Zulqurnain Sabir | Artificial Neural Networks | Best Researcher Award

Published on by Environmental Scientists

Best Researcher Award

Zulqurnain Sabir
Lebanese American University, Lebanon

Zulqurnain Sabir
Affiliation Lebanese American University
Country Lebanon
Scopus ID 56184182600
Documents 301
Citations 8494
h-index 54
Subject Area Artificial Neural Networks
Event Environmental Scientists
ORCID 0000-0001-7466-6233

Zulqurnain Sabir is a researcher affiliated with Lebanese American University whose scholarly work has contributed to the advancement of artificial neural networks and computational mathematics. His publication record, citation impact, and sustained research productivity demonstrate continuous academic engagement and international visibility within interdisciplinary scientific research.[1]

Abstract

This article summarizes the academic profile of Zulqurnain Sabir, highlighting his sustained research activity, publication performance, and scholarly influence in artificial neural networks. His scientific contributions reflect consistent engagement with computational methodologies and interdisciplinary applications.[1]

Keywords

Artificial neural networks, computational mathematics, numerical analysis, scientific computing, differential equations, optimization, machine learning, research excellence.

Introduction

Modern computational research increasingly integrates artificial intelligence with mathematical modelling to address complex scientific problems. Zulqurnain Sabir has contributed to this evolving field through peer-reviewed publications and collaborative academic research.[2]

Research Profile

The research profile demonstrates extensive publication activity supported by strong citation metrics and a high h-index. These indicators suggest sustained scholarly productivity and recognition within the international research community.[1]

Research Contributions

His work emphasizes artificial neural networks, numerical techniques, and mathematical optimization for solving engineering and scientific challenges. These studies support methodological improvements across computational science and applied mathematics.[3]

Publications

With more than 300 indexed publications, the research portfolio reflects consistent authorship in reputable international journals. The publication record illustrates long-term commitment to scientific dissemination and collaborative investigation.[1]

Research Impact

Citation performance exceeding eight thousand references demonstrates broad academic visibility and continued influence among researchers. Such impact indicates that the published work has contributed to ongoing developments in computational research.[4]

Award Suitability

The combination of research productivity, measurable citation impact, and international publication activity supports recognition for research excellence. These achievements align with the objectives of the Environmental Scientists recognition program in acknowledging sustained academic contributions.[5]

Conclusion

Zulqurnain Sabir has established a well-documented scholarly profile through continuous publication, citation growth, and interdisciplinary research. His academic achievements reflect meaningful contributions to computational science and artificial neural network research.[1]

External Links

References

  1. Elsevier. (n.d.). Scopus author details: Zulqurnain Sabir, Author ID 56184182600. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=56184182600
  2. ORCID. (n.d.). Zulqurnain Sabir ORCID record.
    https://orcid.org/0000-0001-7466-6233
  3. Sabir, Z., Muhammad, N., Zhang, S., & Khan, I. (2026). Hybrid radial basis and log-sigmoid neural network using Rprop for dengue–COVID-19 co-infection dynamics. SSRN Electronic Journal (Preprint).
    https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6910764
  4. Sabir, Z., Kobba, T., & Fadel, H. (2026). A computational design-based scale conjugate neural network to solve the nonlinear Rabinovich–Fabrikant model. Journal of Circuits, Systems and Computers. Advance online publication
    https://www.worldscientific.com/doi/10.1142/S0218126626501203
  5. Sabir, Z., Bichbich, I., Umar, M., Salahshour, S., & Bayram, M. (2026). An artificial neural network based on radial basis methodology using delay effects in the Parkinson’s disease model. Computational Biology and Chemistry, 115, Article 109033.

Mahdi Aliyari-Shoorehdeli | Data Science and Analytics | Best Researcher Award

Published on by Environmental Scientists

Best Researcher Award

Mahdi Aliyari-Shoorehdeli
Affiliation K. N. Toosi University of Technology
Country Iran
Scopus ID 16178561500
Documents 243
Citations 3315
h-index 29
Subject Area Data Science and Analytics
Event Environmental Scientists
ORCID 0000-0002-9985-510X

Mahdi Aliyari-Shoorehdeli
K. N. Toosi University of Technology

Mahdi Aliyari-Shoorehdeli is an academic researcher whose work contributes to data science, intelligent systems, computational modeling, and analytics. His publication record, citation impact, and interdisciplinary collaborations demonstrate sustained scholarly activity across internationally recognized scientific platforms.His research spans artificial intelligence, adaptive neuro-fuzzy inference systems (ANFIS), particle swarm optimization, machine learning, optimization algorithms, control engineering, and computational intelligence. [1]

Abstract

This article summarizes the academic profile of Mahdi Aliyari-Shoorehdeli and highlights measurable research achievements in data science and analytics. His scholarly activities demonstrate consistent publication output, recognized citation performance, and active participation in international research collaborations.[2]

Keywords

Data Science, Artificial Intelligence, Machine Learning, Analytics, Intelligent Systems, Computational Modeling, Optimization, Research Impact.

Introduction

The research activities of Mahdi Aliyari-Shoorehdeli focus on analytical methodologies that combine computational intelligence with practical engineering applications. His publications contribute to advancing modern data-driven approaches across multiple scientific disciplines.[3]

Research Profile

With 243 indexed publications, more than 3,300 citations, and an h-index of 29, the researcher has established a strong international academic presence. These indicators reflect sustained productivity and consistent scholarly influence within the research community.[1]

Research Contributions

His studies emphasize intelligent control, optimization techniques, machine learning algorithms, and advanced analytical frameworks. These contributions support the development of efficient computational solutions for complex engineering and scientific problems.

Publications

Research findings have appeared in peer-reviewed journals and conference proceedings covering artificial intelligence, automation, optimization, and computational science. Many publications are indexed in internationally recognized academic databases with DOI identification.[5]

Research Impact

Citation statistics indicate that the research has been referenced by scholars across diverse disciplines, demonstrating continuing academic relevance. The combination of publication quality and interdisciplinary collaboration strengthens the overall research influence.[2]

Award Suitability

The documented publication record, citation performance, and established research profile provide evidence supporting recognition through the Best Researcher Award. These achievements align with academic standards commonly considered during scholarly evaluation processes.

Conclusion

Mahdi Aliyari-Shoorehdeli continues to contribute to data science and analytics through impactful publications and collaborative research. His academic record reflects sustained scholarly engagement and measurable contributions to international scientific literature.

External Links

References

  1. Elsevier. Scopus Author Details: Mahdi Aliyari-Shoorehdeli, Author ID 16178561500.
    https://www.scopus.com/authid/detail.uri?authorId=16178561500
  2. ORCID. Researcher Profile.
    https://orcid.org/0000-0002-9985-510X
  3. Khanesar, M. A., Teshnehlab, M., & Aliyari-Shoorehdeli, M. (2007). A novel binary particle swarm optimization. In Proceedings of the 2007 Mediterranean Conference on Control & Automation (pp. 1–6).
    https://ieeexplore.ieee.org/document/4433821
  4. Aliyari-Shoorehdeli, M., Teshnehlab, M., Sedigh, A. K., & Khanesar, M. A. (2009). Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods. Applied Soft Computing

Mohaddeseh Esmaeili Farsani | Data Science and Analytics | Best Researcher Award

Published on by Environmental Scientists

Best Researcher Award

Mohaddeseh Esmaeili Farsani
Isfahan University, Iran

Mohaddeseh Esmaeili Farsani
Affiliation Isfahan University
Country Iran
Scopus ID aGHJtpkAAAAJ
Documents 4
Citations 1
h-index 1
Subject Area Data Science and Analytics
Event International Environmental Scientists Award

The Best Researcher Award nomination profile of Mohaddeseh Esmaeili Farsani highlights emerging scholarly contributions in the interdisciplinary domains of data science, artificial intelligence, biomedical signal processing, and intelligent healthcare systems. Through collaborative research activities, the researcher has contributed to studies involving clinical artificial intelligence governance, electrocardiogram interpretation, electroencephalography analytics, and contactless physiological monitoring technologies. These works demonstrate engagement with contemporary scientific challenges related to trustworthy AI implementation and healthcare innovation.[1]

Abstract

Mohaddeseh Esmaeili Farsani has participated in research addressing advanced analytical methods for healthcare and biomedical engineering applications. The publication portfolio reflects interests in artificial intelligence governance, physiological signal interpretation, medical imaging analytics, and machine learning methodologies. The research emphasizes responsible AI deployment, regulatory compliance, and evidence-based healthcare technologies while contributing to interdisciplinary scientific knowledge development.[2]

Keywords

Data Science, Artificial Intelligence, Biomedical Engineering, Machine Learning, EEG Analysis, ECG Interpretation, Clinical Informatics, Signal Processing, Healthcare Analytics, Responsible AI.

Introduction

Recent advancements in data science have accelerated the integration of artificial intelligence into healthcare systems. Researchers working at this intersection contribute to the development of predictive models, clinical decision-support tools, and intelligent monitoring platforms. Mohaddeseh Esmaeili Farsani’s scholarly activities align with these developments through collaborative investigations focused on trustworthy and effective AI utilization within biomedical contexts.[3]

Research Profile

The research profile demonstrates engagement with emerging themes in computational healthcare and intelligent biomedical systems. Published works explore clinical evaluation frameworks, AI regulation, human-machine interaction technologies, and standardized methods for physiological data analysis. These areas contribute to improving transparency, reliability, and operational effectiveness in healthcare-oriented artificial intelligence applications.[4]

Research Contributions

  • Contribution to AI governance frameworks for clinical decision-making systems.
  • Participation in systematic reviews of contactless vital-sign monitoring technologies.
  • Research involving deep learning applications for cardiac signal interpretation.
  • Development of methodologies supporting EEG data acquisition and machine learning integration.
  • Promotion of interdisciplinary collaboration between data science and biomedical engineering.

Publications

  1. TRIAGE: Trustworthy Reporting and Assessment for Clinical Gain and Effectiveness of AI Models (Diagnostics, 2026).
  2. Contactless Vital Sign Monitoring Through Intelligent Human–Machine Interaction: A Systematic Review (2026).
  3. CARDIO-AI: Compliance and Artificial Intelligence Regulation for Deep Learning in Electrocardiogram Interpretation (2026).
  4. AI-EEG: Advanced Integration and Machine Learning Standards for EEG Data Acquisition and Processing (2025).

Research Impact

The documented research output contributes to ongoing discussions regarding ethical AI deployment, healthcare data governance, and intelligent biomedical analytics. Although the publication record is at an early stage, the thematic focus addresses areas of substantial scientific and societal relevance. The integration of data science methods with clinical applications supports future advancements in healthcare technology and evidence-driven decision making.[5]

Award Suitability

Based on the available scholarly record, Mohaddeseh Esmaeili Farsani demonstrates active participation in research addressing significant contemporary challenges in artificial intelligence and healthcare analytics. The interdisciplinary nature of the work, together with contributions to responsible AI, biomedical signal processing, and clinical technology evaluation, aligns with the objectives of the International Environmental Scientists Award’s recognition of emerging research excellence and scientific innovation.[6]

Conclusion

Mohaddeseh Esmaeili Farsani’s academic contributions reflect growing engagement with data-driven healthcare innovation and intelligent biomedical systems. The publication portfolio demonstrates participation in research focused on trustworthy AI, advanced signal processing, and clinical technology assessment. These activities support continued scholarly development and provide a foundation for future scientific contributions within data science and analytics.

References

  1. Elsevier. (n.d.). Scopus author details: Mohaddeseh Esmaeili Farsani, Author ID Google Scholar.
    https://scholar.google.com/citations?hl=en&user=aGHJtpkAAAAJ
  2. Fazilati, F., et al. (2026). TRIAGE: Trustworthy Reporting and Assessment for Clinical Gain and Effectiveness of AI Models. Diagnostics.
    https://doi.org/10.3390/diagnostics16050666
  3. Alihosseini, N., et al. (2026). Contactless Vital Sign Monitoring Through Intelligent Human–Machine Interaction.
  4. Rajabi, M.Z., et al. (2026). CARDIO-AI: Compliance and Artificial Intelligence Regulation for Deep Learning in ECG Interpretation.
  5. Marateb, H.R., et al. (2025). AI-EEG: Advanced Integration and Machine Learning Standards for EEG Data Acquisition and Processing.
  6. International Environmental Scientists Award. (n.d.). Award nomination and evaluation framework.
    environmentalscientists.org

May El Barachi | Predictive Analytics and Machine Learning | Best Researcher Award

Published on by Environmental Scientists

Prof. May El Barachi | Predictive Analytics and Machine Learning | Best Researcher Award

Professor and Dean, University of Wollongong in Dubai, United Arab Emirates.

Prof. May El Barachi is a Canadian computer scientist and seasoned academic leader known for her transformative impact on education, research, and innovation. She is currently a Full Professor and Head of the School of Computer Science at the University of Wollongong in Dubai. Over her 15+ year career, she has secured over 5 million AED in research funding, published 110+ high-impact papers, and built a global network of collaborations across academia and industry. Her work bridges cutting-edge AI research with real-world applications in smart systems and sustainable development. A passionate advocate for diversity, inclusion, and lifelong learning, she holds a UAE Golden Visa and is fluent in English, Arabic, and French.

Profile

Scopus

Orcid

Google Scholar

🎓 Education

  • Ph.D. in Computer Science, Concordia University, Montréal, Canada (2004–2009)
    Specialization: Next Generation Networks, Service Engineering, Network Intelligence and Adaptation

  • M.A.Sc. in Electrical and Computer Engineering, Concordia University, Montréal, Canada (2002–2004)
    Specialization: Web Services, IP Telephony, Multimedia Communications

  • B.Sc. in Electronics and Communication Engineering, Arab Academy for Science, Technology & Maritime Transport, Alexandria, Egypt (1995–2000)
    Graduated Valedictorian with Honors; Specialization in Object Recognition, Remote Sensing, and Neural Networks

💼 Professional Experience

Prof. May El Barachi is a visionary academic leader and full professor of Computer Science with over 15 years of experience across the UAE, Canada, and Europe. She currently serves as the Head of the School of Computer Science at the University of Wollongong in Dubai (UOWD), where she has led transformational initiatives, including a 400% increase in enrollment, the development of innovative Master’s programs in AI and Cybersecurity, and the establishment of a widely recognized Executive Learning Program that has trained over 2,500 professionals.

Previously, she held the roles of Associate Dean of Research and Associate Professor at UOWD, where she restructured research clusters around Sustainable Development Goals, drove multi-million-dirham funding acquisition, and played a pivotal role in Ph.D. program development. At Zayed University, she served as Smart Lab Director and co-founder, pioneering research on smart cities and AI-driven systems, while also leading curriculum development and accreditation efforts. Her earlier experience includes postdoctoral research at the University of Quebec (ETS), and roles in industry-academic collaborations at Ericsson Canada and the Ambient Networks Project in Sweden, focusing on web services, context-aware networks, and next-generation telecom systems.

🔬 Research Interest

  • Artificial Intelligence and Machine Learning: including deep learning, computer vision, natural language processing (NLP), reinforcement learning, and ethical AI

  • Emerging Technologies & Applications: smart cities, smart healthcare systems, autonomous systems, and digital transformation

  • Next Generation Networks: context-aware networking, IoT integration, and cloud computing

  • Interdisciplinary Innovation: leveraging AI for societal challenges, particularly in sustainable development, cybersecurity, and educational technology

🏆Author Metrics

  • Publications: 110+ peer-reviewed papers

  • Google Scholar Profile: Google Scholar – Prof. May El Barachi

  • Research Funding Secured: 5.28+ million AED

  • Professional Training Delivered: 2,500+ industry professionals via executive education programs

  • Languages: English, Arabic, French

📚 Publications

1. Evaluating the Impact of COVID-19 on Multimodal Cargo Transport Performance: A Mixed-Method Study in the UAE Context

  • Authors: Rami Aljadiri, Balan Sundarakani, May El Barachi

  • Journal: Sustainability

  • Volume & Issue: 15(22)

  • Article Number: 15703

  • Publication Date: November 7, 2023

  • DOI: 10.3390/su152215703

  • Abstract: This study examines the challenges and opportunities of multimodal cargo transport in the UAE during the COVID-19 pandemic (2020–2022). Utilizing a mixed-method approach, the research involved qualitative interviews with five senior logistics executives and quantitative surveys with 120 participants. Findings indicate a significant relationship between geographical/geopolitical risks and increased shipping costs, emphasizing the need for secure and cost-effective multimodal solutions. The study offers insights for enhancing logistics performance in transit hubs during uncertain times.

2. E2DNE: Energy Efficient Dynamic Network Embedding in Virtualized Wireless Sensor Networks

  • Authors: Vahid Maleki Raee, Amin Ebrahimzadeh, Roch H. Glitho, May El Barachi, Fatna Belqasmi

  • Journal: IEEE Transactions on Green Communications and Networking

  • Volume & Issue: 7(3)

  • Pages: 1309–1325

  • Publication Year: 2023

  • DOI: 10.1109/TGCN.2023.3271230

  • Abstract: The paper introduces E2DNE, a novel approach for energy-efficient dynamic network embedding in virtualized wireless sensor networks (VWSNs). By optimizing resource allocation and reducing energy consumption, E2DNE enhances the performance and sustainability of VWSNs, making them more adaptable to varying network demands.

3. Secure Data Access Using Blockchain Technology Through IoT Cloud and Fabric Environment

  • Authors: Sangeeta Gupta, Premkumar Chithaluru, May El Barachi, Manoj Kumar

  • Journal: Security and Privacy

  • Publication Date: November 23, 2023

  • DOI: 10.1002/spy2.356

  • Abstract: This study addresses the challenges of secure data access in IoT environments by integrating blockchain technology with cloud computing. The proposed framework leverages the Hyperledger Fabric platform to ensure data integrity, confidentiality, and scalability, providing a robust solution for managing IoT data securely.

4. Combining Named Entity Recognition and Emotion Analysis of Tweets for Early Warning of Violent Actions

  • Authors: May El Barachi, Sujith Samuel Mathew, Manar AlKhatib

  • Conference: 2022 7th International Conference on Smart and Sustainable Technologies (SpliTech)

  • Publication Date: July 7–8, 2022

  • DOI: 10.23919/SpliTech55088.2022.9854231

  • Abstract: The paper presents a proactive framework that combines Named Entity Recognition (NER) and emotion analysis to detect early warning signs of potential violent actions from social media content. By analyzing tweets related to the 2020 US presidential election, the study demonstrates the framework’s effectiveness in identifying negative sentiments associated with specific entities, offering a tool for early intervention strategies.

5. A Green, Energy, and Trust-Aware Multi-Objective Cloud Coalition Formation Approach

  • Authors: Souad Hadjres, Fatna Belqasmi, May El Barachi, Nadjia Kara

  • Journal: Future Generation Computer Systems

  • Volume: 111

  • Pages: 52–67

  • Publication Date: October 2020

  • DOI: 10.1016/j.future.2020.04.030

  • Abstract: This research proposes a multi-objective approach for forming cloud coalitions that are energy-efficient and trust-aware. The algorithm considers factors like energy consumption, trust levels among providers, and service quality to form optimal coalitions. Experimental results show improvements in coalition size, provider payoff, and reduced mistrust costs, highlighting the approach’s potential for sustainable cloud computing.

🏁 Conclusion

Prof. May El Barachi is an outstanding and highly qualified nominee for the Best Researcher Award in Predictive Analytics and Machine Learning. Her portfolio exhibits the rare combination of technical depth, real-world applicability, international leadership, and a firm commitment to innovation in AI and societal impact. She not only advances predictive analytics through rigorous research but also through her systemic influence in academia and industry.

Given her publication volume, research funding, academic innovation, and practical AI implementations, she represents a paragon of excellence and leadership in predictive analytics and machine learning.

Abdulrazak Otaru | Modelling and Simulation | Best Researcher Award

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Dr Abdulrazak Otaru | Modelling and Simulation | Best Researcher Award

Assistant Professor, King Faisal University, Saudi Arabia ✨
Dr. Abdulrazak Jinadu Otaru is an Assistant Professor in the Department of Chemical Engineering at King Faisal University, Al Ahsa, Saudi Arabia. With extensive teaching experience, he has held academic roles at King Faisal University and the Federal University of Technology Minna, Nigeria. His expertise spans Chemical Engineering Thermodynamics, Numerical Methods, and Computational Fluid Dynamics (CFD). Dr. Otaru’s research focuses on machine learning applications in modeling thermal degradation and porous material systems.

Profile

Orcid

Education 🎓

Dr. Otaru holds a PhD in Chemical Engineering, specializing in Modelling and Simulation. His education has equipped him with advanced knowledge in CFD, machine learning, and material sciences, allowing him to contribute significantly to his field.

Experience 💼

Dr. Otaru has served as an academic in both undergraduate and postgraduate settings. At King Faisal University, he teaches courses such as Chemical Engineering Thermodynamics, Numerical Methods, and Engineering Computing. Previously, at the Federal University of Technology Minna, he taught subjects like Transport Phenomena, Chemical Reaction Engineering, and Chemical Engineering Design.

Research Interests 🔬

Dr. Otaru’s research revolves around Chemical Engineering, focusing on Computational Fluid Dynamics (CFD), machine learning for material modeling, carbon capture technologies, and the thermo-kinetic analysis of bio-composites. His ongoing projects include studies on the thermal degradation of palm fronds, polyolefin bio-composites, and various zeolite synthesis processes.

Awards 🏆

Dr. Otaru has earned recognition for his contributions to chemical engineering and material sciences, particularly for his innovative use of machine learning techniques in chemical process modeling. His work in computational techniques for sustainable material processing has been highly regarded in both academic and industrial circles.

Publications 📚

Dr. Otaru has published numerous research papers in renowned journals. His recent works include:

Thermal Decomposition of Date Seed/Polypropylene Homopolymer: Machine Learning CDNN, Kinetics, and Thermodynamics published in Polymers, MDPI on 23rd January 2025. Link to the article.

Kinetics Study of the Thermal Decomposition of Date Seed Powder/HDPE Plastic Blends published in Bioresource Technology Reports, Elsevier on 13th January 2025. Link to the article.

Conclusion

Dr. Otaru stands out as a highly deserving candidate for the Best Researcher Award. His exceptional research output, interdisciplinary work, and continuous contributions to the scientific community make him a key figure in the field of Chemical Engineering. With a proven record of academic excellence and an innovative approach to solving complex engineering problems, Dr. Otaru exemplifies the qualities of a distinguished researcher.

 

Sarah Di Grande | Analytics | Best Researcher Award

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Ms. Sarah Di Grande | Analytics | Best Researcher Award

PhD student, University of Catania, Italy

Sarah Di Grande is a driven researcher and data scientist currently pursuing a PhD in Systems, Energy, Computer, and Telecommunications Engineering at the University of Catania, Italy. With expertise in machine learning and a focus on sustainable water-energy optimization, she has contributed extensively to data science applications in renewable energy and smart city initiatives.

Profile

Orcid

Education 🎓

Sarah completed a Master’s in Data Science for Management at the University of Catania in 2022, graduating summa cum laude with a thesis on unsupervised machine learning for photovoltaic systems. She also holds a Bachelor’s degree in Business Economics from the same institution and graduated from Liceo Megara with top honors in 2017. Her studies have centered on advanced machine learning, big data, and data security.

Experience 💼

Currently, Sarah is a PhD student and researcher at the University of Catania, working in collaboration with Darwin Technologies on machine learning-based water-energy optimization. She previously interned as a data scientist at BaxEnergy, where she applied predictive maintenance techniques for photovoltaic panels, gaining hands-on experience in industrial data science applications.

Research Interests 🔬

Her research is dedicated to leveraging artificial intelligence for sustainable energy systems, focusing on machine learning applications in hydropower forecasting, urban traffic prediction, and water distribution network optimization. Sarah’s work aims to enhance resource management and promote sustainability in smart cities.

Awards 🏆

Sarah has received recognition for her innovative contributions, winning the Start-Cup Sicilia 2023 for her work on the “Smart Knee Project,” a device aimed at diagnosing knee osteoarthritis. She also secured second place in the University of Catania’s Start-Cup competition for the same project.

Publications Top Notes📚

Sarah has contributed numerous papers to international conferences and journals, exploring AI in hydropower, water distribution, and urban traffic management. Some key publications include:

“A Proactive Approach for the Sustainable Management of Water Distribution Systems” (2023) in 12th International Conference on Data Science, Technology and Applications – DATA [cited by 10 articles].

“Detection and Prediction of Leakages in Water Distribution Networks” (2023) in DATA 2023 [cited by 7 articles]

“A Machine Learning Approach for Hydroelectric Power Forecasting” (2023) in 14th International Renewable Energy Congress – IREC [cited by 5 articles].

“Data Science for the Promotion of Sustainability in Smart Water Distribution Systems” (2024) in Communications in Computer and Information Science, Springer [cited by 12 articles].