AIMC Topic: Neural Networks, Computer

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Depression Identification Using EEG Signals via a Hybrid of LSTM and Spiking Neural Networks.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Depression severity can be classified into distinct phases based on the Beck depression inventory (BDI) test scores, a subjective questionnaire. However, quantitative assessment of depression may be attained through the examination and categorization...

Artificial intelligence systems in dental shade-matching: A systematic review.

Journal of prosthodontics : official journal of the American College of Prosthodontists
PURPOSE: Uses for artificial intelligence (AI) are being explored in contemporary dentistry, but artificial intelligence in dental shade-matching has not been systematically reviewed and evaluated. The purpose of this systematic review was to evaluat...

Geographical discrimination of Asian red pepper powders using H NMR spectroscopy and deep learning-based convolution neural networks.

Food chemistry
This study investigated an innovative approach to discriminate the geographical origins of Asian red pepper powders by analyzing one-dimensional H NMR spectra through a deep learning-based convolution neural network (CNN). H NMR spectra were collecte...

Domain-informed variational neural networks and support vector machines based leakage detection framework to augment self-healing in water distribution networks.

Water research
The reduction of water leakage is essential for ensuring sustainable and resilient water supply systems. Despite recent investments in sensing technologies, pipe leakage remains a significant challenge for the water sector, particularly in developed ...

Bimodal Feature Analysis with Deep Learning for Autism Spectrum Disorder Detection.

International journal of neural systems
Autism Spectrum Disorder (ASD) is a complex and heterogeneous neurodevelopmental disorder which affects a significant proportion of the population, with estimates suggesting that about 1 in 100 children worldwide are affected by ASD. This study intro...

BASE: Brain Age Standardized Evaluation.

NeuroImage
Brain age, most commonly inferred from T1-weighted magnetic resonance images (T1w MRI), is a robust biomarker of brain health and related diseases. Superior accuracy in brain age prediction, often falling within a 2-3 year range, is achieved predomin...

CNN-Based Facial Expression Recognition with Simultaneous Consideration of Inter-Class and Intra-Class Variations.

Sensors (Basel, Switzerland)
Facial expression recognition is crucial for understanding human emotions and nonverbal communication. With the growing prevalence of facial recognition technology and its various applications, accurate and efficient facial expression recognition has...

Implications of capacity-limited, generative models for human vision.

The Behavioral and brain sciences
Although discriminative deep neural networks are currently dominant in cognitive modeling, we suggest that capacity-limited, generative models are a promising avenue for future work. Generative models tend to learn both local and global features of s...

Even deeper problems with neural network models of language.

The Behavioral and brain sciences
We recognize today's deep neural network (DNN) models of language behaviors as engineering achievements. However, what we know intuitively and scientifically about language shows that what DNNs are and how they are trained on bare texts, makes them p...

Statistical prediction alone cannot identify good models of behavior.

The Behavioral and brain sciences
The dissociation between statistical prediction and scientific explanation advanced by Bowers et al. for studies of vision using deep neural networks is also observed in several other domains of behavior research, and is in fact unavoidable when fitt...