Machine learning and deep learning approach for medical image analysis: diagnosis to detection
Computer-aided detection using Deep Learning (DL) and Machine Learning (ML) shows
tremendous growth in the medical field. Medical images are considered as the actual origin …
tremendous growth in the medical field. Medical images are considered as the actual origin …
An automated conversation system using natural language processing (nlp) chatbot in python
The purpose of this project is to build a ChatBot that utilises NLP (Natural Language
Processing) and assists customers. A ChatBot is an automated conversation system that …
Processing) and assists customers. A ChatBot is an automated conversation system that …
An efficient feature selection and explainable classification method for EEG-based epileptic seizure detection
Epilepsy is a prevalent neurological disorder that poses life-threatening emergencies. Early
electroencephalogram (EEG) seizure detection can mitigate the risks and aid in the …
electroencephalogram (EEG) seizure detection can mitigate the risks and aid in the …
EEG Datasets in Machine Learning Applications of Epilepsy Diagnosis and Seizure Detection
Epilepsy is a common non-communicable, group of neurological disorders affecting more
than 50 million individuals worldwide. Researchers are working to automatically detect …
than 50 million individuals worldwide. Researchers are working to automatically detect …
EEG-based motor imagery channel selection and classification using hybrid optimization and two-tier deep learning
Brain–computer interface (BCI) technology holds promise for individuals with profound
motor impairments, offering the potential for communication and control. Motor imagery (MI) …
motor impairments, offering the potential for communication and control. Motor imagery (MI) …
Software advancements in automatic epilepsy diagnosis and seizure detection: 10-year review
Epilepsy is a chronic neurological disorder that may be diagnosed and monitored using
routine diagnostic tests like Electroencephalography (EEG). However, manual introspection …
routine diagnostic tests like Electroencephalography (EEG). However, manual introspection …
An AI-inspired spatio-temporal neural network for EEG-based emotional status
FM Alotaibi - Sensors, 2023 - mdpi.com
The accurate identification of the human emotional status is crucial for an efficient human–
robot interaction (HRI). As such, we have witnessed extensive research efforts made in …
robot interaction (HRI). As such, we have witnessed extensive research efforts made in …
Improved patient-independent seizure detection using hybrid feature extraction approach with atomic function-based wavelets
The rapidly rising seizure cases and poor patient-to-neurologist ratio necessitate the
development of an efficient automatic seizure detection system. The most commonly used …
development of an efficient automatic seizure detection system. The most commonly used …
EEG Motor Imagery Classification by Feature Extracted Deep 1D-CNN and Semi-Deep Fine-Tuning
The main goal of this paper is to introduce a Motor Imagery (MI) classification system for
electroencephalography (EEG) that is extremely precise. To achieve this goal, we propose …
electroencephalography (EEG) that is extremely precise. To achieve this goal, we propose …
Automated characterization and detection of fibromyalgia using slow wave sleep EEG signals with glucose pattern and D'hondt pooling technique
Fibromyalgia is a soft tissue rheumatism with significant qualitative and quantitative impact
on sleep macro and micro architecture. The primary objective of this study is to analyze and …
on sleep macro and micro architecture. The primary objective of this study is to analyze and …