AIMC Topic: Algorithms

Clear Filters Showing 13701 to 13710 of 28713 articles

Feature engineering solution with structured query language analytic functions in detecting electricity frauds using machine learning.

Scientific reports
Detecting fraud related to electricity consumption is usually a difficult challenge as the input datasets are sometimes unreliable due to missing and inconsistent records, faults, misinterpretation of meter reading remarks, status, etc. In this paper...

Tomographic Ultrasound Imaging in the Diagnosis of Breast Tumors under the Guidance of Deep Learning Algorithms.

Computational intelligence and neuroscience
This study was aimed to discuss the feasibility of distinguishing benign and malignant breast tumors under the tomographic ultrasound imaging (TUI) of deep learning algorithm. The deep learning algorithm was used to segment the images, and 120 patien...

Classification and Detection of Autism Spectrum Disorder Based on Deep Learning Algorithms.

Computational intelligence and neuroscience
Autism spectrum disorder (ASD) is a type of mental illness that can be detected by using social media data and biomedical images. Autism spectrum disorder (ASD) is a neurological disease correlated with brain growth that later impacts the physical im...

Automated pneumothorax triaging in chest X-rays in the New Zealand population using deep-learning algorithms.

Journal of medical imaging and radiation oncology
INTRODUCTION: The primary aim was to develop convolutional neural network (CNN)-based artificial intelligence (AI) models for pneumothorax classification and segmentation for automated chest X-ray (CXR) triaging. A secondary aim was to perform interp...

Machine Learning Based Lens-Free Shadow Imaging Technique for Field-Portable Cytometry.

Biosensors
The lens-free shadow imaging technique (LSIT) is a well-established technique for the characterization of microparticles and biological cells. Due to its simplicity and cost-effectiveness, various low-cost solutions have been developed, such as autom...

Comparative analysis of explainable machine learning prediction models for hospital mortality.

BMC medical research methodology
BACKGROUND: Machine learning (ML) holds the promise of becoming an essential tool for utilising the increasing amount of clinical data available for analysis and clinical decision support. However, the lack of trust in the models has limited the acce...

Predicting Colorectal Cancer Using Residual Deep Learning with Nursing Care.

Contrast media & molecular imaging
Presently, colorectal cancer is the second most dangerous cancer; around 13% of people have been affected; and it requires an effective image analysis and earlier cancer prediction (IAECP) system for reducing the mortality rate. Here, the IAECP syste...

A Liver Damage Prediction Using Partial Differential Segmentation with Improved Convolutional Neural Network.

Journal of healthcare engineering
BACKGROUND: The liver is one of the most significant and most essential organs in the human body. It is divided into two granular lobes, one on the right and one on the left, connected by a bile duct. The liver is essential in the removal of waste pr...

5G Massive MIMO Signal Detection Algorithm Based on Deep Learning.

Computational intelligence and neuroscience
Aiming at the problems of poor signal detection effect caused by many interference factors in large-scale MIMO technology scene, this paper proposes a 5G massive MIMO signal detection algorithm based on deep learning. Firstly, the MIMO system model b...

Broad Echo State Network with Reservoir Pruning for Nonstationary Time Series Prediction.

Computational intelligence and neuroscience
The nonstationary time series is generated in various natural and man-made systems, of which the prediction is vital for advanced control and management. The neural networks have been explored in the time series prediction, but the problem remains in...