Results 181 to 190 of about 371,575 (291)

MNBC: a multithreaded Minimizer-based Naïve Bayes Classifier for improved metagenomic sequence classification. [PDF]

Bioinformatics
Lu R, Dumonceaux T, Anzar M, Zovoilis A, Antonation K, Barker D, Corbett C, Nadon C, Robertson J, Eagle SHC, Lung O, Rudar J, Surujballi O, Laing C.   +13 more
europepmc   +1 more source

Detection of Cardiovascular Disease Risk's Level for Adults Using Naive Bayes Classifier. [PDF]

Healthc Inform Res, 2016
Miranda E, Irwansyah E, Amelga AY, Maribondang MM, Salim M.   +4 more
europepmc   +1 more source

Enabling pandemic‐resilient healthcare: Narrowband Internet of Things and edge intelligence for real‐time monitoring

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract The Internet of Things (IoT) in deploying robotic sprayers for pandemic‐associated disinfection and monitoring has garnered significant attention in recent research. The authors introduce a novel architectural framework designed to interconnect smart monitoring robotic devices within healthcare facilities using narrowband Internet of Things ...
Md Motaharul Islam, Mohammad Kamrul Hasan, Shayla Islam, Mohammed Balfaqih, Ahmed Ibrahim Alzahrani, Nasser Alalwan, Nurhizam Safie, Zaheed Ahmed Bhuiyan, Rahul Thakkar, Taher M. Ghazal   +9 more
wiley   +1 more source

Classification of short-term flood events using stochastic variable selection and Gaussian Naïve Bayes classifier: A case study of Sirajganj district, Bangladesh. [PDF]

Heliyon
Mondal C, Uddin MJ.
europepmc   +1 more source

Prediction of Protein-Protein Interaction Sites Based on Naive Bayes Classifier. [PDF]

Biochem Res Int, 2015
Geng H, Lu T, Lin X, Liu Y, Yan F.
europepmc   +1 more source

RES‐AD: An AutoML‐Driven Robust Ensemble Framework for Real‐Time Anomaly Detection and Energy Forecasting in Smart Grids

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Accurate load forecasting and reliable anomaly detection are critical for the stable operation of modern smart grids (SGs), which increasingly rely on cyber‐connected infrastructures. However, the integration of smart metres and two‐way communication exposes SGs to data integrity attacks that can manipulate consumption measurements, degrade ...
Murad Ali Khan, Naeem Iqbal, Syed Shehryar Ali Naqvi, Muhammad Faseeh, Do‐Hyeun Kim   +4 more
wiley   +1 more source

Dissecting Glioma Heterogeneity: A Deep Hybrid Graph Convolutional Network With Hinge Attention for Causal‐Effect Explainability

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Brain tumour classification is a critical task in medical imaging that requires accurate and interpretable solutions to assist in clinical decision‐making. In this paper, we present GraphConvNet‐X, a novel hybrid model that integrates convolutional neural networks (CNNs) for spatial feature extraction with graph neural networks (GNNs) that ...
Sultanul Arifeen Hamim, Mubasshar U. I. Tamim, S. M. Hasan Mahmud, M. F. Mridha, Zeyar Aung   +4 more
wiley   +1 more source

Mucopolysaccharidosis type II detection by Naïve Bayes Classifier: An example of patient classification for a rare disease using electronic medical records from the Canadian Primary Care Sentinel Surveillance Network. [PDF]

PLoS One, 2018
Ehsani-Moghaddam B, Queenan JA, MacKenzie J, Birtwhistle RV.   +3 more
europepmc   +1 more source

A Validation Method for the Enhanced Fuzzy Min‐Max Neural Network With Application to Heart Disease Data Classification

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT This study investigates early detection of heart diseases using a new Enhanced Fuzzy Min‐Max (EFMM) neural network. Two key modifications are introduced: a refined EFMM contraction procedure during the learning phase to reduce data distortion and information loss, and a new weighted validation method to optimise hyperbox selection and improve ...
Mohammed Falah Mohammed, Osama Nayel Al Sayaydeh, Taha H. Rassem, Chee Peng Lim   +3 more
wiley   +1 more source

Feature selection using a one dimensional naïve Bayes' classifier increases the accuracy of support vector machine classification of CDR3 repertoires. [PDF]

Bioinformatics, 2017
Cinelli M, Sun Y, Best K, Heather JM, Reich-Zeliger S, Shifrut E, Friedman N, Shawe-Taylor J, Chain B.   +8 more
europepmc   +1 more source