AIMC Topic: Neural Networks, Computer

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Trained neural networking framework based skin cancer diagnosis and categorization using grey wolf optimization.

Scientific reports
Skin Cancer is caused due to the mutational differences in epidermis hormones and patch appearances. Many studies are focused on the design and development of effective approaches in diagnosis and categorization of skin cancer. The decisions are made...

Comparing cognition across major transitions using the hierarchy of formal automata.

Wiley interdisciplinary reviews. Cognitive science
The evolution of cognition can be understood in terms of a few major transitions-changes in the computational architecture of nervous systems that changed what cognitive capacities could be evolved by downstream lineages. We demonstrate how the idea ...

Computational scoring and experimental evaluation of enzymes generated by neural networks.

Nature biotechnology
In recent years, generative protein sequence models have been developed to sample novel sequences. However, predicting whether generated proteins will fold and function remains challenging. We evaluate a set of 20 diverse computational metrics to ass...

Classification Method of ECG Signals Based on RANet.

Cardiovascular engineering and technology
BACKGROUND: Electrocardiograms (ECG) are an important source of information on human heart health and are widely used to detect different types of arrhythmias.

TriConvUNeXt: A Pure CNN-Based Lightweight Symmetrical Network for Biomedical Image Segmentation.

Journal of imaging informatics in medicine
Biomedical image segmentation is essential in clinical practices, offering critical insights for accurate diagnosis and strategic treatment approaches. Nowadays, self-attention-based networks have achieved competitive performance in both natural lang...

Enhancing deep learning pre-trained networks on diabetic retinopathy fundus photographs with SLIC-G.

Medical & biological engineering & computing
Diabetic retinopathy disease contains lesions (e.g., exudates, hemorrhages, and microaneurysms) that are minute to the naked eye. Determining the lesions at pixel level poses a challenge as each pixel does not reflect any semantic entities. Furthermo...

Prediction models for bioavailability of Cu and Zn during composting: Insights into machine learning.

Journal of hazardous materials
Bioavailability assessment of heavy metals in compost products is crucial for evaluating associated environmental risks. However, existing experimental methods are time-consuming and inefficient. The machine learning (ML) method has demonstrated exce...

From GPUs to AI and quantum: three waves of acceleration in bioinformatics.

Drug discovery today
The enormous growth in the amount of data generated by the life sciences is continuously shifting the field from model-driven science towards data-driven science. The need for efficient processing has led to the adoption of massively parallel acceler...

Optimizing Image Enhancement: Feature Engineering for Improved Classification in AI-Assisted Artificial Retinas.

Sensors (Basel, Switzerland)
Artificial retinas have revolutionized the lives of many blind people by enabling their ability to perceive vision via an implanted chip. Despite significant advancements, there are some limitations that cannot be ignored. Presenting all objects capt...

DEMA: A Deep Learning-Enabled Model for Non-Invasive Human Vital Signs Monitoring Based on Optical Fiber Sensing.

Sensors (Basel, Switzerland)
Optical fiber sensors are extensively employed for their unique merits, such as small size, being lightweight, and having strong robustness to electronic interference. The above-mentioned sensors apply to more applications, especially the detection a...