Results 181 to 190 of about 113,519 (298)

AI‐based localization of the epileptogenic zone using intracranial EEG

Epilepsia Open, EarlyView.
Abstract Artificial intelligence (AI) is rapidly transforming our lives. Machine learning (ML) enables computers to learn from data and make decisions without explicit instructions. Deep learning (DL), a subset of ML, uses multiple layers of neural networks to recognize complex patterns in large datasets through end‐to‐end learning.
Atsuro Daida, Yipeng Zhang, Sotaro Kanai, Richard Staba, Vwani Roychowdhury, Hiroki Nariai   +5 more
wiley   +1 more source

AI based comparison of prefrontal cortex activity between piano-majors and non-piano-major musicians during score-based playing and Motif-improvisation. [PDF]

PLoS One
Kang HJ, Lee SC, Hwang Y, Ham J, Park M, Park KM.   +5 more
europepmc   +1 more source

Artificial intelligence in preclinical epilepsy research: Current state, potential, and challenges

Epilepsia Open, EarlyView.
Abstract Preclinical translational epilepsy research uses animal models to better understand the mechanisms underlying epilepsy and its comorbidities, as well as to analyze and develop potential treatments that may mitigate this neurological disorder and its associated conditions. Artificial intelligence (AI) has emerged as a transformative tool across
Jesús Servando Medel‐Matus, Cesar Santana‐Gomez, Ruby G. Escalante, Dominique Duncan, Pedro F. Viana, Giulia Sofia Cereda, Naoto Kuroda, Aristea S. Galanopoulou   +7 more
wiley   +1 more source

Compressing and expanding optical matrix-vector multipliers for enabling optical image encoder-decoders and generators. [PDF]

Light Sci Appl
Stern A.
europepmc   +1 more source

Neural Network Models for Solar Irradiance Forecasting in Polluted Areas: A Comparative Study

Energy Science &Engineering, EarlyView.
Pollution‐aware hybrid ensemble model is proposed to forecast solar irradiance across eight diverse cities. The model integrates MLP, RNN, and NARX to handle varying atmospheric pollution levels. The model outperforms state‐of‐the‐art methods with enhanced accuracy and interpretability on standard solar irradiance data set.
Mujtaba Ali, Muhammad Yaqoob Javed, Aamer Bilal Asghar, Khurram Hashmi, Abbas Javed, Basem Alamri, Krzysztof Ejsmont   +6 more
wiley   +1 more source

Correction: Enhancing breast cancer classification using a deep sparse wavelet autoencoder approach. [PDF]

Sci Rep
Alzakari SA, Hassairi S, Alhussan AA, Ejbali R.   +3 more
europepmc   +1 more source

Economic and Technical Assessment of Wind Potential Using SARIMAX Time Series Models: Wind Speed Forecasting and Analysis

Energy Science &Engineering, EarlyView.
The wind energy potential of Khaf was evaluated for 2025 using 15 years of wind data combined with advanced forecasting models, SARIMAX and Prophet. This integrated framework enables precise estimation of wind power density and optimal turbine selection, paving the way for the efficient and sustainable development of wind farms in the region.
Mohammad Amin Valizadeh, Iman Khorsandi Shamir, Abolfazl Ahmadi, Mojtaba Mirhosseini   +3 more
wiley   +1 more source

Artificial Intelligence for Liquid Biopsy: FTIR Spectroscopy and Autoencoder-Based Detection of Cancer Biomarkers in Extracellular Vesicles. [PDF]

Cells
Di Santo R, Niccolini B, Rosa E, De Spirito M, Pizzolante F, Pitocco D, Tartaglione L, Rizzi A, Basile U, Petito V, Gasbarrini A, Gigante G, Ciasca G.   +12 more
europepmc   +1 more source

Performance Monitoring of Photovoltaic Modules Using Machine‐Learning‐Based Solutions: A Survey of Current Trends

Energy Science &Engineering, EarlyView.
The graphical abstract presents the concept of applying machine‐learning algorithms to assess the performance of photovoltaic modules. Data from solar panels are fed to surrogates of intelligent models, to assess the following performance metrics: identifying faults, quantifying energy production and trend degradation over time. The combination of data
Nangamso Nathaniel Nyangiwe, Abraham Dimitri Kapim Kenfack, Nicolas Thantsha, Mandla Msimanga   +3 more
wiley   +1 more source

A Deep Autoencoder Compression-Based Genomic Prediction Method for Whole-Genome Sequencing Data. [PDF]

Biology (Basel)
Song H, Dong T, Wang W, Yan X, Geng C, Bai S, Hu H.   +6 more
europepmc   +1 more source