AIMC Topic: Neural Networks, Computer

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Trustworthy and Human Centric neural network approaches for prediction of landfill methane emission and sustainable waste management practices.

Waste management (New York, N.Y.)
Landfills rank third among the anthropogenic sources of methane gas in the atmosphere, hence there is a need for greater emphasis on the quantification of landfill methane emission for mitigating environmental degradation. However, the estimation and...

Construction and evaluation of machine learning-based predictive models for early-onset preeclampsia.

Pregnancy hypertension
OBJECTIVE: To analyze the influencing factors of early-onset preeclampsia (EOPE). And to construct and validate the prediction model of EOPE using machine learning algorithm.

Can Focusing on One Deep Learning Architecture Improve Fault Diagnosis Performance?

Journal of chemical information and modeling
Machine learning approaches often involve evaluating a wide range of models due to various available architectures. This standard strategy can lead to a lack of depth in exploring established methods. In this study, we concentrated our efforts on a s...

Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence.

Nature cancer
Despite advances in precision oncology, clinical decision-making still relies on limited variables and expert knowledge. To address this limitation, we combined multimodal real-world data and explainable artificial intelligence (xAI) to introduce AI-...

Risk factors and machine learning prediction models for intrahepatic cholestasis of pregnancy.

BMC pregnancy and childbirth
BACKGROUND: Intrahepatic cholestasis of pregnancy (ICP) is a liver disorder that occurs in the second and third trimesters of pregnancy and is associated with a significant risk of fetal complications, including premature birth and fetal death. In cl...

Optimized convolutional neural network using African vulture optimization algorithm for the detection of exons.

Scientific reports
The detection of exons is an important area of research in genomic sequence analysis. Many signal-processing methods have been established successfully for detecting the exons based on their periodicity property. However, some improvement is still re...

Deep learning-based malaria parasite detection: convolutional neural networks model for accurate species identification of Plasmodium falciparum and Plasmodium vivax.

Scientific reports
Accurate malaria diagnosis with precise identification of Plasmodium species is crucial for an effective treatment. While microscopy is still the gold standard in malaria diagnosis, it relies heavily on trained personnel. Artificial intelligence (AI)...

A deep learning based model for diabetic retinopathy grading.

Scientific reports
Diabetic retinopathy stands as a leading cause of blindness among people. Manual examination of DR images is labor-intensive and prone to error. Existing methods to detect this disease often rely on handcrafted features which limit the adaptability a...

Prediction of dry matter intake in growing Black Bengal goats using artificial neural networks.

Tropical animal health and production
Dry matter intake (DMI) determination is essential for effective management of meat goats, especially in optimizing feed utilization and production efficiency. Unfortunately, farmers often face challenges in accurately predicting DMI which leads to w...

Hybrid data augmentation strategies for robust deep learning classification of corneal topographic maptopographic map.

Biomedical physics & engineering express
Deep learning has emerged as a powerful tool in medical imaging, particularly for corneal topographic map classification. However, the scarcity of labeled data poses a significant challenge to achieving robust performance. This study investigates the...