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

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

 

Muhammet Emre Sanci | Robotics | Innovative Research Award

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Dr. Muhammet Emre Sanci | Robotics | Innovative Research Award 

Dr. Muhammet Emre Sanci | Robotics | Istanbul Technical University at Turkey

Robotics expertise forms the foundation of the academic and research journey of Dr. Muhammet Emre Sanci, whose professional path seamlessly blends advanced theoretical knowledge with applied innovation in intelligent systems, nonlinear control, autonomous technologies, and UAV swarm intelligence. Dr. Muhammet Emre Sanci completed his PhD in Mechatronics Engineering at Istanbul Technical University, funded by the Ministry of Education Turkiye, where his thesis focused on adaptive inverse optimal controller design for nonlinear non-affine systems using machine learning methods. He also earned an MSc (Hons) in Electrical and Electronics Engineering at Pamukkale University, focusing on model-based control and autonomy in magnetic levitation systems, and previously completed a BE (Hons, 1st) in Electrical and Electronics Engineering at Anadolu University, where he compared fuzzy PID and PI controllers for DC microgrid energy systems. Adding multidisciplinary strength to his robotics vision, he studied Physics at Vilnius University through Erasmus+ mobility and completed BSc (Hons, 1st) in Physics at Abant Izzet Baysal University. Professionally, Dr. Muhammet Emre Sanci served as a Postdoctoral Fellow at the University of Idaho, advancing research in autonomous robotics, adaptive optimal control, multi-drone swarming systems, and agricultural automation. His work included adaptive disturbance rejection-based fuzzy PID control for UAV swarms, deep learning-based greenhouse automation, irrigation distribution modeling under heterogeneous soil conditions, and drone-based pest management systems, while providing data-driven modeling, algorithm development, and comprehensive documentation for large-scale research initiatives. Earlier, he served at Istanbul Technical University as Teaching & Research Assistant in Control and Automation Engineering, contributing to intelligent control, UAV-based autonomous mine sweeping, multi-agent local path planning, velocity obstacle avoidance, and neural network plus SVR-based nonlinear non-affine system identification strategies. His earlier experience at Pamukkale University integrated Robotics with materials and machining optimization, focusing on magnetic levitation system modeling and composite machining surface roughness prediction. He has extensive teaching expertise across multiple modules including Control Systems, Mechatronics, Real-Time Embedded Systems, Linear Algebra, Probability Theory, Microcontroller Systems, State-Space Methods, Intelligent Systems, Deep Learning, Hardware-in-the-Loop, Electronic Instrumentation, MATLAB Programming, and more, supporting robotics education at undergraduate and postgraduate levels. His research interests remain deeply rooted in Robotics, nonlinear intelligent control, adaptive UAV swarming, multi-agent autonomy, artificial neural networks for system identification, optimal control theory, and real-time embedded computation. His core research skills include nonlinear model design, optimal robotics controller development, multi-UAV coordination, intelligent system modeling, path-planning algorithms, machine learning implementation, and multi-environment dynamic optimization. His innovation has been supported by major competitive grants from national and international science foundations, including drone-based sensing system development for infrastructure inspection and UAV optimization technology for autonomous missions. His excellence is reflected through distinguished graduation achievements, academic honors, international research scholarships, and best presentation recognitions. In conclusion, Dr. Muhammet Emre Sanci stands as a robotics-focused scholar whose interdisciplinary expertise, highly adaptive control systems research, and forward-thinking approach to autonomous UAV swarming technologies significantly advance the global state-of-the-art in intelligent engineering systems, making him an invaluable contributor to emerging frontiers in Robotics.

Profile: Google Scholar

Featured Publications 

Sanci, M. E., Halis, S., & Kaplan, Y. (2017). Optimization of machining parameters to minimize surface roughness in the turning of carbon-filled and glass fiber-filled polytetrafluoroethylene. 2017, 6 citations.
Sanci, M. E., & Günel, G. Ö. (2024). Neural network based adaptive inverse optimal control for non-affine nonlinear systems. 2024, 5 citations.
Sanci, M. E., Uçak, K., & Günel, G. Ö. (2023). A novel adaptive LSSVR based inverse optimal controller with integrator for nonlinear non-affine systems. 2023, 5 citations.
Candan, F., Sanci, M. E., & Li, L. (2024). Vision-based relative navigation and drone swarming control for inspection in GPS-denied environment. 2024, 1 citation.
Sanci, M. E., Halis, S., & Kaplan, Y. (2016). Study on surface roughness of carbon and glass fiber filled polytetrafluoroethylene in turning process. 2016, 1 citation.

 

Zhengquan Piao | Robotics | Best Researcher Award

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Dr. Zhengquan Piao | Robotics | Best Researcher Award

Dr. Zhengquan Piao | Robotics | – Engineer at China North Artificial Intelligence & Innovation Research Institute, China

Zhengquan Piao is an emerging researcher in computer vision, autonomous systems, and intelligent detection technologies. His research reflects a growing focus on advanced methodologies such as deep learning, pattern recognition, and sensor fusion. With several peer-reviewed publications and a rising citation profile, Piao is positioning himself as a significant contributor to the fields of intelligent transportation, object detection, and AI-driven robotics. His research emphasizes practical, scalable solutions that address real-world challenges, particularly in vehicle detection, underground mapping, and smart navigation systems.

Profile Verified:

Scopus

Google Scholar

Education:

Zhengquan Piao received his academic training in computer science and artificial intelligence, where he developed a strong foundation in machine learning, algorithm design, and control theory. His education likely includes postgraduate study from a research-focused institution, possibly Beijing Institute of Technology (BIT), where he deepened his understanding of computer vision, neural networks, and autonomous systems. This academic background has provided him with the analytical and technical tools essential for his cutting-edge research in object recognition and navigation.

Experience:

Professionally, Piao has gained hands-on experience through a range of academic and technical projects that integrate AI with robotics and automation. He has played key roles in designing object detection architectures, enhancing vehicle perception systems, and developing algorithms for real-time localization in complex environments. His participation in national conferences and collaborations with multidisciplinary teams reflects a well-rounded profile of academic research and practical engineering. Piao’s project involvement also demonstrates his ability to work across domains, including transportation safety, aerial imaging, and intelligent mapping.

Research Interest:

Piao’s research interests center around few-shot learning, domain adaptation, autonomous navigation, and sensor-based object detection. He is especially interested in how to enable machines to learn from limited data in resource-constrained environments. His projects often combine LiDAR, camera fusion, deep neural networks, and unsupervised learning to build intelligent systems capable of operating reliably in both structured and unstructured settings. He is also focused on applications in autonomous driving and underground navigation, where accuracy and robustness are critical.

Awards:

While Zhengquan Piao has not yet received formal individual awards, his contributions have begun to gain traction in the academic community, evidenced by a growing number of citations and involvement in collaborative, government-funded research. His compliance with open-access mandates and continued publication in high-quality venues highlight a dedication to research transparency and academic integrity. These efforts position him well for future recognition and academic honors.

Publications:

📘 “Few-shot traffic sign recognition with clustering inductive bias and random neural network” – Pattern Recognition (2020), cited by 38 articles – proposes a novel few-shot learning model for traffic signs.
📙 “AccLoc: Anchor-Free and two-stage detector for accurate object localization” – Pattern Recognition (2022), cited by 25 – introduces an efficient detection method free of anchor boxes.
📗 “Unsupervised domain-adaptive object detection via localization regression alignment” – IEEE Transactions on Neural Networks and Learning Systems (2023), cited by 20 – focuses on domain adaptation in object detection.
📕 “Anchor-free object detection with scale-aware networks for autonomous driving” – Electronics (2022), cited by 3 – improves detection in self-driving vehicle systems.
📓 “An Intelligent Localization Method for Underground Space Targets Based on the Fusion of Camera and LiDAR” – ICIRAC (2024) – addresses underground localization with sensor fusion.
📒 “An Efficient Compression Method for Collaborative 3D Mapping in Confined Space with Limited Resources” – IEEE Conference on Signal, Information and Data (2024) – introduces 3D data compression methods.
📔 “Downsample-Based Improved Dense Point Cloud Registration Framework” – International Conference on Guidance, Navigation and Control (2024) – proposes improvements to point cloud registration for dense environments.

Conclusion:

In summary, Zhengquan Piao is a promising researcher with a clear trajectory of impactful and innovative work. His focus on real-world challenges, including autonomous vehicle perception, few-shot learning, and sensor fusion, demonstrates both originality and technical depth. With growing academic recognition and a solid portfolio of publications, he has established himself as a rising contributor in AI and robotics. Although still early in his academic journey, Piao’s contributions and collaborative spirit strongly position him as a worthy candidate for the Best Researcher Award.

 

 

 

 

Dr. Xin Zhou | Engineering | Best Researcher Award

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Dr. Xin Zhou | Engineering | Best Researcher Award

Dr. Xin Zhou | Engineering – Lecture at Shanghai University of Electric Power, China

Dr. Xin Zhou is a passionate and emerging researcher in the field of automation engineering, currently serving as a lecturer at Shanghai University of Electric Power. With a solid international educational background and hands-on research in robotics and intelligent optimization, he brings both academic insight and practical relevance to his work. Dr. Zhou has focused his career on robotic path planning, artificial intelligence in manufacturing, and intelligent control systems. His rapid contributions to both the theoretical foundations and industrial applications of intelligent robotics make him a promising candidate for the Best Researcher Award.

Education:

Dr. Zhou’s academic path spans several prestigious institutions across China, the UK, and Australia. He received his Ph.D. in Control Science and Engineering from East China University of Science and Technology in 2022, concentrating on intelligent algorithms and robotic optimization. He earned his Master’s degree in Digital Systems and Communication Engineering from the Australian National University (2016–2017), developing skills in communication and embedded systems. His undergraduate training was jointly conducted at the University of Liverpool and Xi’an Jiaotong-Liverpool University (2011–2015), where he majored in Electrical Engineering and Automation, providing a strong technical foundation for his current work.

Profile:

Orcid

Experience:

Since August 2022, Dr. Zhou has been working as a lecturer at the School of Automation Engineering, Shanghai University of Electric Power. In this position, he teaches undergraduate and graduate courses while engaging in active research. He has participated in two completed projects funded by the National Natural Science Foundation of China (NSFC), focusing on welding robotics and production scheduling under uncertainty. Dr. Zhou is also leading a current industry-funded research project on motion planning algorithms for robotic systems used in complex maintenance tasks. His combination of academic research and industrial cooperation demonstrates a comprehensive and practical research profile.

Research Interest:

Dr. Zhou’s primary research interests include robotic path planning, multi-objective optimization, intelligent algorithms, and smart manufacturing systems. He specializes in developing evolutionary algorithms and applying them to real-world robotic control challenges, especially in arc welding scenarios. His work aims to enhance the intelligence, flexibility, and adaptability of autonomous robotic systems, contributing to Industry 4.0 initiatives. He is particularly known for his work on decomposition-based optimization methods and real-time obstacle avoidance strategies.

Awards:

While Dr. Zhou is still early in his career, he has already made notable contributions to applied innovation, as evidenced by three Chinese patents in the area of robotic path planning. These patents include novel systems and methods for arc welding robot navigation and gantry-type robotic control, with the most recent filed in December 2023. His work in patented technologies reflects his practical approach to academic research and commitment to industry-aligned solutions.

Publications:

Dr. Zhou has authored and co-authored several influential journal papers. Below are seven key publications, with emojis, journal names, publication years, and citation notes:

📘 A decomposition-based multiobjective evolutionary algorithm with weight vector adaptation – Swarm and Evolutionary Computation, 2021. Cited for its novel adaptive mechanism in multi-objective optimization.

🤖 An approach for solving the three-objective arc welding robot path planning problem – Engineering Optimization, 2023. Frequently referenced in robotics and optimization studies.

🛠️ Online obstacle avoidance path planning and application for arc welding robot – Robotics and Computer-Integrated Manufacturing, 2022. Cited in real-time control literature.

🔍 A Collision-free path planning approach based on rule-guided lazy-PRM with repulsion field for gantry welding robots – Robotics and Autonomous Systems, 2024. Recent paper gaining citations in dynamic path planning.

📚 A survey of welding robot intelligent path optimization – Journal of Manufacturing Processes, 2021. Serves as a key reference for scholars in the welding robotics field.

🧠 Rule-based adaptive optimization strategies in robotic welding systems – Under review, targeted at IEEE Transactions on Industrial Informatics.

🔄 Multi-objective task sequencing and trajectory planning under dynamic constraints – Manuscript in progress for Journal of Intelligent Manufacturing.

Conclusion:

Dr. Xin Zhou is a standout young researcher whose work in robotic path planning and intelligent optimization has already made a significant impact in the field of automation. His research integrates high-level algorithm development with real-world engineering applications, making his contributions both academically valuable and practically useful. With a growing body of well-cited publications, involvement in both national and industry-sponsored projects, and active innovation through patents, Dr. Zhou is a strong candidate for the Best Researcher Award. His trajectory reflects both dedication and innovation, and he continues to show strong potential to lead transformative work in intelligent automation in the years ahead.

 

 

 

Shashank Pasupuleti | Robotics | Best Researcher Award

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Mr. Shashank Pasupuleti | Robotics | Best Researcher Award

Mr. Shashank Pasupuleti | Robotics – Senior Product and Systems Engineer at Capgemini Engineering, United States

Shashank Pasupuleti is an accomplished Mechanical Systems Engineer with significant contributions to the medical device and robotics industries. With a robust background in system design, validation, and risk analysis, Shashank has demonstrated expertise in bridging engineering innovation with industry compliance. His proficiency in model-based systems engineering (MBSE) and various engineering tools has propelled advancements in product development, especially in robotic surgical platforms. Over the years, his contributions have positively influenced patient care through innovative technologies.

Profile Verified

ORCID | Google Scholar

Education

Shashank holds a Master’s degree in Mechanical Systems from the University of North Texas and a Master of Science in Project Management from the University of the Cumberlands. His academic journey began with a Bachelor’s degree in Mechanical Engineering from Jawaharlal Nehru Technological University. These academic achievements laid the foundation for his expertise in mechanical design, project management, and system engineering methodologies.

Experience

Shashank has over seven years of progressive experience in leading-edge projects across globally recognized organizations. As a Senior Product and Systems Engineer at Capgemini Engineering, he spearheaded the development of system engineering models for risk management, system architecture, and validation processes, enhancing project quality and efficiency. His tenure at THINK Surgical as a Senior System Engineer saw him develop TMINI, a miniature surgical robotic platform, significantly improving precision in total knee replacement surgeries. Additionally, at Auris Health Inc. (Johnson & Johnson), Shashank contributed to the development of the Monarch robotic platform, optimizing testing strategies and supporting regulatory approvals. His early career roles at Fresenius Medical Care and GE Healthcare honed his expertise in verification and validation (V&V) strategies and compliance with FDA and ISO standards.

Research Interests

Shashank’s research interests lie at the intersection of robotics, medical devices, and MBSE. He focuses on advancing system integration techniques and enhancing reliability in medical devices. His dedication to innovation in healthcare robotics is evident in his work on surgical platforms, usability studies, and cybersecurity strategies for regulatory compliance. Shashank also actively explores how digital continuity and data-driven design can transform medical device development, making healthcare safer and more effective.

Awards

Shashank has been consistently recognized for his technical acumen and leadership in engineering projects. He was an integral part of teams that achieved successful 510(k) FDA approvals for medical devices such as the Monarch Bronchoscopy System and TMINI robotic platform. His technical presentations, including his work on MBSE advancements at the INCOSE IS 2023 conference, underscore his role as a thought leader in his domain. His contributions have not only driven innovation but also positioned him as a prominent figure in the medical robotics field.

Publications

“Advanced Sensor Technologies in Autonomous Robots: Improving Real-time Decision Making and Environmental Interaction” – Published in International Journal of Innovative Research and Creative Technology, December 2024. Part of ISSN: 2454-5988. 🌐
Cited by: Articles in progress.
“Elevating Systems Engineering Through Digital Transformation for Interconnected Systems” – Published in International Journal of Leading Research Publication, December 2024. Part of ISSN: 2582-8010. 🔗
Cited by: Articles in progress.
“Engineering the Future: Mastering Systems Design and Resilience” – Published by Eliva Press, November 2024. ISBN: 978-99993-2-174-7. 📚
Cited by: Not available.
“Model-Based Systems Engineering (MBSE) in Medical Device Development: Enhancing Efficiency and Quality” – Presented at INCOSE Symposium 2023, July 2023. 🤖
Cited by: Research in progress.
“The Integration of Robotic Systems in Healthcare Infrastructure: Challenges and Solutions” – Published in Scientific Research and Community, April 29, 2022. Part of ISSN: 2755-9866. 🩺
Cited by: 14 articles.
“System Integration Failures and Their Impact on Patient Safety in Critical Care Settings” – Published in International Journal of Scientific Research in Engineering and Management (IJSREM), October 2021. Part of ISSN: 2582-3930. 🛠️
Cited by: 10 articles.
“The Role of Robotic Systems in Minimally Invasive Surgery: Benefits, Risks, and Future Directions” – Published in International Journal of Scientific Research in Engineering and Management (IJSREM), March 2021. Part of ISSN: 2582-3930. 🦾
Cited by: 18 articles.

Conclusion

Shashank Pasupuleti embodies excellence in engineering, with a career that bridges cutting-edge technology and real-world medical applications. His dedication to advancing healthcare robotics and medical device engineering has led to significant industry contributions, including successful FDA approvals and innovative system designs. With a strong focus on research, leadership, and compliance, Shashank continues to push the boundaries of what is possible in the realm of medical technology. His expertise and achievements make him a deserving candidate for the Best Researcher Award, reflecting his impact on the field and the broader community.

Abdellatif Sadeq | Mechanical | Best Researcher Award

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Assist Prof Dr. Abdellatif Sadeq | Mechanical | Best Researcher Award

Ph.D., Qatar University, Qatar

🌍 Dr. Abdellatif Mohammad Sadeq, born on April 30, 1993, in Jordan, currently resides in Doha, Qatar. Fluent in both Arabic and English, Dr. Sadeq is a distinguished mechanical engineer with a robust academic background and a dedication to teaching and research. He has over eight years of teaching experience and currently serves as the Dean of Academic Affairs and a mechanical engineering lecturer at Qatar Naval Academy. Dr. Sadeq’s research focuses on energy and automotive engineering, emphasizing sustainable energy solutions.

Profile

Google Scholar

 

Education🎓

🎓 Dr. Abdellatif Mohammad Sadeq holds a B.Sc. (2015), M.Sc. (2018), and Ph.D. (2022) in Mechanical Engineering from Qatar University. Additionally, in September 2023, he earned a second M.Sc. in Hybrid and Electric Vehicles Design and Analysis from Skill-Lync Online Platform in Chennai, India. His academic journey is marked by numerous accolades, including certificates of excellence and distinction.

Experience🏫

💼 Dr. Sadeq’s professional journey began in 2015 as a Graduate Teaching and Research Assistant at Qatar University, where he contributed significantly to teaching and research in mechanical engineering. Since October 2023, he has been serving as the Dean of Academic Affairs and a lecturer at Qatar Naval Academy. His roles have encompassed curriculum development, faculty training, and strategic academic planning, ensuring high standards of education and research excellence.

Research Interests📚

🔬 Dr. Sadeq’s research interests lie in energy and automotive engineering, with a particular focus on internal combustion engines, alternative fuels, hydrogen energy, renewable energy utilization, and energy storage techniques. He specializes in system modeling, simulation, and the design of hybrid and electric vehicles. Additionally, he contributes to heat transfer and HVAC systems, integrating management principles to tackle complex engineering challenges.

Awards🏆

🏅 Throughout his academic and professional career, Dr. Sadeq has received numerous awards and certificates, recognizing his excellence in both teaching and research. His commitment to academic and professional growth is evident through his continuous pursuit of knowledge and innovative solutions in mechanical engineering.

Publications

📚 Dr. Abdellatif Mohammad Sadeq’s extensive research work is well-documented in his publications, which are available on platforms like Google Scholar, ResearchGate, and ORCID. Here are some notable publications:

“Enhanced Synthesis and Performance Analysis of Castor Oil-Based Biolubricant for Two-Stroke Engines” [Submitted on May 2024].

“Advanced Demand Response Strategy and Development of Real-Time Data Acquisition Using The IoT-based Grasshopper Optimization Algorithm” [Submitted on May 2024].

“Optimization of In-Cylinder Pressure Prediction in Biodiesel Engines Using Deep Neural Networks” [Submitted on May 2024].

“Performance improvement of phase change material (PCM) based shell-and-tube type latent heat energy storage system utilizing different shaped fins” [Submitted on May 2024].

“Improved Hydrogen Production and Electrical Power in Photovoltaic-Thermal by Using Micro-jet Array Cooling System” [Submitted on May 2024].