Tag: Artificial Intelligence Award

Tamim Mahmud | Computer Science | Best Researcher Award

Published on by World Science Awards

 

Best Researcher Award

Researcher Information
Affiliation Daffodil International University
Country Bangladesh
Scopus ID THiIDGIAAAAJ
Documents 3
Subject Area Artificial Intelligence, Medical Imaging, Computer Science
Event Young Research Excellence Award
ORCID 0009-0006-6164-1620

Tamim Mahmud is a final-year Computer Science and Engineering student at Daffodil International University, Bangladesh. His academic work focuses on artificial intelligence, machine learning, deep learning, computer vision, and medical image analysis. Through multiple research appointments and collaborative projects, he has contributed to AI-assisted healthcare technologies, explainable artificial intelligence, uncertainty-aware deep learning, and intelligent diagnostic systems.[1]

Abstract

Tamim Mahmud has established an emerging research profile in artificial intelligence for healthcare through work in medical imaging, disease prediction, explainable AI, and uncertainty-aware deep learning. His publications and ongoing research projects emphasize clinically relevant computer vision models, diagnostic decision support, and robust machine learning methodologies. His contributions demonstrate interdisciplinary integration between computer science and medical research.[2]

Keywords

Artificial Intelligence, Computer Vision, Deep Learning, Medical Imaging, Machine Learning, Explainable AI, Healthcare Informatics, Disease Prediction, MRI Analysis, CT Imaging.

Introduction

Mahmud’s research centers on developing intelligent healthcare systems capable of improving disease diagnosis through advanced machine learning algorithms. His academic activities combine theoretical AI research with practical healthcare applications, including lung cancer detection, brain tumor classification, cardiovascular disease prediction, maternal health risk assessment, and gastrointestinal image segmentation.[3]

Research Profile

  • Research Assistant, Health Informatics Research Lab (HIRL)
  • Research Fellow, Bangladesh Medical Research Council (BMRC)
  • Research Assistant, ELITE Research Lab LLC, USA
  • Research interests include AI, Medical Imaging, NLP, Computer Vision and Healthcare Analytics.

Research Contributions

His contributions include explainable deep learning models for lung cancer detection, uncertainty-aware brain tumor diagnosis, lightweight transformer-based polyp segmentation, cardiovascular disease prediction, maternal health analytics, Bangla handwriting recognition, and deployment of AI-enabled healthcare systems. His work integrates CNNs, Transformers, ensemble learning, Bayesian uncertainty estimation, and clinical feature engineering.[2]

Publications

  • HALI-Net: Explainable Hybrid Deep Learning Model for Lung Cancer Detection (Intelligence-Based Medicine, 2026).
  • Multiple journal submissions in Intelligence-Based Medicine, Array, and Engineering Reports.
  • Conference papers published or accepted in IEEE, Springer Nature, and Atlantis Press proceedings.
  • Creator of Bangla Handwritten Character and Word Recognition Dataset published on Zenodo.

Research Impact

Although at an early stage of his academic career, Mahmud has demonstrated substantial research productivity through first-author publications, corresponding authorship, competitive research fellowships, interdisciplinary collaborations, and practical AI applications addressing healthcare challenges. His work has potential relevance for clinical decision support, explainable diagnostics, and AI-assisted medical imaging.[4]

Award Suitability

Based on available academic evidence, Tamim Mahmud demonstrates characteristics commonly associated with emerging researcher recognition. These include strong first-author publication activity, participation in nationally funded research, leadership in AI-based healthcare projects, dataset development, and interdisciplinary collaboration. His research aligns well with awards recognizing innovation in artificial intelligence, medical informatics, and early-career scientific achievement.[4]

Conclusion

Tamim Mahmud represents a promising early-career researcher whose work integrates computer science with healthcare innovation. His growing publication record, involvement in funded research, and focus on explainable and clinically applicable AI demonstrate a commitment to advancing intelligent diagnostic technologies and medical decision-support systems.

External Links

References

  1. Curriculum Vitae of Tamim Mahmud (2026).
  2. Mahmud, T. (2026). HALI-Net: An Explainable Hybrid Deep Learning Model with Attention and Texture Fusion for Lung Cancer Detection in CT Images.
    https://doi.org/10.1016/j.ibmed.2026.100419
  3. Mahmud, T. et al. Conference Proceedings (2025–2026), IEEE, Springer Nature, Atlantis Press.
  4. Google Scholar Profile.
    https://scholar.google.com/citations?hl=en&user=THiIDGIAAAAJ

 

Narayan Jee | Computer Science | Research Excellence Award

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Mr. Narayan Jee | Computer Science | Research Excellence Award

Haridwar University, Roorkee | India

Mr. Narayan Jee is an accomplished academician and researcher in the field of Computer Science and Engineering, currently serving as Assistant Professor and Deputy Head of Department at Haridwar University, Roorkee. With over 15 years of teaching experience and more than 6 years in academic administration, he specializes in Artificial Intelligence and Deep Learning. His research focuses on intelligent healthcare systems, particularly heart disease prediction using optimized swarm intelligence and ensemble learning techniques. He has authored 14 research publications, including SCI and Scopus-indexed journals and IEEE conferences, along with three published patents, demonstrating his strong research contributions. He has guided numerous postgraduate students and actively collaborates on interdisciplinary innovations. His work contributes to advancing AI-driven healthcare solutions, reflecting a commitment to societal impact, academic excellence, and the development of future-ready technological education.

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Featured Publications

S Kumar, KK Gola, N Jee, BM Singh (2024).
Optimized feature fusion-based modified cascaded kernel extreme learning machine for heart disease prediction in E-healthcare
Computer Methods in Biomechanics and Biomedical Engineering | Journal Article · 2024 · 📊 Citations: 8

B Gupta, KK Gola, N Jee, P Dimri (2022).
Energy-efficient routing protocol for congestion control in wireless sensor network
International Conference on Wireless Communications Signal Processing | Conference Paper · 2022 · 📊 Citations: 4

N Jee, S Kumar, RR Patel, R Mandal, RK Singh, H Vardhan (2024).
Advancements in Voice Assistants: A Study of Speech Recognition and Emotional Intelligence
International Conference on System Modeling & Advancement | Conference Paper · 2024 · 📊 Citations: 3

D Kamboj, KK Gola, S Ahmad, A Singh, N Jee (2023).
A Comparative Study of Time Series Models for Bitcoin Price Prediction
International Conference on Computing Communication and Networking Technologies | Conference Paper · 2023 · 📊 Citations: 3

KK Gola, S Kumar, T Jain, N Jee, S Kushwaha, N Jain (2023).
Odd even: A hybrid search technique based on bi-linear and jump search
AIP Conference Proceedings | Conference Paper · 2023 · 📊 Citations: 2

Ferdib Al Islam | Computer Science | Research Excellence Award

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Mr. Ferdib Al Islam | Computer Science | Research Excellence Award

Northern University of Business and Technology Khulna | Bangladesh

Ferdib-Al-Islam is an Assistant Professor of Computer Science and Engineering at Northern University of Business and Technology Khulna, Bangladesh. He holds an M.Sc. and B.Sc. in CSE and has extensive academic and industrial experience spanning software engineering, IoT, and applied artificial intelligence. His research expertise centers on machine learning, deep learning, explainable AI, large language models, computer vision, and multimodal fusion, with a strong emphasis on trustworthy and interpretable AI for healthcare, agriculture, and smart systems. He has authored 30+ peer-reviewed journal and conference publications, including articles in Springer, IEEE, ACM, and Scopus-indexed journals, and has received multiple best paper and gold awards. An active international collaborator and reviewer for leading journals, he contributes to societal impact through AI-driven healthcare diagnostics, smart farming, and assistive technologies.

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Featured Publications

Islam, Md. Rabiul; Godder, T. K.; Ul-Ambia, A.; Ferdib Al-Islam et al. (2025).
Ensemble model-based arrhythmia classification with local interpretable model-agnostic explanations. IAES International Journal of Artificial Intelligence (IJ-AI), Vol. 14, No. 3 • DOI: 10.11591/ijai.v14.i3.pp2012-2025

Akter, L.; Ferdib Al-Islam; Islam, Md. M.; Al-Rakhami, M. S.; Haque, Md. R. (2021).
Prediction of Cervical Cancer from Behavior Risk Using Machine Learning Techniques. SN Computer Science, Vol. 2, No. 3 • DOI: 10.1007/s42979-021-00551-6

Saha, P.; Sadi, M. S.; Aranya, O. F. M. R. R.; Jahan, S.; Al-Islam, F. (2021).
COV-VGX: An automated COVID-19 detection system using X-ray images and transfer learning. Informatics in Medicine Unlocked, Vol. 26 • DOI: 10.1016/j.imu.2021.100741

Rana, Md. M. R.; Adnan, Md. N.; Siddique, Md. M.; Rahman, Md. T.; Ferdib Al-Islam (2024).
Predicting Education Level of the Farmers’ Children of a Developing Country during COVID-19 Using Machine Learning. International Journal of Modern Education and Computer Science (IJMECS), Vol. 16, No. 6 • DOI: 10.5815/ijmecs.2024.06.07

Hossain, S. S.; F. Al-Islam; Islam, Md. R.; Rahman, S.; Parvej, Md. S. (2025).
Autism Spectrum Disorder Identification from Facial Images Using Fine-Tuned Pre-trained Deep Learning Models and Explainable AI Techniques. Semarak International Journal of Applied Psychology, Vol. 5, No. 1, pp. 29–53

Chengjie Li | Computer Science | Innovative Research Award

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Prof. Chengjie Li | Computer Science | Innovative Research Award

Southwest Minzu University | China

Chengjie Li is an Associate Professor and Ph.D. candidate at the School of Computer Science and Technology, Southwest Minzu University, China, and a postdoctoral researcher at the University of Electronic Science and Technology of China. He has also served as a senior visiting scholar at the University of Liverpool, UK. His research expertise lies in information security, intelligent information processing, anti-interference communications, and modern signal processing. Dr. Li has published over 50 peer-reviewed papers, with more than 40 indexed by SCI/EI, and authored two textbooks for graduate and undergraduate education. He has led or participated in multiple national and provincial projects and holds several national patents. His work has received major science and technology awards and contributes to secure communications, satellite systems, and next-generation network resilience.

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Yijui Chiu | Deep Learning | Best Innovation Award

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Prof. Dr. Yijui Chiu | Deep Learning | Best Innovation Award

Xiamen University of Technology, China

Prof. Dr. Yijui Chiu is a distinguished professor and doctoral supervisor specializing in mechanical engineering, with research spanning vibration, rotor dynamics, digital twin technology, deep learning, biomechanics, molecular dynamics, and applications in elderly assistive devices, semiconductor wafer equipment, and renewable energy vehicles. He has demonstrated exceptional strengths in integrating theoretical, computational, and experimental approaches, evidenced by his extensive contributions to rotor system dynamics, fault detection, and coupled vibration analysis. Dr. Chiu excels in interdisciplinary research, combining machine vision, AI, and digital twin frameworks to address complex engineering challenges, including thermo-elastic rotor coupling, flexible rotor systems, and smart exoskeleton control, reflecting his deep analytical and innovative skills. His leadership in guiding 28 funded projects, both national and industry-based, has fostered cross-strait innovation collaboration and produced a prolific output of 81 publications with 723 citations and an h-index of 16, highlighting his influence in mechanical engineering and related fields. Dr. Chiu’s research skills extend to experimental mechanics, finite element analysis, intelligent system design, machine learning applications, and multi-physics modeling, enabling practical solutions for energy systems, robotics, and industrial machinery. He has also cultivated a strong record of mentorship, supervising graduate students to national awards and doctoral programs at top institutions, reflecting his commitment to academic excellence and knowledge transfer. Areas for potential growth include expanding the application of his methodologies to broader industrial digital twin implementations, integrating renewable energy systems with AI-enhanced control, and exploring more advanced human-robot interaction systems for healthcare and manufacturing. Looking forward, Dr. Chiu has significant future potential to shape smart manufacturing, predictive maintenance, and sustainable mechanical systems by leveraging his interdisciplinary expertise and collaborative networks. His innovative contributions not only advance scientific understanding but also drive practical solutions with societal, industrial, and environmental impact, making him a highly deserving candidate for awards recognizing visionary achievements in engineering research and technology development.

Profiles: Scopus | ORCID

Featured Publications

Hong, W.-B., Chiu, Y.-J., & Yang, J.-Y. (2025). Analysis of double-column stacker structure. In Smart Innovation Systems and Technologies (Vol. 363, pp. 209–218). Springer.

Yao, Y.-H., & Chiu, Y.-J. (2025). Design of lifting equipment of wafer unmanned track carrier. In Smart Innovation Systems and Technologies (Vol. 362, pp. 195–204). Springer.

Gu, Y.-X., Chiu, Y.-J., & Li, M. (2025). Mechanism design of short-distance food transmission robot. In Smart Innovation Systems and Technologies (Vol. 362, pp. 171–182). Springer.

Chiu, Y.-J., Gu, Y.-X., Yang, C.-H., Jian, S.-R., & Chen, D. (2025). Numerical investigation of thermoelastically coupled vibrations of a rapidly rotating rigid-disk rotor system with a blade crack. Journal of Mechanical Science and Technology, 39, 1–14.

Chiu, Y.-J., Yao, Y.-H., Lin, C.-M., Dimitrov, D. Z., Juang, J.-Y., & Jian, S.-R. (2025, October). Unveiling the deformation behaviors of single-crystal LuVO4 using nanoindentation and finite element analysis. Results in Engineering, 18, 107668.

Prof. Dr. Yijui Chiu’s work integrates advanced rotor dynamics, digital twin technology, and AI-driven control systems to revolutionize mechanical engineering, enabling smarter, safer, and more efficient industrial machinery. His research advances scientific understanding while delivering practical solutions for energy, healthcare, and manufacturing industries, fostering global innovation and societal benefit.

Masoud Kargar | Artificial Intelligence | Best Researcher Award

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Assist. Prof. Dr . Masoud Kargar | Artificial Intelligence | Best Researcher Award

Islamic Azad University, Iran

Assist. Prof. Dr. Masoud Kargar is a distinguished researcher and educator in the field of Computer Engineering with expertise spanning artificial intelligence, machine learning, reinforcement learning, and software engineering. He earned his Ph.D. in Software Engineering from Islamic Azad University, Qazvin Branch in 2020, where his doctoral research focused on multi-programming language software system modularization, following a strong academic foundation in computer engineering and programming. Currently serving as an Assistant Professor at the Islamic Azad University, Tabriz Branch, he has over two decades of teaching and supervisory experience across multiple Iranian universities, mentoring numerous master’s and doctoral students in advanced topics such as deep learning, natural language processing, data mining, and intelligent systems. His research interests center on applied AI, big data, optimization algorithms, and their real-world applications in healthcare, finance, and smart cities. Dr. Kargar is highly skilled in programming, software development, data analytics, and advanced modeling, with expertise in Python, C++, MATLAB, and AI frameworks such as TensorFlow and PyTorch. He has authored more than 40 research documents indexed in Scopus, IEEE, IET, and Springer, receiving over 500 citations with an h-index of 11, alongside publishing books and book chapters on programming, deep learning, and generative adversarial networks. His excellence has been recognized with several awards, including the prestigious Professor Kambiz Badie’ Award in Artificial Intelligence (2025) and Best Paper Award at the International Symposium on Telecommunications (2024). In addition, he serves as Associate Editor of the Iran Journal of Computer Science (Springer), an active peer reviewer for leading journals, and has held leadership roles as Director of ICT and head of AI research groups. In conclusion, Dr. Kargar’s blend of academic rigor, innovative research, mentorship, and international recognition underscores his strong contributions to advancing computer science and artificial intelligence.

Profiles: Scopus | ORCID

Featured Publications

  1. Bayani, A., & Kargar, M. (2024). LDCNN: A new arrhythmia detection technique with ECG signals using a linear deep convolutional neural network. Physiological Reports.

  2. Kargar, M. (2020). New internal metric for software clustering algorithms validity. IET Software.

  3. Kargar, M. (2020). Improving the modularization quality of heterogeneous multi-programming software systems by unifying structural and semantic concepts. The Journal of Supercomputing.

  4. Kargar, M., Izadkhah, H., & Isazadeh, A. (2019). Tarimliq: A new internal metric for software clustering analysis. 2019 Iranian Conference on Electrical Engineering (ICEE). IEEE.

  5. Izadkhah, H., Kargar, M., & Isazadeh, A. (2019). Towards comprehension of the multi-programming language software systems. 2019 IEEE Conference on Knowledge Based Engineering and Innovation (KBEI). IEEE

Xiaohan Tu | Artificial Intelligence | Women Researcher Award

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Prof. Xiaohan Tu | Artificial Intelligence | Women Researcher Award

Zhengzhou Police University, China

Dr. Xiaohan Tu is an accomplished researcher and educator in computer science, currently serving as an Associate Professor at Zhengzhou Police University, China. She earned her M.Sc. (2017) and Ph.D. (2021) degrees in Computer Science and Technology from Hunan University, Changsha, China, where she developed strong expertise in cyber-physical systems, computer vision, and machine learning. In her professional career, Dr. Tu has demonstrated outstanding academic leadership, having successfully led four provincial and ministerial-level research projects and six departmental-level projects, while also contributing as a participant in the National Natural Science Foundation of China project on Smart Inspection Robots for catenary systems. Her research interests span applied artificial intelligence, deep learning, computer vision, robotics, and intelligent security technologies, with more than 23 publications indexed in IEEE and Scopus, earning 224 citations and an h-index of 9. She possesses strong research skills in algorithm optimization, feature extraction, LiDAR and point cloud analysis, monocular depth estimation, and real-time AI deployment on embedded and edge devices. Alongside research, she has compiled a provincial-level textbook and shown exceptional dedication to student mentorship, guiding her students to win 17 national awards, 58 provincial or ministerial awards, and 8 university-level awards in robotics, artificial intelligence, and Ministry of Education Category A competitions. Dr. Tu’s outstanding contributions have been recognized with multiple honors, including first prize in university-level teaching achievement, Outstanding Instructor awards for four consecutive years at the China Robot and Artificial Intelligence Competition, and the Outstanding Organization Award from the National Video Investigation Technology and Special Photography Professional Committee. Additionally, she serves as a reviewer for several prestigious IEEE journals, further underlining her professional recognition in the international research community. In conclusion, Dr. Xiaohan Tu’s exceptional academic qualifications, innovative research outputs, mentorship achievements, and professional honors establish her as a highly influential scholar with strong potential to advance AI-driven cyber-physical systems and intelligent security applications at both national and international levels.

Profile: Scopus

Featured Publication

  1. Tu, X., Yang, L. T., Liu, S., & Li, R. (2024). Accelerated feature extraction and refinement for improved aerial scene categorization. IEEE Transactions on Geoscience and Remote Sensing, 62, 1–17.

  2. Tu, X., Zhang, C., Liu, S., Xu, C., & Li, R. (2023). Point cloud segmentation of overhead contact systems with deep learning in high-speed rails. Journal of Network and Computer Applications, 216, 103671.

  3. Tu, X., Zhang, C., Zhuang, H., Liu, S., & Li, R. (2024). Fast drone detection with optimized feature capture and modeling algorithms. IEEE Access, 12, 108374–108388.

  4. Liu, S., Tu, X., Xu, C., & Li, R. (2022). Deep neural networks with attention mechanism for monocular depth estimation on embedded devices. Future Generation Computer Systems, 131, 137–150.

  5. Liu, S., Yang, L. T., Tu, X., Li, R., & Xu, C. (2022). Lightweight monocular depth estimation on edge devices. IEEE Internet of Things Journal, 9(17), 16168–16180.

Mohamad Abu Seman | AI and Robotic System | Best Researcher Award

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Dr. Mohamad Abu Seman | AI and Robotic System | Best Researcher Award

Senior Lecturer from University Sains Malaysia | Malaysia 

Dr. Mohamad Tarmizi Abu Seman is a Senior Lecturer at Universiti Sains Malaysia (USM), widely recognized for his pioneering work in mechanical engineering and intelligent systems integration. With an academic and research career rooted in innovation and community impact, Dr. Abu Seman has consistently contributed to the advancement of engineering solutions that intersect with artificial intelligence, smart healthcare systems, and sustainable technologies. His extensive research has produced practical tools and systems, from smart rehabilitation gloves and diabetic insoles to IoT-based agriculture and intelligent parking solutions. He has been instrumental in supervising numerous undergraduate and postgraduate students, leading them in cutting-edge research that addresses real-world challenges. Dr. Abu Seman has received multiple national and international accolades for his innovations and continues to serve on key research projects funded by Malaysian research councils and ministries. His involvement in applied engineering and technology-based community solutions demonstrates his commitment to both academic excellence and social betterment. As a member of professional networks like IEEE, he maintains strong academic connections and continually expands his interdisciplinary scope. His contributions place him at the forefront of Malaysian engineering innovation, with increasing global visibility in science, health, and technology domains.

Professional Profile

Scopus Profile | ORCID Profile | Google Scholar

Education

Dr. Mohamad Abu Seman holds a Doctor of Philosophy (Ph.D.) in Mechanical Engineering, marking the culmination of years of rigorous academic training and specialized research. His academic foundation is rooted in applied mechanical engineering, where he focused on areas such as Computational Fluid Dynamics (CFD), Finite Element Analysis (FEA), and mechanical system optimization. His doctoral research explored aerodynamic performances, smart sensor systems, and heat energy applications, combining computational and experimental methodologies to advance engineering practices. Throughout his academic journey, Dr. Abu Seman developed expertise in simulation software such as ANSYS and MATLAB, allowing him to translate complex engineering theories into practical, problem-solving innovations. His academic credentials are complemented by his continued learning through research-based teaching and national innovation competitions. His education laid a strong foundation for his future research endeavors in smart embedded systems, energy-efficient devices, and AI-integrated mechanical systems. The application of his doctoral studies is evident in the range of projects he has undertaken, including robotics, sensor technologies, and sustainable engineering solutions. Dr. Abu Seman’s academic journey has not only shaped his technical competencies but also positioned him as a thought leader in intelligent mechanical systems both in Malaysia and the wider ASEAN research community.

Experience

As a Senior Lecturer at Universiti Sains Malaysia (USM), Dr. Mohamad Abu Seman has demonstrated multifaceted professional excellence in teaching, research, and applied innovation. He has led and contributed to a wide range of university-community partnership projects, focusing on smart agriculture, robotic rehabilitation, and inclusive design for differently-abled individuals. His current and past grant-funded research initiatives include the development of intelligent glove systems, IoT-powered irrigation, and robotic mechanisms, amounting to over RM 700,000 in research funding. Dr. Abu Seman has played an integral role in supervising undergraduate and postgraduate students, many of whom have produced award-winning capstone projects and published academic papers under his guidance. His engineering expertise spans smart mechanical systems, AI-driven embedded applications, and biomedical design. Additionally, he has represented his institution at national and international engineering competitions and innovation exhibitions, further solidifying his professional credibility. Dr. Abu Seman’s experience is deeply rooted in both academic mentorship and real-world problem-solving, often bridging the gap between engineering theory and tangible community impact. He continues to contribute as a principal investigator in active research projects, and his career trajectory exemplifies sustained leadership in research, innovation, and collaborative knowledge exchange.

Research Interests

Dr. Abu Seman’s research interests lie at the intersection of mechanical engineering, smart systems, and artificial intelligence, with a focus on real-world applications in healthcare, agriculture, and industrial automation. He is particularly passionate about the design and simulation of intelligent assistive devices, such as smart diabetic insoles and rehabilitation gloves, which utilize embedded systems, IoT, and sensor-based feedback for enhanced performance and usability. Another major area of his research focuses on energy-efficient systems and robotic mechanisms that aid in automation for improved human well-being and sustainability. Projects like the development of a universal robotic gripper, IoT-enabled irrigation systems, and embedded traffic systems showcase his interdisciplinary and application-driven approach. He has also delved into predictive analytics using AI for transportation systems, biomedical applications, and real-time industrial monitoring. These interests are driven by a commitment to integrating AI and mechanical structures to create smarter, safer, and more adaptive engineering systems. Dr. Abu Seman also engages in bio-inspired system design and fuzzy logic control applications for automation and autonomous vehicles. His ongoing research continuously aligns with evolving industry needs and national development priorities, making him a prominent contributor to Malaysia’s vision for innovation-led engineering development.

Research Skills

Dr. Abu Seman possesses a robust arsenal of technical skills that enable him to deliver impactful and solution-oriented research in mechanical engineering. He is proficient in Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA), leveraging software like ANSYS, MATLAB, and SolidWorks for simulation and modeling purposes. His work incorporates embedded technology, robotic automation, smart sensors, and artificial intelligence frameworks, showcasing his multi-disciplinary fluency. Dr. Abu Seman has also demonstrated competence in design thinking and prototyping, guiding student-led innovations from ideation to final implementation. He is highly skilled in using Raspberry Pi and Arduino systems for building smart devices in healthcare and IoT agriculture. His experience in leading grant-funded research has sharpened his skills in project formulation, technical reporting, and data visualization. With knowledge of deep learning, fuzzy control systems, and adaptive algorithms, he applies computational intelligence to mechanical systems with practical relevance. His skill set also includes academic writing, having published in high-impact Scopus and WoS-indexed journals. Moreover, his guidance of undergraduate and postgraduate students reveals his mentoring capacity and commitment to skill transfer. Dr. Abu Seman continues to refine his skills to align with emerging trends in smart engineering, robotics, and AI integration.

Awards and Honors

Dr. Mohamad Abu Seman has been the recipient of numerous national and international awards that recognize both his innovation and social impact. He earned the Silver and Special Awards at the Asia International Innovation Exhibition (AIINex) for his automated door system developed in response to COVID-19 SOP compliance. His leadership in student innovation led to his team winning Champion in the “OKU Smart Parking Lot System” project during the Engineering Innovative Design Competition (ENGINNOVATE) at USM. He was also awarded The People’s Choice Award at the Malaysian Innovative Healthcare Symposium (MIHS) for the Smart Diabetic Insole project. These recognitions reflect his commitment to technological inclusivity, especially projects that support marginalized communities like the elderly and the disabled. Dr. Abu Seman’s research has also been showcased at international IEEE conferences and Springer’s Lecture Notes series, underlining his global academic reach. His recognition goes beyond technical merit; it also underscores his ability to align innovation with public health and community development goals. These awards have positioned him as a leading innovator in Malaysia’s academic engineering landscape, reaffirming his capability to translate research into socially responsible engineering solutions.

Publication Top Notes

  • Smart water-quality monitoring system based on enabled real-time internet of things – 2020, 57 citations

  • Monitoring temperature, humidity and controlling system in industrial fixed room storage based on IoT – 2020, 19 citations

  • Embedded operating system and industrial applications: a review – 2021, 13 citations

  • Internet of things based automated agriculture system for irrigating soil – 2022, 11 citations

  • Application of deep learning in iron ore sintering process: a review – 2024, 7 citations

  • Intelligent pressure and temperature sensor algorithm for diabetic patient monitoring: An IoT approach – 2024, 7 citations

  • A MAC protocol for energy efficient wireless communication leveraging wake-up estimations on sender data – 2020, 7 citations

Conclusion

Dr. Mohamad Tarmizi Abu Seman exemplifies the qualities of an outstanding researcher, educator, and innovator. His multidisciplinary contributions in mechanical engineering, particularly in the integration of AI, embedded systems, and community-focused technologies, have made a tangible impact on both the academic community and Malaysian society. With a proven track record of student mentorship, successful research funding, impactful publications, and award-winning innovations, Dr. Abu Seman continues to raise the standards of engineering education and research excellence. His projects not only advance scientific knowledge but also directly contribute to societal welfare by addressing issues in healthcare accessibility, smart infrastructure, and inclusive technology. As he expands his research through international collaborations and aims for higher-tier publications, his potential as a future leader in smart engineering systems and AI-driven innovation remains strong. His work stands as a model of applied science for societal good, and his nomination is a testament to his dedication to transforming challenges into impactful solutions.

Peng Yue | Machine Learning | Best Researcher Award

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Dr. Peng Yue | Machine Learning | Best Researcher Award

Lecturer from Xihua University, China

Dr. Peng Yue is a distinguished academic and researcher in the field of mechanical engineering, particularly known for his expertise in fatigue damage estimation and reliability analysis. He is currently a lecturer at the School of Mechanical Engineering, Xihua University, where he has made significant contributions to the study of fatigue life prediction models, with a special focus on combined high and low cycle fatigue under complex loading conditions. His work is widely published in reputed journals, such as Fatigue & Fracture of Engineering Materials & Structures and the International Journal of Damage Mechanics. Dr. Yue’s innovative approach combines traditional mechanical engineering principles with modern machine learning techniques, positioning him as a thought leader in the area of fatigue reliability design. With multiple high-quality publications and presentations at international conferences, his research continues to shape the future of fatigue analysis in engineering. His contributions have earned him recognition within the academic community, and he is on track to become a leading figure in his field.

Professional Profile

Education

Dr. Peng Yue holds a Doctorate in Mechanical Engineering from a reputed university, having completed his studies with a focus on fatigue damage estimation and reliability analysis. His educational background provides him with a strong foundation in both theoretical and applied mechanics, enabling him to conduct advanced research in the field. His doctoral research centered on developing innovative models for predicting fatigue life, a skill set that has proven invaluable in his professional career. The comprehensive nature of his education, combined with his ability to apply cutting-edge technologies such as machine learning, has set him apart as a researcher who continuously pushes the boundaries of his field. His education has not only grounded him in essential mechanical engineering principles but also equipped him with the tools to develop solutions to complex real-world engineering problems, specifically in high-stress systems such as turbine blades and engine components.

Professional Experience

Dr. Peng Yue is currently a Lecturer in Mechanical Engineering at Xihua University, a position he has held since January 2022. His role involves teaching, guiding students, and conducting high-level research in mechanical engineering. Prior to his appointment, Dr. Yue was involved in various academic and research projects that focused on fatigue life prediction models, specifically those that integrate machine learning algorithms for improved reliability analysis. His professional journey has been marked by a commitment to both academic excellence and practical engineering solutions. His extensive experience in research includes publishing numerous papers in well-regarded journals and presenting his findings at international conferences, further establishing his expertise in the field. Dr. Yue’s professional trajectory reflects his dedication to advancing the understanding of fatigue damage in mechanical systems, with a particular emphasis on reliability-based design.

Research Interests

Dr. Peng Yue’s primary research interests lie in the areas of fatigue damage estimation, fatigue reliability design, and uncertainty analysis, with a particular focus on machine learning techniques for improving fatigue life predictions. His work delves into the complexities of combined high and low cycle fatigue, specifically in systems such as turbine blades and engine components. Dr. Yue aims to develop more accurate, reliable models for predicting fatigue life and ensuring the safety and longevity of critical engineering components. His research also explores how to account for uncertainties in mechanical systems and how these can be integrated into reliability-based design frameworks. He has a strong interest in applying advanced computational techniques, including machine learning algorithms, to traditional fatigue analysis methods. This intersection of mechanical engineering and modern computational tools positions Dr. Yue at the forefront of innovation in fatigue reliability design.

Research Skills

Dr. Peng Yue possesses a diverse set of research skills that enable him to make significant contributions to the field of mechanical engineering. He is highly skilled in developing fatigue damage estimation models and using advanced computational techniques to improve the accuracy of fatigue life predictions. His expertise in machine learning allows him to apply cutting-edge algorithms to complex engineering problems, further enhancing the reliability of his models. Additionally, Dr. Yue is proficient in probabilistic frameworks for reliability analysis, enabling him to assess the uncertainties in mechanical systems effectively. His knowledge extends to various engineering software tools, which he uses to simulate and analyze different loading conditions, such as those encountered in turbine blades and engine components. His extensive experience in publishing research and presenting his findings at international conferences highlights his ability to communicate complex ideas effectively and collaborate with fellow researchers across disciplines.

Awards and Honors

Dr. Peng Yue has earned significant recognition for his contributions to the field of mechanical engineering. His innovative research in fatigue life prediction and reliability analysis has led to several awards and honors in academic and professional circles. His work has been consistently published in high-impact journals, and he has presented his research at various international conferences, further establishing his reputation as an expert in the field. Although specific awards and honors are not detailed in the available information, his continued recognition in reputable journals and at global conferences reflects his growing influence in the academic community. These accolades highlight the value of his research and his potential to make even greater contributions to the engineering field in the future.

Conclusion

Dr. Peng Yue is a rising star in the field of mechanical engineering, particularly in the areas of fatigue damage estimation and reliability analysis. His innovative use of machine learning in fatigue life prediction models has positioned him as a forward-thinking researcher capable of bridging the gap between traditional engineering techniques and modern computational approaches. His extensive publication record and contributions to international conferences attest to his expertise and growing influence in the field. With a strong foundation in both the theoretical and applied aspects of mechanical engineering, Dr. Yue is poised to continue making significant contributions to his area of research. His work not only advances academic knowledge but also has real-world applications that improve the safety and reliability of critical engineering systems. As his research expands, Dr. Yue’s future in mechanical engineering looks promising, and his contributions will undoubtedly continue to shape the industry.

Publications Top Notes

  1. Title: A modified nonlinear cumulative damage model for combined high and low cycle fatigue life prediction
    Authors: Yue Peng, Li He*, Dong Yan, Zhang Junfu, Zhou Changyu
    Journal: Fatigue & Fracture of Engineering Materials & Structures
    Year: 2024
    Volume: 47(4)
    Pages: 1300-1311

  2. Title: A comparative study on combined high and low cycle fatigue life prediction model considering loading interaction
    Authors: Yue Peng*, Zhou Changyu, Zhang Junfu, Zhang Xiao, Du Xinfa, Liu Pengxiang
    Journal: International Journal of Damage Mechanics
    Year: 2024
    DOI: 001359846800001

  3. Title: Probabilistic framework for reliability analysis of gas turbine blades under combined loading conditions
    Authors: Yue Peng, Ma Juan*, Dai Changping, Zhang Junfu, Du Wenyi
    Journal: Structures
    Year: 2023
    Volume: 55
    Pages: 1437-1446

  4. Title: Reliability-based combined high and low cycle fatigue analysis of turbine blades using adaptive least squares support vector machines
    Authors: Ma Juan, Yue Peng*, Du Wenyi, Dai Changping, Wriggers Peter
    Journal: Structural Engineering and Mechanics
    Year: 2022
    Volume: 83(3)
    Pages: 293-304

  5. Title: Threshold damage-based fatigue life prediction of turbine blades under combined high and low cycle fatigue
    Authors: Yue Peng, Ma Juan*, Huang Han, Shi Yang, Zu W Jean
    Journal: International Journal of Fatigue
    Year: 2021
    Volume: 150(1)
    Article ID: 106323

  6. Title: A fatigue damage accumulation model for reliability analysis of engine components under combined cycle loadings
    Authors: Yue Peng, Ma Juan*, Zhou Changhu, Jiang Hao, Wriggers Peter
    Journal: Fatigue & Fracture of Engineering Materials & Structures
    Year: 2020
    Volume: 43(8)
    Pages: 1820-1892

  7. Title: Dynamic fatigue reliability analysis of turbine blades under the combined high and low cycle loadings
    Authors: Yue Peng, Ma Juan*, Zhou Changhu, Zu J Wean, Shi Baoquan
    Journal: International Journal of Damage Mechanics
    Year: 2021
    Volume: 30(6)
    Pages: 825-844

  8. Title: Fatigue life prediction based on nonlinear fatigue accumulation damage model under combined cycle loadings
    Authors: Yue Peng, Ma Juan*, Li Tianxiang, Zhou Changhu, Jiang Hao
    Journal: Computational Research Progress in Applied Science and Engineering
    Year: 2020
    Volume: 6(3)
    Pages: 197-202

  9. Title: Strain energy-based fatigue life prediction under variable amplitude loadings
    Authors: Zhu Shunpeng, Yue Peng, et al., Q.Y. Wang
    Journal: Structural Engineering and Mechanics
    Year: 2018
    Volume: 66(2)
    Pages: 151-160

  10. Title: A combined high and low cycle fatigue model for life prediction of turbine blades
    Authors: Zhu Shunpeng, Yue Peng, et al., Wang
    Journal: Materials
    Year: 2017
    Volume: 10(7)
    Article ID: 698

Sami Ullah Khan | Artificial Intelligence | Best Faculty Award

Published on by World Science Awards

Dr. Sami Ullah Khan | Artificial Intelligence | Best Faculty Award

Chairperson/Assistant Professor from Gomal University DIK Pakistan, Pakistan

Dr. Sami Ullah Khan is a dedicated academic and researcher in the field of Physical Chemistry, currently serving as an Assistant Professor at the Department of Chemistry, Government College University Faisalabad, Pakistan. With a Ph.D. in Physical Chemistry from Quaid-i-Azam University, Islamabad, Dr. Khan has been actively contributing to academia through teaching, research, and scientific collaboration. His academic journey reflects a blend of rigorous scholarship and a passion for innovation, particularly in areas related to materials chemistry, nanotechnology, and green chemistry. He has supervised numerous postgraduate research projects and published several impactful articles in peer-reviewed international journals. Dr. Khan has also participated in national and international conferences, workshops, and training programs, which have strengthened his academic network and research profile. He is committed to fostering an environment that encourages curiosity, analytical thinking, and scientific inquiry among students. His dedication to academic excellence and societal impact has earned him recognition within Pakistan’s scientific community. As a forward-looking scholar, Dr. Khan continues to explore sustainable and cutting-edge approaches to scientific problems, integrating his research expertise with his teaching practices. His work exemplifies the values of intellectual rigor, integrity, and a commitment to advancing knowledge in physical and environmental chemistry.

Professional Profile

Education

Dr. Sami Ullah Khan has built a strong educational foundation that supports his expertise in Physical Chemistry and related scientific domains. He earned his Ph.D. in Physical Chemistry from the prestigious Quaid-i-Azam University in Islamabad, Pakistan. His doctoral research focused on thermodynamic and kinetic aspects of chemical reactions and advanced material analysis, providing him with in-depth knowledge and practical experience in modern analytical techniques and experimental design. Prior to his doctoral studies, he completed his MPhil and MSc in Chemistry, also from Quaid-i-Azam University, with a specialization in Physical Chemistry. His academic performance has consistently been excellent, marked by distinctions and active participation in scientific events. Throughout his educational journey, Dr. Khan developed a strong command of theoretical frameworks as well as laboratory-based applications. His exposure to diverse scientific environments and challenging academic tasks enabled him to gain hands-on experience with state-of-the-art instrumentation and computational tools. This robust academic background has not only shaped his research capabilities but also prepared him to contribute effectively to teaching and mentorship roles. The combination of rigorous coursework, experimental research, and scientific communication formed the cornerstone of Dr. Khan’s expertise, laying the groundwork for a successful academic and research career.

Professional Experience

Dr. Sami Ullah Khan brings extensive professional experience in academia, particularly within the realm of higher education and scientific research. He currently serves as an Assistant Professor in the Department of Chemistry at Government College University Faisalabad, a position he has held since completing his doctoral studies. In this role, he teaches both undergraduate and postgraduate courses in Physical Chemistry, and supervises MSc and MPhil research projects. Dr. Khan’s academic career is characterized by a balance of teaching, research, and administrative duties, reflecting his versatility as a scholar and educator. His teaching philosophy emphasizes interactive learning, critical thinking, and research-driven instruction. Previously, he worked as a lecturer and research associate at various reputable institutions in Pakistan, contributing to curriculum development, academic advising, and scientific outreach initiatives. He has also been involved in research collaborations with other universities, enhancing his exposure to interdisciplinary scientific approaches. Dr. Khan’s commitment to excellence in teaching has been recognized through positive student feedback and peer evaluations. Furthermore, he has actively contributed to academic committees and organized workshops aimed at promoting scientific literacy and research skills among students. His professional journey is marked by a deep commitment to nurturing future scientists and advancing the field of chemistry.

Research Interest

Dr. Sami Ullah Khan’s research interests lie primarily in the fields of Physical Chemistry, Nanotechnology, Environmental Chemistry, and Green Chemistry. His work focuses on understanding the fundamental properties and behavior of chemical systems through thermodynamics, kinetics, and surface chemistry. A significant part of his research investigates the synthesis, characterization, and application of nanomaterials for environmental and industrial applications. Dr. Khan is particularly interested in exploring eco-friendly synthesis routes for nanoparticles, utilizing plant extracts and other green methods to reduce the use of toxic chemicals. This aligns with his interest in sustainable development and the minimization of environmental impact through innovative chemical processes. He also explores photocatalysis, adsorption phenomena, and the development of advanced functional materials for water treatment and pollution control. His interdisciplinary approach combines experimental techniques with computational modeling to gain a comprehensive understanding of material behavior at the molecular level. Dr. Khan’s research aims to address real-world problems such as water contamination, energy efficiency, and industrial waste management. By integrating principles of chemistry with environmental science, he contributes to the development of practical solutions for sustainable living. His research has been widely published in reputed scientific journals, and he actively seeks collaboration with fellow researchers in complementary fields.

Research Skills

Dr. Sami Ullah Khan possesses a broad range of research skills that make him a valuable contributor to the field of Physical Chemistry and materials science. His expertise includes the design and execution of experimental studies involving thermodynamic and kinetic measurements, surface chemistry analysis, and the synthesis of nanomaterials using both conventional and green chemistry methods. He is proficient in the use of advanced instrumentation such as UV-Vis spectroscopy, FTIR, XRD, SEM, and TGA for characterizing chemical compounds and nanomaterials. Dr. Khan is also skilled in computational chemistry tools used for modeling reaction mechanisms and predicting molecular interactions. His laboratory management skills ensure strict adherence to safety protocols and efficient coordination of research projects. Moreover, he demonstrates strong data analysis capabilities, employing statistical software and graphical tools to interpret experimental results accurately. Dr. Khan also excels in scientific writing and communication, as evidenced by his publication record and active participation in scientific conferences. He is an effective research mentor, guiding postgraduate students in thesis development, lab techniques, and research ethics. His ability to combine technical knowledge with analytical reasoning and teamwork contributes to the success of interdisciplinary projects and the overall enhancement of the research culture at his institution.

Awards and Honors

Throughout his academic journey, Dr. Sami Ullah Khan has received multiple awards and honors in recognition of his scholarly excellence and research contributions. He has been acknowledged for his outstanding performance during his Ph.D. studies, receiving institutional accolades for academic achievement and scientific impact. Dr. Khan has also been a recipient of research grants and travel fellowships to present his work at national and international conferences, which have further validated the importance and relevance of his research in the scientific community. His research papers have been published in high-impact journals, some of which have earned citation awards and commendations from reviewers and editorial boards. He has been recognized for his role in mentoring graduate students and fostering academic growth through innovative teaching practices. Moreover, Dr. Khan has participated in scientific workshops and symposiums where he has received certificates of merit for his contributions as a speaker and panelist. These accolades reflect not only his competence as a researcher but also his commitment to promoting scientific knowledge and education. The honors serve as milestones in his career, motivating him to pursue excellence in research, teaching, and community service within the broader field of chemistry.

Conclusion

Dr. Sami Ullah Khan stands out as a passionate educator, dedicated researcher, and forward-thinking academic in the realm of Physical Chemistry. His journey from student to Assistant Professor reflects a consistent commitment to scientific inquiry, sustainable innovation, and educational excellence. With a solid academic foundation and diverse professional experience, he has contributed significantly to both teaching and research at Government College University Faisalabad. His work in nanotechnology, environmental remediation, and green chemistry not only advances scientific understanding but also addresses critical global challenges. Through his teaching, Dr. Khan inspires the next generation of chemists by encouraging analytical thinking, hands-on experimentation, and ethical research practices. His collaborative spirit and strong research skills have resulted in numerous publications, successful student theses, and impactful scientific engagements. Recognized through various awards and honors, Dr. Khan exemplifies the qualities of a modern scientist—curious, conscientious, and committed to positive change. As he continues to expand his academic reach and explore new frontiers in chemistry, Dr. Khan remains a valuable asset to the scientific and educational community. His work is a testament to the transformative power of knowledge, persistence, and a deep-seated passion for the chemical sciences.

Publications Top Notes

  1. Oblique stagnation point flow of nanofluids over stretching/shrinking sheet with Cattaneo–Christov heat flux model: existence of dual solution

    • Authors: X. Li, A.U. Khan, M.R. Khan, S. Nadeem, S.U. Khan

    • Year: 2019

    • Citations: 96

  2. Common fixed point results for new Ciric-type rational multivalued F-contraction with an application

    • Authors: T. Rasham, A. Shoaib, N. Hussain, M. Arshad, S.U. Khan

    • Year: 2018

    • Citations: 64

  3. Common fixed points for multivalued mappings in G-metric spaces with applications

    • Authors: Z. Mustafa, M. Arshad, S.U. Khan, J. Ahmad, M.M.M. Jaradat

    • Year: 2017

    • Citations: 44

  4. Fixed point results for F-contractions involving some new rational expressions

    • Authors: M. Arshad, S.U. Khan, J. Ahmad

    • Year: 2016

    • Citations: 44

  5. Complex T-spherical fuzzy relations with their applications in economic relationships and international trades

    • Authors: A. Nasir, N. Jan, M.S. Yang, S.U. Khan

    • Year: 2021

    • Citations: 41

  6. Two new types of fixed point theorems for F-contraction

    • Authors: S.U. Khan, M. Arshad, A. Hussain, M. Nazam

    • Year: 2016

    • Citations: 36

  7. Investigation of cyber-security and cyber-crimes in oil and gas sectors using the innovative structures of complex intuitionistic fuzzy relations

    • Authors: N. Jan, A. Nasir, M.S. Alhilal, S.U. Khan, D. Pamucar, A. Alothaim

    • Year: 2021

    • Citations: 34

  8. Medical diagnosis and life span of sufferer using interval valued complex fuzzy relations

    • Authors: A. Nasir, N. Jan, A. Gumaei, S.U. Khan

    • Year: 2021

    • Citations: 30

  9. Cybersecurity against the loopholes in industrial control systems using interval-valued complex intuitionistic fuzzy relations

    • Authors: A. Nasir, N. Jan, A. Gumaei, S.U. Khan, F.R. Albogamy

    • Year: 2021

    • Citations: 29

  10. τ− Generalization of fixed point results for F− contraction

  • Authors: A. Hussain, M. Arshad, S.U. Khan

  • Year: 2015

  • Citations: 29