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Prof Mo Saraee's Outputs (172)

Gas pipeline defect detection based on improved deep learning approach (2025)
Journal Article
Zhang, T., Ma, C., Liu, Z., ur Rehman, S., Li, Y., & Saraee, M. (2025). Gas pipeline defect detection based on improved deep learning approach. Expert systems with applications, 267, Article 126212. https://doi.org/10.1016/j.eswa.2024.126212

The working conditions of gas pipelines directly impact urban populations and factory operations. However, accurate and rapid detection of gas pipeline defects is challenging. To improve the accuracy of gas pipeline defect detection, we propose an im... Read More about Gas pipeline defect detection based on improved deep learning approach.

Enhancing Infrared Small Target Detection: A Saliency-Guided Multi-Task Learning Approach (2025)
Journal Article
Liu, Z., Zhang, Y., He, J., Zhang, T., ur Rehman, S., Saraee, M., & Sun, C. (in press). Enhancing Infrared Small Target Detection: A Saliency-Guided Multi-Task Learning Approach. IEEE Transactions on Intelligent Transportation Systems, 1-16. https://doi.org/10.1109/TITS.2024.3520424

Object detection in infrared images poses a considerable challenge due to its small-scale targets, low contrast and poor signal-to-clutter ratio, often resulting in a high false alarm rate. To improve the detection accuracy on infrared small targets,... Read More about Enhancing Infrared Small Target Detection: A Saliency-Guided Multi-Task Learning Approach.

Enhancing Drug Discovery and Patient Care through Advanced Analytics with The Power of NLP and Machine Learning in Pharmaceutical Data Interpretation (2024)
Journal Article
R, N., Khan, S. B., kumar, A. V., T R, M., Alojail, M., Sangwan, S. R., & Saraee, M. (in press). Enhancing Drug Discovery and Patient Care through Advanced Analytics with The Power of NLP and Machine Learning in Pharmaceutical Data Interpretation. #Journal not on list, Article 100238. https://doi.org/10.1016/j.slast.2024.100238

This study delves into the transformative potential of Machine Learning (ML) and Natural Language Processing (NLP) within the pharmaceutical industry, spotlighting their significant impact on enhancing medical research methodologies... Read More about Enhancing Drug Discovery and Patient Care through Advanced Analytics with The Power of NLP and Machine Learning in Pharmaceutical Data Interpretation.

Improvement of OLSRv2 massage format and message interval to reduce routing overhead in a D2D communication network (2024)
Conference Proceeding
Bunu, S. M., Alani, O., & Saraee, M. (2024). Improvement of OLSRv2 massage format and message interval to reduce routing overhead in a D2D communication network. In 2024 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) (117-120). https://doi.org/10.1109/CyberC62439.2024.00029

Optimized Link State Routing Protocol (OLSR) is one of the most famous and widely used proactive routing protocols in Device-to-Device (D2D) communication network. The traditional OLSR and OLSRv2 experience significantly higher routing overhead due t... Read More about Improvement of OLSRv2 massage format and message interval to reduce routing overhead in a D2D communication network.

An Improved Routing Protocol for Optimum Quality of Service in Device-to-Device and Energy Efficiency in 5G/B5G (2024)
Journal Article
Bunu, S. M., Alani, O. Y., & Saraee, M. (2024). An Improved Routing Protocol for Optimum Quality of Service in Device-to-Device and Energy Efficiency in 5G/B5G. Future Internet, 16(9), Article 347. https://doi.org/10.3390/fi16090347

Some challenges when implementing the optimized link state routing (OLSR) protocol on real-life devices and simulators are unmanageable: link quality, rapid energy depletion, and high processor loads. The causes of these challenges are link state pro... Read More about An Improved Routing Protocol for Optimum Quality of Service in Device-to-Device and Energy Efficiency in 5G/B5G.

Optimizing Pandemic Control Strategies: A Deep Reinforcement Learning Approach in Public Health Management (2024)
Thesis
Ibraimoh, R. (2024). Optimizing Pandemic Control Strategies: A Deep Reinforcement Learning Approach in Public Health Management. (Thesis). University of Salford

COVID-19, also known as the SARS-CoV-2 coronavirus, has paralysed the world and
forced people to change their lifestyles. Since COVID-19 deaths are increasing daily, the
disease has become a global public health issue. Different countries used diff... Read More about Optimizing Pandemic Control Strategies: A Deep Reinforcement Learning Approach in Public Health Management.

Features in extractive supervised single-document summarization: case of Persian news (2024)
Journal Article
Rezaei, H., Mirhosseini, S. A. M., Shahgholian, A., & Saraee, M. (2024). Features in extractive supervised single-document summarization: case of Persian news. Language Resources and Evaluation, 58(4), 1073-1091. https://doi.org/10.1007/s10579-024-09739-7

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 abstractive or extractive methods. Extractive methods are preferable due to their simplicity compar... Read More about Features in extractive supervised single-document summarization: case of Persian news.

A Decentralised Peer-to-Peer Energy Trading Platform for Residential Homes (2024)
Thesis
Debrah, K. (2024). A Decentralised Peer-to-Peer Energy Trading Platform for Residential Homes. (Thesis). University of Salford

To achieve a sustainable and low-carbon energy system, it is necessary to develop novel solutions for the way household energy is consumed. Homes that have solar photovoltaic (PV) systems, electric vehicles (EVs), and microgrids can potentially trans... Read More about A Decentralised Peer-to-Peer Energy Trading Platform for Residential Homes.

Improving Predictive Process Analytics with Deep Learning and XAI (2024)
Thesis
Obuzor, P. (2024). Improving Predictive Process Analytics with Deep Learning and XAI. (Thesis). University of Salford

In this doctoral thesis, we explore the innovative application of the Tab Transformer
architecture in the realm of predictive process mining, marking a significant advancement in
forecasting subsequent events within activity sequences. Utilising th... Read More about Improving Predictive Process Analytics with Deep Learning and XAI.

Real-Time Application Of Deepfake For De-Identification Privacy Preservation And Data Protection (2024)
Thesis
Inuwa, M. (2024). Real-Time Application Of Deepfake For De-Identification Privacy Preservation And Data Protection. (Thesis). University of Salford

In an era marked by mounting concerns over data privacy and protection, conventional
regulatory measures have proven inadequate against cyber-attacks, complicating data sharing
for research and development. Meanwhile, traditional face de-identifica... Read More about Real-Time Application Of Deepfake For De-Identification Privacy Preservation And Data Protection.

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.

Developing a Framework to Identify Professional Skills Required for Banking Sector Employee in UK using Natural Language Processing (NLP) Techniques (2024)
Thesis
Anthony, G. (2024). Developing a Framework to Identify Professional Skills Required for Banking Sector Employee in UK using Natural Language Processing (NLP) Techniques. (Thesis). University of Salford

The banking sector is changing dramatically, and new studies reveal that many financial institutions are having challenges keeping up with technology advancements and an acute shortage of skilled workers. The banking industry is changing into a dynam... Read More about Developing a Framework to Identify Professional Skills Required for Banking Sector Employee in UK using Natural Language Processing (NLP) 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.

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.