Suchergebnisse - stacked denoising sparse autoencoder (SASSE)~

Latent Feature‐Based Type 2 Diabetes Prediction Using a Hybrid Stacked Sparse Autoencoder and Machine Learning Models.

Autoren: Abdussamad Daud, Hanita Sokkalingam, Rajalingam et al.

Quelle: Engineering Reports; Sep2025, Vol. 7 Issue 9, p1-17, 17p

Schlagwörter: TYPE 2 diabetes, MACHINE learning, CLINICAL decision support systems, METADATA, FORECASTING, FEATURE extraction

A hybrid deep learning framework for denoising acoustic partial discharge signals.

Autoren: Kumar, Chandan Ganguly, Biswarup Dey, Debangshu et al.

Quelle: Signal, Image & Video Processing; Dec2025, Vol. 19 Issue 12, p1-17, 17p

Parameterised Quantum SVM with Data-Driven Entanglement for Zero-Day Exploit Detection.

Autoren: Nhlapo, Steven Jabulani Mutombo, Elodie Ngoie Nkongolo, Mike Nkongolo Wa

Quelle: Computers (2073-431X); Aug2025, Vol. 14 Issue 8, p331, 37p

Schlagwörter: ANOMALY detection (Computer security), COMPUTER network security, MACHINE learning, ENSEMBLE learning, INTERNET security

TQVGModel: Tomato Quality Visual Grading and Instance Segmentation Deep Learning Model for Complex Scenarios.

Autoren: Cong, Peichao Wang, Kun Liang, Ji et al.

Quelle: Agronomy; Jun2025, Vol. 15 Issue 6, p1273, 25p

Schlagwörter: TOMATO harvesting, DEEP learning, FRUIT, SPINE, ROBOTS, TOMATOES

A Brief Perspective on Deep Learning Approaches for 2D Semantic Segmentation.

Autoren: Sulemane, Shazia Fachada, Nuno Matos-Carvalho, João P.

Quelle: Eng (2673-4117); Jul2025, Vol. 6 Issue 7, p165, 32p

Schlagwörter: DEEP learning, IMAGE segmentation, MODEL validation, ARTIFICIAL neural networks, MACHINE learning

Impacts of Climate Change on Oceans and Ocean-Based Solutions: A Comprehensive Review from the Deep Learning Perspective.

Autoren: Qiao, Xin Zhang, Ke Huang, Weimin

Quelle: Remote Sensing; Jul2025, Vol. 17 Issue 13, p2306, 59p

Schlagwörter: CLIMATE change, OCEAN, RENEWABLE energy sources, MARINE ecology, REMOTE sensing, OCEAN acidification, DEEP learning, CARBON sequestration

Advances in Computer Vision and Spectroscopy Techniques for Non-Destructive Quality Assessment of Citrus Fruits: A Comprehensive Review.

Autoren: Yu, Kai Zhong, Mingming Zhu, Wenjing et al.

Quelle: Foods; Feb2025, Vol. 14 Issue 3, p386, 35p

Schlagwörter: CITRUS fruits, COMPUTER vision, MULTISPECTRAL imaging, MULTISENSOR data fusion, NONDESTRUCTIVE testing, CITRUS fruit industry, NUCLEAR magnetic resonance spectroscopy

Comprehensive applications of the artificial intelligence technology in new drug research and development.

Autoren: Chen, Hongyu Lu, Dong Xiao, Ziyi et al.

Quelle: Health Information Science & Systems; 8/8/2024, Vol. 12 Issue 1, p1-17, 17p

A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond.

Autoren: Jia, Pengzhen Zhang, Fuhao Wu, Chaojin et al.

Quelle: Briefings in Bioinformatics; May2024, Vol. 25 Issue 3, p1-22, 22p

Schlagwörter: PROTEIN-ligand interactions, PROTEIN-protein interactions, LIGANDS (Biochemistry), NUCLEIC acids, AMINO acid sequence, CELLULAR signal transduction, DEEP learning

Abstracts presented at the 37th Annual Meeting of The Protein Society.

Quelle: Protein Science: A Publication of the Protein Society; Dec2023, Vol. 32 Issue 12, p1-207, 207p

Deep Learning for Medical Image-Based Cancer Diagnosis.

Autoren: Jiang, Xiaoyan Hu, Zuojin Wang, Shuihua et al.

Quelle: Cancers; Jul2023, Vol. 15 Issue 14, p3608, 72p

Schlagwörter: TUMOR diagnosis, DEEP learning, DIGITAL image processing, X-rays, MAGNETIC resonance imaging, POSITRON emission tomography computed tomography, DIAGNOSTIC imaging, COMPUTER-aided diagnosis, COMPUTED tomography, ARTIFICIAL neural networks, PREDICTION models

Rise of Deep Learning Clinical Applications and Challenges in Omics Data: A Systematic Review.

Autoren: Mohammed, Mazin Abed Abdulkareem, Karrar Hameed Dinar, Ahmed M. et al.

Quelle: Diagnostics (2075-4418); Feb2023, Vol. 13 Issue 4, p664, 30p

Schlagwörter: DEEP learning, CLINICAL medicine, WEB search engines, MULTIOMICS, MODEL validation

Graph Signal Processing, Graph Neural Network and Graph Learning on Biological Data: A Systematic Review.

Autoren: Li, Rui Yuan, Xin Radfar, Mohsen et al.

Quelle: IEEE Reviews in Biomedical Engineering; 2023, Vol. 16, p109-135, 27p

Deep learning: as the new frontier in high-throughput plant phenotyping.

Autoren: Arya, Sunny Sandhu, Karansher Singh Singh, Jagmohan et al.

Quelle: Euphytica; Apr2022, Vol. 218 Issue 4, p1-22, 22p

Schlagwörter: DEEP learning, COMPUTER vision, MACHINE learning, POTENTIAL barrier, CONVOLUTIONAL neural networks, ALGORITHMS

Data analysis methods for defining biomarkers from omics data.

Autoren: Li, Chao Gao, Zhenbo Su, Benzhe et al.

Quelle: Analytical & Bioanalytical Chemistry; Jan2022, Vol. 414 Issue 1, p235-250, 16p

Schlagwörter: BIOMARKERS, DATA analysis, COMBINATORICS, EPIGENOMICS, DIAGNOSIS

Human Language Technologies As a Challenge for Computer Science and Linguistics – 2023

Autoren: Zygmunt Vetulani Patrick Paroubek

Resource Type: eBook.

Schlagworte: Language and languages, Natural language processing (Computer science), Computational linguistics

Categories: COMPUTERS / General

Australasian Conference on Information Systems 2018

Autoren: Australasian Conference on Information Systems (eds.)

Resource Type: eBook.

Categories: COMPUTERS / Database Administration & Management, COMPUTERS / Networking / General, COMPUTERS / Programming / General

Digital Image Enhancement and Reconstruction

Autoren: Shyam Singh Rajput Nafis Uddin Khan Amit Kumar Singh et al.

Resource Type: eBook.

Schlagworte: Digital images, Image processing--Digital techniques

Categories: COMPUTERS / Artificial Intelligence / General

Encyclopedia of Digital Agricultural Technologies

Autoren: Qin Zhang

Resource Type: eBook.

Schlagworte: Agricultural innovations--Encyclopedias

Categories: TECHNOLOGY & ENGINEERING / Agriculture / General, COMPUTERS / Data Science / General, COMPUTERS / Human-Computer Interaction (HCI), TECHNOLOGY & ENGINEERING / Electrical, TECHNOLOGY & ENGINEERING / Engineering (General), TECHNOLOGY & ENGINEERING / Signals & Signal Processing

Audio Source Separation

Autoren: Shoji Makino

Resource Type: eBook.

Schlagworte: Acoustical engineering, Engineering, Source separation (Signal processing), Speech processing systems

Categories: TECHNOLOGY & ENGINEERING / Signals & Signal Processing, SCIENCE / Acoustics & Sound, TECHNOLOGY & ENGINEERING / Acoustics & Sound