[HTML][HTML] Machine learning for the diagnosis of Parkinson's disease: a review of literature
Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and
assessment of clinical signs, including the characterization of a variety of motor symptoms …
assessment of clinical signs, including the characterization of a variety of motor symptoms …
Imperative role of machine learning algorithm for detection of Parkinson's disease: review, challenges and recommendations
Parkinson's disease (PD) is a neurodegenerative disease that affects the neural, behavioral,
and physiological systems of the brain. This disease is also known as tremor. The common …
and physiological systems of the brain. This disease is also known as tremor. The common …
[HTML][HTML] A secure healthcare 5.0 system based on blockchain technology entangled with federated learning technique
In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a
tremendous speed. Security and privacy are key concerns on the IoMT, owing to the huge …
tremendous speed. Security and privacy are key concerns on the IoMT, owing to the huge …
Machine learning methods for cyber security intrusion detection: Datasets and comparative study
The increase in internet usage brings security problems with it. Malicious software can affect
the operation of the systems and disrupt data confidentiality due to the security gaps in the …
the operation of the systems and disrupt data confidentiality due to the security gaps in the …
Early diagnosis of Parkinson's disease using machine learning algorithms
ZK Senturk - Medical hypotheses, 2020 - Elsevier
Parkinson's disease is caused by the disruption of the brain cells that produce substance to
allow brain cells to communicate with each other, called dopamine. The cells that produce …
allow brain cells to communicate with each other, called dopamine. The cells that produce …
Binary grey wolf optimizer with mutation and adaptive k-nearest neighbour for feature selection in Parkinson's disease diagnosis
RR Rajammal, S Mirjalili, G Ekambaram… - Knowledge-Based …, 2022 - Elsevier
Disease identification and classification relies on Feature Selection (FS) to find the relevant
features for accurate medical diagnosis. FS is an optimization problem solved with the help …
features for accurate medical diagnosis. FS is an optimization problem solved with the help …
A hybrid system for Parkinson's disease diagnosis using machine learning techniques
Parkinson's disease is a neurodegenerative disorder that progresses slowly and its
symptoms appear over time, so its early diagnosis is not easy. A neurologist can diagnose …
symptoms appear over time, so its early diagnosis is not easy. A neurologist can diagnose …
A sound based method for fault detection with statistical feature extraction in UAV motors
A Altinors, F Yol, O Yaman - Applied Acoustics, 2021 - Elsevier
The motors of the Unmanned Aerial Vehicle are critical parts, especially when used in
applications such as military and defense systems. The fact that the brushless DC (BLDC) …
applications such as military and defense systems. The fact that the brushless DC (BLDC) …
Computerized analysis of speech and voice for Parkinson's disease: A systematic review
Background and objective Speech impairment is an early symptom of Parkinson's disease
(PD). This study has summarized the literature related to speech and voice in detecting PD …
(PD). This study has summarized the literature related to speech and voice in detecting PD …
A novel automated tower graph based ECG signal classification method with hexadecimal local adaptive binary pattern and deep learning
Electrocardiography (ECG) signal recognition is one of the popular research topics for
machine learning. In this paper, a novel transformation called tower graph transformation is …
machine learning. In this paper, a novel transformation called tower graph transformation is …