Grade Identification of Raw Nongxiangxing Baijiu Based on Fused Data of Near Infrared Spectroscopy and Gas Chromatography-Mass Spectrometry by ZHANG Wei, ZHANG Guiyu, TUO Xianguo, FU Ni, LI Xiaoping, PANG Tingting, LIU Kecai

“…Raw Nongxiangxin Baijiu of different grades were collected during the distillation process, and their near infrared spectroscopy (NIR) data and gas chromatography-mass spectrometry (GC-MS) data were acquired. …”
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Enhancing Brain Tumor Diagnosis with L-Net: A Novel Deep Learning Approach for MRI Image Segmentation and Classification by Lehel Dénes-Fazakas, Levente Kovács, György Eigner, László Szilágyi

“…<b>Methods:</b> We propose L-net, a novel architecture combining U-net for tumor boundary segmentation and a convolutional neural network (CNN) for tumor classification. …”
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RFSoC Modulation Classification With Streaming CNN: Data Set Generation &#x0026; Quantized-Aware Training by Andrew Maclellan, Louise H. Crockett, Robert W. Stewart

“…This paper introduces a novel FPGA-based Convolutional Neural Network (CNN) architecture for continuous radio data processing, specifically targeting modulation classification on the Zynq UltraScale+ Radio Frequency System on Chip (RFSoC) operating in real-time. …”
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CM-YOLO: Typical Object Detection Method in Remote Sensing Cloud and Mist Scene Images by Jianming Hu, Yangyu Wei, Wenbin Chen, Xiyang Zhi, Wei Zhang

“…Moreover, a local-global semantic joint mining (LGSJM) module is utilized, which combines convolutional neural networks (CNNs) and hierarchical selective attention to comprehensively mine global and local semantics, achieving target feature enhancement. …”
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Offline reinforcement learning combining generalized advantage estimation and modality decomposition interaction by Kaixin Jin, Lifang Wang, Xiwen Wang, Wei Guo, Qiang Han, Xiaoqing Yu

“…In intra-modal interaction, the convolutional properties of ConvFormer effectively capture the associative information within respective modalities of states and actions. …”
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Deep learning-based surface deformation tracking with interferometric fringes: A case study in Taiwan by Shih-Teng Chang, Shih-Yuan Lin, Yu-Ching Lin

“…A Fringe-Labeling Model (FLM) was developed to identify deformation regions, followed by a Fringe-Detection Model (FDM) using Faster Region-based Convolutional Neural Networks (Faster R-CNN) to classify deformation magnitudes. …”
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Toward AI-Enabled Approach for Urdu Text Recognition: A Legacy for Urdu Image Apprehension by Kamlesh Narwani, Hongzhi Lin, Sandeep Pirbhulal, Mir Hassan

“…Besides, baseline results are also provided with several state-of-the-art networks, including TextBoxes++, Seglink, DB(ResNet-50) and EAST for text localization and Convolutional Recurrent Neural Network (CRNN) for text recognition. …”
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Research on the UAV Sound Recognition Method Based on Frequency Band Feature Extraction by Jilong Zhong, Aigen Fan, Kuangang Fan, Wenjie Pan, Lu Zeng

“…The sound features were classified and recognized using a Convolutional Neural Network (CNN). The experimental results show that the frequency band feature extraction method has a better recognition effect compared to the classic MFCC feature extraction method.…”
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Coordinate Attention Filtering Depth-Feature Guide Cross-Modal Fusion RGB-Depth Salient Object Detection by Lingbing Meng, Mengya Yuan, Xuehan Shi, Qingqing Liu, Le Zhange, Jinhua Wu, Ping Dai, Fei Cheng

“…Many methods use the same feature interaction module to fuse RGB and depth maps, which ignores the inherent properties of different modalities. In contrast to previous methods, this paper proposes a novel RGB-D salient object detection method that uses a depth-feature guide cross-modal fusion module based on the properties of RGB and depth maps. …”
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A robust deep learning approach for segmenting cortical and trabecular bone from 3D high resolution µCT scans of mouse bone by Amine Lagzouli, Peter Pivonka, David M. L. Cooper, Vittorio Sansalone, Alice Othmani

“…DBAHNet’s hierarchical structure combines transformers and convolutional neural networks to capture long-range dependencies and local features for improved contextual representation. …”
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Drainage Pipeline Multi-Defect Segmentation Assisted by Multiple Attention for Sonar Images by Qilin Jin, Qingbang Han, Jianhua Qian, Liujia Sun, Kao Ge, Jiayu Xia

“…Compared to the coordinate attention and convolutional block attention module attention models, it had a significant precision advantage, and the weight file size is merely 7.0 MB, which is far smaller than the Yolov9 model segmentation weight size. …”
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Lithium-Ion Battery State of Health Degradation Prediction Using Deep Learning Approaches by Talal Alharbi, Muhammad Umair, Abdulelah Alharbi

“…Three deep learning architectures 1D Convolutional Neural Networks (CNN), CNN plus Long Short-Term Memory (LSTM), and CNN plus Gated Recurrent Units (GRU) are used in the centralized approach. …”
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Recent Advances in Deep Learning-Based Spatiotemporal Fusion Methods for Remote Sensing Images by Zilong Lian, Yulin Zhan, Wenhao Zhang, Zhangjie Wang, Wenbo Liu, Xuhan Huang

“…Consequently, spatiotemporal fusion techniques, which integrate images from different sensors, have garnered significant attention. …”
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Detecting Fake News in Urdu Language Using Machine Learning, Deep Learning, and Large Language Model-Based Approaches by Muhammad Shoaib Farooq, Syed Muhammad Asadullah Gilani, Muhammad Faraz Manzoor, Momina Shaheen

“…The research uses methods that look at the features of documents and classes to detect fake news in Urdu. Different models were tested, including machine learning models like Naïve Bayes and Support Vector Machine (SVM), as well as deep learning models like Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM), which used embedding techniques. …”
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Application of deep learning models in gastric cancer pathology image analysis: a systematic scoping review by Sijun Xia, Yuanze Xia, Ting Liu, Yiming Luo, Patrick Cheong-Iao Pang

“…Some models even reached an accuracy of over 95% in GC detection. Convolutional neural networks (CNN) are the most commonly used DL models. …”
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LUNA: Loss-Construct Unsupervised Network Adjustment for Low-Dose CT Image Reconstruction by Ritu Gothwal, Shailendra Tiwari, Shivendra Shivani

“…We propose an unsupervised CT reconstruction technique that leverages the power of Deep convolutional neural networks (Deep CNNs), demonstrating that a randomly initialized neural network can serve as a prior. …”
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Transformer-Based Optimization for Text-to-Gloss in Low-Resource Neural Machine Translation by Younes Ouargani, Noussaim El Khattabi

“…The trials involve optimizing a minimal model, and our complex model with different optimizers; The findings from these trials show that both Adaptive Gradient (AdaGrad) and Adaptive Momentum (Adam) offer significantly better performance than Stochastic Gradient Descent (SGD) and Adaptive Delta (AdaDelta) in the minimal model scenario, however, Adam offers significantly better performance in the complex model optimization task. …”
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A review of machine learning and deep learning for Parkinson’s disease detection by Hajar Rabie, Moulay A. Akhloufi

“…Our evaluation included different algorithms such as support vector machines (SVM), random forests (RF), convolutional neural networks (CNN). …”
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Managing Timing Uncertainties in Worst-Case Design of Machine Learning Applications by Robin Hapka, Rolf Ernst

“…., robot-human collaboration using convolutional neural networks, timing must be considered to operate safely. …”
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ABDviaMSIFAT: Abnormal Crowd Behavior Detection Utilizing a Multi-Source Information Fusion Technique by Ali Ahmad Hamid, S. Amirhassan Monadjemi, Bijan Shoushtarian

“…To solve this issue, we suggest a new method that combines data from various sources with different characteristics to enhance the precision of detecting human behavior in crowds. …”
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