Suleiman Ali Alsaif

@iau.edu.sa

Computer Science Department
Imam Abdulrahman Bin Faisal University

Suleiman Ali Alsaif


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https://researchid.co/saalsaif
23

Scopus Publications

419

Scholar Citations

10

Scholar h-index

10

Scholar i10-index

Scopus Publications

  • Scalable multi-metric association rule learning for explainable book recommendations
    Adel Hidri, Suleiman Ali AlSaif, Eman AlShehri, Minyar Sassi Hidri
    Frontiers in Computer Science, 2026
    Digital reading platforms have grown rapidly, increasing information overload and highlighting the need for efficient and transparent recommendation systems. This study presents a scalable hybrid framework that combines multi-metric association rule learning (ARL) with intelligent filtering strategies to provide clear, high-quality book recommendations at scale. Unlike traditional ARL-based recommenders that depend on a single metric or small datasets, our approach combines support, confidence, and lift measures to identify strong behavioral patterns while maintaining computational efficiency. The framework uses data-reduction strategies that select active users and high-impact items, transforming a sparse rating matrix into a dense, computationally tractable representation. Extensive experiments on a real-world dataset demonstrated that our method significantly outperforms collaborative filtering, neural models, and rule-mining baselines in precision, recall, and normalized discounted cumulative gain (NDCG). The resulting rules are inherently interpretable, enabling clear explanations for recommendations, which is a critical feature of modern personalized systems. This study demonstrates that ARL remains viable when designed with modern scalability constraints in mind, providing an explainable, efficient solution for digital libraries, online platforms, and large-scale recommender systems.
  • Opinion Mining and Analysis Using Hybrid Deep Neural Networks
    Adel Hidri, Suleiman Ali Alsaif, Muteeb Alahmari, Eman AlShehri, Minyar Sassi Hidri
    Technologies, 2025
    Understanding customer attitudes has become a critical component of decision-making due to the growing influence of social media and e-commerce. Text-based opinions are the most structured, hence playing an important role in sentiment analysis. Most of the existing methods, which include lexicon-based approaches and traditional machine learning techniques, are insufficient for handling contextual nuances and scalability. While the latter has limitations in model performance and generalization, deep learning (DL) has achieved improvement, especially on semantic relationship capturing with recurrent neural networks (RNNs) and convolutional neural networks (CNNs). The aim of the study is to enhance opinion mining by introducing a hybrid deep neural network model that combines a bidirectional gated recurrent unit (BGRU) and long short-term memory (LSTM) layers to improve sentiment analysis, particularly addressing challenges such as contextual nuance, scalability, and class imbalance. To substantiate the efficacy of the proposed model, we conducted comprehensive experiments utilizing benchmark datasets, encompassing IMDB movie critiques and Amazon product evaluations. The introduced hybrid BGRU-LSTM (HBGRU-LSTM) architecture attained a testing accuracy of 95%, exceeding the performance of traditional DL frameworks such as LSTM (93.06%), CNN+LSTM (93.31%), and GRU+LSTM (92.20%). Moreover, our model exhibited a noteworthy enhancement in recall for negative sentiments, escalating from 86% (unbalanced dataset) to 96% (balanced dataset), thereby ensuring a more equitable and just sentiment classification. Furthermore, the model diminished misclassification loss from 20.24% for unbalanced to 13.3% for balanced dataset, signifying enhanced generalization and resilience.
  • Enhancing Sensor-Based Human Physical Activity Recognition Using Deep Neural Networks
    Minyar Sassi Hidri, Adel Hidri, Suleiman Ali Alsaif, Muteeb Alahmari, Eman AlShehri
    Journal of Sensor and Actuator Networks, 2025
    Human activity recognition (HAR) is the task of classifying sequences of data into defined movements. Taking advantage of deep learning (DL) methods, this research investigates and optimizes neural network architectures to effectively classify physical activities from smartphone accelerometer data. Unlike traditional machine learning (ML) methods employing manually crafted features, our approach employs automated feature learning with three deep learning architectures: Convolutional Neural Networks (CNN), CNN-based autoencoders, and Long Short-Term Memory Recurrent Neural Networks (LSTM RNN). The contribution of this work is primarily in optimizing LSTM RNN to leverage the most out of temporal relationships between sensor data, significantly improving classification accuracy. Experimental outcomes for the WISDM dataset show that the proposed LSTM RNN model achieves 96.1% accuracy, outperforming CNN-based approaches and current ML-based methods. Compared to current works, our optimized frameworks achieve up to 6.4% higher classification performance, which means that they are more appropriate for real-time HAR.
  • Optimal Convolutional Networks for Staging and Detecting of Diabetic Retinopathy
    Minyar Sassi Hidri, Adel Hidri, Suleiman Ali Alsaif, Muteeb Alahmari, Eman AlShehri
    Information Switzerland, 2025
    Diabetic retinopathy (DR) is the main ocular complication of diabetes. Asymptomatic for a long time, it is subject to annual screening using dilated fundus or retinal photography to look for early signs. Fundus photography and optical coherence tomography (OCT) are used by ophthalmologists to assess retinal thickness and structure, as well as detect edema, hemorrhage, and scarring. The effectiveness of ConvNet no longer needs to be demonstrated, and its use in the field of imaging has made it possible to overcome many barriers, which were until now insurmountable with old methods. Throughout this study, a robust and optimal deep ConvNet is proposed to analyze fundus images and automatically distinguish between healthy, moderate, and severe DR. The proposed model combines the use of the ConvNet architecture taken from ImageNet, data augmentation, class balancing, and transfer learning in order to establish a benchmarking test. A significant improvement at the level of middle class which corresponds to the early stage of DR, which was the major problem in previous studies. By eliminating the need for retina specialists and broadening access to retinal care, the proposed model is substantially more robust in objectively early staging and detecting DR.
  • Dynamic Road Anomaly Detection: Harnessing Smartphone Accelerometer Data with Incremental Concept Drift Detection and Classification
    Imen Ferjani, Suleiman Ali Alsaif
    Sensors, 2024
    Effective monitoring of road conditions is crucial for ensuring safe and efficient transportation systems. By leveraging the power of crowd-sourced smartphone sensor data, road condition monitoring can be conducted in real-time, providing valuable insights for transportation planners, policymakers, and the general public. Previous studies have primarily focused on the use of pre-trained machine learning models and threshold-based methods for anomaly classification, which may not be suitable for real-world scenarios that require incremental detection and classification. As a result, there is a need for novel approaches that can adapt to changing data environments and perform effective classification without relying on pre-existing training data. This study introduces a novel, real-time road condition monitoring technique harnessing smartphone sensor data, addressing the limitations of pre-trained models that lack adaptability in dynamic environments. A hybrid anomaly detection method, combining unsupervised and supervised learning, is proposed to effectively manage concept drift, demonstrating a significant improvement in accuracy and robustness with a 96% success rate. The findings underscore the potential of incremental learning to enhance model responsiveness and efficiency in distinguishing various road anomalies, offering a promising direction for future transportation safety and resource optimization strategies.
  • STARM: STreaming Association Rules Mining in High-Dimensional Data
    Rania Mkhinini Gahar, Olfa Arfaoui, Adel Hidri, Suleiman Ali Alsaif, Minyar Sassi Hidri
    Lecture Notes on Data Engineering and Communications Technologies, 2024
  • Machine Learning-Based Ransomware Classification of Bitcoin Transactions
    Suleiman Ali Alsaif
    Applied Computational Intelligence and Soft Computing, 2023
    Ransomware attacks are one of the most dangerous related crimes in the coin market. To increase the challenge of fighting the attack, early detection of ransomware seems necessary. In this article, we propose a high-performance Bitcoin transaction predictive system that investigates Bitcoin payment transactions to learn data patterns that can recognize and classify ransomware payments for heterogeneous bitcoin networks into malicious or benign transactions. The proposed approach makes use of three supervised machine learning methods to learn the distinctive patterns in Bitcoin payment transactions, namely, logistic regression (LR), random forest (RF), and Extreme Gradient Boosting (XGBoost). We evaluate these ML-based predictive models on the BitcoinHeist ransomware dataset in terms of classification accuracy and other evaluation measures such as confusion matrix, recall, and F1-score. It turned out that the experimental results recorded by the XGBoost model achieved an accuracy of 99.08%. As a result, the resulting model accuracy is higher than many recent state-of-the-art models developed to detect ransomware payments in Bitcoin transactions.
  • Computational Linguistics with Optimal Deep Belief Network Based Irony Detection in Social Media
    Manar Ahmed Hamza, Hala J. Alshahrani, Abdulkhaleq Q. A. Hassan, Abdulbaset Gaddah, Nasser Allheeib, Suleiman Ali Alsaif, Badriyya B. Al-onazi, Heba Mohsen
    Computers Materials and Continua, 2023
    Computational linguistics refers to an interdisciplinary field associated with the computational modelling of natural language and studying appropriate computational methods for linguistic questions. The number of social media users has been increasing over the last few years, which have allured researchers’ interest in scrutinizing the new kind of creative language utilized on the Internet to explore communication and human opinions in a better way. Irony and sarcasm detection is a complex task in Natural Language Processing (NLP). Irony detection has inferences in advertising, sentiment analysis (SA), and opinion mining. For the last few years, irony-aware SA has gained significant computational treatment owing to the prevalence of irony in web content. Therefore, this study develops Computational Linguistics with Optimal Deep Belief Network based Irony Detection and Classification (CLODBN-IRC) model on social media. The presented CLODBN-IRC model mainly focuses on the identification and classification of irony that exists in social media. To attain this, the presented CLODBN-IRC model performs different stages of pre-processing and TF-IDF feature extraction. For irony detection and classification, the DBN model is exploited in this work. At last, the hyperparameters of the DBN model are optimally modified by improved artificial bee colony optimization (IABC) algorithm. The experimental validation of the presented CLODBN-IRC method can be tested by making use of benchmark dataset. The simulation outcomes highlight the superior outcomes of the presented CLODBN-IRC model over other approaches.
  • NLP-Based Bi-Directional Recommendation System: Towards Recommending Jobs to Job Seekers and Resumes to Recruiters
    Suleiman Ali Alsaif, Minyar Sassi Hidri, Imen Ferjani, Hassan Ahmed Eleraky, Adel Hidri
    Big Data and Cognitive Computing, 2022
    For more than ten years, online job boards have provided their services to both job seekers and employers who want to hire potential candidates. The provided services are generally based on traditional information retrieval techniques, which may not be appropriate for both job seekers and employers. The reason is that the number of produced results for job seekers may be enormous. Therefore, they are required to spend time reading and reviewing their finding criteria. Reciprocally, recruitment is a crucial process for every organization. Identifying potential candidates and matching them with job offers requires a wide range of expertise and knowledge. This article proposes a reciprocal recommendation based on bi-directional correspondence as a way to support both recruiters’ and job seekers’ work. Recruiters can find the best-fit candidates for every job position in their job postings, and job seekers can find the best-match jobs to match their resumes. We show how machine learning can solve problems in natural language processing of text content and similarity scores depending on job offers in major Saudi cities scraped from Indeed. For bi-directional matching, a similarity calculation based on the integration of explicit and implicit job information from two sides (recruiters and job seekers) has been used. The proposed system is evaluated using a resume/job offer dataset. The performance of generated recommendations is evaluated using decision support measures. Obtained results confirm that the proposed system can not only solve the problem of bi-directional recommendation, but also improve the prediction accuracy.
  • Learning-Based Matched Representation System for Job Recommendation
    Suleiman Ali Alsaif, Minyar Sassi Hidri, Hassan Ahmed Eleraky, Imen Ferjani, Rimah Amami
    Computers, 2022
    Job recommender systems (JRS) are a subclass of information filtering systems that aims to help job seekers identify what might match their skills and experiences and prevent them from being lost in the vast amount of information available on job boards that aggregates postings from many sources such as LinkedIn or Indeed. A variety of strategies used as part of JRS have been implemented, most of them failed to recommend job vacancies that fit properly to the job seekers profiles when dealing with more than one job offer. They consider skills as passive entities associated with the job description, which need to be matched for finding the best job recommendation. This paper provides a recommender system to assist job seekers in finding suitable jobs based on their resumes. The proposed system recommends the top-n jobs to the job seekers by analyzing and measuring similarity between the job seeker’s skills and explicit features of job listing using content-based filtering. First-hand information was gathered by scraping jobs description from Indeed from major cities in Saudi Arabia (Dammam, Jeddah, and Riyadh). Then, the top skills required in job offers were analyzed and job recommendation was made by matching skills from resumes to posted jobs. To quantify recommendation success and error rates, we sought to compare the results of our system to reality using decision support measures.
  • How to get best predictions for road monitoring using machine learning techniques
    Imen Ferjani, Suleiman Ali Alsaif
    Peerj Computer Science, 2022
  • Performance analysis of machine learning algorithms on networks intrusion detection
    Minyar Sassi Hidri, Suleiman Ali Alsaif, Adel Hidri
    International Journal of Computer Applications in Technology, 2022
  • Stacking-based modelling for improved over-indebtedness predictions
    Suleiman Ali Alsaif, Adel Hidri, Minyar Sassi Hidri
    International Journal of Computer Applications in Technology, 2022
  • Using capsule networks for android malware detection through orientation-based features
    Sohail Khan, Mohammad Nauman, Suleiman Ali Alsaif, Toqeer Ali Syed, Hassan Ahmad Eleraky
    Computers Materials and Continua, 2022
  • The Use of an Incremental Learning Algorithm for Diagnosing COVID-19 from Chest X-ray Images
    Mendel, 2022
  • ECG Classification for Detecting ECG Arrhythmia Empowered with Deep Learning Approaches
    Atta-ur Rahman, Rizwana Naz Asif, Kiran Sultan, Suleiman Ali Alsaif, Sagheer Abbas, Muhammad Adnan Khan, Amir Mosavi
    Computational Intelligence and Neuroscience, 2022
  • Multi-Cloud Integration Security Framework Using Honeypots
    Tahir Alyas, Khalid Alissa, Mohammed Alqahtani, Tauqeer Faiz, Suleiman Ali Alsaif, Nadia Tabassum, Hafiz Hasan Naqvi
    Mobile Information Systems, 2022
  • IoMT-Based Mitochondrial and Multifactorial Genetic Inheritance Disorder Prediction Using Machine Learning
    Atta-ur Rahman, Muhammad Umar Nasir, Mohammed Gollapalli, Suleiman Ali Alsaif, Ahmad S. Almadhor, Shahid Mehmood, Muhammad Adnan Khan, Amir Mosavi
    Computational Intelligence and Neuroscience, 2022
  • Impact of data balancing during training for best predictions
    Suleiman Ali AlSaif, Adel Hidri
    Informatica Slovenia, 2021
  • Towards inferring influential facebook users
    Suleiman Ali Alsaif, Adel Hidri, Minyar Sassi Hidri
    Computers, 2021
  • Large scale data mining for banking credit risk prediction
    Suleiman Ali AlSaif
    Proceedings 2020 International Conference on Computational Science and Computational Intelligence Csci 2020, 2020
  • The efficacy of Facebook in teaching and learning: Studied via content analysis of web log data
    Suleiman Alsaif, Alice S Li, Ben Soh, Sara Alraddady
    Procedia Computer Science, 2019
  • A snapshot survey of Saudi higher education students' perceptions of SNSs and Web 2.0 as a support learning tool based on the proposed social learning hub model
    Suleiman Alsaif, Alice S. Li, Ben Soh
    International Journal of Services Economics and Management, 2017

RECENT SCHOLAR PUBLICATIONS

  • Scalable multi-metric association rule learning for explainable book recommendations
    A Hidri, SA AlSaif, E AlShehri, M Sassi Hidri
    Frontiers in Computer Science 8, 1779096 , 2026
    2026
  • Opinion mining and analysis using hybrid deep neural networks
    A Hidri, SA Alsaif, M Alahmari, E AlShehri, M Sassi Hidri
    Technologies 13 (5), 175 , 2025
    2025
    Citations: 5
  • Enhancing sensor-based human physical activity recognition using deep neural networks
    M Sassi Hidri, A Hidri, SA Alsaif, M Alahmari, E AlShehri
    Journal of Sensor and Actuator Networks 14 (2), 42 , 2025
    2025
    Citations: 8
  • Optimal convolutional networks for staging and detecting of diabetic retinopathy
    M Sassi Hidri, A Hidri, SA Alsaif, M Alahmari, E AlShehri
    Information 16 (3), 221 , 2025
    2025
    Citations: 6
  • Dynamic road anomaly detection: Harnessing smartphone accelerometer data with incremental concept drift detection and classification
    I Ferjani, SA Alsaif
    Sensors 24 (24), 8112 , 2024
    2024
    Citations: 10
  • STARM: Streaming association rules mining in high-dimensional data
    R Mkhinini Gahar, O Arfaoui, A Hidri, SA Alsaif, M Sassi Hidri
    International Conference on Advanced Information Networking and Applications … , 2024
    2024
    Citations: 3
  • Computational Linguistics with Optimal Deep Belief Network Based Irony Detection in Social Media.
    MA Hamza, HJ Alshahrani, AQA Hassan, A Gaddah, N Allheeib, SA Alsaif, ...
    Computers, Materials & Continua 75 (2), 4137 , 2023
    2023
    Citations: 7
  • Machine Learning‐Based Ransomware Classification of Bitcoin Transactions
    SA Alsaif
    Applied Computational Intelligence and Soft Computing 2023 (1), 6274260 , 2023
    2023
    Citations: 35
  • NLP-based bi-directional recommendation system: Towards recommending jobs to job seekers and resumes to recruiters
    SA Alsaif, M Sassi Hidri, I Ferjani, HA Eleraky, A Hidri
    Big Data and Cognitive Computing 6 (4), 147 , 2022
    2022
    Citations: 50
  • Learning-based matched representation system for job recommendation
    SA Alsaif, M Sassi Hidri, HA Eleraky, I Ferjani, R Amami
    Computers 11 (11), 161 , 2022
    2022
    Citations: 42
  • How to get best predictions for road monitoring using machine learning techniques
    I Ferjani, SA Alsaif
    PeerJ Computer Science 8, e941 , 2022
    2022
    Citations: 24
  • Using Capsule Networks for Android Malware Detection Through Orientation-Based Features.
    S Khan, M Nauman, SA Alsaif, TA Syed, HA Eleraky
    Computers, Materials & Continua 70 (3) , 2022
    2022
    Citations: 5
  • Ecg classification for detecting ecg arrhythmia empowered with deep learning approaches
    R Asif, K Sultan, S Alsaif, S Abbas, M Khan, A Mosavi
    Computational Intelligence and Neuroscience 2022, 1-12 , 2022
    2022
    Citations: 5
  • Research Article IoMT-Based Mitochondrial and Multifactorial Genetic Inheritance Disorder Prediction Using Machine Learning
    A Rahman, MU Nasir, M Gollapalli, SA Alsaif, AS Almadhor, S Mehmood, ...
    2022
  • ECG classification for detecting ECG arrhythmia empowered with deep learning approaches
    AU Rahman, RN Asif, K Sultan, SA Alsaif, S Abbas, MA Khan, A Mosavi
    Computational intelligence and neuroscience 2022 (1), 6852845 , 2022
    2022
    Citations: 66
  • Research Article ECG Classification for Detecting ECG Arrhythmia Empowered with Deep Learning Approaches
    A Rahman, RN Asif, K Sultan, SA Alsaif, S Abbas, MA Khan, A Mosavi
    2022
  • Performance analysis of machine learning algorithms on networks intrusion detection
    MS Hidri, SA Alsaif, A Hidri
    International Journal of Computer Applications in Technology 70 (3-4), 285-295 , 2022
    2022
    Citations: 3
  • Stacking-based modelling for improved over-indebtedness predictions
    SA Alsaif, A Hidri, MS Hidri
    International Journal of Computer Applications in Technology 69 (3), 273-281 , 2022
    2022
    Citations: 3
  • Multi‐cloud integration security framework using honeypots
    T Alyas, K Alissa, M Alqahtani, T Faiz, SA Alsaif, N Tabassum, HH Naqvi
    Mobile Information Systems 2022 (1), 2600712 , 2022
    2022
    Citations: 55
  • IoMT‐Based Mitochondrial and Multifactorial Genetic Inheritance Disorder Prediction Using Machine Learning
    A Rahman, MU Nasir, M Gollapalli, SA Alsaif, AS Almadhor, S Mehmood, ...
    Computational Intelligence and Neuroscience 2022 (1), 2650742 , 2022
    2022
    Citations: 33

MOST CITED SCHOLAR PUBLICATIONS

  • ECG classification for detecting ECG arrhythmia empowered with deep learning approaches
    AU Rahman, RN Asif, K Sultan, SA Alsaif, S Abbas, MA Khan, A Mosavi
    Computational intelligence and neuroscience 2022 (1), 6852845 , 2022
    2022
    Citations: 66
  • Multi‐cloud integration security framework using honeypots
    T Alyas, K Alissa, M Alqahtani, T Faiz, SA Alsaif, N Tabassum, HH Naqvi
    Mobile Information Systems 2022 (1), 2600712 , 2022
    2022
    Citations: 55
  • NLP-based bi-directional recommendation system: Towards recommending jobs to job seekers and resumes to recruiters
    SA Alsaif, M Sassi Hidri, I Ferjani, HA Eleraky, A Hidri
    Big Data and Cognitive Computing 6 (4), 147 , 2022
    2022
    Citations: 50
  • Learning-based matched representation system for job recommendation
    SA Alsaif, M Sassi Hidri, HA Eleraky, I Ferjani, R Amami
    Computers 11 (11), 161 , 2022
    2022
    Citations: 42
  • Machine Learning‐Based Ransomware Classification of Bitcoin Transactions
    SA Alsaif
    Applied Computational Intelligence and Soft Computing 2023 (1), 6274260 , 2023
    2023
    Citations: 35
  • IoMT‐Based Mitochondrial and Multifactorial Genetic Inheritance Disorder Prediction Using Machine Learning
    A Rahman, MU Nasir, M Gollapalli, SA Alsaif, AS Almadhor, S Mehmood, ...
    Computational Intelligence and Neuroscience 2022 (1), 2650742 , 2022
    2022
    Citations: 33
  • How to get best predictions for road monitoring using machine learning techniques
    I Ferjani, SA Alsaif
    PeerJ Computer Science 8, e941 , 2022
    2022
    Citations: 24
  • The efficacy of Facebook in teaching and learning: studied via content analysis of web log data
    S Alsaif, AS Li, B Soh, S Alraddady
    Procedia Computer Science 161, 493-501 , 2019
    2019
    Citations: 23
  • Towards inferring influential facebook users
    SA Alsaif, A Hidri, MS Hidri
    Computers 10 (5), 62 , 2021
    2021
    Citations: 13
  • Dynamic road anomaly detection: Harnessing smartphone accelerometer data with incremental concept drift detection and classification
    I Ferjani, SA Alsaif
    Sensors 24 (24), 8112 , 2024
    2024
    Citations: 10
  • Impact of data balancing during training for best predictions
    SA Alsaif, A Hidri
    Informatica 45 (2) , 2021
    2021
    Citations: 9
  • Enhancing sensor-based human physical activity recognition using deep neural networks
    M Sassi Hidri, A Hidri, SA Alsaif, M Alahmari, E AlShehri
    Journal of Sensor and Actuator Networks 14 (2), 42 , 2025
    2025
    Citations: 8
  • Computational Linguistics with Optimal Deep Belief Network Based Irony Detection in Social Media.
    MA Hamza, HJ Alshahrani, AQA Hassan, A Gaddah, N Allheeib, SA Alsaif, ...
    Computers, Materials & Continua 75 (2), 4137 , 2023
    2023
    Citations: 7
  • Optimal convolutional networks for staging and detecting of diabetic retinopathy
    M Sassi Hidri, A Hidri, SA Alsaif, M Alahmari, E AlShehri
    Information 16 (3), 221 , 2025
    2025
    Citations: 6
  • Opinion mining and analysis using hybrid deep neural networks
    A Hidri, SA Alsaif, M Alahmari, E AlShehri, M Sassi Hidri
    Technologies 13 (5), 175 , 2025
    2025
    Citations: 5
  • Using Capsule Networks for Android Malware Detection Through Orientation-Based Features.
    S Khan, M Nauman, SA Alsaif, TA Syed, HA Eleraky
    Computers, Materials & Continua 70 (3) , 2022
    2022
    Citations: 5
  • Ecg classification for detecting ecg arrhythmia empowered with deep learning approaches
    R Asif, K Sultan, S Alsaif, S Abbas, M Khan, A Mosavi
    Computational Intelligence and Neuroscience 2022, 1-12 , 2022
    2022
    Citations: 5
  • From learning management systems to a social learning environment: A comparative review and the implications
    SA Alsaif, AS Li, B Soh, MA AlZain, M Masud
    International Journal of Smart Education and Urban Society (IJSEUS) 10 (1), 1-18 , 2019
    2019
    Citations: 5
  • STARM: Streaming association rules mining in high-dimensional data
    R Mkhinini Gahar, O Arfaoui, A Hidri, SA Alsaif, M Sassi Hidri
    International Conference on Advanced Information Networking and Applications … , 2024
    2024
    Citations: 3
  • Performance analysis of machine learning algorithms on networks intrusion detection
    MS Hidri, SA Alsaif, A Hidri
    International Journal of Computer Applications in Technology 70 (3-4), 285-295 , 2022
    2022
    Citations: 3