NFE-YOLO: A Lightweight and Efficient Detection Network for Low, Slow, and Small Drones by Dan Tian, Chen Wang, Dong Zhou, Xin Yan, Liaoyuan Zeng

“…Specifically, the neck incorporates partial Convolution and C3Faster modules to reduce model size while the EOrthoNet channel attention module improves feature extraction accuracy. …”
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A Lightweight Network for UAV Multi-Scale Feature Fusion-Based Object Detection by Sheng Deng, Yaping Wan

“…This approach introduces a new module, C2f_SEPConv, which incorporates Partial Convolution (PConv) and channel attention mechanisms (Squeeze-and-Excitation, SE), effectively replacing the previous bottleneck and minimizing both the model’s parameter count and computational demands. …”
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Small object detection in complex open-pit mine backgrounds based on improved YOLOv11 by ZHU Yongjun, CAI Guangqi, HAN Jin, MIAO Yanzi, MA Xiaoping, JIAO Wenhua

“…The improved YOLOv11 model introduced a Robust Feature Downsampling (RFD) module to replace the stride convolution downsampling module, effectively preserving the feature information of small objects. …”
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A Transfer Learning-Based VGG-16 Model for COD Detection in UV–Vis Spectroscopy by Jingwei Li, Iqbal Muhammad Tauqeer, Zhiyu Shao, Haidong Yu

“…This paper proposes transforming one-dimensional spectra into two-dimensional spectrum images and employing convolutional neural networks (CNNs) to extract features and model automatically. …”
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Maturity Classification and Quality Determination of Cherry Using VNIR Hyperspectral Images and Comprehensive Chemometrics by Yuzhen Wei, Siyi Yao, Feiyue Wu, Qiangguo Yu

“…To improve the imaging performance, two spectral pretreatment methods (wavelet transform, standard normal variable transformation and detrend), three feature selection methods (successive projection algorithm, genetic algorithm, and shuffled frog leaping algorithm), and four regression modeling methods (principal components regression, partial least squares regression, least square-support vector regression, convolutional neural network) were employed and compared. …”
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Deep learning-based occlusion-aware face mask detection for airborne disease control by Teshome Ayechiluhem Yalew, Sosina M. Gashaw, Aleka Melese Ayalew, Mourad Oussalah

“…This study presents a deep learning-based occlusion-aware face mask detection model designed to identify both proper and improper mask usage, even under partial facial occlusions. A dataset of 4,820 images, including occlusions from hands, objects, and mask misuse, was used to train and evaluate three convolutional neural network models: InceptionV3, MobileNetV2, and DenseNet121. …”
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Integrating Backscattered Electron Imaging and Multi-Feature-Weighted Clustering for Quantification of Hydrated C₃S Microstructure by Xin Wang, Yongjun Luo

“…The results indicate the following: (1) The deep convolutional neural network with guided filtering demonstrates superior performance (mean squared error: 53.52; peak signal-to-noise ratio: 26.35 dB; structural similarity index: 0.8187), enabling high-fidelity preservation of cementitious phases. …”
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Enhancing the YOLOv8 model for realtime object detection to ensure online platform safety by Mohammed Kawser Jahan, Fokrul Islam Bhuiyan, Al Amin, M. F. Mridha, Mejdl Safran, Sultan Alfarhood, Dunren Che

“…Our key contributions include enhancing the cross-stage partial fusion blocks and incorporating three additional convolutional blocks into the model head, leading to better feature extraction and detection capabilities. …”
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A Mobile Deep Learning Classification Model for Diabetic Retinopathy by Daniel Rimaru, Antonio Nehme, Musaed Alhussein, Khaled Mahbub, Khusheed Aurangzeb, Anas Khan

“…In this work, we have developed a new approach, which involves the development of a lightweight convolutional neural network (CNN)-based model for segmentation of retinal vessels and a mobile application for DR grading. …”
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Maize Seed Variety Classification Based on Hyperspectral Imaging and a CNN-LSTM Learning Framework by Shuxiang Fan, Quancheng Liu, Didi Ma, Yanqiu Zhu, Liyuan Zhang, Aichen Wang, Qingzhen Zhu

“…This study introduced an efficient method for maize variety identification by combining hyperspectral imaging with a framework that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. …”
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Deep learning for enhancing automatic classification of M-PSK and M-QAM waveform signals dedicated to single-relay cooperative MIMO 5G systems by Haithem Ben Chikha, Alaa Alaerjan, Randa Jabeur

“…The proposed method leverages a convolutional neural network (CNN) classifier trained on a reduced set of discriminative features, including higher-order statistics and the differential nonlinear phase peak factor, which are extracted from the received signal. …”
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Unsupervised Binary Classifier-Based Object Detection Algorithm with Integrated Background Subtraction Suitable for Use with Aerial Imagery by Gabija Veličkaitė, Ignas Daugėla, Ivan Suzdalev

“…The proposed system, SARGAS, combines a custom convolutional neural network (CNN) classifier with MOG2 background subtraction and partial affine transformations for camera stabilization. …”
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Combined formаtion of a cryptographic key using synchronized artificial neural networks by M. L. Radziukevich, V. F. Golikov

“…As a convolution function, the bitwise addition modulo 2 of the vectors of the weights of the networks is used. …”
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Statistical Analysis of Medium‐Scale Traveling Ionospheric Disturbances Over Japan Based on Deep Learning Instance Segmentation by Peng Liu, Tatsuhiro Yokoyama, Weizheng Fu, Mamoru Yamamoto

“…Therefore, this research proposes a real‐time processing algorithm for dTEC maps based on Mask Region‐Convolutional Neural Network (R‐CNN) model of deep learning instance segmentation to detect wavelike perturbations intelligently with an accuracy of about 80% and a processing speed of about 8 fps. …”
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Monitoring of vegetation chlorophyll content in photovoltaic areas using UAV-mounted multispectral imaging by Ming Li, Weiyi Wang, Haoran Li, Zekun Yang, Jianjun Li

“…The selected features were then used in three modeling strategies—vegetation index–based, texture feature–based, and fused index–texture–based—employing three conventional machine-learning regressors (partial least squares regression, random forest, support vector machine regression) and three deep-learning regressors (back propagation neural network, convolutional neural network, multilayer perceptron). …”
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Nondestructive Detection of Rice Milling Quality Using Hyperspectral Imaging with Machine and Deep Learning Regression by Zhongjie Tang, Shanlin Ma, Hengnian Qi, Xincheng Zhang, Chu Zhang

“…In this study, hyperspectral imaging was employed to estimate the rice milling quality attributes of two rice varieties (Xiushui121 and Zhehujing26). Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Convolutional Neural Networks (CNNs), and Backpropagation Neural Networks (BPNNs) were used to establish both single-task and multi-task models for the prediction of milling quality attributes. …”
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Geographic origin discrimination and quantification of phenolic compounds and moisture in Artemisia argyi folium using NIRS and chemometrics by Lifei Hu, Yifan Wang, Xin Wu, Yuanyuan Shan, Fengxiao Zhu, Fan Zhang, Qiang Yang, Mingxing Liu

“…The results showed that partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) outperformed unsupervised methods, with key wavenumbers in high and low-frequency regions showing similarities, but exhibiting differences mainly in the 7783–6773 cm−1 range. …”
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Detection of Maize Pathogenic Fungal Spores Based on Deep Learning by Yijie Ren, Ying Xu, Huilin Tian, Qian Zhang, Mingxiu Yang, Rongsheng Zhu, Dawei Xin, Qingshan Chen, Qiaorong Wei, Shuang Song

“…To improve the recognition accuracy of various maize disease spores, this study introduced the YOLOv8s-SPM model by incorporating the space-to-depth and convolution (SPD-Conv) layers, the Partial Self-Attention (PSA) mechanism, and Minimum Point Distance Intersection over Union (MPDIoU) loss function. …”
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MFFCI–YOLOv8: A Lightweight Remote Sensing Object Detection Network Based on Multiscale Features Fusion and Context Information by Sheng Xu, Lin Song, Junru Yin, Qiqiang Chen, Tianming Zhan, Wei Huang

“…First, we introduce the lightweight CSP bottleneck with attention module, which utilizes partial convolution calculation and SimAM attention mechanisms to decrease the number of parameters and computational complexity while enhancing feature extraction capabilities. …”
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Traceability of Rizhao green tea origin based on multispectral data fusion strategy and chemometrics by Mengqi Guo, Zhiwei Chen, Zezhong Ding, Dewen Wang, Dandan Qi, Min Lu, Mei Wang, Chunwang Dong

“…By integrating data from near-infrared and hyperspectral technologies, the prediction accuracy of multivariate models (including Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Random Forest (RF), and Convolutional Neural Networks (CNN)) was improved. …”
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