Parameter optimization of 3D convolutional neural network for dry-EEG motor imagery brain-machine interface by Nobuaki Kobayashi, Musashi Ino

“…On the other hand, however, the edge is limited by hardware resources, and the implementation of models with a huge number of parameters and high computational cost, such as deep-learning, on the edge is challenging. …”
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A Seq-to-Seq Temporal Convolutional Network for Volleyball Jump Monitoring Using a Waist-Mounted IMU by Meng Shang, Camilla de Bleecker, Jos Vanrenterghem, Roel de Ridder, Sabine Verschueren, Carolina Varon, Walter de Raedt, Bart Vanrumste

“…Jump monitoring for volleyball players during training or a match can be crucial to prevent injuries, yet the measurement requires considerable workload and cost using traditional methods such as video analysis. …”
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A Deep Convolutional Neural Network Model for Lung Disease Detection Using Chest X-Ray Imaging by Samia Dardouri

“…Despite advancements in imaging diagnostic methods, chest radiographs remain pivotal due to their cost-effectiveness and rapid results. This study proposes an automated system for detecting multiple lung diseases in x-ray and CT scans using a customized convolutional neural network (CNN) alongside pretrained models and an image enhancement approach. …”
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Lung and Colon Cancer Histopathological Image Classification Using 1D Convolutional Channel-based Attention Networks by Nazmul Shahadat

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Covid-19 Detection from Chest X-Ray Images and Hybrid Model Recommendation with Convolutional Neural Networks by Furkan Eryılmaz, Hacer Karacan

“…Within the scope of the related study, the detection of COVID-19 from cost-effective and easily accessible lung X-Ray images was studied. …”
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Fault Classification in Diesel Engines Based on Time-Domain Responses through Signal Processing and Convolutional Neural Network by Gabriel Hasmann Freire Moraes, Ronny Francis Ribeiro Junior, Guilherme Ferreira Gomes

“…Traditional anomaly detection methods, such as thermometry, wear indicators, and radiography, often necessitate significant expertise, involve costly equipment shutdowns, and are limited by high usage costs and accessibility. …”
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Radio Frequency Signal-Based Drone Classification with Frequency Domain Gramian Angular Field and Convolutional Neural Network by Yuanhua Fu, Zhiming He

“…Existing drone classification methods based on radio frequency (RF) signals have low accuracy or a high computational cost. In this paper, we propose a novel RF signal image representation scheme that incorporates a convolutional neural network (CNN), named the frequency domain Gramian Angular Field with a CNN (FDGAF-CNN), to perform drone classification. …”
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Steady-State Visually Evoked Magnetic Signal Classification and BCI Evaluation Based on a Convolutional Neural Network by Yutong Wei, Fudan Zhao, Fengwen Zhao, Shiqiang Zheng, Chaofeng Ye, Liangyu Liu

“…A three-block temporal convolutional neural network (3B-TCN) is developed to classify brain magnetic signals. …”
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ZooCNN: A Zero-Order Optimized Convolutional Neural Network for Pneumonia Classification Using Chest Radiographs by Saravana Kumar Ganesan, Parthasarathy Velusamy, Santhosh Rajendran, Ranjithkumar Sakthivel, Manikandan Bose, Baskaran Stephen Inbaraj

“…Pneumonia, a leading cause of mortality in children under five, is usually diagnosed through chest X-ray (CXR) images due to its efficiency and cost-effectiveness. However, the shortage of radiologists in the Least Developed Countries (LDCs) emphasizes the need for automated pneumonia diagnostic systems. …”
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LSTA-CNN: A Lightweight Spatiotemporal Attention-Based Convolutional Neural Network for ASD Diagnosis Using EEG by Jing Li, Xiangwei Jia, Xinghan Chen, Gongfa Li, Gaoxiang Ouyang

“…Electroencephalography (EEG) is an effective assessment tool to identify autism spectrum disorders with low cost, and deep learning has been applied in EEG analysis for extracting meaningful features in recent years. …”
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Herbify: an ensemble deep learning framework integrating convolutional neural networks and vision transformers for precise herb identification by Farhan Sheth, Ishika Chatter, Manvendra Jasra, Gireesh Kumar, Richa Sharma

“…In addition, the growing dependence on synthetic pharmaceuticals has raised concerns regarding affordability, thereby fostering a renewed interest in herbal medicine as a cost-effective and holistic alternative. In response to this need, the current study introduces a computer vision framework for accurate herb identification. …”
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Combination of Graph and Convolutional Networks for Brain Tumor Segmentation from Multi-Modal MR Images In Clinical Applications by Marjan Vatanpour, Javad Haddadnia, Shahryar Salmani Bajestani

“…To solve the problems related to manual segmentation such as time-cost, inaccuracy and subjectivity, automatic segmentation with deep learning methods is presented. …”
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Development and validation of 3D super-resolution convolutional neural network for 18F-FDG-PET images by Hiroki Endo, Kenji Hirata, Keiichi Magota, Takaaki Yoshimura, Chietsugu Katoh, Kohsuke Kudo

“…High-resolution PET scanners that use silicon photomultipliers and time-of-flight measurements are expensive. Therefore, cost-effective software-based super-resolution methods are required. …”
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Sway frequencies may predict postural instability in Parkinson’s disease: a novel convolutional neural network approach by David Engel, R. Stefan Greulich, Alberto Parola, Kaleb Vinehout, Justus Student, Josefine Waldthaler, Lars Timmermann, Frank Bremmer

“…Our aim was to use a convolutional neural network (CNN) to differentiate people with early to mid-stage PD from healthy age-matched individuals based on spectrogram images obtained from their body sway. …”
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Predicting standardized uptake value of brown adipose tissue from CT scans using convolutional neural networks by Ertunc Erdil, Anton S. Becker, Moritz Schwyzer, Borja Martinez-Tellez, Jonatan R. Ruiz, Thomas Sartoretti, H. Alberto Vargas, A. Irene Burger, Alin Chirindel, Damian Wild, Nicola Zamboni, Bart Deplancke, Vincent Gardeux, Claudia Irene Maushart, Matthias Johannes Betz, Christian Wolfrum, Ender Konukoglu

“…Abstract The standard method for identifying active Brown Adipose Tissue (BAT) is [18F]-Fluorodeoxyglucose ([18F]-FDG) PET/CT imaging, which is costly and exposes patients to radiation, making it impractical for population studies. …”
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Open-Circuit Fault Analysis and Recognition in Three-Level Inverters Based on Recurrence Plot and Convolution Neural Network by Jianjun Yan, Yanxing Huang, Shuai Yuan, Yufan Lu, Zeyu Yu

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Rolling Bearing Fault Diagnosis Using a Deep Convolutional Autoencoding Network and Improved Gustafson–Kessel Clustering by Yaochun Wu, Rongzhen Zhao, Wuyin Jin, Linfeng Deng, Tianjing He, Sencai Ma

“…However, in mechanical fault diagnosis, labeled data are costly and time-consuming to collect. A novel method based on a deep convolutional autoencoding network (DCAEN) and adaptive nonparametric weighted-feature extraction Gustafson–Kessel (ANW-GK) clustering algorithm was developed for the fault diagnosis of bearings. …”
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Unsupervised Learning for Machinery Adaptive Fault Detection Using Wide-Deep Convolutional Autoencoder with Kernelized Attention Mechanism by Hao Yan, Xiangfeng Si, Jianqiang Liang, Jian Duan, Tielin Shi

“…Traditional fault detection methods rely on labeled data, which is costly and labor-intensive to obtain. This paper proposes a novel unsupervised approach, WDCAE-LKA, combining a wide kernel convolutional autoencoder (WDCAE) with a large kernel attention (LKA) mechanism to improve fault detection under unlabeled conditions, and the adaptive threshold module based on a multi-layer perceptron (MLP) dynamically adjusts thresholds, boosting model robustness in imbalanced scenarios. …”
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Intelligent Fault Diagnosis of Hydraulic System Based on Multiscale One-Dimensional Convolutional Neural Networks with Multiattention Mechanism by Jiacheng Sun, Hua Ding, Ning Li, Xiaochun Sun, Xiaoxin Dong

“…Hydraulic systems are critical components of mechanical equipment, and effective fault diagnosis is essential for minimizing maintenance costs and enhancing system reliability. In practical applications, data from hydraulic systems are collected with varying sampling frequencies, coupled with complex interdependencies within the data, which poses challenges for existing fault diagnosis algorithms. …”
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HLSK-CASMamba: hybrid large selective kernel and convolutional additive self-attention mamba for hyperspectral image classification by Xiaoqing Wan, Yupeng He, Feng Chen, Ziqi Sun, Dongtao Mo

“…Abstract Classifying hyperspectral images (HSIs) is a key challenge in remote sensing, with convolutional neural networks (CNNs) and transformer models becoming leading techniques in this area. …”
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