Interpretable Machine Learning Biosignature Detection From Ocean Worlds Analogue CO2 Isotopologue Data by Lily A. Clough, Victoria DaPoian, Jonathan D. Major, Lauren M. Seyler, Brett A. McKinney, Bethany P. Theiling

“…Abstract Future missions to icy ocean worlds (OW) such as Europa and Enceladus will evaluate the habitability and potential for biosignatures on these worlds. …”
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Detection of Fiber-Flaw on Pill Surface Based on Lightweight Network SA-MGhost-DVGG by Jipei Lou, Hongyi Wang, Haodong Liang, Ziwei Wu

“…To overcome the limitations of traditional methods in accuracy and real-time performance, this study introduces SA-MGhost-DVGG, a novel lightweight network for enhanced detection. The proposed network integrates an MGhost module for reducing parameters and computational load, a mixed-channel spatial attention (SA) module to refine features specific to fiber regions, and depthwise separable convolutions (DepSepConv) for efficient dimensionality reduction while preserving feature information. …”
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Diagnosis of Alzheimer Disease Using 2D MRI Slices by Convolutional Neural Network by Fanar E. K. Al-Khuzaie, Oguz Bayat, Adil D. Duru

“…We trained the convolutional neural network structure using the 2D slices to exhibit the deep network weightings that we named as the Alzheimer Network (AlzNet). …”
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Automatic text generation system for endangered languages based on conditional generative adversarial networks by Zhong Luo

“…This paper explores the application of Conditional Generative Adversarial Networks (CGANs) in the field of endangered language text generation. …”
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Research on Change Information Retrieval Method for Energy Infrastructure Based on Optical Remote Sensing Images by Zhibao Wang, Ying Yuan, Man Zhao, Dan Zhou, Lu Bai, Jinhua Tao, Anna Jurek-Loughrey

“…Our model, SCanNet-CDH (Semantic Change Network and Convolutional Deep Hashing), integrates convolutional neural networks with Transformer architecture, utilizing multi-scale features from ResNet-34 and enhancing them with the CSWin Transformer for improved global feature modeling. …”
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Automatic Classification of 5G Waveform-Modulated Signals Using Deep Residual Networks by Haithem Ben Chikha, Alaa Alaerjan, Randa Jabeur

“…Modulation identification plays a crucial role in contemporary wireless communication systems, especially within 5G and future-generation networks that utilize a variety of multicarrier waveforms. …”
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Deep learning network for NMR spectra reconstruction in time-frequency domain and quality assessment by Yao Luo, Wenhan Chen, Zhenhua Su, Xiaoqi Shi, Jie Luo, Xiaobo Qu, Zhong Chen, Yanqin Lin

“…In this study, a joint time-frequency domain deep learning network, referred to as JTF-Net, is proposed. It effectively combines time domain and frequency domain features, exhibiting better reconstruction performance on protein spectra across various dimensions compared to traditional algorithms and single-domain DL methods. …”
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Beam Coefficient Prediction for Antenna Arrays Using Physics-Aware Convolutional Neural Networks by Glendyn D. King, Md Asaduzzaman Towfiq, Ali C. Gurbuz, Bedri A. Cetiner

“…This work introduces a novel approach for predicting the complex element weights of a rectangular planar phased array for a desired beam pattern using physics-aware convolutional neural networks (PA-CNN). An image representation of the desired radiation pattern is provided as input to the proposed network architecture, where PA-CNN learns to recognize spatial features and map input patterns to the amplitude and phase of the antenna element weights. …”
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Medical image segmentation network based on multi-scale cross-attention and wavelet transform by Fan Qin, Yongjie Liang, Chaofeng Yang, Yulong Cao, Jiaying Fan, Peiyuan Wang, Bizhong Wei

“…The network introduces a Multi-Scale Cross-Attention Module (MSCAM) to enhance feature selection capability and long-distance dependency modeling, employs Wavelet Transform Convolution (WTConv) for effective multi-scale feature extraction, and implements a Dynamic Upsampling module (DySample) to improve global context integration and detail reconstruction. …”
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Effective Skin Cancer Diagnosis Through Federated Learning and Deep Convolutional Neural Networks by Mabrook S. Al-Rakhami, Salman A. AlQahtani, Abdulaziz Alawwad

“…Early detection is crucial for successful treatment, and deep learning techniques, particularly deep convolutional neural networks (DCNNs), have shown tremendous potential in this area. …”
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Satellite Remote Sensing Grayscale Image Colorization Based on Denoising Generative Adversarial Network by Qing Fu, Siyuan Xia, Yifei Kang, Mingwei Sun, Kai Tan

“…Secondly, the discriminator network is optimized by proposing a feature statistical discrimination network, which imposes fewer constraints on the generator network. …”
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MixtureRS: A Mixture of Expert Network Based Remote Sensing Land Classification by Yimei Liu, Changyuan Wu, Minglei Guan, Jingzhe Wang

“…To address this, we propose MixtureRS, a compact multimodal network that effectively integrates hyperspectral imagery and LiDAR data for land-use classification. …”
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Dense-TNT: Efficient Vehicle Type Classification Neural Network Using Satellite Imagery by Ruikang Luo, Yaofeng Song, Longfei Ye, Rong Su

“…In addition, due to the rapid development of deep neural network technology, image-based vehicle classification methods can better extract underlying objective features when processing data. …”
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Artificial Neural Networks for Flood Prediction in Current and CMIP6 Climate Change Scenarios by Abderraman R. Amorim Brandão, Dimaghi Schwamback, Frederico C. M. deMenezes Filho, Paulo T. S. Oliveira, Maria Clara Fava

“…ABSTRACT Researchers have widely applied discharge simulation using artificial neural networks (ANNs) and have gained prominence in water resources. …”
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Developing an Artificial Neural Network-Based Grading Model for Energy Consumption in Residential Buildings by Yaser Shahbazi, Sahar Hosseinpour, Mohsen Mokhtari Kashavar, Mohammad Fotouhi, Siamak Pedrammehr

“…This research emphasizes the role of geometric features and natural lighting in energy consumption prediction, highlighting the model’s practicality for early design evaluations and architectural validations.…”
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Heart sound classification based on convolutional neural network with convolutional block attention module by Ximing Huai, Lei Jiang, Lei Jiang, Chao Wang, Peng Chen, Hanchi Li

“…This study presents an enhanced heart sound classification framework based on a Convolutional Neural Network (CNN) integrated with the Convolutional Block Attention Module (CBAM). …”
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A method of fatigue life prediction for rubber materials based on convolutional neural network by Jiao Li, Qi Li, Feng Wang, Liuxiangzi Yang, Mingshan Lou

“…The prediction performance of the CNN model was then evaluated against physical models, BPNN, and SVM models. …”
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Comparative Analysis of Time Series Prediction Algorithms on Multiple Network Function Data of NWDAF by Dasheng Chen, Qi Song, Yinbin Zhang, Ling Li, Zhiming Yang

“…This exponential increase in activity produces a plethora of events, making conventional systems inadequate for the efficient management of 5G networks. In comparison to 4G technology, 5G technology brings forth a host of new features, one of which is the network data analytics function (NWDAF). …”
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Enhancing market trend prediction using convolutional neural networks on Japanese candlestick patterns by Edrees Ramadan Mersal, Kürşat Mustafa Karaoğlan, Hakan Kutucu

“…Subsequently, the model was trained and evaluated multiple times using different combinations of these subsets. …”
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ResCapsnet: a capsule network with CRAM and BiGRU for sound event detection by Bing Sun, Chenglong Liu, Shuguo Yang, Wenwu Wang, Yiduo Mei

“…Deep learning methods such as convolutional neural networks (CNN) and recurrent neural networks (RNN) have achieved promising performance in SED. …”
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