Kanta Prasad Sharma | Computer Science | Best Innovation Award

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Dr. Kanta Prasad Sharma | Computer Science | Best Innovation Award

Associate Professor at Amity University Greater Noida Campus, India

Dr. Kanta Prasad Sharma is a seasoned academic and researcher with over 14 years of experience in the field of Computer Science and Engineering. Currently serving as an Associate Professor at Amity University, Uttar Pradesh, he has held various teaching positions across multiple esteemed institutions. His expertise spans a wide range of research areas, including Internet of Things (IoT), Machine Learning, and Artificial Intelligence, among others. In addition to his teaching, Dr. Sharma has made significant contributions to research, authoring numerous patents and publications. His dedication to education and research has earned him recognition from academic peers and institutions.

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Education

Dr. Sharma holds a Ph.D. in Information Technology from Amity University, Rajasthan, awarded in 2019. He completed his MCA from GLA Institute of Technology & Management, Mathura, in 2007, and his BCA from Rajiv Institute of Technology & Management, Mathura, in 2003. His academic background is rooted in a strong foundation in Computer Science, with a commitment to advancing technology through both teaching and research.

Experience

Dr. Sharma’s career in academia spans over a decade, during which he has held various teaching positions. He is currently an Associate Professor at Amity University, Greater Noida Campus, where he has been contributing to the academic community since September 2024. His previous roles include Assistant Professor positions at GLA University, Chandigarh University, GL Bajaj Group of Institutions, Rajiv Academy for Technology & Management, and several other prestigious institutions. He has also served as a Research Coordinator and Head of Departments, overseeing significant academic and research responsibilities. Additionally, Dr. Sharma has engaged with industry as an Industrial Spoc for Samsung Prism Research Project.

Research Interests

Dr. Sharma’s research interests are deeply entrenched in emerging technologies, focusing primarily on Internet of Things (IoT), Machine Learning, Artificial Intelligence, and their applications in real-world problems. His work explores the intersection of these technologies in areas such as smart healthcare, IoT-based systems, predictive models, and automation. Dr. Sharma is also deeply involved in the development of practical solutions through his innovative research, leading to the publication of patents and articles in reputable international journals. His academic work, especially in IoT and AI, aims to address global challenges by creating efficient and scalable solutions.

Awards

Dr. Sharma has been acknowledged for his contributions to both teaching and research in various capacities. His academic excellence is reflected in his strong research gate scores, citation counts, and the number of patents granted to him at national and international levels. In addition to his academic achievements, he has been nominated for multiple awards in recognition of his significant impact on the academic and research community, particularly in fields like Artificial Intelligence, IoT, and Machine Learning.

Publications

Dr. Sharma has contributed to a number of publications in well-known international journals. Below are some of his selected works:

Sharma, K., et al. (2021). “An IoT-Based Autonomous Firefighting Drone Using Machine Learning,” Journal of Internet of Things, 2021.

Sharma, K., et al. (2021). “IoT System for Monitoring Agriculture,” Agricultural Technology Journal, 2021.

Sharma, K., et al. (2021). “IoT-Based Automatic Door Control System,” Journal of IoT and Automation, 2021.

Sharma, K., et al. (2022). “Intelligent Face Recognition Using Deep Recurrent Neural Networks,” AI and Vision Technology Journal, 2022.

Sharma, K., et al. (2022). “IoT-Based Newborn Care System,” International Journal of Health Systems, 2022.

His work has garnered significant attention in the research community, evidenced by citations from other notable scholars in the fields of IoT, AI, and Machine Learning.

Conclusion

Dr. Kanta Prasad Sharma’s career is a testament to his unwavering dedication to education, innovation, and research. With a rich academic background and extensive professional experience, he continues to make significant contributions to the fields of Computer Science and Engineering. His research on IoT, Machine Learning, and Artificial Intelligence is pushing the boundaries of technological applications, and his work has far-reaching implications for industries such as healthcare, agriculture, and automation. As an academic and researcher, Dr. Sharma remains committed to advancing knowledge and nurturing future generations of engineers and researchers.

Quanming Yao | Automated Machine Learning (AutoML) | AI & Machine Learning Award

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Assist. Prof. Dr. Quanming Yao | Automated Machine Learning (AutoML) | AI & Machine Learning Award

Assistant Professor at Department of Electronic Engineering, Tsinghua University, China

Quanming Yao is a world-class researcher in the field of machine learning, holding the position of Assistant Professor in the Department of Electronic Engineering at Tsinghua University. With a strong academic background and extensive experience in deep learning, Yao’s research focuses on creating efficient and parsimonious solutions in machine learning, particularly in deep networks and graph learning. His work aims to enhance interpretability in AI models and has led to groundbreaking advancements, such as the development of EmerGNN, the first deep learning model that interprets drug-drug interaction predictions for new drugs. His contributions have significantly impacted both academia and industry, leading to the commercialization of his methods in the AI unicorn 4Paradigm.

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Education

Yao earned his Ph.D. in Computer Science and Engineering from the Hong Kong University of Science and Technology (HKUST) between 2013 and 2018. Prior to this, he completed his undergraduate studies at Huazhong University of Science and Technology, where he obtained a degree in Electronic and Information Engineering in 2013.

Experience

Before becoming an assistant professor at Tsinghua University in 2021, Yao worked as a researcher and senior scientist at 4Paradigm Inc. in Hong Kong, from June 2018 to May 2021. In his current academic role, he serves as a Ph.D. advisor, leading research in machine learning and AI, with a specific focus on making deep learning models more efficient and interpretable.

Research Interests

Yao’s research interests revolve around the concept of “parsimonious deep learning,” wherein he explores how simple solutions can lead to substantial improvements in machine learning models. His work is especially notable for its emphasis on automated graph learning methods, which has earned him first place in the Open Graph Benchmark, an equivalent to ImageNet in graph learning. He is also dedicated to the development of deep learning methods that provide interpretable results, particularly in domains like drug discovery, where his innovations have had a direct impact on creating a synthetic biology startup, Kongfoo Technology.

Awards

Yao’s exceptional contributions to the field of machine learning have earned him numerous prestigious awards. These include the Inaugural Intech Prize in 2024, the Aharon Katzir Young Investigator Award in 2023, Forbes 30 Under 30 in the Science & Healthcare Category (China) in 2020, and the Google Ph.D. Fellowship in 2016. He was also recognized as one of the World’s Top 2% Scientists in 2023, highlighting his influence in the global research community.

Publications

Yao has published over 100 papers in top-tier international journals and conferences, with a significant citation record (around 12,000 citations and an h-index of 36). His work includes several landmark papers, such as:

Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network, Nature Computational Science, 2023.

AutoBLM: Bilinear Scoring Function Search for Knowledge Graph Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022.

Efficient Low-rank Tensor Learning with Nonconvex Regularization, Journal of Machine Learning Research (JMLR), 2022.

Co-teaching: Robust Training Deep Neural Networks with Extremely Noisy Labels, Advance in Neural Information Processing Systems (NeurIPS), 2018.

These papers showcase his innovative work in the areas of drug interaction prediction, knowledge graph learning, and robust training of deep neural networks, significantly impacting both theoretical and practical aspects of AI.

Conclusion

Quanming Yao stands out as a leader in machine learning, particularly in deep learning, graph learning, and AI applications in drug discovery. His exceptional academic journey, impactful research, and numerous awards reflect his profound influence in the field. Yao’s contributions to AI are reshaping industries, and his future work promises to continue pushing the boundaries of what is possible with machine learning.

Preethi Iype | Neural Networks | Best Researcher Award

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Mrs. Preethi Iype | Neural Networks | Best Researcher Award

Asst. Professor at St. Thomas Institute for Science and Technology, India

Preethi Elizabeth Iype is an accomplished academician and researcher with over two decades of experience in the field of Electronics and Communication Engineering. She has made significant contributions to the field of microcontrollers, embedded systems, and IoT-based solutions, with a particular emphasis on health monitoring and electric vehicle battery management systems. Her research primarily focuses on the thermal management of semiconductor devices, particularly High Electron Mobility Transistors (HEMT). Throughout her career, she has actively participated in national and international conferences, published in reputed Scopus and Web of Science indexed journals, and contributed to various academic and professional initiatives. She currently serves as an Assistant Professor at St. Thomas Institute for Science and Technology, where she continues to inspire and mentor students in cutting-edge technological domains.

Profile

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Education

Preethi Elizabeth Iype has pursued a strong academic foundation in Electronics and Communication Engineering. She completed her Bachelor of Engineering degree from the University of Madras in 2000. Furthering her expertise, she earned her Master of Engineering from Anna University in 2011. Currently, she has submitted her doctoral thesis and is awaiting her open defense for her Ph.D. in Electronics and Communication Engineering from the College of Engineering, Trivandrum, under the University of Kerala. Her academic journey has been marked by a keen interest in semiconductor device performance, particularly focusing on AlGaN/GaN HEMT technology, and its applications in high-power and high-frequency electronics.

Professional Experience

Preethi Elizabeth Iype has a diverse professional background that spans academia and industry. She started her career as a Software Engineer at Amstor Softech, Technopark, where she worked from June 2001 to June 2004 on software development projects related to hotel management systems and industrial applications. Transitioning into academia, she joined Mar Baselios College of Engineering and later St. Thomas Institute for Science and Technology, where she has been serving as an Assistant Professor since 2005. Her teaching portfolio includes core subjects such as Embedded Systems, Real-Time Systems, Wireless Communication, Solid State Devices, and Microcontrollers. In addition to teaching, she has played a crucial role in guiding student research projects, particularly in IoT and embedded systems applications.

Research Interests

Her primary research interests lie in semiconductor device physics, embedded systems, and IoT-based smart solutions. Specifically, her work focuses on the thermal management of High Electron Mobility Transistors (HEMT) using innovative materials and device architectures. She has conducted extensive research on optimizing the electrical and thermal performance of AlGaN/GaN and AlGaAs/GaAs-based HEMT devices. Additionally, her work extends to the application of artificial intelligence and neural networks in thermal efficiency enhancement. Her research has significant implications for high-power applications, radar systems, and next-generation wireless communication technologies.

Awards and Recognitions

Preethi Elizabeth Iype has been an active contributor to academic and research communities, earning recognition for her contributions. She has received accolades for her research presentations at national and international conferences. As a coordinator and SPOC for the NPTEL Local Chapter and Club President of the National Digital Library, India, she has played a pivotal role in promoting digital learning initiatives among students. Her active participation in workshops and seminars at premier institutes such as IISc Bengaluru and VIT Vellore reflects her commitment to continuous learning and knowledge dissemination.

Selected Publications

Preethi Elizabeth Iype, Dr. Anju S, Dr. V Suresh Babu (2021). “Temperature Dependent DC and AC Performance of AlGaN/GaN HEMT on 4H-SiC.” IEEE Conference Series (ICECCT 2021), DOI: 10.1109/ICECCT52121.2021.961668. Cited by: Multiple IEEE articles.

Preethi Elizabeth Iype, Dr. Geenu Paul, Dr. V Suresh Babu (2021). “Thermal and Electrical Performance of AlGaAs/GaAs based HEMT device on SiC substrate.” Journal of Physics: Conference Series, IOP Publishing, DOI: 10.1088/1742-6596/2070/1/012057. Cited by: Various research papers in semiconductor physics.

Preethi Elizabeth Iype, Dr. Geenu Paul, Dr. V Suresh Babu (2024). “Optimizing electrical and thermal performance in AlGaN/GaN HEMT devices using dual metal gate technology.” Heat Transfer, WILEY, DOI: 10.1002/htj.23099. Cited by: Emerging studies in heat transfer and semiconductor devices.

Preethi Elizabeth Iype, Dr. Geenu Paul, Dr. V Suresh Babu (2024). “Investigation of Thermal Efficiency of Recessed Γ gate over Γ gate, T gate and Rectangular gate AlGaN/GaN HEMT on BGO substrate.” Microelectronics Reliability, Elsevier, DOI: 10.1016/j.microrel.2024.115522. Cited by: Recent works on HEMT technology and reliability.

Preethi Elizabeth Iype, Dr. Geenu Paul, Dr. V Suresh Babu (2024). “Sheaf Attention-Based Osprey Spiking Neural Network for Effective Thermal Management and Self Heating Mitigation in GaAs and GaN HEMTs.” Heat Transfer, WILEY, DOI: 10.1002/htj.23099. Cited by: Studies on AI-based thermal efficiency improvements.

Conclusion

Preethi Elizabeth Iype has demonstrated a remarkable blend of teaching, research, and industry experience over the years. Her expertise in embedded systems, IoT, and semiconductor device physics has been instrumental in shaping young minds and contributing to technological advancements. With her research in thermal management of HEMTs and AI-driven solutions, she continues to pave the way for innovations in high-power electronics and wireless communication. Through her dedication to academia and active participation in professional organizations, she remains a key figure in the field of Electronics and Communication Engineering.

Muhammad Dilshad | Data Privacy and Security | AI & Machine Learning Award

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Mr. Muhammad Dilshad | Data Privacy and Security | AI & Machine Learning Award

Student at Quaid e Azam University Islamabad, Pakistan

Muhammad Dilshad is a dedicated and driven professional in the field of Computer and Information Technology. Holding a Master’s degree in Computer and Information Technology (MCIT) from Quaid-i-Azam University, Islamabad, he specializes in Cybersecurity, Networking, Machine Learning, and Blockchain. With practical experience in network performance monitoring and troubleshooting, he has contributed significantly to optimizing infrastructure security. His research interests revolve around enhancing Internet of Vehicles (IoV) security, employing Federated Learning, and integrating Blockchain technology to build decentralized, tamper-resistant frameworks. Proficient in various programming languages and analytical tools, he continually strives to apply emerging technologies for solving real-world security challenges.

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Education

Muhammad Dilshad began his academic journey with a strong foundation in science and mathematics, completing his Matriculation from BISE DG Khan Board. He then pursued an Intermediate of Computer Science (ICS) from the same board, gaining expertise in programming and computational concepts. His passion for technology led him to obtain a Bachelor of Science in Information Technology (BSIT) from Bahauddin Zakariya University, Multan, where he honed his skills in web development, networking, and database management. He further advanced his knowledge by earning a Master of Science in Information Technology (MSIT) from Quaid-i-Azam University, Islamabad, specializing in Machine Learning, Federated Learning, Blockchain, and Cybersecurity. His academic excellence is reflected in his impressive CGPAs and his continuous learning through various certifications.

Work Experience

Muhammad Dilshad has amassed valuable hands-on experience through his roles at Pakistan Telecommunication Company Limited (PTCL). He completed an internship at PTCL, where he actively monitored network performance, troubleshot connectivity issues, and assisted in optimizing infrastructure using tools like SolarWinds and CRM. He later transitioned into a Technical Support Associate (TSA) role in PTCL’s USD department, where he provided technical support, resolved network issues, and maintained high customer satisfaction ratings. His work has significantly contributed to improving service reliability and network security within the organization.

Research Interest

With a keen interest in cybersecurity, networking, and advanced computing paradigms, Muhammad Dilshad focuses his research on enhancing security frameworks for the Internet of Vehicles (IoV). His work primarily involves using Machine Learning techniques for DDoS attack detection and employing Federated Learning to create more secure, decentralized architectures. His expertise in Blockchain technology enables him to develop tamper-resistant security frameworks that protect critical data integrity. Additionally, he is passionate about applying Data Science methodologies for predictive analytics, improving network security, and optimizing intelligent systems. His research contributions aim to address contemporary challenges in network security and privacy, with a focus on real-world implementations.

Awards

Muhammad Dilshad has been recognized for his outstanding contributions to the field of Information Technology. His innovative research on IoV security and Blockchain applications has earned him nominations for prestigious awards in academia and industry. His work has been appreciated at international conferences, and he has received accolades for his impactful presentations on cybersecurity and emerging technologies. He continues to seek new opportunities to contribute to the scientific community and enhance technological advancements in cybersecurity and networking.

Publications

IOV Cyber Defense: Advancing DDoS Attack Detection with Gini Index in Tree Models (2024) – Published in a reputed journal, this paper explores the effectiveness of tree-based models in detecting cyber threats in IoV environments. Cited by multiple cybersecurity research articles.

Blockchain-Enabled Secure and Efficient DDoS Attack Detection Mechanisms in Connected Internet of Vehicles Using Federated Learning (2024) – Accepted at the 21st International Conference on Frontiers of Information Technology (FIT 2024). Recognized for innovative integration of Blockchain and Federated Learning.

Efficient DDoS Attack Detection in the Internet of Vehicles Using Gini Index and Federated Learning (2024) – Submitted to MDPI Journal, this paper proposes an advanced security mechanism for IoV systems. Highly relevant for researchers in cybersecurity.

Conclusion

Muhammad Dilshad’s dedication to advancing the fields of cybersecurity, networking, and artificial intelligence is evident in his extensive research and professional experience. His expertise in Machine Learning, Blockchain, and Federated Learning continues to contribute significantly to the development of secure, decentralized systems. Through his work at PTCL and his academic pursuits, he has demonstrated a strong commitment to innovation and problem-solving. With a growing list of publications, awards, and research contributions, he remains at the forefront of technological advancements, striving to make impactful changes in network security and intelligent systems.

Ibrahim Yildirim | Statistical Analysis | Best Researcher Award

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Assoc. Prof. Dr. Ibrahim Yildirim | Statistical Analysis | Best Researcher Award

Researcher at Gaziantep University, Turkey

Assoc. Prof. Dr. İbrahim Yıldırım is a distinguished academic in the field of educational sciences, specializing in measurement and evaluation in education. With a strong background in mathematics education and curriculum development, he has made significant contributions to the academic community through his research, publications, and innovative teaching approaches. His work primarily focuses on the integration of technology into education, gamification-based learning, and alternative assessment methods. Throughout his career, he has held various academic and teaching positions, shaping future educators and influencing educational policies.

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Education

Dr. Yıldırım completed his PhD in Educational Sciences at Gaziantep University from 2012 to 2016, where he developed a gamification-based teaching curriculum for his dissertation. He earned his first master’s degree in Educational Sciences at Gaziantep University between 2008 and 2011, focusing on alternative measurement tools in technology-supported mathematics teaching. Additionally, he pursued a combined bachelor’s and master’s degree in Secondary Mathematics Teaching at Dokuz Eylül University from 2003 to 2008. Demonstrating a strong interest in interdisciplinary studies, he also obtained a second bachelor’s degree in Economics from Anadolu University between 2005 and 2009. His foundational education was completed at Konya – İvriz Anatolian Teacher Training High School.

Experience

Dr. Yıldırım has accumulated extensive academic and professional experience in education. Since 2021, he has been serving as an Associate Professor in the Department of Measurement and Evaluation in Education at Gaziantep University. Prior to this, he held the position of Assistant Professor at the same institution from 2019 to 2021. Between 2017 and 2019, he worked at Harran University as an Assistant Professor in Curriculum and Instruction. His early career includes research assistant roles at both Harran University and Gaziantep University. Before transitioning into academia, he taught mathematics at various high schools under the Ministry of National Education from 2008 to 2013, gaining hands-on experience in student assessment and curriculum implementation.

Research Interests

Dr. Yıldırım’s research is primarily focused on educational measurement and evaluation, gamification in education, technology-integrated learning environments, and meta-analysis studies. His work explores how innovative teaching methods, particularly gamification and blended learning, influence student motivation and academic achievement. Additionally, he has contributed to research on assessment design, value-added evaluation models, and professional development programs for educators. His expertise extends to developing and validating assessment tools that enhance educational outcomes.

Awards

Dr. Yıldırım has been recognized for his significant contributions to the field of educational sciences. His research on gamification-based teaching methodologies and technology-enhanced learning environments has received accolades at national and international levels. He has been nominated for various academic excellence awards and has actively participated in high-impact research projects funded by institutions such as TUBITAK. His scholarly contributions and innovative research have positioned him as a leading figure in the educational sciences community.

Selected Publications

Yıldırım, İ. (2017). “The Effects of Gamification-Based Teaching Practices on Student Achievement and Students’ Attitudes toward Lessons.” Internet and Higher Education, 33, 86-97. (Cited by 440 Google Scholar)

Yıldırım, İ. (2019). “Using Facebook Groups to Support Teachers’ Professional Development.” Technology, Pedagogy and Education, 28(5), 589-609. (Cited by 27 Google Scholar)

Yıldırım, İ., Şen, S. (2021). “The Effects of Gamification on Students’ Academic Achievement: A Meta-Analysis Study.” Interactive Learning Environments, 29(8), 1301-1318. (Cited by 108 Google Scholar)

Yıldırım, İ. (2017). “Students’ Perceptions about Gamification of Education: A Q-Method Analysis.” Education and Science, 42(191), 235-246. (Cited by 69 Google Scholar)

Kurt, S. Ç., & Yıldırım, İ. (2018). “The Students’ Perceptions on Blended Learning: A Q Method Analysis.” Educational Sciences: Theory & Practice, 18(2), 427-446. (Cited by 54 Google Scholar)

Yıldırım, İ., & Demir, S. (2013). “Use of Technology-Assisted Mathematics Education and Alternative Measurement Together.” Çukurova University Faculty of Education Journal, 42(1), 65-73. (Cited by 4 Google Scholar)

Yıldırım, İ. & Demir, S. (2016). “Student Opinions on Gamification-Based ‘Teaching Principles and Methods’ Course Curriculum.” International Journal of Curriculum and Instruction Research, 6(11), 85-101. (Cited by 46 Google Scholar)

Conclusion

Assoc. Prof. Dr. İbrahim Yıldırım is a dedicated academic whose work has significantly contributed to the fields of educational measurement, gamification, and technology-enhanced learning. His extensive research, impactful publications, and innovative methodologies have played a crucial role in improving educational practices. Through his continued efforts in teaching, research, and project development, he continues to influence the academic landscape and contribute to the advancement of education. His commitment to integrating modern technological approaches into education has set a strong foundation for future research and practical applications in the field.

Abdultaofeek Abayomi | Machine Learning | Best Researcher Award

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Dr. Abdultaofeek Abayomi | Machine Learning | Best Researcher Award

Researcher at Walter Sisulu University, South Africa

ABDULTAOFEEK ABAYOMI, Ph.D., is a distinguished academic and researcher with a rich career in Information Technology and Computer Science. He holds a Ph.D. from Durban University of Technology, South Africa, and has been an influential figure in various educational institutions, including Mangosuthu University of Technology, where he served as a Postdoctoral Research Fellow and Lecturer. His extensive experience spans roles in teaching, research, and industry, with a specific focus on ICT, machine learning, and telecommunications. Dr. Abayomi’s contributions extend beyond academia, having held positions in major banks and IT firms, where he influenced projects in system analysis, IT infrastructure, and banking operations.

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Education

Dr. Abayomi’s academic journey began with a B.Sc. in Computer Science from the University of Ilorin, Nigeria, where he graduated with a Second Class Upper Division. This was followed by a Master’s in Technology (Computer Science) and an MBA from the Federal University of Technology, Akure, Nigeria. He then pursued a Ph.D. in Information Technology at Durban University of Technology, South Africa, where his doctoral research explored real-time tracking of individuals in distress situations using physiological signals, a significant contribution to the field of IT and human-centered computing.

Experience

Dr. Abayomi’s professional career spans teaching, research, and leadership roles in the technology sector. He has lectured and conducted research at various universities, including Durban University of Technology and Mangosuthu University of Technology in South Africa. Additionally, he has worked as a system analyst and instructor for IT certifications such as MCSE and MCSA at JIT Solutions in Akure, Nigeria. His career in the banking sector includes roles as a Profit Centre Manager and ICT System Administrator at United Bank for Africa Plc., where he contributed to improving operational efficiency and implementing IT solutions. Dr. Abayomi has also been involved in research projects aimed at addressing pressing issues in ICT and society, particularly focusing on the intersection of technology and human needs.

Research Interests

Dr. Abayomi’s research interests lie at the convergence of Information Technology, machine learning, and network systems. His work has explored deep learning, cognitive radio networks, spectrum sensing, and software-defined networks. He is particularly interested in the application of artificial intelligence to solve real-world problems, such as dynamic spectrum access and health insurance prediction. Dr. Abayomi’s research aims to improve the way technology interacts with human and environmental factors, making significant contributions to both academic and applied research.

Awards

Dr. Abayomi has received numerous accolades in recognition of his academic and research excellence. He was honored with the Research Award for Most Productive Postdoctoral Research Fellow in 2022 at Mangosuthu University of Technology, South Africa. He has also been an active participant in international conferences, serving as a session chair for various events such as the 22nd International Conference on Hybrid Intelligent Systems in 2022 and the 13th International Conference on Soft Computing and Pattern Recognition in 2021. His contributions to research are further exemplified by his involvement in winning the South African National Research Foundation’s Infrastructure Bridging Funding in 2016.

Publications

Dr. Abayomi’s scholarly work is well-regarded in academic circles, with several impactful publications in peer-reviewed journals. His notable publications include:

Ukpong, U.C., Idowu-Bismark, O., Adetiba, E., Kala, J.R., Owolabi, E., Oshin, O., Abayomi, A., Dare, O.E. (2025). “Deep reinforcement learning agents for dynamic spectrum access in television whitespace cognitive radio networks.” Scientific African, 27, e02523.

Dare, O.E., Okokpujie, K., Adetiba, E., Idowu-Bismark, O., Abayomi, A., Kala, R.J., Owolabi, E., Ukpong, U.C. (2024). “Development of a Conditional Generative Adversarial Network Model for Television Spectrum Radio Environment Mapping.” IEEE Access, 12, 197632-197644.

Mavundla, K., Thakur, S., Adetiba, E., Abayomi, A. (2024). “Predicting Cross-Selling Health Insurance Products Using Machine-Learning Techniques.” Journal of Computer Information Systems.

Adetiba, E., Uzoatuegwu, P.C., Ifijeh, A.H., Abayomi, A., Obiyemi, O. (2024). “NomadicBTS-2: A Network-in-a-Box with Software-Defined Radio and Web Based App for Multiband Cellular Communication.” International Journal of Computing and Digital Systems, 15(1), 1-16.

Aroba, O.J., Abayomi, A. (2023). “An Implementation of SAP Enterprise Resource Planning – A Case Study of the South African Revenue Services and Taxation Sectors.” Cogent Social Sciences.

These publications reflect his diverse research interests and his significant impact on fields ranging from telecommunications to machine learning and health technology.

Conclusion

Dr. Abayomi’s academic and professional journey is a testament to his dedication to advancing knowledge in Information Technology and its application to solving societal challenges. His work has influenced both the academic community and industry practices, particularly in the areas of cognitive radio networks, machine learning, and ICT solutions for societal development. His numerous accolades and impactful publications underscore his standing as a leading researcher in his field, and his continued contributions promise further advancements in the intersection of technology and human development.

Neeraj Thakur | AI in Healthcare | Best Researcher Award

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Dr. Neeraj Thakur | AI in Healthcare | Best Researcher Award

Postdoctoral Fellow at University of Oklahoma Health Sciences, United States

Dr. Neeraj S. Thakur, currently a Postdoctoral Fellow in the Department of Pharmaceutical Sciences at the University of Oklahoma Health Sciences Center (OUHSC), is a highly motivated researcher specializing in drug delivery systems, theranostic platforms, and AI-based approaches for developing novel diagnostics and treatments. With over a decade of international research experience spanning the USA, Europe, and Asia, Dr. Thakur has led multiple projects related to nanomaterial design, formulation development, and the synthesis of advanced drug delivery systems for treating diseases like cancer and infections. His expertise extends to both small molecules and large biomolecules, utilizing cutting-edge technologies such as polymeric and lipid nanoparticles. He has an extensive publication record with 29 papers, five patents, six book chapters, and significant contributions to the field.

Profile

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Education

Dr. Thakur earned his Ph.D. in Pharmaceutical Technology (Biotechnology) from the National Institute of Pharmaceutical Education and Research (NIPER), Mohali, India, where his research focused on the development of nanoparticle-based fluorescent probes for biomedical applications. He also holds an M.Tech in Pharmaceutical Technology (Biotechnology) from NIPER, Mohali, where he was the class topper, and a B.Pharm degree from Shri G.S. Institute of Technology and Science, Indore. Additionally, he is pursuing a Certificate in Data Science and Machine Learning from the Massachusetts Institute of Technology (MIT).

Experience

Dr. Thakur has held significant roles in various research institutions. As a Postdoctoral Fellow at OUHSC, he established research laboratories and led projects on nanomaterial development for drug delivery systems targeting multiple applications, including inner ear and ocular delivery. Prior to this, he worked at the University of Geneva, Switzerland, where he developed hybrid micelle formulations for transdermal delivery and designed iontophoretic devices. He also contributed to the Center of Innovative and Applied Bioprocessing (CIAB), India, where he led the development of topical drug delivery systems and was involved in several patent filings. These experiences have honed his skills in formulation and analytical development, laboratory management, and industrial collaboration.

Research Interests

Dr. Thakur’s research interests focus on the development of advanced drug delivery systems, particularly using nanomaterials such as polymeric and lipid nanoparticles. His work aims to improve the therapeutic efficacy of drugs by enhancing their delivery to specific sites in the body, such as the inner ear, ocular tissues, and tumors. He is also dedicated to the integration of AI-based approaches for optimizing drug delivery design, ensuring more precise treatments for conditions like cancer and ototoxicity. His passion extends to the creation of theranostic platforms for early disease diagnosis and personalized medicine.

Awards

Throughout his career, Dr. Thakur has received numerous accolades for his contributions to pharmaceutical sciences. Notable among them are the John B. Bruce Scholarship Award (2024), the Best Abstract Award at AAPS Pharm360 (2023), and the prestigious Swiss Government Excellence Scholarship (2020-2021). His earlier achievements include the DST-INSPIRE and CSIR Research Fellowships, which are awarded to the top 1% of students in India. These recognitions underscore his impact on the scientific community and his leadership in advancing pharmaceutical research.

Publications

Dr. Thakur has authored 29 peer-reviewed papers, with some of his recent work focused on innovative drug delivery systems. Key publications include:

Thakur, N.S., et al., “Crosslinked Hybrid Nanoparticle Embedded in Thermogel For Sustained Co-Delivery to Inner Ear,” Journal of Nanobiotechnology, 2024.

Thakur, N.S., et al., “Progress and Promise of Photoresponsive Nanocarriers for Precision Drug Delivery in Cancer,” Journal of Photochemistry & Photobiology C: Photochemistry Reviews, 2024.

Thakur, N.S., et al., “Dual Stimuli-Responsive and Sustained Drug Delivery Nanosensogel for Prevention of Cisplatin-Induced Ototoxicity,” Journal of Controlled Release, 2024.

Paul, S., et al., “Co-Administration of Chemo-Phototherapeutic Loaded Lignin Nanoarchitecture for Skin Cancer and Bacterial Infections,” ACS Applied Nano Materials, 2024.

Kumar, S., et al., “Bioengineered Multi-Walled Carbon Nanotubes Based Biosensors and Applications,” Sensors and Diagnostics, 2023. These works, which cover nanomedicine, drug delivery, and nanomaterial innovation, have significantly contributed to the field of pharmaceutical sciences and have been widely cited.

Conclusion

Dr. Neeraj S. Thakur’s multifaceted career highlights his dedication to advancing pharmaceutical research through innovation in nanotechnology and drug delivery systems. His profound knowledge in both academic and industrial settings, along with his leadership in driving collaborative research projects, continues to position him as a key figure in his field. With a focus on precision medicine, AI-based design, and the development of targeted therapies, Dr. Thakur is making remarkable strides toward improving patient outcomes and advancing the future of drug delivery and diagnostics.

Ameni Chetouane | Computer Science | Best Researcher Award

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Dr. Ameni Chetouane | Computer Science | Best Researcher Award

Contractual assistant at Higher Institute of Computer Science – Tunisia (ISI), Tunisia

Ameni Chetouane is a dedicated doctoral student specializing in computer science, currently pursuing her PhD at the Ecole Nationale des Sciences de l’Informatique (ENSI) at the University of Manouba, Tunisia. Her academic journey began with a Bachelor’s in Applied Computer Networks followed by a Master’s degree, where she concentrated on network technologies and video analysis for traffic congestion detection. She is deeply involved in research aimed at securing Software Defined Networking (SDN) systems against cyber-attacks using Artificial Intelligence (AI) methods.

Profile

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Education

Ameni’s education spans several years, starting with a Bachelor’s degree in Applied Computer Networks from the Institut Supérieur d’Informatique de Mahdia (ISIMA) in 2014. She pursued two Master’s degrees, one focusing on network technologies and telecommunications, and the other on research in computer science, both from the University of Carthage’s Faculté des Sciences de Bizerte (FSB). Her doctoral studies, commenced in 2021, are focused on the application of AI for intrusion detection systems (IDS) in SDN environments, with a goal to combat cyber-attacks.

Experience

Ameni has gained practical teaching experience as a part-time instructor at the Institut Supérieur des Etudes Technologiques de Bizerte and the Faculté des Sciences de Bizerte, where she taught subjects such as database engineering and object-oriented programming. Her internships, including research at LaBRI, University of Bordeaux, and her professional project at Millénia Engineering, have allowed her to apply theoretical knowledge in real-world network and software development projects.

Research Interests

Ameni’s research is primarily focused on the security of SDN environments, particularly in utilizing AI for effective threat detection and mitigation. Her doctoral thesis specifically explores AI-driven solutions for securing SDN systems against Distributed Denial of Service (DDoS) attacks. She aims to improve the performance of IDSs by incorporating machine learning (ML) and continual learning methods into SDN security architectures, ensuring adaptive and real-time defenses against evolving threats.

Awards

Ameni has earned recognition for her academic and research excellence, notably her significant contributions to the field of SDN and AI. Her work has been presented at various international conferences, contributing to advancements in network security research. While specific awards are not listed, her impact within the academic community, through her publications and conference participations, is considerable.

Publications

Ameni Chetouane, Sabra Mabrouk, Imen Jemili, and Mohamed Mosbah. “A comparative study of vehicle detection methods in a video sequence.” International Workshop on Distributed Computing for Emerging Smart Networks, Springer, 2019.

Ameni Chetouane, Sabra Mabrouk, Imen Jemili, and Mohamed Mosbah. “Vision-based vehicle detection for road traffic congestion classification.” Concurrency and Computation: Practice and Experience, 2022.

Ameni Chetouane, Sabra Mabrouk, and Mohamed Mosbah. “Traffic congestion detection: Solutions, open issues, and challenges.” International Workshop on Distributed Computing for Emerging Smart Networks, Springer, 2020.

Ameni Chetouane and Kamel Karoui. “A survey of machine learning methods for DDoS threats detection against SDN.” International Workshop on Distributed Computing for Emerging Smart Networks, Springer, 2022.

Ameni Chetouane, Kamel Karoui, and Ghayth Nemri. “An intelligent ML-based IDS framework for DDoS detection in the SDN environment.” International Conference on Advances in Mobile Computing and Multimedia Intelligence, Springer, 2022.

Ameni Chetouane and Kamel Karoui. “DDoS detection approach based on continual learning in the SDN environment.” International Conference on Hybrid Intelligent Systems, Springer, 2022.

Ameni Chetouane and Kamel Karoui. “Risk-based intrusion detection system in Software Defined Networking.” Concurrency and Computation: Practice and Experience, 2023.

Conclusion

Ameni Chetouane stands out in her field with a robust educational background, strong professional experiences, and an ongoing commitment to researching the intersection of AI and SDN security. Through her published works, she has made significant contributions to securing networks using intelligent methods, focusing on solving complex cyber threats in modern network infrastructures. As she continues her research, her work promises to shape the future of AI-driven cybersecurity in SDN environments.

Yuriy Gusev | AI in Healthcare | Best Use of Data in Healthcare Award

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Assoc. Prof. Dr. Yuriy Gusev | AI in Healthcare | Best Use of Data in Healthcare Award

Director of Health Informatics and Data Science Program at Georgetown University, United States

Yuriy Gusev is an esteemed Associate Professor of Bioinformatics at Georgetown University Medical Center’s Innovation Center for Biomedical Informatics (ICBI) and Department of Oncology. He is recognized for his extensive expertise in computational biology, bioinformatics, and systems biology, with a particular focus on cancer research. Dr. Gusev has dedicated his career to bioinformatics, computational modeling, and the development of innovative bioinformatics tools and methodologies. He also plays a leading role in the Health Informatics and Data Science graduate program, and co-directs the Biostatistics and Bioinformatics Shared Resource at the Lombardi Cancer Center. Throughout his career, Dr. Gusev has contributed significantly to multi-institutional cancer research efforts, particularly through large-scale studies, including the Georgetown Database of Cancer (G-DOC), and various NIH-funded programs.

Profile

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Education

Dr. Gusev’s academic journey began with a Master of Science in Applied Mathematics from State University of St. Petersburg in Russia. He later earned his Ph.D. in Computational Biology from the Central Research Institute of Roentgenology & Radiology in St. Petersburg, Russia. Dr. Gusev further honed his expertise with a postdoctoral position at the Waksman Institute, Rutgers University, where he focused on Computational Modeling in Cancer Research. These experiences laid the foundation for his innovative approach to bioinformatics and cancer research.

Experience

Dr. Gusev’s professional journey spans over three decades, with pivotal positions at several renowned institutions. After his postdoctoral work at Rutgers, he held various roles, including faculty research associate at Johns Hopkins University, senior research scientist at Molecular Staging Inc., and assistant professor at the University of Oklahoma Health Sciences Center. In 2009, he joined Georgetown University as an Associate Professor. Alongside his academic appointments, Dr. Gusev has directed numerous research projects and collaborated extensively in multi-disciplinary research programs across cancer genomics, bioinformatics, and computational biology.

Research Interests

Dr. Gusev’s research interests lie at the intersection of computational biology, bioinformatics, and cancer research. His primary focus includes the study of tumor heterogeneity, chromosomal instability, microRNA, and long-noncoding RNA regulation in cancer. He is particularly invested in the application of computational models and bioinformatics methods to analyze large-scale genomic and transcriptomic data. Dr. Gusev is also passionate about integrating molecular, imaging, and clinical data to advance personalized medicine and precision oncology. His work involves high-throughput data analysis, machine learning techniques for biomarker discovery, and the development of cloud-based platforms to streamline cancer research workflows.

Awards

Dr. Gusev has been recognized with numerous accolades throughout his career. Notable awards include the Charles and Johanna Bush Postdoctoral Fellowship, NSF travel awards for his work in tumor heterogeneity and mathematical population dynamics, and the Executive Leadership Award from the Mid-South Computational Biology and Bioinformatics Society. His contributions to computational cancer research were further acknowledged with the 2008 Executive Leadership Award, and his research impact continues to be recognized by various scientific bodies.

Publications

Dr. Gusev has authored or co-authored numerous influential publications. His research in tumor heterogeneity, chromosomal instability, and microRNA profiling has resulted in multiple highly cited papers. Some key publications include:

Axelrod DE, Gusev Y, Kuczek T. “Persistence of cell cycle times over many generations as determined by heritability of colony sizes of ras oncogene-transformed and non-transformed cells.” Cell Proliferation, 1993, 26(3), 235-249.

Gusev Y, Kagansky V, Dooley WC. “Long-term dynamics of chromosomal instability in cancer: a transition probability model.” Mathematical and Computer Modelling, 2001, 33(12), 1253-1273.

Gusev Y, Bhuvaneshwar K, Song L, Zenklusen JC, Fine H, Madhavan S. “The REMBRANDT study, a large collection of genomic data from brain cancer patients.” Nature Scientific Data, 2018; 5:180158.

Bhuvaneshwar K, Belouali A, Singh V, et al. “G-DOC Plus – an integrative bioinformatics platform for precision medicine.” BMC Bioinformatics, 2016; 17(1):193.

Lei Song, Krithika Bhuvaneshwar, Yue Wang, et al. “CINdex: a bioconductor package for analysis of chromosome instability in DNA copy number data.” Cancer Informatics, 2017, Volume 16, PMID: 29343938.

His works have been cited extensively, contributing to advances in cancer bioinformatics, precision oncology, and the study of molecular biomarkers in cancer.

Conclusion

Dr. Yuriy Gusev has made significant contributions to the field of computational biology and bioinformatics, particularly in cancer research. His work has greatly advanced the understanding of tumor heterogeneity, chromosomal instability, and non-coding RNA regulation in cancer. As an educator, researcher, and leader, he continues to influence the development of bioinformatics tools and platforms that facilitate precision medicine. Dr. Gusev’s expertise in computational modeling, genomic data analysis, and multi-omics integration positions him as a pivotal figure in cancer research and bioinformatics. His ongoing efforts to apply innovative computational approaches to clinical oncology will undoubtedly lead to further breakthroughs in cancer treatment and personalized therapies.

Jale Kalemkuş | Artificial Intelligence | Best Researcher Award

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Assist. Prof. Dr. Jale Kalemkuş | Artificial Intelligence | Best Researcher Award

Assistant Professor at Kafkas University, Turkey

Dr. Jale Kalemkuş is an Assistant Professor at Kafkas University with a strong academic and professional background in primary education. She began her career as a primary school teacher under the Turkish Ministry of National Education from 2008 to 2012 before transitioning to academia as a lecturer in the Child Development Department at Kafkas University. Since 2020, she has been serving as an assistant professor in the same department. With a deep interest in science education and technology-enhanced learning, Dr. Kalemkuş has contributed significantly to research in areas such as artificial intelligence, conceptual change, science process skills, and distance education.

Profile

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Education

Dr. Kalemkuş completed her undergraduate studies at Kocaeli University in the Primary School Teaching Program between 2002 and 2006. She then pursued her master’s degree at Selçuk University in the Primary Education Department from 2006 to 2009. Further advancing her academic credentials, she earned her PhD from Necmettin Erbakan University in the Primary Education Department between 2014 and 2018. Her education has provided her with a strong foundation in pedagogy and research methodologies, enabling her to contribute significantly to the field of primary education and science learning.

Experience

Dr. Kalemkuş’s professional journey reflects a blend of practical teaching experience and academic research. Her tenure as a primary school teacher helped her understand the challenges in early education, leading her to explore innovative teaching strategies. She later transitioned to higher education, where she has been instrumental in teaching and mentoring future educators. Since 2020, she has been engaged in research and academic activities as an assistant professor, focusing on enhancing science education through digital tools and emerging technologies such as artificial intelligence and augmented reality.

Research Interest

Dr. Kalemkuş’s research primarily focuses on integrating modern technological advancements into primary education. Her areas of interest include conceptual change, science process skills, argumentation, laboratory experiments, metacognition, misconceptions in science education, 21st-century skills, augmented reality, distance education, visual programming languages, artificial intelligence, and STEM education. Her studies aim to bridge the gap between traditional educational methods and modern technological interventions to improve students’ academic achievement and engagement.

Awards

Dr. Kalemkuş has been recognized for her contributions to educational research and innovation. She has actively participated in prestigious projects, such as the TÜBİTAK-funded initiative “Teachers Developing AI-Supported Next-Generation Teaching Materials” (Project ID: 224B743). Her work has been cited in reputable academic indexes, reflecting its impact on the field. Her nomination for the Best Researcher Award under the AI Data Scientist Awards underscores her dedication to advancing science education through innovative research methodologies.

Publications

Dr. Kalemkuş has published extensively in peer-reviewed journals indexed in SSCI, ERIC, and TR-Index. Some of her notable publications include:

Kalemkuş, J., & Kalemkuş, F. (2025). Primary school students’ perceptions of artificial intelligence: Metaphor and drawing analysis. European Journal of Education, 60(1), 1-23. https://doi.org/10.1111/ejed.70007

Kalemkuş, J., & Kalemkuş, F. (2024). The effect of designing scientific experiments with visual programming on learning outcomes. Science & Education, 1-23. https://doi.org/10.1007/s11191-024-00546-8

Kalemkuş, J., & Kalemkuş, F. (2023). Effect of the use of augmented reality applications on academic achievement in science education: A meta-analysis. Interactive Learning Environments, 31(9), 6017-6034. https://doi.org/10.1080/10494820.2022.2027458

Kalemkuş, J. (2024). Investigation of primary school teachers’ experiences on teaching science during distance education. Journal of Learning and Teaching in Digital Age, 9(2), 12-28. https://doi.org/10.53850/joltida.1326497

Kalemkuş, J., Bayraktar, Ş., & Çiftçi, S. (2021). Comparative effects of argumentation and laboratory experiments on metacognition, attitudes, and science process skills of primary school children. Journal of Science Learning, 4(2), 113-122. https://doi.org/10.17509/jsl.v4i2.27825

Kalemkuş, J. (2021). Fen bilimleri dersi öğretim programı kazanımlarının 21.yüzyıl becerileri açısından incelenmesi. Anadolu Journal of Educational Sciences International, 11(1), 63-87. https://doi.org/10.18039/ajesi.800552

Kalemkuş, J., Bayraktar, Ş., & Çiftçi, S. (2019). Eğitimde sosyal, zihinsel ve sözlü-yazılı bir aktivite: Argümantasyon. Turkish Studies, 14(4), 2449-2467. https://dx.doi.org/10.29228/TurkishStudies.23024

Conclusion

Dr. Jale Kalemkuş is a dedicated researcher and educator whose work has significantly contributed to the advancement of primary science education. Her integration of artificial intelligence, augmented reality, and other digital tools into education has provided valuable insights into modern learning methodologies. With numerous publications in high-impact journals, active involvement in educational projects, and recognition in the academic community, Dr. Kalemkuş continues to influence the field of primary education by developing innovative teaching strategies and conducting groundbreaking research.