An Attention-Based Residual U-Net for Tumour Segmentation Using Multi-Modal MRI Brain Images by Najme Zehra Naqvi, K. R. Seeja

“…Hence, this research proposes a novel automated deep-learning approach based on U-Net for segmenting Glioma tumours. …”
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TepiSense: A Social Computing-Based Real-Time Epidemic Surveillance System Using Artificial Intelligence by Bilal Tahir, Muhammad Amir Mehmood

“…TepiSense compares the performance of 3 feature extraction techniques, 9 machine/deep learning models, and 3 Large Language Models (LLMs). …”
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Hybridizing Evolutionary Computation and Deep Neural Networks: An Approach to Handwriting Recognition Using Committees and Transfer Learning by Alejandro Baldominos, Yago Saez, Pedro Isasi

“…The field was introduced in the late-1980s, but only in the latest years the field has become mature enough to enable the optimization of deep learning models, such as convolutional neural networks. …”
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Vision-Based Underwater Docking Guidance and Positioning: Enhancing Detection with YOLO-D by Tian Ni, Can Sima, Wenzhong Zhang, Junlin Wang, Jia Guo, Lindan Zhang

“…A cascaded detection and positioning strategy incorporating fused active and passive markers enabled real-time detection of the relative position and pose between the UUV and docking station (DS). A novel deep learning-based network model, YOLO-D, was developed to detect docking markers in real time. …”
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Research progress of MRI-based radiomics in hepatocellular carcinoma by Xiao-Yun Xie, Rong Chen

“…Key search terms included Hepatocellular carcinoma, HCC, Liver cancer, Magnetic resonance imaging, MRI, radiomics, deep learning, machine learning, and artificial intelligence.ResultsA comprehensive analysis of 93 articles underscores the efficacy of MRI radiomics, a noninvasive imaging analysis modality, across various facets of HCC management. …”
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Biomedical named entity recognition using improved green anaconda-assisted Bi-GRU-based hierarchical ResNet model by Ram Chandra Bhushan, Rakesh Kumar Donthi, Yojitha Chilukuri, Ulligaddala Srinivasarao, Polisetty Swetha

“…To overcome these challenges, deep learning (DL) methods have emerged. However, DL-based NER methods may need help identifying long-distance relationships within text and require significant annotated datasets. …”
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A generative deep neural network for pan-digestive tract cancer survival analysis by Lekai Xu, Tianjun Lan, Yiqian Huang, Liansheng Wang, Junqi Lin, Xinpeng Song, Hui Tang, Haotian Cao, Hua Chai

“…Conclusions The experiment indicate that GDEC outperforms better than other deep-learning-based methods, and the interpretable algorithm can select biologically significant genes and potential drugs for DTC treatment.…”
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Transforming traffic accident investigations: a virtual-real-fusion framework for intelligent 3D traffic accident reconstruction by Yanzhan Chen, Qian Zhang, Fan Yu

“…Specifically, a micro-traffic simulator and an autonomous driving simulator are co-simulated to generate high-fidelity traffic accidents. Subsequently, a deep learning-based reconstruction method, i.e., 3D Gaussian splatting (3D-GS), is utilized to construct 3D digitized traffic accident scenes from UAV-based image datasets collected in the traffic simulation environment. …”
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Localization and detection of deepfake videos based on self-blending method by Junfeng Xu, Xintao Liu, Weiguo Lin, Wenqing Shang, Yuefeng Wang

“…Abstract Deepfake technology, which encompasses various video manipulation techniques implemented through deep learning algorithms-such as face swapping and expression alteration-has advanced to generate fake videos that are increasingly difficult for human observers to detect, posing significant threats to societal security. …”
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Classifying IoT Botnet Attacks With Kolmogorov-Arnold Networks: A Comparative Analysis of Architectural Variations by Phuc Hao do, Tran Duc Le, Truong Duy Dinh, van Dai Pham

“…This study aims to evaluate the effectiveness of Kolmogorov-Arnold Networks (KANs) and their architectural variations in classifying IoT botnet attacks, comparing their performance with traditional machine learning and deep learning models. We conducted a comparative analysis of five KAN architectures, including Original-KAN, Fast-KAN, Jacobi-KAN, Deep-KAN, and Chebyshev-KAN, against models like Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRU). …”
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Data Clustering Improves Siamese Neural Networks Classification of Parkinson’s Disease by Mohamed Shalaby, Nahla A. Belal, Yasser Omar

“…Hence, continuous efforts are being made to enhance the diagnosis of PD using deep learning approaches that rely on experienced neurologists. …”
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Hematoxylin and Eosin-stained whole slide image dataset annotated for skin tissue segmentationMendeley Data by Anum Abdul Salam, Muhammad Zeeshan Asaf, Muhammad Usman Akram, Noah Musolff, Samavia Khan, Bassem Rafiq, Babar Rao

“…These systems assist medical specialists by reducing diagnosis time and accelerating the entire diagnostic process. However, deep learning models require substantial amounts of data for training. …”
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Machine learning-based prediction of hemodynamic parameters in left coronary artery bifurcation: A CFD approach by Sara Malek, Arshia Eskandari, Mahkame Sharbatdar

“…Further research is warranted to evaluate the effectiveness of deep learning models and address challenges in patient-specific applications.…”
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Election Prediction on Twitter: A Systematic Mapping Study by Asif Khan, Huaping Zhang, Nada Boudjellal, Arshad Ahmad, Jianyun Shang, Lin Dai, Bashir Hayat

“…Appropriate political labelled datasets are not available, especially in languages other than English. Deep learning needs to be employed in this domain to get better predictions.…”
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Bumblebee social learning outcomes correlate with their flower-facing behaviour by Yuyi Lu, Zhenwei Zhuo, Mark Roper, Lars Chittka, Cwyn Solvi, Fei Peng, Ying Zhou

“…Here we designed a new 2D paradigm suitable for simple top-down high-speed video recording and analysed bumblebees’ observational learning process using a deep-learning-based pose-estimation framework. Two groups of bumblebees observed live conspecifics foraging from either blue or yellow flowers during a single foraging bout, and were subsequently tested for their socially learned colour preferences. …”
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Evolution of artificial intelligence in healthcare: a 30-year bibliometric study by Yaojue Xie, Yuansheng Zhai, Yuansheng Zhai, Guihua Lu, Guihua Lu

“…IntroductionIn recent years, the development of artificial intelligence (AI) technologies, including machine learning, deep learning, and large language models, has significantly supported clinical work. …”
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Latent spectral-spatial diffusion model for single hyperspectral super-resolution by Yingsong Cheng, Yong Ma, Fan Fan, Jiayi Ma, Yuan Yao, Xiaoguang Mei

“…In recent years, significant advances have been achieved in addressing super-resolution (SR) tasks for hyperspectral images, primarily through deep learning-based methodologies. Nevertheless, methods oriented toward optimizing peak signal-to-noise ratio (PSNR) often tend to drive the SR image to an average of several possible SR predictions, resulting in visually over-smoothed outputs. …”
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A Disentangled Representation-Based Multimodal Fusion Framework Integrating Pathomics and Radiomics for KRAS Mutation Detection in Colorectal Cancer by Zhilong Lv, Rui Yan, Yuexiao Lin, Lin Gao, Fa Zhang, Ying Wang

“…Recently, the advancement of machine learning, especially deep learning, has greatly promoted the development of KRAS mutation detection from tumor phenotype data, such as pathology slides or radiology images. …”
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Similarity Learning and Generalization with Limited Data: A Reservoir Computing Approach by Sanjukta Krishnagopal, Yiannis Aloimonos, Michelle Girvan

“…Thus, as opposed to training in the entire high-dimensional reservoir space, the RC only needs to learns characteristic features of these dynamical patterns, allowing it to perform well with very few training examples compared with conventional machine learning feed-forward techniques such as deep learning. In generalization tasks, we observe that RCs perform significantly better than state-of-the-art, feed-forward, pair-based architectures such as convolutional and deep Siamese Neural Networks (SNNs). …”
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Personalized Federated Learning for Heterogeneous Residential Load Forecasting by Xiaodong Qu, Chengcheng Guan, Gang Xie, Zhiyi Tian, Keshav Sood, Chaoli Sun, Lei Cui

“…Accurate load forecasting is critical for electricity production, transmission, and maintenance. Deep learning (DL) model has replaced other classical models as the most popular prediction models. …”
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