IsVoNet8: A Proposed Deep Learning Model for Classification of Some Fish Species by Özge Zencir Tanır, İsmail Akgül, Volkan Kaya

“…In this study, a new convolutional neural network model classifying 8 different belonging to 6 families (Mullidae, Sparidae, Carangidae, Serranidae, Clupeidae, Salmonidae) fish species using deep learning methods was proposed. …”
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A Lightweight GCT-EEGNet for EEG-Based Individual Recognition Under Diverse Brain Conditions by Laila Alshehri, Muhammad Hussain

“…The extracted features were used for subject recognition through a cosine similarity metric that measured the similarity between feature vectors of different EEG trials to identify individuals. The proposed method was evaluated on a large dataset comprising 263 subjects. …”
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Cascaded Feature Fusion Grasping Network for Real-Time Robotic Systems by Hao Li, Lixin Zheng

“…The network employs innovative structural designs, including depth-wise separable convolutions to reduce parameters and enhance computational efficiency; convolutional block attention modules to augment the model’s ability to focus on key features; multi-scale dilated convolution to expand the receptive field and capture multi-scale information; and bidirectional feature pyramid modules to achieve effective fusion and information flow of features at different levels. …”
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A Multi-Scale Feature Extraction Algorithm for Chinese Herbal Medicine Image Classification by Wenbin Dai, Yuxin Ma, Yan Fan, Jun Ma

“…Considering the subtle differences among the data, we proposed a multi-scale feature extraction architecture called MSPyraNet. …”
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Analysis and training of a traffic sign recognition neural network model by A. U. Mentsiev, T. G. Aigumov, E. M. Abdulmukminova

“…The purpose of the research is to develop and train a neural network model based on convolutional neural networks for effective recognition of road signs in images.Method. …”
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Underwater Acoustic Signal LOFAR Spectrogram Denoising Based on Enhanced Simulation by Tianxiang He, Sheng Feng, Jie Yang, Kun Yu, Junlin Zhou, Duanbing Chen

“…Furthermore, the experiments demonstrate that the proposed convolutional denoising model has transferability and generalization, making it suitable for denoising underwater acoustic signal in different marine areas.…”
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HyperKAN: Kolmogorov–Arnold Networks Make Hyperspectral Image Classifiers Smarter by Nikita Firsov, Evgeny Myasnikov, Valeriy Lobanov, Roman Khabibullin, Nikolay Kazanskiy, Svetlana Khonina, Muhammad A. Butt, Artem Nikonorov

“…Specifically, six cutting-edge neural networks were modified, including 1D (1DCNN), 2D (2DCNN), and 3D convolutional networks (two different 3DCNNs, NM3DCNN), as well as transformer (SSFTT). …”
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Wavelet-Driven Multi-Band Feature Fusion for RGB-T Salient Object Detection by Jianxun Zhao, Xin Wen, Yu He, Xiaowei Yang, Kechen Song

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DroneSilient (drone + resilient): an anti-drone system by Meghna Manoj Nair, Harini Sriraman, Gadiparthy Harika Sai, V. Pattabiraman

“…In this study, we present the DroneSilient System, a novel anti-drone system that combines different parts. The DroneSilient system includes components that connect to RF identification technology and image-capture technology. …”
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Nameplate Detection and Recognition of Smart Meter Communication Module Based on ResSE-SegNet by ZHAI Xiaohui, SUN Kai, ZHAO Jifu, SUN Yanling, XING Yu, GUO Kaixuan, WANG Haiying

“…The region where the manufacturer′s name is located in the image is segmented using a deep codec network structure, and an end-to-end convolutional neural network (CNN) model is constructed and trained to identify different manufacturers.Finally, the data set of the communication module image is obtained through the full-dimensional smart meter detection system, and the detection and recognition experiment of the communication module nameplate is carried out. …”
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Retrievals of Biomass Burning Aerosol and Liquid Cloud Properties from Polarimetric Observations Using Deep Learning Techniques by Michal Segal Rozenhaimer, Kirk Knobelspiesse, Daniel Miller, Dmitry Batenkov

“…We present a comparison between the different DL approaches, as well as their comparison to existing algorithms. …”
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Research on UAV Jamming Signal Generation Based on Intelligent Jamming by Haonan Xue, Zhihai Zhuo, Weihao Yan, Yuexia Zhang

“…Simulation results show that, across different communication systems, the generated jamming signal waveforms exhibit strong similarity to the original signal waveforms. …”
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The analysis of sculpture image classification in utilization of 3D reconstruction under K-means++ by Xuhui Wang

“…ResNet50 includes residual blocks, each containing multiple convolutional layers and a skip connection, enabling the network to learn differences between inputs and outputs rather than directly learning outputs, thus improving performance. …”
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AI-Powered Object Detection in Radiology: Current Models, Challenges, and Future Direction by Abdussalam Elhanashi, Sergio Saponara, Qinghe Zheng, Nawal Almutairi, Yashbir Singh, Shiba Kuanar, Farzana Ali, Orhan Unal, Shahriar Faghani

“…Moreover, the need for strong applicable models across different populations and imaging modalities are addressed. …”
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Cloud Computing Resource Scheduling Algorithm Based on Unsampled Collaborative Knowledge Graph Network by Haichuan Sun, Liang Gu, Chenni Dong, Xin Ma, Zeyu Liu, Zhenxi Li

“…The knowledge graph data fragments are processed based on class convolution and human-machine interaction attention mechanism, and different sizes of linear aggregators are used to capture deep level information, completing the design of cloud computing resource scheduling algorithm. …”
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PV Module Soiling Detection Using Visible Spectrum Imaging and Machine Learning by Boris I. Evstatiev, Dimitar T. Trifonov, Katerina G. Gabrovska-Evstatieva, Nikolay P. Valov, Nicola P. Mihailov

“…One of these factors is the soiling of the PV surface, which could be observed in different forms, such as dust and bird droppings. In this study, visible spectrum data and machine learning algorithms were used for the identification of soiling. …”
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Review on Hybrid Deep Learning Models for Enhancing Encryption Techniques Against Side Channel Attacks by Amjed A. Ahmed, Mohammad Kamrul Hasan, Azana H. Aman, Nurhizam Safie, Shayla Islam, Fatima A. Ahmed, Thowiba E. Ahmed, Bishwajeet Pandey, Leila Rzayeva

“…Deep learning is being used in many different fields in the past several years. Convolutional neural networks and recurrent neural networks, for instance, have demonstrated efficacy in text generation and object detection in images, respectively. …”
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Seismic Random Noise Attenuation Using DARE U-Net by Tara P. Banjade, Cong Zhou, Hui Chen, Hongxing Li, Juzhi Deng, Feng Zhou, Rajan Adhikari

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The potential role of synthetic computed tomography in spinal surgery: generation, applications, and implications for future clinical practice by Shreya Sankar, Jake Michael McDonnell, Stacey Darwish, Joseph Simon Butler

“…The review assessed sCT accuracy and clinical feasibility across different medical disciplines, particularly oncology and surgery, with potential applications in orthopedic, neurosurgical, and spinal surgery. sCT has shown significant promise across various medical disciplines. …”
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Real-Time Human Action Recognition With Dynamical Frame Processing via Modified ConvLSTM and BERT by Raden Hadapiningsyah Kusumoseniarto, Zhi-Yuan Lin, Shun-Feng Su, Pei-Jun Lee

“…The effects of ModConvLSTM are verified at different depths. In our proposed architecture, we replace global average pooling (GAP) with Bidirectional Encoder Representations from Transformers (BERT) to address the limitations of temporal processing in a two-dimensional convolutional neural network (2D-CNN). …”
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