AIMC Topic: Neural Networks, Computer

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ACXNet hybrid deep learning model for cross task mental workload estimation using EEG neural manifolds.

Scientific reports
Mental workload is an interdisciplinary construct that significantly influences human performance, particularly in tasks requiring sustained attention and cognitive processing. Effective mental workload assessment is critical for preventing cognitive...

Artificial intelligence in student management systems to enhance academic performance monitoring and intervention.

Scientific reports
In recent years, the integration of artificial intelligence (AI) in student management systems (SMS) has gained significant attention, particularly for monitoring academic performance and predicting at-risk students. Traditional approaches often lack...

Transfer learning with fuzzy decision support for multi-class lung disease classification: performance analysis of pre-trained CNN models.

Scientific reports
Accurate and efficient classification of lung diseases from medical images remains a significant challenge in computer-aided diagnosis systems. This research presents a novel approach integrating transfer learning techniques with fuzzy decision suppo...

Detection and classification of brain tumor using a hybrid learning model in CT scan images.

Scientific reports
Accurate diagnosis of brain tumors is critical in understanding the prognosis in terms of the type, growth rate, location, removal strategy, and overall well-being of the patients. Among different modalities used for the detection and classification ...

Multi-task deep learning framework combining CNN: vision transformers and PSO for accurate diabetic retinopathy diagnosis and lesion localization.

Scientific reports
Diabetic Retinopathy (DR) continues to be the leading cause of preventable blindness worldwide, and there is an urgent need for accurate and interpretable framework. A Multi View Cross Attention Vision Transformer (MVCAViT) framework is proposed in t...

Enhancing peptide identification in metaproteomics through curriculum learning in deep learning.

Nature communications
Metaproteomics offers a powerful window into the active functions of microbial communities, but accurately identifying peptides remains challenging due to the size and incompleteness of protein databases derived from metagenomes. These databases ofte...

A Vision-Language-Guided Multimodal Fusion Network for Glottic Carcinoma Early Diagnosis: Model Development and Validation Study.

JMIR medical informatics
BACKGROUND: Early diagnosis and intervention in glottic carcinoma (GC) can significantly improve long-term prognosis. However, the accurate diagnosis of early GC is challenging due to its morphological similarity to vocal cord dysplasia, with the dif...

Optimized hybrid RNN-GRU model for predictive diagnosis of cardiovascular disease.

Biomedical physics & engineering express
Cardiovascular disease (CVD) continues to be the leading cause of death for individuals all over the globe, and India bears a disproportionate share of the burden associated with this condition. A hybrid deep learning model that combines Recurrent Ne...

Beyond Divisive Normalization: Scalable Feedforward Networks for Multisensory Integration Across Reference Frames.

The Journal of neuroscience : the official journal of the Society for Neuroscience
The integration of multiple sensory inputs is essential for human perception and action in uncertain environments. This process includes reference frame transformations as different sensory signals are encoded in different coordinate systems. Studies...

Gait recognition using spatio-temporal representation fusion learning network with IMU-based skeleton graph and body partition strategy.

PloS one
The precise recognition of human lower limb movements based on wearable sensors is very important for human-computer interaction. However, the existing methods tend to ignore the dynamic spatial information in the process of executing human lower lim...