Fault diagnosis and inference of hoist main bearing based on transfer learning and ontology by Fei DONG, Di ZHANG, Kunpeng GE, Junjie CHEN, Xinyue XU

“…To overcome the challenges still faced by data-driven hoist main bearing fault diagnosis methods, including data imbalance due to a lack of fault samples under real operating conditions, diagnostic performance degradation of fault diagnosis models caused by significant differences in data sample distribution under varying conditions, single fault diagnosis function, and a lack of reasoning analysis and localization for the causes of hoist main bearing system failures, a new fault diagnosis and reasoning method for hoist main bearing systems is studied, which includes two aspects: ① Bearing fault diagnosis based on convolutional neural network transfer learning and domain adaptation. …”
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A fault diagnosis method for rolling bearings in open-set domain adaptation with adversarial learning by Tongfei Lei, Feng Pan, Jiabei Hu, Xu He, Bing Li

“…Abstract The closed-set assumption often fails in practical industrial applications, especially considering diverse working conditions where the data distribution may differ significantly. In light of this, a domain adaptation method with adversarial learning is designed for open-set fault diagnosis. …”
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Neuroevolutionary reinforcing learning of neural networks by Y. A. Bury, D. I. Samal

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HTC-HAD: A Hybrid Transformer-CNN Approach for Hyperspectral Anomaly Detection by Minghua Zhao, Wen Zheng, Jing Hu

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Deep machine learning identified fish flesh using multispectral imaging by Zhuoran Xun, Xuemeng Wang, Hao Xue, Qingzheng Zhang, Wanqi Yang, Hua Zhang, Mingzhu Li, Shangang Jia, Jiangyong Qu, Xumin Wang

“…We found that nCDA images transformed from MSI data showed significant differences in flesh splices of the 20 fish species. …”
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Deep Learning for Glioblastoma Multiforme Detection from MRI: A Statistical Analysis for Demographic Bias by Kebin Contreras, Julio Gutierrez-Rengifo, Oscar Casanova-Carvajal, Angel Luis Alvarez, Patricia E. Vélez-Varela, Ana Lorena Urbano-Bojorge

“…This morphological discrepancy demonstrates the generalisation capacity of the model across anatomical and acquisition differences, achieving an F1-score of 0.88. Furthermore, statistical tests, such as Shapiro–Wilk, Mann–Whitney U, and Chi-square, confirmed the absence of demographic bias in model predictions, based on <i>p</i>-values, confidence intervals, and statistical power analyses supporting its demographic fairness. …”
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RDAU-Net: A U-Shaped Semantic Segmentation Network for Buildings near Rivers and Lakes Based on a Fusion Approach by Yipeng Wang, Dongmei Wang, Teng Xu, Yifan Shi, Wenguang Liang, Yihong Wang, George P. Petropoulos, Yansong Bao

“…To address the above issues, the present study proposes the design of a U-shaped segmentation network of buildings called RDAU-Net that works through extraction and fuses a convolutional neural network and a transformer to segment buildings. …”
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DreamOn: a data augmentation strategy to narrow the robustness gap between expert radiologists and deep learning classifiers by Luc Lerch, Luc Lerch, Lukas S. Huber, Lukas S. Huber, Amith Kamath, Alexander Pöllinger, Aurélie Pahud de Mortanges, Verena C. Obmann, Florian Dammann, Walter Senn, Walter Senn, Mauricio Reyes, Mauricio Reyes, Mauricio Reyes

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Subtypes detection of papillary thyroid cancer from methylation assay via Deep Neural Network by Andrea Colacino, Andrea Soricelli, Michele Ceccarelli, Ornella Affinito, Monica Franzese

“…Methods: To address this issue, we first performed a pan-cancer analysis to train a convolutional 1-D Neural Network (CNN) using supervised learning. …”
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Impacted lower third molar classification and difficulty index assessment: comparisons among dental students, general practitioners and deep learning model assistance by Paniti Achararit, Chawan Manaspon, Chavin Jongwannasiri, Promphakkon Kulthanaamondhita, Chumpot Itthichaisri, Soranun Chantarangsu, Thanaphum Osathanon, Ekarat Phattarataratip, Kraisorn Sappayatosok

“…Among the groups, performance tests without CNN assistance revealed no significant differences in any category. However, compared with DSs, GPs took significantly less time for the class and total time, a trend that persisted when CNN assistance was used. …”
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A dual-phase deep learning framework for advanced phishing detection using the novel OptSHQCNN approach by Srikanth Meda, Vangipuram Sesha Srinivas, Killi Chandra Bhushana Rao, Repudi Ramesh, Narasimha Rao Yamarthi

“…Background Phishing attacks are now regarded as one of the most prevalent cyberattacks that often compromise the security of different communication and internet networks. Phishing websites are created with the goal of generating cyber threats in order to ascertain the user’s financial information. …”
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Improving Solar Radiation Forecasting in Cloudy Conditions by Integrating Satellite Observations by Qiangsheng Bu, Shuyi Zhuang, Fei Luo, Zhigang Ye, Yubo Yuan, Tianrui Ma, Tao Da

“…Forecast errors are related to cloud regimes, of which the cloud amount leads to a maximum relative RMSE difference of about 50% with an additional 5% from cloud variability. …”
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Robust Road Surface Classification Using Time Series Augmented Intelligent Tire Sensor Data and 1-D CNN by Seokchan Kim, Yeong-Jae Kim, Dongwook Lee, Hanmin Lee

“…The robustness to different tires and driving conditions makes the proposed algorithm practical for estimating road surface conditions in real vehicles.…”
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Segmentation of dermatoscopic images of skin lesions. Comparison of methods by A. F. Smalyuk, M. S. Dzeshka, I. D. Kupchykava

“…The proposed algorithm makes it possible to detect differences in images even if there is a significant difference in the brightness and color levels of the compared images, and also ignores small insignificant details, such as noise, dermatoscope optics marks, hair, etc. …”
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A review of plant leaf disease identification by deep learning algorithms by Junmin Zhao, Laixiang Xu, Zizhen Ma, Juncai Li, Xiaowei Wang, Yunchang Liu, Xiaojie Du

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DPD-v2: Generalised deep particle diffusometry for varied particle shapes and experimental conditions by Pranshul Sardana, Steven T. Wereley

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Multi-scale Information Aggregation for Spoofing Detection by Changtao Li, Yi Wan, Feiran Yang, Jun Yang

“…The unique topology of DLA makes possible compounding of speech features from convolution layers at different depths, and therefore the local and the global speech representations can be incorporated simultaneously. …”
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Fine-Tuning-Based Transfer Learning for Building Extraction from Off-Nadir Remote Sensing Images by Bipul Neupane, Jagannath Aryal, Abbas Rajabifard

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Introducing an ensemble method for the early detection of Alzheimer's disease through the analysis of PET scan images by Arezoo Borji, Taha-Hossein Hejazi, Abbas Seifi

“…The classification results show that using deep-learning models to tell the difference between MCI patients gives an overall average accuracy of 93.13% and an Area Under the Curve (AUC)  of 94.4%.…”
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Efficient Method of Road Outlier Recognition Using Deep Learning Coupled with Data Augmentation Approach by Sarfaraz Natha, Fareed Ahmed Jokhio, Muhammad Shafique, Naeem Ahmed, Danish Munir Arain

“…The proposed study investigated different types of Convolutional Neural Network (CNN) pre-trained models with the Data Augmentation (DA) approach to address the frame variance problem in real-time videos. …”
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