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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.

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.

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.

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.

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.

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.

Mars image segmentation with most relevant features among wavelet and color features (2015)
Presentation / Conference
Rashno, A., Saraee, M., & Sadri, S. (2015, April). Mars image segmentation with most relevant features among wavelet and color features. Presented at AI & Robotics (IRANOPEN), 2015

Mars rover is a robot which explores the Mars surface, is equipped to front-line Panoramic Camera (Pancam). Automatic processing and segmentation of images taken by Pancam is one of the most important and most significant tasks of Mars rover since th... Read More about Mars image segmentation with most relevant features among wavelet and color features.

PSA : a hybrid feature selection approach for Persian text classification (2015)
Journal Article
Bagheri, A., Saraee, M., & Nadi, S. (2015). PSA : a hybrid feature selection approach for Persian text classification. Journal of computing and security (Online), 1(4), 261-272

In recent decades, as enormous amount of data being accumulated, the number of text documents is increasing vastly. E-mails, web pages, texts, news and articles are only part of this grow. Thus the need for text mining techniques, including automatic... Read More about PSA : a hybrid feature selection approach for Persian text classification.

A novel feature selection method for text classification using association rules and clustering (2014)
Journal Article
Sheydaei, N., Saraee, M., & Shahgholian, A. (2015). A novel feature selection method for text classification using association rules and clustering. Journal of Information Science, 41(1), 3-15. https://doi.org/10.1177/0165551514550143

Readability and accuracy are two important features of a good classifier. For reasons such as acceptable accuracy, rapid training and high interpretability, associative classifiers have been recently used in many categorization tasks. These features... Read More about A novel feature selection method for text classification using association rules and clustering.

Persian sentiment analyzer : a framework based on a novel feature selection method (2014)
Journal Article
feature selection method. International Journal of Artificial Intelligence, 12(2), 115-129

In the recent decade, with the enormous growth of digital content in internet and databases, sentiment analysis has received more and more attention between information retrieval and natural language processing researchers. Sentiment analysis aims... Read More about Persian sentiment analyzer : a framework based on a novel feature selection method.

MRAR : mining multi-relation association rules (2014)
Journal Article
Ramezani, R., Saraee, M., & Nematbakhsh, M. (2014). MRAR : mining multi-relation association rules. Journal of computing and security (Online), 1(2), 133-158

In this paper, we introduce a new class of association rules (ARs) named “Multi-Relation Association Rules” which in contrast to primitive ARs (that are usually extracted from multi-relational databases), each rule item consists of one entity and... Read More about MRAR : mining multi-relation association rules.

A fuzzy method for discovering cost-effective actions from data (2014)
Journal Article
Kalanat, N., Shamsinejadbabaki, P., & Saraee, M. (2014). A fuzzy method for discovering cost-effective actions from data. Journal of Intelligent and Fuzzy Systems, 28(2), 757-765. https://doi.org/10.3233/IFS-141357

Data mining techniques are often confined to the delivery of frequent patterns and stop short of suggesting how to act on these patterns for business decision-making. They require human experts to post-process the discovered patterns manually. Theref... Read More about A fuzzy method for discovering cost-effective actions from data.

ADM-LDA : an aspect detection model based on topic modelling using the structure of review sentences (2014)
Journal Article
Bagheri, A., Saraee, M., & de Jong, F. (2014). ADM-LDA : an aspect detection model based on topic modelling using the structure of review sentences. Journal of Information Science, 40(5), 621-636. https://doi.org/10.1177/0165551514538744

Probabilistic topic models are statistical methods whose aim is to discover the latent structure in a large collection of documents. The intuition behind topic models is that, by generating documents by latent topics, the word distribution for each t... Read More about ADM-LDA : an aspect detection model based on topic modelling using the structure of review sentences.

Care more about customers: Unsupervised domain-independent aspect detection for sentiment analysis of customer reviews (2013)
Journal Article
Bagheri, A., Saraee, M., & de Jong, F. (2013). Care more about customers: Unsupervised domain-independent aspect detection for sentiment analysis of customer reviews. Knowledge-Based Systems, 52(2013), 201-213. https://doi.org/10.1016/j.knosys.2013.08.011

With the rapid growth of user-generated content on the internet, automatic sentiment analysis of online customer reviews has become a hot research topic recently, but due to variety and wide range of products and services being reviewed on the intern... Read More about Care more about customers: Unsupervised domain-independent aspect detection for sentiment analysis of customer reviews.

Causality-based cost-effective action mining (2013)
Journal Article
Shamsinejadbabki, P., Saraee, M., & Blockeel, H. (2013). Causality-based cost-effective action mining. Intelligent Data Analysis, 17(6), 1075-1091. https://doi.org/10.3233/IDA-130621

In many business contexts, the ultimate goal of knowledge discovery is not the knowledge itself, but putting it to use. Models or patterns found by data mining methods often require further post-processing to bring this about. For instance, in churn... Read More about Causality-based cost-effective action mining.

Natural language processing and information systems : 18th international conference on applications of natural language to information systems, NLDB 2013, Salford, UK, June 2013; Proceedings (2013)
Book
(2013). E. Métais, F. Meziane, M. Saraee, & S. Vadera (Eds.), Natural language processing and information systems : 18th international conference on applications of natural language to information systems, NLDB 2013, Salford, UK, June 2013; Proceedings. Springer

This book constitutes the refereed proceedings of the 18th International Conference on Applications of Natural Language to Information Systems, held in Salford, UK, in June 2013. The 21 long papers, 15 short papers and 17 poster papers presented... Read More about Natural language processing and information systems : 18th international conference on applications of natural language to information systems, NLDB 2013, Salford, UK, June 2013; Proceedings.

Finding association rules in linked data, a centralization approach (2013)
Presentation / Conference
Ramezani, R., Saraee, M., & Nematbakhsh, M. (2013, May). Finding association rules in linked data, a centralization approach. Presented at 21st Iranian Conference on Electrical Engineering (ICEE), 2013, Mashhad, Iran

Linked Data is used in the Web to create typed links between data from different sources. Connecting diffused data by using these links provides new data which could be employed in different applications. Association Rules Mining (ARM) is a data mini... Read More about Finding association rules in linked data, a centralization approach.

Sentiment classification in Persian: Introducing a mutual information-based method for feature selection (2013)
Presentation / Conference
Bagheri, A., Saraee, M., & de Jong, F. (2013, May). Sentiment classification in Persian: Introducing a mutual information-based method for feature selection. Presented at 21st Iranian Conference on Electrical Engineering (ICEE), 2013, Mashhad, Iran

With the enormous growth of online reviews in Internet, sentiment analysis has received more and more attention in information retrieval and natural language processing community. Up to now there are very few researches conducted on sentiment analysi... Read More about Sentiment classification in Persian: Introducing a mutual information-based method for feature selection.

Protein contact map prediction using committee machine approach (2013)
Journal Article
Habibi, N., Saraee, M., & Korbekandi, H. (2013). Protein contact map prediction using committee machine approach. International Journal of Data Mining and Bioinformatics, 7(4), 397-415. https://doi.org/10.1504/IJDMB.2013.054226

A protein contact map is a simplified representation of the protein's spatial structure. In recent years, contact map prediction has received a great deal of attention in Bioinformatics. Committee Machine is a machine learning method which shares t... Read More about Protein contact map prediction using committee machine approach.

Feature selection methods in Persian sentiment analysis (2013)
Journal Article
Saraee, M., & Bagheri, A. (2013). Feature selection methods in Persian sentiment analysis. Lecture notes in computer science, 7934, 303-308. https://doi.org/10.1007/978-3-642-38824-8_29

With the enormous growth of digital content in internet, various types of online reviews such as product and movie reviews present a wealth of subjective information that can be very helpful for potential users. Sentiment analysis aims to use automat... Read More about Feature selection methods in Persian sentiment analysis.

An unsupervised aspect detection model for sentiment analysis of reviews (2013)
Journal Article
Bagheri, A., Saraee, M., & Jong, F. (2013). An unsupervised aspect detection model for sentiment analysis of reviews. Lecture notes in computer science, 7934, 140-151. https://doi.org/10.1007/978-3-642-38824-8_12

With the rapid growth of user-generated content on the internet, sentiment analysis of online reviews has become a hot research topic recently, but due to variety and wide range of products and services, the supervised and domain-specific models are... Read More about An unsupervised aspect detection model for sentiment analysis of reviews.

A multi-armed bandit approach to cost-sensitive decision tree learning (2012)
Presentation / Conference
Lomax, S., Vadera, S., & Saraee, M. (2012, December). A multi-armed bandit approach to cost-sensitive decision tree learning. Presented at 2012 IEEE 12th International Conference on Data Mining Workshops, Brussels, Belgium

Several authors have studied the problem of inducing decision trees that aim to minimize costs of misclassification and take account of costs of tests. The approaches adopted vary from modifying the information theoretic attribute selection measure u... Read More about A multi-armed bandit approach to cost-sensitive decision tree learning.

Preface to the workshop on cost sensitive data mining (2012)
Book Chapter
Vadera, S., Saraee, M., & Lomax, S. (2012). Preface to the workshop on cost sensitive data mining. In J. Vreeken, C. Ling, M. Zaki, A. Siebes, J. Yu, B. Goethals, …X. Wu (Eds.), The 12th IEEE International Conference on Data Mining : Workshops. IEEE. https://doi.org/10.1109/ICDMW.2012.148

Much of the early work on data mining concentrated on developing algorithms that focused on classification accuracy. A more challenging and practical problem is to devise algorithms that learn rules or associations that optimize income and take bette... Read More about Preface to the workshop on cost sensitive data mining.

Privacy preserving mining of association rules on horizontally distributed databases (2012)
Presentation / Conference
distributed databases. Presented at International Conference on Software and Computer Applications ICSCA 2012, Singapore

These protocols are based on two main approaches named as: the Randomization approach and the Cryptographic approach. The first one is based on perturbation of the valuable information while the second one uses cryptographic techniques. The randomiza... Read More about Privacy preserving mining of association rules on horizontally distributed databases.

A new method for compressing massive RFID data to achieve efficient mining (2012)
Journal Article
Hafezi, L., Saraee, M., & Montazeri, M. (2012). A new method for compressing massive RFID data to achieve efficient mining. International journal of computer theory and engineering (Print), 4(5), 694-696. https://doi.org/10.7763/IJCTE.2012.V4.559

Radio Frequency Identification (RFID) technology has been used for many purposes and has had effective results. This technology eases and accelerates many applications, but it has proposed a challenge, and that is the production of such a volume of d... Read More about A new method for compressing massive RFID data to achieve efficient mining.

Robust and cost-effective approach for discovering action rules (2011)
Journal Article
Kalanat, N., Shamsinejad, P., & Saraee, M. (2011). Robust and cost-effective approach for discovering action rules. International journal of machine learning and computing (Online), 1(4), 325-331. https://doi.org/10.7763/IJMLC.2011.V1.48

The main goal of Knowledge Discovery in Databases is to find interesting and usable patterns, meaningful in their domain. Actionable Knowledge Discovery came to existence as a direct respond to the need of finding more usable patterns called acti... Read More about Robust and cost-effective approach for discovering action rules.

Hybrid rule threshold adjustment system for intrusion detection (2011)
Presentation / Conference
Moghimi, M., & Saraee, M. (2011, September). Hybrid rule threshold adjustment system for intrusion detection. Presented at The 8th International ISC Conference on Information Security and Cryptology (ISCISC 2011), September 14-15, 2011 - Ferdowsi University of Mashhad,, Mashhad, Iran

Generally, multiple IDSs generates huge volume of alerts every minute and to manage these alerts, rule-based alert management systems are very important. It is critical to keep the rules inside these systems updated, based on the ever changing networ... Read More about Hybrid rule threshold adjustment system for intrusion detection.

Modeling batch annealing process using data mining techniques for cold rolled steel sheets (2011)
Presentation / Conference
Saraee, M., Moghimi, M., & Bagheri, A. (2011, August). Modeling batch annealing process using data mining techniques for cold rolled steel sheets. Presented at The 17th Annual ACM SIGKDD Conference on Knowledge Discovery and Data Mining, San Diego

The annealing process is one of the important operations in production of cold rolled steel sheets, which significantly influences the final product quality of cold rolling mills. In this process, cold rolled coils are heated slowly to a desired temp... Read More about Modeling batch annealing process using data mining techniques for cold rolled steel sheets.

Examination of the online reputation systems problems and provide solution (2011)
Presentation / Conference
Ehghaghi Kakoli, Z., Nematbakhsh, M., & Saraee, M. (2011, August). Examination of the online reputation systems problems and provide solution. Presented at 13th IEEE Joint International Computer Science and Information Technology Conference (JICSIT 2011), Chongqing, China

Electronic commerce communities are considered to be communities that provide opportunities for the sellers on the one hand and include threats for the purchasers on the other hand. One of the ways to reduce such threats in these open communities... Read More about Examination of the online reputation systems problems and provide solution.

A new unsupervised feature selection method for text clustering based on genetic algorithms (2011)
Journal Article
Shamsinejadbabki, P., & Saraee, M. (2012). A new unsupervised feature selection method for text clustering based on genetic algorithms. Journal of Intelligent Information Systems, 38(3), 669-684. https://doi.org/10.1007/s10844-011-0172-5

Nowadays a vast amount of textual information is collected and stored in various databases around the world, including the Internet as the largest database of all. This rapidly increasing growth of published text means that even the most avid reader... Read More about A new unsupervised feature selection method for text clustering based on genetic algorithms.

Defining a new measure for synchronization of multi-channel epileptic depth-EEG signals based on identification of parameters of a computational model (2011)
Presentation / Conference
Shayegh, F., Amirfattahi, R., Sadri, S., Ansari-Asl, K., & Saraee, M. (2011, July). Defining a new measure for synchronization of multi-channel epileptic depth-EEG signals based on identification of parameters of a computational model. Presented at Intelligent Systems and Control / 742 : Computational Bioscience (ISC 2011), Cambridge, UK

There are various methods to measure the value of synchronization of signals. These methods usually do not consider the sources of the signals. Therefore, these methods usually underestimate the coupling phenomena of the sources of the system that ge... Read More about Defining a new measure for synchronization of multi-channel epileptic depth-EEG signals based on identification of parameters of a computational model.

A new framework for online rule threshold adjustment in intrusion detection (2011)
Presentation / Conference
Moghimi, M., & Saraee, M. (2011, June). A new framework for online rule threshold adjustment in intrusion detection. Presented at 2011 CSI International Symposium on Computer Science and Software Engineering (CSSE), Tehran

Generally, rule-based systems work to make sense of a large volume of alerts generated by the intrusion detection systems (IDSs) every minute. Hence, it is very significant to verify that these systems are error-free and that the rules are suitable f... Read More about A new framework for online rule threshold adjustment in intrusion detection.

Discovering cost-effective action rules (2011)
Presentation / Conference
Kalanat,, N., Shamsinejad, P., & Saraee, M. (2011, June). Discovering cost-effective action rules. Presented at 4th IEEE International Conference on Computer Science and Information Technology (IEEE ICCSIT 2011),, Chengdu, China

Mining informative patterns from databases is the historical task of data mining. But now, mining actionable patterns is becoming the new duty of data mining. Most of machine learning and data mining algorithms only focus on finding patterns and usua... Read More about Discovering cost-effective action rules.

A survey on utilization of data mining approaches for dermatological (skin) diseases prediction (2011)
Journal Article
Barati, E., Saraee, M., Mohammadi, A., & Adibi, N. (2011). A survey on utilization of data mining approaches for dermatological (skin) diseases prediction. #Journal not on list,

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its va... Read More about A survey on utilization of data mining approaches for dermatological (skin) diseases prediction.

Toward a functional ontology of reputation for e-commerce (2011)
Presentation / Conference
Ehghaghi Kakoli, Z., Nematbakhsh, M., & Saraee, M. (2011, March). Toward a functional ontology of reputation for e-commerce. Presented at e-Society 2011, 9th International Conference IADIS, Avila, Spain

In recent years, the expansion of the internet has influenced all aspect of our lives. Trust is an important factor making business transactions possible. In a conventional setting this trust is based on all involved parties knowing each other. Howev... Read More about Toward a functional ontology of reputation for e-commerce.

A hybrid recommender system for dynamic web users (2011)
Journal Article
Nadi, S., Saraee, M., & Bagheri, A. (2011). A hybrid recommender system for dynamic web users. International Journal of Multimedia and Image Processing, 1(1), 3-8

Nowadays, providing tools that eases the interaction of users with websites is a big challenge in e-commerce. Recommender systems are useful tools which adapts the environment of websites compatible with users needs. In this paper, applying a hybrid... Read More about A hybrid recommender system for dynamic web users.

A survey on utilization of data mining approaches for dermatological (skin) diseases prediction (2011)
Journal Article
Barati, E., Saraee, M., Mohammadi, A., Adibi, N., & Ahmadzadeh, M. (2011). A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its va... Read More about A survey on utilization of data mining approaches for dermatological (skin) diseases prediction.

Identification of disease-causing genes using microarray data mining and gene ontology (2011)
Journal Article
Mohammadi, A., Saraee, M., & Salehi, M. (2011). Identification of disease-causing genes using microarray data mining and gene ontology. BMC Medical Genomics, 4(12), 1-9. https://doi.org/10.1186/1755-8794-4-12

Background: One of the best and most accurate methods for identifying disease-causing genes is monitoring gene expression values in different samples using microarray technology. One of the shortcomings of microarray data is that they provide a small... Read More about Identification of disease-causing genes using microarray data mining and gene ontology.

Disordered metabolic evaluation in renal stone recurrence : a data mining approach (2011)
Journal Article
Saraee, M., Givchi, A., Taghi Adl, S., & Eshraghi, A. (2011). Disordered metabolic evaluation in renal stone recurrence : a data mining approach. Journal of Applied Computer Science & Mathematics (Suceava. Online), 5(11), 64-68

Nephrolithiasis is a disease with a high and even rising incidence. It has a high morbidity, generates high costs and has a high recurrence rate. Metabolic evaluation in renal stone formers allows the identification and quantification of risk factors... Read More about Disordered metabolic evaluation in renal stone recurrence : a data mining approach.

A Bayesian network approach for causal action rule mining (2011)
Journal Article
Shamsinejad, P., & Saraee, M. (2011). A Bayesian network approach for causal action rule mining. International journal of machine learning and computing (Online), 1(5), 528-533

Actionable Knowledge Discovery has attracted much interest lately. It is almost a new paradigm shift toward mining more usable and more applicable knowledge in each specific domain. Action Rule is a new tool in this research area that suggests some a... Read More about A Bayesian network approach for causal action rule mining.

FARS: Fuzzy Ant based Recommender System for Web Users (2011)
Journal Article
Nadi, S., Saraee, M., Bagheri, A., & Davarpanh Jazi, M. (2011). FARS: Fuzzy Ant based Recommender System for Web Users. International Journal of Computer Science Issues, 8(1), 203-209

Recommender systems are useful tools which provide an adaptive web environment for web users. Nowadays, having a user friendly website is a big challenge in e-commerce technology. In this paper, applying the benefits of both collaborative and... Read More about FARS: Fuzzy Ant based Recommender System for Web Users.

Application of Self Organizing Map (SOM) to model a machining process (2011)
Journal Article
Saraee, M., Moosavi, S., & Rezapour, S. (2011). Application of Self Organizing Map (SOM) to model a machining process. Journal of Manufacturing Technology Management, 22(6), 818-830. https://doi.org/10.1108/17410381111149666

Purpose: This paper aims to present a practical application of Self Organizing Map (SOM) and decision tree algorithms to model a multi-response machining process and to provide a set of control rules for this process. Design/methodology/approach: S... Read More about Application of Self Organizing Map (SOM) to model a machining process.

A fuzzy recommender system for dynamic prediction of user's behavior (2010)
Presentation / Conference
Nadi, S., Saraee, M., & Davarpanah Jazi, M. (2010, November). A fuzzy recommender system for dynamic prediction of user's behavior. Presented at 2010 International Conference for Internet Technology and Secured Transactions (ICITST), Issue Date: 8-11 Nov. 2010, London

Analyzing and predicting navigational behavior of Web users can lead to more user friendly and efficient websites which is an important issue in Electronic Commerce. Web personalization is a common way for adapting the content of a website to the nee... Read More about A fuzzy recommender system for dynamic prediction of user's behavior.

Proximity user identification using correlogram (2010)
Book Chapter
Shahidi, S., Mazrooei, P., Esfahani, N., & Saraee, M. (2010). Proximity user identification using correlogram. In Intelligent Information Processing (343-351). Berlin: Springer -Velag

This paper represents a technique, applying user action patterns in order to distinguish between users and identify them. In this method, users’ actions sequences are mapped to numerical sequences and each user's profile is generated using autocorre... Read More about Proximity user identification using correlogram.

Data mining approaches on discovering knowledge for decision makers: towards sustainable groundwater resources management (2010)
Presentation / Conference
Taheri, H., Safavi, H., Saraee, M., & Afghari, N. (2010, July). Data mining approaches on discovering knowledge for decision makers: towards sustainable groundwater resources management. Presented at 2010 IEEE International Conference on Advanced Management Science (ICAMS), Chengdu, China

Rapid population growth, increased irrigation, and industrial development, dramatically increased risk of vulnerability in water resources especially groundwater resources all over the world. Because of the complexity of water resources management, t... Read More about Data mining approaches on discovering knowledge for decision makers: towards sustainable groundwater resources management.

A new path planner for autonomous mobile robots based on genetic algorithm (2010)
Presentation / Conference
Shamsinejad, P., Saraee, M., & Sheikholeslam, F. (2010, July). A new path planner for autonomous mobile robots based on genetic algorithm. Presented at he 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), Chengdu, China

One of the most important issues for autonomous mobile robots is finding paths in their environment. A local path planner must be able to design the path immediately and if possible with high accuracy and efficiency. In this p... Read More about A new path planner for autonomous mobile robots based on genetic algorithm.

Mining time series data : case of predicting consumption patterns in steel industry (2010)
Presentation / Conference
Fazel, A., Saraee, M., & Shamsinejad, P. (2010, June). Mining time series data : case of predicting consumption patterns in steel industry. Presented at The 2nd International Conference on Software Engineering and Data Mining, Chengdu, China

Analyzing and predicting with Time series is a method which used in different fields, including consumption pattern analyzing and predicting. In this paper, required amount of inventory items have been predicted with time series. At first, desired da... Read More about Mining time series data : case of predicting consumption patterns in steel industry.

Social networking approach for building trust in e-commerce (2010)
Journal Article
Saraee, M., Shahghlian, A., & Mazrooei, P. (2010). Social networking approach for building trust in e-commerce. Journal of communication and computer, 7(6), 49-53

E-commerce has played a major role for most business functions in today's competitive enterprises. In general, E-commerce has enabled online transactions while the most important factor in a transaction between two individuals is the degree of trust... Read More about Social networking approach for building trust in e-commerce.

The use of genetic algorithm for feature selection in video concept detection (2010)
Presentation / Conference
Momtazpour, M., Saraee, M., & Palhang, M. (2010, May). The use of genetic algorithm for feature selection in video concept detection. Presented at The 18th Iranian Conference on Electrical Engineering (ICEE), 2010, Isfahan Iran

Video semantic concept detection is considered as an important research problem by the multimedia industry in recent years. Classification is the most accepted method used for concept detection, where, the output of the classification system is inter... Read More about The use of genetic algorithm for feature selection in video concept detection.

Application of data mining in traffic management: case of city of Isfahan (2010)
Presentation / Conference
Zamani, Z., Pourmand, M., & Saraee, M. (2010, May). Application of data mining in traffic management: case of city of Isfahan. Presented at 2010 International Conference on Electronic Computer Technology (ICECT), Kuala Lumpur, Malaysia

This paper describes the work investigating the application of data mining tools to aid in the development of traffic signal timing plans. A case study was conducted to illustrate that the use of hierarchical cluster analysis. This approach can be us... Read More about Application of data mining in traffic management: case of city of Isfahan.

Application of data mining in predicting cell phones subscribers behavior employing the contact pattern (2010)
Presentation / Conference
Mansouri, R., Saraee, M., & Amirfattahi, R. (2010, February). Application of data mining in predicting cell phones subscribers behavior employing the contact pattern. Presented at DSDE, Bangalore, India

As telecommunication services becoming competitive, client contract management in this sector has become importance as well. In regards to the fact that a huge volume of telecommunication data especially details of the cell phone conversations exist... Read More about Application of data mining in predicting cell phones subscribers behavior employing the contact pattern.

Privacy-preserving data mining in peer to peer networks (2010)
Book Chapter
Hussain, I., Irakleous, M., Siddiqi, M., & Saraee, M. (2010). Privacy-preserving data mining in peer to peer networks. In proceedings from the Annual International Conference on Data Analysis, Data Quality and Metadata Management. GSTF

In recent years, privacy-preserving data mining has been studied extensively, due to the wide increase of sensitive information on the internet. A number of algorithms and procedures have been designed, some of which are yet to be implemented, but a... Read More about Privacy-preserving data mining in peer to peer networks.

Better classifiers for credit scoring : a comparison study between self organizing maps (SOM) and support vector machine (SVM) (2009)
Presentation / Conference
Shahlaii Moghadam, A., Shalbafzadeh, A., & Saraee, M. (2009, December). Better classifiers for credit scoring : a comparison study between self organizing maps (SOM) and support vector machine (SVM). Presented at 3rd International Conference on Communications and information technology, Vouliagmeni, Athens, Greece

Credit scoring has become an increasingly important area for financial institutions. Self Organizing Maps and Support Vector Machine are two techniques of data mining which are used in different applications of businesses. In this paper, we use descr... Read More about Better classifiers for credit scoring : a comparison study between self organizing maps (SOM) and support vector machine (SVM).

Better classifiers for credit scoring: a comparison study between self organizing maps (SOM) and support vector machine (SVM) (2009)
Presentation / Conference
Shahlaii Moghada, A., Shalbafzadeh, A., & Saraee, M. (2009, December). Better classifiers for credit scoring: a comparison study between self organizing maps (SOM) and support vector machine (SVM). Presented at 3rd International Conference on Communications and Information Technology, Vouliagmeni, Athens, Greece

Credit scoring has become an increasingly important area for financial institutions. Self Organizing Maps (SOM) and Support Vector Machine(SVM) are two techniques of data mining which are being used in different applications of businesses. In this p... Read More about Better classifiers for credit scoring: a comparison study between self organizing maps (SOM) and support vector machine (SVM).

Extracting temporal rules from medical data (2009)
Presentation / Conference
Meamarzadeh, H., Khayyambashi, M., & Saraee, M. (2009, November). Extracting temporal rules from medical data. Presented at The 2009 International Conference on Computer Technology and Development, Kota, Kinabalu, Malaysia

Trauma is the main leading cause of death in children; we need a tool to prevent and predict the outcome in these patients. Data mining is the science of extracting the useful information from a large amount of data sets or databases that leads to... Read More about Extracting temporal rules from medical data.

Data mining cardiovascular risk factors (2009)
Presentation / Conference
Kajabadi, A., Saraee, M., & Asgari, S. (2009, October). Data mining cardiovascular risk factors. Presented at International Conference on Application of Information and Communication Technologies, 2009. AICT 2009., Baku, Azerbaija

Nowadays, medical centers collect various data in different diseases. Investigating these data and obtaining useful results and patterns with respect to the diseases are the aims of using these data. Great amount of these data and confusions results... Read More about Data mining cardiovascular risk factors.

Web search personalization: A fuzzy adaptive approach (2009)
Presentation / Conference
Norouzzadeh, M., Bagheri, A., & Saraee, M. (2009, August). Web search personalization: A fuzzy adaptive approach. Presented at 2nd IEEE International Conference on Computer Science and Information Technology, 2009. ICCSIT 2009, Beijing, China,

Today the growing rate of Web data has become so large and this is the reason for turning search engines into the major decision support systems for the Internet. In this paper, a novel and simple approach is proposed to improve Web search. The appro... Read More about Web search personalization: A fuzzy adaptive approach.

Application of self-organizing map to model a machining process (2009)
Presentation / Conference
Saraee, M., Moosavi, S., & Rezapoor, S. (2009, July). Application of self-organizing map to model a machining process. Presented at The 4th European Conference on Intelligent Management Systems in Operations, Salford, UK

Protein contact map prediction based on an ensemble learning method (2009)
Presentation / Conference
Habibi, N., & Saraee, M. (2009, January). Protein contact map prediction based on an ensemble learning method. Presented at International Conference on Computer Engineering and Technology (ICCET 2009),, Singapore

Contact map is the simplified, 2D representation of protein spatial structure. Contact map prediction is an intermediate step to predict protein 3D structure. Ensemble learning-based model is a collection of learners that is more accurate than a sing... Read More about Protein contact map prediction based on an ensemble learning method.

A new and improved skin detection method using mixed color space (2009)
Book Chapter
Aznaveh, M., Mirzaei, H., Roshan, E., & Saraee, M. (2009). A new and improved skin detection method using mixed color space. In Human-Computer Systems Interaction (471-480). Berlin: Springer. https://doi.org/10.1007/978-3-642-03202-8_37

In this paper a new and robust approach of skin detection is proposed. In the previous proposed system, we introduced a method for skin detection based on RGB vector space. An extended and modified approach based on a mixed color space is presented.... Read More about A new and improved skin detection method using mixed color space.

Applying data mining in medical data with focus on mortality related to accident in children (2008)
Presentation / Conference
Saraee, M., Ehghaghi, Z., Meamarzadeh, H., & Zibanezhad, B. (2008, December). Applying data mining in medical data with focus on mortality related to accident in children. Presented at IEEE International Multitopic Conference, Karachi, Pakistan

Trauma is the main leading cause of death in children; we need a tool to prevent and predict the outcome in these patients. Data mining is the science of extracting the useful information from a large amount of data sets or databases that leads to st... Read More about Applying data mining in medical data with focus on mortality related to accident in children.

Optimizing classification techniques using genetic programming approach (2008)
Presentation / Conference
Saraee, M., & Sadjady, R. (2008, December). Optimizing classification techniques using genetic programming approach. Presented at 12th IEEE International Multitopic Conference, Conquering the Horizons of Future Technology (IEEE INMIC 2008), Karachi, Pakistan

Genetic Programming (GP) is a branch of Genetic Algorithms (GA) that searches for the best operation or computer program in search space of operations. At the same time classification is a data mining technique used to build model of data classes... Read More about Optimizing classification techniques using genetic programming approach.

Fuzzy block matching motion estimation for video compression (2008)
Presentation / Conference
Soroushmehr, S., Samavi, S., & Saraee, M. (2008, December). Fuzzy block matching motion estimation for video compression. Presented at 2008 IEEE 9th Malay International Conference on Communications, Kuala Lumpur, Malaysia,

Motion estimation demands intense computations. To overcome this obstacle, different techniques have been devised. In this paper an efficient spatio-temporal fuzzy search algorithm is proposed to shorten the search time without the loss of accuracy.... Read More about Fuzzy block matching motion estimation for video compression.

Estimating missing value in microarray data using fuzzy clustering and gene ontology (2008)
Presentation / Conference
Mohammadi, A., & Saraee, M. (2008, November). Estimating missing value in microarray data using fuzzy clustering and gene ontology. Presented at IEEE International Conference on Bioinformatics and Biomedicine, 2008. BIBM '08., Philadelphia, PA, USA,

Microarray experiments usually generate data sets with multiple missing expression values, due to several problems. In this paper, a new and robust method based on fuzzy clustering and gene ontology is proposed to estimate missing values in microarra... Read More about Estimating missing value in microarray data using fuzzy clustering and gene ontology.

Building trust in e-commerce using social networks approach (2008)
Presentation / Conference
Shahgholian, A., Mazrooei, P., & Saraee, M. (2008, October). Building trust in e-commerce using social networks approach. Presented at The International Conference on “E-Commerce and Developing Countries (ECDC 08), Isfahan, Iran

Dealing with missing values in microarray data (2008)
Presentation / Conference
Mohammadi, A., & Saraee, M. (2008, October). Dealing with missing values in microarray data. Presented at 4th IEEE International Conference on Emerging Technologies, 2008. ICET 2008, Rawalpindi, Pakistan,

Gene expression profiling plays an important role in a broad range of areas in biology. The raw gene expression data, may contain missing values. It is an important preprocessing step to accurately estimate missing values in microarray data, because... Read More about Dealing with missing values in microarray data.

Mining protein primary structure data using committee machines approach to predict protein contact map (2008)
Presentation / Conference
Habibi, N., Mahdaviani, K., & Saraee, M. (2008, October). Mining protein primary structure data using committee machines approach to predict protein contact map. Presented at 4th IEEE International Conference on Emerging Technologies, 2008. ICET 2008., Rawalpindi, Pakistan,

Committee machines approach has shown to be useful in different applications. Protein primary structure data contain valuable information to extract. In this paper we mine these data and predict protein contact map based on committee machines. Contac... Read More about Mining protein primary structure data using committee machines approach to predict protein contact map.

Finding shortest path with learning algorithms (2008)
Journal Article
Bagheri, A., Akbarzadeh, M., & Saraee, M. (2008). Finding shortest path with learning algorithms. International Journal of Artificial Intelligence, 1(A08),

This paper presents an approach to the shortest path routing problem that uses one of the most popular learning algorithms. The Genetic Algorithm (GA) is one of the most powerful and successful method in stochastic search and optimization techniques... Read More about Finding shortest path with learning algorithms.

A new and improves skin detection method using RGB vector space (2008)
Presentation / Conference
Aznaveh, M., Mirzaei, H., Roshan, E., & Saraee, M. (2008, July). A new and improves skin detection method using RGB vector space. Presented at 5th International Multi-Conference on Systems, Signals and Devices, 2008. IEEE SSD 2008., Amman Jordan

This paper describes a new method for skin detection based on RGB vector space. Skin color has proven to be a useful cue for pre-process of face detection, localization and tracking. Image content filtering, content aware video compression and image... Read More about A new and improves skin detection method using RGB vector space.

A method to resolve the overfitting problem in recurrent neural networks for prediction of complex system's behavior (2008)
Presentation / Conference
Mahdaviani, K., Mazyar, H., Majidi, S., & Saraee, M. (2008, June). A method to resolve the overfitting problem in recurrent neural networks for prediction of complex system's behavior. Presented at IEEE World Congress on Computational Intelligence / IEEE International Joint Conference on Neural Networks, Hong Kong, China

In this paper a new method to resolve the overfitting problem for predicting complex systems' behavior has been proposed. This problem occurs when a neural network loses its generalization. The method is based on the training of recurrent neural... Read More about A method to resolve the overfitting problem in recurrent neural networks for prediction of complex system's behavior.

A new color based method for skin detection using RGB vector space (2008)
Presentation / Conference
Aznaveh, M., Mirzaei, H., Roshan, E., & Saraee, M. (2008, May). A new color based method for skin detection using RGB vector space. Presented at 2008 IEEE Conference on Human System Interactions, Krakow, Poland

This paper describes a new method for skin detection based on RGB vector space. Skin color has proven to be a useful cue for pre-process of face detection, localization and tracking. Image content filtering, content aware video compression and image... Read More about A new color based method for skin detection using RGB vector space.

A new linear appearance-based method in face recognition (2008)
Book Chapter
Hajiarbabi, M., Askari, J., Sadri, S., & Saraee, M. (2008). A new linear appearance-based method in face recognition. In Advances in Communication Systems and Electrical Engineering (579-587). Springer. https://doi.org/10.1007/978-0-387-74938-9_39

Human identification recognition has attracted scientists for many years. During these years, and due to increases in terrorism, the need for such systems has increased much more. The most important biometric systems that have been used during these... Read More about A new linear appearance-based method in face recognition.

Genome-wide efficient attribute selection for purely epistatic models via Shannon entropy (2008)
Journal Article
Manzourolajdad, A., Saraee, M., Mirlohi, A., & Javan, A. (2008). Genome-wide efficient attribute selection for purely epistatic models via Shannon entropy. International Journal of Business Intelligence and Data Mining, 3(4), 390. https://doi.org/10.1504/IJBIDM.2008.022736

Epistasis plays an important role in the genetic architecture of common human diseases. Most complex diseases are believed to have multiple contributing loci that often have subtle patterns which make them fairly difficult to find in large data sets.... Read More about Genome-wide efficient attribute selection for purely epistatic models via Shannon entropy.

Epistasy search in population-based gene mapping using mutual information (2007)
Presentation / Conference
Saraee, M., Nikoofar, H., & Manzour, A. (2007, December). Epistasy search in population-based gene mapping using mutual information. Presented at 2007 IEEE International Symposium on Signal Processing and Information Technology, Cairo, EGYPT

Gene mapping intends to identify the causal genetic regions of a specific phenotype mostly a complex disease. These diseases are believed to have multiple contributing loci that are potentially unknown and often have subtle patterns making them hard... Read More about Epistasy search in population-based gene mapping using mutual information.

An agent-based method for predicting monthly maximum & minimum quote prices (2007)
Presentation / Conference
Mazyar, H., Mahdaviani, K., Majidi, S., & Saraee, M. (2007, November). An agent-based method for predicting monthly maximum & minimum quote prices. Presented at IDMC'07, Tehran, Iran

In this paper a multi agent model for predicting monthly maximum and minimum quote prices has been proposed. This model is based on the training of Elman neural networks and using particle swarm optimization for obtaining the best parameters of the n... Read More about An agent-based method for predicting monthly maximum & minimum quote prices.

Data mining process using clustering: a survey (2007)
Presentation / Conference
Saraee, M., Ahmadian, N., & Narimani, Z. (2007, November). Data mining process using clustering: a survey. Presented at IDMC'07, Tehran, Iran

Clustering is a basic and useful method in understanding and exploring a data set. Clustering is division of data into groups of similar objects. Each group, called cluster, consists of objects that are similar between themselves and dissimilar... Read More about Data mining process using clustering: a survey.

A new and robust apple evaluation method using image processing (2007)
Presentation / Conference
Mirzaei, H., & Saraee, M. (2007, August). A new and robust apple evaluation method using image processing. Presented at First Joint Congress on Fuzzy and Intelligent Systems, Ferdowsi University of Mashhad, Iran

Fruit evaluation is a necessary component of vegetable and fruit sorting system. Recently, machine vision applications for sorting and inspecting some fruit vegetables have been studied by many scientists. In this paper a new and robust computer visi... Read More about A new and robust apple evaluation method using image processing.

Entropy-based epistasy search in SNP case-control studies (2007)
Presentation / Conference
Manzour, A., & Saraee, M. (2007, August). Entropy-based epistasy search in SNP case-control studies. Presented at Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007., Haikou, China

The purpose of gene mapping is to identify the causal genetic regions of a specific phenotype mainly a complex disease. Most complex diseases are believed to have multiple contributing loci often having subtle patterns which make them fairly difficul... Read More about Entropy-based epistasy search in SNP case-control studies.

The evaluation of camera motion, defocusing and noise immunity for linear appearance based methods in face recognition (2007)
Presentation / Conference
Hajiarbabi, M., Askari, J., Sadri, S., & Saraee, M. (2007, July). The evaluation of camera motion, defocusing and noise immunity for linear appearance based methods in face recognition. Presented at World Congress on Engineering 2007 Vol I WCE 2007, July 2 - 4, 2007, London, U.K, London, U.K

Face recognition has assigned a special place to itself because of its low intrusiveness, low cost and effort and acceptable accuracy. There are several methods for recognition and appearance based methods is one of the most popular one. Unfortunatel... Read More about The evaluation of camera motion, defocusing and noise immunity for linear appearance based methods in face recognition.

Face recognition using discrete cosine transform plus linear discriminant analysis (2007)
Presentation / Conference
Hajiarbabi, M., Askari, J., Sadri, S., & Saraee, M. (2007, July). Face recognition using discrete cosine transform plus linear discriminant analysis. Presented at The World Congress on Engineering (WCE 2007), London, U.K., 2-4 July, 2007., London, U.K

Face recognition is a biometric identification methodwhich among the other methods such as, finger printidentification, speech recognition, signature and hand writtenrecognition has assigned a special place to itself. In principle, thebiometric ident... Read More about Face recognition using discrete cosine transform plus linear discriminant analysis.

Using T3, an improved decision tree classifier, for mining stroke-related medical data (2007)
Journal Article
Saraee, M., & Keane, J. (2007). Using T3, an improved decision tree classifier, for mining stroke-related medical data. Methods of Information in Medicine, 46(5), 523-529. https://doi.org/10.1160/ME0317

Objectives: Medical data are a valuable resource from which novel and potentially useful knowledge can be discovered by using data mining. Data mining can assist and support medical decision making and enhance clinical management and investigativ... Read More about Using T3, an improved decision tree classifier, for mining stroke-related medical data.

A novel ensemble classifier-based feature selection method (2006)
Presentation / Conference
Noorbehbahani, F., & Saraee, M. (2006, July). A novel ensemble classifier-based feature selection method. Presented at The Sixth International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2012), Sanpaolo Palace Hotel, Palermo, Italy

Improving similarity search in time series using wavelets (2006)
Journal Article
Liabotis, I., Theodoulidis, B., & Saraee, M. (2006). Improving similarity search in time series using wavelets. International Journal of Data Warehousing and Mining, 2(2), 1116-1137. https://doi.org/10.4018/978-1-59904-951-9.ch064

Sequences constitute a large portion of data stored in databases. Data mining applications require the ability to process similarity queries over a large amount of time series data. The query processing performance is an important factor that needs t... Read More about Improving similarity search in time series using wavelets.

Data mining application : case of road traffic accidents in the UK West Midlands area 2000 (2005)
Presentation / Conference
Saraee, M., Kerry, J., Llyod, M., & Markey, C. (2005, July). Data mining application : case of road traffic accidents in the UK West Midlands area 2000. Presented at 3rd European Conference on Intelligent Management Systems in Operations, University of Salford

For this assignment we are aiming to use data mining techniques in the analysis of data recorded about road traffic accidents in the West Midlands Area in the year 2000. This data will then hopefully provide drivers with guidelines relating to wh... Read More about Data mining application : case of road traffic accidents in the UK West Midlands area 2000.

Application of data mining in medical domain: case of cardiology sickness level (2005)
Presentation / Conference
Saraee, M., Waheed, A., Javed, S., & Nigam, J. (2005, June). Application of data mining in medical domain: case of cardiology sickness level. Presented at Mathematics and Engineering Techniques in Medicine and Biological Scienes, Las Vegas, USA

Accuracy plays a vital role in the medical field of Cardiology as it concerns with the life of an individual. It is very important and crucial while taking decisions, which includes both the past experience and the present situations. Data mining in... Read More about Application of data mining in medical domain: case of cardiology sickness level.

Mining XML data: A clustering approach (2005)
Presentation / Conference
Saraee, M., & Aljibouri, J. (2005, June). Mining XML data: A clustering approach. Presented at DMIN, Las Vegas, USA

XML data has become very popular to represent semi structured data. This has resulted in a growing amount of XML data on the web. This raises a need for languages and tools to manage collections of XML documents as well as to mine interesting informa... Read More about Mining XML data: A clustering approach.

Identifying trends concerning computer science students with dyslexia: A data mining approach (2005)
Presentation / Conference
Saraee, M., Edwards, C., & Peers, A. (2005, June). Identifying trends concerning computer science students with dyslexia: A data mining approach. Presented at International Conference on Computers for People with Special Needs, Las Vegas, USA

With dyslexia becoming more widely accepted as a learning disability, many questions come to light about what is being done to help those affected by it. Are those diagnosed given enough support in early education and are they being encouraged to con... Read More about Identifying trends concerning computer science students with dyslexia: A data mining approach.

Data mining approach to implement a recommendation system for electronic tour guides (2005)
Presentation / Conference
Saraee, M., Khan, S., & Yamaner, S. (2005, June). Data mining approach to implement a recommendation system for electronic tour guides. Presented at CSREA EEE, Las Vegas, USA

In this paper we consider the problem of discovering patterns generated by association rules between items in a database of locations that tourists have visited within the Manchester city centre area. The focus of the paper is on the interactive agen... Read More about Data mining approach to implement a recommendation system for electronic tour guides.

Viability of implementing data mining algorithms as a web service (2005)
Presentation / Conference
Stent, C., Howard, N., Saraee, M., & Thompson, E. (2005, June). Viability of implementing data mining algorithms as a web service. Presented at International Symposium on Web Services and Applications, Las Vegas, USA

This paper describes an experiment into the viability of implementing data mining algorithms within a W3C standards compliant web service. The experiment shows that it can be done by the successful deployment of a prototype based on an implementation... Read More about Viability of implementing data mining algorithms as a web service.

A data mining approach to analysis and prediction of movie ratings (2004)
Presentation / Conference
Saraee, M., White, S., & Eccleston, J. (2004, September). A data mining approach to analysis and prediction of movie ratings. Presented at The Fifth International Conference on Data Mining, Text Mining and their Business Applications,, Malaga, Spain

This paper details our analysis of the Internet Movie Database (IMDb), a free, user-maintained, online resource of production details for over 390,000 movies, television series and video games, which contains information such as title, genre, box-off... Read More about A data mining approach to analysis and prediction of movie ratings.

One scan is enough: Optimising association rules mining (2004)
Presentation / Conference
Saraee, M., & Al-Mejrab, M. (2004, June). One scan is enough: Optimising association rules mining. Presented at The 2004 International Conference on Information and Knowledge Engineering, June 21-24, 2004, Las Vegas, USA

Data mining is as a new area of research has taken its place as one of the most important techniques in the decision making process. Mining association rules is one of simple yet powerful technique in the data mining process The problem of mining ass... Read More about One scan is enough: Optimising association rules mining.

Data mining application : case of road traffic accidents in the UK West Midlands area 2000 (2004)
Presentation / Conference
Saraee, M., Kerry, J., Lloyd, M., & Markey, C. (2004, June). Data mining application : case of road traffic accidents in the UK West Midlands area 2000. Presented at IC-AI, Las Vegas, USA

We are aiming to use data mining techniques in the analysis of data recorded about road traffic accidents in the UK West Midlands Area in the year 2000. This data will then hopefully provide drivers with guidelines relating to what measures can be t... Read More about Data mining application : case of road traffic accidents in the UK West Midlands area 2000.

SVM categorizer: a generic categorization tool using support vector machines (2004)
Presentation / Conference
Kapoutsis, E., Theodoulidis, B., & Saraee, M. (2004, June). SVM categorizer: a generic categorization tool using support vector machines. Presented at IC-AI 2004, Las Vegas, USA

Supervised text categorisation is a significant tool considering the vast amount of structured, unstruc-tured, or semi-structured texts that are available from internal or external enterprise resources. The goal of supervised text categorisation is t... Read More about SVM categorizer: a generic categorization tool using support vector machines.

Determining the locations visited by GPS users: a clustering approach (2004)
Presentation / Conference
Saraee, M., Yamaner, S., Dai, M., & Long, D. (2004, June). Determining the locations visited by GPS users: a clustering approach. Presented at CISST 2004, Las Vegas, USA

The aim of our research is to use the GPS log captured over 2 days from a PDA and try to extract locations the user have visited. For this research, we have logged GPS data over two days when the user was moving at least 2 miles per hour. To achieve... Read More about Determining the locations visited by GPS users: a clustering approach.

A web based management of references (2004)
Presentation / Conference
O'Shea, S., Saraee, M., & Vadera, S. (2004, January). A web based management of references. Presented at The 2004 International Research Conference on Innovations in Information Technology (IIT2004), Dubai, UAE, Dubai, UAE

During the evolution of research from the beginning of a project to the end a large amount of information is accumulated from books, journals, articles, manuals and the internet. Managing all this information is a complex and crucial part, especially... Read More about A web based management of references.

Mining knowledge in train scheduling data (2001)
Presentation / Conference
Saraee, M., & Theodoulidis, B. (2001, July). Mining knowledge in train scheduling data. Presented at The 2nd European Conference on Intelligent Management Systems in Operations, Salford, UK

TempoMiner: towards mining time-oriented data (2000)
Thesis
Saraee, M. TempoMiner: towards mining time-oriented data. (Thesis). University of Manchester, Institute of Science and Technology

The time dimension is a unique and powerful dimension in every enterprise data. In dynamic application such as financial and medical applications representing data as it changes overtime is a common problem. There are diverse applications that requir... Read More about TempoMiner: towards mining time-oriented data.

Pattern discovery in time-oriented data (1998)
Presentation / Conference
Saraee, M., Theodoulidis, B., & Koundourakis, G. (1998, November). Pattern discovery in time-oriented data. Presented at International Conference on Advances in Pattern Recognition, Plymouth, England

We present a data mining system, EasyMiner which has been developed for interactive mining of interesting patterns in time-oriented databases. This system implements a wide spectrum of data mining functions, including generalisation, characterisati... Read More about Pattern discovery in time-oriented data.

EasyMiner: data mining in medical databases (1998)
Presentation / Conference
Saraee, M., Koundourakis, G., & Theodoulidis, B. (1998, October). EasyMiner: data mining in medical databases. Presented at IEE Colloquium on Intelligent Methods in Healthcare and Medical Applications, York, UK

Data mining techniques have rarely been applied to medical domain. The University of Manchester Institute of Science and Technology (UMIST) is currently in the process of experimenting with a data mining project using an extensive clinical database o... Read More about EasyMiner: data mining in medical databases.

Data mining in temporal databases (1998)
Presentation / Conference
Saraee, M., & Theodoulidis, B. (1998, October). Data mining in temporal databases. Presented at Panhellenic Conference on New Information Technology Athens, Hellas, Athens, Greece

Case-control study of stroke and the quality of hypertension control in north west England (1997)
Journal Article
Du, X., Cruickshank, K., McNamee, R., Saraee, M., Sourbutts, J., Summers, A., …Holmes, S. (1997). Case-control study of stroke and the quality of hypertension control in north west England. BMJ, 314(7076), 272-6. https://doi.org/10.1136/bmj.314.7076.272

Objective: To examine the risk of stroke in relation to quality of hypertension control in routine general practice across an entire health district. Design: Population based matched case-control study. Setting: East Lancashire Health District with... Read More about Case-control study of stroke and the quality of hypertension control in north west England.

Case control study of stroke and quality of hypertension control in Northwest of England (1996)
Presentation / Conference
Du, X., Cruickshank, J., Saraee, M., McNamee, R., Hannaford, O., Sourbutts, J., …Theodoulidis, B. (1996, August). Case control study of stroke and quality of hypertension control in Northwest of England. Presented at The XIV International Scientific Meeting of the International Epidemiological Association, Nagoya, Japan

Knowledge discovery in temporal databases (1995)
Presentation / Conference
Saraee, M., & Theodoulidis, B. (1995, February). Knowledge discovery in temporal databases. Presented at IEE Colloquium on Knowledge Discovery in Databases, London, UK

Knowledge discovery in databases is the process of applying statistical, machine learning and other techniques to conventional database systems. Our survey in knowledge discovery systems has indicated that up to date there is no knowledge discovery s... Read More about Knowledge discovery in temporal databases.

Applying NLP to build a cold reading chatbot
Presentation / Conference
Tracey, P., Saraee, M., & Hughes, C. Applying NLP to build a cold reading chatbot. Presented at ISEEIE 2021: 2021 International Symposium on Electrical, Electronics and Information Engineering, Seoul, Republic of Korea

Chatbots are computer programs designed to simulate conversation by interacting with a human user. In this paper we present a chatbot framework designed specifically to aid prolonged grief disorder (PGD) sufferers by replicating the techniques perfor... Read More about Applying NLP to build a cold reading chatbot.

Data science in public mental health : a new analytic framework
Presentation / Conference
Silva, H., Saraee, M., & Saraee, M. Data science in public mental health : a new analytic framework. Presented at IEEE Symposium on Computers and Communications June 30 - July 3, 2019 – Barcelona, Spain

Understanding public mental health issues and finding solutions can be complex and requires advanced techniques, compared to conventional data analysis projects. It is important to have a comprehensive project management process to ensure that... Read More about Data science in public mental health : a new analytic framework.

Optimum parameter machine learning classification and prediction of Internet of Things (IoT) malwares using static malware analysis techniques
Thesis
Shaukat, S. (in press). Optimum parameter machine learning classification and prediction of Internet of Things (IoT) malwares using static malware analysis techniques. (Dissertation). University of Salford

Application of machine learning in the field of malware analysis is not a new concept, there have been lots of researches done on the classification of malware in android and windows environments. However, when it comes to malware analysis in the int... Read More about Optimum parameter machine learning classification and prediction of Internet of Things (IoT) malwares using static malware analysis techniques.

Latent dirichlet markov allocation for sentiment analysis
Presentation / Conference
Bagheri, A., Saraee, M., & de Jong, F. Latent dirichlet markov allocation for sentiment analysis. Presented at The Fifth European Conference on Intelligent Management Systems in Operations (IMSIO 5), Thinklab, University of Salford

In recent years probabilistic topic models have gained tremendous attention in data mining and natural language processing research areas. In the field of information retrieval for text mining, a variety of probabilistic topic models have been used t... Read More about Latent dirichlet markov allocation for sentiment analysis.

A state of the art survey on semantic web mining
Journal Article
Quboa, Q., & Saraee, M. A state of the art survey on semantic web mining. Intelligent Information Management, 05(01), 10-17. https://doi.org/10.4236/iim.2013.51002

The integration of the two fast-developing scientific research areas Semantic Web and Web Mining is known as Semantic Web Mining. The huge increase in the amount of Semantic Web data became a perfect target for many researchers to apply Data Mining t... Read More about A state of the art survey on semantic web mining.

A novel method in scam detection and prevention using data mining approaches
Presentation / Conference
Mokhtari, M., Saraee, M., & Haghshenas, A. A novel method in scam detection and prevention using data mining approaches. Presented at IDMC2008, Amir Kabir University, Tehran Iran

‘Scam’ is a fraudulence message by criminal intent sent to internet user mailboxes. Many approaches have been proposed to filter out unsolicited messages known as ‘spam’ from legitimate messages known as ‘ham’. However up to this date no suitable app... Read More about A novel method in scam detection and prevention using data mining approaches.

Mining GPS logs to augment location models
Presentation / Conference
Saraee, M., & Yamaner, S. Mining GPS logs to augment location models. Presented at The Sixth International Conference on Data Mining, Text Mining and their Business Applications May 25 – 27, 2005, Skiathos, Greece., 2005, Skiathos, Greece

The availability of mobile computing and satellite technologies make it possible to develop applications that are aware of user location.However, as the amount of collected data grows quickly, coming up with techniques that ease interpretation of suc... Read More about Mining GPS logs to augment location models.

Improving genetic algorithm with the help of novel twin removal method
Presentation / Conference
Imani, M., Pakizeh, E., & Saraee, M. Improving genetic algorithm with the help of novel twin removal method. Presented at 10th IASTED International Conference on Artificial Intelligence and Applications, held February 15-17, 2010 in Innsbruck, Austria., Innsbruck, Austria

Evolutionary Algorithms is one of the fastest growing areas of computer science. The simple Genetic Algorithm is fairly representative of other EAs. As they all use the same steps, significant researches in this area focus on Genetic Algorithm (GA).... Read More about Improving genetic algorithm with the help of novel twin removal method.

Iris disease classifying using neuro-fuzzy medical diagnosis machine
Book Chapter
Moein, S., Saraee, M., & Moein, M. Iris disease classifying using neuro-fuzzy medical diagnosis machine. In The Sixth International Symposium on Neural Networks (ISNN 2009) (359-368). Springer Berlin / Heidelberg,. https://doi.org/10.1007/978-3-642-01216-7_38

Disease diagnosis is an essential task in the medical world. The use of computers in the practice of medicine is becoming more and more crucial. In this paper, we propose an intelligent system to help us diagnose the Iris disease. This system is base... Read More about Iris disease classifying using neuro-fuzzy medical diagnosis machine.

Classifying advanced malware into families based on instruction link analysis
Thesis
Tabatabaei, S. (in press). Classifying advanced malware into families based on instruction link analysis. (Dissertation). University of Salford

With the ever-increasing growth of network resources, a great number of organizations are extremely dependent on the internet for operational activities as such, exposing their sensitive and confidential information to intrusion or invasion by sabote... Read More about Classifying advanced malware into families based on instruction link analysis.