My recent publications

Using the Ethereum Blockchain to Secure a Crowdfunding System in the Real Estate Sector

Published in Lecture Notes in Networks and Systems book series (LNNS,volume 635), 2023

Abstract : The covid-19 crisis has severely affected the dynamics of the real estate sector, which is facing various financial and structural problems. The current real estate world is complicated by the lack of transparency in transactions such as rental, purchase, and sale, and it does not reach the level of confidentiality and authenticity of operational data. In addition, real estate financing brings together several players such as banks, notaries, and others, which makes the acquisition of real estate very expensive. With the advent of blockchain technology, many fields such as finance, accounting, and real estate have received a positive impact using the benefits of this technology. This article aims to reorganize real estate into a next-generation digitized system based on blockchain technology, by proposing a crowdfunding model that aims to eliminate intermediate, costs. Moreover, this model can allow to customers, who do not have immediate financing, the possibility of acquiring real estate. We also present the implementation of this model through smart contracts and the blockchain to set up a decentralized platform that ensures the security, traceability, and transparency of transactions. Read more

Recommended citation: Boulsane, H. A., Afdel, K., & El Hajjami, S. (2023). Using the Ethereum Blockchain to Secure a Crowdfunding System in the Real Estate Sector. In Artificial Intelligence and Smart Environment: ICAISE’2022 (pp. 818-823). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-26254-8_119

Machine Learning System for Fraud Detection. A Methodological Approach for a Development Platform

Published in Lecture Notes in Networks and Systems book series (LNNS,volume 211), 2021

Abstract : The democratization and massification use of credit cards lead inexorably to a high number of fraudulent transactions. Generally, the fraud detection is part of the anomaly detection problem. In this field, current approaches and techniques are constantly looking for optimized solutions to detect anomalies. Faced with a massive and growing data volume, these methods are put to the test, and thus lead to a large number of undetected anomalies. Real time fraud detection requires the design and implementation of scalable techniques capable of ingesting and analyzing massive amounts of data continuously. Recent advances in storage, data analytics processing, and open-source solutions open up new perspectives in the anomaly detection field and in particular fraud. In this article, we are interested in the design of a fraud detection system (FDS) based on open-sources Big Data technologies. Thus, a general methodology is proposed based on the formalization, the implementation and the technical design of a platform for fraud detection. The formalization part consists of four layers: distributed storage, data processing, model building, and finally the model evaluation. The implementation part uses Spark distributed data processing system. In particular, we are based on its framework dedicated to machine learning, called MLlib. The technical design part of the platform is based on the latest Big Data technologies such as Hadoop, Yarn, Livy etc. Read more

Recommended citation: El Hajjami, S., Malki, J., Berrada, M., Mostafa, H., Bouju, A. (2021). Machine Learning System for Fraud Detection. A Methodological Approach for a Development Platform. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2021. Lecture Notes in Networks and Systems, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-030-73882-2_10

Machine Learning Facing Behavioral Noise Problem in an Imbalanced Data Using One Side Behavioral Noise Reduction: Application to a Fraud Detection

Published in International Journal of Computer and Information Engineering Vol:15, No:3, 2021, 2021

Abstract : With the technological revolution of Internet and the information overload, adaptive E-learning has become the promising solution for educational institutions since it enhances students’ learning process according to many factors such as their learning styles. Learning styles are a criteria of great import in E-learning environment because they can help the system to effectively personalize students’ learning process. Generally, the traditional way of detecting students’ learning style is based on asking students to fill out a questionnaire. However, using this static technique presents many problems. Some of these problems include the lack of self-awareness of students of their learning preferences. In addition, almost all students are bored when they are asked to fill out a questionnaire. Thus, in this work, we present an automatic approach for detecting students’ learning style based on web usage mining. It consists in classifying students’ log files according to a specific learning style model (Felder and Silverman model) using clustering algorithms (K-means algorithm). In order to test the efficiency of our work, we use a real-world dataset gathered from an E-learning system. Experimental results show that our approach provide promising results. Read more

Recommended citation: El Hajjami, S., Malki, J., Bouju, A., & Berrada, M. (2021). Machine Learning Facing Behavioral Noise Problem in an Imbalanced Data Using One Side Behavioral Noise Reduction: Application to a Fraud Detection. International Journal of Computer and Information Engineering, 15(3), 194-205. https://publications.waset.org/pdf/10011899

Machine learning for anomaly detection. Performance study considering anomaly distribution in an imbalanced dataset

Published in IEEE, 2021

Abstract : The continuous dematerialization of real-world data greatly contributes to the increase in the volume of data exchanged. In this case, anomaly detection is increasingly becoming an important task of data analysis in order to detect abnormal data, which is of particular interest and may require action. Recent advances in artificial intelligence approaches, such as machine learning, are making an important breakthrough in this area. Typically, these techniques have been designed for balanced data sets or that have certain assumptions about the distribution of data. However, the real applications are rather confronted with an imbalanced data distribution, where normal data are present in large quantities and abnormal cases are generally very few. This makes anomaly detection similar to looking for the needle in a haystack. In this article, we develop an experimental setup for comparative analysis of two types of machine learning techniques in their application to anomaly detection systems. We study their performance taking into account anomaly distribution in an imbalanced dataset. Read more

Recommended citation: El Hajjami, S., Malki, J., Berrada, M., & Fourka, B. (2020, November). Machine learning for anomaly detection. performance study considering anomaly distribution in an imbalanced dataset. In 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech) (pp. 1-8). IEEE. http://doi.org/10.1109/CloudTech49835.2020.9365887

A machine learning based approach to reduce behavioral noise problem in an imbalanced data: application to a fraud detection

Published in IEEE, 2020

Abstract : The question of class imbalance has become more pronounced with the application of learning algorithms in real applications. It has received significant attention in the machine learning and data mining community. This problem is present in fraud detection, medical diagnostics, and a number of other areas where training data contains significantly more representatives of one class (called the majority class) than the other class (called the minority class). Machine learning techniques struggle to deal with imbalanced data by focusing on minimizing the error rate for the majority class while ignoring the minority class, which is the most interesting from a learning point of view and also involves a high cost when it is not well classified. However, the imbalance ratio is not the only cause of poor performance when learning from imbalanced data. Another critical factor that accompanies imbalanced data in the real world is the presence of a number of instances of the two classes being overlapped in feature space. This problem is commonly referred to as class overlap and we have called it “behavioral noise”. In this paper, we propose One Side Behavioral Noise Reduction (OSBNR) approach to deal with the problem of class imbalance in the presence of a behavioral noise level. OSBNR is based on two stages. Firstly, a clustering is applied to groups similar instances of the minority class in multiple behavior clusters. Secondly, we select and eliminate instances of the majority class, considered as behavioral noise, which overlap with the behavior clusters of the minority class. The results of experiments conducted on a representative public dataset confirm that the proposed approach is effective for class imbalance problem in the presence of behavioral noise. Read more

Recommended citation: El Hajjami, S., Malki, J., Bouju, A., & Berrada, M. (2020, October). A machine learning based approach to reduce behavioral noise problem in an imbalanced data: application to a fraud detection. In 2020 International Conference on Intelligent Data Science Technologies and Applications (IDSTA) (pp. 11-20). IEEE. http://doi.org/10.1109/IDSTA50958.2020.9264114

Using Semantic Web Technologies and Multi- agent System for Multi-dimensional Analysis of Open Health Data

Published in Journal of Information & Knowledge ManagementVol. 19, No. 03, 2050021 (2020) , 2020

Abstract : Recent years have seen Social Web becoming a global phenomenon, which is being in- creasingly important in our daily lives. Millions of users are chatting on the Web and social networks and expressing their feelings and opinions about the latest outbreaks, symptoms, illnesses and new drugs. These opinions contain a large amount of data, which are destined to become a major source of infor- mation for business intelligence, as they are largely informative and therefore interesting to be dealt with in a decision-making process, in order to evaluate and improve the performance of health system. However, this source of information is currently underutilised. This work describes an approach to creating an analytical health framework that allows the integration and multi-dimensional analysis of available health data, with particular attention to socially generated data, using Semantic Web (SW) technologies and multi-agent systems. Read more

Recommended citation: El Hajjami, S., Berrada, M., Harti, M., & Diallo, G. (2020). Using semantic web technologies and multi-agent system for multi-dimensional analysis of open health data. Journal of Information & Knowledge Management, 19(03), 2050021. https://doi.org/10.1142/S0219649220500215

Healthcare Social Data Platform Based on Linked Data and Machine Learning

Published in Advances in Intelligent Systems and Computing book series (AISC,volume 1076), 2019

Abstract : The healthcare system is facing very important challenges in order to improve the whole system performance. Different communities are interested in this subject from different perspectives ranging from technical issues to organizational aspects. An important aspect of this research area is to consider social network data within the system especially because of the rapid and growing development of social networks. It can be general social networks, like Facebook or twitter but also others dedicated as PatientsLikeMe. This social network proliferation generates complex problems and locks when we want to take into account the resulting large amounts of data, created continuously, within the healthcare system. We call these data “social data”. The aim of this work is to demonstrate that is possible and feasible to build promising alternatives of the traditional healthcare system to improve the quality of services and reduce cost. In our opinion, taking into account “social data” can provide efficient healthcare decisional support systems to help healthcare operators to make optimal and efficient decisions in dynamic and complex environments. Our approach involves data extraction from multiple social networks, data aggregation, and the development of a semantic model in order to answer high-level users’ queries. In addition, we show how an analytical tool can help operators to understand data. Lastly, we present a model of machine learning which aims to detect the Sentiments of users expressed toward a given medication and the “TOP TRENDING” of care and treatments used for a given disease. Read more

Recommended citation: El Hajjami, S., Berrada, M., & Fhiyil, S. (2020). Healthcare Social Data Platform Based on Linked Data and Machine Learning. In Embedded Systems and Artificial Intelligence (pp. 291-304). Springer, Singapore. https://doi.org/10.1007/978-981-15-0947-6_28

Towards an Agent-Based Approach for Multidimensional Analyses of Semantic Web Data

Published in 2017 Intelligent Systems and Computer Vision (ISCV), 2017

Abstract : OLAP analytical systems are essential technologies in decision-making processes; they provide an efficient way to carry out complex analysis in a simpler and faster way to decision-makers. In today’s dynamic and competitive business contexts, the stored internal data within companies does no longer provide enough information for decision-making processes. Therefore, decision analysis systems could be improved by including external data available through the semantic web in order to provide multiple perspectives to decision makers. In this article, we describe a preliminary approach based on the use of multi-agent systems for multidimensional analysis of external data coming from the semantic web also gives a short review of recent research works combining business intelligence and semantic web technologies. The proposed approach is based on an evolutionary architecture by dint of the “agents” technology. The different stages of the analysis are considered tasks that will be assimilated to services, managed by agents. Read more

Recommended citation: El Hajjami, S., Berrada, M., Harti, M., & Diallo, G. (2017, April). Towards an agent-based approach for multidimensional analyses of semantic web data. In 2017 Intelligent Systems and Computer Vision (ISCV) (pp. 1-6). IEEE. https://doi.org/10.1109/ISACV.2017.8054933

Salma EL HAJJAMI

My recent publications