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
A Shoeibi, N Ghassemi, M Khodatars… - … Signal Processing and …, 2022 - Elsevier
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 …
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 …
[HTML][HTML] 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 …
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 …
[HTML][HTML] 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 …
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 …
Deep air quality forecasts: suspended particulate matter modeling with convolutional neural and long short-term memory networks
Public health risks arising from airborne pollutants, eg, Total Suspended Particulate (TSP)
matter, can significantly elevate ongoing and future healthcare costs. The chaotic behaviour …
matter, can significantly elevate ongoing and future healthcare costs. The chaotic behaviour …
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 …