Optimizing Pre-Trained Models for Medical Dataset Classification with a Fine-Tuning Approach by N. Kumar, T. Christopher

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A Novel Two-Stage Deep Learning Model for Network Intrusion Detection: LSTM-AE by Vanlalruata Hnamte, Hong Nhung-Nguyen, Jamal Hussain, Yong Hwa-Kim

“…This kind of research enables the identification of the most efficient algorithm for identifying future cyber-attacks. …”
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Multi-Branch CNN-LSTM Fusion Network-Driven System With BERT Semantic Evaluator for Radiology Reporting in Emergency Head CTs by Selene Tomassini, Damiano Duranti, Abdallah Zeggada, Carlo Cosimo Quattrocchi, Farid Melgani, Paolo Giorgini

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Advancing irrigation uniformity monitoring through remote sensing: A deep-learning framework for identifying the source of non-uniformity by Ígor Boninsenha, Daran R. Rudnick, Everardo C. Mantovani, Higor de Q. Ribeiro

“…Class-specific metrics demonstrated high precision, recall, and F1 scores for most patterns, though underrepresented classes, like mechanical issues, showed lower performance. …”
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A non-sub-sampled shearlet transform-based deep learning sub band enhancement and fusion method for multi-modal images by Sudhakar Sengan, Praveen Gugulothu, Roobaea Alroobaea, Julian L. Webber, Abolfazl Mehbodniya, Amr Yousef

“…The primary objective of Medical Image Fusion (MIF) is to generate a fused image that retains the most informative features from the Source Images (SI), thereby enhancing the reliability of clinical decision-making systems. …”
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Diagnosis of Lung Cancer Using Endobronchial Ultrasonography Image Based on Multi-Scale Image and Multi-Feature Fusion Framework by Huitao Wang, Takahiro Nakajima, Kohei Shikano, Yukihiro Nomura, Toshiya Nakaguchi

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Enhancing water saturation predictions from conventional well logs in a carbonate gas reservoir with a hybrid CNN-LSTM model by Ali Gohari Nezhad, Mohammad Emami Niri

“…Results showed that the CNN-LSTM model was the most effective, with an R-squared accuracy score of 0.859, while the Archie equation had the least R-squared accuracy of 0.322 in the validation dataset. …”
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Categorical classification of skin cancer using a weighted ensemble of transfer learning with test time augmentation by Aliyu Tetengi Ibrahim, Mohammed Abdullahi, Armand Florentin Donfack Kana, Mohammed Tukur Mohammed, Ibrahim Hayatu Hassan

“…Skin cancer is the abnormal development of cells on the surface of the skin and is one of the most fatal diseases in humans. It usually appears in locations that are exposed to the sun, but can also appear in areas that are not regularly exposed to the sun. …”
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Novel deep learning for multi-class classification of Alzheimer’s in disability using MRI datasets by Sumaiya Binte Shahid, Maleeha Kaikaus, Md. Hasanul Kabir, Mohammad Abu Yousuf, A. K. M. Azad, A. S. Al-Moisheer, Naif Alotaibi, Salem A. Alyami, Touhid Bhuiyan, Mohammad Ali Moni, Mohammad Ali Moni, Mohammad Ali Moni

“…IntroductionAlzheimer’s disease (AD) is one of the most common neurodegenerative disabilities that often leads to memory loss, confusion, difficulty in language and trouble with motor coordination. …”
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TCN-GRU Based on Attention Mechanism for Solar Irradiance Prediction by Zhi Rao, Zaimin Yang, Xiongping Yang, Jiaming Li, Wenchuan Meng, Zhichu Wei

“…The global horizontal irradiance (GHI) is the most important metric for evaluating solar resources. …”
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Two-Stage Video Violence Detection Framework Using GMFlow and CBAM-Enhanced ResNet3D by Mohamed Mahmoud, Bilel Yagoub, Mostafa Farouk Senussi, Mahmoud Abdalla, Mahmoud Salaheldin Kasem, Hyun-Soo Kang

“…The attention mechanism provided by CBAM enables the network to focus on the most relevant regions in the frames, improving the detection of violent actions. …”
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A Self-Attention Enhanced Deep CNN-LSTM-Based Irregular Surface Recognition Approach for Integration Into Lower Limb Prosthesis Systems to Ensure Safety Through Predictive Walking by Norazian Subari, Kamarul Hawari Ghazali, Yuanfa Ji

“…However, significant challenges persist when users encounter irregular surfaces, as most prosthetic systems lack the capability to dynamically adapt to surface variations. …”
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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 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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