An Efficient Optimal CapsNet Model-Based Computer-Aided Diagnosis for Gastrointestinal Cancer Classification by Fahdah A Almarshad, Prasanalakshmi Balaji, Liyakathunisa Syed, Eman Aljohani, Santhi Muttipoll Dharmarajlu, Thavavel Vaiyapuri, Nourah Ali AlAseem

“…Besides, the SOADL-GCC technique uses a capsule network (CapsNet) model for deriving the feature vectors from preprocessed images. …”
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Development of a clinical decision support system for breast cancer detection using ensemble deep learning by Jasjeet Kaur Sandhu, Chetna Sharma, Amandeep Kaur, Saroj Kumar Pandey, Anurag Sinha, J. Shreyas

“…The team improves its capacity to extricate intricate patterns and features from medical imaging data by incorporating the Kelm Extreme Learning Machine (KELM), Deep Belief Network (DBN), and other DL architectures. …”
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AG-MS3D-CNN multiscale attention guided 3D convolutional neural network for robust brain tumor segmentation across MRI protocols by Umesh Kumar Lilhore, R. Sunder, Sarita Simaiya, Majed Alsafyani, M. D. Monish Khan, Roobaea Alroobaea, Hamed Alsufyani, Abdullah M. Baqasah

“…To address these challenges, we propose AG-MS3D-CNN, an attention-guided multiscale 3D convolutional neural network for brain tumor segmentation. Our model integrates local and global contextual information through multiscale feature extraction and leverages spatial attention mechanisms to enhance boundary delineation, particularly in complex tumor regions. …”
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An Enhanced Measurement of Epicardial Fat Segmentation and Severity Classification using Modified U-Net and FOA-guided XGBoost by Rajalakshmi K, Palanivel Rajan S

“…The proposed method integrates a modified squeeze-and-excitation (MSE) block and a multi-scale dense (MS-D) convolutional neural network (CNN) to improve feature extraction. In addition, a metaheuristic optimization algorithm from falcon optimization algorithm (FOA) is used for efficient feature selection. …”
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A prior-knowledge-guided dynamic attention mechanism to predict nocturnal hypoglycemic events in type 1 diabetes by Xia Yu, Zi Yang, Xinzhuo Wang, Xiaoyu Sun, Ruiting Shen, Hongru Li, Mingchen Zhang

“…Then, we propose a prior-knowledge-guided attention mechanism to enhance the network's learning capability and interpretability. …”
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Improving the prediction of functional recovery in older adults with stroke in geriatric rehabilitation using an inertial measurement unit combined with the Utrecht Scale for Evalu... by Jules J.M. Kraaijkamp, Margot W.M. de Waal, Niels H. Chavannes, Wilco P. Achterberg, Eléonore F. van Dam van Isselt, Michiel Punt

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Implementation of MF block in CNN for advanced REB fault diagnosis by M. Pandiyan, Narendiranath Babu T.

“…Further, the Multi Feature (MF) block has been used in the architecture of the C-CNN model for better accuracy. …”
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Efficient Sedimentary Facies Recognition Using Vision Transformer and Weakly Supervised Deep Multi-View Clustering by Hao Wu, Yu-Jie Dai, Xin-Yu Liu

“…Sedimentary facies recognition plays a crucial role in geological exploration and oil-gas resource evaluation. However, traditional recognition methods are limited by their ability to extract local features and efficiently utilize unlabeled data, making it difficult to effectively handle complex sedimentary facies characteristics. …”
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Class-weighted Dempster–Shafer in dual-level fusion for multimodal fake real estate listings detection by Maifuza Mohd Amin, Nor Samsiah Sani, Mohammad Faidzul Nasrudin

“…Results The CWDS-DLF was evaluated on the property listing website dataset and achieved an F1 score of 96% and an accuracy of 93%. …”
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Leveraging deep learning to discover interpretable cellular spatial biomarkers for prognostic predictions based on hepatocellular carcinoma histology by Huijuan Hu, Tianhua Tan, Yerong Liu, Wei Liang, Wei Zhang, Jinsong Zhang, Ju Cui, Jinghai Song, Xuefei Li

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Prediction of hepatitis-C virus using statistical learning models by Shalini Kumari, Subhajit Das, Prashant Kumar Sonker, Agni Saroj, Mukesh Kumar

“…The study includes dataset of 615 HCV patients from the UCI Machine Learning Repository for illustrative purposes and analyzed it through machine learning models such as naive Bayes (NB), random forest (RF), support vector machine (SVM), logistic regression (LR), decision trees (DT), and artificial neural network (ANN). The models were evaluated using various performance metrics, and a comparative analysis using non-parametric tests was conducted to evaluate the statistical significance of the model. …”
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Insights into the X-shaped fissure propagation and interaction mechanisms of rock specimens: 3D printing experiments and DEM simulations by Yiqing Sun, Wei Huang, Lei Gan, Zhenzhong Shen, Hongwei Zhang, Wenbing Zhang

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A Lightweight GCT-EEGNet for EEG-Based Individual Recognition Under Diverse Brain Conditions by Laila Alshehri, Muhammad Hussain

“…To overcome these challenges, we propose a lightweight neural network model, GCT–EEGNet, which is based on the design ideas of a CNN model and incorporates an attention mechanism to pay attention to the appropriate frequency bands for extracting discriminative features relevant to the identity of a subject despite diverse brain conditions. …”
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A novel integration of multi-stocked gated variant recurrent units and Kolmogorov-Arnold tuned deep training networks for anchoring the intrusion detection against computer attacks by M. Saritha, Saidireddy Malgireddy

“…The proposed framework introduces the FKA-DLN based classification network to learn the micro-level features that fuels detection performance. …”
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Artificial Intelligence on the Identification of Beiguan Music by Yu-Hsin CHANG, Shu-Nung YAO

“…Based on the strategy of social tagging, the procedure of this research includes: evaluating the qualifying features of 48 Beiguan music recordings, quantifying 11 music indexes representing tempo and instrumental features, feeding these sets of quantized data into a three-layered ANN, and executing three rounds of testing, with each round containing 30 times of identification. …”
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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

“…The combined network mechanisms preserve the spatial information loss during the contraction process so that the decoder can locate the features more accurately by retaining the high-resolution features, enabling precise location in seismic image denoising. …”
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Classification of Biological Data using Deep Learning Technique by Azha Javed, Muhammad Javed Iqbal

“…The main concern is how to extract the useful characteristics of sequences as the input features for the network. These sequences are increasing exponentially over the decades. …”
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A Scalable Data-Driven Surrogate Model for 3D Dynamic Wind Farm Wake Prediction Using Physics-Inspired Neural Networks and Wind Box Decomposition by Qiuyu Lu, Yuqi Cao, Pingping Xie, Ying Chen, Yingming Lin

“…A physics-inspired NN architecture featuring an autoencoder for spatial feature extraction and latent space dynamics for temporal evolution is introduced, motivated by the time–space decoupling structure observed in the Navier–Stokes equations. …”
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Research into the Application of ResNet in Soil: A Review by Wenjie Wu, Lijuan Huo, Gaiqiang Yang, Xin Liu, Hongxia Li

“…ResNet outperforms traditional methods by effectively mitigating the vanishing gradient problem, enabling deeper network training, enhancing feature extraction, and improving accuracy in complex pattern recognition tasks. …”
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AI-Powered Mobile App for Nuclear Cataract Detection by Alicja Anna Ignatowicz, Tomasz Marciniak, Elżbieta Marciniak

“…The evaluation included a range of convolutional neural network architectures, from larger models like VGG16 and ResNet50, to lighter alternatives such as VGG11, ResNet18, MobileNetV2, and EfficientNet-B0. …”
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