Results 101 to 110 of about 234,412 (208)

AE-MCDM: an autoencoder-based multi-criteria decision-making approach for unsupervised feature selection

The Journal of Supercomputing
Feature selection is a fundamental technique for reducing the dimensionality of high-dimensional data by identifying the most relevant features while discarding redundant or irrelevant ones. In unsupervised settings, where labeled data are unavailable and labeling is costly, effective feature selection becomes even more challenging. This paper proposes
Hashemi, Amin, Dowlatshahi, Mohammad Bagher, Farshidi, Siamak, Moradi, Parham   +3 more
openaire   +1 more source

Abstracts

Molecular Oncology, Volume 20, Issue S1, Page 1-692, August 2026.
Abstracts submitted to the ‘EACR 2026 Congress: Innovative Cancer Science’, from 08–11 June 2026 and accepted by the Congress Organising Committee are published in this Supplement of Molecular Oncology, an affiliated journal of the European Association for Cancer Research (EACR).
wiley   +1 more source

A hybrid Fuzzy-Autoencoder based approach for resilient zero-day malware detection under adversarial conditions

Discover Artificial Intelligence
Amid the growing use of artificial intelligence (AI) and machine learning, the risk of adversarial zero-day malware attacks has increased and thus, enhancements to intelligent detection systems are necessary.
Shivangi Mehta, Lata Gadhavi, Mohendra Roy, Aishwarya Mishra   +3 more
doaj   +1 more source

Model Validation for Multivariate Functional Responses via Autoencoder-Based Dual-Layer Feature Extraction

Mathematics
Model validation for complex simulation models with multivariate functional responses poses significant challenges, as it involves the dual coupling of physical correlations among variables and field correlations in time-series data.
Dengyu Wu, Xiaodong Zhang, Daobo Sun, Haidong Lin, Jinhui Li, Baoqiang Zhang   +5 more
doaj   +1 more source

Data Augmentation for Text Classification Using Autoencoders

IEEE Access
Deep learning models have greatly improved various natural language processing tasks. However, their effectiveness depends on large data sets, which can be difficult to acquire.
Mustafa Cataltas, Ilyas Cicekli, Nurdan Akhan Baykan   +2 more
doaj   +1 more source

Leveraging autoencoder models and data augmentation to uncover transcriptomic diversity of gingival keratinocytes in single cell analysis

Scientific Reports
Periodontitis, a chronic inflammatory condition of the periodontium, is associated with over 60 systemic diseases. Despite advancements, precision medicine approaches have had limited success, emphasizing the need for deeper insights into cellular ...
Pradeep Kumar Yadalam, Prabhu Manickam Natarajan, Carlos M. Ardila   +2 more
doaj   +1 more source

FM-AE: Frequency-masked Multimodal Autoencoder for Zinc Electrolysis Plate Contact Abnormality Detection

CoRR
2023 The 34th Chinese Process Control Conference (CPCC 2023)
Canzong Zhou, Can Zhou 0005, Hongqiu Zhu, Tianhao Liu   +3 more
openaire   +3 more sources

BIRCH-AE: A Hierarchical Ensemble Framework for Scalable E-Commerce User Segmentation with Autoencoder-Enhanced Feature Learning

The rapid expansion of e-commerce platforms has intensified demand for scalable, high-quality user segmentation systems capable of efficiently processing millions of behavioral records.
Ibrahim, Hamidah, Li, Caili, Li, Caiwen, Sidi, Fatimah, Ishak, Iskandar, Zolkepli, Maslina   +5 more
core   +1 more source

AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder and Regularization by Denoising

, 2023
Spectral unmixing has been extensively studied with a variety of methods and used in many applications. Recently, data-driven techniques with deep learning methods have obtained great attention to spectral unmixing for its superior learning ability to ...
Zhao, Min, Dobigeon, Nicolas, Chen, Jie
core  

Autoencoder-based detector for distinguishing process anomaly and sensor failure

Anomaly detection is a frequently discussed topic in manufacturing. However, the issues of anomaly detection are typically attributed to the manufacturing process or equipment itself. In practice, the sensor responsible for collecting data and monitoring
Chia-Yen Lee (4389754), Chien Ho (1872865), Kai Chang (149548)   +2 more
core   +1 more source