Christos Roumeliotis | Computer Science | Young Scientist Award

Posted on 05/11/202405/11/2024 by ScienceFather

Mr. Christos Roumeliotis | Computer Science | Young Scientist Award

Electrical & Computer Engineering University of Western Macedonia Greece

Christos Roumeliotis is an accomplished Electrical and Computer Engineer specializing in Biomedical Technology, Healthcare, and Blockchain applications in energy. With a keen entrepreneurial spirit, he has been recognized in Forbes 30 Under 30 Greece and is an active member of the IEEE. As a young leader, he has held notable positions in the IEEE Student Branch and worked in various technology-driven roles.

Profile

Orcid.org

🎓 Education:

Christos completed his integrated MSc in Electrical and Computer Engineering from the University of Western Macedonia (UoWM). His academic journey has been complemented by a Reciprocal Scholarship and active participation in IEEE initiatives.

💼 Experience:

Christos serves as a Business Development Partner at Because Group, focusing on innovative marketing solutions. He co-founded Innovation Bee, where he leads as CEO, providing strategic AI-driven solutions across industries. He also co-founded Gridustry, a blockchain-based energy certification and trading company, aiming to optimize green energy market systems.

🔬 Research Interests:

Christos’s research spans Biomedical Technology for health solutions, Blockchain in energy, and smart contracts. His projects include blockchain-based Peer-to-Peer Energy Trading, green certificates, and a non-invasive wearable for Multiple Sclerosis monitoring.

🏆 Awards:

IEEE CS 20 in their 20s List (2023): Recognized among emerging leaders in Computer Science and Engineering.
Forbes 30 Under 30 Greece (2023): Featured among Greece’s dynamic young professionals.
Green Cities Competition (2022): Won 2nd place for innovative solutions in sustainable city development.

📄 Publications Top Notes:

“A Comprehensive Survey of Blockchain in IoT,” 2024. Intelligent Computing on IoT 2.0, Taylor & Francis. Co-authored with Konstantina Banti and others, this survey highlights IoT innovations and blockchain applications across industries.

“Blockchain and Digital Twins in Smart Industry 4.0,” 2024. Designs, DOI. This review discusses blockchain-integrated digital twins, analyzing Industry 4.0 applications and benefits.

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

Posted on 19/10/2024 by ScienceFather

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.

Mubarak Albathan | AI | Best Researcher Award

Posted on 20/09/202420/09/2024 by ScienceFather

Dr Mubarak Albathan | AI | Best Researcher Award

Dr Mubarak Albathan , Imam Mohammad Ibn Saud Islamic University (IMSIU) ,Saudi Arabia

Dr. Mubarak Albathan is the Head of the Computer and Information Sciences Research Center and an Assistant Professor at Imam Muhammad Ibn Saud Islamic University in Riyadh, Saudi Arabia. He has a robust academic background, holding a PhD in Data Mining from Queensland University of Technology (QUT). With over a decade of experience in higher education, Dr. Albathan has made significant contributions to the fields of computer science and data analytics. He serves as a consultant to the Vice Rector for Graduate Studies and Scientific Research and has held various leadership roles in academia. Dr. Albathan is passionate about integrating advanced technologies into educational frameworks and enhancing research capabilities in the region. His work aims to bridge the gap between theoretical research and practical applications, driving innovation in data-driven solutions across various industries.

Publication Profile

Google Scholar

Strengths for the Award

Dr. Mubarak Albathan has demonstrated exceptional academic and research capabilities, exemplified by his extensive publication record and impactful research contributions. His work spans various critical areas, including data mining, machine learning, and healthcare applications, showcasing his versatility and innovation. Notably, he has received accolades such as the Best Student Paper Award and the International Publication Award, affirming his standing in the research community. As Head of the Computer and Information Sciences Research Center, he leads initiatives that enhance research quality and foster collaboration. Dr. Albathan’s commitment to integrating advanced technologies into practical solutions further underscores his qualifications for this prestigious award.

Areas for Improvement

While Dr. Albathan has a robust publication record, increasing the frequency of solo-authored publications could enhance his visibility as an independent researcher. Additionally, engaging in more interdisciplinary collaborations could broaden his research impact and foster innovative approaches. Expanding his outreach efforts to disseminate research findings beyond academic circles may also enhance community engagement and application of his work.

Education

Dr. Mubarak Albathan earned his PhD in Data Mining from Queensland University of Technology (QUT) in 2015. He completed his Master’s degree in Network Computing at Monash University in 2009, where he developed a strong foundation in network systems and computational techniques. Prior to that, he received his Bachelor’s degree in Computer Science from Al-Imam Muhammad Ibn Saud Islamic University in 2004. This comprehensive educational background has equipped Dr. Albathan with the skills and knowledge necessary to excel in both academic and practical applications of computer science. His studies have focused on various aspects of computing, data mining, and network systems, leading him to engage in cutting-edge research and contribute to significant advancements in technology and education.

Experience

Dr. Mubarak Albathan has extensive experience in academia and research management. Currently, he is the Head of the Computer and Information Sciences Research Center, a position he has held since 2023. He has also served as a consultant to the Vice Rector for Graduate Studies and Scientific Research since 2019. His previous roles include Deputy Director of the Electronic Scientific Research Portal initiative at the Ministry of Education from 2017 to 2019 and Vice-Chair of the Computer Science Department at his university from 2016 to 2017. Dr. Albathan has been involved in several academic projects and has acted as a sessional academic at QUT. His earlier experience includes supervising a diploma program in Computer Applications, showcasing his commitment to education and professional development in the field of computer science.

Awards and Honors

Dr. Mubarak Albathan has received numerous accolades for his academic and research contributions. He was awarded the Best Student Paper Award at the 2014 IEEE/WIC/ACM International Conferences on Web Intelligence in Warsaw, Poland, recognizing his exceptional research in the field. In 2015, he was honored with the International Publication Award from Imam Muhammad Ibn Saud Islamic University for his prolific contributions to scholarly publications. Dr. Albathan’s work has been recognized internationally, with his participation in several prestigious conferences, including the IEEE International Conference on Data Mining and the Australasian Joint Conference on Artificial Intelligence. His commitment to advancing knowledge in computer science and data mining continues to be acknowledged through various awards, highlighting his impact on the academic community and his dedication to research excellence.

Research Focus

Dr. Mubarak Albathan’s research focuses on data mining, machine learning, and their applications in various domains, including healthcare, agriculture, and cybersecurity. His work emphasizes the development of optimized algorithms for pattern recognition and classification, particularly in complex datasets. Dr. Albathan is particularly interested in leveraging advanced technologies such as deep learning to address real-world challenges, such as disease diagnosis through image analysis and enhancing security protocols in IoT networks. His collaborative research projects have led to significant advancements in understanding and improving data-driven systems. Dr. Albathan’s commitment to integrating theoretical research with practical applications makes him a key contributor to the field, driving innovation and supporting the development of efficient, scalable solutions that benefit multiple sectors.

Publications Top Notes

Mobile-HR: An Ophthalmologic-Based Classification System for Diagnosis of Hypertensive Retinopathy Using Optimized MobileNet Architecture. 🩺
Leveraging Ethereum Platform for Development of Efficient Tractability System in Pharmaceutical Supply Chain. 💊
EfficientPNet—An Optimized and Efficient Deep Learning Approach for Classifying Disease of Potato Plant Leaves. 🌾
Computer-Aided Diagnosis of COVID-19 from Chest X-ray Images Using Hybrid-Features and Random Forest Classifier. 🦠
A Supervised Method to Enhance Distance-based Neural Networks’ Clustering Performance by Discovering Perfect Representative Neurons. 🧠
Effective 20 Newsgroups Dataset Cleaning. 📊
Relevance Feature Discovery for Text Mining. 📖
Using Extended Random Set to Find Specific Patterns. 🔍
Interpreting Discovered Patterns in Terms of Ontology Concepts. 📚
Enhanced N-gram Extraction Using Relevance Feature Discovery. 🌐
Using Patterns Co-occurrence Matrix for Cleaning Closed Sequential Patterns for Text Mining. 📈
A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women. 🎗️
Optimized Deep Learning Techniques for Disease Detection in Rice Crop Using Merged Datasets. 🌱
Detection of Depression Trends in Literary Cyber Writers Using Sentiment Analysis and Machine Learning. 📖
Deep-Ocular: Improved Transfer Learning Architecture Using Self-Attention and Dense Layers for Recognition of Ocular Diseases. 👁️
ROAST-IoT: A Novel Range-Optimized Attention Convolutional Scattered Technique for Intrusion Detection in IoT Networks. 🔒
Enhancing Cloud-Based Security: A Novel Approach for Efficient Cyber-Threat Detection Using GSCSO-IHNN Model. ☁️

Conclusion

Dr. Mubarak Albathan is a highly qualified candidate for the Best Researcher Award. His impressive educational background, extensive experience, and significant contributions to research make him a standout in his field. By focusing on areas for improvement, he can further solidify his impact on academia and industry. Recognizing his achievements through this award would not only honor his dedication but also inspire future researchers in the field.

Jing Wang | Artificial Intelligence | Best Researcher Award

Posted on 11/09/202411/09/2024 by ScienceFather

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:

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.
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.
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.
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.
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:

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.
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.
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

Posted on 27/08/2024 by ScienceFather

Ms. Ritu Tanwar | Artificial intelligence | Best Researcher Award

Research Scholar | NIT Uttarakhand | India

Best Researcher Award

Strengths for the Award

Innovative Research Focus: Ms. Tanwar’s research is at the cutting edge of deep learning, artificial intelligence, and stress recognition. Her focus on multimodal physiological signals for affective state recognition and wearable technology is highly relevant and forward-looking.

Strong Academic Foundation: She is pursuing a PhD at the National Institute of Technology, Uttarakhand, with a well-defined thesis on deep learning frameworks for affective state recognition. Her previous education, including an M.Tech. in Emotion Recognition and a B.Tech. in Electronics & Communication Engineering, complements her current research focus.

Quality Publications: Ms. Tanwar has a strong publication record with peer-reviewed journal articles in high-impact journals like Engineering Applications of Artificial Intelligence and Computers in Biology and Medicine. Her conference papers and book chapters also demonstrate her active engagement with the academic community.

Recognition and Support: She has received a Senior Research Fellow Scholarship, highlighting her recognized potential in her field. Her involvement in teaching and supervision further indicates her commitment to academic excellence and leadership.

Technical Skills: Proficiency in Python, MATLAB, and deep learning frameworks enhances her ability to conduct high-quality research. Her experience with various software tools and programming languages supports her research in data analysis and machine learning.

Areas for Improvement

Broader Impact and Application: While Ms. Tanwar’s work is innovative, expanding the application of her research to practical, real-world scenarios could enhance its impact. Exploring collaborations with industry partners could provide valuable insights into the practical applications of her research findings.

Interdisciplinary Integration: Integrating her research with other disciplines, such as psychology or healthcare, could provide a more comprehensive understanding of stress recognition and its applications. This interdisciplinary approach might strengthen her research outcomes and broaden her impact.

Public Engagement and Outreach: Increasing her presence in public forums and engaging with broader audiences could amplify the reach of her research. Participating in outreach activities and science communication initiatives might help in translating her research for non-specialist audiences.

Conclusion

Ms. Ritu Tanwar demonstrates significant promise as a researcher, with a strong foundation in innovative areas of deep learning and stress recognition. Her research contributions are noteworthy, and she has established a solid track record with quality publications and academic achievements.

For the “Best Researcher Award,” Ms. Tanwar’s strengths in cutting-edge research, quality publications, and technical expertise make her a strong candidate. Addressing the suggested areas for improvement could further enhance her research impact and recognition in the field.

Short Bio

👩‍🔬 Ms. Ritu Tanwar is a dedicated Research Scholar in Electronics Engineering at the National Institute of Technology, Uttarakhand, India. With a focus on stress and emotion recognition through innovative technologies, she is pursuing a PhD under the supervision of Dr. Pankaj Kumar Pal and Dr. Ghanapriya Singh. Her extensive background in deep learning and artificial intelligence positions her as a notable contributor to the field of physiological signal analysis.

Profile

Orcid

Education

🎓 PhD (pursuing) – April 2021-present
Department of Electronics Engineering, National Institute of Technology, Uttarakhand, India
Thesis: A deep learning framework for affective state recognition using multimodal physiological signals
Thesis Supervisors: Dr. Pankaj Kumar Pal and Dr. Ghanapriya Singh

🎓 M. Tech. – July 2018
Department of Electronics & Communication Engineering, University Institute of Engineering & Technology, Kurukshetra, India
Thesis: Emotion Recognition from Audio Signals
Thesis Supervisor: Dr. Deepti Chaudhary

🎓 B. Tech. – July 2013
Department of Electronics & Communication Engineering, University Institute of Engineering & Technology, Kurukshetra, India

Experience

📚 Teaching Assistant
Department of Electronics Engineering, National Institute of Technology, Uttarakhand, India

Microcontroller and Interfacing (Jan–May 2024)
Digital Signal Processing (July–Dec 2021, July–Dec 2023)
Speech Signal Processing (July–Dec 2022)
Image Processing (Jan–July 2022)

📝 Supervision Experience
National Institute of Technology, Uttarakhand, India

Undergraduate Supervision: Kunal Kavi and Shivam Purwal (Completion year: 2024)

Research Interests

🔬 Stress and Emotion Recognition: Focused on understanding and analyzing stress and emotional states through physiological signals.
🧠 Data Analysis and Deep Learning: Leveraging advanced data analysis techniques and deep learning models to enhance emotion and stress recognition.
🤖 Artificial Intelligence and Machine Learning: Applying AI and ML technologies to improve the accuracy and effectiveness of stress recognition systems.

Awards

🏆 Senior Research Fellow Scholarship – 2021-present
Awarded for exceptional research potential and academic performance in the field of Electronics Engineering.

Publications

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. Second International Conference on Artificial Intelligence: Towards Sustainable Intelligence, Springer (Accepted).
Tanwar, R., Singh, G., & Pal, P. K. (2024, July). Wearables Based Personalised Stress Recognition Using Signal Processing and Hybrid Deep learning Model. 2024 2nd International Conference on Computer, Electronics, Electrical Engineering and their Applications (IC2E3), IEEE.
Tanwar, R., Singh, G., & Pal, P. K. (2023, July). FuSeR: Fusion of wearables data for StrEss Recognition using explainable artificial intelligence models. 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), IEEE.
Tanwar, R., Phukan, O. C., Singh, G., & Tiwari, S. (2022). CNN-LSTM Based Stress Recognition Using Wearables. CEUR Workshop Proceedings, Springer.

Ritu Tanwar | Artificial intelligence | Best Researcher Award

Posted on 25/08/202401/09/2024 by ScienceFather

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.

Yusuf KARADEDE | Artificial Intelligence | Best Researcher Award

Posted on 25/08/202425/08/2024 by ScienceFather

Assist Prof Dr. Yusuf KARADEDE | Artificial Intelligence | Best Researcher Award

Doctor, Gaziantep Islam Science and Technology University, Faculty of Engineering and Natural Sciences, Department of Industrial Engineering, 27010 Gaziantep, Turkey

Profile

Scopus

Strengths for the Award

Dr. Yusuf Karadede’s research in stochastic processes, heuristic algorithms, and stochastic optimization underscores his deep expertise and innovative approach in industrial engineering. His doctoral thesis and subsequent work have made significant contributions to the fields of simulation and stochastic modeling. Notably, his publications in esteemed journals like Soft Computing and Energy highlight his ability to tackle complex problems with advanced computational techniques.

Dr. Karadede’s diverse range of scientific activities demonstrates his commitment to advancing both theoretical and applied aspects of his field. His development of novel models such as the ProFiVaS model for financial indicators, showcased in his recent publication in Expert Systems with Applications, exemplifies his forward-thinking approach and impact on financial modeling.

Areas for Improvement

While Dr. Karadede’s research is highly impactful, expanding the scope of his research to include interdisciplinary approaches could further enhance the applicability of his work. For instance, integrating his stochastic models with emerging technologies like machine learning could offer new insights and broaden the impact of his research. Additionally, increasing collaboration with international research groups might provide new perspectives and enhance the global reach of his contributions.

Academic Background:

Bachelor’s Degree: Mathematics, Suleyman Demirel University, 2006-2010
Master’s Degree: Industrial Engineering, Suleyman Demirel University, 2011-2014
Doctorate (Ph.D.): Industrial Engineering, Suleyman Demirel University, 2015-2020

Professional Experience:

Kafkas University: Faculty of Engineering and Architecture, Department of Industrial Engineering (2014-2015)
Suleyman Demirel University: Faculty of Engineering, Department of Industrial Engineering (2015-2020)
Gaziantep Islam Science and Technology University: Department of Industrial Engineering (2020-Present)

Research Interests:

Stochastic Processes and Models
Simulation
Heuristic Algorithms
Stochastic Optimization

Awards and Scholarships:

TÜBİTAK 2210-C Program Scholarship (2013-2014)
TÜBİTAK 2211-C Program Scholarship (2018-2020)

Publications Top Notes:

Karadede, Y., Özdemir, G. (2018). A hierarchical soft computing model for parameter estimation of curve-fitting problems. Soft Computing, 22(20), 6937-6964.

Karadede, Y., Ozdemir, G., Aydemir, E. (2017). Breeder Hybrid Algorithm Approach for Natural Gas Demand Forecasting Model. Energy, 141, 1269-1284.

Akdeniz, F., Biçil, M., Karadede, Y., Özbek, F. E., Özdemir, G. (2018). Application of real valued genetic algorithm on prediction of higher heating values of various lignocellulosic materials. Energy, 160, 1047-1054.

Karadede, Y. (2024). A novel stochastic ProFiVaS model based on decomposition of stochastic Vasicek differential equation for modeling and simulating financial indicators. Expert Systems with Applications

Conclusion

Dr. Yusuf Karadede’s distinguished research in stochastic processes and optimization positions him as a strong candidate for the Best Researcher Award. His innovative contributions, including high-impact publications and successful research projects funded by prestigious institutions like TÜBİTAK, highlight his significant achievements and potential for future breakthroughs. His work not only advances theoretical understanding but also offers practical solutions to real-world problems, making him a deserving nominee for this esteemed accolade.

NEHA KATIYAR | Machine Learning | Best Research Article Award

Posted on 25/07/202425/07/2024 by ScienceFather

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.