Faster Dynamic Graph CNN: Faster Deep Learning on 3D Point Cloud Data by Jinseok Hong, Keeyoung Kim, Hongchul Lee

“…However, it has been difficult to apply such data as input to a convolutional neural network (CNN) or recurrent neural network (RNN) because of their unstructured and unordered features. …”
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Lightweight framework for misbehavior detection in internet of vehicles by Yujing Gong, Bin-Jie Hu

“…Therefore, a careful balance between runtime cost and space complexity must be considered when deploying lightweight neural networks in practical applications.…”
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Artificial Intelligence Driving Innovation in Textile Defect Detection by Ahmet Ozek, Mine Seckin, Pinar Demircioglu, Ismail Bogrekci

“…It delves into the types of defects occurring at various production stages, assesses the strengths and weaknesses of conventional and automated approaches, and underscores the pivotal role of deep learning models, especially Convolutional Neural Networks (CNNs), in achieving high precision in defect identification. …”
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Impact of Pretrained Deep Neural Networks for Tomato Leaf Disease Prediction by Mohamed Bouni, Badr Hssina, Khadija Douzi, Samira Douzi

“…This article identifies tomato leaf disease using a deep convolutional neural network (CNN) and transfer learning. …”
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Hybrid Capsule Network for precise and interpretable detection of malaria parasites in blood smear images by Bader Alawfi

“…The model was evaluated on four benchmark malaria datasets (MP-IDB, MP-IDB2, IML-Malaria, MD-2019) and assessed for both intra- and cross-dataset performance.ResultsHybrid CapNet achieves superior accuracy with significantly reduced computational cost (1.35M parameters, 0.26 GFLOPs), rendering it suitable for mobile diagnostic applications. …”
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A soil organic carbon mapping method based on transfer learning without the use of exogenous data by Jingfeng Han, Mujie Wu, Yanlong Qi, Xiaoning Li, Xiao Chen, Jing Wang, Jinlong Zhu, Qingliang Li

“…Accurate and cost-effective mapping of soil organic carbon (SOC) is critical for understanding carbon dynamics and informing sustainable land management. …”
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Enhancing Lidar-Based 3D Classification Through an Improved Deep Learning Framework With Residual Connections by Shih-Lin Lin, Jun-Yi Wu

“…Through systematic adjustments—such as increasing convolutional kernel size and quantity, incorporating dropout and Batch Normalization layers, and integrating residual connections—we achieve substantial accuracy gains. …”
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Leaf disease detection and classification in food crops with efficient feature dimensionality reduction. by Khasim Syed, Shaik Salma Asiya Begum, Anitha Rani Palakayala, G V Vidya Lakshmi, Sateesh Gorikapudi

“…Dimensionality reduction techniques are employed to enhance computational performance by reducing the dimensionality of inner layers. Convolutional Neural Networks (CNNs), originally designed to recognize critical image components, now learn features across multiple layers. …”
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FruitNet: Lightweight CNN for High-Throughput Image-Based Fruit Yield Estimation by Yadav Kamlesh Kumar, Tandan Gajendra

“…Empirical evaluations confirm that FruitNet matches the accuracy of more complex models at the cost of much less inference time and resource consumption. …”
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Machine Learning-Based Approaches for Breast Density Estimation from Mammograms: A Comprehensive Review by Khaldoon Alhusari, Salam Dhou

“…The most commonly utilized models are support vector machines (SVMs) and convolutional neural networks (CNNs), with classification accuracies ranging from 76.70% to 98.75%. …”
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Equivariant spherical CNNs for accurate fiber orientation distribution estimation in neonatal diffusion MRI with reduced acquisition time by Haykel Snoussi, Davood Karimi

“…In this study, we propose a rotationally equivariant Spherical Convolutional Neural Network (sCNN) framework tailored for neonatal dMRI. …”
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Recent Advancements in Hyperspectral Image Reconstruction from a Compressive Measurement by Xian-Hua Han, Jian Wang, Huiyan Jiang

“…Furthermore, we review benchmark datasets, evaluation metrics, and prevailing challenges including spectral distortion, computational cost, and generalizability across diverse conditions. …”
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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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Evaluation of Deep Learning Models for Polymetallic Nodule Detection and Segmentation in Seafloor Imagery by Gabriel Loureiro, André Dias, José Almeida, Alfredo Martins, Eduardo Silva

“…The initial results suggest that transformer-based methods perform better in most evaluation metrics, but at the cost of higher computational resources. Furthermore, recent versions of You Only Look Once (YOLO) have obtained competitive results in terms of mean average precision.…”
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Medium density EMG armband for gesture recognition by Eisa Aghchehli, Eisa Aghchehli, Milad Jabbari, Chenfei Ma, Matthew Dyson, Kianoush Nazarpour

“…To enhance decoding accuracy, we introduced a novel spatio-temporal convolutional neural network that integrates spatial information from additional EMG sensors with temporal dynamics. …”
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A Spaceborne Passive Localization Algorithm Based on MSD-HOUGH for Multiple Signal Sources by Liting Zhang, Hao Huan, Tao Ran, Shangyu Zhang, Yushu Zhang, Hao Ding

“…The estimated source number determines when the MSD will be terminated. Finally, a PSA cost function is established based on the estimated Doppler parameter to achieve signal source localization. …”
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A Diagnosis Method for Noise and Intermittent Faults in Analog Circuits Based on the Fusion of Multiscale Fuzzy Entropy Features and Amplitude Features by Junyou Shi, Yilei Hou, Zili Wang, Zhilin Yang, Zhenyang Lv

“…Finally, the two features are fed into a convolutional neural network for diagnosis. The method is applied to two typical analog circuits. …”
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Automated Detection of the Kyphosis Angle Using a Deep Learning Approach: A Cross-Sectional Study on Young Adults by Onur Kocak, Cansel Ficici, Ilknur Ezgi Dogan, Ziya Telatar, Nihan Ozunlu Pekyavas

“…In regard to clinical diagnosis and evaluation methods, high-cost radiological measurements and a variety of non-radiological clinical methods are employed. …”
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Construction and Recording Method of a Three-Dimensional Model to Automatically Manage Thermal Abnormalities in Building Exteriors by Jonghyeon Yoon, Sangjun Hwang, Kyonghoon Kim, Sanghyo Lee

“…This study proposes an automated three-dimensional (3D)-modeling method that combines convolutional neural networks (CNNs) with unmanned aerial vehicle (UAV) technology for the efficient management of thermal anomalies in building exteriors. …”
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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

“…Abstract In view of the problem that the high noise and data redundancy in the voiceprint signal of the wind turbine blade lead to insufficient diagnostic accuracy and real-time performance and increase the acquisition cost, this paper combines sparse representation, compressed sensing, and deep learning technology to apply a new wind turbine blade damage detection method, aiming to enhance the accuracy and real-time performance of wind turbine blade damage diagnosis. …”
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