Fault Diagnosis Method of Permanent Magnet Synchronous Motor Demagnetization and Eccentricity Based on Branch Current by Zhiqiang Wang, Shangru Shi, Xin Gu, Zhezhun Xu, Huimin Wang, Zhen Zhang

“…Finally, to further improve diagnostic accuracy, a cascaded convolutional neural network based on dilated convolutional layers and multi-scale convolutional layers is designed as the diagnostic model. …”
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Fault Detection in Induction Machines Using Learning Models and Fourier Spectrum Image Analysis by Kevin Barrera-Llanga, Jordi Burriel-Valencia, Angel Sapena-Bano, Javier Martinez-Roman

“…Induction motors are essential components in industry due to their efficiency and cost-effectiveness. This study presents an innovative methodology for automatic fault detection by analyzing images generated from the Fourier spectra of current signals using deep learning techniques. …”
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Scenario-adaptive wireless fall detection system based on few-shot learning by Yuting ZENG, Suzhi BI, Lili ZHENG, Xiaohui LIN, Hui WANG

“…A scenario robust fall detection system based on few-shot learning (FDFL) in wireless environment was designed.The performance of existing fall detection methods based on Wi-Fi channel state information (CSI) degrades significantly across scenarios, which requires collecting and marking a large number of CSI samples in each application scenario, resulting in high cost for large-scale deployment.Therefore, the method of few-shot learning was introduced, which can maintain the performance of fall detection with high accuracy when the number of annotated samples in unfa-miliar scenes is insufficient.The proposed FDFL was mainly divided into two stages, source domain meta-training and target domain meta-learning.The meta training stage of the source domain consists of two parts: data preprocessing and classification training.In the data preprocessing stage, the collected original CSI amplitude and phase data were denoised and segmented.In the classification training stage, a large number of processed source domain data samples were used to train a CSI feature extractor based on convolutional neural network.In the meta-learning stage of the target domain, the limited labeled data sampled in the target domain was effectively extracted based on the feature extractor trained in the meta-training module, and then a lightweight machine learning classifier was trained to detect the fall behavior under the cross-scene.Through several experiments in different scenarios, FDFL can achieve an average accuracy of 95.52% for the four classification tasks of falling, sitting, walking and sit down with only a small number of samples in the target domain, and maintain robust detection accuracy for changes in test environment, personnel target and equipment location.…”
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Advancing ADMET prediction for major CYP450 isoforms: graph-based models, limitations, and future directions by Asmaa A. Abdelwahab, Mustafa A. Elattar, Sahar Ali Fawzi

“…Traditional approaches, while foundational, often face challenges related to cost, scalability, and translatability. This review provides a comprehensive exploration of how graph-based computational techniques, including Graph Neural Networks (GNNs), Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), have emerged as powerful tools for modeling complex CYP enzyme interactions and predicting ADMET properties with improved precision. …”
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Navigating the Challenges and Opportunities of Tiny Deep Learning and Tiny Machine Learning in Lung Cancer Identification by Yasir Salam Abdulghafoor, Auns Qusai Al-Neami, Ahmed Faeq Hussein

“…The combination of lightweight Convolutional Neural Networks and limited resources could produce a portable model with low computational cost that has the ability to substitute the skill and experience of doctors needed in urgent cases. …”
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Posture Monitoring of Patients in Radiotherapy Scenarios Based on Stacked Grayscale 3-Channel Images by Yang Zhang, Ziwen Wei, Zhihua Liu, Xiaolong Wu, Junchao Qian

“…Conclusion: In this study, we introduced a cost-effective and highly accurate method for recognizing patient’s postures during radiotherapy. …”
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The Application of a Marine Weather Data Reconstruction Model Based on Deep Super-Resolution in Ship Route Optimization by Shangfu Li, Junfu Yuan, Zhizheng Wu

“…However, due to the insufficient coverage of the maritime network, the high cost of satellite communication, and the limited bandwidth, it is difficult for ships to obtain high-resolution weather data during route planning. …”
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DeepLASD countermeasure for logical access audio spoofing by Hamed Al-Tairi, Ali Javed, Tasawer Khan, Abdul Khader Jilani Saudagar

“…The model incorporates a SincConv layer for interpretable spectral processing, along with residual convolutional blocks that integrate attention for improved feature extraction. …”
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Recent Trends and Advances in Utilizing Digital Image Processing for Crop Nitrogen Management by Bhashitha Konara, Manokararajah Krishnapillai, Lakshman Galagedara

“…However, precise crop N management (PNM) is hindered by its intensive data requirements, high cost, and time requirements. Digital image processing (DIP) offers a promising approach to overcoming these challenges, and numerous studies have explored its application in N management. …”
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Intelligent Hybrid SHM-NDT Approach for Structural Assessment of Metal Components by Romaine Byfield, Ahmed Shabaka, Milton Molina Vargas, Ibrahim Tansel

“…In contrast, SHM employs permanently installed, cost-effective sensors to enable continuous monitoring, though often with reduced detail. …”
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Study of Cathode Materials for Na-Ion Batteries: Comparison Between Machine Learning Predictions and Density Functional Theory Calculations by Claudio Ronchetti, Sara Marchio, Francesco Buonocore, Simone Giusepponi, Sergio Ferlito, Massimo Celino

“…The limitations associated with lithium’s supply chain, cost, and safety concerns have prompted the exploration of alternative battery chemistries. …”
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ECGConVT: A Hybrid CNN and Vision Transformer Model for Enhanced 12-Lead ECG Images Classification by Mudassar Khalid, Charnchai Pluempitiwiriyawej, Abdulkadhem A. Abdulkadhem, Imran Afzal, Tien Truong

“…Electrocardiogram (ECG) tests have emerged as widely employed, low-cost and non-invasive procedures for evaluating electrical activities of the heart and diagnosing cardiovascular ailments. …”
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MDRN: Multi-distillation residual network for efficient MR image super-resolution by Liwei Deng, Jingyi Chen, Xin Yang, Sijuan Huang

“…However, current SR methods often use the complex convolutional network for feature extraction, which is difficult to train and not suitable for limited computation resources in the medical scenario. …”
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Clinical Applicability and Cross-Dataset Validation of Machine Learning Models for Binary Glaucoma Detection by David Remyes, Daniel Nasef, Sarah Remyes, Joseph Tawfellos, Michael Sher, Demarcus Nasef, Milan Toma

“…Early and accurate detection is critical to prevent vision loss, yet traditional diagnostic methods such as optical coherence tomography and visual field tests face challenges in accessibility, cost, and consistency, especially in under-resourced areas. …”
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Fungi-Kcr: a language model for predicting lysine crotonylation in pathogenic fungal proteins by Yong-Zi Chen, Yong-Zi Chen, Xiaofeng Wang, Zhuo-Zhi Wang, Haixin Li, Haixin Li

“…However, the experimental identification of Kcr sites remains challenging due to the high cost and time-consuming nature of mass spectrometry-based techniques.MethodsTo address this limitation, we developed Fungi-Kcr, a deep learning-based model designed to predict Kcr modification sites in fungal proteins. …”
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NRNH-AR: A Small Robotic Agent Using Tri-Fold Learning for Navigation and Obstacle Avoidance by Carlos Vasquez-Jalpa, Mariko Nakano, Martin Velasco-Villa, Osvaldo Lopez-Garcia

“…The proposed algorithm was evaluated in four critical aspects: computational cost, learning stability, required memory size, and operation speed. …”
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Artificial intelligence and Artificial Neural Networks in toxicology: challenges, perspectives and applications (Narrative review) by Sara Karimi ZEVERDEGANI, Elham SABER, Samira BARAKAT

“…Regularized and fully connected convolutional neural networks cannot detect and detect discrete changes in toxicity related two-dimensional data patterns. …”
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Redefining failure detection in PV Systems: a comparative study of GPT-4o and ResNet's computer vision in aerial infrared imagery analysis by Gallmetzer Sandra, Sondoqah Mousa, Turri Evelyn, Koester Lukas, Louwen Atse, Moser David

“…Aerial infrared thermography has become an essential tool for detecting anomalies in photovoltaic modules due to its cost-effectiveness and scalability. Continuous monitoring through advanced fault detection and classification methods can maintain optimal system performance and extend the life of PV modules. …”
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Citrus Disease Classification Model Based on Improved ConvNeXt by Jichi Yan, Yongbin Mo, Yannan Yu, Shiqing Dou, Rongfeng Yang

“…Early diagnosis of citrus diseases directly affects the yield and quality of citrus cultivation, and a citrus disease classification model based on improved ConvNeXt is proposed to address the problems of high cost and low efficiency of traditional citrus disease detection methods. …”
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Research and Application of Structural Plane Identification for Roadway Surrounding Based on Deep Learning by Qiang Xu, Ze Xia, Gang Huang, Xuehua Li, Xu Gao, Yukuan Fan

“…Compared with traditional classification methods, the proposed method rapidly recognizes and classifies structural planes in borehole images at low cost, with precision, and in a non-destructive and automated manner.…”
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