Mohamed Lazaar

1.5k total citations
65 papers, 870 citations indexed

About

Mohamed Lazaar is a scholar working on Artificial Intelligence, Information Systems and Signal Processing. According to data from OpenAlex, Mohamed Lazaar has authored 65 papers receiving a total of 870 indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Artificial Intelligence, 16 papers in Information Systems and 12 papers in Signal Processing. Recurrent topics in Mohamed Lazaar's work include Sentiment Analysis and Opinion Mining (12 papers), Network Security and Intrusion Detection (10 papers) and Anomaly Detection Techniques and Applications (9 papers). Mohamed Lazaar is often cited by papers focused on Sentiment Analysis and Opinion Mining (12 papers), Network Security and Intrusion Detection (10 papers) and Anomaly Detection Techniques and Applications (9 papers). Mohamed Lazaar collaborates with scholars based in Morocco, France and Israel. Mohamed Lazaar's co-authors include Yassine Al-Amrani, Kamal Eddine El Kadiri, Mohammed Al Achhab, Mohamed Ettaouil, Youssef Ghanou, Mohamed Khaldi, Oussama Mahboub, Nabil Alami, Mostafa El Mallahi and Karim El Moutaouakil and has published in prestigious journals such as SHILAP Revista de lepidopterología, Knowledge-Based Systems and Engineering Applications of Artificial Intelligence.

In The Last Decade

Mohamed Lazaar

62 papers receiving 813 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mohamed Lazaar Morocco 13 444 267 133 128 95 65 870
Keli Xiao United States 16 452 1.0× 208 0.8× 75 0.6× 116 0.9× 87 0.9× 50 1.0k
Pauli Miettinen Germany 14 307 0.7× 167 0.6× 96 0.7× 112 0.9× 89 0.9× 43 791
Rasha Kashef Canada 13 280 0.6× 180 0.7× 102 0.8× 54 0.4× 52 0.5× 39 555
Rajendra Pamula India 19 573 1.3× 260 1.0× 271 2.0× 96 0.8× 57 0.6× 69 1.1k
Aparna S. Varde United States 15 351 0.8× 235 0.9× 116 0.9× 46 0.4× 106 1.1× 116 867
Geeta Sikka India 17 344 0.8× 287 1.1× 183 1.4× 128 1.0× 272 2.9× 76 955
Zengxiang Li Singapore 12 474 1.1× 479 1.8× 317 2.4× 54 0.4× 76 0.8× 53 997
Shuhan Yuan United States 15 594 1.3× 157 0.6× 369 2.8× 134 1.0× 92 1.0× 40 978
Khin Lwin United Kingdom 13 233 0.5× 193 0.7× 95 0.7× 100 0.8× 40 0.4× 19 763
Adebayọ Olusọla Adetunmbi Nigeria 11 685 1.5× 317 1.2× 291 2.2× 144 1.1× 70 0.7× 46 1.0k

Countries citing papers authored by Mohamed Lazaar

Since Specialization
Citations

This map shows the geographic impact of Mohamed Lazaar's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mohamed Lazaar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mohamed Lazaar more than expected).

Fields of papers citing papers by Mohamed Lazaar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mohamed Lazaar. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mohamed Lazaar. The network helps show where Mohamed Lazaar may publish in the future.

Co-authorship network of co-authors of Mohamed Lazaar

This figure shows the co-authorship network connecting the top 25 collaborators of Mohamed Lazaar. A scholar is included among the top collaborators of Mohamed Lazaar based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mohamed Lazaar. Mohamed Lazaar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Lazaar, Mohamed, et al.. (2025). Systematic Review and Framework for AI-Driven Tacit Knowledge Conversion Methods and Machine Learning Algorithms for Ontology-Based Chatbots in E-Learning Platforms. International Journal of Interactive Mobile Technologies (iJIM). 19(1). 126–139. 2 indexed citations
2.
Lazaar, Mohamed, et al.. (2025). Using AI and NLP for Tacit Knowledge Conversion in Knowledge Management Systems: A Comparative Analysis. Technologies. 13(2). 87–87. 3 indexed citations
3.
Lazaar, Mohamed, et al.. (2025). Bridging Tacit Knowledge and Explicit Knowledge: An Ontological Model for Effective Knowledge Conversion. International Journal of Engineering Pedagogy (iJEP). 15(2). 93–105. 1 indexed citations
4.
Lazaar, Mohamed, et al.. (2024). An end-to-end learning approach for enhancing intrusion detection in Industrial-Internet of Things. Knowledge-Based Systems. 294. 111785–111785. 26 indexed citations
5.
Lazaar, Mohamed, et al.. (2024). Leveraging optuna for hyperparameter tuning in GANs: a novel solution for class imbalance in IoT datasets. Engineering Research Express. 6(4). 45257–45257. 1 indexed citations
6.
Lazaar, Mohamed, et al.. (2024). Fine-tuned SegFormer for enhanced fetal head segmentation. Procedia Computer Science. 251. 350–357. 2 indexed citations
7.
Lazaar, Mohamed, et al.. (2024). Security, QoS and energy aware optimization of cloud-edge data centers using game theory and homomorphic encryption: Modeling and formal verification. Results in Engineering. 24. 102902–102902. 1 indexed citations
8.
Lazaar, Mohamed, et al.. (2024). Realtime ransomware process detection using an advanced hybrid approach with machine learning within IoT ecosystems. Engineering Research Express. 7(1). 15211–15211. 1 indexed citations
9.
Lazaar, Mohamed, et al.. (2024). Towards a Deep Learning Approach for IoT Attack Detection Based on a New Generative Adversarial Network Architecture and Gated Recurrent Unit. Journal of Network and Systems Management. 32(4). 4 indexed citations
10.
Lazaar, Mohamed, et al.. (2023). An enhanced recommender system based on heterogeneous graph link prediction. Engineering Applications of Artificial Intelligence. 124. 106553–106553. 15 indexed citations
11.
Lazaar, Mohamed, et al.. (2023). Performance Evaluation of CNN and Pre-trained Models for Malware Classification. Arabian Journal for Science and Engineering. 48(8). 10355–10369. 17 indexed citations
12.
13.
Lazaar, Mohamed, et al.. (2022). Imbalanced tabular data modelization using CTGAN and machine learning to improve IoT Botnet attacks detection. Engineering Applications of Artificial Intelligence. 118. 105669–105669. 94 indexed citations
14.
Lazaar, Mohamed, et al.. (2022). Improving Machine Learning Models for Malware Detection Using Embedded Feature Selection Method. IFAC-PapersOnLine. 55(12). 771–776. 12 indexed citations
15.
Lazaar, Mohamed, et al.. (2020). Investment of Classic Deep CNNs and SVM for Classifying Remote Sensing Images. Advances in Science Technology and Engineering Systems Journal. 5(5). 652–659. 4 indexed citations
16.
Lazaar, Mohamed, et al.. (2019). Impact of Feature selection on content-based recommendation system. 1–6. 10 indexed citations
17.
Lazaar, Mohamed, et al.. (2019). MAPSS: An Intelligent Architecture for the Pedagogical Support. International Journal of Emerging Technologies in Learning (iJET). 14(14). 19–19. 4 indexed citations
18.
Lazaar, Mohamed, et al.. (2018). Self-Organizing Maps and Principal Component Analysis to Improve Classification Accuracy. Research Journal of Applied Sciences Engineering and Technology. 15(5). 190–196. 4 indexed citations
19.
Ettaouil, Mohamed & Mohamed Lazaar. (2012). Compression of Medical Images using Improved Kohonen Algorithm. Acta chimica slovenica. 70(1). 41–45. 1 indexed citations
20.
Ettaouil, Mohamed, Mohamed Lazaar, & Youssef Ghanou. (2012). Vector Quantization by improved kohonen algorithm. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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