Drias, Yassine

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https://hdl.handle.net/20.500.11779/2672
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driasy@mef.edu.tr
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02.02. Department of Computer Engineering
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Current Staff
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ORCID ID
0000-0002-8896-6170
Scopus Author ID
56440023300
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WoS Researcher ID
AAD-4014-2019
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Yassine Drias.jpg (158.11 KB)

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NO POVERTY
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DECENT WORK AND ECONOMIC GROWTH
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INDUSTRY, INNOVATION AND INFRASTRUCTURE
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REDUCED INEQUALITIES
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SUSTAINABLE CITIES AND COMMUNITIES
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Scholarly Output

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JournalCount
-- 33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 -- Istanbul; Isik University Sile Campus -- 2114503
32nd IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2024 -- Tarsus Univ Campus, Mersin, TURKEY2
Symposium on Quantum Sciences, Applications and Challenges (QSAC) -- SEP 24-25, 2023 -- Alger Acad Sci & Tech, Algiers, ALGERIA2
-- 2nd International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2025 -- Antalya -- 2131041
International Conference on Intelligent and Fuzzy Systems (INFUS) -- JUL 16-18, 2024 -- Istanbul Tech Univ, Canakkale, TURKEY1
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Now showing 1 - 10 of 16
  • Thumbnail Image
    Conference Object
    Graph Theory-Based Fraud Detection in Banking Check Transactions
    (Institute of Electrical and Electronics Engineers Inc., 2025) Behsi Z.; Memis E.C.; Ertugrul S.; Sayar A.; Gunes P.; Seydioglu S.; Cakar T.; Gunes, Peri; Memis, Emir Cetin; Sayar, Alperen; Cakar, Tuna; Ertugrul, Seyit; Seydioglu, Sarper; Behsi, Zeynep
    Traditional banking fraud detection systems rely on rule-based approaches that analyze individual transactions in isolation, failing to capture complex relationship patterns indicative of coordinated fraud schemes such as check-kiting and artificial credit score manipulation. We p resent our study, a novel similarity-based graph theory approach that constructs weighted networks between check issuers using Jaccard Similarity Index and employs advanced graph analysis to identify suspicious entity clusters without requiring complete transaction relationship data. Our approach combines Jaccard Similarity Index for behavioral pattern analysis (addressing payee information unavailability) with comprehensive graph analysis including centrality measures, community detection, and anomaly identification. Through comprehensive evaluation on real banking data containing 458,399 transactions from 121,647 unique issuers - the largest confirmed dataset in fraud detection literature - we demonstrate the effectiveness of our methodology. Following parameter optimization using grid search methodology (similarity threshold: 0.55, risk percentile: 0.75), our study achieves competitive detection rates in optimal configurations with an average F1-score of 0.447 (±0.164) and peak performance reaching an F1-score of 0.557, while providing superior network topology analysis with 0.923 clustering coefficient. The system operates under significant data privacy constraints, lacking personal identification information (names, account numbers, IDs) and complete payee data. Despite these limitations, our study outperforms traditional approaches by leveraging similarity-based indirect relationships, and we project that performance could reach 85-95% levels with complete data access. © 2025 IEEE.
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    Conference Object
    Makine Öğrenimi ve Çok Boyutlu Anket Verileri Kullanılarak Öğrenci Başarısının Tahmini: Eğitim Programı Üzerine Bir Uygulama
    (Institute of Electrical and Electronics Engineers Inc., 2025) Behsi, Zeynep; Dereli, Serhan; Cakar, Tuna; Patel, Jay; Cicek, Gultekin; Drias, Yassine
    This study develops a machine learning model integrating survey data and performance metrics to predict student success in the UpSchool education program. Students' personality traits assessed by DISC analysis, financial management, social, and emotional skills were clustered into "Successful,""Unsuccessful,"and "Moderately Successful"groups using K-means clustering. The SMOTE technique addressed data imbalance issues, and algorithms such as Logistic Regression, Random Forest, LightGBM, and AdaBoost were tested. After hyperparameter optimization, AdaBoost and LightGBM achieved the highest predictive performance. Results demonstrated the effectiveness of machine learning models in forecasting student success in educational programs. Future studies are recommended to enhance model performance through expanded datasets and to validate the model's applicability across diverse educational contexts. © 2025 Elsevier B.V., All rights reserved.
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    Citation - WoS: 1
    Citation - Scopus: 1
    Fuzzy Elephant Herding Optimization and DBSCAN for Emergency Transportation: A Case Study for the 2023Turkiye Earthquake
    (Springer international Publishing Ag, 2024) Drias, Yassine; Drias, Habiba
    In recent times, our planet has experienced numerous natural disasters across all continents. The damage caused by these disasters has been so extensive that Emergency Medical Services (EMS) proved incapable of handling the situation. In this article, we present a novel approach for urgent disaster transport with the aim of minimizing loss of life. In this context, we are investigating the Density-Based Spatial Clustering of Applications with Noise algorithm (DBSCAN) to cluster the large geographic zone affected by the 2023 earthquake in Turkiye. The clustering is done based on hospitals' capacity on one hand and damages on the other hand. The ambulance dispatching task is then tackled using a new fuzzy version of Elephant Herding Optimization called FEHO. This approach addresses the challenge of dispatching ambulances to cover emergency locations effectively and optimally in the clustered regions. Experiments conducted on real data demonstrate the effectiveness of our approach in managing emergency transportation and highlight its potential to minimize the number of casualties.
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    Neural Decoding of Brand Perception and Preferences: Understanding Consumer Behavior Through Fnirs and Machine Learning
    (Ieee, 2024) Çakar, Tuna; Girisken, Yener; Tuna, Esin; Filiz, Gozde; Drias, Yassine
    This research examines the link between consumer brand perceptions and neural activity by employing Functional Near-Infrared Spectroscopy (fNIRS) and machine learning techniques. The study analyzes the neural projections of participants' reactions to brand-associated adjectives, processing data collected from 168 individuals through machine learning algorithms. The findings underscore the significance of the lateral regions of the prefrontal cortex in the decision- making process related to brand perceptions. The aim is to understand how brands are perceived when associated with various adjectives and to develop this understanding through neural patterns using machine learning models. This study demonstrates the potential of integrating neural data with machine learning methods in the field of applied neuroscience.
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    Conference Object
    Exploring Generative AI and Unsupervised Learning for Digital Soil Classification: A Case Study of Algeria
    (Institute of Electrical and Electronics Engineers Inc., 2025) Belkadi, W.H.; Drias, Y.; Drias, H.; Bouchelkia, H.; Hamdous, S.
    Accurate soil type mapping is vital for sustainable agriculture and land management. Yet, Algeria remains under-represented in global soil databases. To address this, we propose a pipeline combining Generative AI for data extraction with unsupervised learning for soil classification. After harmonizing Algerian soil data, we evaluate four clustering algorithms - K-means, DBSCAN, HDBSCAN, and Self-Organizing Maps - under various preprocessing settings. Internal and external metrics guide model selection. K-means and DBSCAN produced the most coherent clusters, while SOM best aligned with FAO soil types. RuleFit was then used to extract interpretable rules defining each cluster. This work highlights the potential of AI-based, interpretable clustering for digital soil mapping in data-scarce regions like Algeria. © 2025 Elsevier B.V., All rights reserved.
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    Article
    Methane Emissions Forecasting Using Hybrid Quantum-Classical Deep Learning Models: Case Study of North Africa
    (Springer, 2025) Belkadi, Widad Hassina; Drias, Yassine; Drias, Habiba; Ferkous, Sarah; Khemissi, Maroua
    This study explores climate change by predicting methane emissions in North Africa using classical and quantum deep learning methods. Using data from Sentinel-5P, we developed hybrid quantum-classical models, such as quantum long short-term memory (QLSTM) and quantum-gated recurrent unit networks (QGRUs), along with a novel hybrid architecture combining quantum convolutional neural networks (QCNNs) with LSTM and GRU, namely QCNN-LSTM and QCNN-GRU. The results show that these quantum models, especially the proposed hybrid architectures, outperform classical models by approximately seven percent in root-mean-squared error with fewer training epochs. These findings highlight the potential of quantum methodologies for enhancing environmental monitoring accuracy. Future research will aim to refine model performance, incorporate explainable AI techniques, and expand to forecasting other greenhouse gases, contributing to climate change mitigation efforts.
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    Master Thesis
    Toprak Özelliklerini ve İklim Değişikliğini Tahmin Etmek için Derin Öğrenme
    (2025) Çelik, Nurçin; Drias, Yassine
    Toprak, bitki büyümesi için gerekli olan temel besinleri, mineralleri ve elementleri sağlamakla kalmayıp aynı zamanda iklim düzenlemesinde ve daha geniş ekosistem işleyişinde hayati bir rol oynayan temel bir doğal kaynaktır. Bu yüksek lisans tezi, arazi örtüsü, topografya, iklim verileri ve diğer mekansal faktörler gibi temel çevresel değişkenleri entegre ederek Türkiye genelinde Dijital Toprak Haritalama (DSM) uygulamalarında modern Yapay Zeka (AI) metodolojilerinin uygulanmasını iyileştirmeyi amaçlamaktadır. Bu hedefe ulaşmak için, bu değişkenlere dayalı olarak toprak özelliklerini tahmin etme performanslarını değerlendirmek üzere yedi farklı makine öğrenimi modelinin karşılaştırmalı analizi yürütülmüştür.
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    Conference Object
    Citation - Scopus: 2
    AI-Driven Digital Soil Mapping: Leveraging Generative AI, Ensemble Learning and Geospatial Technologies for Data-Scarce Regions
    (Springer Science and Business Media Deutschland GmbH, 2025) Drias, Yassine; Drias, Habiba; Belkadi, Widad Hassina; Cakar, Tuna; Abdelhamid, Zakaria; Bensemmane, Riad Yacine
    This study presents a methodology for generating highresolution digital soil maps by integrating artificial intelligence (AI) with geospatial technologies. The research begins with comprehensive data collection and the extraction of relevant soil properties with the help of generative AI. To improve predictive accuracy, ensemble learning algorithms were employed due to their ability to capture complex relationships within soil characteristics. Additionally, a structured preprocessing pipeline was developed to refine and standardize the collected soil data, ensuring its suitability for modeling. The model's performance was evaluated using spatial cross-validation techniques to identify the most effective predictive approach. To validate the proposed methodology, experiments were conducted in northern Algeria, a region characterized by diverse landscapes ranging from arid zones to fertile plains. The results demonstrate the potential of AI-driven approaches to enhance soil mapping, particularly in regions where high-quality and up-to-date soil data are limited. This study underscores the efficacy of AI and geospatial technologies in advancing precision agriculture and land management.
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    Conference Object
    Citation - WoS: 1
    Quantum Recurrent Neural Networks for Soil Profiles Prediction in Turkiye
    (Springer international Publishing Ag, 2024) Drias, Yassine; Siouane, Alaa Eddine; Çakar, Tuna
    In this article, we introduce a new approach for soil profile prediction using Quantum Recurrent Neural Networks (QRNNs). By harnessing the power of quantum computing, QRNNs present a promising solution to overcome the limitations of conventional soil mapping techniques. We begin by proposing a classical Recurrent Neural Networks (RNNs) architecture for soil profiles prediction, followed by the design of its quantum counterpart with QRNNs. Focusing on the application of our model in Turkiye, we leverage geospatial data from diverse sources, including climate, vegetation, and land relief data, to showcase the efficacy of QRNNs in soil classification and resource monitoring. Our results reveal a remarkable accuracy score and computational efficiency. Moreover, we delve into the broader implications of quantum computing for digital mapping and explore potential avenues for future research. Emphasizing the integration of quantum computing techniques with digital soil mapping, we foresee a promising advancement in sustainable soil management practices, aiding decision-making processes in agriculture, environmental planning, and natural resource management.
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    Conference Object
    Secure Information Foraging Using Fully Homomorphic Encryption and Agnes Clustering
    (Springer Science and Business Media Deutschland GmbH, 2024) Drias, Yassine; Drias, Habiba; Çakar, Tuna; Tiloult, Aya
    In the ever-expanding landscape of social media, users struggle with navigating an overwhelming volume of information. This research introduces an innovative approach to Information Foraging by incorporating data encryption as a core component-a novel perspective never before explored in this context. The goal is to fortify the confidentiality and integrity of users’ critical data, setting a standard for safeguarding information from external threats. Within this work, we employ Fully Homomorphic Encryption in conjunction with AGNES clustering. While Fully Homomorphic Encryption ensures robust data protection, the model’s efficiency is guaranteed through a hierarchical clustering structure facilitated by AGNES. The evaluation was carried out on a dataset encompassing over 900,000 posts obtained from the social network X, covering a diverse array of topics. The results underscore the model’s competence in efficiently and securely identifying relevant information while upholding users’ privacy. Furthermore, a comparative analysis with existing approaches from the literature highlights the superiority of our proposal, establishing a new frontier in the integration of data encryption within the Information Foraging paradigm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.