A Federated Learning-Based Framework for Accurately Identifying Human Activity in the Environment by Nwadher Suliman Al-Blihed, Dina M. Ibrahim

“…Analysis of the data generated by HAR devices may involve deep learning models and algorithms of different kinds. These data are personal and may include some sensitive data. …”
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IT Equipment Automatic Configuration Method Based on Energy Consumption Prediction by CHEN Xiaojiang, HUANG Hongcong, CAI Xuelong, DING Bo

“…Next, an attention module is used to weight the features for different importance levels, and the final energy consumption prediction is obtained. …”
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Foreign Body Detection Method for Transmission Equipment Based on Edge Computing and Deep Learning by Yanqiao LU, Cuiying SUN, Hongwei CAO, Hongwei YAN

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Fault Diagnosis Method for Transformer Winding Based on the Load Normalized Lissajous Graphical Analysis of Leakage Magnetic Field by Bowen ZHANG, Jian FENG, Bowen WANG, Feiran YANG, Yitong XING

“…This study proposes a transformer winding fault diagnosis method based on Lissajous graphics and convolutional neural networks (CNN).Methods First, a simulation model consistent with an actual transformer is developed, and the simulation system is utilized to obtain magnetic flux leakage signal data from different measurement points outside the winding under both normal and fault conditions. …”
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Detection of Architectural Dysplastic Features from Histopathological Imagery of Oral Mucosa Using Neural Networks by Watchanan Chantapakul, Sirikanlaya Vetchaporn, Sansanee Auephanwiriyakul, Nipon Theera-Umpon, Ritipong Wongkhuenkaew, Uklid Yeesarapat, Nutchapon Chamusri, Mansuang Wongsapai

“…This paper proposes a neural network architecture for detecting dysplastic features of epithelial architecture, including irregular epithelial stratification and bulbous rete ridges. The different combinations of atrous convolution, batch normalization, global pooling, and dropout are discussed regarding their effects, along with an ablation study. …”
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The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015–2024) by Jacek Lukasz Wilk-Jakubowski, Lukasz Pawlik, Damian Frej, Grzegorz Wilk-Jakubowski

“…Additionally, this article analyzes contributions from different countries, highlighting temporal and methodological trends in this field. …”
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How Resilient Are Kolmogorov–Arnold Networks in Classification Tasks? A Robustness Investigation by Ahmed Dawod Mohammed Ibrahum, Zhengyu Shang, Jang-Eui Hong

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AirStrum: A virtual guitar using real-time hand gesture recognition and strumming technique by Beulah ARUL, Shashank PANDA, Tushar NAIR

“…Finally, the proposed system can play different sounds by inferring both the played chord and the strumming velocity from human actions. …”
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Deep Learning for Multi-Tissue Cancer Classification of Gene Expressions (GeneXNet) by Tarek Khorshed, Mohamed N. Moustafa, Ahmed Rafea

“…Our model achieves 98.9% classification accuracy on human samples representing 33 different cancer tumor types across 26 organ sites. …”
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Hyperspectral Imaging for Enhanced Skin Cancer Classification Using Machine Learning by Teng-Li Lin, Arvind Mukundan, Riya Karmakar, Praveen Avala, Wen-Yen Chang, Hsiang-Chen Wang

“…The current study investigates the use of ten different machine learning algorithms for the purpose of classification of AK, BCC, and SK, including convolutional neural network (CNN), random forest (RF), you only look once (YOLO) version 8, support vector machine (SVM), ResNet50, MobileNetV2, Logistic Regression, SVM with stochastic gradient descent (SGD) Classifier, SVM with logarithmic (LOG) Classifier and SVM- Polynomial Classifier, in assessing the capability of the system to differentiate AK from BCC and SK with heightened accuracy. …”
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Improving TMJ Diagnosis: A Deep Learning Approach for Detecting Mandibular Condyle Bone Changes by Kader Azlağ Pekince, Adem Pekince, Buse Yaren Kazangirler

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Extraction of built-up areas using Sentinel-1 and Sentinel-2 data with automated training data sampling and label noise robust cross-fusion neural networks by Yu Li, Patrick Matgen, Marco Chini

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Pure data correction enhancing remote sensing image classification with a lightweight ensemble model by Huaxiang Song, Hanglu Xie, Yingying Duan, Xinyi Xie, Fang Gan, Wei Wang, Jinling Liu

“…Abstract The classification of remote sensing images is inherently challenging due to the complexity, diversity, and sparsity of the data across different image samples. Existing advanced methods often require substantial modifications to model architectures to achieve optimal performance, resulting in complex frameworks that are difficult to adapt. …”
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Self-Correlation Network With Triple Contrastive Learning for Hyperspectral Image Classification With Noisy Labels by Kwabena Sarpong, Mohammad Awrangjeb, Md. Saiful Islam, Islam Helmy

“…Furthermore, we incorporate structure-level representation learning to address inconsistencies across different projections. By doing so, we mitigate the classifier from overfitting to noisy labels. …”
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Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism by Wangyong Guo, Shijin Liu, Liguo Weng, Xingyu Liang

“…The Channel Attention module is then applied to weight different feature channels, highlighting important information and reducing redundancy. …”
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Establishing a GRU-GCN coordination-based prediction model for miRNA-disease associations by Kai-Cheng Chuang, Ping-Sung Cheng, Yu-Hung Tsai, Meng-Hsiun Tsai

“…Conclusions By introducing innovative label-preprocessing methods, this study addressed the relationships between miRNAs and diseases, and improved the ambiguity of the results in different experiments. Based on these refined label definitions, we developed a DL-based model to refine and predict the results of associations between miRNAs and diseases. …”
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Synergistic use of SAR satellites with deep learning model interpolation for investigating of active landslides in Cuenca, Ecuador by Mohammad Amin Khalili, Silvio Coda, Domenico Calcaterra, Diego Di Martire

“…To this aim, we have used Long-Short Term Memory (LSTM) and Convolutional Neural Networks (CNN) as two different Deep Learning Algorithms (DLAs) to integrate results in the temporal and spatial domain, respectively. …”
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SSMSFuse: A Spectral and Spatial Multiscale Coupling Fusion Model for Hyperspectral and Multispectral Image by Siyuan Liu, Yingchao Fan, Qi Hu, Bing Li, Yudong Zhang, Shuaiqi Liu

“…Finally, to achieve interactive coupling of dual-branch information, we designed a spatial–spectral guidance fusion block to fuse features at different scales to avoid loss of spatial and spectral details. …”
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Astronomical Image Superresolution Reconstruction with Deep Learning for Better Identification of Interacting Galaxies by Jiawei Miao, Liangping Tu, Hao Liu, Jian Zhao

“…High-resolution images of galaxies can identify fine structures within galaxies, which are essential for identifying and distinguishing different substructures within merging systems. However, due to observational and instrumental limitations, galaxy data is often collected at low resolution. …”
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Comprehensive Evaluation of Techniques for Intelligent Chatter Detection in Micro-Milling Processes by Guilherme Serpa Sestito, Wesley Angelino De Souza, Alessandro Roger Rodrigues, Maira Martins Da Silva

“…This study exploits experimental data from chatter and chatter-free cuts during machining operations with commercial COSAR-60 low-carbon steel under two different grain sizes (as received and ultra-fined). …”
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