A survey of detection of Parkinson's disease using artificial intelligence models with multiple modalities and various data preprocessing techniques.

Parkinson's disease (PD) is a neurodegenerative brain disorder that causes symptoms such as tremors, sleeplessness, behavioral problems, sensory abnormalities, and impaired mobility, according to the World Health Organization (WHO). Artificial intelligence, machine learning (ML), and deep learning (DL) have been used in recent studies (2015-2023) to improve PD diagnosis by categorizing patients and healthy controls based on similar clinical presentations. This study investigates several datasets, modalities, and data preprocessing techniques from the collected data.

Artificial neural network based models for predicting the effluent quality of a combined upflow anaerobic sludge blanket and facultative pond: Performance evaluation and comparison of different algorithms.

The main objective of this work was to test different artificial neural network (ANN) based models, i.e. the ANN feed forward back propagation (ANN-FFBP), deep feed forward backpropagation (DFFBP), and deep cascade forward back propagation (DCFBP) models, for predicting the effluent quality of an upflow anaerobic sludge blanket-facultative pond (UASB-FP) system.

Automatic identification of insomnia using optimal antisymmetric biorthogonal wavelet filter bank with ECG signals.

Sleep is a fundamental human physiological activity required for adequate working of the human body. Sleep disorders such as sleep movement disorders, nocturnal front lobe epilepsy, insomnia, and narcolepsy are caused due to low sleep quality. Insomnia is one such sleep disorder where a person has difficulty in getting quality sleep.

Wind Turbine Gearbox Condition Monitoring Based on Class of Support Vector Regression Models and Residual Analysis.

The intelligent condition monitoring of wind turbines reduces their downtime and increases reliability. In this manuscript, a feature selection-based methodology that essentially works on regression models is used for identifying faulty scenarios. Supervisory control and data acquisition (SCADA) data with 1009 samples from one year and one month before failure are considered.

Biotransformation of flower waste composting: Optimization of waste combinations using response surface methodology.

Flower waste (FW) is disposed off in the rivers or mixed with solid waste for landfilling that pollutes the environment and causes harmful effects on human health and aquatic life. It is rich in nutrient content and easily converted into the compost. Therefore, the objective of the present research was to optimize the combinations of flower waste and cow dung during agitated pile composting using response surface methodology.