Search Results - Deep stacked Autoencoder

Customer Behavior Classification Using Deep Stacked Autoencoder with Dragonfly Optimization

Authors: Gajendran K S Arunkumar Thangavelu

Source: Journal of Machine and Computing. :2230-2240

Big Data Analytics Cyber Forensic Framework Using Enhanced Sunflower Jaya Optimization- Based Deep Stacked Autoencoder

Authors: Sabaresan V Sharon Samraj J Manjunath N

Source: 2025 6th International Conference for Emerging Technology (INCET). :1-7

Hybrid optimization based deep stacked autoencoder for routing and intrusion detection

Authors: Mythili Boopathi

Source: Web Intelligence. 23:3-22

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Dense_Deepsanet: Dense_Deep Stacked Autoencodernet-Based Intrusion Detection in Cloud Computing.

Authors: Ramakrishna, V. Jaya Dayananda, K.

Source: Journal of Information & Knowledge Management. Oct2025, Vol. 24 Issue 5, p1-26. 26p.

Subject Terms: *Cloud computing, *Artificial neural networks, *Electronic data processing, Intrusion detection systems (Computer security), Data transformations (Statistics), Deep learning, Data augmentation, Autoencoder

A Hybridized Deep Stacked Autoencoder Model for Botnet Detection

Authors: Juliet David Shekarau Rabi Mustapha Khalid Haruna

Source: Kasu Journal of Computer Science. 1:1-15

Transformer Fault Diagnosis Based on Adversarial Generative Networks and Deep Stacked Autoencoder

Authors: Lei Zhang Zhongyang Xu Chen Lu et al.

Source: Heliyon
Heliyon, Vol 10, Iss 9, Pp e30670-(2024)

Subject Terms: H1-99, Transformer, Science (General), Sample expansion, Chromatographic data, 05 social sciences, 01 natural sciences, Social sciences (General), Q1-390, Adversarial neural networks, 0502 economics and business, Fault data, Research Article, 0105 earth and related environmental sciences

Access URL: https://pubmed.ncbi.nlm.nih.gov/38765093
https://doaj.org/article/cb773fe3f16d416894048132c551cb21

On the Evaluation of Energy-Efficient Deep Learning Using Stacked Autoencoders on Mobile GPUs

Authors: Falcao, Gabriel Alexandre, Luís Marques, J. et al.

Contributors: Falcao, Gabriel Alexandre, Luís Marques, J. et al.

Subject Terms: Parallel processing, Mobile GPU, Low-power, Energy savings, Deep Learning, Stacked Autoencoders

File Description: application/pdf

Availability: http://hdl.handle.net/10400.6/8152

A Fuzzy-Based Deep Kronecker Stacked Autoencoder for Attack Detection in Social IoT.

Authors: S., Divya R., Tanuja

Source: Engineering, Technology & Applied Science Research; Oct2025, Vol. 15 Issue 5, p26189-26193, 5p

Subject Terms: FUZZY logic, INTERNET of things, SECURITY management, DATA fusion (Statistics), FUZZY neural networks, SECURITY systems, DEEP learning

Deep Stacked Autoencoder-Based Long-Term Spectrum Prediction Using Real-World Data

Authors: Guangliang Pan Qihui Wu Guoru Ding et al.

Source: IEEE Transactions on Cognitive Communications and Networking. 9:534-548

Improving Code Smell Detection Using Deep Stacked Autoencoder

Authors: Kadem K. Rehef Ahmed S. Abbas

Source: Lecture Notes in Networks and Systems ISBN: 9789819633807

Emotion Recognition using Deep Stacked Autoencoder with Softmax Classifier

Authors: M. Mohana P. Subashini

Source: 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS). :864-872

Botnet attack detection in IoT using hybrid optimisation enabled deep stacked autoencoder network

Authors: Mahesh Babu Arrama Archana Kalidindi

Source: International Journal of Bio-Inspired Computation. 22:77-88

Gated Stacked Target-Related Autoencoder: A Novel Deep Feature Extraction and Layerwise Ensemble Method for Industrial Soft Sensor Application

Authors: Qingqiang Sun Zhiqiang Ge

Source: IEEE Transactions on Cybernetics. 52:3457-3468

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://pubmed.ncbi.nlm.nih.gov/32833658
https://www.ncbi.nlm.nih.gov/pubmed/32833658
https://europepmc.org/article/MED/32833658
https://ieeexplore.ieee.org/abstract/document/9174659

Computer Vision with Optimal Deep Stacked Autoencoder-based Fall Activity Recognition for Disabled Persons in the IoT Environment

Authors: Eatedal Alabdulkreem Radwa Marzouk Mesfer Alduhayyem et al.

Source: Journal of Disability Research. 2

Subject Terms: Artificial neural network, Artificial intelligence, Ambient Intelligence, Computer Networks and Communications, Feature (linguistics), Non-contact Physiological Monitoring Technology, Internet of Things, Biomedical Engineering, Activity Recognition in Pervasive Computing Environments, FOS: Medical engineering, Activity Recognition, Engineering, Computer security, Field (mathematics), Machine learning, FOS: Mathematics, Geography, Internet of Things and Edge Computing, Pure mathematics, Deep learning, Linguistics, Human Activity Analysis, Autoencoder, Computer science, Process (computing), FOS: Philosophy, ethics and religion, 3. Good health, Philosophy, Operating system, Action Recognition, Activity recognition, Computer Science, Physical Sciences, FOS: Languages and literature, Fall Detection, Feature extraction, Computer Vision and Pattern Recognition, Benchmark (surveying), Mathematics, Geodesy

Deep Stacked Autoencoder-Based Automatic Emotion Recognition Using an Efficient Hybrid Local Texture Descriptor

Authors: Shanthi Pitchaiyan Nickolas Savarimuthu

Source: Journal of Information Technology Research. 15:1-26

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://www.igi-global.com/viewtitle.aspx?TitleId=282708

Robust Gesture Recognition and Classification for Visually Impaired Persons Using Growth Optimizer with Deep Stacked Autoencoder

Authors: Mashael Maashi Mohammed Abdullah Al-Hagery Mohammed Rizwanullah et al.

Source: Journal of Disability Research, Vol 2, Iss 2, Pp 100-111 (2023)

Subject Terms: Vocational rehabilitation. Employment of people with disabilities, HD7255-7256

File Description: electronic resource

Relation: https://www.scienceopen.com/hosted-document?doi=10.57197/JDR-2023-0029; https://doaj.org/toc/1658-9912; https://doaj.org/toc/2676-2633

Access URL: https://doaj.org/article/6203150d42e645da83f0dbf128f101c5

Feature selection and hybrid CNNF deep stacked autoencoder for botnet attack detection in IoT

Authors: Archana Kalidindi Mahesh Babu Arrama

Source: Computers and Electrical Engineering. 122:109984

Positional-attention based bidirectional deep stacked AutoEncoder for aspect based sentimental analysis

Authors: S. Anjali Devi M. Sitha Ram Pulugu Dileep et al.

Source: Big Data Research. 39:100505

LDSAE: LeNet Deep Stacked Autoencoder for Secure System to Mitigate the Errors of Jamming Attack in Cognitive Radio Network

Authors: Chandrakant Athavale Chhaya K.P. Patil

Source: International Journal of Networking and Virtual Organisations. 31

DensePILAE: a feature reuse pseudoinverse learning algorithm for deep stacked autoencoder

Authors: Jue Wang Ping Guo Yanjun Li

Source: Complex & Intelligent Systems. 8:2039-2049

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

Access URL: https://link.springer.com/content/pdf/10.1007/s40747-021-00516-5.pdf
https://link.springer.com/content/pdf/10.1007/s40747-021-00516-5.pdf
https://link.springer.com/article/10.1007/s40747-021-00516-5