Unraveling symptom interplay: a network analysis of procrastination in gifted students.

Background: This study explores the intricate web of symptoms experienced by academically gifted high school students, focusing on procrastination, rumination, perfectionism, and cognitive flexibility. The well-being of these gifted adolescents remains a pivotal concern, and understanding the dynamics of these symptoms is vital.

Methods: A diverse sample of 207 academically gifted high school students from Mashhad, Iran, participated in this study.

Tuning attention based long-short term memory neural networks for Parkinson's disease detection using modified metaheuristics.

Parkinson's disease (PD) is a progressively debilitating neurodegenerative disorder that primarily affects the dopaminergic system in the basal ganglia, impacting millions of individuals globally. The clinical manifestations of the disease include resting tremors, muscle rigidity, bradykinesia, and postural instability. Diagnosis relies mainly on clinical evaluation, lacking reliable diagnostic tests and being inherently imprecise and subjective.

A novel firefly algorithm approach for efficient feature selection with COVID-19 dataset.

Feature selection is one of the most important challenges in machine learning and data science. This process is usually performed in the data preprocessing phase, where the data is transformed to a proper format for further operations by machine learning algorithm. Many real-world datasets are highly dimensional with many irrelevant, even redundant features.

Feature Selection by Hybrid Brain Storm Optimization Algorithm for COVID-19 Classification.

A large number of features lead to very high-dimensional data. The feature selection method reduces the dimension of data, increases the performance of prediction, and reduces the computation time. Feature selection is the process of selecting the optimal set of input features from a given data set in order to reduce the noise in data and keep the relevant features.

Hybridized sine cosine algorithm with convolutional neural networks dropout regularization application.

Deep learning has recently been utilized with great success in a large number of diverse application domains, such as visual and face recognition, natural language processing, speech recognition, and handwriting identification. Convolutional neural networks, that belong to the deep learning models, are a subtype of artificial neural networks, which are inspired by the complex structure of the human brain and are often used for image classification tasks. One of the biggest challenges in all deep neural networks is the overfitting issue, which happens when the model performs well on the training data, but fails to make accurate predictions for the new data that is fed into the model.

Modified firefly algorithm for workflow scheduling in cloud-edge environment.

Edge computing is a novel technology, which is closely related to the concept of Internet of Things. This technology brings computing resources closer to the location where they are consumed by end-users-to the edge of the cloud. In this way, response time is shortened and lower network bandwidth is utilized.