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Ziti, Soumia
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Prediction of chronic diseases based on ML packages using spark MLlib Oussous, Aicha; Ez-Zahout, Abderrahmane; Ziti, Soumia
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1121-1129

Abstract

Heart disease, diabetes, and breast cancer pose significant global health challenges, and effectively addressing these chronic diseases necessitates a coordinated international effort. The integration of machine learning and predictive analytics offers promising solutions for tackling these issues. Our study presents a unified model that utilizes the random forest (RF) algorithm and SparkMLlib to predict these three diseases, testing the model on three distinct datasets and evaluating its performance using scientific metrics, including the receiver operating characteristic (ROC) curve, accuracy, precision, recall, and F1-score. Furthermore, we aim to investigate whether variations in medical data and contextual factors impact the results. The findings indicate that while the model shows strong overall performance, its effectiveness may differ for each disease due to factors such as data characteristics, disease-specific features, model behavior, and various biological and medical considerations; understanding these factors is essential for improving model performance and ensuring its appropriate use in clinical environments.
Digital afterlife: challenges and technological innovations in pursuit of immortality Ouhnni, Hamid; Ziti, Soumia; El Bouchti, Karim; Meryam, Belhiah; Lagmiri, Souad Najoua
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1387-1406

Abstract

Digital immortality, the idea of endless life and ultimate happiness in a virtual afterlife, has become a subject of human fascination. This article reports the results of a comprehensive research project focused on identifying the challenges and potential options related to digital immortality. Analyzing 39 relevant studies, our research concentrates on two main themes: the barriers to achieve the digital immortality and the tools created to preserve digital memories. Our findings reveal that the challenges associated with digital immortality are deeply rooted in legal, ethical, and social issues. Importantly, our focus is the challenges related to digital content left by the deceased, its collection method, and integrity in digital immortality research, as content forms the basis for achieving this objective. Furthermore, the research highlights the need for more advanced technology, as the number of studies is limited and current progress is primarily future-oriented. However, our analysis demonstrates that the digital content left by the deceased is paramount, as it constitutes the raw material for achieving digital immortality.
Reviewing chronic ailments: predicting diseases with a multi-symptom approach Oussous, Aicha; Ez-Zahout, Abderrahmane; Ziti, Soumia
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp418-427

Abstract

The integration of machine learning (ML) techniques is now indispensable in healthcare, especially in addressing the challenges posed by chronic illnesses, which present a significant global health concern due to their unpredictable nature. This study compares ML techniques employed in the diagnosis and treatment of chronic conditions such as diabetes, liver disease, thyroid disease, breast cancer, heart disease, Alzheimer’s disease, and others. Two primary criteria guided the selection of diseases under investigation. Firstly, those extensively studied with ML methods, and secondly, those leveraging ML models to resolve issues or yield promising results. The research concludes that in real-time clinical practice, there is no universally proven method for selecting the optimal course of action due to each method’s unique advantages and disadvantages. While a hybrid technique may exhibit slightly slower speed growth, it holds the potential to enhance the accuracy and performance of a model.
Processing queries on encrypted document-based database Belhaj, Abdelilah; Ziti, Soumia; Elbouchti, Karim; Falih, Noureddine; Lagmiri, Souad Najoua
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1299-1309

Abstract

Big  data is a set of technologies and strategies for storing and analyzing large volumes of data in order to learn from it and make predictions. Since non-relational databases such as document-based have been applied in various contexts, the privacy protection must be taken into account by strengthening security to prevent the exposure of user data. In this paper, we focus mainly on secret sharing scheme that supports secure query with data interoperability to design a practical model for document-based databases, especially MongoDB. This approach, being based on secure query processing by defining elementary and suitable operators, allows us to perform operational computations and aggregations on encrypted data in the non-relational document database MongoDB. The obtained results, in the present work, could find places in various fields where data privacy and security are primordial such as healthcare, cloud computing, financial services, artificial intelligence and machine learning, in which user data remains secure and confidential during processing.
Enhancing privacy in document-oriented databases using searchable encryption and fully homomorphic encryption Belhaj, Abdelilah; Ziti, Soumia; Lagmiri, Souad Najoua; El Bouchti, Karim
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1661-1672

Abstract

In cloud-based not only SQL (NoSQL) databases, maintaining data privacy and the integrity are critically challenged by the risks of unauthorized external access and potential threats from malicious insiders. This paper presents a proxy-based solution that provides privacy-preserving by combining searchable encryption and brakerski-fan-vercauteren (BFV) fully homomorphic encryption (FHE) to facilitate secure search and aggregate query execution on encrypted data. Through extensive performance evaluations and security analyses, we show that our approach offers a robust solution for privacy-preserving data operations, with performance overhead introduced by the use of FHE. This solution gives an opportunity for a robust framework for secure data management and querying in NoSQL databases, with promising implications for practical deployment and future research. This work represents a significant advancement in the secure handling of data in NoSQL oriented databases, supplying a practical solution for privacy-conscious organizations.
Co-Authors Belhaj, Abdelilah El Bouchti, Karim Elbouchti, Karim Ez-zahout, Abderrahmane Falih, Noureddine Lagmiri, Souad Najoua Meryam, Belhiah Ouhnni, Hamid Oussous, Aicha