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AI-based epileptic seizure detection and prediction in internet of healthcare things: a systematic review
Epilepsy is a neurological condition affecting around 50 million individuals worldwide,
reported by the World Health Organization. This is identified as a hypersensitive disease by …
reported by the World Health Organization. This is identified as a hypersensitive disease by …
Machine learning algorithms for epilepsy detection based on published EEG databases: A systematic review
Epilepsy is the only neurological condition for which electroencephalography (EEG) is the
primary diagnostic and important prognostic clinical tool. However, the manual inspection of …
primary diagnostic and important prognostic clinical tool. However, the manual inspection of …
Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies
Epileptic seizures are one of the most crucial neurological disorders, and their early
diagnosis will help the clinicians to provide accurate treatment for the patients. The …
diagnosis will help the clinicians to provide accurate treatment for the patients. The …
Epileptic seizures detection in EEG signals using fusion handcrafted and deep learning features
Epilepsy is a brain disorder disease that affects people's quality of life.
Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper …
Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper …
Epileptic seizure classification based on random neural networks using discrete wavelet transform for electroencephalogram signal decomposition
An epileptic seizure is a brief episode of symptoms and signs caused by excessive electrical
activity in the brain. One of the major chronic neurological diseases, epilepsy, affects …
activity in the brain. One of the major chronic neurological diseases, epilepsy, affects …
Automated FBSE-EWT based learning framework for detection of epileptic seizures using time-segmented EEG signals
Epilepsy is a neurological disorder that has severely affected many people's lives across the
world. Electroencephalogram (EEG) signals are used to characterize the brain's state and …
world. Electroencephalogram (EEG) signals are used to characterize the brain's state and …
Sensor-based human activity recognition using deep stacked multilayered perceptron model
The recent development of machines exhibiting intelligent characteristics involves numerous
techniques including computer hardware and software architecture development. Many …
techniques including computer hardware and software architecture development. Many …
A machine learning-based model to estimate PM2. 5 concentration levels in Delhi's atmosphere
During the last many years, the air quality of the capital city of India, Delhi had been
hazardous. A large number of people have been diagnosed with Asthma and other …
hazardous. A large number of people have been diagnosed with Asthma and other …
Intelligent multiobjective optimization for high-performance concrete mix proportion design: A hybrid machine learning approach
S Yang, H Chen, Z Feng, Y Qin, J Zhang, Y Cao… - … Applications of Artificial …, 2023 - Elsevier
The concrete mix proportion design process is complex but important, especially in cold,
ocean, underground and other complex engineering environments. In this study, a hybrid …
ocean, underground and other complex engineering environments. In this study, a hybrid …
Potential application of metal-organic frameworks (MOFs) for hydrogen storage: Simulation by artificial intelligent techniques
Metal-organic frameworks are a new class of materials for hydrogen adsorption/storage
applications. The hydrogen storage capacity of this structure is typically related to pressure …
applications. The hydrogen storage capacity of this structure is typically related to pressure …