Quasi-Newton optimised Kolmogorov-Arnold Networks for wind farm power prediction.

Having accurate and effective wind energy forecasting that can be easily incorporated into smart networks is important. Appropriate planning and energy generation predictions are necessary for these infrastructures. The production of wind energy is linked to instability and unpredictability.

Exploring vision transformers and XGBoost as deep learning ensembles for transforming carcinoma recognition.

Early detection of colorectal carcinoma (CRC), one of the most prevalent forms of cancer worldwide, significantly enhances the prognosis of patients. This research presents a new method for improving CRC detection using a deep learning ensemble with the Computer Aided Diagnosis (CADx). The method involves combining pre-trained convolutional neural network (CNN) models, such as ADaRDEV2I-22, DaRD-22, and ADaDR-22, using Vision Transformers (ViT) and XGBoost.

Effective Alzheimer's disease detection using enhanced Xception blending with snapshot ensemble.

Alzheimer's disease (AD), a prevalent neurodegenerative disorder, leads to progressive dementia, which impairs decision-making, problem-solving, and communication. While there is no cure, early detection can facilitate treatments to slow its progression. Deep learning (DL) significantly enhances AD detection by analyzing brain imaging data to identify early biomarkers, improving diagnostic accuracy and predicting disease progression more precisely than traditional methods.

Hybrid raven roosting intelligence framework for enhancing efficiency in data clustering.

The field of data exploration relies heavily on clustering techniques to organize vast datasets into meaningful subgroups, offering valuable insights across various domains. Traditional clustering algorithms face limitations in terms of performance, often getting stuck in local minima and struggling with complex datasets of varying shapes and densities. They also require prior knowledge of the number of clusters, which can be a drawback in real-world scenarios.

Corrigendum to "WCN24-1105 PHARMACOGENETIC RISK FACTORS FOR TENOFOVIR-INDUCED KIDNEY TUBULAR DYSFUNCTION AMONG HIV-POSITIVE ADULT NIGERIANS" [ Volume 9, Issue 4, Supplement, April 2024, Page S339].

[This corrects the article DOI: 10.1016/j.ekir.