Fatemeh Barati | Computer Science | Innovative Research Award

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Innovative Research Award

Fatemeh Barati
Kharazmi University, Iran

Fatemeh Barati
Affiliation Kharazmi University
Country Iran
Scopus ID Not provided
Google Scholar ID QCQGhrgAAAAJ
Documents 1
Subject Area Computer Science
Event International Academic Achievements & Awards
ORCID 0009-0005-2611-3016

Fatemeh Barati is a researcher associated with Kharazmi University, Iran, with an academic profile identified in the field of Computer Science. Her available researcher information includes a Google Scholar profile and an ORCID identifier, providing persistent scholarly identity markers for research discovery and attribution. The available institutional and researcher information also associates her with computational and bioinformatics-oriented academic activity. [1] [2]

Abstract

The Innovative Research Award recognizes researchers whose scholarly activities demonstrate originality, methodological development, and potential contribution to the advancement of knowledge. Fatemeh Barati, affiliated with Kharazmi University in Iran, is presented in the Computer Science area on the basis of the available academic profile information, researcher identifiers, and documented scholarly record. Her profile is associated with computer science and computational research interests, including bioinformatics-related academic activity. [1] The available profile information records one scholarly document and provides persistent identifiers through Google Scholar and ORCID. [2]

Keywords

  • Fatemeh Barati
  • Innovative Research Award
  • Computer Science
  • Kharazmi University
  • Bioinformatics
  • Computational Research
  • Research Innovation

Introduction

Computer Science encompasses computational theory, algorithms, software systems, artificial intelligence, data analysis, information processing, and interdisciplinary computational methods. Contemporary research increasingly connects computer science with biological sciences, engineering, health, and other knowledge domains. Within this environment, researchers contribute by developing computational approaches capable of addressing complex analytical and practical problems.

Barati’s academic profile is associated with Kharazmi University and the Computer Science subject area. A publicly available researcher profile identifies her as holding an M.Sc. in Computer Science from Kharazmi University and describes participation in a bioinformatics research environment. [4]

Research Profile

The available evidence places Barati within an interdisciplinary computational research context. Bioinformatics is an area in which computer science techniques are applied to biological data, requiring knowledge of algorithms, data processing, computational modelling, and scientific interpretation. Such interdisciplinary work illustrates the role of computing methodologies in contemporary research environments. [4]

Her scholarly identity is further supported by an ORCID record and a Google Scholar author profile. These identifiers provide mechanisms for distinguishing researchers, connecting publications, and improving the discoverability of scholarly contributions. [2]

Research Contributions

The documented research record available for this profile is limited, and therefore specific contribution claims should be interpreted conservatively. A scholarly work indexed under the name Fatemeh Barati addresses integrated generation, transmission, and natural-gas network expansion planning, demonstrating the application of computational optimization and engineering analysis to complex infrastructure planning problems. The work describes a multi-period framework and computational methodology for large-scale systems. [5]

This type of research illustrates how computational methods can support decision-making in complex systems. The broader significance of such work lies in combining mathematical modelling, algorithmic techniques, and domain-specific knowledge to address practical research questions.

Publications

The supplied academic profile records 1 document. Because publication attribution should be based on verified researcher identifiers and bibliographic records, the available publication information should be considered alongside the researcher’s Google Scholar and ORCID profiles. [2]

Research Impact

The potential impact of computer science research is often measured not only through publication output but also through methodological usefulness, reproducibility, interdisciplinary application, and contribution to practical problem solving. Barati’s documented association with computational research provides a basis for considering her work within this broader framework. The combination of computer science training and interdisciplinary research activity is relevant to modern data-intensive and computationally supported scientific investigation.

Award Suitability

The Innovative Research Award is intended to recognize research demonstrating innovation, scholarly relevance, and potential contribution to a field. The International Academic Achievements & Awards program identifies innovation and research excellence among its recognition objectives. Barati’s academic association with Kharazmi University, Computer Science subject area, documented researcher identifiers, and available scholarly record provide relevant evidence for consideration within this recognition category.

Final assessment of award suitability should remain based on the complete nomination materials, publication evidence, research contributions, and supporting documentation reviewed by the appropriate award committee.

Conclusion

Fatemeh Barati is an academic researcher associated with Kharazmi University and the Computer Science field. Her available profile information indicates an interdisciplinary computational orientation, supported by Google Scholar and ORCID identifiers. The documented scholarly record provides a foundation for recognizing her engagement with computational research, while additional verified publications and research evidence can further establish the breadth and impact of her academic contributions. [2] [3]

References

  1. Google Scholar. (n.d.). Fatemeh Barati, author profile.
    https://scholar.google.com/citations?hl=en&user=QCQGhrgAAAAJ
  2. ORCID. (n.d.). Fatemeh Barati, ORCID record.
    https://orcid.org/0009-0005-2611-3016
  3. Rayazi. (n.d.). Fatemeh Barati — bioinformatics research group profile.
  4. Barati, F., Nateghi, A., Seifi, H., & Sepasian, M. S. (2013). Generation and transmission expansion planning with considering natural gas network. Iranian Conference on Electrical Engineering. DOI: 10.1109/IranianCEE.2013.6599577.
    https://doi.org/10.1109/IranianCEE.2013.6599577
  5. Barati, F., Seifi, H., Sepasian, M. S., Nateghi, A., & Catalão, J. P. S. (2014). Multi-Period Integrated Framework of Generation, Transmission, and Natural Gas Grid Expansion Planning for Large-Scale Systems. IEEE Transactions on Power Systems. DOI: 10.1109/TPWRS.2014.2365705.
    https://doi.org/10.1109/TPWRS.2014.2365705

Divya Gautam | Computer Science | Innovative Research Award

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Innovative Research Award

Divya Gautam
Affiliation STME, SVKM’s NMIMS, Indore
Country India
Scopus ID 57315556700
Documents 17
Citations 81
h-index 4
Subject Area Computer Science
Event International Academic Achievements & Awards
ORCID 0000-0002-1395-997X

Divya Gautam
STME, SVKM’s NMIMS, Indore, India

The Innovative Research Award recognizes researchers whose scholarly contributions demonstrate originality, scientific rigor, and meaningful impact within their respective disciplines. Divya Gautam has established a research portfolio in computer science with particular emphasis on mobile ad hoc networks (MANET), denial-of-service attack detection, machine learning-based network security, and optimization techniques. Her publications have contributed to understanding secure communication mechanisms and intelligent intrusion detection methods for decentralized wireless environments.[1]

Abstract

Divya Gautam’s research primarily investigates cybersecurity challenges within mobile ad hoc networks, focusing on intelligent techniques for detecting and mitigating denial-of-service attacks. Her work integrates support vector machines, particle swarm optimization, and pattern-based analytical methods to improve network resilience and communication reliability. The resulting publications collectively contribute toward advancing practical security frameworks for decentralized wireless systems while supporting broader developments in computer science research.[2]

Keywords

Computer Science, Cybersecurity, Mobile Ad Hoc Networks, DoS Detection, Machine Learning, Network Security, Support Vector Machine, Optimization Algorithms

Introduction

Modern wireless communication environments require effective mechanisms for detecting malicious activities while maintaining reliable network performance. Mobile ad hoc networks remain particularly vulnerable because of their decentralized architecture and dynamic topology. Research conducted by Divya Gautam addresses these challenges through computational intelligence techniques capable of improving attack detection accuracy while reducing network disruption.[3]

Research Profile

According to Scopus, Divya Gautam has authored seventeen indexed publications with eighty-one citations and an h-index of four. Her scholarly activities are concentrated within computer science, particularly network security, intrusion detection, wireless communication, intelligent optimization algorithms, and secure distributed networking. These metrics indicate an active and developing research profile with contributions appearing in journals, conference proceedings, and scholarly book chapters.[1]

Research Contributions

  • Development of pattern-based techniques for detecting DoS and DDoS attacks.
  • Application of Support Vector Machine methodologies for intelligent intrusion detection.
  • Integration of Particle Swarm Optimization with machine learning for improved classification.
  • Comparative evaluation of attack mitigation techniques in MANET environments.
  • Contribution to secure wireless networking research through conference and journal publications.

Publications

  1. A Comparative Study of DoS Attack Detection and Mitigation Techniques in MANET (2020), Lecture Notes in Networks and Systems.DOI:
    10.1007/978-981-15-2071-6_50
  2. Fixed points results in b-metric spaces (2020), Materials Today: Proceedings.DOI:
    10.1016/j.matpr.2021.05.147
  3. Pattern Based Detection and Mitigation of DoS Attacks in MANET Using SVM-PSO (2020).DOI:
    10.1007/978-3-030-44758-8_16
  4. Detection of DoS attacks in MANET using LIBSVM (2019), International Journal of Engineering and Advanced Technology.
  5. Pattern based detection of DDoS attacks in MANET (2019), International Journal of Innovative Technology and Exploring Engineering.

Research Impact

The available publication record demonstrates continued engagement with contemporary cybersecurity challenges affecting wireless communication networks. Citation activity reflects scholarly visibility, while the combination of journal articles, conference papers, and book chapters indicates dissemination across multiple academic publishing platforms. The research has practical relevance for secure communication systems, intelligent network monitoring, and machine learning-based threat detection.[4]

Award Suitability

Based on the documented publication record, indexed research output, citation performance, and focus on innovative cybersecurity methodologies, Divya Gautam demonstrates characteristics commonly associated with recognition through an Innovative Research Award. Her research emphasizes methodological development, practical applicability, interdisciplinary integration, and contribution to secure wireless networking technologies.[5]

Conclusion

Divya Gautam’s academic portfolio reflects sustained contributions to computer science, particularly in network security and intelligent attack detection for mobile ad hoc networks. Through the integration of machine learning and optimization techniques, her publications support ongoing developments in cybersecurity research while contributing to the broader understanding of resilient wireless communication systems. The documented scholarly record provides a solid foundation for academic recognition within innovation-oriented research awards.

References

  1. Elsevier. (n.d.). Scopus Author Details: Divya Gautam, Author ID 57315556700. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57315556700
  2. Gautam, D., & Tokekar, V. (2020). A Comparative Study of DoS Attack Detection and Mitigation Techniques in MANET. Lecture Notes in Networks and Systems, Springer. DOI: https://doi.org/10.1007/978-981-15-2071-6_50
  3. Gautam, D., & Tokekar, V. (2020). Pattern Based Detection and Mitigation of DoS Attacks in MANET Using SVM-PSO. Intelligent Computing Applications for Sustainable Real-World Systems.DOI: https://doi.org/10.1007/978-3-030-44758-8_16
  4. Gautam, D., & Tokekar, V. (2020). Fixed Points Results in b-Metric Spaces. Materials Today: Proceedings.
    https://doi.org/10.1016/j.matpr.2021.05.147
  5. SVKM’s NMIMS. (n.d.). Faculty Profile: Dr. Divya Gautam, Assistant Professor, Computer Engineering.
    https://indore.nmims.edu/faculty/dr-divya-gautam/

Md Minhajul Amin | Computer Science | Best Innovator Award

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Best Innovator Award

Md Minhajul Amin
Affiliation Intento Analytics
Country United States
Google Scholar 8Cc4X70AAAAJ
Documents 11
Citations 86
h-index 4
Subject Area Computer Science
Event International Academic Achievements & Awards

Md Minhajul Amin
Intento Analytics, United States

Md Minhajul Amin is a data analyst and researcher specializing in business analytics, artificial intelligence, healthcare informatics, project management, and big data applications. His multidisciplinary research integrates predictive analytics, machine learning, digital healthcare, and operational decision-making. Through academic publications, IEEE conference papers, industrial projects, and editorial activities, he has contributed to advancing evidence-based management and intelligent data-driven systems.[1]

Abstract

Md Minhajul Amin has established an interdisciplinary research portfolio connecting artificial intelligence, predictive analytics, healthcare systems, fraud detection, digital communication, and project management. His work emphasizes practical analytical solutions that improve organizational efficiency and support evidence-based decision-making across healthcare and business environments.[2]

Keywords

Business Analytics, Artificial Intelligence, Machine Learning, Healthcare Analytics, Telemedicine, Big Data, Project Management, Data Visualization, Predictive Analytics, Digital Transformation.

Introduction

Holding an M.S. in Information Systems from Central Michigan University, Md Minhajul Amin combines academic research with industry experience in data analytics. His research focuses on solving real-world organizational challenges through statistical analysis, visualization, artificial intelligence, and business intelligence technologies.[3]

Research Profile

  • IEEE conference contributor.
  • Associate Editor at IFR Discovery.
  • Editorial Board Member of The Science Post Journal.

Research Contributions

His publications cover ethical business analytics, AI-powered project management, fraud detection, cancer classification using machine learning, telemedicine implementation, customer segmentation, healthcare dashboards, RF communication systems, and network management technologies. His research demonstrates the practical integration of advanced analytics into healthcare, engineering, and organizational management.[4]

Publications

  • Ethical Challenges in Business Analytics.
  • Developing a Project Management Dashboard for Telehealth.
  • Business Analytics in the Era of Big Data.
  • AI-Powered Personalized Marketing.
  • Classification of Cancer Stages Using Machine Learning.

Research Impact

His scholarly output has attracted citations across business analytics, healthcare informatics, artificial intelligence, and project management disciplines. His professional activities further include patented AI-driven analytical devices and collaborative research addressing practical industry challenges.

Award Suitability

Considering his multidisciplinary research portfolio, peer-reviewed publications, IEEE conference participation, editorial responsibilities, industrial analytics experience, and measurable research impact, Md Minhajul Amin demonstrates qualifications aligned with recognition under an Innovative Research Award category.

Conclusion

Md Minhajul Amin continues contributing to applied research that bridges artificial intelligence, business analytics, healthcare systems, and project management. His academic achievements and industry experience illustrate the growing role of data-driven methodologies in addressing modern organizational and societal challenges.

External Links

References

    1. Amin, M. M., Munmun, Z. S., Atayeva, J., Ahmed, S. W., Shamim, I., & Akter, M. H. (2025).
      Developing a Project Management Dashboard for Telehealth Implementation.
      https://www.researchgate.net/publication/392591167_Developing_a_Project_Management_Dashboard_for_Telehealth_Implementation
    2. Google Scholar. (2026).
      Md Minhajul Amin – Google Scholar Profile.
      https://scholar.google.com/citations?user=8Cc4X70AAAAJ&hl=en
    3. ResearchGate. (2026).
      Md Minhajul Amin – Research Profile.
      https://www.researchgate.net/profile/Md-Minhajul-Amin
    4. LinkedIn. (2026).
      Md Minhajul Amin – Professional Profile.
      https://www.linkedin.com/in/md-minhajul-amin-cmu

Kashif Mazhar | Computer Science | Research Excellence Award

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Mr. Kashif Mazhar | Computer Science | Research Excellence Award

Mr. Kashif Mazhar | Computer Science | Research Scholar at Motilal Nehru National Institute of Technology Allahabad | India

Computer Science professional Mr. Kashif Mazhar is an accomplished Assistant Professor and Doctoral Researcher recognized for his strong academic foundation and impactful research in Artificial Intelligence and Data Science. Mr. Kashif Mazhar currently serves as an Assistant Professor in the School of Computer Science (Data Science Cluster) at the University of Petroleum and Energy Studies (UPES), Dehradun, while pursuing his Ph.D. at Motilal Nehru National Institute of Technology (MNNIT) Allahabad, where his doctoral research focuses on Explainable Artificial Intelligence (XAI) for brain tumor MRI classification and segmentation using advanced deep learning models integrated with LIME, SHAP, and Grad-CAM. Mr. Kashif Mazhar holds an M.Tech and B.Tech from the University of Allahabad and has over five years of combined teaching and research experience, including roles as Teaching Assistant at MNNIT Allahabad, Researcher at IIM Jammu, and Data Science Instructor at Simplilearn. His research interests span Explainable AI, Medical Imaging, Social Network Analysis, and AI-driven financial analytics, supported by strong research skills in Python, data analysis, supervision, and scientific reporting. Mr. Kashif Mazhar has published in high-impact Q1 journals and is UGC-NET qualified and GATE certified, reflecting his academic excellence and competitive merit. In conclusion, Mr. Kashif Mazhar exemplifies a forward-looking academic whose interdisciplinary expertise, teaching leadership, and commitment to trustworthy AI position him as a promising contributor to future advancements in Computer Science.

Citation Metrics (Google Scholar)

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                🟦 Citations      🟥 Documents     🟩 h-index

View Google Scholar Profile

Featured Publications

Model-agnostic explainable artificial intelligence methods in finance: a systematic review, recent developments, limitations, challenges and future directions
– Artificial Intelligence Review, 2025
Decoding the black box: LIME-assisted understanding of Convolutional Neural Network (CNN) in classification of social media tweets
– Social Network Analysis and Mining, 2024
A survey on methods for explainability in deep learning models
– International Conference on Machine Intelligence, Tools, and Applications, 2024

Guda Vanitha | Computer Science | Best Researcher Award

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Dr. Guda Vanitha | Computer Science | Best Researcher Award

Associate Professor, Chaitanya Bharathi Institute of Technology,(A), India

Dr. G. Vanitha is an accomplished educator and Assistant Professor in the Department of Computer Science Engineering at Chaitanya Bharathi Institute of Technology. With 17 years of teaching experience, she holds a Ph.D. in Computer Science Engineering from Jawaharlal Nehru Technological University, Hyderabad. Her research focuses on Natural Language Processing, particularly in event time relation extraction. Dr. Vanitha has authored a textbook, holds multiple patents, and has received numerous awards for her contributions to academia.

Profile

Google Scholar

 

🎓 Education:

Dr. Vanitha completed her Ph.D. in Computer Science Engineering from Jawaharlal Nehru Technological University, Hyderabad in 2021. She also holds an M.Tech in Computer Science Engineering (2012) and a B.Tech in Computer Science Engineering (2006) from Bharat Institute of Engineering Technology, JNTUH, Hyderabad. Additionally, she pursued a Diploma in Computer Science Engineering and completed her SSC from the Board of Secondary School of Education.

💼 Experience:

She has been serving as an Assistant Professor in Computer Science and Engineering at Chaitanya Bharathi Institute of Technology since April 2007. Prior to this, she held an ad-hoc position in the same department from August 2006 to April 2007.

🔬 Research Interests:

Dr. Vanitha’s research interests include Language Theory, Data Engineering, Machine Learning, and Artificial Intelligence. Her work focuses on developing frameworks for event extraction and representation in natural language texts.

🏆 Awards:

Dr. Vanitha received the “Pre-eminent Researcher National Award 2022” from Chennai Teacher’s Council (CTC) in recognition of her outstanding contributions to research.

📚 Publications:

Covid19 Patterns Analyzation Using Machine Learning, International Journal of Interdisciplinary Cycle Research (JICR), 2021.

Building Graph for Events and Time in Natural Language Text, International Journal of Innovative Technology and Exploring Engineering (IJITEE), 2020.

Heart Disease Prediction Using Hybrid Technique, Journal of Interdisciplinary Cycle Research (JICR), 2020.

Event Extraction And Classification From English Articles, International Journal of Recent Technology and Engineering (IJRTE), 2019.

Event-Time Relation in Natural Language Text, International Journal of Engineering and Advanced Technology (IJEAT), 2019.

Performance Analysis of Learning Models on Medical Documents, International Journal of Innovative Research in Technology (IJIRT), 2018.