AIMC Topic: Algorithms

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Comparative evaluation of machine learning algorithms for greenhouse gas emission forecasting: a case study of Turkey (2012-2021).

Environmental monitoring and assessment
Accurate forecasting of greenhouse gas (GHG) emissions is essential for assessing climate change dynamics and developing evidence-based environmental policies. This study aims to comparatively evaluate the prediction performance of various machine le...

MAFNet: A novel adaptive multi-scale model for fine-grained grading of diabetic retinopathy.

Scientific reports
Diabetic Retinopathy (DR) is a leading cause of blindness worldwide, and its early detection and accurate grading play a crucial role in clinical intervention. To address the dual limitations of existing methods in multi-scale lesions feature fusion ...

A machine learning approach for detecting WPA3 downgrade attacks in next-generation Wi-Fi systems.

PloS one
This paper presents a hybrid adaptive approach based on machine learning (ML) for classifying incoming traffic, feature selection and thresholding, aimed at enhancing downgrade attack detection in Wi-Fi Protected Access 3 (WPA3) networks. The fast pr...

DE-HRNet: Detail enhanced high-resolution network for human pose estimation.

PloS one
Scale variation is a challenge in human pose estimation. The scale variations of human body are related to the accuracy and robustness of posture estimation. For example, the prediction accuracy of smaller joints (such as ankles and wrists) is less t...

An intelligent diagnosis method for cardiovascular diseases based on the CNN-CBAM-GRU model.

PloS one
Early diagnosis of cardiovascular diseases (CVDs) is essential for improving patient outcomes. As a primary diagnostic modality, electrocardiogram (ECG) signals pose challenges for automatic classification due to their complex temporal and morphologi...

An integrated genetic algorithm-machine learning approach for morphological optimization of high-rise residential districts in Yulin.

PloS one
The pursuit of global carbon neutrality necessitates addressing the dual challenge of enhancing solar energy utilization while improving thermal comfort in high-rise residential areas, particularly in Yulin, northern Shaanxi, China, where abundant so...

Improving attachment style clustering with ROCKET and CatBoost: Insights from EEG analysis.

PloS one
Understanding attachment styles is essential in psychology and neuroscience, yet predicting them using objective neural data remains challenging. This study explores the use of machine learning (ML) models and EEG analysis to improve attachment style...

Kinematical error analysis and autonomous calibration of a 5PUS-RPUR parallel robot.

PloS one
Kinematic calibration is essential for improving the absolute accuracy of parallel robots, but conventional identification methods often struggle with the complex, non-linear coupling of their numerous geometric error parameters. This can lead to con...

The efficacy of machine learning algorithms in evaluating factors associated with shunt-dependent hydrocephalus after subarachnoid hemorrhage: a systematic review and meta-analysis.

Neurosurgical review
The identification of factors associated with chronic shunt-dependent hydrocephalus (CSDH) following spontaneous subarachnoid hemorrhage (SAH) remains challenging, despite numerous studies. Early recognition of patients at higher risk for requiring s...

Evaluating Undersampling Schemes and Deep Learning Reconstructions for High-Resolution 3D Double Echo Steady State Knee Imaging at 7 T: A Comparison Between GRAPPA, CAIPIRINHA, and Compressed Sensing.

Investigative radiology
OBJECTIVE: The 3-dimensional (3D) double echo steady state (DESS) magnetic resonance imaging sequence can image knee cartilage with high, isotropic resolution, particularly at high and ultra-high field strengths. Advanced undersampling techniques wit...