Usage of Neural-Based Predictive Modeling and IIoT in Wind Energy Applications by Adrian-Nicolae Buturache, Stelian Stancu

“…The adoption of wind energy has grown significantly in recent years. New, cost-effective technologies have been developed, led by customer awareness of green technologies and a legal framework proposed at the European Union level. …”
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YOLOv8-RBean: Runner Bean Leaf Disease Detection Model Based on YOLOv8 by Hongbing Chen, Haoting Zhai, Jinghuan Hu, Hongrui Chen, Changji Wen, Yizhe Feng, Kun Wang, Zhipeng Li, Guangyao Wang

“…The model enhances detection performance through three key innovations: (1) the BeanConv module, which integrates depthwise separable convolution and pointwise convolution to improve multi-scale feature extraction; (2) a lightweight LA attention mechanism that incorporates spatial, channel, and coordinate information to enhance feature representation; and (3) a lightweight BLBlock structure built upon DWConv and LA attention, which optimizes computational efficiency while maintaining high accuracy. …”
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Finger drawing on smartphone screens enables early Parkinson's disease detection through hybrid 1D-CNN and BiGRU deep learning architecture. by Zhaohui Zhu, E Wu, Pengfei Leng, Jiajun Sun, Mingming Ma, Zhigeng Pan

“…Our hybrid model combined multi-scale convolutional feature extraction (using parallel 1D-Convolutional branches) with bidirectional temporal pattern recognition (via gated recurrent unit [GRU] networks) to analyze movement abnormalities and detect the disease.…”
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Sugarcane Feed Volume Detection in Stacked Scenarios Based on Improved YOLO-ASM by Xiao Lai, Guanglong Fu

“…At the target detection level, we integrate a Convolutional Block Attention Module (CBAM) into the YOLOv5s backbone network. …”
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Deep learning approach for automated ‘Kent’ mango maturity grading in compliance with Peruvian standards by Orlando Salazar-Campos, Javier Moran Ruiz, José Luis Peralta, Mirian Rubio Cieza, Breysi Salazar Medina, Johonathan Salazar-Campos

“…Deep learning, particularly convolutional neural networks (CNNs), has significantly advanced automated fruit classification based on image analysis. …”
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3D-BCLAM: A Lightweight Neurodynamic Model for Assessing Student Learning Effectiveness by Wei Zhuang, Yunhong Zhang, Yuan Wang, Kaiyang He

“…This model cleverly integrates Bidirectional Convolutional Long Short-Term Memory (BCL) and dynamic attention mechanism, in order to efficiently capture emotional dynamic changes in time series with extremely low computational cost. 3D-BCLAM can achieve a comprehensive evaluation of students’ learning outcomes, covering not only the cognitive level but also delving into the emotional dimension for detailed analysis. …”
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Enhanced YOLOv8-based pavement crack detection: A high-precision approach. by ZuXuan Zhang, HongLi Zhang, TongJia Zhang

“…Firstly, this paper introduces deep separable Convolution (DWConv) into YOLOv8 backbone network to capture crack information more flexibly and improve the recognition accuracy of the model. …”
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LANA-YOLO: Road defect detection algorithm optimized for embedded solutions by Paweł TOMIŁO

“…In addition, the article presents Basic Involution Block (BIB) that uses the involution layer to provide better performance at a lower cost than convolution layers. The model was compared with other architectures on a public dataset as well as on a dataset specially created for these purposes. …”
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Multimode Flex-Interleaver Core for Baseband Processor Platform by Rizwan Asghar, Dake Liu

“…The presented hardware enables the mapping of vital types of interleavers including multiple block interleavers and convolutional interleaver onto a single architecture. …”
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A lightweight steel surface defect detection network based on YOLOv9 by Tianyi Zheng, Ling Yu, Yongbao Shi, Fanglin Niu

“…Next, we replace the regular convolution blocks in the model network with spatial-to-depth convolutions, further reducing the model’s computational complexity while retaining global feature information. …”
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Investigation and Implementation of Multi-Stereo Camera System Integration for Robust Localization in Urban Environments by A. Rai, E. Mounier, E. Mounier, P. R. M. de Araujo, A. Noureldin, A. Noureldin, K. Jain

“…Beyond these complexities, environmental conditions like signal-blocking skyscrapers, unpredictable obstacles, and the high costs of precision sensing add further convolution. …”
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Hybrid deep learning framework for robust time-series classification: Integrating inception modules with residual networks by Duong Thi Kim Chi, Nguyen Thi Mai Trang, Tran Ba Minh Son, Nguyen Ngoc Thao, Thanh Q. Nguyen

“…While recurrent neural networks (RNNs) such as LSTM and GRU have shown promise in modeling sequential dependencies, they often suffer from limitations like vanishing gradients and high computational cost when handling long sequences. To overcome these issues, convolutional neural networks (CNNs), particularly the Inception architecture, have emerged as powerful alternatives due to their ability to capture multiscale local patterns efficiently. …”
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MPVT: An Efficient Multi-Modal Prompt Vision Tracker for Visual Target Tracking by Jianyu Xie, Yan Fu, Junlin Zhou, Tianxiang He, Xiaopeng Wang, Yuke Fang, Duanbing Chen

“…The fully connected head network module addresses the shortcomings of traditional convolutional head networks such as inductive biases. …”
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Foreign Object Detection on Insulators Based on Improved YOLO v3 by Huankun ZHANG, Junyi LI, Bin ZHANG

“…A dense network is designed to replace one of the convolutional layers of the original network in order to realize the multi-layer feature reuse and fusion of the insulator, which improves the detection accuracy. …”
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Single-Pixel Imaging Based on Enhanced Multi-Network Prior by Jia Feng, Qianxi Li, Jiawei Dong, Qing Zhao, Hao Wang

“…Owing to the high sensitivity, low cost, and wide spectrum, it acquires extensive applications across various domains. …”
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The TDGL Module: A Fast Multi-Scale Vision Sensor Based on a Transformation Dilated Grouped Layer by Leilei Xie, Fenghua Zhu, Zhixue Wang

“…Traditional spatial pyramid pooling methods fuse multi-scale feature information but lack adaptability in dynamically adjusting convolution operations based on their actual needs. …”
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Portable motorized telescope system for wind turbine blades damage detection by Alejandro Carnero, Cristian Martín, Manuel Díaz

“…Abstract Wind turbines are among the fastest‐growing sources of energy production and the maintenance operations include regular inspection of their blades, causing considerable downtime and cost. In addition, the manual inspection process involves a great risk. …”
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Obtaining Rotational Stiffness of Wind Turbine Foundation from Acceleration and Wind Speed SCADA Data by Jiazhi Dai, Mario Rotea, Nasser Kehtarnavaz

“…First, a convolutional neural network model is applied to map acceleration and wind speed data within a moving window to corresponding moment and tilt values. …”
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A Real-Time Green and Lightweight Model for Detection of Liquefied Petroleum Gas Cylinder Surface Defects Based on YOLOv5 by Burhan Duman

“…The architecture integrates ghost convolution and ECA blocks to improve feature extraction with less computational overhead in the network’s backbone. …”
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Pretraining-improved Spatiotemporal graph network for the generalization performance enhancement of traffic forecasting by Xiangyue Zhang, Chao Li, Ling Ji, Yuyun Kang, Mingming Pan, Zhuo Liu, Qiang Qi

“…However, this approach increases the computational cost of the model. Additionally, adding or replacing datasets in a trained model requires retraining, which decreases prediction accuracy and increases time cost. …”
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