CLASSIFICATION OF MICROCONTROLLER INTEGRATED CIRCUIT ON THE POCKET OF JEDEC TRAY USING CONVOLUTIONAL NEURAL NETWORK IN EMBEDDED MACHINE LEARNING SYSTEM by Mark M. Pallones, King Harold A. Recto

“…This study proposes an MVS classifying MCU IC, JEDEC matrix tray pocket, and background using a Convolutional Neural Network (CNN) deployed in OpenMV H7 Plus, a low-power, memory-constrained Embedded Machine Learning System (EMLS). …”
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Porosity Analysis and Thermal Conductivity Prediction of Non-Autoclaved Aerated Concrete Using Convolutional Neural Network and Numerical Modeling by Alexey N. Beskopylny, Evgenii M. Shcherban’, Sergey A. Stel’makh, Diana Elshaeva, Andrei Chernil’nik, Irina Razveeva, Ivan Panfilov, Alexey Kozhakin, Emrah Madenci, Ceyhun Aksoylu, Yasin Onuralp Özkılıç

“…This paper considers the process of analyzing the visual structure of non-autoclaved aerated concrete products, namely their porosity, using the YOLOv11 convolutional neural network, with a subsequent prediction of one of the most important properties—thermal conductivity. …”
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Solar Cycle Prediction Using a Temporal Convolutional Network Deep-learning Model with a One-step Pattern by Cui Zhao, Kun Liu, Shangbin Yang, Jinchao Xia, Jingxia Chen, Jie Ren, Shiyuan Liu, Fangyuan He

“…In this paper a solar cycle prediction method based on a one-step pattern is proposed with the temporal convolutional network neural network model, in which historical data are input and only one value is predicted at a time. …”
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Local-Global Feature Extraction Network With Dynamic 3-D Convolution and Residual Attention Transformer for Hyperspectral Image Classification by Qiqiang Chen, Zhengyang Li, Junru Yin, Wei Huang, Tianming Zhan

“…Currently, convolutional neural network (CNN) and transformer-based hyperspectral image (HSI) classification methods have attracted significant attention owing to their effective feature representation capabilities. …”
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A Novel Multistep Wavelet Convolutional Transfer Diagnostic Framework for Cross-Machine Bearing Fault Diagnosis by Lujia Zhao, Yuling He, Hai Zheng, Derui Dai

“…Firstly, a multistep time shift wavelet convolutional network (MTSWCN) based on the multiscale technique and wavelet transform is proposed to explore the diversity information regarding original vibration data and enhance the feature expression ability. …”
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The Lightweight Fracture Segmentation Algorithm for Logging Images Based on Fully 3D Attention Mechanism and Deformable Convolution by Qishun Yang, Liyan Zhang, Zihan Xi, Yu Qian, Ang Li

“…The challenge of fracture segmentation remains a significant obstacle in imaging logging interpretation within the current oil and gas exploration and development field. …”
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Transforming Early Breast Cancer Detection: A Deep Learning Approach Using Convolutional Neural Networks and Advanced Classification Techniques by Arun Kumar Singh, Bireshwar Dass Mazumdar, Rohit Raja, Kamred Udham Singh, Ankit Kumar, Mohd Asif Shah

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Knock Detection with Ion Current and Vibration Sensor: A Comparative Study of Logistic Regression and Neural Networks by Ola Björnsson, Per Tunestål

“…In this work, we applied both logistic regression and neural networks, including fully connected (FCNN) and convolutional neural networks (CNN), to classify knock events based on these indicators. …”
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Prediction of Atmospheric Refractivity From Clutter Power Images Using a Convolutional Neural Network and a Trilinear Atmospheric Model by Taekyeong Jin, Doyoung Jang, Hosung Choo

“…In this paper, we propose a method for predicting atmospheric conditions from clutter power images using a convolutional neural network (CNN). The proposed refractivity from clutter (RFC) method employs a CNN model that utilizes clutter power images as input. …”
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A Spatio-Temporal Joint Diagnosis Framework for Bearing Faults via Graph Convolution and Attention-Enhanced Bidirectional Gated Networks by Zhiguo Xiao, Xinyao Cao, Huihui Hao, Siwen Liang, Junli Liu, Dongni Li

“…To address these challenges, this paper proposes a joint diagnosis framework integrating graph convolutional networks (GCNs) with attention-enhanced bidirectional gated recurrent units (BiGRUs). …”
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Deep Learning for Predicting Spheroid Viability: Novel Convolutional Neural Network Model for Automating Quality Control for Three-Dimensional Bioprinting by Zyva A. Sheikh, Oliver Clarke, Amatullah Mir, Narutoshi Hibino

“…In this study, we build a convolutional neural network (CNN) model to efficiently and accurately predict spheroid viability, using a phase-contrast image of a spheroid as its input. …”
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The Automatic Joint Teeth Segmentation in Panoramic Dental Images using Mask Recurrent Convolutional Neural Networks with Residual Feature Extraction: by Raghavendra H. Bhalerao, Abhijeet Ashok Salunke, Shristi Sharan, Kamlesh Kumar, Priyank Rathod, Prince Kumar, Manish Chaturvedi, Nandlal Bharwani, Krupa Shah, Dhruv Patel, Keval Patel, Vikas Warikoo, Manisha Abhijeet Salunke, Shashank Pandya

“…We propose a novel approach to automatic joint teeth segmentation using the pioneer Mask Recurrent Convolutional Neural Network (MRCNN) model for dental image segmentation. …”
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Enhancing occluded and standard bird object recognition using fuzzy-based ensembled computer vision approach with convolutional neural network by L. Richard, G. H. Dhruthi, M. Ashwin Kumar, Anoushka Ghosh, R. Arumuga Arun, N. Priyanka

“…Convolutional Neural Networks (CNNs) provide a more reliable option for feature extraction and classification. …”
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Incorporating convolutional and transformer architectures to enhance semantic segmentation of fine-resolution urban images by Xizi Yu, Shuang Li, Yu Zhang

“…Though convolutional neural networks (CNN) exhibit promise in image semantic segmentation, they have limitations in capturing global context information, resulting in inaccuracies in segmenting small object features and object boundaries. …”
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DeepRice6mA: A convolutional neural network approach for 6mA site prediction in the rice Genome. by Hussam Alsharif

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Human Action Recognition from Videos Using Motion History Mapping and Orientation Based Three-Dimensional Convolutional Neural Network Approach by Ishita Arora, M. Gangadharappa

“…The frames were trained with a 3D Convolution Neural Network model, thus saving time and increasing the Classification Correction Rate (CCR). …”
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A Neural Network with Multiscale Convolution and Feature Attention Based on an Electronic Nose for Rapid Detection of Common Bunt Disease in Wheat Plants by Zhizhou Ren, Kun Liang, Yihe Liu, Xiaoxiao Wu, Chi Zhang, Xiuming Mei, Yi Zhang

“…Compared to traditional and current deep learning models, GFNN demonstrates superior performance, particularly in small-sample-size scenarios. …”
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A transfer learning-based graph convolutional network for dynamic security assessment considering loss of synchronism of wind turbines and unknown faults by Sasan Azad, Mohammad Taghi Ameli, Amjad Anvari-Moghaddam, Miadreza Shafie-khah

“…To tackle these challenges, this paper introduces a new dynamic security index that considers the effects of loss of synchronism in power electronics-based units on DSA. Also, a graph convolutional network (GCN)-based model is developed to improve DSA accuracy by incorporating the topological information of the power system in the form of an adjacency matrix. …”
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A hybrid data-driven approach for rainfall-induced landslide susceptibility mapping: Physically-based probabilistic model with convolutional neural network by Hong-Zhi Cui, Bin Tong, Tao Wang, Jie Dou, Jian Ji

“…To explore rainfall-induced LSM, this study proposes a hybrid model that combines the physically-based probabilistic model (PPM) with convolutional neural network (CNN). The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure (POF) considering the slope stability mechanism under rainfall conditions. …”
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Weak-lensing Mass Reconstruction of Galaxy Clusters with a Convolutional Neural Network. II. Application to Next-generation Wide-field Surveys by Sangjun Cha, M. James Jee, Sungwook E. Hong, Sangnam Park, Dongsu Bak, Taehwan Kim

“…., we demonstrated that many of these pitfalls of traditional mass reconstruction can be mitigated using a deep learning approach based on a convolutional neural network (CNN). In this paper, we present our improvements and report on the detailed performance of our CNN algorithm applied to next-generation wide-field (WF) observations. …”
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