. Experimental results demonstrate that MiniNet significantly reduces parameters and training time while achieving high accuracy.
Article
Computer Science, Artificial Intelligence
Chandra Sekhar Vorugunti, Viswanath Pulabaigari, Prerana Mukherjee, Avinash Gautam
Summary: Online signature verification is a framework used to authenticate the legitimacy of a signature by learning the writer's specific characteristics. A depthwise separable convolution-based framework and a dimensionality reduction-based feature extraction technique are proposed in this study. Extensive experiments on multiple datasets demonstrate the high accuracy of the framework in signature classification.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Mathematical & Computational Biology
Sohaib Asif, Ming Zhao, Xuehan Chen, Yusen Zhu
Summary: Kidney stone disease is a common and serious health problem worldwide. This study proposes a lightweight and high-performance model, StoneNet, for the detection of kidney stones. Experimental results show that StoneNet outperforms other models in terms of accuracy and complexity, and can assist radiologists in faster diagnosis of kidney stones.
INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES
(2023)
Article
Engineering, Multidisciplinary
Xueyi Li, Peng Yuan, Xiangkai Wang, Daiyou Li, Zhijie Xie, Xiangwei Kong
Summary: This paper proposes an improved adaptive batch normalization (AdaBN) transfer learning bearing fault diagnosis method for batch normalization (BN) in traditional deep learning architecture. The method preprocesses the raw vibration signals and trains a depthwise sparable convolution neural model. Features are extracted by depthwise convolution and point convolution in the network. A small amount of labeled data is classified using transfer learning methods, and the experimental validation showed that the accuracy of the bearing fault diagnosis method using AdaBN reached 85%.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2023)
Article
Computer Science, Hardware & Architecture
Jooyeon Choi, Hyeonuk Sim, Sangyun Oh, Sugil Lee, Jongeun Lee
Summary: This article proposes a novel logarithmic quantization-based deep neural network (DNN) architecture for depthwise separable convolution (DSC) networks. The architecture is improved with selective two-word logarithmic quantization (STLQ), which enhances accuracy while maintaining the speed and area advantage of logarithmic quantization. Additionally, a novel architecture and compile-time optimization technique address the synchronization problem caused by variable-latency processing elements (PEs). Experimental results demonstrate that this architecture is significantly faster and more area efficient compared to previous DSC accelerator architectures and accelerators utilizing logarithmic quantization.
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Chenghong Xiao, Shuyuan Yang, Zhixi Feng
Summary: In this article, a novel end-to-end automatic modulation classification (AMC) model called complex-valued depthwise separable convolutional neural network (CDSCNN) is proposed, which adopts complex-valued operation units for tailored feature learning for AMC. With an overall accuracy of 62.63% on the RadioML2016.10a dataset, CDSCNN outperforms its counterparts by 1%-11%. After fine-tuning on the RadioML2016.10b dataset, the overall accuracy reaches 63.15%, demonstrating the robust recognition and generalization capability of CDSCNN. Moreover, CDSCNN exhibits lower model complexity compared to other methods.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Automation & Control Systems
Yanlei Xu, Yuting Zhai, Bin Zhao, Yubin Jiao, ShuoLin Kong, Yang Zhou, Zongmei Gao
Summary: This study proposed a weed recognition method using depthwise separable convolutional neural network based on deep transfer learning to improve accuracy. The Xception model was used and refined with XGBoost classifier to achieve higher classification accuracy. Experimental results showed significant improvement in weed recognition accuracy, demonstrating the promising ability of the proposed method for image detection and precise recognition results.
INTELLIGENT AUTOMATION AND SOFT COMPUTING
(2021)
Article
Biology
Prasad Dutande, Ujjwal Baid, Sanjay Talbar
Summary: This study proposes a deep learning-based methodology for delineating lung cancer tumors. By using a pre-processing method, two novel deep learning networks, and an ensemble strategy, this method outperforms traditional methods and other segmentation networks in terms of performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Artificial Intelligence
Zahid Younas Khan, Zhendong Niu
Summary: A novel deep learning model, CNN-DSCK, is proposed for rating prediction by utilizing product reviews. This model uses two parallel CNN networks with Depthwise Separable Convolutions to extract features from user and item reviews, select important information through different kernels, pool and concatenate the information, and extract higher-order features through a fully connected layer. Extensive experiments show that CNN-DSCK significantly outperforms state of the art baseline models in real-world datasets.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Yiru Wei, Zhiliang Zhu, Hai Yu, Wei Zhang
Summary: X-ray baggage inspection is crucial for detecting threat objects at controlled access points. The limitations of manual detection can be overcome by automated detection models, with our proposed model showing superior performance in accuracy and speed, with enhanced precision of threat object regions.
JOURNAL OF REAL-TIME IMAGE PROCESSING
(2021)
Article
Remote Sensing
Ke Zhang, Inuwa Mamuda Bello, Yu Su, Jingyu Wang, Ibrahim Maryam
Summary: This article proposes a lightweight multiscale segmentation framework that achieves high accuracy pixel prediction with relatively low computational overhead by embedding sparse network architecture and depthwise separable convolution at the multiscale level.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2022)
Article
Computer Science, Information Systems
Safa Bouguezzi, Hana Ben Fredj, Tarek Belabed, Carlos Valderrama, Hassene Faiedh, Chokri Souani
Summary: In this paper, a novel FPGA-based Ad-MobileNet model is introduced, utilizing the Ad-depth engine and multiple activation functions, achieving 88.76% classification accuracy on the CIFAR-10 dataset and saving 41% of hardware resources compared to the baseline model.
Article
Engineering, Biomedical
Denghuang Zhao, Zhixin Qiu, Yujie Jiang, Xincheng Zhu, Xiaojun Zhang, Zhi Tao
Summary: In recent years, deep learning methods have achieved satisfactory results in automatic pathological voice detection (APVD). However, most of these methods lack interpretability, which limits their generalization performance in practical applications. This paper proposes an interpretable neural network architecture, IMBFN, based on clear feature extraction logic and a comprehensive result judgment method, to improve the effectiveness and generalization performance of APVD.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyu Kong, Ke Zhang
Summary: Human behavior is influenced by emotions, and predicting behavior through emotion classification from text is significant for decision-making. Efficiently extracting emotional tendencies from text data is a challenge, but a upgraded CNN model proposed in this study improves the downsides and shows better performance in sentiment analysis tasks.
PEERJ COMPUTER SCIENCE
(2023)
Article
Computer Science, Interdisciplinary Applications
Alireza Karimi, Reza Razaghi, Siddharth Daniel D'costa, Saeed Torbati, Sina Ebrahimi, Seyed Mohammadali Rahmati, Mary J. Kelley, Ted S. Acott, Haiyan Gong
Summary: This study investigated the biomechanical properties of the conventional aqueous outflow pathway using fluid-structure interaction. The results showed that the distribution of aqueous humor wall shear stress within this pathway is not uniform, which may contribute to our understanding of the underlying selective mechanisms.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Robert V. Bergen, Jean-Francois Rajotte, Fereshteh Yousefirizi, Arman Rahmim, Raymond T. Ng
Summary: This article introduces a 3D generative model called TrGAN, which can generate medical images with important features and statistical properties while protecting privacy. By evaluating through a membership inference attack, the fidelity, utility, and privacy trade-offs of the model were studied.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Hoda Mashayekhi, Mostafa Nazari, Fatemeh Jafarinejad, Nader Meskin
Summary: In this study, a novel model-free adaptive control method based on deep reinforcement learning (DRL) is proposed for cancer chemotherapy drug dosing. The method models the state variables and control action in their original infinite spaces, providing a more realistic solution. Numerical analysis shows the superior performance of the proposed method compared to the state-of-the-art RL-based approach.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Hao Sun, Bao Li, Liyuan Zhang, Yanping Zhang, Jincheng Liu, Suqin Huang, Xiaolu Xi, Youjun Liu
Summary: In cases of moderate stenosis in the internal carotid artery, the A1 segment of the anterior cerebral artery or the posterior communicating artery within the Circle of Willis may show a hemodynamic environment with high OSI and low TAWSS, increasing the risk of atherosclerosis development and stenosis in the CoW.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Ilaria Toniolo, Paola Pirini, Silvana Perretta, Emanuele Luigi Carniel, Alice Berardo
Summary: This study compared the outcomes of endoscopic sleeve gastroplasty (ESG) and laparoscopic sleeve gastrectomy (LSG) in weight loss surgery using computational models of specific patients. The results showed significant differences between the two procedures in terms of stomach volume reduction and mechanical stimulation. A predictive model was proposed to support surgical planning and estimation of volume reduction after ESG.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Chun-You Chen, Ya-Lin Chen, Jeremiah Scholl, Hsuan-Chia Yang, Yu-Chuan (Jack) Li
Summary: This study evaluated the overall performance of a machine learning-based CDSS (MedGuard) in triggering clinically relevant alerts and intercepting inappropriate drug errors and LASA drug errors. The results showed that MedGuard has the ability to improve patients' safety by triggering clinically valid alerts.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Lingzhi Tang, Xueqi Wang, Jinzhu Yang, Yonghuai Wang, Mingjun Qu, HongHe Li
Summary: In this paper, a dynamical local feature fusion net for automatically recognizing aortic valve calcification (AVC) from echocardiographic images is proposed. The network segments high-echo areas and adjusts the selection of local features to better integrate global and local semantic representations. Experimental results demonstrate the effectiveness of the proposed approach.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
You-Lei Fu, Wu Song, Wanni Xu, Jie Lin, Xuchao Nian
Summary: This study investigates the combination of surface electromyographic signals (sEMG) and deep learning-based CNN networks to study the interaction between humans and products and the impact on body comfort. It compares the advantages and disadvantages of different CNN networks and finds that DenseNet has unique advantages over other algorithms in terms of accuracy and ease of training, while mitigating issues of gradient disappearance and model degradation.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Moritz Rempe, Florian Mentzel, Kelsey L. Pomykala, Johannes Haubold, Felix Nensa, Kevin Kroeninger, Jan Egger, Jens Kleesiek
Summary: In this study, a deep learning-based skull stripping algorithm for MRI was proposed, which works directly in the complex valued k-space and preserves the phase information. The results showed that the algorithm achieved similar results to the ground truth, with higher accuracy in the slices above the eye region. This approach not only preserves valuable information for further diagnostics, but also enables immediate anonymization of patient data before being transformed into the image domain.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Ziyang Chen, Laura Cruciani, Elena Lievore, Matteo Fontana, Ottavio De Cobelli, Gennaro Musi, Giancarlo Ferrigno, Elena De Momi
Summary: In this paper, a deep learning-based approach is proposed to recover 3D information of intra-operative scenes, which can enhance the safety of robot-assisted surgery by implementing depth estimation using stereo images.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Ao Leng, Bolun Zeng, Yizhou Chen, Puxun Tu, Baoxin Tao, Xiaojun Chen
Summary: This study presents a novel training system for zygomatic implant surgery, which offers a more realistic simulation and training solution. By integrating visual, haptic, and auditory feedback, the system achieves global rigid-body collisions and soft tissue simulation, effectively improving surgeons' proficiency.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Yingjie Wang, Xueqing Yin
Summary: This study developed an integrated computational model combining coronary flow and myocardial perfusion models to achieve physiologically accurate simulations. The model has the potential for clinical application in diagnosing insufficient myocardial perfusion.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Nitzan Avidan, Moti Freiman
Summary: This study aims to enhance the generalization capabilities of DNN-based MRI reconstruction methods for undersampled k-space data. By introducing a mask-aware DNN architecture and training method, the under-sampled data and mask are encoded within the model structure, leading to improved performance. Rigorous testing on the widely accessible fastMRI dataset reveals that this approach demonstrates better generalization capabilities and robustness compared to traditional DNN methods.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Enhao Zhang, Saeed Miramini, Lihai Zhang
Summary: This study investigates the combined effects of osteoporosis and diabetes on fracture healing process by developing numerical models. The results show that osteoporotic fractures have higher instability and disruption in mesenchymal stem cells' proliferation and differentiation compared to non-osteoporotic fractures. Moreover, when osteoporosis coexists with diabetes, the healing process of fractures can be severely impaired.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Yunhao Bai, Wenqi Li, Jianpeng An, Lili Xia, Huazhen Chen, Gang Zhao, Zhongke Gao
Summary: This study proposes an effective MIL method for classifying WSI of esophageal cancer. The use of self-supervised learning for feature extractor pretraining enhances feature extraction from esophageal WSI, leading to more robust and accurate performance. The proposed framework outperforms existing methods, achieving an accuracy of 93.07% and AUC of 95.31% on a comprehensive dataset of esophageal slide images.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)