All Outputs (157)

Early dementia detection with speech analysis and machine learning techniques (2024)
Journal Article
Jahan, Z., Khan, S. B., & Saraee, M. (in press). Early dementia detection with speech analysis and machine learning techniques. #Journal not on list, 5(1), 65. https://doi.org/10.1007/s43621-024-00217-2

This in-depth study journey explores the context of natural language processing and text analysis in dementia detection, revealing their importance in a variety of fields. Beginning with an examination of the widespread and influence of text data. Th... Read More about Early dementia detection with speech analysis and machine learning techniques.

Optimizing the Parameters of Relay Selection Model in D2D Network (2024)
Conference Proceeding
Bunu, S. M., Saraee, M., & Alani, O. (2024). Optimizing the Parameters of Relay Selection Model in D2D Network. . https://doi.org/10.1109/ISRITI60336.2023.10467664

The Fifth generation (5G) cellular network's traffic load is certain to expand significantly in the near future as a result of its flexibility, high speed, increased bandwidth, better connectivity and low latency. Consequently, it is necessary to inv... Read More about Optimizing the Parameters of Relay Selection Model in D2D Network.

Multiclass Classification and Defect Detection of Steel tube using modified YOLO (2023)
Conference Proceeding
Saraee, M., & khan, S. (2023). Multiclass Classification and Defect Detection of Steel tube using modified YOLO.

Steel tubes are widely used in hazardous high pressure environments such as petroleum, chemicals, natural gas and shale gas. Defects in steel tubes have serious negative consequences. Using deep learning object recognition to identify and detect defe... Read More about Multiclass Classification and Defect Detection of Steel tube using modified YOLO.

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.

Designing and Implementing a Blockchain-based Platform for the Exchange of Peerto-Peer Energy Trading and Modelling Vehicle-to-Grid(V2G) Residential Community (2023)
Journal Article
Debrah, P., & Saraee, M. (2023). Designing and Implementing a Blockchain-based Platform for the Exchange of Peerto-Peer Energy Trading and Modelling Vehicle-to-Grid(V2G) Residential Community. #Journal not on list, 6(1), 68

The expansion of renewable energy on the national grid has been a struggle throughout the past decade. Rooftop solar photovoltaics (PV) and electric vehicle to Grid (V2G) can function as either load or distributed energy sources. Consequently, presum... Read More about Designing and Implementing a Blockchain-based Platform for the Exchange of Peerto-Peer Energy Trading and Modelling Vehicle-to-Grid(V2G) Residential Community.

Deriving Environmental Risk Profiles for Autonomous Vehicles From Simulated Trips (2023)
Journal Article
Anih, J., Kolekar, S., Dargahi, T., Babaie, M., Saraee, M., & Wetherell, J. (2023). Deriving Environmental Risk Profiles for Autonomous Vehicles From Simulated Trips. IEEE Access, 11, 38385-38398. https://doi.org/10.1109/access.2023.3261245

The commercial adoption of Autonomous Vehicles (AVs) and the positive impact they are expected to have on traffic safety depends on appropriate insurance products due to the high potential losses. A significant proportion of these losses are expected... Read More about Deriving Environmental Risk Profiles for Autonomous Vehicles From Simulated Trips.

LIFT the AV: Location InFerence aTtack on Autonomous Vehicle Camera Data (2023)
Conference Proceeding
Adeboye, O., Abdullahi, A., Dargahi, T., Babaie, M., & Saraee, M. (2023). LIFT the AV: Location InFerence aTtack on Autonomous Vehicle Camera Data. . https://doi.org/10.1109/ccnc51644.2023.10060796

Connected and autonomous vehicles (CAVs) are one of the main representatives of cyber-physical systems (CPS), where the digital data generated in several forms, such as geolocation, distance, and camera data, are used for the physical functionality o... Read More about LIFT the AV: Location InFerence aTtack on Autonomous Vehicle Camera Data.

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.

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.

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.

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.

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.