Restricted Boltzmann machine with Sobel filter dense adversarial noise secured layer framework for flower species recognition by Shyamala Devi M, Sundaravadivazhagan Balasubaramanian, Balasubramaniam S, Mohd Asif Shah

“…The first contribution deals with the dataset preparation by means of feature extraction through the use of the Sobel filter and the Restricted Boltzmann Machine (RBM) neural network approach through unsupervised learning. …”
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Evaluation of the conventional and synchrotron X-ray tomography applied to heterogeneous oil reservoir rocks by Guilherme José Ramos, Paula Campos de Oliveira, Rodrigo Surmas, Leandro de Paulo Ferreira, Henning Markötter, Nikolay Kardjilov, Ingo Manke, Luciano Andrey Montoro, Augusta Isaac

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A multi-source image fusion algorithm for typical electrical equipment with dual-branch network structure by NIE Qixin, XIAO Zhiyun, BAO Tengfei, JIN Xu, GAO Wenqiang, GUO Hao

“…In this paper, the visible and infrared images of substation electrical equipment are used as the research object, and the network model is designed by deep learning method with auto-encoder as the backbone network, in which the encoder adopts the designed dual-branch network structure of densely connected branch and enhanced branch, one branch is densely connected branch using dense block connection and self-attention mechanism to extract edge and detail features, and the other branch is enhanced branch using a strengthened branch with an improved feature pyramid network (FPN) structure to enhance the global information. …”
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Static Voltage Stability Evaluation and Correction Strategy Based on Stage Solving Strategy and SIFT by Yan Wen, Xiaoming Liu, Yongzhi Zhou, Huiwen Zhang, Donglei Sun

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Action recognition using attention-based spatio-temporal VLAD networks and adaptive video sequences optimization by Zhengkui Weng, Xinmin Li, Shoujian Xiong

“…In this paper, a novel attention-based spatio-temporal VLAD network (AST-VLAD) with self-attention model is developed to aggregate the informative deep features across the video according to the adaptive deep feature selected. …”
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Leveraging Deep Spatiotemporal Sequence Prediction Network with Self-Attention for Ground-Based Cloud Dynamics Forecasting by Sheng Li, Min Wang, Minghang Shi, Jiafeng Wang, Ran Cao

“…These components are integrated into cloud dynamic feature mining units, which concurrently extract spatiotemporal features to strengthen unified spatiotemporal modeling. …”
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Discussion of a Simple Method to Generate Descriptive Images Using Predictive ResNet Model Weights and Feature Maps for Recurrent Cervix Cancer by Destie Provenzano, Jeffrey Wang, Sharad Goyal, Yuan James Rao

“…These generated images could be useful for evaluating the explainability of predictive models and to assist radiologists with the identification of features likely to predict disease course.…”
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Analyzing feature importance for older pedestrian crash severity: A comparative study of DNN models, emphasizing road and vehicle types with SHAP interpretation by Rocksana Akter, Susilawati Susilawati, Hamza Zubair, Wai Tong Chor

“…Recognizing the importance of road safety modeling, the study explores Deep Neural Networks (DNN) with features like hidden layers, batch normalization, Rectified Linear Unit (ReLU) activation, and dropout to predict crash severity, interpreting decisions using SHapley Additive exPlanations (SHAP) for crashes involving older pedestrians. …”
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Graph-Aware Multimodal Deep Learning for Classification of Diabetic Retinopathy Images by Amina Zedadra, Ouarda Zedadra, Mahmoud Yassine Salah-Salah, Antonio Guerrieri

“…DRdiag integrates multiple modalities: the Fundus images and the duration of disease evolution which is an important factor in the diagnosis of DR as the duration of the disease directly influences the onset and progression of retinal lesions, leveraging two distinct models: a Convolutional Neural Network (CNN) based on DenseNet121 for image feature extraction and a Graph Neural Network (GNN) for capturing complex relationships between patient features. …”
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Klasifikasi Katarak Berdasarkan Optic Disc Citra Fundus Smartphone: Perbandingan Ekstraksi Ciri Tekstur Dan Metode Neural Network by Muchtar Ali Setyo Yudono, Fabrobi Fazlur Ridha, Dani Mardiyana, Faturrohman Al-Ghozi, Aldi Maulana

“…This research aims to develop an automatic cataract classification system using smartphone-based fundus cameras, offering a more cost-effective and portable solution compared to conventional devices. The study evaluates the performance of three neural network algorithms: Backpropagation Neural Network (BPNN), Probabilistic Neural Network (PNN), and Radial Basis Function Neural Network (RBFNN) for cataract classification. …”
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Construction and evaluation of a diagnostic model for Alzheimer’s disease based on mitophagy-related genes by Jiarui Liu, Mengyu Yan, Lihua Chen, Weihua Yu, Yang Lü

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Evaluating AI Methods for Pulse Oximetry: Performance, Clinical Accuracy, and Comprehensive Bias Analysis by Ana María Cabanas, Nicolás Sáez, Patricio O. Collao-Caiconte, Pilar Martín-Escudero, Josué Pagán, Elena Jiménez-Herranz, José L. Ayala

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Multidimensional State Data Reduction and Evaluation of College Students’ Mental Health Based on SVM by Han Peiqing

“…A model experiment containing internal and external personality tendency classification, anxiety, and depression dichotomy was designed using logistic regression analysis, information entropy, and SVM algorithm to construct the feature dimensions of the network behavior data, combined with the labeled data of mental state to derive the sample data set for model experiments. …”
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Integrating fast iterative filtering and ensemble neural network structure with attention mechanism for carbon price forecasting by Wang Zhong, Wang Yue, Wang Haoran, Tang Nan, Wang Shuyue

“…Subsequently, an integrated prediction model, AM-TCN-LSTM, is constructed, incorporating the attention mechanism (AM), temporal convolutional networks (TCN), and long short-term memory (LSTM) neural networks. …”
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Versatility Evaluation of Landslide Risk with Window Sizes and Sampling Techniques Based on Deep Learning by Fudong Ren, Koichi Isobe

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EnGCI: enhancing GPCR-compound interaction prediction via large molecular models and KAN network by Weihao Liu, Xiaoli Li, Bo Hang, Pu Wang

“…These features are then processed by a Kolmogorov-Arnold network (KAN) for decision-making. …”
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RSNet: Compact-Align Detection Head Embedded Lightweight Network for Small Object Detection in Remote Sensing by Qing Dong, Tianxin Han, Gang Wu, Baiyou Qiao, Lina Sun

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SACNN‐IDS: A self‐attention convolutional neural network for intrusion detection in industrial internet of things by Mimonah Al Qathrady, Safi Ullah, Mohammed S. Alshehri, Jawad Ahmad, Sultan Almakdi, Samar M. Alqhtani, Muazzam A. Khan, Baraq Ghaleb

“…This paper proposes a self‐attention convolutional neural network (SACNN) architecture for the detection of malicious activity in IIoT networks and an appropriate feature extraction method to extract the most significant features. …”
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Hysteretic curve characteristics in rectangular shear walls predicted by machine learning by Jungui Dong, Ce Chen

“…Results show IEG-ML high accuracy and efficiency, particularly with a backpropagation network optimized by the dung beetle algorithm (DBO), making it a robust tool for seismic evaluation.…”
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DynamicG2B: Dynamic Node Classification with Layered Graph Neural Networks and BiLSTM by Fattah Muhammad Tahabi, Xiao Luo

“…Our method applies relevant attention weights at different time steps to classify nodes in a supervised manner, utilizing dynamic edges and node feature information. Our evaluation of two benchmark datasets shows that DynamicG2B outperforms seven state-of-the-art baseline models in node classification in dynamic graphs. …”
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