On the Control of the Technical Condition of Elevator Ropes Based on Artificial Intelligence and Computer Vision Technology by A. V. Panfilov, A. R. Yusupov, A. A. Korotkiy, B. F. Ivanov

“…The malfunctions of the elevator mechanical equipment related to the defective indices of the ropes are listed. There is a difference in the documentary fixation of defective indices and rejection rates of ropes of lifting structures.   …”
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Dehazing algorithm for coal mining face dust and fog images based on a semi-supervised network by Meng ZHAO, Yuzhong WEI, Zheng LI, Junming ZHANG, Junda CHEN, Xiaofeng LIU

“…The decoder consists of pixel shuffle layers and convolutional layers, progressively recovering higher-resolution feature maps. …”
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Maize and soybean yield prediction using machine learning methods: a systematic literature review by Ramandeep Kumar Sharma, Jasleen Kaur, Gary Feng, Yanbo Huang, Chandan Kumar, Yi Wang, Sandhir Sharma, Johnie Jenkins, Jagmandeep Dhillon

“…Results revealed the temperature, precipitation, historical crop yield, normalized difference vegetation index (NDVI), and soil pH to be the most utilized ML features for yield prediction research. …”
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Improved MobileVit deep learning algorithm based on thermal images to identify the water state in cotton by Kaijun Jin, Jihong Zhang, Ningning Liu, Miao Li, Zhanli Ma, Zhenhua Wang, Jinzhu Zhang, Feihu Yin

“…This approach incorporates the Efficient Channel Attention (ECA) mechanism into the Fusion component of the MobileVit model, optimizes the first convolution in the Fusion component by replacing it with Depthwise Separable Convolution (DsConv), and substitutes the Local representation with the MobileOne block. …”
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CNN-Based Medical Ultrasound Image Quality Assessment by Siyuan Zhang, Yifan Wang, Jiayao Jiang, Jingxian Dong, Weiwei Yi, Wenguang Hou

“…As such, the medical ultrasound IQA on basis of convolutional neural network (CNN) is quantitatively studied in this paper. …”
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Research on bearing fault diagnosis based on a multimodal method by Hao Chen, Shengjie Li, Xi Lu, Qiong Zhang, Jixining Zhu, Jiaxin Lu

“…In parallel, 13 key features are extracted from the original vibration data in the time-frequency domain. Convolutional neural networks are then employed for deep feature extraction. …”
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Quality assurance of hyperspectral imaging systems for neural network supported plant phenotyping by Justus Detring, Abel Barreto, Anne-Katrin Mahlein, Stefan Paulus

“…To test the spatial accuracy at different working distances, the sine-wave-based spatial frequency response (s-SFR) was analysed. …”
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GLIHamba: global–local context image harmonization based on Mamba by Jinsheng SUN, Jiao PAN, Yu GUO, Chao YAO

“…In contrast, region-based matching methods treat the foreground and background regions as two different styles or domains. Although these methods achieve global consistency in harmonization results, they often overlook the spatial differences between the two regions. …”
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A Hybrid AI Approach for Fault Detection in Induction Motors Under Dynamic Speed and Load Operations by Muhammad Irfan Ishaq, Muhammad Adnan, Muhammad Ali Akbar, Amine Bermak, Nimra Saeed, Maaz Ansar

“…From existing literature, conventional fault diagnosis approaches in an IM struggle to reliably identify fault patterns at different speeds, particularly under variable speed and changing load conditions. …”
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Multi-modal denoised data-driven milling chatter detection using an optimized hybrid neural network architecture by Haining Gao, Haoyu Wang, Hongdan Shen, Shule Xing, Yong Yang, Yinlin Wang, Wenfu Liu, Lei Yu, Mazhar Ali, Imran Ali Khan

“…Multi-modal data features of different machining states are then obtained using time–frequency domain methods and Markov transition field methods. …”
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Deep Learning for Visual Leading of Ships: AI for Human Factor Accident Prevention by Manuel Vázquez Neira, Genaro Cao Feijóo, Blanca Sánchez Fernández, José A. Orosa

“…To address this issue, this study explores the use of convolutional neural networks (CNNs), evaluating different training strategies and hyperparameter configurations to assist officers in identifying deviations from proper visual leading. …”
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Graph neural networks for mechanical property prediction of 2D fiber composites by Erdem Caliskan, Reza Abedi, Massimiliano Lupo Pasini

“…This work investigates the ability of graph neural networks (GNNs) to homogenize 2D fiber composite microstructures. We use different inhomogeneity and anisotropy indices to motivate and show that the Volume Elements (VEs) used in ML methods should ideally be far from their Representative Volume Element (RVE) size limit and, consequently, are notably anisotropic. …”
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SOH Estimation Method for Lithium-Ion Batteries Using Partial Discharge Curves Based on CGKAN by Shengfeng He, Wenhu Qin, Zhonghua Yun, Chao Wu, Chongbin Sun

“…Finally, multiple experiments under different conditions are conducted, and the results demonstrate that the proposed CGKAN method, by integrating the individual advantages of 1D-CNN, BiGRU, and KAN, efficiently captures complex nonlinear patterns in battery health features and maintains stable performance across various operating conditions.…”
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Optical Fiber Vibration Signal Recognition Based on the EMD Algorithm and CNN-LSTM by Kun Li, Yao Zhen, Peng Li, Xinyue Hu, Lixia Yang

“…Experimental results demonstrate that this method effectively identifies three different types of vibration signals collected from a real-world environment, achieving a recognition accuracy of 97.3% for intrusion signals. …”
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Application of Machine Learning in Construction Productivity at Activity Level: A Critical Review by Ying Terk Lim, Wen Yi, Huiwen Wang

“…Noticeably, artificial neural networks, convolutional neural networks, support vector machines, and even deep learning demonstrating have been adopted due to their effectiveness in different functionalities and processes in CPM. …”
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Research on damage detection technology for wind turbine blade acoustic signals by fusion of sparse representation, compressive sensing and deep learning by Liang Wang, Chun Yang, Chao Yuan, Yanan Liu, Yanqing Chen

“…It has good adaptability under different computing resources, and the processing delay does not exceed 0.45s under complex environments and large data volumes. …”
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Beyond Nyquist: A Comparative Analysis of 3D Deep Learning Models Enhancing MRI Resolution by Soumick Chatterjee, Alessandro Sciarra, Max Dünnwald, Anitha Bhat Talagini Ashoka, Mayura Gurjar Cheepinahalli Vasudeva, Shudarsan Saravanan, Venkatesh Thirugnana Sambandham, Pavan Tummala, Steffen Oeltze-Jafra, Oliver Speck, Andreas Nürnberger

“…In order to overcome these limitations, super-resolution MRI deep-learning-based techniques can be utilised. In this work, different state-of-the-art 3D convolution neural network models for super resolution (RRDB, SPSR, UNet, UNet-MSS and ShuffleUNet) were compared for the super-resolution task with the goal of finding the best model in terms of performance and robustness. …”
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DeepTransIDS: Transformer-Based Deep learning Model for Detecting DDoS Attacks on 5G NIDD by Kumar Harshdeep, Konatham Sumalatha, Rohit Mathur

“…Unlike traditional IDS approaches that rely on Convolutional Neural Networks, this work uses the self-attention mechanism of Transformers to enhance the classification performance for multi-class network intrusion detection. …”
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Multimodal rapid identification of growth stages and discrimination of growth status for Morchella by Ning Jia, Chunjun Zheng

“…By introducing multi-stage input embedding, enhanced position encoding, and optimized Transformer Encoder layers, the performance of the model in identifying different growth stages of Morchella mushrooms is significantly improved. …”
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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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