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A recurrent neural network-based framework to non-linearly model behaviorally relevant neural dynamicsMany existing methods for exploring the link between neural ... network architecture and training approach." The researchers trained their RNN-based model using a four-step optimization algorithm.
Magnetic materials are in high demand. They're essential to the energy storage innovations on which electrification depends ...
This paper presents a solution addressing the challenges associated with increased screen time during remote work. Leveraging specialized hardware and convolutional neural networks, our real-time ...
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IEEE Spectrum on MSNNew Machine Vision Is More Energy Efficient—and More HumanA I vision models have improved dramatically over the past decade. Yet these gains have led to neural networks which, though effective, don’t share many characteristics with human vision. For example, ...
In today’s world AI is becoming more popular, to improve the quality, and result of their product. So, we have also taken some groundbreaking step in the realm of plant disease detection, utilizing ...
Musical instrument recognition is the act of counting on machine learning or the application of signal processing to isolate and classify various musical instruments in an audio track. Such capability ...
To analyse the DeepFakes, which are AI-generated synthetic media that impersonate real people and pose substantial threats to digital content security, privacy, and authenticity. This research ...
Cancer, a disease that does not discriminate; impacting people from all walks of life. Numerous types of cancer affect humans, each with distinct characteristics and treatment approaches. One of the ...
In this paper, we propose an end-to-end trainable Convolutional Neural Network (CNN) architecture called the M-net, for segmenting deep (human) brain structures from Magnetic Resonance Images (MRI). A ...
Renesas Electronics Corporation, a supplier of advanced semiconductor solutions, has introduced the RA8P1 microcontroller ...
The sense of touch is essential for robots to perform various daily tasks. Artificial Neural Networks have shown significant promise in advancing robotic tactile learning. However, due to the changing ...
Human motor learning is a neural process essential for acquiring new motor skills and adapting existing ones, which is fundamental to everyday activities. Neurological disorders such as Parkinson’s ...
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