| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Kainat Khan, Rahul Katary. MCBERT: A Multi-Modal Framework for the Diagnosis of Autism Spectrum Disorder. Biological psychology. 2024-12-26. PMID:39722324. |
in this paper, we developed a novel multimodal asd diagnosis architecture, referred to as multi-head cnn with bert (mcbert), which integrates bidirectional encoder representations from transformers (bert) for meta-features and a multi-head convolutional neural network (mcnn) for the brain image modality. |
2024-12-26 |
2024-12-29 |
Not clear |
| Anna-Katharina Meißner, Tobias Blau, David Reinecke, Gina Fürtjes, Lili Leyer, Nina Müller, Niklas von Spreckelsen, Thomas Stehle, Abdulkader Al Shugri, Reinhard Büttner, Roland Goldbrunner, Marco Timmer, Volker Neuschmeltin. Image Quality Assessment and Reliability Analysis of Artificial Intelligence-Based Tumor Classification of Stimulated Raman Histology of Tumor Biobank Samples. Diagnostics (Basel, Switzerland). vol 14. issue 23. 2024-12-17. PMID:39682609. |
due to the relatively low number of rare tumor representations in cnn training datasets, a valid prediction of rarer entities remains limited. |
2024-12-17 |
2024-12-21 |
Not clear |
| Bei Gong, Ida Puteri Mahsan, Junhua Xia. Federated learning-driven collaborative recommendation system for multi-modal art analysis and enhanced recommendations. PeerJ. Computer science. vol 10. 2024-12-09. PMID:39650398. |
this system uses pre-trained convolutional neural networks (cnn) and bidirectional encoder representation from transformers (bert) models to extract features from image and text data. |
2024-12-09 |
2024-12-12 |
Not clear |
| Igor V Tetko, Ruud van Deursen, Guillaume Godi. Be aware of overfitting by hyperparameter optimization! Journal of cheminformatics. vol 16. issue 1. 2024-12-09. PMID:39654058. |
we also extended the previous analysis by adding a representation learning method based on natural language processing of smiles called transformer cnn. |
2024-12-09 |
2024-12-12 |
Not clear |
| Vinay Priy Mishra, Yogendra Narain Singh, Feroz Khan, Malay Kishore Dutt. SeqDPI: A 1D-CNN approach for predicting binding affinity of kinase inhibitors. Journal of computational chemistry. vol 46. issue 1. 2024-12-07. PMID:39644133. |
the proposed seqdpi model extract the relevant drug and protein features from the one dimensional sequential representation of the dataset considered using optimized cnn networks that deploy convolutions on varying length of amino acid subsequence's to capture hidden pattern, the convolved drug- protein features obtained are then used as an input to l2 penalized feed forward neural network which matches the local residue patterns in protein classes with molecular fingerprints of drugs to predict the binding strength for all drug target pairs. |
2024-12-07 |
2024-12-10 |
Not clear |
| Shumei Ding, Jia Zheng, Cangzhi Ji. DeepMEns: an ensemble model for predicting sgRNA on-target activity based on multiple features. Briefings in functional genomics. 2024-11-11. PMID:39528429. |
the first part uses one-hot encoding, wherein 0-1 representation of the secondary structure is used as the input to the convolutional neural network (cnn) with transformer encoder. |
2024-11-11 |
2024-11-17 |
Not clear |
| Yun Wang, Longguang Wang, Kunhong Li, Yongjian Zhang, Dapeng Oliver Wu, Yulan Gu. Cost Volume Aggregation in Stereo Matching Revisited: A Disparity Classification Perspective. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol PP. 2024-11-11. PMID:39527433. |
with the help of homogeneous region representations, efficient and informative cost aggregation can be achieved with only a shallow 3d cnn. |
2024-11-11 |
2024-11-17 |
Not clear |
| Sonam Aggarwal, Isha Gupta, Ashok Kumar, Sandeep Kautish, Abdulaziz S Almazyad, Ali Wagdy Mohamed, Frank Werner, Mohammad Shokouhifa. GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images. Mathematical biosciences and engineering : MBE. vol 21. issue 8. 2024-11-01. PMID:39483096. |
gastrofuse-net was developed by combining features extracted from two different cnn models with different numbers of layers, integrating shallow and deep representations to capture diverse aspects of the abnormalities. |
2024-11-01 |
2024-11-03 |
human |
| Xiang Hu, Jingyi Li, Taigang Li. Alg-MFDL: A multi-feature deep learning framework for allergenic proteins prediction. Analytical biochemistry. 2024-10-31. PMID:39481588. |
then, these three protein representations were fused and used as inputs to train the convolutional neural network (cnn). |
2024-10-31 |
2024-11-03 |
Not clear |
| Omnia Magdy, Mohamed Abd Elaziz, Abdelghani Dahou, Ahmed A Ewees, Ahmed Elgarayhi, Mohammed Salla. Bone scintigraphy based on deep learning model and modified growth optimizer. Scientific reports. vol 14. issue 1. 2024-10-28. PMID:39465262. |
the first phase in the proposed model is the feature extraction and it was conducted based on integrating the mobile vision transformer (mobilevit) model in our framework to capture highly complex representations from raw medical imagery using two primary components including vit and lightweight cnn featuring a limited number of parameters. |
2024-10-28 |
2024-10-30 |
Not clear |
| Muhammad Tahir, Shahid Hussain, Fawaz Khaled Alarfa. An Integrated Multi-Model Framework Utilizing Convolutional Neural Networks Coupled with Feature Extraction for Identification of 4mC Sites in DNA Sequences. Computers in biology and medicine. vol 183. 2024-10-26. PMID:39461102. |
following the initial step, the obtained distributed representation of the dna sequence seamlessly enters the cnn, triggering a crucial convolution operation wherein a set of adaptable filters systematically convolves across the sequence to detect vital local patterns. |
2024-10-26 |
2024-10-30 |
Not clear |
| Yaqian Guo, Xin Wang, Ce Li, Shihui Yin. Domain adaptive semantic segmentation by optimal transport. Fundamental research. vol 4. issue 5. 2024-10-21. PMID:39431138. |
specifically, we first generated the output space via a cnn owing to its superior of feature representation. |
2024-10-21 |
2024-10-23 |
Not clear |
| Cheng-Che Chuang, Yu-Chen Liu, Yu-Yen O. DeepNeoAG: Neoantigen epitope prediction from melanoma antigens using a synergistic deep learning model combining protein language models and multi-window scanning convolutional neural networks. International journal of biological macromolecules. 2024-10-04. PMID:39366619. |
in this study, we present deepneoag, a novel framework combines the global sequence-level information captured by a pre-trained plm with the local sequence-based information features extracted by a multi-window scanning cnn, enabling a comprehensive representation of the protein's mutational landscape. |
2024-10-04 |
2024-10-07 |
Not clear |
| Peiyang Wei, Mingsheng Shang, Jiesan Zhou, Xiaoyu Sh. Efficient adaptive learning rate for convolutional neural network based on quadratic interpolation egret swarm optimization algorithm. Heliyon. vol 10. issue 18. 2024-09-25. PMID:39318797. |
specifically, we adopt the cnn to extract rich feature representations. |
2024-09-25 |
2024-09-27 |
Not clear |
| Xian Wei, Yingjie Liu, Xuan Tang, Shui Yu, Mingsong Che. Integrating Convolution and Sparse Coding for Learning Low-Dimensional Discriminative Image Representations. IEEE transactions on neural networks and learning systems. vol PP. 2024-09-18. PMID:39292572. |
second, to avoid the high computational cost of direct sparse coding on hd cnn features, we learn sparse representation (sr) over a task-driven dictionary in the space with the feature being orthogonally projected. |
2024-09-18 |
2024-09-21 |
Not clear |
| Anyi Wang, Tao Zhu, Qifeng Men. Spectrum Sensing Method Based on STFT-RADN in Cognitive Radio Networks. Sensors (Basel, Switzerland). vol 24. issue 17. 2024-09-14. PMID:39275703. |
to address the common issues in traditional convolutional neural network (cnn)-based spectrum sensing algorithms in cognitive radio networks (crns), including inadequate signal feature representation, inefficient utilization of feature map information, and limited feature extraction capabilities due to shallow network structures, this paper proposes a spectrum sensing algorithm based on a short-time fourier transform (stft) and residual attention dense network (radn). |
2024-09-14 |
2024-09-17 |
Not clear |
| M Latha, P Santhosh Kumar, R Roopa Chandrika, T R Mahesh, V Vinoth Kumar, Suresh Guluwad. Revolutionizing breast ultrasound diagnostics with EfficientNet-B7 and Explainable AI. BMC medical imaging. vol 24. issue 1. 2024-09-03. PMID:39223507. |
to address these issues, we propose a methodology that leverages efficientnet-b7, a scalable cnn architecture, combined with advanced data augmentation techniques to enhance minority class representation and improve model robustness. |
2024-09-03 |
2024-09-05 |
Not clear |
| Ming Li, Zekun Yang, Jiehua Yan, Haoran Li, Wangzhong Y. Dune Morphology Classification and Dataset Construction Method Based on Unmanned Aerial Vehicle Orthoimagery. Sensors (Basel, Switzerland). vol 24. issue 15. 2024-08-10. PMID:39124020. |
however, convolutional neural network (cnn) models exhibit robust feature representation capabilities for images and have achieved excellent results in image classification, providing a new method for studying dune morphology classification. |
2024-08-10 |
2024-08-13 |
Not clear |
| Keyvan Mahjoory, Andreas Bahmer, Molly J Henr. Convolutional neural networks can identify brain interactions involved in decoding spatial auditory attention. PLoS computational biology. vol 20. issue 8. 2024-08-08. PMID:39116183. |
to this end, our cnn model was specifically designed to learn pairwise interaction representations for 10 cortical regions from five-second inputs. |
2024-08-08 |
2024-08-12 |
human |
| Adam White, Margarita Saranti, Artur d'Avila Garcez, Thomas M H Hope, Cathy J Price, Howard Bowma. Predicting recovery following stroke: Deep learning, multimodal data and feature selection using explainable AI. NeuroImage. Clinical. vol 43. 2024-07-13. PMID:39002223. |
additionally, we introduce the novel approach of training a convolutional neural network (cnn) on images that combine regions-of-interests (rois) extracted from mris, with symbolic representations of tabular data. |
2024-07-13 |
2024-07-16 |
human |