Results 161 to 170 of about 165,078 (298)

Riemannian Geometry for the Classification of Brain States with Intracortical Brain Recordings

Advanced Intelligent Systems, EarlyView.
Geometric machine learning is applied to decode brain states from invasive intracortical neural recordings, extending Riemannian methods to the invasive regime where data is scarcer and less stationary. A Minimum Distance to Mean classifier on covariance manifolds uses geodesic distances to outperform convolutional neural networks while reducing ...
Arnau Marin‐Llobet, Sergio Sánchez‐Manso, Arnau Manasanch, Lluc Tresserras, Xinhe Zhang, Yining Hua, Hao Zhao, Melody Torao‐Angosto, Maria V Sanchez‐Vives, Leonardo Dalla Porta   +9 more
wiley   +1 more source

Cryogenic Neuromorphic Synaptic Behavior in 180 nm Silicon Transistors for Emerging Computing Systems

Advanced Intelligent Systems, EarlyView.
This study investigates the neuromorphic plasticity behavior of 180 nm bulk complementary metal oxide semiconductor (CMOS) transistors at cryogenic temperatures. The observed hysteresis data reveal a signature of synaptic behavior in CMOS transistors at 4 K.
Fiheon Imroze, Bhavani Yalagala, Naveen Kumar, Mostafa Elsayed, Meraj Ahmad, Robert Graham, Vihar Georgiev, Hadi Heidari, Martin Weides   +8 more
wiley   +1 more source

Machine Learning‐Based Standard Compact Model Binning Parameter Extraction Methodology for Integrated Circuit Design of Next‐Generation Semiconductor Devices

Advanced Intelligent Systems, EarlyView.
This study presents a neural network‐based methodology for Berkeley Short‐Channel IGFET Model–Common Multi‐Gate parameter extraction of gate‐all‐around field effect transistors, integrating binning adaptive sampling and transformer neural networks to efficiently capture current–voltage and capacitance–voltage characteristics.
Jaeweon Kang, Johyeon Kim, Sueyeon Kim, Hyunbo Cho, Jongwook Jeon   +4 more
wiley   +1 more source

Model interpretability on private-safe oriented student dropout prediction. [PDF]

PLoS One
Liu H, Mao M, Li X, Gao J.
europepmc   +1 more source

Multitarget Recognition of Flower Images Based on Lightweight Deep Neural Network and Transfer Learning

Advanced Intelligent Systems, EarlyView.
This article proposes a lightweight YOLOv4‐based detection model using MobileNetV3 or CSPDarknet53_tiny, achieving 30+ FPS and higher mAP. It also presents a ShuffleNet‐based classification model with transfer learning and GAN‐augmented images, improving generalization and accuracy.
Qingyang Liu, Yanrong Hu, Hongjiu Liu
wiley   +1 more source

A PSO weighted ensemble framework with SMOTE balancing for student dropout prediction in smart education systems. [PDF]

Sci Rep
Jain A, Dubey AK, Khan S, Panwar A, Alkhatib M, Alshahrani AM.   +5 more
europepmc   +1 more source

Predicting Postresection Colorectal Liver Metastases Recurrence Using Advanced Graph Neural Networks with Explainability and Causal Inference

Advanced Intelligent Systems, EarlyView.
This study introduces a framework that combines graph neural networks with causal inference to forecast recurrence and uncover the clinical and pathological factors driving it. It further provides interpretability, validates risk factors via counterfactual and interventional analyses, and offers evidence‐based insights for treatment planning ...
Jubair Ahmed, Md. Abdur Rahman, Mohaimenul Azam Khan Raiaan, Sami Azam   +3 more
wiley   +1 more source

Student dropout prediction through machine learning optimization: insights from moodle log data. [PDF]

Sci Rep
Rebelo Marcolino M, Reis Porto T, Thompsen Primo T, Targino R, Ramos V, Marques Queiroga E, Munoz R, Cechinel C.   +7 more
europepmc   +1 more source

Cross‐Modal Characterization of Thin‐Film MoS2 Using Generative Models

Advanced Intelligent Systems, EarlyView.
Cross‐modal learning is evaluated using atomic force microscopy (AFM), Raman spectroscopy, and photoluminescence spectroscopy (PL) through unsupervised learning, regression, and autoencoder models. Autoencoder models are used to generate spectroscopy data from the microscopy images.
Isaiah A. Moses, Chen Chen, Joan M. Redwing, Wesley F. Reinhart   +3 more
wiley   +1 more source

RPSLearner: A Novel Approach Based on Random Projection and Deep Stacking Learning for Categorizing Non‐Small Cell Lung Cancer

Advanced Intelligent Systems, EarlyView.
Identifying non‐small cell lung cancer (NSCLC) subtypes is essential for precision cancer treatment. Conventional methods are laborious, or time‐consuming. To address these concerns, RPSLearner is proposed, which combines random projection and stacking ensemble learning for accurate NSCLC subtyping. RPSLearner outperforms state‐of‐the‐art approaches in
Xinchao Wu, Jieqiong Wang, Shibiao Wan
wiley   +1 more source