Results 61 to 70 of about 13,513 (170)

Financial Time Series Uncertainty: A Review of Probabilistic AI Applications

Journal of Economic Surveys, EarlyView.
ABSTRACT Probabilistic machine learning models offer a distinct advantage over traditional deterministic approaches by quantifying both epistemic uncertainty (stemming from limited data or model knowledge) and aleatoric uncertainty (due to inherent randomness in the data), along with full distributional forecasts.
Sivert Eggen, Tord Johan Espe, Kristoffer Grude, Morten Risstad, Rickard Sandberg   +4 more
wiley   +1 more source

AE SemRL: Learning Semantic Association Rules with Autoencoders

Association Rule Mining (ARM) is the task of learning associations among data features in the form of logical rules. Mining association rules from high-dimensional numerical data, for example, time series data from a large number of sensors in a smart environment, is a computationally intensive task.
Karabulut, Erkan, Degeler, Victoria, Groth, Paul   +2 more
openaire   +2 more sources

An Overview of Deep Learning Techniques for Big Data IoT Applications

International Journal of Communication Systems, Volume 39, Issue 4, 10 March 2026.
Reviews deep learning integration with cloud, fog, and edge computing in IoT architectures. Examines model suitability across IoT applications, key challenges, and emerging trends Provides a comparative analysis to guide future deep learning research in IoT environments.
Gagandeep Kaur, Vipin Balyan, Sindhu Hak Gupta   +2 more
wiley   +1 more source

An Analysis of Image Classification Using Wavelet-Based Autoencoder Architecture and Extreme Learning Machine

Journal of Electrical and Computer Engineering
In recent machine learning applications, promising outcomes have emerged through the integration of Deep Learning (DL) and Extreme Learning Machine (ELM) techniques with wavelet networks (WN), leading to high classification accuracy.
Salwa Said, Salima Hassairi, Siwar Yahia, Ridha Ejbali, Mourad Zaied   +4 more
doaj   +1 more source

Critical Review for One‐Class Classification: Recent Advances and Reality Behind Them

WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 1, March 2026.
This review presents a new taxonomy to summarize one‐class classification (OCC) algorithms and their applications. The main argument is that OCC should not learn multiple classes. The paper highlights common violations of OCC involving multiple classes.
Toshitaka Hayashi, Dalibor Cimr, Hamido Fujita, Richard Cimler   +3 more
wiley   +1 more source

Explaining anomalies through semi-supervised Autoencoders

Array
This work tackles the problem of designing explainable by design anomaly detectors, which provide intelligible explanations to abnormal behaviors in input data observations.
Fabrizio Angiulli, Fabio Fassetti, Luca Ferragina, Simona Nisticò   +3 more
doaj   +1 more source

Cellpose+, a Morphological Analysis Tool for Feature Extraction of Stained Cell Images

Advanced Intelligent Discovery, Volume 2, Issue 1, February 2026.
We introduce Cellpose plus, a morphological and geometrical analysis tool for feature extraction of stained cell images built over Cellpose, a state‐of‐the‐art cell segmentation framework. We also introduce a dataset of DAPI and FITC stained cells to which our new method is applied.
Israel A. Huaman, Fares D. E. Ghorabe, Sofya S. Chumakova, Alexandra A. Pisarenko, Alexey E. Dudaev, Tatiana G. Volova, Galina A. Ryltseva, Sviatlana A. Ulasevich, Ekaterina I. Shishatskaya, Ekaterina V. Skorb, Pavel S. Zun   +10 more
wiley   +1 more source

Multi-View Spectral Clustering via ELM-AE Ensemble Features Representations Learning

IEEE Access, 2020
Spectral cluster based on multi-view data has proven effective for clustering multi-source real-world data because consensus and complementary information of multi-view data ensure the result of clustering.
Lijuan Wang, Shifei Ding
doaj   +1 more source

AI‐Enhanced Surface‐Enhanced Raman Scattering for Accurate and Sensitive Biomedical Sensing

Advanced Intelligent Discovery, Volume 2, Issue 1, February 2026.
AI‐SERS advances spectral interpretation with greater precision and speed, enhancing molecular detection, biomedical analysis, and imaging. This review explores its essential contributions to biofluid analysis, disease identification, therapeutic agent evaluation, and high‐resolution biomedical imaging, aiding diagnostic decision‐making.
Seungki Lee, Rowoon Park, Ho Sang Jung
wiley   +1 more source

An abnormal traffic detection method for chain information management system network based on convolutional neural network

Frontiers in Physics
Chain information management system is widely used, providing convenience for the operation and management of enterprises. However, the problem of abnormal network traffic becomes increasingly prominent currently.
Chao Liu, Chunxiang Liu, Changrong Liu
doaj   +1 more source