All Outputs (16)

Understanding Public Perceptions and Behaviours Towards COVID-19 Vaccination: A Multifaceted Analysis (2025)
Presentation / Conference Contribution

The rapid growth of social media platforms has led to an overwhelming influx of unstructured textual data, which provides valuable insights into public sentiment. However, analyzing this data presents significant challenges due to its informal langua... Read More about Understanding Public Perceptions and Behaviours Towards COVID-19 Vaccination: A Multifaceted Analysis.

Comparative Evaluation of Machine Learning and Signature-Based NIDS for Multi-Class and Binary Threat Detection (2025)
Presentation / Conference Contribution

Network Intrusion Detection Systems (NIDS) are essential in safeguarding networks from evolving cyber threats. Traditional signature-based NIDS, such as Snort, struggle with zero-day attacks and lack adaptability. This study presents a unified, empir... Read More about Comparative Evaluation of Machine Learning and Signature-Based NIDS for Multi-Class and Binary Threat Detection.

Analyzing and Predicting Housing Affordability Trends in the UK Using Machine Learning (2025)
Presentation / Conference Contribution

Housing affordability has become an increasingly critical issue in the United Kingdom, driven by rising house prices and rent levels that outpace income growth. This study leverages machine learning and data-driven techniques to analyse and predict a... Read More about Analyzing and Predicting Housing Affordability Trends in the UK Using Machine Learning.

Metaverse Innovation Canvas: A Tool for Extended Reality Product/Service Development (2025)
Presentation / Conference Contribution

This study investigated the factors contributing to the failure of augmented reality (AR) and virtual reality (VR) startups in the emerging metaverse landscape. Through an in-depth analysis of 29 failed AR/VR startups from 2016 to 2022, key pitfalls... Read More about Metaverse Innovation Canvas: A Tool for Extended Reality Product/Service Development.

A Comparative Study on the Characteristics and Behaviours of Social Media Users in Response to Fake News on X(Twitter) (2024)
Presentation / Conference Contribution

Social media has proven to be a game- changer in providing avenues for individuals from diverse back- grounds to communicate, exchange ideas, and enable friendships and businesses to reach new heights. Despite these profound advantages, it also has i... Read More about A Comparative Study on the Characteristics and Behaviours of Social Media Users in Response to Fake News on X(Twitter).

Improving Predictive Process Analytics with Deep Learning and XAI (2024)
Thesis

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

A systematic review of research on cheating in online exams from 2010 to 2021 (2022)
Journal Article
Noorbehbahani, F., Mohammadi, A., & Aminazadeh, M. (2022). A systematic review of research on cheating in online exams from 2010 to 2021. Education and Information Technologies, 27(6), 8413-8460. https://doi.org/10.1007/s10639-022-10927-7

In recent years, online learning has received more attention than ever before. One of the most challenging aspects of online education is the students' assessment since academic integrity could be violated due to various cheating behaviors in online... Read More about A systematic review of research on cheating in online exams from 2010 to 2021.

Ensemble Deep Learning for Aspect-based Sentiment Analysis (2021)
Journal Article

Sentiment analysis is a subfield of Natural Language Processing (NLP) which tries to process a text to extract opinions or attitudes towards topics or entities. Recently, the use of deep learning methods for sentiment analysis has received noticeable... Read More about Ensemble Deep Learning for Aspect-based Sentiment Analysis.

Proposing a Meta-Heuristic Approach for the Long Tail Problem of Recommender Systems (2021)
Presentation / Conference Contribution

Recommendation systems are a solution for providing appropriate suggestions to users and helping them in the decision-making process. In the most recommendation systems, the purpose is to offer items tailored to the user's interests based on the past... Read More about Proposing a Meta-Heuristic Approach for the Long Tail Problem of Recommender Systems.

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.

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.

A survey on utilization of data mining approaches for dermatological (skin) diseases prediction (2011)
Journal Article

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.

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.

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.

Improving gene ranking methods using fuzzy clustering and gene ontology (2008)
Presentation / Conference
Mohammadi, A., & Saraee, M. (2008, September). Improving gene ranking methods using fuzzy clustering and gene ontology. Paper presented at International Conference of the European Molecular Biology Network (EMBnet), Martina Franca, Italy