AIMC Topic: Algorithms

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Comparative investigation of lung adenocarcinoma and squamous cell carcinoma transcriptome to reveal potential candidate biomarkers: An explainable AI approach.

Computational biology and chemistry
Patients with Non-Small Cell Lung Cancer (NSCLC) present a variety of clinical symptoms, such as dyspnea and chest pain, complicating accurate diagnosis. NSCLC includes subtypes distinguished by histological characteristics, specifically lung adenoca...

GQEO: Nearest neighbor graph-based generalized quadrilateral element oversampling for class-imbalance problem.

Neural networks : the official journal of the International Neural Network Society
The class imbalance problem is one of the difficult factors affecting the performance of traditional classifiers. The oversampling technique is the most common way to solve the class imbalance problem. They alleviate the performance impact of the cla...

Chaotic recurrent neural networks for brain modelling: A review.

Neural networks : the official journal of the International Neural Network Society
Even in the absence of external stimuli, the brain is spontaneously active. Indeed, most cortical activity is internally generated by recurrence. Both theoretical and experimental studies suggest that chaotic dynamics characterize this spontaneous ac...

RePaIR: Repaired pruning at initialization resilience.

Neural networks : the official journal of the International Neural Network Society
Over the past decade, the size of neural network models has gradually increased in both breadth and depth, leading to a growing interest in the application of neural network pruning. Unstructured pruning provides fine-grained sparsity and achieves be...

Improving robustness by action correction via multi-step maximum risk estimation.

Neural networks : the official journal of the International Neural Network Society
Certifying robustness against external uncertainties throughout the control process to reduce the risk of instability is very important. Most existing approaches based on adversarial learning use a fixed parameter to adjust the intensity of adversari...

MrSeNet: Electrocardiogram signal denoising based on multi-resolution residual attention network.

Journal of electrocardiology
Electrocardiography (ECG) is a widely used, non-invasive, and cost-effective diagnostic method that plays a crucial role in the early detection and management of cardiac conditions. However, the ECG signal is easily disrupted by various noise signals...

Highly Responsive Robotic Prosthetic Hand Control Considering Electrodynamic Delay.

Sensors (Basel, Switzerland)
As robots become increasingly integrated into human society, the importance of human-machine interfaces continues to grow. This study proposes a faster and more accurate control system for myoelectric prostheses by considering the Electromechanical D...

Deep Learning Unravels Differences Between Kinematic and Kinetic Gait Cycle Time Series from Two Control Samples of Healthy Children Assessed in Two Different Gait Laboratories.

Sensors (Basel, Switzerland)
We investigate the application of deep learning in comparing gait cycle time series from two groups of healthy children, each assessed in different gait laboratories. Both laboratories used similar gait analysis protocols with minimal differences in ...

Porter 6: Protein Secondary Structure Prediction by Leveraging Pre-Trained Language Models (PLMs).

International journal of molecular sciences
Accurately predicting protein secondary structure (PSSP) is crucial for understanding protein function, which is foundational to advancements in drug development, disease treatment, and biotechnology. Researchers gain critical insights into protein f...

Distributed aggregative optimization with affine coupling constraints.

Neural networks : the official journal of the International Neural Network Society
This paper investigates a distributed aggregative optimization problem subject to coupling affine inequality constraints, in which local objective functions depend not only on their own decision variables but also on an aggregation of all the agents'...