HDCTfusion: Hybrid Dual-Branch Network Based on CNN and Transformer for Infrared and Visible Image Fusion by Wenqing Wang, Lingzhou Li, Yifei Yang, Han Liu, Runyuan Guo

“…The effectiveness of the proposed method in this paper is verified on different experimental datasets, and it is better than most of the current advanced fusion algorithms.…”
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Inverse Kinematics for Robotic Manipulators via Deep Neural Networks: Experiments and Results by Ana Calzada-Garcia, Juan G. Victores, Francisco J. Naranjo-Campos, Carlos Balaguer

“…Different training datasets, normalization techniques, and orientation representations are tested, and custom metrics are introduced to evaluate position and orientation errors. …”
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CMPF-UNet: a ConvNeXt multi-scale pyramid fusion U-shaped network for multi-category segmentation of remote sensing images by Ning Li, Xiaopeng Yu, Miao Yu

“…Most U-shaped convolutional neural network (CNN) methods have the problems of insufficient feature extraction and fail to fully utilize global/multi-scale context information, which makes it difficult to distinguish similar objects and shadow occluded objects in remote sensing images. …”
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Adversarial attacks dataset for low light image enhancementMendeley Data by Axel Martinez, Matthieu Olague, Gustavo Olague, Emilio Hernandez, Julio Cesar Lopez-Arredondo

“…Enhancing images in low-light conditions is a field of research where deep convolutional neural networks have shown considerable effectiveness. …”
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Ocular Disease Detection Using Fundus Images: A Hybrid Approach of Grad-CAM and Multiscale Retinex Preprocessing With VGG16 Deep Features and Fine KNN Classification by Shreemat Kumar Dash, Kante Satyanarayana, Santi Kumari Behera, Sudarson Jena, Ashoka Kumar Ratha, Prabira Kumar Sethy, Aziz Nanthaamornphong

“…This research investigates the application of deep feature extraction for classifying eight different ocular diseases. The VGG16, a pretrained convolutional neural network (CNN) model, was employed for feature extraction, while the fine k-nearest neighbor (KNN) classifier was utilized for classification. …”
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Prescription Recommendation Algorithm Based on Herbal Property-Driven Compatibility Mechanism Semantic Modeling by Geng Xueru, Zhang Jiantong, Luo Tao, Hou Jianchen, Tao Xiaohua

“…This is followed by aggregating higher-order heterogeneous path information of nodes through a graph convolutional network model. Finally, an attention mechanism is employed to fuse information from symptom interaction graphs, symptom-herb interaction graphs, and herb interaction graphs, distinguishing the influence of different dimensions of TCM semantic information. …”
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Presentation Attack Detection using iris periocular visual spectrum images by Andrés Valenzuela, Juan E. Tapia, Violeta Chang, Christoph Busch

“…The analysis was carried out by evaluating the performance of five Convolutional Neural Networks (CNN) using both facial and periocular iris images for PAD with two different attack instruments. …”
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Intelligent Testing Method for Multi-Point Vibration Acquisition of Pile Foundation Based on Machine Learning by Ke Wang, Weikai Zhao, Juntao Wu, Shuang Ma

“…The model’s performance was assessed using statistical error metrics, including the correlation coefficient R², mean absolute error (MAE), and variance accounted for (VAF), with comparative evaluations conducted across different model frameworks. Results show that both the convolutional neural network (CNN) and the long short-term memory neural network (LSTM) consistently achieved high accuracy in identifying the location of the first reflection point in the pile shaft, with R² values greater than 0.98, MAE below 0.41 (m), and VAF greater than 98%. …”
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Limited-angle x-ray nano-tomography with machine-learning enabled iterative reconstruction engine by Chonghang Zhao, Mingyuan Ge, Xiaogang Yang, Yong S. Chu, Hanfei Yan

“…To tackle this challenge, we propose an approach dubbed Perception Fused Iterative Tomography Reconstruction Engine, which integrates a convolutional neural network (CNN) with perceptional knowledge as a smart regularizer into an iterative solving engine. …”
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Deep learning in defects detection of PV modules: A review by Katleho Masita, Ali Hasan, Thokozani Shongwe, Hasan Abu Hilal

“…This review explores the application of deep learning (DL) methods, particularly convolutional neural networks (CNNs), in the identification and classification of PV module defects. …”
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Multi-Scale Long- and Short-Range Structure Aggregation Learning for Low-Illumination Remote Sensing Imagery Enhancement by Yu Cao, Yuyuan Tian, Xiuqin Su, Meilin Xie, Wei Hao, Haitao Wang, Fan Wang

“…Profiting from the surprising non-linear expressive capacity, deep convolutional neural networks have inspired lots of progress in low illumination (LI) remote sensing image enhancement. …”
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Software Defect Prediction Based On Deep Learning Algorithms : A Systematic Literature Review by Akhlas Hasan, Shayma Mohi-Aldeen

“…The top three DL algorithms used in building SDP models and used in predicting software bugs were convolutional neural network (CNN), long-short-term memory (LSTM), and bidirectional LSTM. …”
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Expanding the Reach of Structured EHR Data with Clinical Notes by Seda Bilaloglu, Vincent J Major, Himanshu Grover, Isabel Metzger, Yindalon Aphinyanaphongs

“…Patients of both groups do die in the following months suggesting the two approaches identify different patient phenotypes which supplement one another. …”
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SiNC: Saliency-injected neural codes for representation and efficient retrieval of medical radiographs. by Jamil Ahmad, Muhammad Sajjad, Irfan Mehmood, Sung Wook Baik

“…Neuronal activation features termed as neural codes from different CNN layers are comprehensively studied to identify most appropriate features for representing radiographs. …”
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Multi-task deep learning framework for enhancing Mayo endoscopic score classification in ulcerative colitis by Jaehyuk Lee, Eunchan Kim

“…Future studies should explore integrating multiple convolutional neural network-based models to further boost classification accuracy.…”
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Bridging neuroscience and AI: a survey on large language models for neurological signal interpretation by Sreejith Chandrasekharan, Jisu Elsa Jacob

“…Traditional deep neural networks, such as convolutional networks, sequence-to-sequence networks, and hybrids of such neural networks were proven to be effective for a wide range of neurological disease classifications. …”
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Automatic serving method of volleyball training robot based on improved YOLOv5 and improved Hough transform by Tao Sun, Xiaolong He, Jiajun Zhang

“…Abstract Volleyball training robots play an important role in modern sports training, and their automatic serving technology can simulate serving modes in different scenarios, providing athletes with diverse training programs. …”
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Wind energy system fault classification using deep CNN and improved PSO‐tuned extreme gradient boosting by Chun‐Yao Lee, Edu Daryl C. Maceren

“…The effectiveness of the proposed method is demonstrated through validation conducted on a different imbalanced dataset showing superior performance metrics in terms of accuracy. …”
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Predictive Modeling of Dairy Sales Using Multi-Perspective Fusion Bi-LSTM Integrated with Universal Scale CNN: Insights from the Dairy Supply Chain by Naveen D. Chandavarkar, Dr. Soumya S

“…The proposed research uses dairy supply chain dataset to assess the proposed model CNN (Convolutional Neural Network). The present research uses Universal Scale CNN, specifically 1D-CNN, that is able to acquiring the features in ideal and in effective rates. …”
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Diagnosis Retinal Disease by using Deep Learning Models by attallh salih, Manar Kashmoola

“…The proposed model consists of three different convolutional neural network (CNN) models to be used in this approach and compare the results of each one with others. …”
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