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Alex Shenfield

Publications
10
Citations
94
Publications by Year
20122024
Co-Authors:
Show All Co-Authors (15)
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Alex Shenfield

Affiliation

Sheffield Hallam University
Advanced Food Innovation Centre (AFIC)

Publication Topics

Biography

I am currently Professor of Machine Learning at Sheffield Hallam University. I joined Sheffield Hallam University in November 2013 from Manchester Metropolitan University and am a Senior Member of the IEEE (SMIEEE) and a Fellow of the Higher Education Academy (FHEA). I am also the Digital Connectivity research theme lead for the National Centre of Excellence for Food Engineering (NCEFE). My main research interests are in the field of machine learning and particularly in its application to real-world problems in image processing and pattern recognition, healthcare, and Industry 4.0.
Publications
10
Citations
94
Publications by Year
20122024
Co-Authors:
Show All Co-Authors (15)
Author's Published Works

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AI-Based Surrogate Models of Digital Twins for Industrial Processes

Emmanuel Lwele;Alex Shenfield

2024 IEEE Industrial Electronics and Applications Conference (IEACon)
Year: 2024 | Conference Paper |
Digital twins represent virtual replicas of physical systems, integrating real-time data and advanced analytics to monitor, simulate, and optimize industrial processes. This research delves into the application of AI-based surrogate models to improve the efficiency and accuracy of digital twins for industrial processes. The study employs machine learning techniques to develop computationally effic...Show More

AI-Based Surrogate Models of Digital Twins for Industrial Processes

Emmanuel Lwele;Alex Shenfield

Year: 2024 | Conference Paper |

Anomaly Detection for the Internet-of-Medical-Things

Alan Reji;Bernardi Pranggono;Jims Marchang;Alex Shenfield

2023 IEEE International Conference on Communications Workshops (ICC Workshops)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
Cybersecurity for the Internet of Medical Things (IoMT) is a very concerning issue because of emerging cyber threats and security incidents targeting IoMT devices all over the world. The healthcare system has near-zero tolerance for inexplicability. In this paper, we propose a machine learning-based anomaly detection for the IoMT and evaluate the performance using a realistic public dataset. We im...Show More

Anomaly Detection for the Internet-of-Medical-Things

Alan Reji;Bernardi Pranggono;Jims Marchang;Alex Shenfield

Year: 2023 | Conference Paper |

Machine learning for mortality risk prediction with changing patient demographics

Richard Wainwright;Alex Shenfield

2023 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
Year: 2023 | Conference Paper |
Over the last 25-30 years there has been significant work carried out in producing risk prediction models for patients admitted to intensive care units. The most recent of these models in widespread use is the Intensive Care National Audit and Research Centre (ICNARC) model developed in 2007 which uses data from more than 230,000 admissions to UK intensive care units to develop and validate a UK b...Show More

Machine learning for mortality risk prediction with changing patient demographics

Richard Wainwright;Alex Shenfield

Year: 2023 | Conference Paper |

Accurate Nuclei Segmentation in Breast Cancer Tumour Biopsies

Surya Kasturi;William T. Tran;Alex Shenfield

2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
Year: 2022 | Conference Paper |
Cited by: Papers (3)
Breast cancer identification is a arduous process and diagnosing it using Haematoxylin and Eosin (H&E) stained pathology images is a significant challenge, with pathologists struggling to segment cancer nuclei accurately. This study will evaluate the efficacy of different methods utilising deep learning techniques for breast cancer nuclei segmentation, with a particular emphasis on U-Net architect...Show More

Accurate Nuclei Segmentation in Breast Cancer Tumour Biopsies

Surya Kasturi;William T. Tran;Alex Shenfield

Year: 2022 | Conference Paper |

Deep Learning Meets Cognitive Radio: Predicting Future Steps

Alex Shenfield;Zaheer Khan;Hamed Ahmadi

2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)
Year: 2020 | Conference Paper |
Cited by: Papers (2)
Learning the channel occupancy patterns to reuse the underutilised spectrum frequencies without interfering with the incumbent is a promising approach to overcome the spectrum limitations. In this work we proposed a Deep Learning (DL) approach to learn the channel occupancy model and predict its availability in the next time slots. Our results show that the proposed DL approach outperforms existin...Show More

Deep Learning Meets Cognitive Radio: Predicting Future Steps

Alex Shenfield;Zaheer Khan;Hamed Ahmadi

Year: 2020 | Conference Paper |

Voltage Sag Estimation in Sparsely Monitored Power Systems Based on Deep Learning and System Area Mapping

Huilian Liao;Jovica V. Milanović;Marcos Rodrigues;Alex Shenfield

IEEE Transactions on Power Delivery
Year: 2018 | Volume: 33, Issue: 6 | Journal Article |
Cited by: Papers (58)
This paper proposes a voltage sag estimation approach based on a deep convolutional neural network. The proposed approach estimates the sag magnitude at unmonitored buses regardless of the system operating conditions and fault location and characteristics. The concept of system area mapping is also introduced via the use of bus matrix, which maps different patches in input matrix to various areas ...Show More

Voltage Sag Estimation in Sparsely Monitored Power Systems Based on Deep Learning and System Area Mapping

Huilian Liao;Jovica V. Milanović;Marcos Rodrigues;Alex Shenfield

Year: 2018 | Volume: 33, Issue: 6 | Journal Article |

A novel hybrid differential evolution strategy applied to classifier design for mortality prediction in adult critical care admissions

Alex Shenfield;Marcos Rodrigues;Hossam Nooreldeen;Jeronimo Moreno-Cuesta

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
Year: 2017 | Conference Paper |
Cited by: Papers (2)
The optimisation of classifier performance in pattern recognition and medical prognosis tasks is a complex and poorly miderstood problem. Classifier performance is greatly affected by the choice of artificial neural network architecture and starting weights and biases - yet there exists very little guidance in the literature as to how to choose these parameters. Recently evolutionary artificial ne...Show More

A novel hybrid differential evolution strategy applied to classifier design for mortality prediction in adult critical care admissions

Alex Shenfield;Marcos Rodrigues;Hossam Nooreldeen;Jeronimo Moreno-Cuesta

Year: 2017 | Conference Paper |

Multi-objective evolution of artificial neural networks in multi-class medical diagnosis problems with class imbalance

Alex Shenfield;Shahin Rostami

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
Year: 2017 | Conference Paper |
Cited by: Papers (15)
This paper proposes a novel multi-objective optimisation approach to solving both the problem of finding good structural and parametric choices in an ANN and the problem of training a classifier with a heavily skewed data set. The state-of-the-art CMA-PAES-HAGA multi-objective evolutionary algorithm [41] is used to simultaneously optimise the structure, weights, and biases of a population of ANNs ...Show More

Multi-objective evolution of artificial neural networks in multi-class medical diagnosis problems with class imbalance

Alex Shenfield;Shahin Rostami

Year: 2017 | Conference Paper |

A multi objective approach to evolving artificial neural networks for coronary heart disease classification

Alex Shenfield;Shahin Rostami

2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
Year: 2015 | Conference Paper |
Cited by: Papers (6)
The optimisation of the accuracy of classifiers in pattern recognition is a complex problem that is often poorly understood. Whilst numerous techniques exist for the optimisation of weights in artificial neural networks (e.g. the Widrow-Hoff least mean squares algorithm and back propagation techniques), there do not exist any hard and fast rules for choosing the structure of an artificial neural n...Show More

A multi objective approach to evolving artificial neural networks for coronary heart disease classification

Alex Shenfield;Shahin Rostami

Year: 2015 | Conference Paper |

CMA-PAES: Pareto archived evolution strategy using covariance matrix adaptation for Multi-Objective Optimisation

Shahin Rostami;Alex Shenfield

2012 12th UK Workshop on Computational Intelligence (UKCI)
Year: 2012 | Conference Paper |
Cited by: Papers (6)
The quality of Evolutionary Multi-Objective Optimisation (EMO) approximation sets can be measured by their proximity, diversity and pertinence. In this paper we introduce a modular and extensible Multi-Objective Evolutionary Algorithm (MOEA) capable of converging to the Pareto-optimal front in a minimal number of function evaluations and producing a diverse approximation set. This algorithm, calle...Show More

CMA-PAES: Pareto archived evolution strategy using covariance matrix adaptation for Multi-Objective Optimisation

Shahin Rostami;Alex Shenfield

Year: 2012 | Conference Paper |

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