Environmental Sensitivity in AI Tree Bark Detection: Identifying Key Factors for Improving Classification Accuracy by Charles Warner, Fanyou Wu, Rado Gazo, Bedrich Benes, Songlin Fei

“…We investigated three environmental variables—time of day (lighting conditions), bark moisture content (wet or dry), and cardinal direction of observation—to identify sources of classification inaccuracies. …”
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Intelligent Stress Detection Using ECG Signals: Power Spectrum Imaging with Continuous Wavelet Transform and CNN by Rodrigo Mateo-Reyes, Irving A. Cruz-Albarran, Luis A. Morales-Hernandez

“…This study proposes a model based on depth-separable convolutional neural networks (DSCNN) to analyze heart rate variability (HRV) and detect stress. …”
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DHS-YOLO: Enhanced Detection of Slender Wheat Seedlings Under Dynamic Illumination Conditions by Xuhua Dong, Jingbang Pan

“…Our methodology builds upon the YOLOv11 architecture with three principal enhancements: First, the Dynamic Slender Convolution (DSC) module employs deformable convolutions to adaptively capture the elongated morphological features of wheat leaves. …”
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Handwritten Words Image Character Extraction Adaptive Algorithm Based on the Multi-branch Structure by GUO Xiaojing, ZHAO Xiaoyuan, ZOU Songlin

“…First, the enhanced re-parameterized structure across multiple stages and branches achieves an effect equivalent to variable convolution. Second, the refined classifier with fully convolutional layers combines features from specific intermediate layers with the output layer, resulting in improved precision for complex and similar words. …”
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Deep Time Series Intelligent Framework for Power Data Asset Evaluation by Lihong Ge, Xin Li, Li Wang, Jian Wei, Bo Huang

“…It can simultaneously capture short-term local features and long-term global trends in power data, help to deeply mine spatial correlations and local patterns in data, effectively extract fine relationships between variables and optimize information flow. In the evaluation of the complex and rich Solar-Power dataset and Electricity dataset, TSENet achieved significant performance improvements over other state-of-the-art baseline methods.Through the synergistic design of deep convolutional structures and an efficient memory mechanism, it effectively addresses issues such as inadequate modeling of long-term dependencies, insufficient extraction of short-term features, and high prediction volatility, thereby significantly enhancing both the accuracy and robustness of forecasting in power asset evaluation tasks.…”
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Design and modeling of a nanocomposite system for demineralization of sweet whey by Mina Rezapour, Mohsen Esmaiili, Mehdi Mahmoudian, Alireza Behrooz Sarand

“…The effects of various process variables, including, transmembrane pressure (TMP), Reynolds number, feed pH, and temperature, on the rejection of the minerals were surveyed. …”
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A Novel Multi-Task Learning-Based Approach to Multi-Energy System Load Forecasting by Zain Ahmed, Mohsin Jamil, Ashraf Ali Khan

“…These optimal inputs are fed to D-TCNet (Deep – Temporal Convolution Network). This network uses multi-layer perceptrons (MLP) to encode the spatial relationship among exogenous variables which is fed to a Temporal Convolutional Network (TCN). …”
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Traffic Accident’s Severity Prediction: A Deep-Learning Approach-Based CNN Network by Ming Zheng, Tong Li, Rui Zhu, Jing Chen, Zifei Ma, Mingjing Tang, Zhongqiang Cui, Zhan Wang

“…To promote the prediction accuracy, a novel traffic accident’s severity prediction-convolutional neural network (TASP-CNN) model for traffic accident’s severity prediction is proposed that considers combination relationships among traffic accident’s features. …”
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Predictive and Explainable Artificial Intelligence for Weight Loss After Sleeve Gastrectomy: Insights from Wide and Deep Learning with Medical Image and Non-Image Data by Jaechan Park, Sungsoo Park, Kwang-Sig Lee, Yeongkeun Kwon

“…Here, the WAD model combined a convolutional neural network (CNN) for image data and a linear layer for non-image data (EMR). …”
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Machine vision-based recognition of safety signs in work environments by Jesús-Ángel Román-Gallego, María-Luisa Pérez-Delgado, Miguel A. Conde, Marcos Luengo Viñuela

“…This article presents a system designed for the application of image recognition in the realm of Occupational Risk Prevention—a concern of paramount importance due to the imperative of preventing workplace accidents as falls, collisions, or other types of accidents for the benefit of both workers and enterprises. In this study, convolutional neural networks are employed due to their exceptional efficacy in image recognition. …”
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Autoencoder Reconstruction of Cosmological Microlensing Magnification Maps by Somayeh Khakpash, Federica B. Bianco, Georgios Vernardos, Gregory Dobler, Charles Keeton

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In-Wheel Motor Fault Diagnosis Method Based on Two-Stream 2DCNNs with DCBA Module by Junwei Zhu, Xupeng Ouyang, Zongkang Jiang, Yanlong Xu, Hongtao Xue, Huiyu Yue, Huayuan Feng

“…To address the challenge of fault diagnosis for in-wheel motors in four-wheel independent driving systems under variable driving conditions and harsh environments, this paper proposes a novel method based on two-stream 2DCNNs (two-dimensional convolutional neural networks) with a DCBA (depthwise convolution block attention) module. …”
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ANN-SVM-IP: An Innovative Method for Rapidly and Efficiently Detecting and Classifying of External Defects of Apple Fruits by Nashaat M. Hussain Hassan, Mohamed M. Hassan Mahmoud, Mohamed A. Ismeil, M. Mourad Mabrook, A. A. Donkol, A. M. Mabrouk

“…The first phase attempts to detect exterior defects in apples by applying two proposed convolution kernels that were capable of identifying damaged sections of apples. …”
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Active Acoustics in Concert Halls – A New Approach by Wieslaw WOSZCZYK

“…The virtual acoustic response is created using low-latency convolution and a three-way temporal segmentation of the measured impulse responses. …”
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A Deep Learning Framework for the Classification of Brazilian Coins by Debabrata Swain, Viral Rupapara, Amro Nour, Santosh Satapathy, Biswaranjan Acharya, Shakti Mishra, Ali Bostani

“…Our proposed deep learning framework leverages state-of-the-art convolutional neural networks (CNNs) to address these challenges. …”
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Hybrid Neural Network Models to Estimate Vital Signs from Facial Videos by Yufeng Zheng

“…The hybrid model integrates convolutional neural network (CNN), convolutional long short-term memory (convLSTM), and video vision transformer (ViViT) architectures to ensure comprehensive analysis. …”
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Visual design element recognition of garment based on multi-view image fusion by Meng Fanyu

“…The image texture characteristic variables can be utilized to classify the defects. …”
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MultiModal Ensemble Approach Leveraging Spatial, Skeletal, and Edge Features for Enhanced Bangla Sign Language Recognition by Khan Abrar Shams, Md. Rafid Reaz, Mohammad Ryan Ur Rafi, Sanjida Islam, Md. Shahriar Rahman, Rafeed Rahman, Md. Tanzim Reza, Mohammad Zavid Parvez, Subrata Chakraborty, Biswajeet Pradhan, Abdullah Alamri

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Research on Unsupervised Domain Adaptive Bearing Fault Diagnosis Method Based on Migration Learning Using MSACNN-IJMMD-DANN by Xiaoxu Li, Jiahao Wang, Jianqiang Wang, Jixuan Wang, Qinghua Li, Xuelian Yu, Jiaming Chen

“…To address the problems of feature extraction, cost of obtaining labeled samples, and large differences in domain distribution in bearing fault diagnosis on variable operating conditions, an unsupervised domain-adaptive bearing fault diagnosis method based on migration learning using MSACNN-IJMMD-DANN (multi-scale and attention-based convolutional neural network, MSACNN, improved joint maximum mean discrepancy, IJMMD, domain adversarial neural network, DANN) is proposed. …”
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Deep Learning-Based Multiclass Framework for Real-Time Melasma Severity Classification: Clinical Image Analysis and Model Interpretability Evaluation by Zhang J, Jiang Q, Chen Q, Hu B, Chen L

“…Future work will integrate multimodal data for more comprehensive assessment.Keywords: melasma, deep learning, convolutional neural networks, MASI, clinical decision support…”
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