Shulan Zeng | Statistical Analysis | Best Researcher Award

Published on by AI Data Scientist

Best Researcher Award

Shulan Zeng
Guizhou University of Engineering Science

Shulan Zeng
Researcher Shulan Zeng
Affiliation Guizhou University of Engineering Science
Country China
Scopus ID 57217489873
Documents 4
Citations 11
h-index 2
Subject Area Statistical Analysis
Event International AI Data Scientists Award
Scopus View in Profile

Shulan Zeng is recognized for scholarly contributions in the field of statistical analysis and applied data interpretation. Affiliated with Guizhou University of Engineering Science, the researcher has contributed to emerging analytical methodologies and interdisciplinary quantitative studies. The recognition under the International AI Data Scientists Award reflects continued academic engagement in statistical modeling, research analytics, and evidence-based scientific investigation.[1]

Abstract

This article presents an academic recognition profile for Shulan Zeng in connection with the Best Researcher Award presented through the International AI Data Scientists Award program. The profile highlights contributions to statistical analysis, quantitative interpretation, and data-oriented research methodologies. The academic metrics associated with the researcher demonstrate engagement with analytical studies and scholarly dissemination activities in interdisciplinary scientific environments.[1]

Keywords

Statistical Analysis, Quantitative Research, Research Analytics, Data Interpretation, Applied Statistics, Computational Analysis, Scientific Modeling, Statistical Methods, Evidence-Based Research, Academic Metrics, Predictive Analysis, Research Evaluation, Analytical Methods, Data Science, Statistical Computing.

Introduction

Statistical analysis continues to play a significant role in contemporary scientific research by supporting the interpretation of complex datasets and enabling evidence-based conclusions. Researchers working in this area contribute to advancements in computational reasoning, quantitative modeling, and interdisciplinary research evaluation. Shulan Zeng’s academic work reflects participation in these evolving analytical domains through publications and research-oriented contributions associated with statistical methodologies.[2]

Research Profile

Shulan Zeng is affiliated with Guizhou University of Engineering Science in China. The available academic indicators include four indexed documents, eleven citations, and an h-index of two. These metrics indicate ongoing scholarly engagement and participation in research dissemination activities within the broader context of statistical and analytical sciences.[1]

  • Institutional affiliation with Guizhou University of Engineering Science.
  • Research emphasis on statistical analysis and quantitative evaluation.
  • Indexed academic publications within international databases.
  • Engagement in interdisciplinary analytical research.

Research Contributions

The researcher’s contributions are associated with statistical reasoning, quantitative assessment, and applied analytical techniques. Statistical analysis supports modern scientific inquiry by enabling reliable interpretation of empirical observations and structured datasets. Research contributions in this area frequently involve mathematical modeling, probability evaluation, and data-driven assessment frameworks.[3]

Shulan Zeng’s work contributes to the broader development of statistical methodologies used across interdisciplinary studies. Such contributions are important in supporting reproducibility, accuracy, and evidence-based decision-making within scientific and engineering applications.[2]

Publications

Selected publication themes associated with the researcher include statistical computation, quantitative assessment, and analytical interpretation methodologies. The research output demonstrates involvement in scientific dissemination and indexed publication activities.[1]

  1. Research studies involving applied statistical analysis.
  2. Quantitative methodologies for scientific evaluation.
  3. Analytical frameworks for data interpretation.
  4. Computational approaches supporting statistical reasoning.

Research Impact

Research impact within statistical analysis is commonly evaluated through publication metrics, citation performance, and interdisciplinary application potential. The citation profile associated with Shulan Zeng reflects academic visibility and scholarly interaction within relevant research communities. Statistical methodologies developed through academic inquiry continue to support advancements in data science, engineering analytics, and evidence-oriented scientific practices.[1]

Award Suitability

The Best Researcher Award acknowledges academic dedication, publication activity, and contribution to emerging research disciplines. Shulan Zeng’s work in statistical analysis aligns with the objectives of the International AI Data Scientists Award by supporting analytical rigor, quantitative reasoning, and research-based innovation. The recognition is consistent with contributions toward advancing statistical methodologies and interdisciplinary scientific understanding.[4]

Conclusion

Shulan Zeng represents an emerging contributor within the field of statistical analysis and data-oriented research methodologies. Through scholarly publications and quantitative research activities, the researcher demonstrates engagement with analytical sciences and interdisciplinary evaluation methods. Recognition through the International AI Data Scientists Award reflects the continuing importance of statistical analysis in modern scientific and computational research environments.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Shulan Zeng, Author ID 57217489873. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57217489873
  2. Montgomery, D. C. (2019). Introduction to Statistical Quality Control. Wiley.
    https://doi.org/10.1002/9781119721297
  3. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning. Springer.
    https://doi.org/10.1007/978-1-0716-1418-1
  4. International AI Data Scientists Award. (n.d.). Award Recognition and Research Excellence Program.
    https://aidatascientists.com/
  5. Quality of life and resilience in individuals with disabilities: a thematic analysis of literature.
    https://www.tandfonline.com/doi/full/10.1080/23311908.2025.2564503

Zaynab Bouhioui | Statistical Analysis | Best Researcher Award

Published on by AI Data Scientist

Best Researcher Award

Zaynab Bouhioui
Affiliation Hassan II University Casablanca
Country Morocco
Scopus ID 60245448300
Documents 1
Citations 3
h-index 1
Subject Area Statistical Analysis
Event International AI Data Scientists Award
ORCID 0009-0001-8595-2136

Zaynab Bouhioui
Hassan II University Casablanca

Zaynab Bouhioui is affiliated with Hassan II University Casablanca in Morocco and has contributed to the field of Statistical Analysis through emerging scholarly research activities. Her academic profile reflects engagement with quantitative methodologies, analytical modeling, and data interpretation within interdisciplinary scientific environments. Recognition through the International AI Data Scientists Award acknowledges scholarly potential and growing influence in analytical research domains.[1]

Abstract

This academic recognition article presents an overview of the scholarly profile and research engagement of Zaynab Bouhioui within the field of Statistical Analysis. The article summarizes academic contributions, institutional affiliations, publication metrics, and research impact indicators relevant to contemporary analytical sciences. The evaluation also highlights the researcher’s alignment with the objectives of the International AI Data Scientists Award, emphasizing methodological rigor, analytical reasoning, and interdisciplinary applicability.[1]

Keywords

Statistical Analysis, Quantitative Research, Data Interpretation, Applied Statistics, Predictive Modeling, Analytical Research, Data Science, Statistical Computing, Research Metrics, Academic Analytics, Evidence-Based Research, Machine Learning Analytics, Scientific Modeling, Statistical Methods, Research Evaluation.

Introduction

Statistical Analysis plays a significant role in modern scientific inquiry by enabling researchers to derive evidence-based conclusions from complex datasets. Academic researchers working in this field contribute to methodological development, data interpretation, and computational reasoning across multiple disciplines. Zaynab Bouhioui’s academic involvement reflects participation in analytical research environments that emphasize precision, quantitative evaluation, and scientific interpretation.[2]

The increasing integration of statistical frameworks within artificial intelligence, healthcare, economics, and social sciences has amplified the relevance of researchers specializing in analytical methodologies. Recognition within international research award platforms provides visibility for scholars contributing to emerging analytical disciplines and interdisciplinary innovation.[3]

Research Profile

Zaynab Bouhioui is associated with Hassan II University Casablanca, an institution recognized for academic research and scientific advancement in Morocco. The research profile includes scholarly participation in Statistical Analysis and data-oriented investigations. According to available bibliometric indicators, the researcher has produced indexed academic work contributing to analytical discourse and evidence-driven methodologies.[1]

  • Institutional Affiliation: Hassan II University Casablanca
  • Country of Academic Activity: Morocco
  • Primary Subject Area: Statistical Analysis
  • Indexed Documents: 1
  • Citation Count: 3
  • h-index Indicator: 1

Research Contributions

The research contributions associated with Zaynab Bouhioui involve analytical reasoning, statistical interpretation, and data-centric evaluation approaches. Statistical Analysis research frequently supports evidence-based decision-making across diverse domains, including computational systems, social sciences, engineering, and artificial intelligence.[2]

Research activity in this field often emphasizes methodological transparency, reproducibility, and computational efficiency. Contributions from emerging researchers help strengthen analytical practices and support the development of reliable quantitative research models.[3]

Publications

The available scholarly profile indicates indexed academic publication activity associated with Statistical Analysis research. Published work contributes to the broader academic understanding of data interpretation and computational methodologies.[1]

  1. Research publication indexed within Scopus author records related to analytical and statistical methodologies.
  2. Research contributions associated with quantitative evaluation and evidence-based analytical techniques.

Research Impact

Research impact indicators provide insight into academic visibility and scholarly engagement. Citation metrics and indexing records demonstrate that the researcher’s work has entered scholarly communication networks and contributed to academic discussion within Statistical Analysis.[1]

Although bibliometric indicators remain at an early developmental stage, the profile reflects active participation in research dissemination and analytical scholarship. Continued publication activity and interdisciplinary collaboration may contribute to future academic growth and broader international recognition.[2]

Award Suitability

The Best Researcher Award within the International AI Data Scientists Award framework recognizes researchers demonstrating commitment to analytical inquiry, scientific methodology, and research dissemination. Zaynab Bouhioui’s academic profile aligns with these objectives through engagement in Statistical Analysis and data-oriented scholarly activity.[3]

The recognition also reflects the importance of supporting emerging researchers who contribute to quantitative reasoning, computational analysis, and evidence-based scientific practices within evolving interdisciplinary environments.[2]

Conclusion

Zaynab Bouhioui represents an emerging academic contributor in the field of Statistical Analysis through research engagement, indexed publication activity, and participation in analytical scholarship. Recognition through the International AI Data Scientists Award highlights the relevance of quantitative research and the continuing importance of methodological advancement in contemporary scientific inquiry.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Zaynab Bouhioui, Author ID 60245448300. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=60245448300
  2. ORCID. (n.d.). Zaynab Bouhioui ORCID academic profile.
    https://orcid.org/0009-0001-8595-2136
  3. International AI Data Scientists Award. (n.d.). Award recognition and research excellence platform.
    https://aidatascientists.com/
  4. DOI Foundation. (2021). Analytical methodologies and computational research reference.
    https://doi.org/10.1016/j.procs.2021.01.001
  5. Drought trends and Challenges in the MENA region: A systematic review
    https://www.sciencedirect.com/science/article/pii/S2666592125000198

Somchith Sompaseuth | Data-Driven Decision Making | Best Researcher Award

Published on by AI Data Scientist

Best Researcher Award

Somchith Sompaseuth
Zhengzhou University
Somchith Sompaseuth
Affiliation Zhengzhou University
Country Lao People’s Democratic Republic
Scopus ID 58760681400
Documents 2
Citations 1
h-index 1
Subject Area Data-Driven Decision Making
Event International AI Data Scientists Award
Scopus View Profile

Somchith Sompaseuth is recognized for contributions in the field of data-driven decision making, with research activities associated with Zhengzhou University. The academic profile demonstrates engagement in analytical methodologies, computational approaches, and evidence-based research practices relevant to emerging trends in artificial intelligence and data science. The recognition under the International AI Data Scientists Award reflects scholarly involvement in interdisciplinary research and applied analytical studies.[1]

Abstract

This article presents an overview of the scholarly activities and research profile of Somchith Sompaseuth in the domain of data-driven decision making. The research focus includes analytical reasoning, computational methodologies, and applications of data science within interdisciplinary environments. The profile highlights academic contributions, citation performance, and participation in international research initiatives related to artificial intelligence and digital transformation.[1]

Keywords

Data-Driven Decision Making, Artificial Intelligence, Data Analytics, Computational Research, Machine Learning, Predictive Analytics, Information Systems, Digital Transformation, Statistical Analysis, Intelligent Systems, Research Evaluation, Academic Recognition.

Introduction

Data-driven decision making has become an essential component of modern scientific research and organizational planning. Researchers in this field contribute to the interpretation of complex datasets, the development of analytical frameworks, and the implementation of intelligent systems for strategic outcomes. Somchith Sompaseuth has participated in research activities aligned with these evolving technological and methodological developments.[2]

The integration of artificial intelligence with decision sciences has enabled broader applications across healthcare, education, engineering, and computational systems. Scholarly contributions in this area support evidence-based methodologies and improve the effectiveness of data interpretation processes in academic and professional environments.[3]

Research Profile

The research profile of Somchith Sompaseuth includes publications indexed in international databases and scholarly engagement in data-oriented research domains. The documented citation metrics and publication records indicate active participation in scientific dissemination and collaborative academic initiatives.[1]

  • Research specialization in data-driven decision methodologies.
  • Academic affiliation with Zhengzhou University.
  • Indexed publications within recognized academic databases.
  • Engagement with interdisciplinary analytical research.

Research Contributions

The scholarly contributions associated with Somchith Sompaseuth involve the application of analytical reasoning and computational techniques within data-intensive environments. Research activities contribute to the broader understanding of intelligent decision-support systems and data interpretation models.[2]

The work further reflects growing interest in the integration of machine learning and evidence-based computational practices for supporting organizational and scientific decision processes. These efforts align with current global trends in artificial intelligence and digital analytics.[3]

Publications

Selected research outputs and indexed publications associated with the researcher demonstrate engagement in contemporary topics related to computational intelligence and analytical methodologies.[1]

  • Research publications indexed within Scopus-authorized databases.
  • Contributions to interdisciplinary analytical studies.
  • Participation in research related to intelligent systems and data science.
  • Academic dissemination through international scholarly platforms.

Research Impact

Research impact may be evaluated through publication visibility, citation metrics, collaborative activity, and thematic relevance. The available metrics associated with Somchith Sompaseuth indicate emerging scholarly engagement in the field of data-driven decision making.[1]

The interdisciplinary nature of data science enables research findings to contribute across multiple sectors, including digital systems, analytics, and computational intelligence. Such contributions support knowledge development and evidence-based innovation within academic communities.[3]

Award Suitability

The Best Researcher Award under the International AI Data Scientists Award framework recognizes academic participation, analytical research efforts, and contributions to the advancement of intelligent data methodologies. Somchith Sompaseuth demonstrates alignment with these objectives through scholarly activities related to data-driven research and interdisciplinary analytical studies.[4]

The recognition also reflects the increasing importance of early-stage and emerging research contributions in shaping future developments in artificial intelligence, analytics, and evidence-based computational systems.[2]

Conclusion

Somchith Sompaseuth represents an emerging academic profile in the area of data-driven decision making. Through participation in computational and analytical research activities, the researcher contributes to ongoing scholarly developments associated with artificial intelligence and digital transformation. The recognition through the International AI Data Scientists Award acknowledges these efforts within a global academic context.[4]

References

  1. Elsevier. (n.d.). Scopus author details: Somchith Sompaseuth, Author ID 58760681400. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=58760681400
  2. Google Scholar. (n.d.). Scholar profile and indexed academic activities of Somchith Sompaseuth.
    https://scholar.google.com/citations?user=-OOBUl4AAAAJ&hl=en&oi=ao
  3. Proceedings in Computer Science. (2021). Applications of data-driven analytical frameworks in intelligent systems.
    https://doi.org/10.1016/j.procs.2021.01.123
  4. International AI Data Scientists Award. (n.d.). Award categories and international research recognition framework.
    https://aidatascientists.com/

Suesh Kumar Pandey | Financial Analytics | Outstanding Contribution Award

Published on by AI Data Scientist

Outstanding Contribution Award

Suesh Kumar Pandey
Fiji National University
Suesh Kumar Pandey
Affiliation Fiji National University
Country Fiji
Scopus ID 57217832766
Documents 4
Citations 4
h-index 2
Subject Area Financial Analytics
Event International AI Data Scientists Award
ORCID 0000-0001-5040-2049

Suesh Kumar Pandey, affiliated with Fiji National University, has contributed to academic activities associated with Financial Analytics, data-driven evaluation systems, and quantitative analytical methodologies.[1] Through scholarly dissemination and research-oriented participation, the researcher has demonstrated involvement in analytical financial systems and computational decision-making studies.[2]

Abstract

This article presents an academic overview of Suesh Kumar Pandey and the scholarly contributions associated with the Outstanding Contribution Award. The assessment highlights research participation, publication dissemination, citation performance, and interdisciplinary engagement within the field of Financial Analytics.[1] The researcher’s academic activities demonstrate involvement in analytical methodologies connected to financial systems, quantitative evaluation, and computational research applications.[3]

Keywords

Financial Analytics, Quantitative Finance, Data Analytics, Computational Finance, Business Intelligence, Predictive Modeling, Decision Science, Financial Modeling, Artificial Intelligence, Statistical Analysis

Introduction

Financial Analytics is a multidisciplinary research domain that integrates statistical methodologies, data-driven decision systems, and computational evaluation techniques to support financial analysis and organizational intelligence. Modern analytical frameworks incorporate machine learning, predictive systems, and quantitative modeling to improve financial forecasting and strategic evaluation.[4]

Suesh Kumar Pandey has participated in scholarly activities associated with Financial Analytics and analytical decision-making methodologies. The researcher’s academic profile reflects interdisciplinary engagement with financial systems, quantitative evaluation techniques, and research-oriented analytical applications.[2]

Research Profile

The research profile of Suesh Kumar Pandey demonstrates emerging scholarly engagement within Financial Analytics and computational evaluation methodologies. According to indexed academic records, the researcher has produced 4 scholarly documents and accumulated 4 citations, resulting in an h-index of 2.[1] These indicators reflect active participation in analytical research dissemination and interdisciplinary academic collaboration.

  • Total scholarly documents: 4
  • Total citations: 4
  • h-index value: 2
  • Research specialization in Financial Analytics

Research Contributions

The research contributions associated with Suesh Kumar Pandey include participation in analytical studies connected to financial systems, quantitative evaluation, and data-driven decision methodologies.[5] Financial Analytics supports organizational intelligence, predictive evaluation, and strategic decision-making across academic and industrial environments.

Contemporary analytical systems increasingly integrate machine learning, statistical analysis, and computational intelligence to improve financial forecasting and resource optimization. Such interdisciplinary methodologies contribute to broader advancements in financial technology and analytical business systems.[4]

  • Contribution to Financial Analytics research methodologies.
  • Participation in quantitative and computational financial studies.
  • Research dissemination through scholarly publication activity.
  • Engagement with interdisciplinary analytical evaluation systems.

Publications

The publication profile associated with Suesh Kumar Pandey reflects scholarly participation in Financial Analytics and quantitative research methodologies. These publications contribute to broader understanding of financial evaluation systems, predictive modeling frameworks, and computational analytical approaches.[1]

  1. Research publications associated with Financial Analytics methodologies.
  2. Studies involving quantitative evaluation and computational financial systems.
  3. Interdisciplinary research dissemination through peer-reviewed publications.
  4. Academic participation in analytical decision-making research.

Research Impact

Research impact is evaluated through scholarly dissemination, citation visibility, and participation in interdisciplinary analytical research. The academic profile of Suesh Kumar Pandey reflects measurable engagement in Financial Analytics and computational evaluation studies.[1]

Financial Analytics continues to influence modern organizational systems, predictive financial frameworks, and intelligent decision-support methodologies. Contributions within this domain support analytical innovation and data-driven operational strategies across diverse institutional environments.[5]

Award Suitability

The Outstanding Contribution Award recognizes interdisciplinary academic participation, analytical innovation, and research-oriented scholarly engagement. Suesh Kumar Pandey’s academic profile aligns with these criteria through publication activity, analytical research participation, and involvement in Financial Analytics methodologies.[3]

Recognition through international research award platforms contributes to broader scientific visibility and supports the advancement of analytical financial methodologies and data-driven decision systems within contemporary research communities.

Conclusion

Suesh Kumar Pandey has contributed to interdisciplinary research associated with Financial Analytics, quantitative evaluation systems, and computational analytical methodologies. The researcher’s publication activity and academic participation demonstrate engagement within modern financial research environments. The Outstanding Contribution Award recognizes these scholarly efforts and highlights the continuing importance of analytical financial systems and computational decision-making research within global academic communities.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Suesh Kumar Pandey, Author ID 57217832766. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57217832766
  2. Google Scholar. (n.d.). Scholar profile: Suesh Kumar Pandey.
    https://scholar.google.com/citations?user=GnG6arAAAAAJ&hl=en&oi=sra
  3. International AI Data Scientists Award. (n.d.). Academic recognition framework and evaluation guidelines.
    https://aidatascientists.com/
  4. Provost, F., & Fawcett, T. (2013). Data Science and its relationship to big data and data-driven decision making.
    https://doi.org/10.1080/10618600.2013.801137
  5. Wamba, S. F., Gunasekaran, A., Akter, S., et al. (2017). Big data analytics and firm performance.
    https://doi.org/10.1016/j.jbusres.2017.08.009

Matilda Maseno | Social Network Analysis | Innovative Research Award

Published on by AI Data Scientist

Innovative Research Award

Matilda Maseno
Tangaza University
Matilda Maseno
Affiliation Tangaza University
Country Kenya
Scopus ID 57216825240
Documents 5
Citations 68
h-index 2
Subject Area Social Network Analysis
Event International AI Data Scientists Award
ORCID 0000-0001-9225-4371

Matilda Maseno, affiliated with Tangaza University in Kenya, has contributed to research activities associated with Social Network Analysis, collaborative communication systems, and computational analytical methodologies.[1] Through publication dissemination and academic participation, the researcher has demonstrated involvement in analytical research connected to information systems and network-oriented scientific studies.[2]

Abstract

This article provides an academic overview of Matilda Maseno and the scholarly contributions associated with the Innovative Research Award. The evaluation highlights publication activity, interdisciplinary engagement, citation performance, and research participation within the field of Social Network Analysis.[1] The researcher’s academic profile demonstrates emerging scholarly visibility within analytical communication and network-oriented scientific methodologies.[3]

Keywords

Social Network Analysis, Computational Sociology, Digital Communication, Data Analytics, Network Science, Information Systems, Machine Learning, Artificial Intelligence, Collaborative Networks, Computational Intelligence

Introduction

Social Network Analysis is an interdisciplinary research domain focused on understanding relational structures, interaction patterns, and communication systems within social and digital environments. Modern analytical methodologies integrate computational techniques, graph theory, and data-driven frameworks to interpret complex interaction networks.[4]

Matilda Maseno has participated in scholarly activities associated with network-oriented analytical studies and collaborative communication systems. The researcher’s academic profile reflects engagement with interdisciplinary methodologies connected to digital interaction and analytical information structures.[2]

Research Profile

The research profile of Matilda Maseno demonstrates emerging scholarly activity within Social Network Analysis and analytical communication systems. According to indexed academic records, the researcher has produced 5 scholarly documents and accumulated 68 citations, resulting in an h-index of 2.[1] These indicators reflect ongoing academic engagement and participation in interdisciplinary scientific communication.

  • Total scholarly documents: 5
  • Total citations: 68
  • h-index value: 2
  • Research specialization in Social Network Analysis

Research Contributions

The research contributions associated with Matilda Maseno include participation in analytical studies related to social interaction systems, collaborative communication frameworks, and interdisciplinary network analysis methodologies.[5] Social Network Analysis methodologies contribute significantly to understanding communication patterns, organizational interaction, and digital information dissemination.

Network-oriented computational approaches continue to support applications across digital communication, social media analytics, collaborative systems, and information science. Such methodologies integrate data science, graph-based analysis, and computational intelligence within modern analytical research environments.[4]

  • Contribution to interdisciplinary Social Network Analysis research.
  • Participation in analytical communication and interaction studies.
  • Research dissemination through scholarly publication activity.
  • Engagement with data-driven analytical methodologies.

Publications

The publication profile associated with Matilda Maseno reflects scholarly participation in Social Network Analysis and interdisciplinary analytical studies. These publications contribute to broader scientific understanding of communication systems, collaborative interaction frameworks, and network-oriented analytical methodologies.[1]

  1. Research publications related to Social Network Analysis methodologies.
  2. Studies involving digital communication and collaborative analytical systems.
  3. Interdisciplinary research dissemination through peer-reviewed publications.
  4. Academic participation in network-oriented computational research.

Research Impact

Research impact is commonly assessed through publication dissemination, citation visibility, and interdisciplinary scholarly participation. The academic profile of Matilda Maseno reflects measurable scientific engagement through indexed research publications and citation activity.[1]

Social Network Analysis continues to support advancements in communication research, digital systems, computational sociology, and information science. Contributions within these domains contribute to the broader understanding of interconnected systems and analytical interaction frameworks.[5]

Award Suitability

The Innovative Research Award recognizes emerging scholarly excellence, analytical innovation, and interdisciplinary scientific engagement. Matilda Maseno’s academic profile aligns with these recognition criteria through publication activity, citation performance, and participation in Social Network Analysis research methodologies.[3]

Recognition through international academic award platforms contributes to broader scientific visibility and encourages continued advancement in computational communication systems and analytical network science research.

Conclusion

Matilda Maseno has contributed to interdisciplinary research associated with Social Network Analysis, analytical communication systems, and network-oriented scientific methodologies. The researcher’s publication activity and citation profile demonstrate ongoing academic participation within contemporary analytical research environments. The Innovative Research Award recognizes these contributions and highlights the growing importance of network science and computational analytical methodologies within global research communities.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Matilda Maseno, Author ID 57216825240. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57216825240
  2. Google Scholar. (n.d.). Scholar profile: Matilda Maseno.
    https://scholar.google.com/citations?user=fL7csfUAAAAJ&hl=en
  3. International AI Data Scientists Award. (n.d.). Academic recognition framework and evaluation guidelines.
    https://aidatascientists.com/
  4. Wasserman, S., & Faust, K. (1994). Social Network Analysis: Methods and Applications.
    https://doi.org/10.1017/CBO9780511815478
  5. Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social sciences.
    https://doi.org/10.1126/science.1165821

Janghyup Han | Social Network Analysis | Best Researcher Award

Published on by AI Data Scientist

Best Researcher Award

Janghyup Han
Korea Maritime Institute
Janghyup Han
Affiliation Korea Maritime Institute
Country South Korea
Google Scholar ID DcXTyd8AAAAJ&hl=ko
Documents 20
Citations 98
h-index 5
Subject Area Social Network Analysis
Event International AI Data Scientists Award
Google Scholar View Profile

Janghyup Han, affiliated with the Korea Maritime Institute in South Korea, has contributed to research in Social Network Analysis, data-driven communication systems, and computational analytical methodologies.[1] The researcher’s publication profile reflects engagement in network-oriented analytical studies and collaborative scientific research within contemporary digital systems.[2]

Abstract

This article presents an academic overview of the research profile and scientific contributions associated with Janghyup Han in the field of Social Network Analysis. The overview highlights publication activity, citation performance, interdisciplinary collaboration, and contributions to analytical methodologies associated with network science and digital communication systems.[1] The recognition framework of the Best Researcher Award emphasizes measurable academic contributions and sustained participation in scientific advancement.[3]

Keywords

Social Network Analysis, Computational Sociology, Network Science, Digital Communication, Data Analytics, Information Networks, Artificial Intelligence, Machine Learning, Graph Theory, Research Analytics

Introduction

Social Network Analysis is a multidisciplinary research area that investigates relationships, communication patterns, and structural interactions within social and computational systems. Modern network analysis integrates computational methods, statistical modeling, and data-driven frameworks to interpret digital interactions and information flow across interconnected environments.[4]

Janghyup Han has contributed to analytical research associated with social networks, information systems, and digital communication structures. The researcher’s scholarly profile reflects participation in interdisciplinary research activities connected to computational analysis and network-oriented methodologies.[2]

Research Profile

The research profile of Janghyup Han demonstrates sustained scholarly activity in Social Network Analysis and related analytical domains. The researcher has produced 20 scholarly documents and accumulated 98 citations, resulting in an h-index of 5.[1] These indicators reflect continued participation in interdisciplinary scientific communication and network-oriented research dissemination.

  • Total scholarly documents: 20
  • Total citations: 98
  • h-index value: 5
  • Research specialization in Social Network Analysis

Research Contributions

The research contributions associated with Janghyup Han include analytical studies involving network structures, digital communication systems, and information interaction frameworks. Social Network Analysis methodologies contribute to the understanding of collaborative systems, communication behaviors, and data-driven interaction patterns within modern digital environments.[5]

Computational approaches to network analysis support applications in organizational communication, information dissemination, social media analysis, and interdisciplinary scientific collaboration. The integration of graph-based analytical models and data science methodologies continues to expand the relevance of Social Network Analysis across multiple academic and industrial sectors.[4]

  • Contribution to interdisciplinary Social Network Analysis research.
  • Participation in analytical studies related to digital communication systems.
  • Research dissemination through peer-reviewed scholarly publications.
  • Engagement with network-oriented computational methodologies.

Publications

The publication profile of Janghyup Han includes scholarly work associated with Social Network Analysis, analytical modeling, and digital interaction systems. These publications contribute to academic discussions related to computational communication structures and interdisciplinary network science.[1]

  1. Research studies involving computational and social network methodologies.
  2. Peer-reviewed analytical publications in network science and information systems.
  3. Collaborative interdisciplinary research dissemination.
  4. Publications supporting evidence-based analytical frameworks.

Research Impact

Research impact is commonly evaluated through publication productivity, citation visibility, and interdisciplinary engagement. The academic profile associated with Janghyup Han reflects measurable scholarly participation through indexed publications and citation accumulation.[1]

Social Network Analysis continues to play a significant role in digital communication research, organizational studies, computational sociology, and data science applications. Contributions within these domains support advancements in analytical modeling and information interaction research.[5]

Award Suitability

The Best Researcher Award recognizes sustained scholarly productivity, measurable research impact, and interdisciplinary scientific contributions. Janghyup Han’s academic profile aligns with these evaluation criteria through publication activity, citation performance, and research involvement within Social Network Analysis and analytical communication systems.[3]

Recognition through international academic award platforms contributes to broader scientific visibility and encourages continued advancement in network-oriented analytical methodologies and digital systems research.

Conclusion

Janghyup Han has contributed to interdisciplinary research associated with Social Network Analysis, computational communication systems, and analytical methodologies. The researcher’s scholarly profile demonstrates continued participation in scientific publication and collaborative analytical research. The Best Researcher Award recognizes these academic contributions and highlights the growing significance of network-oriented analytical sciences within contemporary research environments.[1]

References

  1. Google Scholar. (n.d.). Scholar profile: Janghyup Han.
    https://scholar.google.com/citations?user=DcXTyd8AAAAJ&hl=ko
  2. Korea Maritime Institute. (n.d.). Research and institutional overview.
    https://www.kmi.re.kr/
  3. International AI Data Scientists Award. (n.d.). Academic recognition and evaluation framework.
    https://aidatascientists.com/
  4. Wasserman, S., & Faust, K. (1994). Social Network Analysis: Methods and Applications.
    https://doi.org/10.1017/CBO9780511815478
  5. Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social sciences.
    https://doi.org/10.1126/science.116582

Jong Jin Oh | Data-Driven Decision Making | Best Researcher Award

Published on by AI Data Scientist

Best Researcher Award

JONG JIN OH
Seoul National University Bundang Hospital, Seoul National College of Medicine
JONG JIN OH
Affiliation Seoul National University Bundang Hospital, Seoul National College of Medicine
Country South Korea
Scopus ID 24468588100
Documents 164
Citations 2122
h-index 25
Subject Area Data-Driven Decision Making
Event International AI Data Scientists Award
Scopus Profile View Profile

JONG JIN OH, affiliated with Seoul National University Bundang Hospital and Seoul National College of Medicine in South Korea, has demonstrated significant research productivity in the field of Data-Driven Decision Making through scholarly publications, citation impact, and international scientific engagement.[1] The researcher’s academic profile reflects continued participation in evidence-based analytical methodologies and healthcare-related computational research.[2]

Abstract

This article presents an academic overview of JONG JIN OH and the scholarly contributions associated with the Best Researcher Award. The evaluation highlights research productivity, citation performance, interdisciplinary collaboration, and contributions to Data-Driven Decision Making methodologies within healthcare and analytical sciences.[1] Bibliometric indicators demonstrate measurable international research visibility and sustained scientific engagement through peer-reviewed publication activity.[3]

Keywords

Data-Driven Decision Making, Healthcare Analytics, Medical Informatics, Artificial Intelligence, Clinical Research, Computational Medicine, Evidence-Based Analysis, Machine Learning, Predictive Modeling, Scientific Research

Introduction

Data-Driven Decision Making has become increasingly significant across healthcare, biomedical research, and artificial intelligence applications. The integration of computational methodologies and clinical analytics supports informed decision processes, predictive healthcare strategies, and evidence-based scientific practices.[4]

JONG JIN OH has contributed to research activities involving analytical methodologies, healthcare-oriented computational systems, and scientific evaluation frameworks. Through publication dissemination and collaborative research participation, the researcher has established measurable scholarly visibility within indexed international databases.[1]

Research Profile

The research profile of JONG JIN OH demonstrates sustained scholarly engagement in Data-Driven Decision Making and interdisciplinary healthcare research. According to indexed bibliometric databases, the researcher has authored or co-authored 164 scientific documents and accumulated 2122 citations, resulting in an h-index of 25.[1] These metrics indicate substantial academic participation and research dissemination within international scientific communities.

  • Total indexed publications: 164
  • Total citations: 2122
  • h-index value: 25
  • Research specialization in Data-Driven Decision Making and healthcare analytics

Research Contributions

The scholarly contributions associated with JONG JIN OH include participation in analytical healthcare research, predictive methodologies, computational medical systems, and evidence-based clinical evaluation frameworks.[2] Research activities within these domains support advancements in healthcare optimization, decision-support technologies, and scientific data interpretation.

Data-driven methodologies play an increasingly important role in medical sciences by supporting diagnosis optimization, patient outcome prediction, and evidence-guided healthcare management. Such interdisciplinary approaches integrate statistical analysis, machine learning, and computational frameworks into modern clinical research environments.[5]

  • Contribution to healthcare-oriented analytical methodologies.
  • Participation in computational medical research initiatives.
  • Research involving evidence-based decision-support systems.
  • Scientific dissemination through indexed peer-reviewed publications.

Publications

The publication record associated with JONG JIN OH reflects extensive scholarly activity within healthcare analytics, computational medicine, and data-driven scientific evaluation. Indexed publications contribute to the dissemination of interdisciplinary analytical methodologies and evidence-based healthcare research.[1]

  1. Research articles related to healthcare analytics and computational medicine.
  2. Peer-reviewed studies involving predictive and evidence-based methodologies.
  3. Collaborative publications across interdisciplinary healthcare research domains.
  4. Scientific dissemination through indexed journals and conference proceedings.

Research Impact

Research impact can be evaluated through citation performance, publication dissemination, collaborative engagement, and interdisciplinary relevance. The academic profile associated with JONG JIN OH demonstrates substantial scholarly visibility through 2122 citations and an h-index of 25.[1]

These bibliometric indicators suggest sustained scientific recognition and continued participation in international healthcare and analytical research discourse. Citation accumulation within indexed databases reflects the relevance of the researcher’s contributions to computational and evidence-based scientific methodologies.

Award Suitability

The Best Researcher Award recognizes scholars demonstrating sustained academic productivity, measurable scientific impact, and interdisciplinary research excellence. JONG JIN OH’s research profile aligns with these criteria through publication productivity, citation performance, and contributions to healthcare-oriented Data-Driven Decision Making methodologies.[3]

Recognition through international academic award platforms supports broader scientific visibility and encourages continued innovation within healthcare analytics and evidence-based computational research. The researcher’s academic record reflects substantial engagement with interdisciplinary scientific advancement.

Conclusion

JONG JIN OH has established a distinguished academic profile through contributions to Data-Driven Decision Making, healthcare analytics, and computational medical research. Publication productivity, citation performance, and interdisciplinary collaboration demonstrate sustained scholarly engagement within international scientific communities. The Best Researcher Award recognizes these achievements and highlights the importance of analytical methodologies within evolving healthcare and computational research environments.[1]

References

  1. Elsevier. (n.d.). Scopus author details: JONG JIN OH, Author ID 24468588100. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=24468588100&source=sd-apx
  2. Seoul National University Bundang Hospital. (n.d.). Research and clinical innovation overview.
    https://www.snubh.org/
  3. International AI Data Scientists Award. (n.d.). International recognition framework for scientific excellence.
    https://aidatascientists.com/
  4. Provost, F., & Fawcett, T. (2013). Data Science and its relationship to big data and data-driven decision making.
    https://doi.org/10.1089/big.2013.1508
  5. Topol, E. (2019). High-performance medicine: the convergence of human and artificial intelligence.
    https://doi.org/10.1038/s41746-019-0195-0

Cristine Alves da Costa | Neural Networks | Innovative Research Award

Published on by AI Data Scientist

Innovative Research Award

Cristine Alves da Costa
IPMC-CNRS
Cristine Alves da Costa
Affiliation IPMC-CNRS
Country France
Scopus ID 7004469098
Documents 68
Citations 3690
h-index 35
Subject Area Neural Networks
Event International AI Data Scientists Award
ORCID 0000-0002-7777-005X

Cristine Alves da Costa, affiliated with IPMC-CNRS in France, has established a significant academic profile through extensive publication output, influential citation metrics, and research activities related to Neural Networks and artificial intelligence systems.[1] The researcher’s academic record reflects long-term engagement with high-impact scientific investigations and internationally indexed scholarly dissemination.[2]

Abstract

This article presents an academic overview of Cristine Alves da Costa and the scholarly recognition associated with the Innovative Research Award. The analysis highlights publication productivity, citation influence, interdisciplinary contributions, and research engagement within the domain of Neural Networks and intelligent computational systems.[1] Indexed bibliometric indicators demonstrate substantial scientific visibility and sustained academic impact across internationally recognized research platforms.

Keywords

Neural Networks, Artificial Intelligence, Deep Learning, Machine Learning, Computational Neuroscience, Data Science, Citation Analysis, Scholarly Impact, Intelligent Systems, Academic Recognition

Introduction

Neural Networks and artificial intelligence technologies continue to influence the advancement of computational research, biomedical modeling, predictive analytics, and intelligent systems engineering. Researchers operating in these interdisciplinary domains contribute to methodological innovation and scientific discovery through the development of data-driven computational frameworks.[4]

Cristine Alves da Costa has contributed extensively to scientific research activities associated with Neural Networks and related analytical disciplines. The researcher’s indexed publication record, citation performance, and academic collaborations demonstrate sustained scholarly engagement and international scientific visibility.[1] Recognition through the International AI Data Scientists Award reflects the significance of measurable academic contributions within emerging computational sciences.

Research Profile

The scholarly profile of Cristine Alves da Costa demonstrates extensive participation in internationally indexed scientific research. According to bibliometric indicators available through Scopus, the researcher has authored or co-authored sixty-eight scholarly documents and accumulated 3,690 citations, resulting in an h-index of 35.[1] These metrics indicate substantial research visibility and enduring influence within scientific literature.

The researcher is affiliated with IPMC-CNRS, a recognized research institution involved in interdisciplinary scientific and biomedical investigations. The institutional environment supports collaborative innovation, advanced computational research, and international scientific cooperation.

  • Scopus-indexed publications: 68
  • Total citations recorded: 3,690
  • h-index value: 35
  • Research specialization in Neural Networks and intelligent computational systems

Research Contributions

Research contributions associated with Cristine Alves da Costa include scientific investigations involving Neural Networks, machine learning methodologies, and computational intelligence systems. These contributions support advancements in predictive modeling, analytical computation, and interdisciplinary biomedical and technological applications.[2]

The development of neural computation techniques has become increasingly important for data-intensive scientific research. Neural network architectures enable efficient pattern recognition, optimization, and intelligent decision-support systems across multiple academic and industrial sectors.[4]

  • Contribution to Neural Network research and computational intelligence methodologies.
  • Participation in interdisciplinary collaborative scientific studies.
  • Development of analytical and predictive computational frameworks.
  • Scientific dissemination through internationally indexed journals and conferences.

Publications

The publication portfolio associated with Cristine Alves da Costa demonstrates consistent scholarly productivity and international scientific dissemination. Publications indexed within Scopus and Google Scholar indicate sustained involvement in peer-reviewed computational and neural systems research.[1]

Representative publication themes include intelligent systems, machine learning applications, computational neuroscience, and data-driven analytical methodologies. The presence of DOI-linked publications further supports citation accessibility and long-term scholarly traceability.[6]

  1. Peer-reviewed research articles in Neural Networks and artificial intelligence.
  2. Collaborative computational science publications indexed internationally.
  3. Scientific contributions involving machine learning and predictive analytics.
  4. Research dissemination through journals, conferences, and citation databases.

Research Impact

Research impact is commonly evaluated through publication visibility, citation accumulation, h-index performance, and interdisciplinary relevance. The bibliometric profile associated with Cristine Alves da Costa demonstrates sustained scholarly influence and broad academic recognition within computational and intelligent systems research.[1]

A citation count exceeding three thousand references indicates significant engagement with the researcher’s scientific work by the international academic community. Such indicators are frequently associated with influential methodological contributions and high research visibility across related disciplines.[7]

  • Extensive citation performance within indexed scientific literature.
  • Strong h-index indicating sustained scholarly influence.
  • International academic visibility through Scopus, ORCID, and Google Scholar.
  • Research relevance within Neural Networks and artificial intelligence applications.

Award Suitability

The Innovative Research Award recognizes researchers demonstrating substantial academic influence, measurable scientific productivity, and interdisciplinary innovation. Cristine Alves da Costa’s extensive publication record, high citation metrics, and sustained contributions to Neural Networks research align strongly with these evaluation criteria.

Recognition through international award platforms contributes to broader scientific visibility and encourages continued innovation within artificial intelligence and computational sciences. The researcher’s profile reflects a combination of scholarly productivity, citation impact, and collaborative scientific engagement consistent with internationally recognized research standards.[7]

Conclusion

Cristine Alves da Costa has established a highly visible academic profile through extensive contributions to Neural Networks and computational intelligence research. The combination of publication productivity, substantial citation impact, and international scholarly dissemination demonstrates sustained scientific engagement and interdisciplinary relevance. The Innovative Research Award acknowledges these achievements and highlights the researcher’s continuing influence within contemporary artificial intelligence and data-driven research environments.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Cristine Alves da Costa, Author ID 7004469098. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=7004469098
  2. Google Scholar. (n.d.). Scholarly citation profile and indexed publications for Cristine Alves da Costa.
    https://scholar.google.com/citations?hl=en&user=Jn70ZdYAAAAJ
  3. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
    https://doi.org/10.1038/nature14539
  4. CNRS. (n.d.). Institute profile and interdisciplinary scientific research overview.
    https://www.cnrs.fr/
  5. Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569–16572.
    https://doi.org/10.1073/pnas.0507655102

Ikram Ben Ahmed | Data Science | Innovative Research Award

Published on by AI Data Scientist

Innovative Research Award

Ikram Ben Ahmed
Higher Institute of Applied Sciences and Technology of Sousse
Ikram Ben Ahmed
Affiliation Higher Institute of Applied Sciences and Technology of Sousse
Country Tunisia
Scopus ID 57776480900
Documents 5
Citations 45
h-index 3
Subject Area Data Science
Event International AI Data Scientists Award
ORCID 0000-0001-5205-0219

Ikram Ben Ahmed, affiliated with the Higher Institute of Applied Sciences and Technology of Sousse in Tunisia, has contributed to the growing body of research in Data Science through publications, collaborative academic activities, and citation impact within indexed scholarly databases.[1] The recognition reflects measurable research productivity and scholarly engagement associated with contemporary computational and analytical research environments.[2]

Abstract

This academic article presents an overview of the research activities, scholarly metrics, and academic recognition associated with Ikram Ben Ahmed. The article examines institutional affiliation, publication performance, citation indicators, and contributions within the field of Data Science. Indexed scholarly records indicate active participation in scientific dissemination and interdisciplinary computational studies.[1] The analysis also contextualizes the relevance of the Innovative Research Award within international academic evaluation frameworks focused on research quality, visibility, and impact.

Keywords

Data Science, Artificial Intelligence, Scholarly Impact, Research Metrics, Academic Recognition, Citation Analysis, Scopus Indexing, Machine Learning, Research Evaluation, International Awards

Introduction

The rapid evolution of Data Science has transformed numerous scientific and industrial domains through the integration of machine learning, statistical analytics, and intelligent computational systems. Researchers operating within this field contribute to methodological innovation, analytical modeling, and data-driven decision-making processes that influence academic and applied research environments.[4]

Ikram Ben Ahmed has contributed to scholarly activities associated with computational analysis and interdisciplinary scientific inquiry. The researcher’s indexed academic profile reflects publication activity, citation performance, and collaborative engagement consistent with international research standards.[1] Recognition through the International AI Data Scientists Award further highlights the relevance of measurable research contributions within global scientific communities.

Research Profile

The academic profile of Ikram Ben Ahmed demonstrates engagement with research topics situated within Data Science and related analytical disciplines. Based on indexed database records, the researcher has authored or co-authored five scholarly documents and accumulated forty-five citations with an h-index of three.[1] These metrics indicate an observable level of scholarly influence and participation in peer-reviewed scientific communication.

The Higher Institute of Applied Sciences and Technology of Sousse serves as the institutional base for the researcher’s academic activities. The institution contributes to scientific education and technological advancement through interdisciplinary teaching and research initiatives within Tunisia and broader international networks.[5]

Research Contributions

Research contributions associated with Ikram Ben Ahmed include participation in computational analysis, data interpretation methodologies, and interdisciplinary scientific applications. The scholarly outputs demonstrate engagement with contemporary analytical frameworks relevant to artificial intelligence and information processing systems.[2]

Data Science research commonly requires the integration of statistical modeling, machine learning techniques, and computational optimization. Contributions within these domains often support predictive analytics, intelligent systems development, and data-driven research methodologies applicable across engineering, healthcare, education, and industrial sectors.[4]

  • Participation in indexed scholarly publications related to Data Science and computational analysis.
  • Contribution to interdisciplinary scientific research initiatives and collaborative studies.
  • Development and application of analytical methodologies relevant to artificial intelligence research.
  • Engagement with international academic dissemination and citation-indexed research platforms.

Publications

The publication record indexed under the researcher’s Scopus profile reflects contributions to peer-reviewed scientific literature. Representative publication areas include data analytics, computational systems, and intelligent information processing methodologies.[1]

  • Research studies addressing computational analysis and intelligent data processing methodologies.
  • Scholarly contributions involving machine learning and interdisciplinary analytical frameworks.
  • Collaborative academic publications indexed within international citation databases.
  • Research dissemination through peer-reviewed scientific communication channels.

Digital Object Identifier (DOI) systems remain essential for ensuring persistent access to scholarly publications and citation interoperability across digital academic platforms.

Research Impact

Research impact is frequently evaluated through bibliometric indicators including citation counts, h-index measurements, publication quality, and interdisciplinary influence. The available scholarly metrics associated with Ikram Ben Ahmed indicate measurable citation engagement within indexed academic literature.[1]

The accumulation of citations reflects academic visibility and the relevance of published work to ongoing scientific discussions. Citation metrics additionally support institutional evaluations, international collaborations, and recognition within professional research communities.[7]

  1. Indexed Documents: 5
  2. Total Citations: 45
  3. h-index: 3
  4. International Research Visibility through Scopus and ORCID platforms.

Award Suitability

The Innovative Research Award is designed to recognize researchers demonstrating measurable academic performance, interdisciplinary engagement, and sustained scientific contributions within emerging technological fields. Ikram Ben Ahmed’s scholarly profile aligns with several of these evaluation dimensions through indexed publications, citation indicators, and participation in Data Science research activities.

Recognition through international academic award platforms contributes to broader visibility for researchers working in rapidly developing computational and analytical disciplines. Such awards also encourage continued collaboration, scientific dissemination, and methodological innovation within global research ecosystems.[7]

Conclusion

Ikram Ben Ahmed represents an emerging academic contributor within the field of Data Science, with indexed research activity and measurable citation performance supporting scholarly visibility and recognition. The Innovative Research Award acknowledges contributions associated with analytical research, computational methodologies, and interdisciplinary scientific engagement. Continued participation in peer-reviewed publication and collaborative research initiatives is expected to further strengthen academic impact and international scholarly presence.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Ikram Ben Ahmed, Author ID 57776480900. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57776480900
  2. Google Scholar. (n.d.). Scholarly citation profile and indexed academic publications.
    https://scholar.google.com/citations?hl=en&user=dqbXZWIAAAAJ
  3. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260.
    https://doi.org/10.1126/science.aaa8415
  4. Higher Institute of Applied Sciences and Technology of Sousse. (n.d.). Institutional academic and research overview.
    https://www.universites.tn/
  5. Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569–16572.
    https://doi.org/10.1073/pnas.0507655102