Results 41 to 50 of about 142,938 (289)

A new approach for diabetes risk detection using quadratic interpolation flower pollination neural network

Applied Computer Science
This study aims to evaluate and compare five algorithms in diabetes detection, namely Flower Pollination Neural Network (FPNN), Particle Swarm Optimization Neural Network (PSONN), Bat Artificial Neural Network (BANN), Stochastic Gradient Descent (SGD ...
Yulianto Triwahyuadi POLLY, Adriana FANGGIDAE, Juan Rizky Mannuel LEDOH, Clarissa Elfira AMOS PAH, Bertha S. DJAHI, Kisan Emiliano Rape TUPEN   +5 more
doaj   +1 more source

RaMBat: Accurate identification of medulloblastoma subtypes from diverse data sources with severe batch effects

Molecular Oncology, EarlyView.
To integrate multiple transcriptomics data with severe batch effects for identifying MB subtypes, we developed a novel and accurate computational method named RaMBat, which leveraged subtype‐specific gene expression ranking information instead of absolute gene expression levels to address batch effects of diverse data sources.
Mengtao Sun, Jieqiong Wang, Shibiao Wan
wiley   +1 more source

Artificial Intelligence Algorithms for Rapeseed Fields Mapping Using Sentinel-1 Time Series: Temporal Transfer Scenario and Ground Sampling Constraints

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023
This study aims to enhance rapeseed field detection accuracy using Sentinel-1 (S1) time series data and addressing challenges in collecting ground samples.
Saeideh Maleki, Nicolas Baghdadi, Cassio Fraga Dantas, Sami Najem, Hassan Bazzi, Nuria Pantaleoni Reluy, Dino Ienco, Mehrez Zribi   +7 more
doaj   +1 more source

F1 Score Based Weighted Asynchronous Federated Learning

International Journal for Research in Applied Science and Engineering Technology
Abstract: The domain of federated learning has observed remarkable developments in recent years, enabling collaborative model training while preserving data privacy. This paper discusses several recent advancements in the field of federated learning, particularly in asynchronous and weighted federated learning.
Sneha Sree Yarlagadda, Sai Harshith Tule, Karthik Myada   +2 more
openaire   +1 more source

Systemic T Cell Receptor Profiling Reveals Adaptive Immune Activation and Potential Immune Signatures of Diagnosis and Brain Atrophy in Epilepsy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Epilepsy is increasingly associated with immune dysregulation and inflammation. The T cell receptor (TCR), a key mediator of adaptive immunity, shows repertoire alterations in various immune‐mediated diseases. The unique TCR sequence serves as a molecular barcode for T cells, and clonal expansion accompanied by reduced overall TCR ...
Yong‐Won Shin, Sang Bin Hong, Yong Woo Shin, Inpyeong Hwang, Jaeseong Oh, Jihyeon Choi, Narae Kim, Jangsup Moon, Keun‐Hwa Jung, Kyung‐Il Park, Ki‐Young Jung, Kon Chu, Sang Kun Lee   +12 more
wiley   +1 more source

Comparison of Different Machine Learning Algorithms to Classify Epilepsy Seizure from EEG Signals

Engineering Proceedings
Recurrent seizures are a symptom of a central nervous system disease called epilepsy. The duration of these seizures lasts less than a few seconds or sometimes minutes. There are very few ways to record seizures, and one of them is EEG.
Pankaj Kunekar, Chanchal Kumawat, Vaishnavi Lande, Sushant Lokhande, Ram Mandhana, Malhar Kshirsagar   +5 more
doaj   +1 more source

SNUPN‐Related Muscular Dystrophy: Novel Phenotypic, Pathological and Functional Protein Insights

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective SNUPN‐related muscular dystrophy or LGMDR29 is a new entity that covers from a congenital or childhood onset pure muscular dystrophy to more complex phenotypes combining neurodevelopmental features, cataracts, or spinocerebellar ataxia. So far, 12 different variants have been described.
Nuria Muelas, Pablo Iruzubieta, Alberto Damborenea, Laura Pérez‐Fernández, Inmaculada Azorín, Juan Carlos Jiménez García, Ana Töpf, Pilar Martí, Lorena Fores‐Toribio, María Manterola, Rosana Blanco‐Mañez, Oihane Pikatza‐Menoio, Sonia Alonso‐Martín, Volker Straub, Aitziber L. Cortajarena, Adolfo López de Munain, David De Sancho, Lorea Blázquez, Juan J. Vilchez   +18 more
wiley   +1 more source

Real-Time Computing Strategies for Automatic Detection of EEG Seizures in ICU

Applied Sciences
Developing interfaces for seizure diagnosis, often challenging to detect visually, is rising. However, their effectiveness is constrained by the need for diverse and extensive databases.
Laura López-Viñas, Jose L. Ayala, Francisco Javier Pardo Moreno   +2 more
doaj   +1 more source

Deep Learning–Assisted Differentiation of Four Peripheral Neuropathies Using Corneal Confocal Microscopy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Peripheral neuropathies contribute to patient disability but may be diagnosed late or missed altogether due to late referral, limitation of current diagnostic methods and lack of specialized testing facilities. To address this clinical gap, we developed NeuropathAI, an interpretable deep learning–based multiclass classification ...
Chaima Ben Rabah, Ioannis N. Petropoulos, Mark Stettner, Maryam Ferdousi, Uazman Alam, Nathan Efron, Ahmed Serag, Rayaz A. Malik   +7 more
wiley   +1 more source

Acoustic Measures Capture Speech Dysfunction in Spinocerebellar Ataxia

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Spinocerebellar ataxias (SCA) are hereditary cerebellar degenerative disorders with a common feature of dysarthria, involving impaired phonatory and articulatory control of speech, thereby affecting social communication. In this study, we investigated whether acoustic measures could objectively measure speech dysfunction and identify
Zena Fadel, Charlotte Hennessey, Hannah Lee, Pia Parekh, Sheng‐Han Kuo, Ami Kumar   +5 more
wiley   +1 more source