Drone-Based Digital Phenotyping to Evaluating Relative Maturity, Stand Count, and Plant Height in Dry Beans (Phaseolus vulgaris L.) by Leonardo Volpato, Evan M. Wright, Francisco E. Gomez

“…In this study, RGB drone imagery and deep learning (DL) approaches are explored to measure relative maturity (RM), stand count (SC), and plant height (PH), potentially offering higher throughput, accuracy, and cost-effectiveness than traditional methods. A time series of drone images was utilized to estimate dry bean RM employing a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) model. …”
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An Efficient Semantic Segmentation Framework with Attention-Driven Context Enhancement and Dynamic Fusion for Autonomous Driving by Jia Tian, Peizeng Xin, Xinlu Bai, Zhiguo Xiao, Nianfeng Li

“…Recognizing the limitations of convolutional networks in modeling long-range dependencies and capturing global semantic context, the model incorporates an attention-based feature extraction component. …”
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An improved lightweight method based on EfficientNet for birdsong recognition by Haolun He, Hui Luo

“…Abstract In the context of birdsong recognition, conventional modeling approaches often involve a significant number of parameters and high computational costs, rendering them unsuitable for deployment in embedded field monitoring devices. …”
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Research on lightweight malware classification method based on image domain by SUN Jingzhang, CHENG Yinan, ZOU Binghui, QIAO Tonghua, FU Sizheng, ZHANG Qi, CAO Chunjie

“…To address the high deployment costs and long prediction times associated with traditional malware classification methods, a lightweight malware visualization classification method was proposed. …”
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A Systematic Review of CNN Architectures, Databases, Performance Metrics, and Applications in Face Recognition by Andisani Nemavhola, Colin Chibaya, Serestina Viriri

“…This study provides a comparative evaluation of face recognition databases and Convolutional Neural Network (CNN) architectures used in training and testing face recognition systems. …”
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TCN-QRNN model for short term energy consumption forecasting with increased accuracy and optimized computational efficiency by Lesia Mochurad, Roman Levkovych

“…In this work, a new model is proposed that combines the advantages of Temporal Convolutional Networks (TCN) and Quasi-Recurrent Neural Networks (QRNN) for energy consumption forecasting. …”
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Deep learning of the particulate and mineral-associated organic carbon fractions using a compositional transform and mid-infrared spectroscopy by Mingxi Zhang, Zefang Shen, Lewis Walden, Farid Sepanta, Zhongkui Luo, Lei Gao, Oscar Serrano, Raphael A. Viscarra Rossel

“…We used the centred log ratio (CLR) method to transform the data compositionally and then modelled POCmac, POCmic, POC (POCmac + POCmic), and MAOC with the spectra, using convolutional neural networks (CNN) and cubist for benchmarking. …”
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SpAi: A machine-learning supported experimental workflow for high-throughput spheroid production and analysis by Nedim Hacıosmanoğlu, Murat Alp Güngen, Eylul Gulsen Yilmaz, Emre Ece, Alphan Uzun, Arda Taşcan, Burak M. Görmüş, Ismail Eş, Fatih Inci

“…We herein improved spheroid analysis by including a convolutional neural network (CNN) model, specifically U-Net, into a graphical user interface (GUI). …”
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Advanced AI-Driven Thermographic Analysis for Diagnosing Diabetic Peripheral Neuropathy and Peripheral Arterial Disease by Albert Siré Langa, Jose Luis Lázaro-Martínez, Aroa Tardáguila-García, Irene Sanz-Corbalán, Sergi Grau-Carrión, Ibon Uribe-Elorrieta, Arià Jaimejuan-Comes, Ramon Reig-Bolaño

“…This study collected thermographic data from diabetic patients and employed convolutional neural networks (CNNs) and vision transformers (ViTs) to classify individuals as healthy or affected by DPN or PAD (not healthy). …”
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Estimation of lower limb torque: a novel hybrid method based on continuous wavelet transform and deep learning approach by Shu Xu, Tao Wang, Zenghui Ding, Yu Wang, Tongsheng Wan, Dezhang Xu, Xianjun Yang, Ting Sun, Meng Li

“…Traditional gait analysis techniques, such as optical motion capture systems and biomechanical force platforms, are limited by high costs, operational complexity, and restricted applicability. …”
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Intelligent Detection Algorithm for Concrete Bridge Defects Based on SATH–YOLO Model by Lanlin Zou, Ao Liu

“…Lastly, a lightweight TDMDH detection head with shared convolution and dynamic feature selection further reduced computational costs. …”
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The Cultural Value Validity of Digital Media Art Based on Deep Learning Network Model by Yuan Ruan

“…In order to solve this problem, this paper proposes a deep learning neural network model based on a dual-core compression activation module. The convolution kernels of different sizes in one module are used to extract the overall features and local details of the image, and another module is used to achieve the main goal. …”
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YOLO11-ARAF: An Accurate and Lightweight Method for Apple Detection in Real-World Complex Orchard Environments by Yangtian Lin, Yujun Xia, Pengcheng Xia, Zhengyang Liu, Haodi Wang, Chengjin Qin, Liang Gong, Chengliang Liu

“…First, to enhance the model’s ability to capture apple-specific features, we replaced the original C3k2 module with the CARConv convolutional layer. Second, to reinforce feature learning in visually challenging orchard environments, the enhanced attention module AFGCAM was embedded into the model architecture. …”
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A Real-Time ETP Outlet Monitoring Framework Leveraging Environmental IoT, Colorimetry, and Learning Theory by Md. Jahid Hasan Mridha, Md. Mehedi Hossain, Sazid Md. Imran, Sk Ayub Al Wahid, Anika Tahsin, Md. Golam Rabiul Alam, Ashis Talukder, Ahmed Wasif Reza

“…This research utilizes IoT (Internet of Things)–based real-time monitoring to enhance industrial wastewater management. A Convolutional Neural Network (CNN) is used for video-based binary classification to detect the On/Off state of ETPs. …”
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Future of Alzheimer's detection: Advancing diagnostic accuracy through the integration of qEEG and artificial intelligence by Sahar Rezaei, Farzan Asadirad, Alireza Motamedi, Mohammadsadegh Kamran, Farzaneh Parsa, Haniyeh Samimi, Parna Ghannadikhosh, Mahdi Zahmatyar, Seyed Ali Hosseinzadeh, Hossein Arabi

“…Results demonstrate that AI-enhanced qEEG analysis achieves remarkable diagnostic accuracy, with Linear Discriminant Analysis (LDA) reaching 93.18% accuracy and 97.92% Area Under Curve (AUC). Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) also showed promising results, with some models achieving up to 100% sensitivity in specific classifications. …”
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Individual behavior tracking of heifers by using object detection algorithm YOLOv4 by Sarah Jahn, Gabriel Schmidt, Lisa Bachmann, Robert Kammerer, Helen Louton, Anne K. Schütz, Timo Homeier-Bachmann

“…Since manual evaluation is time-consuming and cost-intensive, video surveillance offers an opportunity to obtain an unbiased insight. …”
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STSA‐Based Early‐Stage Detection of Small Brain Tumors Using Neural Network by Nafiul Hasan, Md. Masud Rana, Md Mahmudul Hasan, AKM Azad, Dil Afroz, Md Mostafizur Rahman Komol, Mousumi Aktar, Mohammad Ali Moni

“…The proposed methodology was benchmarked against Support Vector Machine (SVM), K‐Nearest Neighbor (KNN), Random Forest Classifier (RFC), and Graph Convolutional Neural Network (GCN), demonstrating superior classification performance across different tumor sizes. …”
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NDVI Estimation Throughout the Whole Growth Period of Multi-Crops Using RGB Images and Deep Learning by Jianliang Wang, Chen Chen, Jiacheng Wang, Zhaosheng Yao, Ying Wang, Yuanyuan Zhao, Yi Sun, Fei Wu, Dongwei Han, Guanshuo Yang, Xinyu Liu, Chengming Sun, Tao Liu

“…RGB images were used to extract conventional features, including color indices (CIs), texture features (TFs), and vegetation coverage, while convolutional features (CFs) were extracted using the deep learning network ResNet50 to optimize the model. …”
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WindDefNet: A Multi-Scale Attention-Enhanced ViT-Inception-ResNet Model for Real-Time Wind Turbine Blade Defect Detection by Majad Mansoor, Xiyue Tan, Adeel Feroz Mirza, Tao Gong, Zhendong Song, Muhammad Irfan

“…WindDefNet utilizes modified cross-convolutional blocks, including the powerful Inception-ResNet hybrid, to capture both fine-grained and high-level features from input images. …”
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Load Optimization for Connected Modern Buildings Using Deep Hybrid Machine Learning in Island Mode by Seyed Morteza Moghimi, Thomas Aaron Gulliver, Ilamparithi Thirumarai Chelvan, Hossen Teimoorinia

“…A hybrid Machine Learning (ML) model combining Long Short-Term Memory (LSTM) and a Convolutional Neural Network (CNN) is proposed to improve the performance. …”
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