AIMC Topic: Neural Networks, Computer

Clear Filters Showing 5821 to 5830 of 31376 articles

Automatic Sleep Stage Classification Using Nasal Pressure Decoding Based on a Multi-Kernel Convolutional BiLSTM Network.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Sleep quality is an essential parameter of a healthy human life, while sleep disorders such as sleep apnea are abundant. In the investigation of sleep and its malfunction, the gold-standard is polysomnography, which utilizes an extensive range of var...

Neural patient-specific 3D-2D registration in laparoscopic liver resection.

International journal of computer assisted radiology and surgery
PURPOSE: Augmented reality guidance in laparoscopic liver resection requires the registration of a preoperative 3D model to the intraoperative 2D image. However, 3D-2D liver registration poses challenges owing to the liver's flexibility, particularly...

Brief Review and Primer of Key Terminology for Artificial Intelligence and Machine Learning in Hypertension.

Hypertension (Dallas, Tex. : 1979)
Recent breakthroughs in artificial intelligence (AI) have caught the attention of many fields, including health care. The vision for AI is that a computer model can process information and provide output that is indistinguishable from that of a human...

A depth analysis of recent innovations in non-invasive techniques using artificial intelligence approach for cancer prediction.

Medical & biological engineering & computing
The fight against cancer, a relentless global health crisis, emphasizes the urgency for efficient and automated early detection methods. To address this critical need, this review assesses recent advances in non-invasive cancer prediction techniques,...

A novel fractional-order memristive Hopfield neural network for traveling salesman problem and its FPGA implementation.

Neural networks : the official journal of the International Neural Network Society
This paper proposes a novel fractional-order memristive Hopfield neural network (HNN) to address traveling salesman problem (TSP). Fractional-order memristive HNN can efficiently converge to a globally optimal solution, while conventional HNN tends t...

Adaptive Decision Spatio-temporal neural ODE for traffic flow forecasting with Multi-Kernel Temporal Dynamic Dilation Convolution.

Neural networks : the official journal of the International Neural Network Society
Traffic flow prediction is crucial for efficient traffic management. It involves predicting vehicle movement patterns to reduce congestion and enhance traffic flow. However, the highly non-linear and complex patterns commonly observed in traffic flow...

Optimized multi-head self-attention and gated-dilated convolutional neural network for quantum key distribution and error rate reduction.

Network (Bristol, England)
Quantum key distribution (QKD) is a secure communication method that enables two parties to securely exchange a secret key. The secure key rate is a crucial metric for assessing the efficiency and practical viability of a QKD system. There are severa...

Streak artefact removal in x-ray dark-field computed tomography using a convolutional neural network.

Medical physics
BACKGROUND: Computed tomography (CT) relies on the attenuation of x-rays, and is, hence, of limited use for weakly attenuating organs of the body, such as the lung. X-ray dark-field (DF) imaging is a recently developed technology that utilizes x-ray ...

Convolutional neural network for automated tooth segmentation on intraoral scans.

BMC oral health
BACKGROUND: Tooth segmentation on intraoral scanned (IOS) data is a prerequisite for clinical applications in digital workflows. Current state-of-the-art methods lack the robustness to handle variability in dental conditions. This study aims to propo...

Early detection of cardiorespiratory complications and training monitoring using wearable ECG sensors and CNN.

BMC medical informatics and decision making
This research study demonstrates an efficient scheme for early detection of cardiorespiratory complications in pandemics by Utilizing Wearable Electrocardiogram (ECG) sensors for pattern generation and Convolution Neural Networks (CNN) for decision a...