Maryam Kheirabadi | Computer Science | Women Researcher Award

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Women Researcher Award

Maryam Kheirabadi
Affiliation Islamic Azad University
Country Iran
Scopus ID 57210153054
Documents 16
Citations 710
h-index 9
Subject Area Computer Science
Event International Academic Achievements & Awards
ORCID 0000-0001-8980-4299

Maryam Kheirabadi
Islamic Azad University, Iran

The Women Researcher Award recognizes distinguished scholarly achievement, research excellence, scientific innovation, and sustained academic contributions by women researchers. Maryam Kheirabadi has established a research profile in computer science, artificial intelligence, medical image analysis, and intelligent healthcare systems through peer-reviewed publications indexed in Scopus. Her work demonstrates interdisciplinary applications of deep learning, convolutional neural networks, recurrent neural networks, and medical decision-support systems, contributing to diagnostic technologies and intelligent recommendation systems.[1]

Abstract

Maryam Kheirabadi’s scholarly work focuses on applying artificial intelligence techniques to healthcare, medical image processing, pattern recognition, and educational recommendation systems. Her publications emphasize convolutional neural networks, deep learning architectures, image classification, recurrent neural networks with attention mechanisms, and computer-assisted diagnosis of gastric diseases. These contributions illustrate the integration of computer science methodologies into practical healthcare applications while advancing intelligent computational models for diagnostic accuracy and automated decision support.[2]

Keywords

Artificial Intelligence, Deep Learning, Computer Science, Medical Image Analysis, Convolutional Neural Networks, Healthcare Informatics, Machine Learning, Clinical Decision Support

Introduction

Artificial intelligence has become an increasingly influential discipline for solving complex healthcare challenges. The combination of machine learning, deep neural networks, and biomedical image analysis has improved diagnostic accuracy, automated disease detection, and clinical decision support. Within this evolving research landscape, Maryam Kheirabadi has contributed to studies addressing intelligent diagnostic systems, neural network optimization, and educational recommender systems, demonstrating interdisciplinary collaboration between computer science and healthcare research.[2]

Research Profile

According to the supplied Scopus metrics, Maryam Kheirabadi has authored sixteen indexed publications, received more than seven hundred citations, and achieved an h-index of nine. Her research portfolio centers on artificial intelligence, image processing, neural networks, biomedical signal analysis, and intelligent software systems. Her publications demonstrate continued engagement with computational approaches that support clinical diagnosis and intelligent educational technologies.[1]

Research Contributions

  • Development of deep convolutional neural network models for gastric cancer diagnosis using tongue image analysis.
  • Research on intelligent educational recommender systems employing recurrent neural networks and attention mechanisms.
  • Advancement of MRI brain tumor classification through convolutional neural network architectures.
  • Application of biomedical image processing techniques for healthcare diagnostics.
  • Contribution to explainable and data-driven medical decision-support technologies.

Publications

  • Diagnosis of Gastric Cancer via Classification of the Tongue Images using Deep Convolutional Networks (2021). Journal of Information Systems and Telecommunication.
  • Dynamic Educational Recommender System Based on Improved Recurrent Neural Networks Using Attention Technique (2021). Applied Artificial Intelligence. DOI: https://doi.org/10.1080/08839514.2021.2005298
  • Increasing the Accuracy in the Diagnosis of Stomach Cancer Based on Color and Lint Features of Tongue (2021). Biomedical Signal Processing and Control. DOI: https://doi.org/10.1016/j.bspc.2021.102782
  • Y-net: A Reducing Gaussian Noise Convolutional Neural Network for MRI Brain Tumor Classification with NADE Concatenation (2021). Biomedical Physics and Engineering Express.
    DOI: https://doi.org/10.1088/2057-1976/ac107b

Research Impact

The reported citation record reflects meaningful scholarly visibility within computer science and healthcare-related artificial intelligence research. Her studies contribute to the development of intelligent diagnostic systems, automated disease classification, medical image interpretation, and educational recommendation technologies. These interdisciplinary contributions support ongoing research into machine learning applications that improve analytical performance and healthcare decision-making.[3]

Award Suitability

Maryam Kheirabadi demonstrates qualifications consistent with the objectives of the Women Researcher Award through sustained scientific publication, interdisciplinary innovation, measurable citation performance, and contributions to artificial intelligence applications in healthcare. Her research profile reflects active engagement in internationally indexed scholarship and supports recognition for advancing computational methods with practical societal applications.[1]

Conclusion

Maryam Kheirabadi’s publication portfolio illustrates the integration of artificial intelligence with healthcare and intelligent information systems. Her contributions to deep learning, neural network design, biomedical imaging, and recommendation systems have strengthened interdisciplinary research in computer science. The documented publication record, citation performance, and research focus collectively support recognition within academic achievement and research excellence programs.

References

  1. Elsevier. (n.d.). Scopus Author Details: Maryam Kheirabadi, Author ID 57210153054. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57210153054
  2. Kheirabadi, M., et al. (2021). Diagnosis of Gastric Cancer via Classification of the Tongue Images using Deep Convolutional Networks. Journal of Information Systems and Telecommunication.
    http://www.scopus.com/inward/record.url?eid=2-s2.0-85111735812&partnerID=MN8TOARS
  3. Kheirabadi, M., et al. (2021). Dynamic Educational Recommender System Based on Improved Recurrent Neural Networks Using Attention Technique. Applied Artificial Intelligence. DOI: https://doi.org/10.1080/08839514.2021.2005298
  4. Kheirabadi, M., et al. (2021). Increasing the Accuracy in the Diagnosis of Stomach Cancer Based on Color and Lint Features of Tongue. Biomedical Signal Processing and Control. DOI: https://doi.org/10.1016/j.bspc.2021.102782
  5. Kheirabadi, M., et al. (2021). Y-net: A Reducing Gaussian Noise Convolutional Neural Network for MRI Brain Tumor Classification with NADE Concatenation. Biomedical Physics and Engineering Express. DOI: https://doi.org/10.1088/2057-1976/ac107b

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/

Quang Minh Tran | Computer Science | Innovative Research Award

Published on by Academic Achievements

Innovative Research Award

Quang Minh Tran
Affiliation University of Wollongong
Country Australia
ORCID 0009-0007-9413-2600
Documents 2
Subject Area Computer Science
Event International Academic Achievements & Awards

Quang Minh Tran
Institution: University of Wollongong,

Quang Minh Tran is a researcher in the field of Computer Science whose recent work focuses on trustworthy artificial intelligence, deepfake audio detection, adversarial machine learning, and multimedia security. His research investigates the robustness of deep learning systems against sophisticated adversarial attacks while contributing to the development of reliable forensic methods for synthetic audio detection. These studies address important challenges in AI security, digital trust, and the protection of multimedia systems against manipulation.[1]

Abstract

This article presents an academic profile of Quang Minh Tran in recognition of research contributions to Computer Science, particularly in adversarial machine learning and deepfake audio detection. His work examines the resilience of artificial intelligence systems under universal adversarial perturbations while advancing forensic methods capable of identifying manipulated synthetic speech. The research contributes to improving the security, reliability, and robustness of AI-enabled multimedia technologies.[2]

Keywords

Computer Science, Artificial Intelligence, Deepfake Audio Detection, Adversarial Machine Learning, Multimedia Security, Universal Adversarial Perturbations, AI Robustness, Audio Forensics, Digital Trust, Machine Learning Security.

Introduction

The increasing adoption of artificial intelligence has intensified concerns regarding the misuse of generative technologies, including deepfake audio. Detecting synthetic speech while maintaining robustness against adversarial attacks represents a significant challenge in AI security. Quang Minh Tran’s research explores these issues through systematic evaluation of deepfake detectors and vocoder fingerprint detectors, supporting the development of trustworthy AI systems suitable for practical deployment.[2]

Research Profile

  • Research field: Computer Science.
  • Primary interests include AI security and multimedia forensics.
  • Research emphasizes adversarial robustness of deep learning systems.
  • Investigates deepfake audio detection and vocoder fingerprint analysis.
  • Contributes to trustworthy artificial intelligence and secure multimedia applications.

Research Contributions

Quang Minh Tran has contributed to the evaluation of adversarial robustness in deepfake audio detection systems through comprehensive analysis of universal adversarial perturbations. His work investigates vulnerabilities in deep learning-based forensic models while identifying approaches that improve detector resilience. These contributions are relevant to cybersecurity, digital media authentication, trustworthy AI, and the broader development of reliable machine learning systems capable of operating under adversarial conditions.[2]

Publications

  • Evaluating Adversarial Robustness of Deepfake Audio Detectors and Vocoder Fingerprint Detectors Against Universal Adversarial Perturbations. Future Internet, 2026. DOI:10.3390/fi18070344
  • Evaluating Adversarial Robustness of Deepfake Audio Detectors and Vocoder Fingerprint Detectors Against Universal Adversarial Perturbations. Preprint, 2026. DOI:10.20944/preprints202606.0272.v1

Research Impact

The research addresses an increasingly important area of artificial intelligence by strengthening the understanding of adversarial vulnerabilities affecting deepfake detection technologies. The findings provide valuable insights for researchers, cybersecurity practitioners, and developers seeking to improve the resilience of AI-based forensic systems. This work contributes to ongoing efforts aimed at enhancing digital trust, secure communication, and responsible deployment of artificial intelligence.[2]

Award Suitability

Based on the available scholarly publications, Quang Minh Tran demonstrates emerging research contributions in artificial intelligence security, adversarial machine learning, and multimedia forensics. His work addresses contemporary challenges involving deepfake detection and AI robustness using rigorous scientific methodology. These contributions provide a sound academic basis for consideration within the Innovative Research Award category of the International Academic Achievements & Awards program.[1]

Conclusion

Quang Minh Tran’s research advances the field of Computer Science by addressing the robustness and security of artificial intelligence systems against adversarial manipulation. His investigations into deepfake audio detection and multimedia forensics contribute to the growing body of knowledge supporting trustworthy AI technologies. The combination of technical innovation, practical relevance, and scientific rigor reflects meaningful scholarly progress within the rapidly evolving domain of AI security.

References

  1. ORCID. (n.d.). Quang Minh Tran ORCID Record.
    https://orcid.org/0009-0007-9413-2600
  2. Future Internet. (2026). Evaluating Adversarial Robustness of Deepfake Audio Detectors and Vocoder Fingerprint Detectors Against Universal Adversarial Perturbations.
    https://doi.org/10.3390/fi18070344
  3. Preprints.org. (2026). Evaluating Adversarial Robustness of Deepfake Audio Detectors and Vocoder Fingerprint Detectors Against Universal Adversarial Perturbations.
    https://doi.org/10.20944/preprints202606.0272.v1

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

Shen Zhang | Engineering | Innovative Research Award

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Mr. Shen Zhang | Engineering | Innovative Research Award

Central South Architectural Design Institute Co., Ltd. | China

Dr. Zhang Shen is a distinguished leader in digital construction and sustainable engineering, currently serving as Digital Director at Central South Architectural Design Institute and Chairman of Zhongda Digital Technology (Hubei) Co., Ltd.. A Ph.D. graduate of Wuhan University, he is widely recognized for pioneering China’s first Building Lifecycle Management (PLM) platform, enabling fully model-driven, drawing-free construction. His innovations integrate BIM, IoT, and prefabrication technologies to enhance efficiency, reduce carbon emissions, and improve structural resilience. Dr. Zhang has authored over 90 academic publications and holds numerous patents and software copyrights. A recipient of prestigious national honors, including the State Council Special Allowance, he continues to advance intelligent construction practices aligned with global sustainability and digital transformation goals.

Citation Metrics (Scopus)

200

150

100

50

0

Citations
156

Documents
20

h-index
6

🟦 Citations 🟥 Documents 🟩 h-index

Featured Publications

 

Getachew Getu Enyew | Computer Science | Research Excellence Award

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Mr. Getachew Getu Enyew | Computer Science | Research Excellence Award

Information Network Security Administration (INSA) & Addis Ababa Science and Technology University | Ethiopia

Getachew Getu Enyew is an AI/ML Engineer and emerging researcher specializing in artificial intelligence, machine learning, and autonomous systems. He holds an M.Sc. in Electrical and Computer Engineering from Addis Ababa Science and Technology University, Ethiopia. His research focuses on intelligent decision-making systems, robotics perception, computer vision, and anomaly detection for real-world applications, particularly in cybersecurity and critical infrastructure. Currently working at the Information Network Security Administration (INSA), he develops AI-driven solutions for threat detection and leads MLOps integration for scalable deployment of machine learning models. He has authored multiple research papers on topics such as traffic accident prediction, industrial fault diagnosis, and AI-based intrusion detection, and has presented his work at national conferences. His contributions aim to advance safe, adaptive, and trustworthy AI systems with strong societal and industrial impact.

Featured Publications

Artificial Intelligence in Fault Diagnosis of Industrial Machinery: A Comprehensive Review
– Structural Control and Health Monitoring (2025) | Citations: 1

 

Angeliki Antoniou | AI | Best Researcher Award

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Assoc. Prof. Dr. Angeliki Antoniou | AI | Best Researcher Award

Assoc. Prof. Dr. Angeliki Antoniou | AI | Associate Professor at University of West Attica | Greece

Assoc. Prof. Dr. Angeliki Antoniou is a distinguished scholar in the field of Human-Computer Interaction (HCI), Educational Technologies, and Digital Cultural Heritage, currently serving at the University of West Attica, Department of Archival, Library and Information Studies, Greece. She earned her Doctor of Informatics (Ph.D.) from the University of Peloponnese, focusing on adaptive educational technologies for museums, and holds an MSc in Human-Computer Interaction with Ergonomics from University College London (UCL). Additionally, she possesses undergraduate degrees in Psychology from the University of Kent and Early Childhood Education from the National and Kapodistrian University of Athens, illustrating her interdisciplinary foundation that bridges education, psychology, and informatics. Professionally, Assoc. Prof. Dr. Angeliki Antoniou has accumulated extensive teaching and research experience across institutions such as the University of Peloponnese and the University of West Attica, where she has led courses in cognitive psychology, human-computer interaction, and digital learning environments. Her research interests include user-centered design, cognitive modeling, serious games, digital storytelling, and technology-enhanced museum learning. She has successfully contributed to and coordinated several international and national projects on cultural heritage technologies, and her work is well-cited in high-impact academic journals indexed in Scopus and IEEE. Assoc. Prof. Dr. Angeliki Antoniou’s research skills encompass experimental design, usability evaluation, qualitative and quantitative analysis, and the development of adaptive systems for education and culture. She has received academic recognition for her leadership in interdisciplinary research, along with honors for her contributions to digital culture and innovation in educational informatics. In conclusion, Assoc. Prof. Dr. Angeliki Antoniou exemplifies academic excellence, innovative vision, and global impact through her scholarly research, educational leadership, and enduring contributions to the advancement of digital cultural heritage and human-computer interaction.

Profile: Google Scholar

Featured Publications 

  1. Lykourentzou, I., Antoniou, A., Naudet, Y., & Dow, S. P. (2016). Personality matters: Balancing for personality types leads to better outcomes for crowd teams. Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing. Citations: 158

  2. Theodoropoulos, A., & Antoniou, A. (2022). VR games in cultural heritage: A systematic review of the emerging fields of virtual reality and culture games. Applied Sciences, 12(17), 8476. Citations: 108

  3. Antoniou, A., & Lepouras, G. (2010). Modeling visitors’ profiles: A study to investigate adaptation aspects for museum learning technologies. Journal on Computing and Cultural Heritage (JOCCH), 3(2), 1–19. Citations: 84

  4. Lykourentzou, I., Claude, X., Naudet, Y., Tobias, E., Antoniou, A., & Lepouras, G. (2013). Improving museum visitors’ quality of experience through intelligent recommendations: A visiting style-based approach. Workshop Proceedings of the 9th International Conference on Intelligent Environments. Citations: 76

  5. Antoniou, A., Lepouras, G., Bampatzia, S., & Almpanoudi, H. (2013). An approach for serious game development for cultural heritage: Case study for an archaeological site and museum. Journal on Computing and Cultural Heritage (JOCCH), 6(4), 1–19. Citations: 69

  6. Katifori, A., Perry, S., Vayanou, M., Antoniou, A., Ioannidis, I. P., & McKinney, S. (2020). “Let them talk!” Exploring guided group interaction in digital storytelling experiences. Journal on Computing and Cultural Heritage (JOCCH), 13(3), 1–30. Citations: 67

  7. Antoniou, A., Katifori, A., Roussou, M., Vayanou, M., Karvounis, M., & Kyriakidi, M. (2016). Capturing the visitor profile for a personalized mobile museum experience: An indirect approach. Proceedings of the Digital Heritage International Congress. Citations: 60

 

Ms. Vidushi Sharma | Automation | Women Researcher Award

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Ms. Vidushi Sharma | Automation | Women Researcher Award

Ms. Vidushi Sharma | Automation – Project Manager at Artech LLC, United States

 

Vidushi Sharma is a distinguished technology leader and researcher with over 15 years of impactful experience across diverse industries including banking, healthcare, retail, and finance. She is known for blending cutting-edge research in Artificial Intelligence (AI), Machine Learning (ML), and Robotic Process Automation (RPA) with enterprise-scale digital transformation. Sharma has consistently demonstrated the ability to drive innovation and implement technology solutions that deliver measurable results. Her unique blend of academic rigor and practical leadership makes her a prominent figure in the global digital landscape.

Profile Verified:

Google Scholar

Education:

Vidushi Sharma holds a Bachelor’s degree in Information Technology, which provided a solid foundation in computer science and systems engineering. She further enhanced her managerial acumen with a Postgraduate Diploma in Human Resource Management, facilitating her ability to lead teams and manage organizational change. In addition to formal academic qualifications, she has earned several professional certifications including Google Cloud, SAFe Agile, Artificial Intelligence and Machine Learning, ITIL v3, and UiPath RPA, making her a highly versatile and forward-thinking researcher and practitioner.

Experience:

With a professional journey spanning over 15 years, Sharma has served in senior leadership and technology roles at top-tier companies such as Citigroup, Vanguard, Tech Mahindra, NCR Corporation, IBM, and Hewitt Associates. In her current role as Program Delivery Manager at Citigroup, she leads global transformation initiatives involving automation, AI, and cloud migration. Her career includes successfully managing large-scale digital projects, transforming business operations through DevOps, and enhancing productivity through RPA and intelligent automation. Her proven expertise in tools like JIRA, Confluence, and ServiceNow supports Agile and scalable project delivery.

Research Interest:

Vidushi Sharma’s research interests center on the integration of Artificial Intelligence and Robotic Process Automation into traditional business workflows, particularly in sectors such as healthcare, fintech, retail, and HR. Her work explores how intelligent systems can optimize operations, improve decision-making, and provide cost-efficient solutions. She also investigates human-machine collaboration, ethical AI deployment, and the scalability of automation across sectors. Her early research in epidemiology and chemistry highlights her interdisciplinary strength and scientific curiosity, enabling her to address complex problems with holistic approaches.

Awards:

Sharma’s innovative contributions have been widely recognized. She received the Outstanding Digital Innovation of the Year 2024 award from Asia International Research, which acknowledges groundbreaking work in automation and AI for enterprise solutions. Other honors include the Standing Ovation Award, Bravo Award, and Achiever’s Award from industry leaders, affirming her excellence in program management, team leadership, and strategic innovation. Her consistent receipt of team and individual awards across her career reflects her dedication to excellence, collaboration, and transformative leadership.

Publications:

  • 🧬 “Epidemiology of Chronic Suppurative Otitis Media and Deafness in a Rural Area” – The Indian Journal of Pediatrics, 1995, Cited by 61
  • 🧪 “Synthesis, Characterization and Biological Activity of a Novel Schiff Base” – Journal of Molecular Structure, 2024, Cited by 9
  • 💻 “Online Education During COVID-19 Pandemic: Challenges and Solutions” – Turkish Online Journal of Qualitative Inquiry, 2021, Cited by 3
  • 🤖 “AI and RPA in Financial Services – Enhancing Fraud Prevention” – International Journal of Multidisciplinary Research in Global Environment (IJMRGE), 2024
  • 🏪 “The Future of Automation in FinTech: Hyper-Automating Operations” – International Journal of Innovative Research in Computer and Technology (IJIRCT), 2025
  • 🏥 “AI-Powered RPA in Healthcare – Improving Patient Outcomes” – IJMRGE, 2024
  • 🛒 “Impact of Automation on Retail Logistics – AI Solutions for Supply Chains” – International Journal of Scientific Research and Engineering Management (IJSREM), 2023

Conclusion:

Vidushi Sharma represents a unique blend of research intelligence, technological mastery, and leadership capability. Her journey from early scientific research to pioneering AI and automation in global enterprises demonstrates her adaptability and vision. Through her work, she exemplifies the transformative power of women in research and innovation. Her commitment to advancing knowledge and solving real-world problems positions her as an ideal recipient of the Women Researcher Award. Sharma’s contributions are not just impactful—they are inspirational, paving the way for future generations of female scientists and tech leaders.

 

 

Haitham Adarbah | IoT | Best Researcher Award

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Dr. Haitham Adarbah | IoT | Best Researcher Award 

Postdoctoral Researcher  | Texas A&M University |  United States

Research for Best Researcher Award

Strengths for the Award

  1. Innovative Research Contributions: Haitham Adarbah’s research, particularly in areas such as vehicular networks, 5G technologies, and artificial intelligence, demonstrates a strong capacity for innovation. His work on enhancing communication protocols for connected autonomous vehicles and developing secure routing protocols for IoT is cutting-edge and addresses critical technological challenges.
  2. Extensive Publication Record: Adarbah’s impressive list of publications in high-impact journals and conferences showcases his substantial contributions to the field of computer science. His research has been widely recognized and disseminated, highlighting his role as a leading scholar in wireless networks and related technologies.
  3. Successful Grant Acquisition: His ability to secure funding for research projects, such as the TRC-funded project on secure RPL routing protocols, underscores his competency in attracting financial support and managing substantial research initiatives.
  4. Educational Impact: As an educator, Adarbah has significantly influenced the academic development of his students. His dedication to teaching diverse subjects in computer science and mentoring both undergraduate and graduate students is commendable.
  5. Cross-Functional Collaboration: Adarbah actively engages with both academic and industry collaborators, facilitating advancements in his research areas and promoting collaborative learning environments.

Areas for Improvement

  1. Broader Research Horizons: While Adarbah’s focus on wireless networks and vehicular technologies is highly specialized, expanding his research scope to include emerging areas such as quantum computing or advanced cybersecurity could enhance his impact and visibility in the broader computing field.
  2. Increased Interdisciplinary Work: Further interdisciplinary collaborations, particularly with fields such as healthcare or environmental sciences, could provide new perspectives and applications for his research, potentially leading to groundbreaking discoveries.
  3. Enhanced Public Engagement: Increasing public engagement and outreach through popular science articles, public talks, or community-based projects could improve the visibility of his research and its societal impact.
  4. International Collaboration: Expanding collaborations with international researchers and institutions could offer new opportunities for joint research projects and enhance the global impact of his work.
  5. Involvement in Professional Societies: Greater involvement in professional societies and editorial boards of high-impact journals could further solidify his reputation as a leading researcher and provide more platforms for sharing his insights.

Conclusion

Haitham Adarbah’s achievements in research and education make him a strong candidate for the Research for Best Researcher Award. His innovative contributions to wireless networks and communication technologies, coupled with his dedication to teaching and successful grant acquisition, highlight his exceptional qualifications. While there are areas for potential growth, such as expanding research horizons and increasing public engagement, Adarbah’s current accomplishments and impact on the field are noteworthy and deserving of recognition. His continued efforts in advancing technology and education underscore his commitment to excellence and innovation in computer science.

Short Biography 📚

Dr. Haitham Adarbah is an Assistant Professor in Computer Science at Texas A&M University-Corpus Christi, known for his dynamic approach to teaching and research. With a rich background in computer science, Dr. Adarbah excels in instructing both undergraduate and graduate courses, mentoring students, and advancing academic development. His research focus spans 5G and 6G communication protocols, vehicular networks, and AI algorithms, complemented by his active role in grant writing and conference presentations. He is celebrated for his innovative contributions and effective communication skills in regional and national computing events.

Profile

SCOPUS

Education 🎓

  • Postdoctoral Researcher (Ongoing)
    Texas A&M University, Corpus Christi, TX
    Focus: Enhancing connectivity and control for autonomous vehicles using AI algorithms in the 5G-6G era.
  • Doctor of Philosophy (PhD) in Wireless Networks
    De Montfort University, Leicester, UK
    Dissertation: Bandwidth and Energy-Efficient Route Discovery for Noisy Mobile Ad-Hoc Networks.
  • Master of Science in Computer Science
    Amman Arab University, Amman, Jordan
  • Bachelor of Science in Computer Science
    AL-Zaytoonah University of Jordan, Amman, Jordan

Experience 🏢

Dr. Adarbah has extensive experience in academia, having served as an IT Lecturer at Gulf College, Muscat, Oman, from April 2009 to August 2024. During this tenure, he taught a variety of subjects including Networks, Digital Solutions, and Artificial Intelligence. He also developed and delivered engaging lesson plans, fostered collaborative learning environments, and led numerous student research initiatives. His role involved evaluating student performance, mentoring, and orchestrating professional training sessions.

Research Interests 🔍

Dr. Adarbah’s research interests include:

  • 5G and 6G Communication Systems: Developing protocols for enhanced connectivity.
  • Vehicular Networks and V2X Systems: Exploring advanced communication technologies for autonomous vehicles.
  • Artificial Intelligence and Machine Learning: Applying AI to optimize network performance.
  • IoT and Wireless Networks: Investigating secure and efficient routing protocols.

Awards 🏆

  • Research Grant Appreciation from the Ministry of Higher Education – Research & Innovation of the Sultanate of Oman.
  • Teaching Excellence Award for the academic year 2015-16, recognizing his exceptional teaching and mentoring skills.

Publications 📄

Sobouti, M. J., Adarbah, H. Y., et al. “Efficient Fuzzy-Based 3-D Flying Base Station Positioning and Trajectory for Emergency Management in 5G and Beyond Cellular Networks.” IEEE Systems Journal (2024).

Al-Harrasi, A. S., Adarbah, H., et al. “Exploring the Adoption of Big Data Analytics in the Oil and Gas Industry: A Case Study.” Journal of Business, Communication & Technology (in press) (2024).

Adarbah, H. Y., Sookhak, M., & Atiquzzaman, M. “A Digital Twin Environment for 5G Vehicle-to-Everything: Architecture and Open Issues.” Proceedings of the Int’l ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, & Ubiquitous Networks (PE-WASUN’23) (2023).

Adarbah, H. Y., Moghadam, M. F., et al. “Security Challenges of Selective Forwarding Attack and Design a Secure ECDH-Based Authentication Protocol to Improve RPL Security,” IEEE Access vol. 11, pp. 11268-11280, (2023).

Adarbah, H. Y., Al-Badi, A., & Golzar, J. “The Impact of Emerging Data Sources and Social Media on Decision Making: A Culturally Responsive Framework” International Journal of Society, Culture & Language Volume 11, Issue 1, pp. 16-29, (2023).

Adarbah, H. Y., Al-Badi, A. H. “Banking on the Cloud: Insights into Security and Smooth Operations” Journal of Business, Communication & Technology (2023, in Press).

Adarbah, H. Y., Ahmad, S. “Impact of Selective Forwarding Attacks on the Performance of RPL Routing Protocol in the Internet of Things” International Journal of Research Publication and Reviews Volume 3, Issue 6, pp. 1479-1487, (2022).

Adarbah, H. Y., Goode, M. M. H. “Key Demand Factors in Professional Business Courses: A Mixed-Methods Study” Journal of Business, Communication & Technology Volume 1, Issue 2, pp. 44-53, (2022).

Adarbah, H. Y., Jajarmi, H. “Active Learning for Omani At-Risk Students through Educational Technology: A Case of Content-Based Language Instruction” International Journal of Society, Culture & Language Volume 10, Issue 2, pp. 82-91, (2022).

Adarbah, H. Y., Ahmad, S. “Channel-Adaptive Probabilistic Broadcast in Route Discovery Mechanism of MANETs” Journal of Communications Software and Systems (JCOMSS) Volume 15, Issue 1, pp. 34-44, March (2019).