Search Results - Modified stacked sparse autoencoder~

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

Authors: He, Zhaopeng Shi, Tielin Chen, Xu

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

Subject Terms: 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.

Authors: Fouda, Walaa Mirzaliev, Sanjar Abokhoza, Reneh

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

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

RETRACTED: Optimization using Internet of Agent based Stacked Sparse Autoencoder Model for Heart Disease Prediction.

Authors: Baviskar, Vaishali Verma, Madhushi Chatterjee, Pradeep et al.

Source: Expert Systems; Dec2024, Vol. 41 Issue 12, p1-20, 20p

Subject Terms: CONVOLUTIONAL neural networks, MACHINE learning, OPTIMIZATION algorithms, NOSOLOGY, CLASSIFICATION algorithms

Visual Motion Segmentation in Crowd Videos Based on Spatial-Angular Stacked Sparse Autoencoders.

Authors: Hafeezallah, Adel Al-Dhamari, Ahlam Abu-Bakar, Syed Abd Rahman

Source: Computer Systems Science & Engineering; 2023, Vol. 47 Issue 1, p593-611, 19p

Subject Terms: COLLECTIVE behavior, IMAGE segmentation, DEEP learning, DATA analysis, ACCURACY

SSAE‐KPLS: A quality‐related process monitoring via integrating stacked sparse autoencoder with kernel partial least squares.

Authors: Ye, Zhenyu Wu, Ping He, Yuchen et al.

Source: Canadian Journal of Chemical Engineering; Oct2023, Vol. 101 Issue 10, p5858-5873, 16p

Subject Terms: LEAST squares, FEATURE extraction, MANUFACTURING processes, PRODUCT quality

Breath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network.

Authors: Aslam, Muhammad Aqeel Xue, Cuili Chen, Yunsheng et al.

Source: Scientific Reports; 2/17/2021, Vol. 11 Issue 1, p1-12, 12p

Subject Terms: STOMACH cancer, DEEP learning, NEURAL circuitry, MORTALITY, COMPUTER-aided design

Intrusion Detection Method Based on Stacked Sparse Autoencoder and Sliced GRU for Connected Healthcare Systems.

Authors: Gu, Zhaoyang Wang, Liangliang Li, Jinguo et al.

Source: Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ); Feb2023, Vol. 48 Issue 2, p2061-2074, 14p

Subject Terms: INTRUSION detection systems (Computer security), RECURRENT neural networks, CYBERTERRORISM, COVARIANCE matrices

Secondary Pulmonary Tuberculosis Identification Via pseudo-Zernike Moment and Deep Stacked Sparse Autoencoder.

Authors: Wang, Shui-Hua Satapathy, Suresh Chandra Zhou, Qinghua et al.

Source: Journal of Grid Computing; Mar2022, Vol. 20 Issue 1, p1-16, 16p

High Parameter Frequency Resolution Encoding Scheme for Spatial Audio Objects Using Stacked Sparse Autoencoder.

Authors: Wu, Yulin Hu, Ruimin Wang, Xiaochen et al.

Source: Neural Processing Letters; Apr2022, Vol. 54 Issue 2, p817-833, 17p

Subject Terms: VIDEO coding, SOUND systems, AUDIO communication, VIRTUAL reality, ARTIFICIAL neural networks, AUDIO equipment

Deep network approach with stacked sparse autoencoders in detection of DDoS attacks on SDN‐based VANET.

Authors: Polat, Huseyin Turkoglu, Muammer Polat, Onur

Source: IET Communications (Wiley-Blackwell); Dec2020, Vol. 14 Issue 22, p4089-4100, 12p

Five-category classification of pathological brain images based on deep stacked sparse autoencoder.

Authors: Jia, Wenjuan Muhammad, Khan Wang, Shui-Hua et al.

Source: Multimedia Tools & Applications; Feb2019, Vol. 78 Issue 4, p4045-4064, 20p

Subject Terms: MAGNETIC resonance imaging, ENCODING, COMPUTED tomography, IMAGE processing, CONJUGATE gradient methods

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

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

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

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

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

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

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

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

Deep network approach with stacked sparse autoencoders in detection of DDoS attacks on SDN‐based VANET.

Authors: Polat, Huseyin Turkoglu, Muammer Polat, Onur

Source: IET Communications (Wiley-Blackwell); Dec2020, Vol. 14 Issue 22, p4089-4100, 12p

Predicting the Remaining Useful Life of an Aircraft Engine Using a Stacked Sparse Autoencoder with Multilayer Self-Learning.

Authors: Ma, Jian Su, Hua Zhao, Wan-lin et al.

Source: Complexity; 7/30/2018, p1-13, 13p

Subject Terms: AIRPLANE motors, LOGISTIC regression analysis

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

Authors: SHEEBA D. LINGESHWARI G. ANORELIN A.

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

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

An intelligent grinding burn detection system based on two-stage feature selection and stacked sparse autoencoder.

Authors: Guo, Weicheng Li, Beizhi Shen, Shouguo et al.

Source: International Journal of Advanced Manufacturing Technology; Aug2019, Vol. 103 Issue 5-8, p2837-2847, 11p

Subject Terms: DEEP learning, FEATURE selection, HILBERT-Huang transform, MARAGING steel, ACOUSTIC emission, MACHINE learning, SIGNAL processing

Predicting Protein Interactions Using a Deep Learning Method-Stacked Sparse Autoencoder Combined with a Probabilistic Classification Vector Machine.

Authors: Wang, Yanbin You, Zhuhong Li, Liping et al.

Source: Complexity; 12/10/2018, p1-12, 12p

Subject Terms: PROTEIN-protein interactions, MACHINE learning

Weighted time series fault diagnosis based on a stacked sparse autoencoder.

Authors: Lv, Feiya Wen, Chenglin Liu, Meiqin et al.

Source: Journal of Chemometrics; Sep2017, Vol. 31 Issue 9, pn/a-N.PAG, 16p

Subject Terms: CHEMICAL industry, STATISTICS, FAULT diagnosis, THRESHOLDING algorithms, MULTIVARIATE analysis, NONLINEAR analysis

Application of Artificial Intelligence Technologies as an Intervention for Promoting Healthy Eating and Nutrition in Older Adults: A Systematic Literature Review.

Authors: Kalu, Kingsley Ataguba, Grace Onifade, Oyepeju et al.

Source: Nutrients. Oct2025, Vol. 17 Issue 20, p3223. 33p.