A Robust Multispectral Reconstruction Network from RGB Images Trained by Diverse Satellite Data and Application in Classification and Detection Tasks by Xiaoning Zhang, Zhaoyang Peng, Yifei Wang, Fan Ye, Tengying Fu, Hu Zhang

“…First, to supplement paired natural color RGB and multispectral images, the Houston hyperspectral dataset was used to train a convolutional neural network Model-TN for generating natural color RGB images from true color images combining CIE standard colorimetric system theory. …”
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A STacked Adaptive Residual PINN (STAR-PINN) Approach to 2D Time-Domain Magnetic Diffusion in Nonlinear Materials by Shayan Dodge, Sami Barmada, Alessandro Formisano

“…The key advantage of this new architecture is the ability to refine predictions through multiple lightweight PINN blocks to achieve accurate results with lower computational cost and less architectural complexity than more advanced neural networks like Recurrent Neural Networks or Convolutional Neural Networks. …”
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A Power-Efficient 0.5668 TOPS/W Digital Logic Accelerator Implemented Using 40-nm CMOS Process for Underwater Object Recognition Usage by Chua-Chin Wang, Shih-Heng Luo, Hsin-Che Wu, Ralph Gerard B. Sangalang, Chewn-Pu Jou, Harry Hsia, Lan-Chou Cho

“…DNN (deep neural network) and CNN (convolution neural network) have been widely used in real-time artificial intelligent (AI) applications, particularly image or video recognitions, because they have been proved physically in many occasions. …”
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Deep vision-based real-time hand gesture recognition: a review by Cui Cui, Mohd Shahrizal Sunar, Goh Eg Su

“…However, some issues remain in edge blurring generated by complex backgrounds, rotation inaccuracy induced by fast movement, and delay caused by computing cost. Recently, the emergence of deep learning has ameliorated these issues, convolution neural network (CNN) enhanced edge clarity, long-short term memory (LSTM) improved rotation accuracy, and attention mechanism optimized response time. …”
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PPLA-Transformer: An Efficient Transformer for Defect Detection with Linear Attention Based on Pyramid Pooling by Xiaona Song, Yubo Tian, Haichao Liu, Lijun Wang, Jinxing Niu

“…Additionally, the incorporation of partial convolution into the model improves local feature extraction, further enhancing detection precision. …”
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Advanced investing with deep learning for risk-aligned portfolio optimization. by Minh Duc Nguyen

“…We combine two prediction models, Long Short-Term Memory (LSTM) and One-Dimensional Convolutional Neural Network (1D-CNN), with three portfolio frameworks: Mean-Variance with Forecasting (MVF), Risk Parity Portfolio (RPP), and Maximum Drawdown Portfolio (MDP). …”
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An attack detection method based on deep learning for internet of things by Yihan Yu, Yu Fu, Taotao Liu, Kun Wang, Yishuai An

“…Firstly, a genetic algorithm is used for feature selection; secondly, a cost-sensitive function is employed to address the scarcity of attack traffic in IoT; and finally, a combination of Convolutional Neural Networks and Long Short Term Memory Network is utilized to extract spatiotemporal information from the network. …”
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Advancements and Challenges in Character Recognition: A Comparative Analysis of CNN and Deep Learning Approaches by Yang Ximin

“…This paper provides a comprehensive review of character recognition technologies, focusing on the application of Convolutional Neural Networks (CNN) and deep learning methodologies. …”
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Optimizing Sensor Placement for Event Detection: A Case Study in Gaseous Chemical Detection by Priscile Fogou Suawa, Christian Herglotz

“…Effective algorithms and well-planned sensor locations are required for reliable results. Using deep convolutional neural networks (DCNNs) and decision tree (DT) methods, we implemented and tested detection models on a public dataset of chemical substances collected at five locations. …”
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High-Accuracy Recognition Method for Diseased Chicken Feces Based on Image and Text Information Fusion by Duanli Yang, Zishang Tian, Jianzhong Xi, Hui Chen, Erdong Sun, Lianzeng Wang

“…Key innovations include the following: (1) Integrating MASA(Manhattan self-attention)and DSconv (Depthwise Separable convolution) into the backbone network to mitigate feature confusion. (2) Utilizing a pre-trained BERT to extract textual semantic features, reducing annotation dependency and cost. (3) Designing a lightweight Gated Cross-Attention (GCA) module for dynamic multimodal fusion, achieving a 41% parameter reduction versus cross-modal transformers. …”
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BG-YOLO: A Bidirectional-Guided Method for Underwater Object Detection by Ruicheng Cao, Ruiteng Zhang, Xinyue Yan, Jian Zhang

“…A feature-guided module connects the shallow convolution layers of the two branches. When training the image enhancement branch, the object detection subnet in the enhancement branch guides the image enhancement subnet to be optimized towards the direction that is most conducive to the detection task. …”
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Edge Artificial Intelligence: Real-Time Noninvasive Technique for Vital Signs of Myocardial Infarction Recognition Using Jetson Nano by H. M. Mohan, S. Anitha, Rifai Chai, Sai Ho Ling

“…A real-time embedded solution persuaded from “edge AI” is implemented using the state-of-the-art convolution neural networks: single shot detector Inception V2, single shot detector MobileNet V2, and Internet of Things embedded GPU platform NVIDIA’s Jetson Nano. …”
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ST-YOLO: A defect detection method for photovoltaic modules based on infrared thermal imaging and machine vision technology. by Hanfei Xie, Baoxi Yuan, Chengyu Hu, Yujie Gao, Feng Wang, Chunlan Wang, Yuqian Wang, Peng Chu

“…First, it introduces the C2f-SCconv convolution module, which is based on SCconv convolution. …”
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Flow Visualization In Closed Loop Pulsating Heat Pipe (CLPHP) Using Deep Learning Techniques by N Santhi Sree, P Bhramara, Medidi Veerabhadra Rao, Anthani Kamala Priya, G Avinash, K Sindhu

“…YOLOv5 based Convolutional Neural Network (CNN) is used to identify and classify the flow in CLPHP. …”
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Digital Melting Curve Analysis for Multiplex Quantification of Nucleic Acids on Droplet Digital PCR by Xiaoqing Dai, Meng Cao, Zunliang Wang

“…This approach eliminates the need for complex fluorescent probe design, reducing both costs and dependence on fluorescence channels. We developed a convolutional neighborhood search algorithm to correct droplet displacement during heating, ensuring precise tracking and accurate extraction of melting curves. …”
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Leveraging two-dimensional pre-trained vision transformers for three-dimensional model generation via masked autoencoders by Muhammad Sajid, Kaleem Razzaq Malik, Ateeq Ur Rehman, Tauqeer Safdar Malik, Masoud Alajmi, Ali Haider Khan, Amir Haider, Seada Hussen

“…In vision, attention is used in conjunction with convolutional networks or to replace individual convolutional network elements while preserving the overall network design. …”
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Enhancing classification efficiency in capsule networks through windowed routing: tackling gradient vanishing, dynamic routing, and computational complexity challenges by Gangqi Chen, Zhaoyong Mao, Junge Shen, Dongdong Hou

“…Abstract Capsule networks overcome the two drawbacks of convolutional neural networks: weak rotated object recognition and poor spatial discrimination. …”
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A Joint Optimization Model for Energy and Reserve Capacity Scheduling With the Integration of Variable Energy Resources by M. Wajahat Hassan, Thamer Alquthami, Ahmad H. Milyani, Ashfaq Ahmad, Muhammad Babar Rasheed

“…First, the load demand is predicted through a convolutional neural network (CNN) by taking the ISO-NECA hourly real-time data. …”
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Enhancing energy consumption forecasting for electric vehicle charging stations with Time Series Dense Encoder (TiDE) by Amril Nazir, Abdul Khalique Shaikh, Aftab Ahmed Khan, Abdul Salam Shah, Nadia Khalique

“…The transformers have handled time series forecasting better with larger datasets like the Temporal Fusion Transformer and the Temporal Convolutional Network. However, they still need help with issues, specifically the higher computation cost and larger dataset for the training process. …”
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Just a Single-Layer CNN for Stochastic Modeling: A Discriminator-Free Approach by Evangelos Rozos

“…In this study, we propose a simpler stochastic scheme based on a single convolutional neural network (CNN) used as a generator, replacing the discriminator component of the GAN with a specifically designed cost function. …”
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