Suchergebnisse - residual inception‐based autoencoder

Generative Adversarial Network Based Improved Transfer Learning Models for Chronic Heart Failure Detection.

Autoren: Gawande, Namrata Goyal, Dinesh Sankhla, Kriti

Quelle: Ingénierie des Systèmes d'Information; Apr2025, Vol. 30 Issue 4, p1087-1100, 14p

Schlagwörter: HEART failure, GENERATIVE adversarial networks, DATA fusion (Statistics), HEART sounds, TRANSFER of training, MACHINE learning, CONVOLUTIONAL neural networks, CLASSIFICATION algorithms

Parallel Symmetric Appearance‐Motion Framework With Diffusion and Refinement Blocks for Video Anomaly Detection System.

Autoren: Prasad, Kavitapu Naga Siva Shankara Vara Haritha, Dasari

Quelle: Concurrency & Computation: Practice & Experience; 8/30/2025, Vol. 37 Issue 18/20, p1-15, 15p

Schlagwörter: DEEP learning, FEATURE extraction, SECURITY systems, VIDEO processing

A novel YOLOv11 framework for enhanced tomato disease detection.

Autoren: Eliwa, Entesar Hamed I. Abd El-Hafeez, Tarek

Quelle: PeerJ Computer Science; Oct2025, p1-29, 29p

Schlagwörter: TOMATO diseases & pests, DEEP learning, PLANT health, FOOD security, DETECTION algorithms, AGRICULTURE

NextGen lung disease diagnosis with explainable artificial intelligence.

Autoren: Veeramani, Nirmala S.A, Reshma Sherine S, Sakthi Prabha et al.

Quelle: Scientific Reports; 9/26/2025, Vol. 15 Issue 1, p1-16, 16p

Schlagwörter: LUNG diseases, COVID-19, DIAGNOSIS, ARTIFICIAL intelligence, MACHINE learning, IMAGE processing, CHEST X rays

Performance of ResNet‐18 and InceptionResNetV2 in Automated Detection of Diabetic Retinopathy.

Autoren: Asare, Akwasi Adjei Broni, Alvin Asare Dickson, Alex Kwasi et al.

Quelle: Medicine Advances; Sep2025, Vol. 3 Issue 3, p231-241, 11p

Small Sample Palmprint Recognition Based on Image Augmentation and Dynamic Model-Agnostic Meta-Learning.

Autoren: Zhou, Xiancheng Bai, Huihui Dong, Zhixu et al.

Quelle: Electronics (2079-9292); Aug2025, Vol. 14 Issue 16, p3236, 20p

Schlagwörter: PALMPRINT recognition, DATA augmentation, FEATURE extraction, MACHINE learning, ACCESS control

Automated lumbar spine segmentation in MRI using an enhanced U-Net with inception module and dual-output mechanism.

Autoren: Silveira, Jaysel Theresa S., Girisha Kundapur, Poornima Panduranga

Quelle: Scientific Reports; 11/10/2025, Vol. 15 Issue 1, p1-19, 19p

Schlagwörter: IMAGE segmentation, MAGNETIC resonance imaging, FEATURE extraction, DEEP learning, COMPUTER-assisted image analysis (Medicine), SEGMENTATION (Biology), LUMBAR vertebrae

Infrared Thermography Anomaly Detection Using VAESIMCA Approach.

Autoren: Petersen, Akam Brabrand, Mikkel Kucheryavskiy, Sergey

Quelle: Sensing & Imaging; 6/12/2025, Vol. 26 Issue 1, p1-20, 20p

Blockchain-based framework for secure medical data sharing and disease diagnosis using optimized deep belief networks.

Autoren: Sharma, Nikhil Shambharkar, Prashant Giridhar

Quelle: Cluster Computing; Dec2025, Vol. 28 Issue 16, p1-38, 38p

Schlagwörter: DIAGNOSIS of epilepsy, DATA security, CRYPTOGRAPHY, MEDICAL technology, BLOCKCHAINS, HEALTH information exchanges, DEEP learning, ARTIFICIAL neural networks

BACA-Net: a band-adaptive collaborative attention network for hyperspectral image classification.

Autoren: Liao, Jianshang Wang, Liguo Han, Yuqi

Quelle: All Earth; Dec2025, Vol. 37 Issue 1, p1-28, 28p

Toward Smarter Orthopedic Care: Classifying Plantar Footprints from RGB Images Using Vision Transformers and CNNs.

Autoren: Ramírez-Rios, Lidia Yolanda Olguín-Tiznado, Jesús Everardo Ramos-Acosta, Edgar Rene et al.

Quelle: Journal of Imaging; Nov2025, Vol. 11 Issue 11, p414, 24p

Schlagwörter: FOOTPRINTS, CONVOLUTIONAL neural networks, ORTHOPEDIC surgery, DIGITAL images, DIAGNOSIS, TRANSFORMER models, MUSCULOSKELETAL system diseases

SMMTM: Motor imagery EEG decoding algorithm using a hybrid multi-branch separable convolutional self-attention temporal convolutional network.

Autoren: Cao, DianGuo Yu, ZhenYuan Wang, Jinqiang et al.

Quelle: PLoS ONE; 10/23/2025, Vol. 20 Issue 10, p1-19, 19p

Schlagwörter: BRAIN-computer interfaces, CONVOLUTIONAL neural networks, MOTOR imagery (Cognition), MULTISENSOR data fusion

Predicting stress corrosion cracking in downhole environments: a Bayesian network approach for duplex stainless steels.

Autoren: Rojas Zuniga, Abraham Bakhtiari, Sam Aldrich, Chris et al.

Quelle: NPJ Materials Degradation; 10/8/2025, Vol. 9 Issue 1, p1-26, 26p

Schlagwörter: STRESS corrosion cracking, DUPLEX stainless steel, BAYESIAN analysis, DEEP learning, PETROLEUM industry equipment, PITTING corrosion, MACHINE learning

Emerging deep learning approaches for urban satellite image analysis: a survey on classification, segmentation, and change detection.

Autoren: Soni, Tannu Kumar Pujari, Pushpalata

Quelle: Evolutionary Intelligence; Oct2025, Vol. 18 Issue 5, p1-45, 45p

Forecasting electricity prices in the spot market utilizing wavelet packet decomposition integrated with a hybrid deep neural network.

Autoren: Heping Jia Yuchen Guo Xiaobin Zhang et al.

Quelle: Global Energy Interconnection; Oct2025, Vol. 8 Issue 5, p874-890, 17p

Schlagwörter: ELECTRICITY pricing, FORECASTING, FREQUENCY-domain analysis, CONVOLUTIONAL neural networks, LONG short-term memory, DEEP learning, MARKET volatility

Bidirectional feature fusion via cross-attention transformer for chrysanthemum classification.

Autoren: Chen, Yifan Yang, Xichen Yan, Hui et al.

Quelle: Pattern Analysis & Applications; Jun2025, Vol. 28 Issue 2, p1-16, 16p

Schlagwörter: TRANSFORMER models, DEEP learning, TRUST, VALUE (Economics), GROWTH industries, CHRYSANTHEMUMS

STIT-Net- A Wavelet based Convolutional Transformer Model for Motor Imagery EEG Signal Classification in the Sensorimotor Bands.

Autoren: S, Chrisilla Shantha SelvaKumari, R.

Quelle: Clinical EEG & Neuroscience; Jan2026, Vol. 57 Issue 1, p88-100, 13p

Temporal convolutional transformer for EEG based motor imagery decoding.

Autoren: Altaheri, Hamdi Karray, Fakhri Karimi, Amir-Hossein

Quelle: Scientific Reports; 9/26/2025, Vol. 15 Issue 1, p1-19, 19p

Schlagwörter: BRAIN-computer interfaces, MOTOR imagery (Cognition), TRANSFORMER models, ELECTROENCEPHALOGRAPHY, CONVOLUTIONAL neural networks

Exploring the role of preprocessing combinations in hyperspectral imaging for deep learning colorectal cancer detection.

Autoren: Tkachenko, Mariia Huber, Benjamin Hamotskyi, Serhii et al.

Quelle: Scientific Reports; 9/23/2025, Vol. 15 Issue 1, p1-15, 15p

Schlagwörter: DEEP learning, COLORECTAL cancer, SIGNAL processing, DATA quality, COMPUTER-assisted image analysis (Medicine), SIGNAL filtering, HYPERSPECTRAL imaging systems

Msst-eegnet: multi-scale spatio-temporal feature extraction using inception and temporal pyramid pooling for motor imagery classification.

Autoren: Mishra, Rashmi Agrawal, R. K. Kirar, Jyoti Singh

Quelle: Cognitive Neurodynamics; 9/20/2025, Vol. 19 Issue 1, p1-25, 25p