Application of social media communication for museum based on the deep mediatization and artificial intelligence by Hongkai Wang, Chao Song, Hongming Li

“…Abstract Based on deep mediatization theory and artificial intelligence (AI) technology, this study explores the effective improvement of museums’ social media communication by applying Convolutional Neural Network (CNN) technology. Firstly, the social media content from four different museums is collected, a dataset containing tens of thousands of images is constructed, and a CNN-based model is designed for automatic identification and classification of image content. …”
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Research on Lightweight Small Object Detection Algorithm Based on Context Representation by Li Qiang, Cui Jianghui

“…The convolutional self-attention model is employed into traditional feature pyramid network (FPN) structures to reduce information loss during up-sampling and down-sampling processes, and a differential detection head is used to detect targets of different sizes. …”
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Self-adaptive spatial-temporal network based on heterogeneous data for air quality prediction by Feng Chang, Liang Ge, Siyu Li, Kunyan Wu, Yaqian Wang

“…In order to effectively aggregate spatial information, we employ a self-adaptive graph convolution module that can learn the latent spatial correlations of air quality automatically. …”
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Domain Adaptation for Satellite-Borne Multispectral Cloud Detection by Andrew Du, Anh-Dzung Doan, Yee Wei Law, Tat-Jun Chin

“…This underperformance stems from the domain gap, i.e., differences in the underlying distributions of the data generated by the different sensors in previous and future missions. …”
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Vision Transformers (ViTs) for Feature Extraction and Classification of AI-Generated Visual Designs by Qing Yun

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A Novel Lightweight Algorithm for Sonar Image Recognition by Gang Wan, Qi He, Qianqian Zhang, Hanren Wang, Huanru Sun, Xinnan Fan, Pengfei Shi

“…Secondly, the original 1 × 1 convolution is replaced with the redesigned multi-scale convolution Res2Net in the MV2 module to enhance the ability of the algorithm to learn global and local features. …”
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The impacts of training data spatial resolution on deep learning in remote sensing by Christopher Ardohain, Songlin Fei

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Implementation of real time root crop leaf classification using CNN on raspberry-Pi microprocessor by M.D. Rakesh, M. Jeevankumar, S.B. Rudraswamy

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Deep learning modeling of manufacturing and build variations on multistage axial compressors aerodynamics by Giuseppe Bruni, Sepehr Maleki, Senthil K. Krishnababu

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Boost Cyclone Intensity Estimation via Processing Motion Information and Embedding Rotation Invariance by Xiaoping Cheng, Xinxin Kong, Jianfang Fei, Xiaogang Huang

“…To ensure that the feature extraction module of the network possesses rotation invariance, each basic convolution operation is replaced with a rotation invariant convolution operation. …”
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Hybrid CNN-GCN Network for Hyperspectral Image Classification by Cuiping Shi, Diling Liao, Liguo Wang

“…In recent years, convolutional neural networks (CNNs) have been impressive due to their excellent feature representation abilities, but it is difficult to learn long-distance spatial structures information. …”
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Identification of line status changes using phasor measurements through deep learning networks by N. E. Gotman, G. P. Shumilova

“…The combination of different CNN architectures and the number of time slices from the moment of line status change are used to detect the power grid topology. …”
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Deep-Learning-Based Solar Flare Prediction Model: The Influence of the Magnetic Field Height by Lei Hu, Zhongqin Chen, Long Xu, Xin Huang

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Fish oocyte morphology detection using neural networks: a comparison of YOLO architectures by Yanna Leidy Ketley Fernandes Cruz, Ewaldo Eder Carvalho Santana, Isa Rosete Araujo Nascimento, Antonio Fhillipi Maciel Silva, Raimunda Nonata Fortes Carvalho Neta, José Ribamar de Souza Torres-Junior

“…In this paper, we propose the use of YOLO, a family of convolutional neural networks, for oocyte morphology detection of Centropomus undecimalis fish. …”
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Detection of single and dual pulmonary diseases using an optimized vision transformer by Almughamisi Naif, Abosamra Gibrael, Albar Adnan, Saleh Mostafa

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Estimating the effect of age-induced defocus on object detection performance of automotive cameras by Amit Pandey, Stephan Kühn, Marcel Kettelgerdes, Bernhard Wunderle, Gordon Elger

“…This work investigates the effect of defocus on safety-critical object detection by comparing various pre-trained object detection models, primarily convolutional neural networks. These models are compared in their performance against static scenes with different levels of defocus. …”
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Deep Learning Algorithm Analysis of Potato Disease Classification for System on Chip Implementation by John Adebisi, Sesham Srinu, Varqa Mitonga

“…The broad range of crops has witnessed setbacks in different capacities due to climate change among other factors, hence leading to diseases and infections; thereby leading to negatively impacted nutrition. …”
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Deep Learning-Based Multistage Fire Detection System and Emerging Direction by Tofayet Sultan, Mohammad Sayem Chowdhury, Mejdl Safran, M. F. Mridha, Nilanjan Dey

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A Unified Approach to Voice Classification: Leveraging Spectrograms, Mel Spectrograms, and Statistical Features by Muhammad Talha, Huma Ghafoor, Seung Yeob Nam

“…This study presents a multi-input neural network architecture for voice classification that integrates two parallel convolutional neural networks (CNNs) for spectrogram and Mel spectrogram images, along with a fully connected dense network for six handpicked numerical statistical features from time domain signal. …”
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PLANT DISEASE DETECTION USING DEEP LEARNING by Amrita, Sanjay Kumar Nayak, Rajiv Kumar, Sakshi Tomar

“…In this paper, several deep learning (DL) models are proposed to recognize the multiple classes of diseases present in plants from the images of leaves taken under various resolutions and different environmental conditions. Employing a Deep Convolutional Neural Network (CNN) in multi-class classification for detecting plant diseases can be beneficial in the early identification of these diseases and also in dealing with the negative impact of these diseases on agriculture. …”
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