Nazli Tokatli | Computer Science and Artificial Intelligence | Innovative Research Award

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Innovative Research Award

Nazli Tokatli — Istanbul Health and Technology University,  Turkey

Nazli Tokatli
Affiliation Istanbul Health and Technology University
Country Turkey
Scopus ID 57212214663
Documents 23
Citations 88
h-index 5
Subject area Computer Science and Artificial Intelligence
Event International Research Scientist Awards
ORCID 0000-0001-9840-4211

Nazli Tokatli is a researcher affiliated with Istanbul Health and Technology University, Turkey, whose scholarly profile is associated with computer science and artificial intelligence. The available bibliometric information records 23 documents, 88 citations, and an h-index of 5 in Scopus. These indicators provide a quantitative snapshot of indexed research output and citation activity, while the researcher’s publication record offers the appropriate basis for examining individual contributions, thematic interests, and scholarly relevance. The Innovative Research Award recognizes research-related achievements within the context of the International Research Scientist Awards. [1]

Abstract

Nazli Tokatli, affiliated with Istanbul Health and Technology University in Turkey, has a research profile associated with computer science and artificial intelligence. Scopus records 23 documents, 88 citations, and an h-index of 5, providing quantitative indicators of scholarly publication and citation activity. This article summarizes the available profile information and outlines how research contributions may be assessed through publication relevance, methodological rigor, reproducibility, and potential applications. The Innovative Research Award provides a recognition framework for examining research achievements. Detailed assessment requires verification of individual publications, their subject matter, and documented contributions to the relevant scientific fields. [2]

Keywords

Computer science, artificial intelligence, machine learning, computational methods, intelligent systems, data analysis, algorithm development, and research innovation. These terms describe potential indexing categories rather than verified subjects of specific publications. The final keyword selection should be refined against the titles, abstracts, and keywords of the researcher’s indexed articles to ensure accurate representation of actual research themes.[3]

Introduction

Nazli Tokatli’s academic profile is situated within the broad disciplines of computer science and artificial intelligence, areas that encompass computational theory, intelligent algorithms, data-driven systems, and technological applications. Evaluating research in these fields requires consideration of the problems addressed, methods employed, and reproducibility of reported findings. Institutional affiliation and bibliometric records provide useful context, but publication-level evidence remains necessary to characterize an individual’s specific research interests and achievements accurately. [1]

Research Profile

Nazli Tokatli’s available Scopus profile lists 23 documents, 88 citations, and an h-index of 5. The h-index indicates that five publications have received at least five citations each, subject to the database’s current records and author attribution. These indicators can help describe publication activity and citation visibility but do not independently establish research quality, originality, or practical significance. Profile details should be checked against the current database record before formal evaluation or republication. [1] [2]

Research Contributions

Nazli Tokatli’s research contributions should be evaluated through verified publications, research questions, methodological choices, and documented findings in computer science and artificial intelligence. Relevant evidence may include algorithmic improvements, computational analysis, model evaluation, or applications to real-world problems when supported by published work. Without a verified publication list and article-level review, specific innovations cannot be attributed reliably. Assessment should therefore prioritize original results, transparent methods, reproducibility, and relevance to the research community.[3]

Publications

Nazli Tokatli’s Scopus author record reports 23 indexed documents. A complete publication analysis should examine article titles, publication venues, dates, co-authors, abstracts, and available DOI identifiers to identify recurring themes and assess individual contributions. Google Scholar provides a complementary discovery resource, although its coverage and citation counts may differ from Scopus. Specific publication titles and DOI links should be added only after verification against the original publisher records and author profiles. [1]

Research Impact

Nazli Tokatli’s recorded 88 citations indicate that indexed publications have received scholarly references within the database’s coverage. Citation totals can help identify research visibility but vary with publication age, discipline, collaboration patterns, and database coverage. A balanced assessment should consider citation context, methodological reliability, subsequent use of findings, and evidence of scientific or societal relevance. Responsible interpretation combines quantitative indicators with qualitative examination instead of treating any single metric as a definitive measure of research excellence.

Award Suitability

Nazli Tokatli’s bibliometric profile offers preliminary information for consideration within the Innovative Research Award category of the International Research Scientist Awards. A rigorous recognition assessment should review verified publications, originality, contribution to the field, research integrity, and evidence of influence. The supplied metrics provide context but do not independently confirm award eligibility or selection. Any final determination should follow the event’s published criteria and documented review process, with claims of recognition distinguished from nomination or consideration.[3]

Conclusion

Nazli Tokatli is affiliated with Istanbul Health and Technology University, Turkey, and has a Scopus profile reporting 23 documents, 88 citations, and an h-index of 5. These figures provide a concise overview of indexed scholarly activity in the context of computer science and artificial intelligence. A fuller evaluation of research significance requires publication-level verification and qualitative assessment of original contributions. The Innovative Research Award offers a framework for presenting research-related achievements, provided that all claims are supported by reliable academic records and applicable award criteria. [1]

References

    1. Elsevier. (n.d.). Scopus author details: Nazli Tokatli, Author ID 57212214663. Scopus. Author metrics presented in this article are based on the information supplied for this profile and should be checked against the live record.
      https://www.scopus.com/authid/detail.uri?authorId=57212214663
    2. ORCID. (n.d.). Nazli Tokatli: ORCID researcher identifier 0000-0001-9840-4211. ORCID.
      https://orcid.org/0000-0001-9840-4211
    3. Google. (n.d.). Google Scholar author profile. Google Scholar. Use the profile to identify publications and related scholarly records; individual publication metadata should be confirmed against publisher pages.
      https://scholar.google.com/citations?user=sOqjhTsAAAAJ&hl=en&oi=sra

Usman Anjum | Tsetlin Machines | Innovative Research Award

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Innovative Research Award

Usman Anjum, Ottawa University

Usman Anjum
Researcher Usman Anjum
Affiliation Ottawa University
Country United States
Scopus ID 57367380200
Documents 12
Citations 53
h-index 5
Subject Area Tsetlin Machines
Event International Research Scientist Awards
ORCID
0000-0002-9280-772X

The Innovative Research Award recognizes scholarly contributions and emerging advancements in computational intelligence, machine learning, and interdisciplinary scientific research. Usman Anjum of Ottawa University has been associated with research developments involving Tsetlin Machines and related computational methodologies that contribute to evolving analytical frameworks within artificial intelligence research.[1]

Abstract

This article presents an overview of the academic profile and research activities associated with Usman Anjum and the Innovative Research Award under the International Research Scientist Awards initiative. The profile highlights scholarly engagement in Tsetlin Machine research, computational intelligence, and machine learning methodologies. The article further examines research output metrics, publication visibility, and interdisciplinary relevance within modern artificial intelligence studies.[1][2]

Keywords

Tsetlin Machines, Artificial Intelligence, Machine Learning, Computational Intelligence, Pattern Recognition, Research Innovation, Scholarly Impact, Data Analytics, Intelligent Systems, Scientific Awards

Introduction

The International Research Scientist Awards aim to recognize researchers contributing to scientific advancement through original investigations, interdisciplinary collaboration, and publication activity. Within this context, Usman Anjum has been identified for contributions associated with computational learning systems and Tsetlin Machine methodologies, which are increasingly explored in explainable artificial intelligence and data-driven decision systems.[1]

Tsetlin Machines represent a symbolic machine learning approach designed to enhance interpretability while maintaining competitive predictive performance. Such methodologies have gained increasing visibility in domains requiring transparent reasoning processes and computational efficiency.[2]

Research Profile

Usman Anjum is affiliated with Ottawa University in the United States and has contributed to scholarly research connected with computational intelligence and machine learning systems. According to available Scopus indexing data, the research profile includes twelve indexed documents with citation activity reflecting ongoing academic engagement in the field.[1]

  • Primary research area: Tsetlin Machines
  • Indexed scholarly documents: 12
  • Citation count: 53
  • Research visibility through Scopus indexing
  • Association with emerging explainable AI methodologies

Research Contributions

Research involving Tsetlin Machines focuses on interpretable pattern recognition systems that rely on propositional logic and automated clause learning. Such approaches are particularly relevant in contexts where explainability and reduced computational overhead are considered essential.[2]

The broader research significance of these studies includes applications in predictive analytics, classification systems, healthcare informatics, cybersecurity, and intelligent automation. The growing interest in symbolic machine learning architectures demonstrates the continuing relevance of alternative approaches to conventional neural network frameworks.[1]

  • Research emphasis on explainable machine learning models
  • Exploration of logic-based computational systems
  • Contributions to interpretable AI methodologies
  • Participation in interdisciplinary computational research
  • Academic engagement with data-driven analytical frameworks

Publications

The publication portfolio associated with Usman Anjum reflects involvement in machine learning and computational intelligence research. Indexed works contribute to the growing body of literature on explainable artificial intelligence and symbolic learning architectures.[1]

  1. Research related to Tsetlin Machine methodologies and interpretable classification systems.
  2. Investigations into computational learning mechanisms for intelligent systems.
  3. Studies contributing to explainable artificial intelligence frameworks.
  4. Applications of symbolic machine learning for data analysis and decision support.

Research Impact

The research impact associated with Usman Anjum can be evaluated through indexed publications, citation metrics, and participation in evolving machine learning research domains. Citation indicators and publication visibility suggest continuing engagement with scholarly communities focused on computational intelligence and explainable AI.[1]

Research concerning Tsetlin Machines has gained attention because of its potential balance between interpretability and computational performance. These characteristics are increasingly relevant in academic and industrial environments emphasizing ethical AI and transparent algorithmic systems.

Award Suitability

The Innovative Research Award acknowledges individuals demonstrating measurable scholarly engagement, publication activity, and contributions to advancing scientific knowledge. Based on indexed research activity, publication output, and involvement in machine learning methodologies, Usman Anjum represents a suitable candidate profile for recognition within interdisciplinary computational research categories.[1]

  • Documented scholarly publication record
  • Research engagement in explainable artificial intelligence
  • Indexed citation visibility
  • Interdisciplinary computational research relevance
  • Contribution to emerging symbolic learning methodologies

Conclusion

The Innovative Research Award article highlights the academic profile and research activities associated with Usman Anjum and Ottawa University. The documented scholarly contributions in Tsetlin Machines and explainable machine learning systems illustrate participation in a rapidly evolving field of artificial intelligence research. Through indexed publications, citation metrics, and interdisciplinary relevance, the profile demonstrates alignment with the objectives of the International Research Scientist Awards program.[1]

References

    1. Elsevier. (n.d.). Scopus author details: Usman Anjum, Author ID 57367380200. Scopus.
      https://www.scopus.com/authid/detail.uri?authorId=57367380200
    2. ORCID. (n.d.). ORCID profile: Usman Anjum.
      https://orcid.org/0000-0002-9280-772X
    3. Anjum, U., & Zhan, J. (n.d.). A Novel Tsetlin Machine with Enhanced Generalization.
      https://www.mdpi.com/2079-9292/13/19/3825

Meziane Iftene | Artificial Neural Networks | Research Excellence Award

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Dr. Meziane Iftene | Artificial Neural Networks | Research Excellence Award

Agence Spatiale Algérienne | Algeria

Dr. Meziane Iftene is a researcher at the Algerian Space Agency specializing in remote sensing, deep learning, and geospatial data fusion. He has authored and co-authored overpeer-reviewed publications in leading international conferences and journals. His work focuses on hyperspectral unmixing, very high-resolution image classification, and AI-driven environmental monitoring, including forest mapping and precision agriculture. He has led R&D initiatives, supervised nanosatellite projects, and contributed to national space system development such as the Alsat-3 program. Through international collaborations, his research advances GeoAI solutions for sustainable environmental management and intelligent Earth observation systems.

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Featured Publications


Very high resolution images classification by fusing deep convolutional neural networks.

– In Proceedings of the 5th International Conference on Advanced Computer Science and Technology (ACSAT 2017). (2017). Cited By: 6

Transfering super resolution convolutional neural network for remote sensing data sharpening.

– In Proceedings of the 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS 2018). (2018). Cited By: 5

End-to-end change detection in satellite remote sensing imagery.

– In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2021) (pp. 4356–4359). (2021). Cited By; 3

 

Jobish Vallikavungal Devassia | Computer Science | Best Researcher Award

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Prof. Dr. Jobish Vallikavungal Devassia | Computer Science | Best Researcher Award

Professor | Tecnológico De Monterrey | Mexico

Prof. Dr. Jobish Vallikavungal Devassia is a distinguished researcher in Computer Science, specializing in scheduling algorithms, operations research, and optimization for flexible job-shop and parallel machine systems. He has authored over 10 high-impact publications in top-tier journals, including Computers & Industrial Engineering, International Journal of Production Research, and IEEE Access, accumulating more than 112 citations. His work integrates advanced techniques such as rough sets, multidimensional fuzzy logic, and dynamic scheduling methods, often in collaboration with international scholars from Mexico, Spain, and France. His research advances efficient industrial operations, resource optimization, and practical applications in manufacturing and logistics, impacting both academia and industry globally.

 

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Featured Publications

The generalized flexible job shop scheduling problem.

– Computers & Industrial Engineering. (2021). Cited By: 50

Flexible job-shop scheduling problem with resource recovery constraints.

– International Journal of Production Research. (2018). Cited By: 41

A parallel machine batch scheduling problem in a brewing company.

– The International Journal of Advanced Manufacturing Technology. (2016). Cited By: 32

Mokhtar Ferhi | Computer Science and Artificial Intelligence | Research Excellence Award

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Dr. Mokhtar Ferhi | Computer Science and Artificial Intelligence | Research Excellence Award

University of Jendouba | Tunisia

Dr. Mokhtar Ferhi is a researcher at Université de Jendouba, Tunisia, specializing in heat transfer, fluid mechanics, magnetohydrodynamics (MHD), nanofluid convection, and numerical simulation methods, particularly the Lattice Boltzmann Method. He has authored 27 peer-reviewed publications, receiving 140 citations with an h-index of 6 (Scopus). His work focuses on entropy generation, energy optimization, and thermal performance enhancement in cavities and micro-heat exchangers. Ferhi collaborates internationally with experts across North Africa, Europe, and the Middle East, contributing to advances in energy-efficient thermal systems with applications in sustainable engineering and heat exchanger design.

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Featured Publications

 

Saeed Banaeian Far | Computer Science | Best Researcher Award

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Assist. Prof. Dr. Saeed Banaeian Far | Computer Science | Best Researcher Award

Assist. Prof. | Blockchain and Metaverse research lab | Iran

Assist. Prof. Dr. Saeed Banaeian Far is a leading researcher in applied cryptography, blockchain systems, security protocols, and emerging Metaverse technologies. He is affiliated with the Blockchain and Metaverse Research Lab (BMRL) and has made influential contributions to decentralized finance, digital twins, NFTs, Web3, privacy-preserving protocols, and quantum-secure blockchain architectures. With over 44 peer-reviewed publications in high-impact journals and conferences, his work has received 1,045 citations, reflecting strong global academic influence. He actively collaborates with international scholars and interdisciplinary teams, advancing secure digital infrastructures with significant societal impact in finance, governance, healthcare, and next-generation virtual ecosystems.

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Featured Publications

 

Efendi Nasibov | Computer Science | Research Excellence Award

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Prof. Dr. Efendi Nasibov | Computer Science | Research Excellence Award

Dokuz Eylul University | Turkey

Prof. Dr. Efendi Nasiboğlu is a researcher in Computer Sciences at Dokuz Eylül University, İzmir, Turkey. He has authored over 107 scholarly publications indexed in Scopus and Web of Science, accumulating more than 1,101 citations with an h-index of 16. His research expertise spans fuzzy systems, regression modeling, computational intelligence, machine learning, and applied data analysis, with contributions to both theoretical foundations and real-world applications in engineering, manufacturing, healthcare, and smart systems. Dr. Nasiboğlu actively collaborates with international researchers and has published in reputable journals and conferences, contributing to methodological advancements with measurable societal and technological impact.

 

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Featured Publications


On the nearest parametric approximation of a fuzzy number

Fuzzy Sets and Systems  (2008). Citations: 107

A new unsupervised approach for fuzzy clustering

– Fuzzy Sets and Systems. (2007). Citations : 91

Public transport route planning: Modified Dijkstra’s algorithm

– International Conference on Computer Science and Engineering. (2017). Citations :  76

Yongbin Zhao | Computer Science | Best Researcher Award

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Dr. Yongbin Zhao | Computer Science | Best Researcher Award

School of Information Science and Technology | Shijiazhuang Tiedao University | China

Dr. Yongbin Zhao is a researcher at Shijiazhuang Tiedao University, China, recognized for his growing contributions to computational science, network analysis, and data-driven systems research. With a Scopus-indexed publication record comprising 27 scholarly documents and 67 citations across 66 citing sources, he has established an emerging academic profile characterized by interdisciplinary inquiry and collaborative engagement. His current h-index of 5 reflects the consistent impact and relevance of his research in both theoretical and applied domains.Dr. Zhao’s work spans multiple high-value fields including blockchain security complex network modeling and agricultural trade analytics. His recent publication on multi-key fully homomorphic encryption algorithms introduces secure computation frameworks tailored for blockchain environments contributing to the advancement of privacy-preserving technologies in decentralized systems. Another notable study focusing on the evolution and robustness of the global soybean trade network demonstrates his ability to integrate physics-based modeling with international agricultural economics offering meaningful insights for global trade stability and food-system resilience.He has collaborated with more than 40 co-authors reflecting a strong international and interdisciplinary research network. His contributions extend beyond academic outputs providing analytical tools and conceptual frameworks that support secure data infrastructures enhance trade-network understanding and contribute to more resilient socio-economic systems.Through his sustained research activity and collaborative leadership Dr. Yongbin Zhao continues to contribute to scientific knowledge with societal and technological relevance strengthening the interface between computational innovation and global system analysis.

Profiles : Scopus | Research Gate

Featured Publications

1.Author(s). (2025). Research on multi-key fully homomorphic encryption algorithms suitable for blockchain. Cluster Computing.

2.Author(s). (2024). The evolution and robustness analysis of global soybean trade network. International Journal of Modern Physics C. Cited By : 2

Dr. Zhao’s contributions in homomorphic encryption and network robustness drive innovation in blockchain security and international supply-chain analytics. His research supports industry in building safer digital platforms and more efficient global trade systems.

RaulJavier ChangTam | Data Science and Analytics | Best Researcher Award

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Prof. Dr. RaulJavier ChangTam | Data Science and Analytics | Best Researcher Award

Profesor Investigador | Universidad Latina de Costa Rica | Costa Rica

Prof. Dr. RaulJavier ChangTam  is a multidisciplinary researcher whose work connects technology adoption, entrepreneurship, and sustainable innovation in Latin America. His research explores how emerging technologies influence entrepreneurial ecosystems, digital transformation, and user behavior within both business and social contexts. Drawing upon models such as UTAUT2, his studies provide empirical insights into the acceptance and use of new technologies by entrepreneurs, SMEs, and professionals in fields like healthcare and finance. He also contributes to studies on sustainable finance and FinTech, emphasizing the role of digital platforms and neobanks in promoting environmentally responsible entrepreneurship. Beyond business and technology, his work extends into socio-cultural domains examining global consumption patterns such as sneaker culture and analyzing trade dynamics in ornamental species markets. Chang-Tam’s ongoing research reflects a strong orientation toward understanding the intersection of innovation, digitalization, and sustainability, with an applied perspective that links academic insight to real-world economic and cultural transformations. His research has received 3 citations across 9 documents, with an h-index of 1.

Profiles : ORCID | Scopus  | ResearchGate

Featured Publications

1. Taboada Álvarez, J. E., Chang-Tam, R. J., Rueda Varón, M. J., & Hunter Torrealba, R. (2025). Analysis of the entrepreneurial motivational demand in a learning management process in incubators.

2. Araya, J. L. G., Robles Herrera, A. E., & Chang-Tam, R. J. (2025). Analysis of trends in exports and imports of continental and marine ornamental species of aquariums in Costa Rica.

3. Chang-Tam, R. J., Caldera-Gutiérrez, V., & Rivera Shaik, V. (2025). Influence of IT technology on the development of SME entrepreneurs in Costa Rica: Applied study of the adapted model of the Unified Theory of Acceptance and Use of Technology (UTAUT2).

4. Chang-Tam, R. J., Garita Quesada, R., Masís Muñoz, R., & Chang Caldera, A. P. (2025). Influence of new IT technology trends in dental care processes between dental professionals and patients: An analysis of the UTAUT2 theory.

5. Palos-Sanchez, P. R., Chang-Tam, R. J., & Folgado-Fernández, J. A. (2025). The role of neobanks and fintech in sustainable finance and technology: The customer/user perspective for entrepreneurs. Sustainable Technology and Entrepreneurship, 100109.

Mohammed M Alenazi | Computer Science and Artificial Intelligence | Best Researcher Award

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Dr. Mohammed M Alenazi | Computer Science and Artificial Intelligence | Best Researcher Award

Assistance Professor | University of Tabuk | Saudi Arabia

Dr. Mohammed M. Alenazi is an Assistant Professor of Computer Engineering at the University of Tabuk, Saudi Arabia, whose research focuses on the intersection of energy-efficient communication networks, machine learning, and distributed systems. His work advances intelligent computing architectures that optimize performance, reduce energy consumption, and enable sustainability in next-generation networks. Dr. Alenazi has contributed to several impactful studies, including energy-efficient neural network embedding in IoT over passive optical networks, distributed machine learning in cloud–fog environments, and AI-driven frameworks for 6G-IoT-based remote cardiac monitoring. His research extends to federated learning for low-latency IoT communications, hybrid cloud edge architectures for real-time cryptocurrency forecasting with blockchain integration, and machine learning-optimized energy management for resilient residential microgrids with electric vehicle integration. His scholarly output, cited over 50 times with an h-index of 4 and i10-index of 3, reflects growing recognition in the domains of sustainable networking and intelligent systems. Dr. Alenazi’s work combines AI, IoT, and cloud–fog computing to create adaptive, energy-aware solutions for smart environments, healthcare, and industrial systems. Through his innovative contributions, he continues to enhance the efficiency, reliability, and intelligence of modern communication infrastructures, positioning his research at the forefront of AI-powered green networking and distributed intelligence for the evolving digital ecosystem.

Profiles : ORCID | Scopus | Google Scholar | ResearchGate

Featured Publications

1. Alenazi, M. M., Yosuf, B. A., El-Gorashi, T., & Elmirghani, J. M. H. (2020). Energy efficient neural network embedding in IoT over passive optical networks. Cited By : 13

2.Yosuf, B. A., Mohamed, S. H., Alenazi, M. M., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2021). Energy-efficient AI over a virtualized cloud fog network. Cited By : 12

3.Alenazi, M. M., Yosuf, B. A., Mohamed, S. H., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2021). Energy-efficient distributed machine learning in cloud fog networks. Cited By : 10

4.Banga, A. S., Alenazi, M. M., Innab, N., Alohali, M., Alhomayani, F. M., Algarni, M. H., et al. (2024). Remote cardiac system monitoring using 6G-IoT communication and deep learning. Cited By : 6

5.Alenazi, M. M., Yosuf, B. A., Mohamed, S. H., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2022). Energy efficient placement of ML-based services in IoT networks. Cited By : 4