Multistage adaptive cyberattack in power systems with CNN identification feedback loops by Mohannad Alhazmi, Alexis Pengfei Zhao, Xi Cheng, Chenlu Yang

“…The proposed CDB-TAS model comprises: (i) a Preliminary Reconnaissance Phase, where a Convolutional Neural Network (CNN) identifies the most vulnerable buses via real-time anomaly detection; (ii) an Escalation Phase, where a Double Deep Q-Network (Double DQN) dynamically refines the attack strategy based on grid response and demand profiles; and (iii) a Sustained Attack Phase, which maintains high-intensity disruptions while minimizing detection through continuous feedback adaptation. …”
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SGCL-LncLoc: An Interpretable Deep Learning Model for Improving lncRNA Subcellular Localization Prediction with Supervised Graph Contrastive Learning by Min Li, Baoying Zhao, Yiming Li, Pingjian Ding, Rui Yin, Shichao Kan, Min Zeng

“…Then, SGCL-LncLoc applies graph convolutional networks to learn the comprehensive graph representation. …”
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A Systematic Survey of AI Models in Financial Market Forecasting for Profitability Analysis by Bilal Hassan Ahmed Khattak, Imran Shafi, Abdul Saboor Khan, Emmanuel Soriano Flores, Roberto Garcia Lara, Md. Abdus Samad, Imran Ashraf

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A prediction model for the mechanical properties of SUS316 stainless steel ultrathin strip driven by multimodal data mixing by Zhenhua Wang, Pengzhan Wang, Yunfei Liu, Yuanming Liu, Tao Wang

“…Specifically, the MLP branch is used to extract the rolling process data features, and the ResNet with the addition of a convolutional block attention module (CBAM) is used to extract the microstructure features. …”
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Radiative Transfer Model-Integrated Approach for Hyperspectral Simulation of Mixed Soil-Vegetation Scenarios and Soil Organic Carbon Estimation by Asmaa Abdelbaki, Robert Milewski, Mohammadmehdi Saberioon, Katja Berger, José A. M. Demattê, Sabine Chabrillat

“…A simulated EO disturbed soil spectral library (DSSL) was created, significantly expanding the EU LUCAS cropland soil spectral library. A 1D convolutional neural network (1D-CNN) was trained on this database to predict Soil Organic Carbon (SOC) content. …”
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A systematic review of multimodal fake news detection on social media using deep learning models by Maged Nasser, Noreen Izza Arshad, Abdulalem Ali, Hitham Alhussian, Faisal Saeed, Aminu Da'u, Ibtehal Nafea

“…The findings showed that the Transformer models and Recurrent Neural Networks (RNNs) are the most popular deep learning techniques for detecting multimodal fake news, followed by the Convolutional Neural Networks (CNNs) techniques. …”
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Forecasting very short-term power load with hybrid interpretable deep models by Zhihe Yang, Jiandun Li, Chang Liu, Haitao Wang

“…Experiment results demonstrate that the hybrid model based on Convolutional Neural Network (CNN) and BiLSTM outperforms several state-of-the-art solutions. …”
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Accurate and real-time brain tumour detection and classification using optimized YOLOv5 architecture by M. Saranya, R. Praveena

“…This integration of detection and segmentation models presents one of the most effective techniques for enhancing the diagnostic performance of the system to add value within the medical imaging field. …”
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EFCNet enhances the efficiency of segmenting clinically significant small medical objects by Lingjie Kong, Qiaoling Wei, Chengming Xu, Xiaofeng Ye, Wei Liu, Min Wang, Yanwei Fu, Han Chen

“…Notably, smaller objects benefit most, highlighting EFCNet’s effectiveness where conventional models underperform. …”
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Alzheimer’s Prediction Methods with Harris Hawks Optimization (HHO) and Deep Learning-Based Approach Using an MLP-LSTM Hybrid Network by Raheleh Ghadami, Javad Rahebi

“…<b>Method:</b> This proposal methodology involves sourcing Alzheimer’s disease-related MRI images and extracting features using convolutional neural networks (CNNs) and the Gray Level Co-occurrence Matrix (GLCM). …”
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Computational methods and technical means of processing signals of side electromagnetic emanation by Danil A. Shinyaev, Leonid N. Kessarinskiy, Egor A. Simakhin

“…Due to the 10-bit noise-resistant encoding of video information for digital data transmission interfaces, signal analysis and image restoration are most difficult. Since this encoding expands the bandwidth for side electromagnetic radiation and leads to a nonlinear display of the observed signal and a decrease in the intensity of radiation from the display pixels. …”
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Autonomous detection of nail disorders using a hybrid capsule CNN: a novel deep learning approach for early diagnosis by Gunjan Shandilya, Sheifali Gupta, Salil Bharany, Ateeq Ur Rehman, Upinder Kaur, Hafizan Mat Som, Seada Hussen

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Multi-stream feature fusion of vision transformer and CNN for precise epileptic seizure detection from EEG signals by Qi Li, Wei Cao, Anyuan Zhang

“…Abstract Background Automated seizure detection based on scalp electroencephalography (EEG) can significantly accelerate the epilepsy diagnosis process. However, most existing deep learning-based epilepsy detection methods are deficient in mining the local features and global time series dependence of EEG signals, limiting the performance enhancement of the models in seizure detection. …”
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Data-Driven Optimized Load Forecasting: An LSTM-Based RNN Approach for Smart Grids by Muhammad Asghar Majeed, Sotdhipong Phichaisawat, Furqan Asghar, Umair Hussan

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Steering-Angle Prediction and Controller Design Based on Improved YOLOv5 for Steering-by-Wire System by Cunliang Ye, Yunlong Wang, Yongfu Wang, Yan Liu

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Supervised machine learning prediction and investigation of nonlinear optical rectification in Ge/Si0.15Ge0.85 asymmetric coupled triangle quantum wells by A. Cherni, N. Yahyaoui, N. Zeiri, M.L. Bouazizi, M.Al. Zahrani

“…Among the three ML models, the DT model yields the most accurate predictions, with RMSE values between 0.0038 and 0.0053 and MAE values between 0.0020 and 0.0027 across all considered LR values. …”
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More Accurate Constraints for Self-Supervised Learning in Remote Sensing Images-Based Object Detection by Shangdong Zheng, Zebin Wu, Yang Xu, Qian Liu, Zhihui Wei

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MoAGL-SA: a multi-omics adaptive integration method with graph learning and self attention for cancer subtype classification by Lei Cheng, Qian Huang, Zhengqun Zhu, Yanan Li, Shuguang Ge, Longzhen Zhang, Ping Gong

“…Next, three-layer graph convolutional networks are employed to extract omic-specific graph embeddings. …”
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An air target intention data extension and recognition model based on deep learning by Bo Cao, Qinghua Xing, Longyue Li, Weijie Lin

“…Finally, the temporal block based on dilated causal convolution is built to solve the problem of temporal feature extraction. …”
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MEFA-Net: Multilevel Feature Extraction and Fusion Attention Network for Infrared Small-Target Detection by Jingcui Ma, Nian Pan, Dengyu Yin, Di Wang, Jin Zhou

“…Specifically, the dilated direction-sensitive convolution block (DDCB) is devised to collaboratively extract local detail features, contextual features, and Gaussian salient features via ordinary convolution, dilated convolution and parallel strip convolution. …”
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