Drias, Yassine
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driasy@mef.edu.tr
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02.02. Department of Computer Engineering
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Sustainable Development Goals
2
ZERO HUNGER
3
Research Products
8
DECENT WORK AND ECONOMIC GROWTH
2
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
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11
SUSTAINABLE CITIES AND COMMUNITIES
1
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12
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2
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13
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1
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38
Citations
107
h-index
6
Documents
27
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58
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14
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2
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1800/1
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1
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0
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3
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3
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1
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1
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WoS Citations per Publication
0.21
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| Journal | Count |
|---|---|
| -- 33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 -- Istanbul; Isik University Sile Campus -- 211450 | 3 |
| 32nd IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2024 -- Tarsus Univ Campus, Mersin, TURKEY | 2 |
| Symposium on Quantum Sciences, Applications and Challenges (QSAC) -- SEP 24-25, 2023 -- Alger Acad Sci & Tech, Algiers, ALGERIA | 2 |
| Lecture Notes in Networks and Systems -- 23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023 -- 11 December 2023 through 13 December 2023 -- Olten -- 315609 | 1 |
| Lecture Notes in Networks and Systems -- 7th International Conference on Intelligent and Fuzzy Systems, INFUS 2025 -- Istanbul – 336089 | 1 |
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Now showing 1 - 10 of 14
Conference Object İnternet Trafik Hızının Tahmininde Derin Öğrenme ve Ağaç Tabanlı Modellerin Karşılaştırılması(Institute of Electrical and Electronics Engineers Inc., 2025) Filiz, Gozde; Altıntaş, Suat; Yıldız, Ayşenur; Kara, Erkan; Drias, Yassine; Çakar, TunaThis study addresses the prediction of internet traffic speed using time-dependent data from an internet service provider through different modeling approaches. On an anonymized dataset, the performance of the moving average method, various deep learning models (N-BEATS, N-HITS, TimesNet, TSMixer, LSTM), and the XGBoost regression model enhanced with feature engineering was compared. Time series cross-validation and random hyperparameter search were used for model training. According to the results, the XGBoost model achieved the highest accuracy with 98.7% explained variance (R2), while among the deep learning models, N-BEATS and N-HITS achieved the best performance with R2 values around 90%. The findings indicate that tree-based methods supported by carefully selected features can offer higher accuracy and computational efficiency compared to complex deep learning models in internet traffic forecasting. © 2025 Elsevier B.V., All rights reserved.Article Quantum FP-Growth Algorithm Using GPU Simulation-Application to Digital Soil Mapping(Elsevier, 2026) Belkadi, Widad Hassina; Drias, Yassine; Drias, HabibaThis study introduces a novel quantum version of the FP-growth algorithm for frequent itemset mining, leveraging the combined strengths of classical FP-growth and quantum machine learning. Key contributions include the theoretical and practical framework for Quantum FP-growth, along with a comprehensive analysis of its time and space complexity. We implemented Quantum FP-growth using IBM Qiskit and conducted a comparative evaluation of various quantum amplitude estimation (QAE) methods, including Canonical QAE, Faster QAE, Maximum Likelihood QAE, and Iterative QAE for support estimation. Our findings reveal that Iterative QAE surpasses the other methods in both accuracy and speed. Additionally, we explored the advantages of GPU simulation with IBM Qiskit and NVIDIA cuQuantum. Notably, this research marks the first application of a quantum frequent itemset mining algorithm to a real-world dataset in Digital Soil Mapping (DSM), pioneering the use of quantum technologies in soil science. This study underscores the potential of quantum computing to revolutionize data mining and promote sustainable soil management practices.Conference Object The Application of Two Bayesian Personalized Ranking Approaches Based on Item Recommendation From Implicit Feedback(Ieee, 2024) Tagtekin, Burak; Sahin, Zeynep; Çakar, Tuna; Drias, YassineThe present study has aimed to provide a different ranking approach that will be used actively in a sector-specific application regarding the optimization of item ranking presented to the users. The current online approach in several different applications still holds a manual ranking algorithm whose parameters are determined by the data specialists with adequate domain-knowledge. The obtained findings from the present study indicate that the optimized Bayesian Personalized Ranking models will be used for providing a suitable, data-driven input for the ranking system that would serve to be personalized. The outcomes of the present study also demonstrate that the model using LearnBPR optimized with a stochastic gradient descent algorithm outperform the other similar methods. The sample model outputs were also investigated by a user sample to ensure that the algorithm was working correctly. The next potential step is to provide a normalization process to include the extracted information to the current ranking system and observe the performance of this new algorithm with the A/B tests conducted.Conference Object Yapay Öğrenme Tabanlı Mikrofaktoring Skorlama Modeli ve Kredi Risk Yönetim Sistemi Geliştirilmesi(Institute of Electrical and Electronics Engineers Inc., 2025) Sayar, Alperen; Ates, Yigit; Ertugrul, Seyit; Turan, Elif Naz; Drias, Yassine; Çakar, TunaCredit scoring systems are critical tools used by factoring institutions to assess the credit risks of SME businesses seeking microloans. This study presents a comprehensive predictive modeling framework that achieves 82.67% ROC-AUC with 65.34% Gini score on test data, demonstrating robust discriminative capability despite significant class imbalance. Our ensemble approach outperforms individual boosting models by leveraging their complementary strengths in payment behavior analysis and fraud detection. The raw data was cleaned, transformed, and optimized using the Polars library, with specialized features for detecting fraud patterns and time-based risk indicators. When implementing a score threshold of 950, our model significantly improves the detection of non-performing loans (NPL) compared to traditional rule-based approaches by reducing the net deficit from 6.59% to 2.62%. When applied to previously rejected applications, the model projects a potential 762.57% increase in transaction count and 747.05% growth in transaction volume. © 2025 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 1Quantum Fp-Growth for Association Rules Mining(Springer international Publishing Ag, 2024) Belkadi, Widad Hassina; Drias, Yassine; Drias, HabibaQuantum computing, based on quantum mechanics, promises revolutionary computational power by exploiting quantum states. It provides significant advantages over classical computing regarding time complexity, enabling faster and more efficient problem-solving. This paper explores the application of quantum computing in frequent itemset mining and association rules mining, a crucial task in data mining and pattern recognition. We propose a novel algorithm called Quantum FP-Growth (QFP-Growth) for mining frequent itemsets. The QFP-Growth algorithm follows the traditional FP-Growth approach, constructing a QF-list, then the QFP-tree, a quantum radix tree, to efficiently mine frequent itemsets from large datasets. We present a detailed analysis of each step in the QFP-Growth algorithm, providing insights into its time complexity and computational efficiency. Our algorithm outperforms classical FP-Growth with a quadratic improvement in error dependence, showcasing the power of quantum algorithms in data mining. To validate the effectiveness of our approach, we conducted experiments using the IBM QASM simulator, qiskit. The results demonstrate the efficiency and effectiveness of our QFP-Growth algorithm in mining frequent itemsets from a transactional database.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.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, MarouaThis 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.Master Thesis Toprak Özelliklerini ve İklim Değişikliğini Tahmin Etmek için Derin Öğrenme(2025) Çelik, Nurçin; Drias, YassineToprak, 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.Conference Object Citation - Scopus: 2AI-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 YacineThis 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.Conference Object Citation - WoS: 1Quantum Recurrent Neural Networks for Soil Profiles Prediction in Turkiye(Springer international Publishing Ag, 2024) Drias, Yassine; Siouane, Alaa Eddine; Çakar, TunaIn 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.
