Pixels relationship analysis for extracting building footprints by A. Emelyanov, A. Emelyanov, V. Knyaz, V. Knyaz, V. Kniaz, V. Kniaz, D. Artist

“…First, a convolutional neural network is used to train the binary semantic segmentation model and then regularization and vectorization processes are performed. …”
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Comparative analysis of random forest and deep learning approaches for automated acute lymphoblastic leukemia detection using morphologicaland textural features by Windra Swastika, Kestrilia Rega Prilianti, Paulus Lucky Tirma Irawan, Hendry Setiawan

“…We propose: (1) a Random Forest classifier using carefully engineered morphological and textural features, and (2) a Convolutional Neural Network (CNN)architecture for automated feature learning from microscopic blood cell images. …”
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A unified approach for weakly supervised crack detection via affine transformation and pseudo label refinement by Zhongmin Huangfu, Yibo Jiao, Fupeng Wei, Ge Shi, Hangcheng Dong

“…However, their labels are substantially worse quality than those of manual labeling. Current deep neural network visual interpretation approaches have issues including erroneous target localization. …”
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Modeling energy consumption indexes of an industrial cement ball mill for sustainable production by Saeed Chehreh Chelgani, Rasoul Fatahi, Ali Pournazari, Hamid Nasiri

“…To fill the gap, this study developed a CL by examining different AI models (Random Forest, Support Vector Regression, Convolutional Neural Network, extreme gradient boosting, CatBoost, and SHapley Additive exPlanations) for modeling energy consumption indexes of a close ball mill circuit in a cement plant to address the effectiveness of operating variables. …”
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Fault Classification of 3D-Printing Operations Using Different Types of Machine and Deep Learning Techniques by Satish Kumar, Sameer Sayyad, Arunkumar Bongale

“…The ML models such as k-nearest neighbor (KNN), decision tree (DT), extra trees (ET), and random forest (RF) with convolutional neural network (CNN) as a DL model are used to classify the variable operation printing parameters. …”
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Research on fusion generation algorithm of visual communication and product design based on AIGC technology by Guoying Chen, Xiaofeng Lan, Kai Liu, Can Cheng

“…This paper makes an in-depth analysis of the requirements of fusion of visual communication and product design, and puts forward the basic framework of fusion generation algorithm and the method of dynamic fusion of visual communication and product design elements based on convolutional neural network. Finally, the effectiveness and advantages of the proposed algorithm are verified by experimental analysis.…”
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Deep Learning Model-Based Detection of Anemia from Conjunctiva Images by Najmus Sehar, Nirmala Krishnamoorthi, C. Vinoth Kumar

“…A total of 764 conjunctiva images were augmented to 4,315 images using the deep convolutional generative adversarial network model to prevent overfitting and enhance model robustness. …”
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The effectiveness of a novel artificial intelligence (AI) model in detecting oral and dental diseases by Ravi Rathod, Saffa Dean, Christopher Sproat

“…Method A unique AI machine-learning model was built using a convolutional neural network (CNN) model and trained using a dataset of over five thousand images. …”
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Multi-source data fusion-based knowledge transfer for unmanned aerial vehicle flight data anomaly detection and recovery by Lei Yang, Shaobo Li, Liya Yu, Caichao Zhu, Congbao Wang

“…First, a data-driven framework based on one-dimensional convolutional neural network and bi-directional long short-term memory (1D CNN-BiLSTM) with parameter selection and residual smoothing (1DCB-PSRS) is proposed. …”
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Enhancing prostate cancer segmentation in bpMRI: Integrating zonal awareness into attention-guided U-Net by Chao Wei, Zheng Liu, Yibo Zhang, Lianhui Fan

“…First, pretraining a convolutional neural network (CNN)-based attention-guided U-Net model for segmenting the region of interest which is carried out in the prostate zone. …”
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Small-Target Detection Algorithm Based on STDA-YOLOv8 by Cun Li, Shuhai Jiang, Xunan Cao

“…Due to the inherent limitations of detection networks and the imbalance in training data, small-target detection has always been a challenging issue in the field of target detection. …”
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TinySurveillance: An Extra Low-Power Event-Based Surveillance Method for UAVs by Arash Farahdel, Alimul H. Khan, Hossein Keshmiri, Khan A. Wahid

“…The server colorizes the grayscale images using a convolutional neural network trained by the colored images. …”
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Identification of diabetic retinopathy lesions in fundus images by integrating CNN and vision mamba models. by Zenglei Liu, Ailian Gao, Hui Sheng, Xueling Wang

“…Considering these analyses, this work presents a comprehensive deep learning model that combines convolutional neural network and vision mamba models. …”
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An Accelerated FPGA-Based Parallel CNN-LSTM Computing Device by Xin Zhou, Wei Xie, Han Zhou, Yongjing Cheng, Ximing Wang, Yun Ren, Shandong Yuan, Liuwen Li

“…Recently, the combination of convolutional neural network (CNN) and long short-term memory (LSTM) exhibits better performance than single network architecture. …”
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HAMF: A Novel Hierarchical Attention-Based Multi-Modal Fusion Model for Parkinson’s Disease Classification and Severity Prediction by Anitha Rani Palakayala, P. Kuppusamy, D. Kothandaraman, Gunakala Archana, Jaideep Gera

“…An accuracy of 94.2 % was achieved, thus improving by 4–5 %, compared to the existing methodologies. Temporal Convolutional Network (TCN) which can capture long-range temporal dependencies, was used in the longitudinal severity estimation task, achieving a Mean Squared Error (MSE) of 0.12 in disease progression forecasting. …”
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FDC-TA-DSN Ship Classification Model and Dataset Construction Based on Complex-Valued SAR by Gui Gao, Yucong He, Jinghao Zhao, Sijie Li, Meixiang Wang, Gang Yang, Xi Zhang

“…The experimental results show that, compared with the current popular networks, such as DSN, ResNet, VGG, etc., FDC-TA-DSN has achieved better performance, and the network has good generalization ability in SAR data classification.…”
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Using Machine Learning for Analysis of Wideband Acoustic Immittance and Assessment of Middle Ear Function in Infants by Shan Peng, Yukun Zhao, Xinyi Yao, Huilin Yin, Bei Ma, Ke Liu, Gang Li, Yang Cao

“…Design: In this study, we developed five machine learning models—feedforward neural network, convolutional neural network, kernel density estimation, random forest, and support vector machine—to extract features from wideband acoustic immittance data collected from newborns aged 2–6 months. …”
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ST-YOLO: a deep learning based intelligent identification model for salt tolerance of wild rice seedlings by Qiong Yao, Qiong Yao, Pan Pan, Pan Pan, Xiaoming Zheng, Xiaoming Zheng, Guomin Zhou, Guomin Zhou, Guomin Zhou, Jianhua Zhang, Jianhua Zhang

“…Diversified feature extraction paths are introduced to enhance the ability of feature extraction; Introducing CAFM (Context Aware Feature Modulation) convolution and attention fusion modules into the backbone network to enhance feature representation capabilities while improving the fusion of features at various scales; Design a more flexible and effective spatial pyramid pooling layer using deformable convolution and spatial information enhancement modules to improve the model’s ability to represent target features and detection accuracy.ResultsThe experimental results show that the improved algorithm improves the average precision by 2.7% compared with the original network; the accuracy rate improves by 3.5%; and the recall rate improves by 4.9%.ConclusionThe experimental results show that the improved model significantly improves in precision compared with the current mainstream model, and the model evaluates the salt tolerance level of wild rice varieties, and screens out a total of 2 varieties that are extremely salt tolerant and 7 varieties that are salt tolerant, which meets the real-time requirements, and has a certain reference value for the practical application.…”
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TomaFDNet: A multiscale focused diffusion-based model for tomato disease detection by Rijun Wang, Rijun Wang, Yesheng Chen, Fulong Liang, Xiangwei Mou, Xiangwei Mou, Guanghao Zhang, Hao Jin

“…The model utilizes a multi-scale focus-diffusion network (MSFDNet) alongside an efficient parallel multi-scale convolutional module (EPMSC) to significantly enhance the extraction of multi-scale features. …”
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Severity Classification of Parkinson’s Disease via Synthesis of Energy Skeleton Images from Videos Produced in Uncontrolled Environments by Nejib Ben Hadj-Alouane, Arav Dhoot, Monia Turki-Hadj Alouane, Vinod Pangracious

“…<b>Methods:</b> Leveraging deep learning techniques, our approach synthesizes Skeleton Energy Images (SEIs) from gait sequences and employs three advanced models—a Convolutional Neural Network (CNN), a Residual Network (ResNet), and a Vision Transformer (ViT)—to analyze these images. …”
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