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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.

Geometrical-based approach for robust human image detection (2018)
Journal Article
Al-Hazaimeh, O., Al-Nawashi, M., & Saraee, M. (2019). Geometrical-based approach for robust human image detection. Multimedia Tools and Applications, 78(6), 7029-7053. https://doi.org/10.1007/s11042-018-6401-y

In recent years, object detection and classification has been gaining more attention, thus, there are several human object detection algorithms being used to locate and recognize human objects in images. The research of image processing and analyzing... Read More about Geometrical-based approach for robust human image detection.

Analyzing data streams using a dynamic compact stream pattern algorithm (2018)
Presentation / Conference
Oyewale, A., Hughes, C., & Saraee, M. (2018, July). Analyzing data streams using a dynamic compact stream pattern algorithm. Presented at The Eighth International Conference on Advances in Information Mining and Management, Barcelona, Spain

In order to succeed in the global competition, organizations need to understand and monitor the rate of data influx. The acquisition of continuous data has been extremely outstretched as a concern in many fields. Recently, frequent patterns in data s... Read More about Analyzing data streams using a dynamic compact stream pattern algorithm.

Finding influential users for different time bounds in social networks using multi-objective optimization (2018)
Journal Article
Mohammadi, A., & Saraee, M. (2018). Finding influential users for different time bounds in social networks using multi-objective optimization. Swarm and Evolutionary Computation, 40, 158-165. https://doi.org/10.1016/j.swevo.2018.02.003

Online social networks play an important role in marketing services. Influence maximization is a major challenge, in which the goal is to find the most influential users in a social network. Increasing the number of influenced users at the end of a d... Read More about Finding influential users for different time bounds in social networks using multi-objective optimization.

Optimisation techniques for finding connected components in large graphs using GraphX (2018)
Thesis
Turifi, M. (in press). Optimisation techniques for finding connected components in large graphs using GraphX. (Thesis). The University of Salford

The problem of finding connected components in undirected graphs has been well studied. It is an essential pre-processing step to many graph computations, and a fundamental task in graph analytics applications, such as social network analysis, web gr... Read More about Optimisation techniques for finding connected components in large graphs using GraphX.

SSAM : towards supervised sentiment and aspect modeling on different levels of labeling (2017)
Journal Article
Zahedi, E., & Saraee, M. (2018). SSAM : towards supervised sentiment and aspect modeling on different levels of labeling. Soft Computing, 22(23), 7989-8000. https://doi.org/10.1007/s00500-017-2746-9

Abstract In recent years people want to express their opinion on every online service or product, and there are now a huge number of opinions on the social media, online stores and blogs. However, most of the opinions are presented in plain text and... Read More about SSAM : towards supervised sentiment and aspect modeling on different levels of labeling.

A distributed joint sentiment and topic modeling using spark for big opinion mining (2017)
Book Chapter
Zahedi, E., Saraee, M., & Baniasadi, Z. (2017). A distributed joint sentiment and topic modeling using spark for big opinion mining. In Iranian Conference on Electrical Engineering (ICEE), 2017 (1475-1480). IEEE. https://doi.org/10.1109/IranianCEE.2017.7985276

Opinion data are produced rapidly by a large and uncontrolled number of opinion holders in different domains (public, business, politic and etc). The volume, variety and velocity of such data requires an opinion mining model to be also adopted with t... Read More about A distributed joint sentiment and topic modeling using spark for big opinion mining.

Particle emissions from Euro 6 diesel cars during real world driving conditions (2017)
Presentation / Conference
Babaie, M., Cooper, J., Molden, N., Silva, C., & Saraee, M. (2017, July). Particle emissions from Euro 6 diesel cars during real world driving conditions. Poster presented at 7th International Congress of Energy and Environment Engineering and Management, Canary Islands, Spain

CO, NOx, HC and Particle mass have been monitored in different vehicle emission standards and Particle number (PN) has been added to standards recently. The EU has proposed a solid particle PN limit in Euro 5b and Euro 6. The PN limit for low duty v... Read More about Particle emissions from Euro 6 diesel cars during real world driving conditions.

Effective pixel classification of Mars images based on ant colony optimization feature selection and extreme learning machine (2016)
Journal Article
Rashno, A., Nazari, B., Sadri, S., & Saraee, M. (2017). Effective pixel classification of Mars images based on ant colony optimization feature selection and extreme learning machine. Neurocomputing, 226, 66-79. https://doi.org/10.1016/j.neucom.2016.11.030

one of the most important tasks of Mars rover, a robot which explores the Mars surface, is the process of automatic segmentation of images taken by front-line Panoramic Camera (Pancam). This procedure is highly significant since the transformation co... Read More about Effective pixel classification of Mars images based on ant colony optimization feature selection and extreme learning machine.

Mining the crime survey to support crime profiling (2016)
Presentation / Conference
Wu, J., Meziane, F., Saraee, M., Aspin, R., & Hope, T. (2016, October). Mining the crime survey to support crime profiling. Presented at 2016 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM), Reggio Calabria, Italy

Crime surveys are conducted to record crimes by the Office for National Statistics (ONS) in the United Kingdom every year. They contain rich information about crime. They record the crimes that are not reported to the police. However, their exploitat... Read More about Mining the crime survey to support crime profiling.

Semantic aware Bayesian network model for actionable knowledge discovery in linked data (2016)
Presentation / Conference
Alharbi, H., & Saraee, M. (2016, July). Semantic aware Bayesian network model for actionable knowledge discovery in linked data. Presented at 12th International Conference, MLDM 2016, New York, NY, USA

The majority of the conventional mining algorithms treat the mining process as an isolated data-driven procedure and overlook the semantic of the targeted data. As a result, the generated patterns are abundant and end users cannot act upon them seaml... Read More about Semantic aware Bayesian network model for actionable knowledge discovery in linked data.

A novel framework for intelligent surveillance system based on abnormal human activity detection in academic environments (2016)
Journal Article
Al-Nawashi, M., Al-Hazaimeh, O., & Saraee, M. (2016). A novel framework for intelligent surveillance system based on abnormal human activity detection in academic environments. Neural Computing and Applications, 27(4), https://doi.org/10.1007/s00521-016-2363-z

Abnormal activity detection plays a crucial role in surveillance applications, and a surveillance system thatcan perform robustly in an academic environment has become an urgent need. In this paper, we propose a novel framework for an automatic re... Read More about A novel framework for intelligent surveillance system based on abnormal human activity detection in academic environments.

Natural language processing and information systems : 21st International Conference on Applications of Natural Language to Information Systems, NLDB 2016, Salford, UK, June 22-24, 2016, Proceedings (2016)
Book
(2016). E. Metais, F. Meziane, M. Saraee, V. Sugumaran, S. Vadera, E. Métais, …S. Vadera (Eds.), Natural language processing and information systems : 21st International Conference on Applications of Natural Language to Information Systems, NLDB 2016, Salford, UK, June 22-24, 2016, Proceedings. Springer. https://doi.org/10.1007/978-3-319-41754-7

This volume of the lecture notes in computer science (LNCS) contains the papers presented at the 21st International Conference on application of Natural Language to Information Systems, held at MediacityUK, University of Salford on the 22-24 June 201... Read More about Natural language processing and information systems : 21st International Conference on Applications of Natural Language to Information Systems, NLDB 2016, Salford, UK, June 22-24, 2016, Proceedings.

Simulated annealing least squares twin support vector machine (SA-LSTSVM) for pattern classification (2016)
Journal Article
Sartakhti, J., Afrabandpey, H., & Saraee, M. (2017). Simulated annealing least squares twin support vector machine (SA-LSTSVM) for pattern classification. Soft Computing, 21(15), 4361-4373. https://doi.org/10.1007/s00500-016-2067-4

Least squares twin support vector machine (LSTSVM) is a relatively new version of support vector machine (SVM) based on non-parallel twin hyperplanes. Although, LSTSVM is an extremely efficient and fast algorithm for binary classification, its p... Read More about Simulated annealing least squares twin support vector machine (SA-LSTSVM) for pattern classification.

Time-sensitive influence maximization in social networks (2015)
Journal Article
Mohammadi, A., Saraee, M., & Mirzaei, A. (2015). Time-sensitive influence maximization in social networks. Journal of Information Science, 41(6), 765-778. https://doi.org/10.1177/0165551515602808

One of the fundamental issues in social networks is the influence maximization problem, where the goal is to identify a small subset of individuals such that they can trigger the largest number of members in the network. In real-world social networks... Read More about Time-sensitive influence maximization in social networks.