Christian Peluso | Artificial Intelligence | Excellence in Ethical AI Development Award

Published on by Computer Science Engineering

Dr. Christian Peluso | Artificial Intelligence | Excellence in Ethical AI Development Award

Libero professionista | Consiglio Nazionale delle RicercheThis link is disabled | Italy

Dr. Christian Peluso is a researcher specializing in artificial intelligence with expertise in federated learning, deep learning, and cybersecurity, focusing on privacy-preserving systems for mobile and distributed environments. His research aims to develop intelligent models capable of processing complex and varied data while safeguarding user privacy and ensuring compliance with data protection regulations. Christian earned his Master’s degree in Artificial Intelligence from the University of Pisa with the highest distinction, presenting a thesis titled PrivNet: Advancing Mobile Security through Privacy-Preserving Federated Learning for Malware Detection, which introduced an innovative federated learning approach for mobile malware analysis using convolutional neural networks optimized for image-based data. He has actively collaborated with the Consiglio Nazionale delle Ricerche (CNR) and several academic and research institutions, contributing to projects that merge AI, cybersecurity, and data privacy. His publications, including “PrivNet: Advancing Mobile Security through Privacy-Preserving Federated Learning for Malware Detection” and “An Approach for Privacy-Preserving Mobile Malware Detection Through Federated Machine Learning,” reflect his deep involvement in advancing secure and decentralized AI solutions. He has also contributed to research on explainability-driven malware analysis using deep learning, aimed at improving model interpretability and aiding analysts in identifying malicious software components efficiently. Christian’s technical proficiency covers Python, machine learning frameworks, and reverse engineering methodologies, enabling him to design intelligent systems with strong analytical and practical impact. His academic achievements and professional experiences in software engineering, mobile application security, and AI-driven analysis demonstrate a consistent pursuit of excellence and innovation. His work not only strengthens theoretical understanding in federated machine learning but also delivers practical tools for protecting digital ecosystems. Through his commitment to research, collaboration, and ethical AI development, he continues to contribute meaningfully to the evolving landscape of artificial intelligence and data security. 17 Citations 3 Documents 2 h-index View h-index

Profiles: Google Scholar | Scopus | ORCID
Featured Publication

Iadarola, G., Casolare, R., Martinelli, F., Mercaldo, F., Peluso, C., & Santone, A. (2021). A semi-automated explainability-driven approach for malware analysis through deep learning. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE. Cited by: 19

Ciaramella, G., Martinelli, F., Mercaldo, F., Peluso, C., & Santone, A. (2024). An approach for privacy-preserving mobile malware detection through federated machine learning. In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024). SciTePress.
Cited by: 5

Peluso, C., Ciaramella, G., Mercaldo, F., Santone, A., & Martinelli, F. (2024). A federated learning-based Android malware detector through differential privacy. In International Conference on Computer Aided Systems Theory (EUROCAST 2024) (pp. 307–319).

Sirous Rafiei Asl | Computer Vision | Best Researcher Award

Published on by Computer Science Engineering

Dr. Sirous Rafiei Asl | Computer Vision | Best Researcher Award

Medical Student | Ahvaz Jundishapur University of Medical Sciences | Iran

Dr. Safa Najafi’s research focuses on the intersection of medical education and parasitology, with particular attention to Leishmaniasis and other parasitic diseases prevalent in tropical and subtropical regions. Her work emphasizes evaluating medical students’ knowledge, awareness, and performance toward parasitic infections to identify gaps that hinder effective disease prevention and control. Through descriptive and analytical studies, she explores the relationship between demographic factors, clinical exposure, and academic performance in shaping medical students’ understanding of zoonotic diseases such as Leishmania infections. The findings of her research highlight that enhanced awareness and practical performance among future healthcare professionals play a critical role in public health preparedness and vector control strategies. Safa Najafi also investigates behavioral and environmental determinants of disease transmission and advocates for integrating targeted educational programs, including mobile-based learning and seminar-based interventions, into medical curricula to strengthen clinical competencies and promote early prevention. Her studies contribute to developing evidence-based strategies to reduce leishmaniasis transmission by bridging the gap between theoretical knowledge and field application. By analyzing key epidemiological factors, her research supports the design of culturally relevant training programs that empower medical students and healthcare providers to adopt preventive practices effectively. This work aligns with broader goals in global health to mitigate the burden of parasitic diseases through informed medical practice and community education. Overall, her research advances understanding of how educational approaches can shape health behavior and influence disease outcomes, reinforcing the significance of awareness, attitudes, and practices in sustainable disease management. Safa Najafi’s scholarly contributions are reflected in her academic record, with 2 Citations, 3 Documents, and an h-index of 1. View h-index.

Profiles: Google Scholar |  Scopus | ORCID
Featured Publication

Elahi, R. K., Asl, S., & Shahian, F. (2013). Study on the effects of various doses of Tribulus terrestris extract on epididymal sperm morphology and count in rat. Iranian Journal of Reproductive Medicine, 11(3), 207–212. Citations: 46

Mahdavinia, M., Alizadeh, S., Vanani, A. R., Dehghani, M. A., Shirani, M., et al. (2019). Effects of quercetin on bisphenol A-induced mitochondrial toxicity in rat liver. Iranian Journal of Basic Medical Sciences, 22(5), 499. Citations: 18

Moradi, M., Montazeri, E. A., Rafiei Asl, S., Pormohammad, A., Farshadzadeh, Z., et al. (2025). In vitro and in vivo antibacterial and antibiofilm activity of zinc sulfate (ZnSO₄) and carvacrol (CV) alone and in combination with antibiotics against Pseudomonas aeruginosa. Antibiotics, 14(4), 367. Citations: 5

Rafiei-Asl, S., Gh., K., Jalali, S. M., Jamshidian, J., & Rezaie, A. (2021). Protective effects of bromelain against cadmium-induced pulmonary intoxication in rats: A histopathologic and cytologic study. Archives of Razi Institute, 76(5), 1427–1436. Citations: 3

Rafiei-Asl, S., Khadjeh, G., Jalali, S. M., Jamshidian, J., & Rezaie, A. (2020). Investigating the protective effects of bromelain against inflammatory marker alterations induced by cadmium pulmonary intoxication in rat. Iranian Veterinary Journal, 16(2), 75–88. Citations: 3