Suchergebnisse - "stacked sparse autoencoder(SSAE)"

Improved Human Activity Recognition Using Stacked Sparse Autoencoder (SSAE) Algorithm

Autoren: Firman Aziz Nurul Fathanah Mustamin Muhammad Rijal et al.

Quelle: JOIV: International Journal on Informatics Visualization, Vol 9, Iss 4, Pp 1469-1475 (2025)

Schlagwörter: classification, har, smartphones, svm, ssae, Computer software, QA76.75-76.765

Dateibeschreibung: electronic resource

Relation: https://joiv.org/index.php/joiv/article/view/3079; https://doaj.org/toc/2549-9610; https://doaj.org/toc/2549-9904

Zugangs-URL: https://doaj.org/article/b3862fba0473469baccb6c99d6abb913

Novel Pelican Optimization Algorithm (POA) With Stacked Sparse Autoencoder (SSAE) Based IDS for Network Security

Autoren: R. Kanimozhi A. Neela Madheswari

Quelle: Transactions on Emerging Telecommunications Technologies. 36

Automatic Nucleus Detection of Pap Smear Images using Stacked Sparse Autoencoder (SSAE)

Autoren: Ito Wasito Moh. Faturrahman Ratna Mufidah et al.

Quelle: Proceedings of the 1st International Conference on Algorithms, Computing and Systems. :9-13

Schlagwörter: 0206 medical engineering, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 3. Good health

Zugangs-URL: https://dl.acm.org/doi/10.1145/3127942.3127946

Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images

Autoren: Lei Xiang Jun Xu Anant Madabhushi et al.

Quelle: IEEE Transactions on Medical Imaging. 35:119-130

Schlagwörter: Cell Nucleus, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Histocytochemistry, Image Processing, Computer-Assisted, 0202 electrical engineering, electronic engineering, information engineering, Humans, Breast Neoplasms, Female, 02 engineering and technology, Algorithms, 3. Good health

Zugangs-URL: https://europepmc.org/articles/pmc4729702?pdf=render
https://pubmed.ncbi.nlm.nih.gov/26208307

Stacked Sparse Autoencoder (SSAE) based framework for nuclei patch classification on breast cancer histopathology

Autoren: Renlong Hang Jun Xu Lei Xiang et al.

Quelle: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI). :999-1002

Schlagwörter: 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: http://or.nsfc.gov.cn/bitstream/00001903-5/150390/1/1000006955751.pdf
http://ieeexplore.ieee.org/document/6868041/
https://ieeexplore.ieee.org/document/6868041/
https://dblp.uni-trier.de/db/conf/isbi/isbi2014.html#XuXHW14
https://doi.org/10.1109/ISBI.2014.6868041
http://or.nsfc.gov.cn/handle/00001903-5/150390

Novel Pelican Optimization Algorithm (POA) With Stacked Sparse Autoencoder (SSAE) Based IDS for Network Security.

Autoren: Kanimozhi, R. Neela Madheswari, A.

Quelle: Transactions on Emerging Telecommunications Technologies; May2025, Vol. 36 Issue 5, p1-20, 20p

Myocardium Detection by Deep SSAE Feature and Within-Class Neighborhood Preserved Support Vector Classifier and Regressor

Autoren: Yanmin Niu Lan Qin Xuchu Wang

Quelle: Sensors, Vol 19, Iss 8, p 1766 (2019)

Schlagwörter: myocardium detection, cardiac magnetic resonance, region proposal, support vector classifier and regressor, stacked sparse autoencoder (SSAE), Chemical technology, TP1-1185

Relation: https://www.mdpi.com/1424-8220/19/8/1766; https://doaj.org/toc/1424-8220; https://doaj.org/article/514c027bf42f4be5a00fb438b3d7beee

Verfügbarkeit: https://doi.org/10.3390/s19081766
https://doaj.org/article/514c027bf42f4be5a00fb438b3d7beee

Online prediction of composite material drilling quality based on multi-sensor fusion.

Autoren: Liu, Wei Cui, ·Jiacheng Lu, Yongkang et al.

Quelle: Journal of Intelligent Manufacturing. Dec2025, Vol. 36 Issue 8, p5889-5901. 13p.

Schlagwörter: *INDUSTRIAL applications, *MACHINING, *FORECASTING, *BAYESIAN analysis, COMPOSITE materials, MULTISENSOR data fusion, MANUFACTURING process management, PREDICTION models

A hybrid metaheuristic federated learning approach based attack detection system for multi-cloud environment.

Autoren: Verma, Rashmi Jailia, Manisha

Quelle: Journal of Cloud Computing (2192-113X); 11/27/2025, Vol. 14 Issue 1, p1-22, 22p

Schlagwörter: FEDERATED learning, CLOUD computing, CLOUD computing security measures, INTRUSION detection systems (Computer security), DATA privacy, ANOMALY detection (Computer security), MACHINE learning, METAHEURISTIC algorithms

An intelligent fusion-based transfer learning model with artificial protozoa optimiser for enhancing gesture recognition to aid visually impaired people.

Autoren: Mengash, Hanan Abdullah Alqadi, Basma S. Marzouk, Radwa

Quelle: Scientific Reports; 11/21/2025, Vol. 15 Issue 1, p1-20, 20p

Schlagwörter: PEOPLE with visual disabilities, ARTIFICIAL intelligence, HUMAN-computer interaction, IMAGE processing, SIGN language, DEEP learning, TRANSFER of training, HUMAN activity recognition

HYBRID DEEP LEARNING NETWORK-BASED APPROACH FOR AIR COMBAT MANEUVER DECISION-MAKING.

Autoren: SHEEBA D. LINGESHWARI G. ANORELIN A.

Quelle: i-Manager's Journal on Computer Science; Sep2025, Vol. 13 Issue 2, p83-93, 11p

Schlagwörter: LONG short-term memory, DEEP learning, DECISION making, METADATA, AIR warfare, ENSEMBLE learning, AUTONOMOUS robots

Deep learning with leagues championship algorithm based intrusion detection on cybersecurity driven industrial IoT systems.

Autoren: Alotaibi, Saud S. Alghamdi, Turki Ali

Quelle: Scientific Reports; 8/19/2025, Vol. 15 Issue 1, p1-17, 17p

Schlagwörter: DEEP learning, INTRUSION detection systems (Computer security), AUTOENCODERS, ALGORITHMS, CYBER physical systems, INTERNET security, CYBERTERRORISM, FEATURE selection

An Innovative Study for Tool Wear Prediction Based on Stacked Sparse Autoencoder and Ensemble Learning Strategy.

Autoren: He, Zhaopeng Shi, Tielin Chen, Xu

Quelle: Sensors (14248220); Apr2025, Vol. 25 Issue 8, p2391, 14p

Schlagwörter: ARTIFICIAL neural networks, MACHINE learning, ENSEMBLE learning, NUMERICAL control of machine tools, AUTOENCODERS, DEEP learning

Social Media Platform Based Evaluation of Teaching Style on Online Education System using Heuristic Search with Stacked Sparse Autoencoder Model.

Autoren: Fouda, Walaa Mirzaliev, Sanjar Abokhoza, Reneh

Quelle: Journal of Intelligent Systems & Internet of Things; 2025, Vol. 14 Issue 2, p115-126, 12p

Schlagwörter: SOCIAL media in education, VIRTUAL classrooms, ONLINE education, OPTIMIZATION algorithms, TEACHING methods

The classification of absence seizures using power-to-power cross-frequency coupling analysis with a deep learning network.

Autoren: Medvedev, A.V. Lehmann, B.

Quelle: Frontiers in Neuroinformatics; 2025, p1-11, 11p

Schlagwörter: BRAIN waves, AUTOENCODERS, ARTIFICIAL intelligence, DEEP learning, DATABASES, UNIVERSITY hospitals

Piranha Foraging Optimization Algorithm with Deep Learning Enabled Fault Detection in Blockchain-Assisted Sustainable IoT Environment.

Autoren: Assiri, Haitham

Quelle: Sustainability (2071-1050); Feb2025, Vol. 17 Issue 4, p1362, 24p

Utilizing chaos game representation for enhanced classification of SARS-CoV-2 variants with stacked sparse autoencoders.

Autoren: Coutinho, Maria G. F. Câmara, Gabriel B. M. Barbosa, Raquel de M. et al.

Quelle: Neural Computing & Applications; Nov2024, Vol. 36 Issue 31, p19823-19837, 15p

Schlagwörter: ARTIFICIAL neural networks, WHOLE genome sequencing, SARS-CoV-2, DEEP learning, VIRAL genomes

Transient Stability Assessment Model With Sample Selection Method Based on Spatial Distribution.

Autoren: Li, Yongbin Wang, Yiting Li, Jian et al.

Quelle: Complexity; 10/17/2024, Vol. 2024, p1-12, 12p

Schlagwörter: PHASOR measurement, SUPPORT vector machines, DEEP learning, SAMPLE size (Statistics), SAMPLING methods, ELECTRIC transients

Analysis and Review of Extraordinary Machine Learning Approaches

Autoren: Tyagi, Nitin Gupta, Sandeep Pratap Srivastava, Arun et al.

Quelle: International Journal of Engineering and Technology; Vol. 7 No. 4.39 (2018): Special Issue 39; 915-920 ; 2227-524X ; 10.14419/ijet.v7i4.39

Schlagwörter: Random Recursive Support Vector Machine (R2SVM), Stacked Sparse autoencoder (SSAE), Stacked Denoising Autoencoder (SDAE), Convolutional Neural Network (CNN), Support Vector Machine (SVM)

Dateibeschreibung: application/pdf

Relation: http://www.sciencepubco.com/index.php/IJET/article/view/27728/14398; http://www.sciencepubco.com/index.php/IJET/article/view/27728

Verfügbarkeit: http://www.sciencepubco.com/index.php/IJET/article/view/27728
https://doi.org/10.14419/ijet.v7i4.39.27728

A battle royale optimization with feature fusion-based automated fruit disease grading and classification.

Autoren: Sree, S. Rama Lydia, E. Laxmi Anupama, C. S. S. et al.

Quelle: AIMS Mathematics; 2024, Vol. 9 Issue 5, p11432-11451, 20p

Schlagwörter: CONVOLUTIONAL neural networks, NOSOLOGY, COMPUTER vision, FRUIT, DEEP learning, PRECISION farming