Huxiong Li | Artificial Intelligence | Artificial Intelligence

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Prof. Dr. Huxiong Li | Artificial Intelligence | Artificial Intelligence

Professor | Shaoxing University | China

Prof. Dr. Huxiong Li is a leading researcher in artificial intelligence, specializing in 3D vision, intelligent perception, urban digital twins, and complex network control. He has made significant contributions through innovative research, demonstrated by his extensive publications, patents, and leadership of multiple national and international projects. His work bridges AI technologies with practical applications in cultural heritage preservation and smart city infrastructure, reflecting a strong interdisciplinary approach. Over the years, he has fostered collaborations with global institutions, enhancing the reach and impact of his research. Prof. Li’s guidance of numerous projects has not only advanced scientific understanding but also facilitated industrial implementation of AI technologies. His research demonstrates consistent excellence, originality, and societal relevance, positioning him as a prominent figure in geospatial artificial intelligence. According to Scopus, his measurable research impact includes 28 citations, 9 documents, and an h-index of 402.

Profiles: Scopus | ORCID

Featured Publications

1. Reducing the clustering challenge in the IoT using two disjoint convex hulls. Scientific Reports, 2025.

2. Integrating InSAR coherence and air pollution detection satellites to study the impact of war on air quality. International Journal of Applied Earth Observation and Geoinformation, 2025.

 

Bushra Naz | Deep learning | Best Researcher Award

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Dr. Bushra Naz | Deep learning | Best Researcher Award

Associate professor at Mehran University of Engineering and Technology| Pakistan

Dr. Bushra Naz is an accomplished academic and researcher with expertise in artificial intelligence, deep learning, image processing, hyperspectral image classification, and pattern recognition. Serving as an Associate Professor and PhD supervisor, she has made significant contributions to advancing knowledge through impactful research and dedicated mentorship. Her funded projects include innovative solutions in speech emotion recognition, assistive technologies for visually impaired individuals, water quality monitoring, and sustainable agriculture, reflecting a strong focus on societal benefit. She has published widely, reviewed for leading international journals, and actively participated in global conferences as a session chair and committee member. Her achievements are further recognized through prestigious scholarships, research fellowships, and honors that demonstrate her academic excellence and leadership. With a commitment to bridging theory and practice, Dr. Naz continues to drive interdisciplinary collaborations and inspire future researchers, positioning herself as a leader in advancing AI-driven solutions for real-world challenges.

Professional Profile 

Google Scholar

Education

Dr. Bushra Naz has a strong academic foundation in computer systems and engineering, beginning with a bachelor’s degree in Computer Systems Engineering, followed by a master’s degree in Communication Systems and Networks. She pursued her doctoral studies at Nanjing University of Science and Technology, China, where she completed a PhD in Computer Science and Engineering with a research focus on machine learning and hyperspectral image classification. Her doctoral thesis explored advanced elastic-net representation methods for image classification, demonstrating her early commitment to innovative AI-driven solutions. She also earned international recognition during her doctoral journey, supported by prestigious scholarships and fellowships, which allowed her to gain global exposure and strengthen her research expertise. With a solid academic trajectory rooted in both national and international institutions, Dr. Naz has combined technical depth with interdisciplinary knowledge, equipping her with the skills to pursue cutting-edge research while training the next generation of scholars and professionals.

Experience

Dr. Bushra Naz brings extensive academic and research experience spanning over a decade. She began her professional journey as a laboratory lecturer, progressively advancing to lecturer, assistant professor, and currently serves as an associate professor in the Department of Computer Systems Engineering at Mehran University of Engineering and Technology, Jamshoro. In these roles, she has taught a diverse range of subjects including microprocessors, operating systems, digital image processing, machine learning, deep learning, and artificial intelligence, shaping the technical skills of numerous students. Beyond teaching, she has taken on leadership roles in departmental committees, project supervision, curriculum review, and outcome-based education implementation. Her responsibilities also include supervising undergraduate, master’s, and doctoral research projects, many of which align with pressing technological and societal challenges. Through her experience, she has built a reputation as a dedicated educator, innovative researcher, and academic leader who seamlessly integrates research and teaching to drive meaningful outcomes.

Research Focus

Dr. Bushra Naz’s research focus lies in the application of artificial intelligence and machine learning to solve complex real-world problems. Her expertise covers deep learning, neural networks, hyperspectral imaging, image classification, object detection, and pattern recognition. She has conducted pioneering research in spectral-spatial methods for image classification, advancing techniques in optimization and sparse representation. Her projects span diverse domains, including speech emotion recognition, augmented reality-based navigation for the visually impaired, IoT-driven water quality monitoring, crop sensing for sustainable agriculture, and accident detection systems. This interdisciplinary approach highlights her commitment to applying AI solutions for societal impact, sustainability, and technological innovation. In addition, she actively contributes as a reviewer for high-impact journals and participates in international conferences as a session chair, strengthening global research dialogue. By integrating technical rigor with practical application, Dr. Naz continues to expand the frontiers of AI research while addressing challenges that directly benefit communities and industries.

Award and Honor

Dr. Bushra Naz’s academic excellence and research contributions have been recognized through numerous awards and honors at national and international levels. She received the prestigious China Scholarship Council award for her PhD studies and was further distinguished with the ELITE Scholarship as the Best Foreign Student during her doctoral program. Her excellence in research was acknowledged with honor certificates and rewards for her publications in IEEE journals. Earlier in her career, she earned the Higher Education Commission of Pakistan’s fully funded scholarship for her master’s studies and received merit-based scholarships during her undergraduate years. She also secured the UNESCO/People’s Republic of China Co-Sponsored Fellowship as a senior research scholar, reflecting her growing international recognition. These accolades not only highlight her academic dedication but also underscore her ability to compete successfully at global platforms. Collectively, her awards showcase her talent, perseverance, and impactful contributions to engineering and computer science research.

Publication Top Notes

  • Title: Sustainable Higher Education Reform Quality Assessment Using SWOT Analysis with Integration of AHP and Entropy Models: A Case Study of Morocco
    Year: 2021
    Citations: 64

  • Title: Spatial-Hessian-feature-guided variational model for pan-sharpening
    Year: 2015
    Citations: 50

  • Title: Fast superpixel based subspace low rank learning method for hyperspectral denoising
    Year: 2018
    Citations: 44

  • Title: Bilayer elastic net regression model for supervised spectral-spatial hyperspectral image classification
    Year: 2016
    Citations: 28

  • Title: Hybrid LSTM Self-Attention Mechanism Model for Forecasting the Reform of Scientific Research in Morocco
    Year: 2021
    Citations: 25

  • Title: Onion Crop Monitoring with Multispectral Imagery using Deep Neural Network
    Year: 2021
    Citations: 14

  • Title: A machine learning framework for major depressive disorder (MDD) detection using non-invasive EEG signals
    Year: 2025
    Citations: 13

  • Title: Sustainable higher education reform quality assessment using SWOT Analysis with integration of AHP and Entropy models: A case study of Morocco
    Year: 2021
    Citations: 13

  • Title: Local and nonlocal context-aware elastic net representation-based classification for hyperspectral images
    Year: 2017
    Citations: 8

  • Title: Hyperspectral image classification via Elastic Net Regression and bilateral filtering
    Year: 2015
    Citations: 8

Conclusion

Dr. Bushra Naz has established herself as a distinguished researcher and academic leader with a significant impact in the fields of artificial intelligence, machine learning, and hyperspectral image analysis. Her extensive research portfolio demonstrates a balance of theoretical innovation and practical application, addressing societal challenges such as sustainable agriculture, water quality monitoring, assistive technologies, and mental health detection. With a strong record of high-impact publications, international collaborations, research supervision, and active participation in conferences and editorial roles, she has consistently contributed to advancing knowledge and mentoring future researchers. Her achievements are further reinforced by prestigious awards, fellowships, and funded projects that recognize her scholarly excellence and leadership. Overall, Dr. Naz exemplifies the qualities of a visionary researcher—innovative, dedicated, and socially responsible—making her a highly deserving candidate for recognition through the Best Researcher Award.

Mian Usman Sattar | Artificial Intelligence | Best Researcher Award

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Dr. Mian Usman Sattar | Artificial Intelligence | Best Researcher Award

Lecturer at University of Derby, United Kingdom

Dr. Mian Usman Sattar is an accomplished academic and researcher specializing in information systems, business intelligence, and data analytics, with extensive teaching and leadership experience across universities in the United Kingdom, Malaysia, and Pakistan. He has led innovative academic programs, introduced contemporary specialization tracks, and integrated emerging technologies into curricula. His research achievements are supported by multiple prestigious national and international grants, reflecting both expertise and the trust of funding bodies. As a program leader, department chair, and cluster head, he has demonstrated strong leadership in shaping academic directions and fostering institutional collaborations. His expertise spans artificial intelligence for business, data-driven marketing, enterprise systems, and big data analytics, complemented by a track record of academic honors and recognition as an approved PhD supervisor. Through his global exposure, research contributions, and commitment to advancing knowledge, Dr. Sattar has significantly impacted academia and industry, making him a distinguished figure in his field.

Professional Profile 

Google Scholar | Scopus Profile

Education

Dr Mian Usman Sattar holds a diverse and robust academic background, reflecting his dedication to continuous learning and specialization in technology and business domains. He earned his PhD in Informatics from the Malaysian University of Science and Technology, focusing on advanced aspects of information systems and analytics. His postgraduate qualifications include an MS in IT Management from the University of Sunderland, postgraduate diplomas in computer science and communication & computer technology, and an MSc in Computer Science from Government College University, Lahore. His academic progression demonstrates a steady advancement from foundational computer science knowledge to applied IT management and specialized research in informatics. Currently, he is pursuing a Postgraduate Certificate leading to FHEA from the University of Derby, reflecting his commitment to enhancing teaching and academic leadership. This diverse educational portfolio has equipped him with a strong blend of technical expertise, managerial insight, and pedagogical skills essential for his academic and research pursuits.

Experience

Dr. Mian Usman Sattar has an extensive career spanning academia, research leadership, and industry roles in the United Kingdom, Pakistan, and Malaysia. He is currently a Lecturer and Program Leader in Information Technology at the University of Derby, where he oversees academic programs and fosters student engagement. His previous academic roles include Assistant Professor positions at Beaconhouse National University and the University of Management and Technology, where he led academic clusters, introduced new specialization tracks, and managed industry collaborations. Beyond academia, he has held managerial and technical roles such as Deputy Manager (MIS) and Assistant Network Administrator, as well as leadership in training and consultancy for information security. Throughout his career, he has taught a wide range of subjects including business analytics, artificial intelligence for business, enterprise resource planning, and data-driven marketing. His experience reflects a blend of teaching, research supervision, academic program development, and practical industry engagement across multiple disciplines and sectors.

Research Focus

Dr. Mian Usman Sattar’s research centers on business intelligence, data analytics, enterprise systems, and information security, with a strong emphasis on integrating disruptive technologies into organizational and educational contexts. His work explores the use of artificial intelligence, machine learning, and big data analytics to enhance business decision-making and operational efficiency. He is particularly interested in the application of data-driven strategies in marketing, enterprise resource planning, and digital transformation. His research approach is both academic and applied, aiming to bridge the gap between theory and real-world implementation. By leading and participating in funded research projects, he has contributed to advancing knowledge in areas such as analytics governance, trust in digital systems, and emerging technologies for business competitiveness. His interdisciplinary focus enables collaboration across computing, engineering, and management fields, making his research relevant to both academia and industry. This focus reflects a commitment to innovation, problem-solving, and societal impact through technology-driven solutions.

Award and Honor

Dr. Mian Usman Sattar has earned multiple prestigious awards and honors that underscore his research excellence, academic leadership, and professional contributions. He received a PhD Fellowship from the Malaysian University of Science and Technology, recognizing his academic potential and research capability. His work has been supported by competitive grants from organizations such as the Pakistan Science Foundation, Malaysia Digital Economy Corporation, Malaysia Toray Science Foundation, and TWAS-COMSTECH, totaling significant funding for technology-driven research projects. He was awarded a travel grant by the Higher Education Commission of Pakistan to present his research internationally, reflecting recognition from the national academic community. Additionally, he holds the distinction of being an HEC Approved PhD Supervisor, affirming his capability to guide doctoral-level research. These achievements demonstrate his ability to secure funding, contribute to high-impact projects, and earn recognition for his scholarly and professional excellence at both national and international levels.

Publications Top Notes

  • Title: Predicting student performance in higher educational institutions using video learning analytics and data mining techniques
    Authors: R Hasan, S Palaniappan, S Mahmood, A Abbas, KU Sarker, MU Sattar
    Year: 2020
    Citations: 236

  • Title: Effects of virtual reality training on medical students’ learning motivation and competency
    Authors: MU Sattar, S Palaniappan, A Lokman, A Hassan, N Shah, Z Riaz
    Year: 2019
    Citations: 167

  • Title: Motivating medical students using virtual reality based education
    Authors: M Sattar, S Palaniappan, A Lokman, N Shah, U Khalid, R Hasan
    Year: 2020
    Citations: 148

  • Title: Whole-genome sequencing as a first-tier diagnostic framework for rare genetic diseases
    Authors: H Nisar, B Wajid, S Shahid, F Anwar, I Wajid, A Khatoon, MU Sattar, …
    Year: 2021
    Citations: 30

  • Title: An efficient computer vision-based approach for acute lymphoblastic leukemia prediction
    Authors: A Almadhor, U Sattar, A Al Hejaili, U Ghulam Mohammad, U Tariq, …
    Year: 2022
    Citations: 26

  • Title: Validation method for digital flow meter for fuel vendors
    Authors: P Megantoro, DA Husnan, MU Sattar, A Maseleno, O Tanane
    Year: 2020
    Citations: 19

  • Title: A review: Emerging trends of big data in higher educational institutions
    Authors: R Hasan, S Palaniappan, S Mahmood, VR Naidu, A Agarwal, B Singh, …
    Year: 2020
    Citations: 17

  • Title: Design of laboratory scale fluid level measurement device based on arduino
    Authors: NF Apsari, P Megantoro, MU Sattar, A Maseleno, O Tanane
    Year: 2020
    Citations: 12

  • Title: User experience design in virtual reality medical training application
    Authors: MU Sattar, S Palaniappan, A Lokman, N Shah, Z Riaz, U Khalid
    Year: 2021
    Citations: 11

  • Title: Multi-stage intelligent smart lockdown using sir model to control covid 19
    Authors: A Ghaffar, S Alanazi, M Alruwaili, MU Sattar, W Ali, M Humayun, …
    Year: 2021
    Citations: 10

  • Title: Arduino-based digital advanced audiometer
    Authors: NH Wijaya, M Ibrahim, N Shahu, MU Sattar
    Year: 2021
    Citations: 10

  • Title: Block-chain-security advancement in medical sector for sharing medical records
    Authors: R Abid, B Aslam, M Rizwan, F Ahmad, MU Sattar
    Year: 2019
    Citations: 10

  • Title: Customer Satisfaction Affects the Customer Loyalty: Evidence from Telecommunication Sector in Pakistan
    Authors: MU Sattar, B Sattar
    Year: 2012
    Citations: 10

  • Title: eDify: Enhancing Teaching and Learning Process by Using Video Streaming Server
    Authors: R Hasan, S Palaniappan, S Mahmood, KU Sarker, MU Sattar, A Abbas, …
    Year: 2021
    Citations: 9

  • Title: Intelligent digital twin to make robot learn the assembly process through deep learning
    Authors: B Ahmad
    Year: 2021
    Citations: 9

Conclusion

The publication record of Dr. Mian Usman Sattar reflects a strong and diverse research portfolio with impactful contributions in education technology, virtual reality in medical training, artificial intelligence applications, data analytics, and biomedical research. Several of his works, particularly those on predicting student performance and virtual reality in education, have received significant citation counts, indicating strong relevance and recognition in the academic community. His collaborations span across interdisciplinary fields, integrating computing, engineering, healthcare, and business domains, which enhances the applicability and reach of his research. The steady stream of publications in reputable journals and conferences, coupled with high-impact studies, demonstrates his ability to address current challenges with innovative, technology-driven solutions. Overall, the breadth, depth, and influence of his research output position him as a noteworthy and influential scholar whose work continues to contribute meaningfully to academia and industry alike.

María Inmaculada Mohino-Herranz | Artificial Intelligence| Best Research Article Award

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Dr. María Inmaculada Mohino-Herranz | Artificial Intelligence| Best Research Article Award

Investigador, INTA, Spain

Inmaculada Mohíno Herranz is a distinguished researcher in the fields of signal processing, pattern recognition, and emotion detection. She currently works at the National Institute of Aerospace Technology (INTA), bringing her extensive expertise in physiological signal analysis to the forefront of innovative research. 🌟 Her career reflects a commitment to advancing technology and science, contributing to both academia and industry.

Publication profile

Scopus

Education

Inmaculada holds an impressive academic background, beginning with her M.Eng. in Telecommunication Engineering (2010), followed by a second degree in Electronics Engineering (2012), and a Master’s degree in Information and Communication Technologies (2015). 📚 She culminated her academic journey with a Ph.D. in Information and Communication Technologies (2017, with honors) from the University of Alcalá, Madrid, Spain. 🎓

Experience

She has built a solid career in academia and research, having worked at the Signal Theory and Communications Department of the University of Alcalá, where she was part of the Applied Signal Processing research group until 2021. 📡 Currently, she continues her research at INTA, contributing to projects related to aerospace technology. She has also been actively involved in supervising final degree and master’s projects, shaping future innovators. 👩‍🏫

Research Focus

Inmaculada’s research revolves around physiological signal processing, pattern recognition, emotion recognition, and stress detection. 💡 Her work is especially significant in understanding how physiological data can be used to monitor emotional states, which has applications ranging from healthcare to technology-enhanced well-being. 💻

Awards and Honors

Inmaculada has received recognition for her outstanding contributions to the field of Information and Communication Technologies, including supervising several successful degree projects and participating in numerous public and private-funded research initiatives. 🏆 Her efforts in academic and industrial projects further solidify her reputation as a leading researcher.

Publication Top Notes

Inmaculada Mohíno Herranz has authored various impactful papers. She has published nine journal papers, six of which are indexed in the Journal Citation Report. 📄 She has also written a book chapter and around 20 conference papers, showcasing her active engagement in research dissemination.

Metrological analysis on measuring techniques used to determine solubility of solids in supercritical carbon dioxide – Published in Measurement: Journal of the International Measurement Confederation (2025), this article has no citations yet.

Initializing the weights of a multilayer perceptron for activity and emotion recognition – Published in Expert Systems with Applications (2024), this article has no citations yet.

Introducing the ReaLISED Dataset for Sound Event Classification – Published in Electronics (2022), cited by two articles.

Linear detector and neural networks in cascade for voice activity detection in hearing aids – Published in Applied Acoustics (2021), cited by one article.

A wrapper feature selection algorithm: An emotional assessment using physiological recordings from wearable sensors – Published in Sensors (2020), this open-access article focuses on emotion assessment using physiological data from wearable sensors.

Jing Wang | Artificial Intelligence | Best Researcher Award

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Dr. Jing Wang | Artificial Intelligence | Best Researcher Award

Assistant Professor, Southeast University, China

Jing Wang is an assistant researcher at the School of Computer Science and Engineering, Southeast University, China. With a Ph.D. from Southeast University under Prof. Xin Geng, Jing has made significant strides in machine learning, focusing on multi-label learning and explainable machine learning. Jing is a recognized contributor to multiple esteemed journals and conferences, with impactful research on label distribution learning.

Publication Profile

ORCID

Strengths for the Award:

  1. Solid Academic Background: The candidate has pursued advanced degrees in Computer Science from reputable institutions, including a Ph.D. from Southeast University under the supervision of renowned professors.
  2. Focused Research Interests: The candidate’s research concentrates on machine learning, with a particular emphasis on multi-label learning and explainable machine learning—fields of significant current interest.
  3. Prolific Publication Record: The candidate has authored numerous high-quality journal and conference papers, many in well-regarded venues such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Pattern Analysis and Machine Intelligence, and AAAI Conference on Artificial Intelligence.
  4. Academic Service and Leadership: The candidate has served as a lead guest editor and guest editor for special issues in reputable journals and has been a program committee member and reviewer for major conferences and journals, showcasing their commitment to advancing their field.
  5. Collaboration and Recognition: The candidate’s work involves collaboration with other established researchers, and they have published in leading journals and conferences, reflecting their recognition and influence in the research community.

Areas for Improvement:

  1. Research Impact and Application: While the candidate has published extensively, there is limited information on the real-world impact and applications of their research. Emphasizing how their work has been applied or can be applied to solve practical problems in industry or society could strengthen their profile.
  2. Awards and Honors: Although the candidate has made notable academic contributions, there is no mention of individual awards or recognitions, which could further validate their research impact and excellence.
  3. International Collaboration and Diversity of Research Areas: Expanding collaborations beyond their current network, potentially with international researchers from diverse fields, could enhance their research’s global reach and interdisciplinary impact.

 

🎓 Education

Ph.D. in Computer Science from Southeast University, China, supervised by Prof. Xin Geng. M.Sc. in Computer Science from Northeast University, China, supervised by Prof. Xingwei Wang. B.Sc. in Computer Science from Suzhou University of Science and Technology, China.

🏆 Experience

Jing Wang serves as an assistant researcher at the School of Computer Science and Engineering, Southeast University, China. Jing actively contributes to the academic community as a guest editor for renowned journals and as a program committee (PC) member and reviewer for prestigious conferences, including AAAI, UAI, and ECML.

🔍 Research Focus

Jing Wang’s research delves into machine learning, with a particular emphasis on multi-label learning and explainable machine learning. Jing’s work is notable for pioneering approaches in label distribution learning, leveraging common and label-specific feature fusion spaces, and developing innovative methodologies for driver distraction detection and open-world few-shot learning.

🏅 Awards and Honors

Lead Guest Editor for IEEE Transactions on Consumer Electronics on “When Consumer Electronics Meet Large Models: Opportunities and Challenges.” Guest Editor for the International Journal of Machine Learning and Cybernetics on “Reliable and Interpretable Machine Learning: Theory, Methodologies, Applications, and Beyond.” Program Committee Member for AAAI-23, UAI-24, and ECML-24.Reviewer for several high-impact journals, including IEEE TNNLS, IEEE TMM, IEEE TAI, IEEE JBHI, and Medical Image Analysis (MIA).

📚 Publications Top Notes

Jing Wang has authored numerous high-impact papers in top-tier journals and conferences. Key publications include works on label distribution learning in Pattern Recognition and IEEE Transactions on Neural Networks and Learning Systems, contributing to the understanding of label-specific feature fusion and fuzzy label correlation in machine learning. Jing’s research on “Driver Distraction Detection Using Semi-supervised Lightweight Vision Transformer” has been recognized for its innovative application in Engineering Applications of Artificial Intelligence.

Jing Wang, Fu Feng, Jianhui Lv, and Xin Geng. “Residual k-Nearest Neighbors Label Distribution Learning.” Pattern Recognition (PR), 2024, in press.

Zhiyun Zhang, Jing Wang†, and Xin Geng. “Label Distribution Learning by Utilizing Common and Label-Specific Feature Fusion Space.” International Journal of Machine Learning and Cybernetics, 2024, in press.

Jing Wang, Zhiqiang Kou, Yuheng Jia, Jianhui Lv, and Xin Geng. “Label Distribution Learning by Exploiting Fuzzy Label Correlation.” IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 2024, in press.

Zhiqiang Kou, Jing Wang, Yuheng Jia, and Xin Geng.* “Inaccurate Label Distribution Learning.” IEEE Transactions on Circuits and Systems for Video Technology (IEEE TCSVT), 2024, in press.

Jing Wang and Xin Geng. “Explaining the Better Generalization of Label Distribution Learning for Classification.” SCIENCE CHINA Information Sciences (SCIS), 2024, in press.

Conclusion:

The candidate demonstrates a strong research profile with a solid foundation in machine learning, a prolific publication record, and active involvement in the academic community. Their focused research in multi-label learning and explainable AI aligns well with contemporary challenges and advancements in artificial intelligence. To strengthen their candidacy for the Best Researcher Award, they could emphasize the practical impact of their research, seek additional recognitions or awards, and pursue more diverse and international collaborations. Overall, the candidate is highly suitable for the award, with a promising future in their research career.

Ritu Tanwar | Artificial intelligence | Best Researcher Award

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Ms. Ritu Tanwar | Artificial intelligence | Best Researcher Award

Research Scholar, NIT Uttarakhand, India

Ms. Ritu Tanwar is a dedicated Research Scholar at the National Institute of Technology, Uttarakhand, India, specializing in stress and emotion recognition through advanced machine learning techniques. Her innovative research harnesses deep learning and artificial intelligence to interpret physiological signals, contributing significantly to the field of affective computing. Ritu’s academic journey and teaching roles underline her commitment to advancing both theoretical and practical aspects of her research.

Profile

Scopus

Research for “Best Researcher Award” for Ms. Ritu Tanwar

Strengths for the Award

Ms. Ritu Tanwar, currently pursuing her PhD at the National Institute of Technology, Uttarakhand, has demonstrated exceptional strengths in her field of research. Her primary area of focus—stress and emotion recognition through physiological signals—highlights her deep engagement with cutting-edge technology and data analysis. Ritu’s work utilizes advanced techniques in deep learning and machine learning to address significant challenges in affective state recognition.

Innovative Research Contributions: Ritu’s research integrates multimodal physiological signals to enhance stress recognition, showcasing her ability to develop and implement novel frameworks. Her attention-based hybrid deep learning models for wearable stress recognition, published in prestigious journals like Engineering Applications of Artificial Intelligence and Computers and Electrical Engineering, underline her proficiency in blending theory with practical application.

High-Impact Publications: Her publications in high-impact journals and conferences, including Computers in Biology and Medicine and the International Conference on Artificial Intelligence, reflect the substantial impact of her work on the field. Her innovative models, such as the CNN-LSTM based stress recognition system, are well-received and contribute to advancing the state of the art in affective computing.

Diverse Expertise: Ritu’s skill set spans various domains, from deep learning and artificial intelligence to data analysis and signal processing. Her ability to apply these skills effectively in her research demonstrates a well-rounded expertise that is crucial for a leading researcher.

Areas for Improvement

While Ms. Tanwar’s achievements are commendable, there are areas where she could further enhance her profile:

Broader Research Collaboration: Expanding her collaborative network with researchers from diverse fields could provide new insights and foster interdisciplinary approaches. Engaging in more collaborative projects could also increase the visibility and applicability of her research outcomes.

Broadened Publication Scope: Although Ritu has published extensively, diversifying her publication portfolio to include more interdisciplinary journals or higher-impact venues could further amplify the reach and influence of her research.

Enhanced Outreach: Increasing her participation in academic and industry conferences, workshops, and seminars could boost her professional network and provide more platforms to showcase her research. Additionally, contributing to review articles or special issues in her field could enhance her visibility as a thought leader.

Education 🎓

Ms. Tanwar is currently pursuing a PhD in Electronics Engineering at the National Institute of Technology, Uttarakhand, India, focusing on developing a deep learning framework for affective state recognition using multimodal physiological signals (April 2021-present). She earned her M.Tech. in Electronics & Communication Engineering from the University Institute of Engineering & Technology, Kurukshetra, India, with a thesis on emotion recognition from audio signals (July 2018). Her foundational B.Tech. in Electronics & Communication Engineering was also completed at the same institute (July 2013).

Experience 💼

Ms. Tanwar has a robust academic background, having worked as a Teaching Assistant at the National Institute of Technology, Uttarakhand, where she taught courses on Microcontroller and Interfacing, Digital Signal Processing, and Speech & Image Processing. Her research experience includes contributions as an Assistant/Associate Supervisor for undergraduate students and active participation in administrative and outreach activities, including her roles as Session Coordinator and Reviewer for the IC2E3 IEEE Conference.

Research Interests 🔬

Ms. Tanwar’s research interests are centered around stress and emotion recognition, physiological signals, and advanced data analysis techniques. She specializes in applying deep learning, machine learning, and artificial intelligence to improve the accuracy and applicability of affective state recognition systems.

Awards 🏆

Senior Research Fellow Scholarship (2021-present): Awarded for her exceptional research capabilities and contributions to her field.

Publication Recognition: Her work has been accepted and recognized in leading journals and conferences, reflecting her significant contributions to the field of artificial intelligence and machine learning.

Publications Top Notes

Tanwar, R., Phukan, O. C., Singh, G., Pal, P. K., & Tiwari, S. (2024). Attention based hybrid deep learning model for wearable based stress recognition. Engineering Applications of Artificial Intelligence, 127, 107391.

Tanwar, R., Singh, G., & Pal, P. K. (2024). A Hybrid Transposed Attention Based Deep Learning Model for Wearable and Explainable Stress Recognition. Computers and Electrical Engineering (Accepted).

Tanwar, R., Singh, G., & Pal, P. K. (2024). Explainable Artificial Intelligence System For Stress Recognition Using Multimodal Physiological Signals. Computers in Biology and Medicine (under review).

Tanwar, R., Singh, G., & Pal, P. K. (2024). Stress-Wed: Stress recognition autoencoder using Wearables Data. In Second International Conference on Artificial Intelligence: Towards Sustainable Intelligence. Springer (Accepted).

Conclusion

Ms. Ritu Tanwar’s research on stress and emotion recognition using physiological signals is both innovative and impactful, making her a strong candidate for the “Best Researcher Award.” Her contributions to deep learning and machine learning in affective computing are significant, and her academic and teaching experiences add to her profile as a dedicated and knowledgeable researcher. By addressing areas for improvement, such as expanding collaboration and publication scope, Ritu can further strengthen her position as a leading researcher in her field. Her ongoing research promises to make substantial contributions to both theoretical and applied aspects of artificial intelligence and emotion recognition.

NEHA KATIYAR | Machine Learning | Best Research Article Award

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MS. NEHA KATIYAR | Machine Learning | Best Research Article Award

RESEARCH SCHOLAR, Bennett university, India

 

Neha Katiyar is an Assistant Professor at Noida Institute of Technology, Greater Noida, India. With a robust background in Information Technology and Computer Science, she has contributed significantly to academia through teaching, research, and project management.

Profile

Scopus

Education

🎓 Doctorate
Bennett University, Greater Noida (July 2023 – Present)
Department: Computer Science & Engineering

🎓 Master of Technology
Madan Mohan Malviya University of Technology, Gorakhpur (Aug 2018 – July 2020)
Department: Information Technology and Computer Application
Percentage: 69% (First Division)

🎓 Bachelor of Technology
Sir Chootu Ram Institute of Engineering & Technology, Meerut (July 2015 – June 2018)
University: Chaudhary Charan Singh University, Meerut, U.P
Course: Information Technology
Percentage: 74% (First Division)

🎓 Diploma in Engineering
Government Girls Polytechnic, Lucknow (July 2010 – December 2013)
University: Board of Technical Education, Lucknow UP
Course: Information Technology
Percentage: 70% (First Division)

🎓 High School
Soni Pariya Inter College, Farrukhabad (Apr 2009 – Mar 2010)
Board: Board of High School and Intermediate Education, U.P.
Percentage: 58% (Second Division)

Experience

💼 Assistant Professor
Noida Institute of Technology, Greater Noida (11 April 2022 – 17 May 2023)
Responsibilities included evaluation of copies, research work, academic work, and preparation of question banks and presentations.

💼 Academic Associate
Indian Institute of Management, Rohtak (22 July 2021 – 7 April 2022)
Assisted faculties, conducted empirical research, managed conferences, and evaluated copies.

💼 Research Assistant
Ajay Kumar Garg Engineering College, Ghaziabad (12 Oct 2020 – 16 Jul 2021)
Worked on a project titled “Compressed parallel wavelet tree based on semantic search” funded by the Council of Science And Technology, Uttar Pradesh (UPCST).

Research Interest

🔬 Neha’s research interests include Cyber Security, Internet of Things (IoT), Machine Learning, and Artificial Intelligence. She has actively participated in various projects and research works, contributing to advancements in these fields.

Publications Top Notes

📚 Neha has authored several research papers and articles in reputed journals and conferences. Below are some of her notable publications:

Diabetes detection using IoT techniques and platform: A Survey – Published in the 1st International Conference on Recent Trends in Computer Science and Information Technology (ICRCSIT-20) at St. Martin’s Engineering College Secunderabad Telangana, on 17-18 June 2020.

A review: Target Based Sentiment Analysis using Machine Learning – Published in the 4th International Conference on Microelectronics and Telecommunications at SRM Institute of Science and Technology NCR Campus, on 26-27 Sept. 2020 (Springer Conference).

Index Optimization using Wavelet Tree and Compression – Published in the 2nd International Conference on Data Analytics and Management Conference (ICDAM 2021) at Panipat Institute of Engineering and Technology, on 26 June 2021.

A Survey on Wavelet Tree ensembles with Machine Learning and its classification – Published in 2021 at Sreyas Institute of Engineering and Technology, Hyderabad, on 9-10 July 2021.

A perspective towards 6G Networks – Published in the 5th ICMETE21 at SRM Institute of Science and Technology NCR Campus, on 24-25 Sept. 2021 (Springer Conference).

Trending IoT platform Middleware layer – Published in Taylor and Francis Group Journal on 3 May 2023.