Fatma Zahra Sayadi | Deep Learning | Best Innovation Award

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Prof. Fatma Zahra Sayadi | Deep Learning | Best Innovation Award

Associate Professor | University of Sousse | Tunisia

Fatma Elzahra Sayadi is a highly accomplished researcher and academic specializing in electronics and microelectronics, with current research focused on video surveillance systems, real-time processing, and signal compression. She earned her PhD in electronics for real-time systems from the University of Bretagne Sud in collaboration with the University of Monastir and has also completed her engineering and master’s studies in electrical and electronic systems. She has extensive professional experience as a maître de conférences and previously as a maître assistante and assistant technologist, teaching courses in microprocessors, multiprocessors, programming, circuit testing, and industrial electronics. Her research interests include signal processing, parallel architectures, microelectronics, real-time systems, and communication networks. She has actively participated in national and international research projects and collaborations with institutions in France, Italy, Germany, and Morocco. Her work has been published in over 37 journal articles, 40 conference papers, and six book chapters, and she has supervised several doctoral and master’s theses. She has been recognized with awards such as the first prize at the Women in Research Forum at the University of Sharjah and contributes to professional communities as a reviewer, evaluator, and organizer of academic events. She is skilled in research methodologies, signal and data analysis, electronic system design, and digital education innovation. Her academic contributions have been cited by 395 documents, with 69 documents contributing to her citations, and she has an h-index of 13.

Profile: Google Scholar | Scopus | ORCID 

Featured Publications

  1. Basly, H., Ouarda, W., Sayadi, F. E., Ouni, B., & Alimi, A. M. (2020). CNN-SVM learning approach based human activity recognition. In International Conference on Image and Signal Processing (pp. 271–281). 77 citations.

  2. Bouaafia, S., Khemiri, R., Sayadi, F. E., & Atri, M. (2020). Fast CU partition-based machine learning approach for reducing HEVC complexity. Journal of Real-Time Image Processing, 17(1), 185–196. 53 citations.

  3. Haggui, O., Tadonki, C., Lacassagne, L., Sayadi, F., & Ouni, B. (2018). Harris corner detection on a NUMA manycore. Future Generation Computer Systems, 88, 442–452. 48 citations.

  4. Basly, H., Ouarda, W., Sayadi, F. E., Ouni, B., & Alimi, A. M. (2022). DTR-HAR: Deep temporal residual representation for human activity recognition. The Visual Computer, 38(3), 993–1013. 40 citations.

  5. Bouaafia, S., Khemiri, R., Messaoud, S., Ben Ahmed, O., & Sayadi, F. E. (2022). Deep learning-based video quality enhancement for the new versatile video coding. Neural Computing and Applications, 34(17), 14135–14149. 35 citations.

Sheilla Ann Pacheco | Machine Learning | Best Researcher Award

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Assist. Prof. Dr. Sheilla Ann Pacheco | Machine Learning | Best Researcher Award

Faculty at North Eastern Mindanao State University | Philippines

Dr. Sheilla Ann Pacheco is an accomplished academic and researcher with extensive experience in computer science, particularly in the fields of machine learning, image processing, and adversarial defense. With over nine years of academic service, she has established herself as a dedicated educator, mentor, and innovator who contributes significantly to both research and teaching. Her work spans practical and theoretical domains, addressing challenges in privacy-preserving AI, biometrics, and medical applications such as breast cancer prediction. Dr. Pacheco is actively involved in presenting her research at national and international conferences, where she has received recognition for her contributions. She is also engaged with professional organizations such as IEEE and ACM, which allows her to remain connected with global advancements in her field. Combining strong technical expertise, leadership in research, and dedication to academic growth, she continues to advance computer science while inspiring students and peers alike.

Professional Profiles 

Google Scholar | Scopus Profile | ORCID Profile 

Education

Dr. Sheilla Ann Pacheco has pursued her academic journey with determination and excellence in the field of computer science. She earned her Bachelor of Science in Computer Science from Surigao del Sur State University, laying the foundation for her career in research and academia. She further advanced her studies by completing her Master of Science in Computer Science at the same institution, where she deepened her knowledge of programming, data processing, and research methodologies. To further enhance her expertise, she is currently completing her Doctor of Philosophy in Computer Science at the Technological Institute of the Philippines, focusing on advanced topics such as machine learning, adversarial defense, and computational intelligence. Her academic path highlights her continuous commitment to lifelong learning and growth in her field. Through her education, she has developed the strong theoretical and practical background that now underpins her teaching, supervision, and impactful research contributions.

Professional Experience

Dr. Sheilla Ann Pacheco has built a solid professional career as an academic and researcher in the field of information technology. She currently serves as an Assistant Professor at North Eastern Mindanao State University, where she teaches a variety of courses, supervises research, and contributes to the development of the academic community. Over the years, she has guided students in their research projects, emphasizing innovation and practical applications of computer science in areas such as artificial intelligence and data processing. Her experience is not only limited to classroom teaching but also extends to participation in academic conferences, workshops, and seminars where she presents her work and collaborates with other professionals. Her professional journey demonstrates a balance of academic leadership, technical expertise, and a commitment to advancing knowledge. Through her role, she continues to inspire students and colleagues while contributing to the university’s mission of research and innovation.

Research Interest

Dr. Sheilla Ann Pacheco’s research interests lie in the fields of machine learning, image processing, adversarial defense, and privacy-preserving artificial intelligence. She has a particular focus on developing intelligent solutions that enhance the security and accuracy of biometric recognition systems, as reflected in her work on SARGAN-based face recognition and hidden adversarial attacks on facial biometrics. In addition, she explores federated learning models that aim to protect user privacy while enabling effective AI applications. Her research also extends to healthcare, where she has contributed to studies such as breast cancer prediction using ensemble techniques. These areas highlight her commitment to addressing real-world challenges through innovative technologies. By integrating theoretical models with applied solutions, her research contributes both to the scientific community and to society at large. Her future directions aim to expand collaborations in international research networks and further explore secure, ethical, and intelligent AI applications.

Research Skill

Dr. Sheilla Ann Pacheco possesses a wide range of research skills that enable her to excel in both academic and applied studies. Her expertise includes image processing, machine learning, and adversarial defense, which she has applied in developing innovative solutions for biometric recognition and healthcare prediction models. She is proficient in programming, data analysis, and the use of advanced computational tools, allowing her to conduct rigorous and high-quality research. Her skills in academic writing and presentation have enabled her to publish and present her work at reputable conferences and to effectively communicate her findings to diverse audiences. She is also skilled in research supervision, guiding students through the research process and fostering a culture of inquiry and innovation. Combined with strong organizational and leadership skills, she demonstrates the ability to collaborate with peers, contribute to multidisciplinary projects, and advance knowledge in her field through impactful and practical research outcomes.

Publications Top Notes

Title: Enhanced content-based image retrieval using multivisual features fusion
Authors: SAB Pacheco, M Goyani, ZG Rehman, SF Rehman, T Champaneria, …
Year: 2025
Citation: 1

Title: Robust Face Recognition Under Adversarial Attack Using SARGAN Model and Improved Cross Triple MobileNetV1
Authors: SAB Pacheco, JE Estrada, MM Goyani
Year: 2025

Title: Least Variance based Modeling of Heart Disease Prediction System using Ensemble Technique
Authors: SA Pacheco, JP Bangoy, ZG Rehman, SF Rehman, SV Goyani, …
Year: 2025

Title: Hidden adversarial attack on facial biometrics – a comprehensive survey
Authors: MMG Sheilla Ann Bangoy Pacheco, Jheanel Espiritu Estrada
Year: 2025

Title: A Comprehensive Survey on Federated Learning and Its Applications in Health Care
Authors: SAB Pacheco
Year: 2024

Title: Performance of Students in Computer Programming: An Analysis
Authors: IB Christian, SA Pacheco
Year: 2023

Title: Breast Cancer Prediction using Ensemble Technique
Authors: SAB Pacheco
Year: 2022

Title: Trends and Analysis of Graduate Programs
Authors: SAB Pacheco
Year: 2022

Conclusion

Dr. Sheilla Ann Pacheco is a deserving candidate for the Best Researcher Award due to her impactful contributions in machine learning, image processing, and adversarial defense, which address critical challenges in biometrics, privacy-preserving AI, and healthcare applications. Her research outputs, academic leadership, and active involvement in professional organizations highlight her commitment to advancing both scientific knowledge and the research community. With her strong academic foundation, proven dedication, and potential for expanding her influence through future international collaborations and innovative projects, she is well-positioned to make even greater contributions to research and society in the years ahead.

Minh-Son Dao | Deep Learning | Best Researcher Award

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Dr. Minh-Son Dao | Deep Learning | Best Researcher Award

Researcher at The National Institute of Information and Communications Technology (NICT), Japan.

Dr. Minh-Son DAO is a distinguished Senior Researcher and Research Manager at the Big Data Integration Research Center, National Institute of Information and Communications Technology (NICT), Japan. With over two decades of research and leadership experience across academia and government, he leads cutting-edge initiatives in artificial intelligence, big data analytics, and smart IoT systems. He has played a pivotal role in Japan’s Society 5.0 vision through projects like MMCRAI and collaborative smart-city platforms. Dr. DAO is also a committed educator, serving as a thesis supervisor and adjunct lecturer across multiple international universities. His work has earned him numerous accolades, including multiple Best Challenge Awards, national recognitions, and research excellence honors. With over 100 peer-reviewed publications and international partnerships spanning Europe and Asia, he continues to bridge academic rigor with real-world impact. His current focus lies in multimodal AI frameworks and data-driven societal innovation.

Professional Profile

Suitability For Best Researcher Award – Dr. Minh-Son Dao

Dr. Minh-Son DAO exemplifies the qualities of an outstanding researcher through his sustained, interdisciplinary contributions to artificial intelligence, big data analytics, and smart IoT systems. With over 20 years of research leadership, a strong publication record (100+ peer-reviewed papers), and international collaboration across Europe and Asia, he has significantly influenced both theoretical advancements and real-world applications. His active role in Japan’s Society 5.0 vision and the development of the MMCRAI framework further underscore his commitment to data-driven societal innovation. Dr. DAO also demonstrates excellence in mentoring, editorial roles, and academic service, enriching the broader research ecosystem.

Education

Dr. Minh-Son DAO holds a Ph.D. in Information and Communications Technology from Trento University, Italy, where his research focused on similarity measures and shape matching using genetic algorithms. His doctoral dissertation introduced the Edge Potential Function (EPF), a novel contribution to shape-based image retrieval. Prior to that, he earned a Master’s degree in Computer Science from Vietnam National University, specializing in handwritten character recognition using Convolutional Neural Networks—an early demonstration of his interest in deep learning. His Bachelor’s degree, also in Computer Science from the University of HCM City, Vietnam, emphasized image processing and hypertext applications. These academic milestones laid a strong foundation in AI, machine learning, and multimedia processing, enabling him to merge theoretical knowledge with practical innovation throughout his career. His educational journey reflects a continuous pursuit of excellence across diverse computational and applied domains.

Experience

Dr. Minh-Son DAO brings over 20 years of extensive research and leadership experience across Asia and Europe. Currently, he serves as Research Manager and Senior Researcher at NICT Japan, spearheading national AI and Smart IoT initiatives. His prior roles include Deputy Director and Senior Assistant Professor at Universiti Teknologi Brunei, where he also founded the ELEDIA@UTB lab focused on smart farming and wireless technologies. He has held prestigious research roles at Trento University, Osaka University (as a JSPS Fellow), and GraphiTech Italy. He has supervised more than 40 postgraduate students, co-authored over 100 publications, and led multi-institutional projects in smart cities, multimedia analytics, and health informatics. His teaching portfolio spans creative multimedia, data science, and database systems. Known for building strong global research networks, Dr. DAO has established successful collaborations with institutions in Norway, Ireland, Vietnam, and Switzerland, playing a vital role in cross-disciplinary and cross-cultural scientific advancements.

Professional Development

Dr. Minh-Son DAO has consistently invested in professional development to enhance his academic and leadership capabilities. He completed the UTB Faculty Development Program and the Foundations of University Learning and Teaching at Universiti Teknologi Brunei, gaining proficiency in teaching pedagogy, assessment strategies, and flipped classroom techniques. He also holds Oracle certifications in SQL, PL/SQL, and web application development. His involvement as a guest editor for high-impact journals such as IEEE ACCESS, ACM TOMM, and Frontiers in Big Data, along with his participation as program committee member for numerous international conferences, highlights his role as a thought leader in multimedia, AI, and big data. Dr. DAO frequently chairs and organizes conferences and workshops, including ICMLSC, ICCRD, and MediaEval. His holistic development in research, teaching, industry consulting, and international collaboration exemplifies a well-rounded professional commitment to lifelong learning and knowledge dissemination in cutting-edge computing technologies.

Research Focus

Dr. Minh-Son DAO’s research primarily focuses on multidisciplinary applications of Artificial Intelligence, Big Data Analytics, and Smart IoT systems, aligning closely with the vision of a data-driven, intelligent society (Society 5.0). His most notable initiative, the Multimodal and Cross-modal AI Framework (MMCRAI), demonstrates his commitment to converting raw multimodal data into actionable insights across domains like environmental monitoring, health informatics, multimedia forensics, and smart cities. He has applied his research to real-world challenges such as air pollution prediction, disaster management, and cheapfake detection. His work spans from foundational AI techniques to practical societal applications, including the integration of sensor networks, robotics, and citizen-driven data platforms. Through collaborative international projects, he explores the intersections between cyber-physical-social systems, smart urban planning, and sustainable development. This focus enables him to address complex problems with scalable, intelligent solutions that impact public health, education, urban resilience, and digital media integrity.

Research Skills

Dr. Minh-Son DAO possesses a comprehensive suite of research skills that bridge theoretical and applied domains. He is proficient in machine learning, deep learning, multimedia retrieval, and big data analytics, often applying these in cross-modal and multimodal AI frameworks. His technical abilities include programming in C++, R, SQL, HTML/JavaScript, and Python, and working with AI tools such as TensorFlow and Keras. Dr. DAO’s expertise spans data fusion, smart sensor integration, pattern recognition, event detection, and AI-based forecasting models, enabling him to tackle large-scale and heterogeneous data sources. Additionally, he has extensive experience in research project management, proposal writing, international collaboration, and supervising graduate students. His editorial and peer-review roles in IEEE, Springer, and Elsevier journals further reflect his analytical and evaluative skill set. These capabilities have allowed him to lead multi-disciplinary teams and create impactful AI-driven solutions for urban management, environmental monitoring, and personalized health analytics.

Awards and Honors

Dr. Minh-Son DAO has received numerous national and international awards recognizing his research excellence and innovation. Notably, he won the Best Challenge Awards at ICMR 2023 and ACM MM 2022 for his groundbreaking work in cheap fake detection. He was honored with the Excellent Performance Award by Japan’s NICT in 2022, reflecting his leadership in national projects. Earlier, he earned first-place awards at prestigious competitions such as image CLEF 2018 and Media Eval 2017 for his contributions to multimedia understanding and disaster response. He received the Research Excellence Mid-Career Academic Award from University Technology Brunei in 2017. His early career was marked by competitive international fellowships, including the JSPS International Fellowship (Japan) and ERCIM Fellowship (Europe), and he was awarded Vietnam’s highest youth scientific honor, the Creative Youth Medal. These accolades affirm his sustained contributions to AI, data science, and societal innovation across multiple countries and disciplines.

Conclusion

Dr. Minh-Son DAO’s profile aligns exceptionally well with the criteria for a Best Researcher Award. His work bridges high-impact research, global collaboration, and societal benefit. His innovations in AI and multimodal systems, combined with his leadership in international research initiatives and dedication to mentorship, make him a deserving candidate. His recognition through prestigious awards and fellowships across continents further validates his global research excellence.

Publication Top Notes

1. Deep learning for mobile multimedia: A survey

  • Authors: K Ota, MS Dao, V Mezaris, FGBD Natale

  • Journal: ACM Transactions on Multimedia Computing, Communications, and Applications

  • Cited by: 188

  • Year: 2017

Summary:
This comprehensive survey explores how deep learning techniques have been adapted and optimized for mobile multimedia applications. It covers both theoretical advancements and practical implementation challenges. The paper also discusses energy efficiency and processing limitations of mobile devices. It has become a foundational reference in mobile multimedia research.

2. Exploring convolutional neural network architectures for EEG feature extraction

  • Authors: I Rakhmatulin, MS Dao, A Nassibi, D Mandic

  • Journal: Sensors, Vol. 24(3), Article 877

  • Cited by: 62

  • Year: 2024

Summary:
This paper investigates CNN-based methods for extracting features from EEG signals, a key step in brain-computer interface development. Multiple CNN architectures are compared for performance and accuracy. The study demonstrates significant improvement in signal interpretation. It contributes to the emerging field of AI-powered neuro technology.

3. Daily human activities recognition using heterogeneous sensors from smartphones

  • Authors: MS Dao, TA Nguyen-Gia, VC Mai

  • Journal: Procedia Computer Science, Vol. 111, pp. 323–328

  • Cited by: 34

  • Year: 2017

Summary:
The paper presents a method for recognizing daily human activities using various smartphone sensors. It highlights sensor fusion techniques to improve detection accuracy. The approach is lightweight and suitable for real-time implementation. It holds potential for fitness, health, and smart environment applications.

4. A real-time complex event discovery platform for cyber-physical-social systems

  • Authors: MS Dao, S Pongpaichet, L Jalali, K Kim, R Jain, K Zettsu

  • Conference: International Conference on Multimedia Retrieval

  • Cited by: 34

  • Year: 2014

Summary:
This work proposes a real-time platform for discovering complex events from integrated cyber, physical, and social sources. It focuses on fusing multi-modal data streams for event detection. The platform is designed for smart city and situational awareness applications. It bridges the gap between social sensing and real-time analytics.

5. Edge potential functions (EPF) and genetic algorithms (GA) for edge-based matching of visual objects

  • Authors: MS Dao, FGB De Natale, A Massa

  • Journal: IEEE Transactions on Multimedia, Vol. 9(1), pp. 120–135

  • Cited by: 33

  • Year: 2006

Summary:
This paper introduces edge potential functions (EPF) combined with genetic algorithms for visual object matching. It enhances robustness in noisy or occluded conditions. The method shows improvements in object recognition performance. It contributes foundational techniques for multimedia and computer vision systems.

Divya Mishra | Machine Learning | Best Researcher Award

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Assoc . Prof . Dr . Divya Mishra | Machine Learning | Best Researcher Award

Associate Professor at GL Bajaj Institute of Technology & Management, Greater Noida, India

Dr. Divya Mishra is a passionate and accomplished academician and AI researcher with over 13 years of cross-sectoral experience spanning academia, research, and industry. Currently serving as an Associate Professor in CSE-AIML at NIET and pursuing post-doctoral research remotely at Infrastructure University Kuala Lumpur (IUKL), her work centers on AI-driven sustainable e-governance. She brings deep expertise in machine learning, deep learning, and neural networks, underpinned by practical software development experience in Java and Python. Her PhD research addressed call drop prediction in mobile networks using an ANN-based model, resulting in near-perfect accuracy. Dr. Mishra is actively engaged in impactful research projects, including patents, edited books, and IEEE conference publications, while serving as a reviewer, session chair, and technical program committee member in prestigious forums. With a commitment to transparency, innovation, and sustainability in digital transformation, she is a leading voice in AI applications for public administration and smart solutions.

Professional Profile 

Education🎓

Dr. Divya Mishra holds a robust academic background in computer science and electronics. She earned her Ph.D. in Computer Science and Engineering from Noida International University in August 2021, with research focused on mitigating mobile network call drops using deep learning. She previously completed her M.Tech in Computer Science (Full-Time) from the same institution with a stellar CGPA of 9.2, securing a Gold Medal. Her postgraduate studies include an MCA from U.P. Technical University in 2011 with 77.4%, and a BCA from IGNOU, New Delhi. She also holds a Diploma in Electronics Engineering from B.T.E. Lucknow with 72.95%. Her academic journey reflects a consistent trajectory of academic excellence, technical competence, and multidisciplinary learning. Recognized for her honors during MCA by the Governor of Uttar Pradesh, Dr. Mishra’s educational path has equipped her with the theoretical and applied foundation required for her advanced research in AI, machine learning, sustainable systems, and digital governance.

Professional Experience📝

Dr. Divya Mishra boasts over 13 years of versatile professional experience across academia, industry, and research. She currently serves as an Associate Professor in the CSE-AIML Department at NIET, Greater Noida, since May 2025, while also pursuing post-doctoral research on AI-driven e-governance at IUKL, Malaysia. Her academic tenure includes Assistant Professor roles at GL Bajaj Institute and GNIOT, where she taught and mentored students in AI, ML, and data analytics. Previously, she was a Research Scholar at Noida International University, contributing significantly to AI-based telecom systems. Her industrial experience includes software development roles at Tripti e Solutions, Apex TG India Pvt. Ltd., and IIHT Ltd, where she also served as Center Head. She began her technical journey as a Diploma Trainee at Indian Telephone Industries Ltd. Her multifaceted experience enables her to seamlessly integrate theoretical concepts with practical applications in her teaching and research efforts.

Research Interest🔎

Dr. Divya Mishra’s research interests lie at the intersection of artificial intelligence, machine learning, deep learning, and sustainable governance systems. She is particularly passionate about developing intelligent, real-time AI-driven solutions for public administration, telecom, e-governance, and smart environmental monitoring. Her doctoral research focused on mitigating call drops in mobile networks through ANN-based models integrated into a real-time mobile application. Her post-doctoral focus extends into AI-powered sustainable e-governance frameworks, emphasizing transparency and accountability. She is also involved in multidisciplinary projects such as wildlife monitoring using deep learning, hand sign language recognition, waste classification, and emotion recognition from voice, reflecting her commitment to using AI for societal benefit. Dr. Mishra’s work spans practical AI implementations in healthcare, energy optimization, VANET security, and IoT systems. Through her edited books, patents, and publications, she continues to explore innovative intersections of AI with sustainability, data integrity, and policy, aligning her research with global digital transformation agendas.

Award and Honor🏆

Dr. Divya Mishra has received numerous accolades recognizing her academic excellence, impactful research, and leadership in AI. Notably, she was honored with the Shakti Award 2024 by Jansharnam NGO on Women’s Day for her outstanding contributions to technology and education. She also received the Gold Medal during her M.Tech, and her MCA degree was conferred by the Governor of Uttar Pradesh, recognizing her academic honors. She was appreciated for her contributions at international conferences like IICS 2021, and awarded the Quality Contribution Award by GNIOT, Greater Noida. Additionally, her leadership as an Innovation Ambassador at GL Bajaj’s Innovation Cell and roles as session chair and reviewer for multiple IEEE and Springer conferences further validate her active participation in shaping global research discourse. Her recognitions from institutional and national forums reflect her continuous drive toward academic excellence, innovative research, and meaningful community contributions in AI and governance.

Research Skill🔬

Dr. Divya Mishra possesses an extensive and dynamic research skill set across the AI landscape. She is proficient in programming languages like Python, Java, and C, and has a deep command over machine learning, deep learning, neural networks, and data analysis. Her expertise includes developing intelligent algorithms for real-time applications, evidenced by her ANN-based call drop prediction model and integration into the MyTelecomApp. She has published and reviewed numerous peer-reviewed papers, contributed to edited books, and filed multiple AI-driven patents across domains such as environment, health, and security. Dr. Mishra excels in research writing, patent drafting, project conceptualization, and conference management. She also has experience in hands-on technical training and mentoring, contributing to student development and curriculum design. Her interdisciplinary skills allow her to translate complex AI frameworks into socially impactful, sustainable solutions, making her a versatile and effective researcher in applied artificial intelligence and digital innovation ecosystems.

Conclusion💡

Dr. Divya Mishra demonstrates strong qualifications, multidisciplinary impact, and innovative leadership that make her a highly suitable candidate for the Best Researcher Award. Her ongoing postdoctoral work, numerous publications, patents, and reviewer engagements speak to her active and impactful research career. With minor enhancements in global collaborations, funding portfolios, and citation metrics, her candidacy would become even more compelling.

Publications Top Noted✍

  1. Title: Self-optimization in LTE: An approach to reduce call drops in mobile network
    Authors: D. Mishra, A. Mishra
    Year: 2018
    Citations: 8

  2. Title: Sentimental Voice Recognition: An Approach to Analyse the Emotion by Voice
    Authors: A. Gupta, D. Mishra
    Year: 2024
    Citations: 2

  3. Title: Neural Network: A Way to Know Consumer Satisfaction During Voice Call
    Authors: D. Mishra, S. Mishra
    Year: 2022
    Citations: 2

  4. Title: Performance Enhanced and Improvised Approach to Reduce Call Drops Using LTE-SON
    Authors: D. Mishra, A. Mishra
    Year: 2019
    Citations: 2

  5. Title: Drowsiness Alert System: An Approach To Save The Life
    Authors: A. Chandra, D. Mishra, B. Shaw, A. Gupta
    Year: 2023
    Citations: 1

  6. Title: Mobility Robustness Optimization Using ANN for Call Drop Prediction
    Authors: D. Mishra, S. Yadav
    Year: 2020
    Citations: 1

  7. Title: Fine tuning of MapReduce jobs using parallel K Map clustering
    Authors: D. Mishra, S. Yadav
    Year: 2019
    Citations: 1

  8. Title: Empowering Sustainable Waste Management: A Comparative Study of Machine Learning Models for Citizen Engagement
    Authors: D. Mishra, R. Kumar, A.B. bin Abdul Hamid
    Year: 2025

  9. Title: Machine Learning: A Self-Optimized Boon for Deaf and Mute to Recognize Real-Time Hand Sign Language
    Authors: P. Pandey, D. Mishra
    Year: 2025

  10. Title: Character Detection: An Approach to Clarify the Texts Using Machine Learning
    Authors: B. Shaw, D. Mishra
    Year: 2025

  11. Title: Intellicam: A Self-Optimizing Approach to Detect Burglary using Machine Learning
    Authors: A. Chandra, D. Mishra
    Year: 2025

  12. Title: Integrating Cryptographic Techniques with Machine Learning Algorithms for Enhanced Data Privacy and Information Security: A Mathematical Framework
    Authors: G. Merlin Florrence, D. Mishra, G. Ghule, P.K. Sahu, Singh
    Year: 2024

  13. Title: A Mathematical Framework for Enhancing IoT Security in VANETs: Optimizing Intrusion Detection Systems through Machine Learning Algorithms
    Authors: D. Mishra, S. Moudgi, D. Virmani, Y.P. Faniband, A.B. Nandyal, P.K. Sahu
    Year: 2024

  14. Title: YOLO: A way to identify gemstone and predict its relevant finger to wear
    Authors: D. Mishra, S. Mishra
    Year: 2023

  15. Title: Instant Energy Products: An Analysis
    Authors: D.M. Mohasin Haque, Irfan Ahamad
    Year: 2023

  16. Title: Mid–Point Sorting Algorithm: A New Way to Sort
    Authors: A. Garg, V. Patel, D. Mishra
    Year: 2022

  17. Title: A review on call drop
    Authors: D. Mishra, A. Mishra
    Year: 2016

Prof. Kalfalla Awedat | Deep Learning | Best Researcher Award

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Prof. Kalfalla Awedat | Deep Learning | Best Researcher Award

Professor at SUNY Morrisville College, United States

👨‍🎓 Profiles

Google Scholar

Publications

Advanced fault detection in photovoltaic panels using enhanced U-Net architectures

  • Authors: Khalfalla Awedat, Gurcan Comert, Mustafa Ayad, Abdulmajid Mrebit
    Journal: Machine Learning with Applications
    Year: 2025

COVID-CLNet: COVID-19 Detection With Compressive Deep Learning Approach

  • Authors: Khalfalla Awedat, Almabrok Essa
    Journal: International Journal of Computer Vision and Image Processing (IJCVIP)
    Year: 2022

Novel Robust Augmentation Approach Based on Sensing Features for Data Classification

  • Authors: Masoud M Alajmi, Khalfalla A Awedat
    Journal: IEEE Access
    Year: 2021

COVID-CLNet: COVID-19 Detection with Compressive Deep Learning Approaches

  • Authors: Khalfalla Awedat, Almabrok Essa
    Journal: arXiv preprint arXiv:2012.02234
    Year: 2020

Efficient face recognition using regularized adaptive non-local sparse coding

  • Authors: Masoud Alajmi, Khalfalla Awedat, Almabrok Essa, Fawaz Alassery, Osama S Faragallah
    Journal: IEEE Access
    Year: 2019

Dr. Aiai Wang | Deep Learning | Best Researcher Award

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Dr. Aiai Wang | Deep Learning | Best Researcher Award

Doctorate at University of Science and Technology Beijing, China

👨‍🎓 Profiles

Scopus

Publications

Quantitative Analysis of Pore Characteristics of Nanocellulose Reinforced Cementitious Tailings Fills Using 3D Reconstruction of CT Images

  • Authors: Wang, Aiai; Cao, Shuai; Yilmaz, Erol
    Journal: Journal of Materials Research and Technology
    Year: 2023

Prof. Haigen Hu | Deep Learning | Best Researcher Award

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Prof. Haigen Hu | Deep Learning | Best Researcher Award

Professor at Zhejiang University of Technology, China

👨‍🎓 Profiles

Scopus

Google Scholar

Publications

Multipath and noise resilient direction of arrival method for low-cost mechanical arm calibration

  • Authors: Hanmo Chen, Qianwei Zhou, Haigen Hu, Baoqing Li
    Journal: Computers and Electrical Engineering
    Year: 2025

Pruning Networks only Using Few-shot Pre-training Based on Gradient Similarity Frequency

  • Authors: Haigen Hu, Huihuang Zhang, Qianwei Zhou, Tieming Chen
    Journal: IEEE Transactions on Artificial Intelligence
    Year: 2025

An anchor-free instance segmentation method for cells based on mask contour

  • Authors: Qi Chen, Huihuang Zhang, Qianwei Zhou, Qiu Guan, Haigen Hu
    Journal: Applied Intelligence
    Year: 2025

RMFDNet: Redundant and Missing Feature Decoupling Network for salient object detection

  • Authors: Qianwei Zhou, Jintao Wang, Jiaqi Li, Haigen Hu, Keli Hu
    Journal: Engineering Applications of Artificial Intelligence
    Year: 2025

A comprehensive survey on contrastive learning

  • Authors: Haigen Hu, Xiaoyuan Wang, Yan Zhang, Qi Chen, Qiu Guan
    Journal: Neurocomputing
    Year: 2024

Ms. Beenish Khalid | Deep Learning | Best Researcher Award

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Ms. Beenish Khalid | Deep Learning | Best Researcher Award

National University of Sciences and Technology , Islamabad, Sweden

👨‍🎓 Profiles

Orcid

Google Scholar

Publications

A triple-shallow CNN with genetic algorithm channel selection method for classifying EEG complex limb movements

  • Author: Beenish Khalid, Ali Hassan, Muhammad Yasin, Muhammad Salman, Muhammad Fasih Uddin Butt, Wadood Abdul, Imran Khan Niazi
    Journal: Biomedical Signal Processing and Control
    Year: 2025

EMD and VMD in Pre-Movement EEG Signal Analysis: A Hybrid Mode Selection to Classify Upper Limb Complex Movements Using Statistical Features

  • Author: Beenish Khalid, Ali Hassan, Ehsan Ullah Munir, Imran Khan Niazi
    Journal: 2023 IEEE 20th International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET)
    Year: 2023

EEG Compression Using Motion Compensated Temporal Filtering and Wavelet Based Subband Coding

  • Author: Beenish Khalid, Muhammad Majid, Imran Fareed Nizami, Syed Muhammad Anwar, Majdi Alnowamii
    Journal: IEEE Access
    Year: 2020

Ms. Chetna Kwatra | Deep Learning | Women Researcher Award

Published on by Computer Vision Awards

Ms. Chetna Kwatra | Deep Learning | Women Researcher Award

Lovely Professional University, India

👨‍🎓 Profiles

Scopus

Orcid

Publications

Harnessing ensemble deep learning models for precise detection of gynaecological cancers

  • Authors: C.V. Kwatra, Chetna Vaid, H. Kaur, Harpreet, S.P. Potharaju, Sai Prasad, D.B. Jadhav, Devyani Bhamare, S.B. Tambe, Sagar B.
    Journal: Clinical Epidemiology and Global Health
    Year: 2025

Dr. Meng Wang | Deep Learning | Best Researcher Award

Published on by Computer Vision Awards

Dr. Meng Wang | Deep Learning | Best Researcher Award

Doctorate at Xi’an Polytechnic University, China

👨‍🎓 Profiles

Orcid

Publications

Place Your Next Branch with MILE-RUN: Min-dist Location Selection over User Movement

  • Author: Meng Wang
    Journal: Information Sciences
    Year: 2018

PINOCCHIO: Probabilistic Influence-Based Location Selection over Moving Objects

  • Author: Meng Wang
    Journal: IEEE Transactions on Knowledge and Data Engineering (TKDE)
    Year: 2016