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Regularized Contrastive Masked Autoencoder Model for Machinery Anomaly Detection Using Diffusion-Based Data Augmentation (2023)
Journal Article
Zahedi, E., Saraee, M., Masoumi, F. S., & Yazdinejad, M. (in press). Regularized Contrastive Masked Autoencoder Model for Machinery Anomaly Detection Using Diffusion-Based Data Augmentation. #Journal not on list, 16(9), 431. https://doi.org/10.3390/a16090431

Unsupervised anomalous sound detection, especially self-supervised methods, plays a crucial role in differentiating unknown abnormal sounds of machines from normal sounds. Self-supervised learning can be divided into two main categories: Generative a... Read More about Regularized Contrastive Masked Autoencoder Model for Machinery Anomaly Detection Using Diffusion-Based Data Augmentation.

Immediate and accessible grief treatment via cold reading chatbots (2023)
Thesis
Tracey, P. (2023). Immediate and accessible grief treatment via cold reading chatbots. (Thesis). University of Salford

This thesis presents a potential solution for prolonged grief disorder (PGD) sufferers waiting for psychological aid, by simulating the cold reading process through a chatbot model. PGD occurs in approximately 10% of all bereavements, and there is cu... Read More about Immediate and accessible grief treatment via cold reading chatbots.

Real Time Crime Prediction Using Social Media (2023)
Thesis
Jimoh, F. (2023). Real Time Crime Prediction Using Social Media. (Thesis). University of Salford

There is no doubt that crime is on the increase and has a detrimental influence on a nation's economy despite several attempts of studies on crime prediction to minimise crime rates. Historically, data mining techniques for crime prediction models of... Read More about Real Time Crime Prediction Using Social Media.

DeepClean : a robust deep learning technique for autonomous vehicle camera data privacy (2022)
Journal Article
Adeboye, O., Dargahi, T., Babaie, M., Saraee, M., & Yu, C. (2022). DeepClean : a robust deep learning technique for autonomous vehicle camera data privacy. IEEE Access, https://doi.org/10.1109/ACCESS.2022.3222834

Autonomous Vehicles (AVs) are equipped with several sensors which produce various forms of data, such as geo-location, distance, and camera data. The volume and utility of these data, especially camera data, have contributed to the advancement of h... Read More about DeepClean : a robust deep learning technique for autonomous vehicle camera data privacy.

DeepClean: a robust deep learning approach for autonomous vehicle camera data privacy (2022)
Thesis
Adeboye, O. DeepClean: a robust deep learning approach for autonomous vehicle camera data privacy. (Thesis). University of Salford

Autonomous Vehicles (AVs) generate several forms of tracking data, such as geolocation, distance, and camera data. The utility of these data, especially camera data for computer vision projects, has contributed to the advancement of high-performance... Read More about DeepClean: a robust deep learning approach for autonomous vehicle camera data privacy.

Machine learning-based optimized link state routing protocol for D2D communication in 5G/B5G (2022)
Presentation / Conference
Bunu, S., Saraee, M., & Alani, O. (2022, September). Machine learning-based optimized link state routing protocol for D2D communication in 5G/B5G. Presented at The 4th International Conference on Electrical Engineering and Informatics (ICELTICs) 2022, Banda Aceh, Indonesia

Device to Device (D2D) communication in Fifth Generation (5G) and unavoidable in Beyond Fifth Generation (B5G) technology is designed to increase network capacity by offloading backhaul links and base stations traffic and improving the performanc... Read More about Machine learning-based optimized link state routing protocol for D2D communication in 5G/B5G.

A framework for dynamic heterogeneous information networks change discovery based on knowledge engineering and data mining methods (2021)
Thesis
Umer, S. A framework for dynamic heterogeneous information networks change discovery based on knowledge engineering and data mining methods. (Thesis). University of Salford

Information Networks are collections of data structures that are used to model interactions in social and living phenomena. They can be either homogeneous or heterogeneous and static or dynamic depending upon the type and nature of relations between... Read More about A framework for dynamic heterogeneous information networks change discovery based on knowledge engineering and data mining methods.

Towards forecasting and prediction of faults in electricity distribution network : a novel data mining & machine learning approach (2020)
Thesis
Silva, H. Towards forecasting and prediction of faults in electricity distribution network : a novel data mining & machine learning approach. (Thesis). University of Salford

The electricity supply system includes a large-scale power generation installation and a convoluted network of electrical circuits that work together to efficiently and reliably supply electricity to consumers. Faults in the electricity distribution... Read More about Towards forecasting and prediction of faults in electricity distribution network : a novel data mining & machine learning approach.

Predicting average annual electricity outage using electricity distribution network operator's performance indicators (2020)
Presentation / Conference
Silva, C., & Saraee, M. (2020, February). Predicting average annual electricity outage using electricity distribution network operator's performance indicators. Presented at 2020 Advances in Science and Engineering Technology International Conferences (ASET), Dubai, United Arab Emirates

Electricity Distribution network operators (DNO) may receive a monetary reward or have a penalty reliant on their performance against the target set by the regulators. Customer minutes lost (CML) is one of the primary performance indicators which lea... Read More about Predicting average annual electricity outage using electricity distribution network operator's performance indicators.

Electricity distribution network : seasonality and the dynamics of equipment failures related network faults (2020)
Presentation / Conference
Silva, C., & Saraee, M. (2020, February). Electricity distribution network : seasonality and the dynamics of equipment failures related network faults. Presented at 2020 Advances in Science and Engineering Technology International Conferences (ASET), Dubai, United Arab Emirates

Power systems are inclined to frequent failures due to equipment malfunctions in the network. Equipment malfunctions can occur in any of the equipment in the network such as transformers, switchgear, overground cables or underground cables. Any failu... Read More about Electricity distribution network : seasonality and the dynamics of equipment failures related network faults.

Predictive modelling in mental health : a data science approach (2019)
Presentation / Conference
Saraee, M., Silva, H., & Saraee, M. (2019, November). Predictive modelling in mental health : a data science approach. Presented at 2019 IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (IEEE CSUDET), Penang, Malaysia

In national and local level, understanding of factors associated with public health issues like mental health is paramount important. This framework evaluation aims to use the decision Tree technique to improve the degree of understanding of the ment... Read More about Predictive modelling in mental health : a data science approach.

Predicting road traffic accident severity using decision trees and time-series calendar heatmaps (2019)
Presentation / Conference
Silva, H., & Saraee, M. (2019, November). Predicting road traffic accident severity using decision trees and time-series calendar heatmaps. Presented at The 6th IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (2019 IEEE CSUDET), Penang, Malaysia

The European Commission estimates that around 135,000 people are seriously injured on Europe's roads each year. The road traffic injuries are a significant but neglected global general public health problem, needing rigorous attempts for effectiv... Read More about Predicting road traffic accident severity using decision trees and time-series calendar heatmaps.

Analyzing frequent patterns in data streams using a dynamic compact stream pattern algorithm (2019)
Thesis
Oyewale, A. (in press). Analyzing frequent patterns in data streams using a dynamic compact stream pattern algorithm. (Thesis). University of Salford

As a result of modern technology and the advancement in communication, a large amount of data streams are continually generated from various online applications, devices and sources. Mining frequent patterns from these streams of data is now an impor... Read More about Analyzing frequent patterns in data streams using a dynamic compact stream pattern algorithm.

Features in extractive supervised single-document summarization : case of Persian news (2019)
Journal Article
Rezaei, H., Moeinzadeh, S., Shahgholian, A., & Saraee, M. (2019). Features in extractive supervised single-document summarization : case of Persian news

Text summarization has been one of the most challenging areas of research in NLP. Much effort has been made to overcome this challenge by using either the abstractive or extractive methods. Extractive methods are more popular, due to their simplicity... Read More about Features in extractive supervised single-document summarization : case of Persian news.

Natural Language Processing and Information Systems : 24th International Conference on applications of natural language to information systems, NLDB 2019, Salford, UK, June 26–28, 2019, proceedings (2019)
Book
24th International Conference on applications of natural language to information systems, NLDB 2019, Salford, UK, June 26–28, 2019, proceedings. Switzerland: Springer Nature Switzerland. https://doi.org/10.1007/978-3-030-23281-8

This book constitutes the refereed proceedings of the 24th International Conference on Applications of Natural Language to Information Systems, NLDB 2019, held in Salford, UK, in June 2019. The 21 full papers and 16 short papers were carefully re... Read More about Natural Language Processing and Information Systems : 24th International Conference on applications of natural language to information systems, NLDB 2019, Salford, UK, June 26–28, 2019, proceedings.

Understanding causes of low voltage (LV) faults in electricity distribution network using association rule mining and text clustering (2019)
Presentation / Conference
Silva, H., & Saraee, M. (2019, June). Understanding causes of low voltage (LV) faults in electricity distribution network using association rule mining and text clustering. Presented at 3RD IEEE Industrial and Commercial Power System Europe (I&CPS), Genoa, Italy

In-depth understanding of a fault cause in electricity distribution network has always been of paramount importance to Distributed Network Operators (DNO) for a reliable power supply. Faults in the network have direct effect on its stability, availab... Read More about Understanding causes of low voltage (LV) faults in electricity distribution network using association rule mining and text clustering.

Analyzing data streams using a dynamic compact stream pattern algorithm (2019)
Journal Article
Oyewale, A., Hughes, C., & Saraee, M. (2019). Analyzing data streams using a dynamic compact stream pattern algorithm

A growing number of applications that generate massive streams of data need intelligent data processing and online analysis. Data & Knowledge Engineering (DKE) has been known to stimulate the exchange of ideas and interaction between these two rela... Read More about Analyzing data streams using a dynamic compact stream pattern algorithm.

Diabetics’ self-management systems : drawbacks and potential enhancements (2019)
Presentation / Conference
Darwish, F., Silva, H., & Saraee, M. (2019, March). Diabetics’ self-management systems : drawbacks and potential enhancements. Presented at 2nd International Conference on Geoinformatics and Data Analysis (ICGDA), Prague, Czech Republic

Diabetes is a pandemic that is growing globally, and by the year 2030 it is expected to effect three people every 10 minutes. In the UK, it is estimated that by 2025, 5 million people will have diabetes. Diabetes is currently costing the British Nati... Read More about Diabetics’ self-management systems : drawbacks and potential enhancements.

Classification of advance malware for autonomous vehicles by using stochastic logic (2018)
Presentation / Conference
Alsadat tabatabaei, S., Saraee, M., & Dehghantanha, A. (2018, September). Classification of advance malware for autonomous vehicles by using stochastic logic. Presented at 11th IEEE International Conference on Developments in eSystems Engineering DeSE2018, Cambridge, UK

Connectivity of vehicles allows the seamless power of communication over the internet but is not without its cyber risks. Many IoT communication systems - such as vehicle-to-vehicle or vehicle-to-roadside - may require latencies below a few tens of... Read More about Classification of advance malware for autonomous vehicles by using stochastic logic.

Diabetes self-management system : review of existing systems and potential enhancements (2018)
Presentation / Conference
systems and potential enhancements. Presented at 11th IEEE International Conference on Developments in eSystems Engineering (DeSE2018), Cambridge, UK

Diabetes is a global pandemic with growing devastating human, social and economic impacts. By 2025 it is estimated that in the UK five million people will be diagnosed with diabetes and by 2030 diabetes will claim three lives every ten minutes. Accor... Read More about Diabetes self-management system : review of existing systems and potential enhancements.