Alireza Daneshkhah
Biographic Data
| ID | 7858118 |
|---|---|
| NAME | Alireza Daneshkhah |
| GIVEN NAMES | Alireza |
| FAMILY NAME | Daneshkhah |
| SIGNATURE | DANESHKHAH A |
| AFFILIATIONS | Coventry University |
| ORCID | 0000-0001-7751-4307 |
| VERIFIED | Yes |
| TOTAL WORKS | 9 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 9 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2006 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Urban heat island dynamics and risk stratification across local climate zones during extreme heat events
The performance of various machine learning methods for Parkinson’s disease recognition
Developing Graph Convolutional Networks and Mutual Information for Arrhythmic Diagnosis Based on Multichannel ECG Signals
Cardiovascular diseases, like arrhythmia, as the leading causes of death in the world, can be automatically diagnosed using an electrocardiogram (ECG). The ECG-based diagnostic has notably resulted in reducing human errors. The main aim of this study is to increase the accuracy of arrhythmia diagnosis and classify various types of arrhythmias in individuals (suffering from cardiovascular diseases) using a novel graph convolutional network (GCN) b…
Examining Type 1 Diabetes Mathematical Models Using Experimental Data
Type 1 diabetes requires treatment with insulin injections and monitoring glucose levels in affected individuals. We explored the utility of two mathematical models in predicting glucose concentration levels in type 1 diabetic mice and determined disease pathways. We adapted two mathematical models, one with β-cells and the other with no β-cell component to determine their capability in predicting glucose concentration and determine type 1 diabet…
Using Machine Learning Algorithms to Develop a Clinical Decision-Making Tool for Covid-19 Inpatients
The proposed probabilistic models were able to predict, using feature selected risk factors, the probability of the mentioned outcomes. Overall, our findings demonstrate reliable, multivariable, quantitative predictive models for four outcomes, which utilise readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as risk stratification and c…
Economic Evaluation of Mental Health Effects of Flooding Using Bayesian Networks
The appraisal of appropriate levels of investment for devising flooding mitigation and to support recovery interventions is a complex and challenging task. Evaluation must account for social, political, environmental and other conditions, such as flood state expectations and local priorities. The evaluation method should be able to quickly identify evolving investment needs as the incidence and magnitude of flood events continue to grow. Quantifi…
The prevalence of stress, anxiety and depression within front-line healthcare workers caring for Covid-19 patients
The prevalence of sleep disturbances among physicians and nurses facing the Covid-19 patients
Uncertain Judgements
Elicitation is the process of extracting expert knowledge about some unknown quantity or quantities, and formulating that information as a probability distribution. Elicitation is important in situations, such as modelling the safety of nuclear installations or assessing the risk of terrorist attacks, where expert knowledge is essentially the only source of good information. It also plays a major role in other contexts by augmenting scarce observ…
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Uncertain Judgements
Elicitation is the process of extracting expert knowledge about some unknown quantity or quantities, and formulating that information as a probability distribution. Elicitation is important in situations, such as modelling the safety of nuclear installations or assessing the risk of terrorist attacks, where expert knowledge is essentially the only source of good information. It also plays a major role in other contexts by augmenting scarce observ…
The prevalence of stress, anxiety and depression within front-line healthcare workers caring for Covid-19 patients
The prevalence of sleep disturbances among physicians and nurses facing the Covid-19 patients
Using Machine Learning Algorithms to Develop a Clinical Decision-Making Tool for Covid-19 Inpatients
The proposed probabilistic models were able to predict, using feature selected risk factors, the probability of the mentioned outcomes. Overall, our findings demonstrate reliable, multivariable, quantitative predictive models for four outcomes, which utilise readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as risk stratification and c…
Economic Evaluation of Mental Health Effects of Flooding Using Bayesian Networks
The appraisal of appropriate levels of investment for devising flooding mitigation and to support recovery interventions is a complex and challenging task. Evaluation must account for social, political, environmental and other conditions, such as flood state expectations and local priorities. The evaluation method should be able to quickly identify evolving investment needs as the incidence and magnitude of flood events continue to grow. Quantifi…
Developing Graph Convolutional Networks and Mutual Information for Arrhythmic Diagnosis Based on Multichannel ECG Signals
Cardiovascular diseases, like arrhythmia, as the leading causes of death in the world, can be automatically diagnosed using an electrocardiogram (ECG). The ECG-based diagnostic has notably resulted in reducing human errors. The main aim of this study is to increase the accuracy of arrhythmia diagnosis and classify various types of arrhythmias in individuals (suffering from cardiovascular diseases) using a novel graph convolutional network (GCN) b…
Examining Type 1 Diabetes Mathematical Models Using Experimental Data
Type 1 diabetes requires treatment with insulin injections and monitoring glucose levels in affected individuals. We explored the utility of two mathematical models in predicting glucose concentration levels in type 1 diabetic mice and determined disease pathways. We adapted two mathematical models, one with β-cells and the other with no β-cell component to determine their capability in predicting glucose concentration and determine type 1 diabet…
The performance of various machine learning methods for Parkinson’s disease recognition
Urban heat island dynamics and risk stratification across local climate zones during extreme heat events
Computer Science (3 works) · Internal Medicine (3 works) · Medicine (3 works) · Artificial Intelligence (2 works) · Data mining (2 works) · Mathematics (2 works) · Psychology (2 works) · Adjacency matrix (1 work) · Agricultural risk and resilience (1 work) · Akaike information criterion (1 work)