Rishabh Anand | Computer Science | Best Researcher Award

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Dr. Rishabh Anand | Computer Science | Best Researcher Award

Associate Vice President at India

Dr. Rishabh Anand is a distinguished professional with over 19 years of experience spanning technology, business management, and academia. His expertise lies in program and delivery management, strategic leadership, and digital transformation, with a strong foundation in IT and education. As a thought leader, he has successfully integrated academic theories with real-world business applications, fostering innovation and excellence. His global experience across the USA, UK, India, Denmark, France, the Middle East, and ASEAN has given him a unique perspective on technology and business evolution. Dr. Anand is known for his mentorship and coaching abilities, shaping the next generation of professionals and students through his academic and industry engagements. His ability to drive strategic initiatives, coupled with his passion for education and research, has positioned him as a leader in the fields of artificial intelligence, machine learning, and digital transformation.

Professional Profile

Education

Dr. Rishabh Anand has an impressive academic background with multiple degrees spanning technology, management, and psychology. He earned his B.E. in Electronics and Communication Engineering from Dronacharya College of Engineering, MDU, in 2006. His passion for advanced technical research led him to pursue an M.Tech in Electronics and Communication Engineering from the Indian Institute of Technology (IIT), Delhi, in 2010. Expanding his expertise into business and finance, he completed an MBA in Finance from New York University (NYU) in 2014. Understanding the significance of human behavior in technology and business, he pursued an MS in Psychology from the University of Texas at Dallas in 2016. His dedication to research culminated in a Ph.D. in Computer Science from the University of Bristol, UK, in 2020. Further solidifying his expertise, he completed a dual postdoctoral degree in Artificial Intelligence and Machine Learning from Sรฃo Paulo State University, Brazil, in 2024.

Professional Experience

Dr. Anand has an extensive professional career, demonstrating expertise in global technology, business strategy, and academic leadership. He has been a key figure at Google India Private Limited since 2006, leading strategic initiatives, managing multi-million-dollar IT projects, and driving digital transformation across various industries. As a Program and Delivery Manager, he has played a pivotal role in managing large-scale engineering teams, ensuring efficiency, innovation, and profitability. His work spans industries such as airlines, pharmaceuticals, financial services, FMCG, tourism, logistics, and technology. He has successfully transitioned over 350-400 roles globally, demonstrating his expertise in workforce transformation and leadership. In academia, he has mentored students and professionals, bridging the gap between theoretical learning and industry expectations. His extensive experience working with C-suite executives and leading digital initiatives has established him as a global thought leader in technology-driven business solutions.

Research Interest

Dr. Rishabh Anandโ€™s research interests primarily focus on artificial intelligence, machine learning, digital transformation, and strategic IT management. His work revolves around integrating cutting-edge AI and ML technologies into business strategies to enhance efficiency, automation, and customer experience. He is deeply invested in enterprise IT strategies, cybersecurity, cloud computing, and predictive analytics, ensuring that businesses stay ahead in the digital era. His interest in digital transformation includes process automation, technology adoption in organizations, and data-driven decision-making frameworks. With his background in psychology, he also explores human-computer interaction, cognitive computing, and behavioral AI. Through his published case studies and academic collaborations, Dr. Anand continues to contribute valuable insights into how AI and digital solutions can drive innovation and economic growth. His research aims to bridge the gap between academia and industry, ensuring that emerging technologies align with real-world business challenges.

Awards and Honors

Dr. Rishabh Anand has received multiple awards and recognitions for his contributions to technology, research, and academia. He was recognized for his excellence in digital transformation and IT strategy at Google India, where he led high-impact projects, driving profitability and innovation. His โ€œThinking Breakthroughโ€ workshops have received industry recognition for aligning client visions with cutting-edge business and IT strategies. As a dedicated mentor, he has been honored for his contributions to student career development and academic excellence. His research publications on AI, digital transformation, and strategic IT management have been acknowledged in international conferences and journals. Dr. Anandโ€™s work in mentorship and workforce transformation has also earned him leadership awards from various professional organizations. With a stellar career spanning technology, business, and academia, he continues to be an influential figure in shaping the future of AI, machine learning, and enterprise IT solutions.

Conclusion

Dr. Rishabh Anand is a strong contender for the Best Researcher Award, given his significant contributions to research, industry-academia collaboration, and leadership in digital transformation. Strengthening his publication record and patents could further solidify his case as an outstanding researcher.

Publications Top Noted

Industry 4.0 Technologies

Author: Dr. Rishabh Anand (2025)
Publisher: S Chand and Company Ltd

Smart Factories for Industry 5.0 Transformation (Industry 5.0 Transformation Applications)

Authors: Dr. Rishabh Anand, R. Nidhya, Manish Kumar, S. Karthik, S. Balamurugan (2025)
Publisher: Wiley-Scrivener

Foundation Course in Universal Human Values and Professional Ethics

Author: Dr. Rishabh Anand (2025)
Publisher: CBS Publishers and Distributors Pvt. Ltd.

Blockchain Technology

Author: Dr. Rishabh Anand (2023)
Publisher: Khanna Publishers

Computer Organization and Architecture (Designing for Performance)

Authors: Dr. Rishabh Anand, R.S. Salaria (2023)
Publisher: Khanna Publishers

Digital Signal Processing: An Introduction

Author: Dr. Rishabh Anand (2022)
Publisher: Mercury Learning & Information

Wireless Communication

Author: Dr. Rishabh Anand (2022)
Publisher: S Chand And Company Ltd

An Integrated Approach to Software Engineering

Author: Dr. Rishabh Anand (2021)
Publisher: Khanna Publishers

Digital Signal Processing

Author: Dr. Rishabh Anand (2021)
Publisher: Khanna Publishers

Object-Oriented Programming using C++

Author: Dr. Rishabh Anand (2021)
Publisher: Khanna Publishers

Optical Fiber Communications

Author: Dr. Rishabh Anand (2021)
Publisher: Khanna Publishers

Satellite Communications

Author: Dr. Rishabh Anand (2021)
Publisher: Khanna Publishers

Nanotechnology

Author: Dr. Rishabh Anand (2020)
Publisher: Khanna Publishers

Digital Electronics

Author: Dr. Rishabh Anand (2019)
Publisher: Khanna Book Publishing Company

Signals and Systems

Author: Dr. Rishabh Anand (2018)
Publisher: Khanna Book Publishing Company

Mobile Computing

Author: Dr. Rishabh Anand (2017)
Publisher: Khanna Publishers

Computer Networks

Author: Dr. Rishabh Anand (2016)
Publisher: Satya Prakashan

Linear Integrated Circuits

Author: Dr. Rishabh Anand (2014)
Publisher: Khanna Book Publishing Company

Electromagnetic Field Theory

Author: Dr. Rishabh Anand (2013)
Publisher: Khanna Book Publishing Company

Computer Graphics

Author: Dr. Rishabh Anand (2013)
Publisher: Khanna Book Publishing Company

Digital System Design Using VHDL

Author: Dr. Rishabh Anand (2013)
Publisher: Khanna Book Publishing Company

Intelligent Instrumentation for Engineers

Author: Dr. Rishabh Anand (2013)
Publisher: Khanna Book Publishing Company

Software Project Management

Author: Dr. Rishabh Anand (2013)
Publisher: S K Kataria and Sons

Wireless and Mobile Computing

Author: Dr. Rishabh Anand (2013)
Publisher: S K Kataria and Sons

Network Management

Author: Dr. Rishabh Anand (2012)
Publisher: Not Specified

Neural Networks

Author: Dr. Rishabh Anand (2012)
Publisher: Satya Prakashan

Communication Systems: Analog and Digital

Author: Dr. Rishabh Anand (2011)
Publisher: Khanna Book Publishing Company

 

merve pฤฑnar | Machine Learning | Best Researcher Award

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Dr. merve pฤฑnar | Machine Learning | Best Researcher Award

Research Ass, Marmara University, Turkey

Merve Pinar is a Research Assistant in the Faculty of Technology, Computer Engineering Department at Marmara University, Turkey. She has been pursuing her doctorate since 2023 at Marmara University in the field of Computer Engineering. Her academic journey includes a postgraduate degree from the Institute for Graduate Studies in Pure and Applied Sciences (2019-2022) and an undergraduate degree from ร‡anakkale Onsekiz Mart University, where she studied Engineering (2009-2013). Merveโ€™s work primarily focuses on artificial intelligence, machine learning, and their applications in various fields, especially healthcare. She is dedicated to exploring innovative solutions using deep learning and pattern recognition techniques. Her contributions to the academic community include publications in respected journals and conferences. She also actively collaborates with other researchers to advance the field.

Profile 

Education

  • Doctorate (2023-Present): Marmara University, Faculty of Technology, Computer Engineering, Turkey.
  • Postgraduate (2019-2022): Marmara University, Institute for Graduate Studies in Pure and Applied Sciences, Turkey. Dissertation: โ€œDerinรถฤŸrenme yรถntemleri kullanฤฑlarak beyin tรผmรถrรผ tiplerinin ve sฤฑnฤฑrlarฤฑnฤฑn tahminlenmesiโ€ (Prediction of brain tumor types and boundaries using deep learning methods).
  • Undergraduate (2009-2013): ร‡anakkale Onsekiz Mart University, Faculty of Engineering, Turkey.

Merveโ€™s academic background provides a solid foundation in computer engineering, artificial intelligence, and data science. She continues to pursue advanced studies, focusing on leveraging machine learning and deep learning methods to address complex problems in health and technology.

Research Focus

Merve Pinarโ€™s research focuses on the intersection of artificial intelligence, machine learning, and medical applications. Her primary interests are database management, data structures, pattern recognition, and deep learning. She specializes in using AI techniques for medical imaging, particularly in the classification and segmentation of brain tumor types using MRI and surgical microscope images. Her work aims to enhance diagnostic tools, improving the accuracy and efficiency of healthcare systems. Additionally, she is involved in hyperparameter optimization for big data applications, which helps improve recommendation systems. Merveโ€™s interdisciplinary research is positioned at the cutting edge of AI, combining computer engineering with real-world applications, particularly in healthcare technology, where deep learning plays a crucial role in revolutionizing diagnostics and treatment strategies.

Publications

  • Deep Learning-Assisted Segmentation and Classification of Brain Tumor Types on Magnetic Resonance and Surgical Microscope Images ๐Ÿง ๐Ÿ’ป (2024)
  • Hyperparameter Optimization for Recommendation Systems with Big Data ๐Ÿ“Š๐Ÿ” (2017)

รlvaro Figueira | Artificial Intelligence | Best Paper Award

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Assist. Prof. Dr. รlvaro Figueira | Artificial Intelligence | Best Paper Award

Professor Auxiliar, FCUP โ€“ Universidade do Porto, Portugal

Profile

Orcid

รlvaro Figueira is a distinguished academic and researcher in the field of Computer Science, currently serving as a Professor (Prof. Auxiliar) at Universidade do Porto, Faculdade de Ciรชncias in Portugal. With a robust academic background and extensive experience, his research focuses on data mining, machine learning, social network analysis, and eLearning. Figueiraโ€™s passion for technology and innovation is evident in his contribution to various scientific fields, including data visualization and text mining, where his work aims to bridge theory with practical applications. With years of experience in teaching and leading research initiatives, Figueira is a prominent figure in his discipline. ๐Ÿ“š๐Ÿ’ป

Education

รlvaro Figueiraโ€™s academic journey is distinguished by his advanced qualifications in Computer Science. He obtained his Bachelorโ€™s (BSc) degree from Universidade do Porto, followed by a Masterโ€™s (MSc) from Imperial College London. He continued his academic excellence by completing a Ph.D. at Universidade do Porto in 2004, where he focused on Computer Science. Additionally, Figueira pursued Post-Graduation in Business Intelligence and Analytics at Porto Business School in 2017, further enhancing his expertise. ๐ŸŽ“๐Ÿ“–

Experience

Throughout his career, รlvaro Figueira has amassed a wealth of academic and professional experience. He is currently a Professor at Universidade do Porto, where he teaches and supervises students in the field of Computer Science. He has also worked on a variety of research projects related to eLearning, data science, and machine learning, particularly focused on how these technologies can improve education and business practices. His previous experience includes a prestigious Masterโ€™s thesis position at Imperial College London. ๐ŸŒ๐Ÿ“Š

Research Interests

รlvaro Figueiraโ€™s research interests span a wide array of cutting-edge fields within Computer Science. His primary focus areas include Data Mining, Text Mining, Machine Learning, Social Network Analysis, Data Visualization, and eLearning. Figueiraโ€™s work aims to apply computational techniques to improve the analysis of large datasets, making significant strides in understanding and enhancing social networks and educational systems. His research has contributed to the advancement of automated assessment systems and the optimization of learning processes. ๐Ÿ“ˆ๐Ÿ”

Award

รlvaro Figueiraโ€™s contributions to computer science and education have been recognized with various awards and accolades. Notably, his research has been funded by several prestigious grants, including those from the Fundaรงรฃo para a Ciรชncia e Tecnologia I.P. and Instituto de Engenharia de Sistemas e Computadores. His excellence in research is further highlighted by his numerous publications in top-tier journals, where he continues to make an impact in the fields of data science and machine learning. ๐Ÿ†๐ŸŽ–๏ธ

Publications Top Notes

รlvaro Figueiraโ€™s publication record reflects his significant contributions to the fields of data science, machine learning, and eLearning. Some of his recent publications include:

โ€œTopic Extraction: BERTopicโ€™s Insight into the 117th Congressโ€™s Twitterverseโ€ โ€“ Informatics (2024).

โ€œClustering source code from automated assessment of programming assignmentsโ€ โ€“ International Journal of Data Science and Analytics (2024).

โ€œComparing Semantic Graph Representations of Source Code: The Case of Automatic Feedback on Programming Assignmentsโ€ โ€“ Computer Science and Information Systems (2024).

โ€œGANs in the Panorama of Synthetic Data Generation Methodsโ€ โ€“ ACM Transactions on Multimedia Computing, Communications, and Applications (2024).

โ€œOn the Quality of Synthetic Generated Tabular Dataโ€ โ€“ Mathematics (2023).

โ€œBibliometric Analysis of Automated Assessment in Programming Education: A Deeper Insight into Feedbackโ€ โ€“ Electronics (2023).

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

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

Investigador, INTA, Spain

Inmaculada Mohรญno 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. ๐ŸŒŸ 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 Masterโ€™s degree in Information and Communication Technologies (2015). ๐Ÿ“š She culminated her academic journey with a Ph.D. in Information and Communication Technologies (2017, with honors) from the University of Alcalรก, Madrid, Spain. ๐ŸŽ“

Experience

She has built a solid career in academia and research, having worked at the Signal Theory and Communications Department of the University of Alcalรก, where she was part of the Applied Signal Processing research group until 2021. ๐Ÿ“ก 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 masterโ€™s projects, shaping future innovators. ๐Ÿ‘ฉโ€๐Ÿซ

Research Focus

Inmaculadaโ€™s research revolves around physiological signal processing, pattern recognition, emotion recognition, and stress detection. ๐Ÿ’ก 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. ๐Ÿ’ป

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. ๐Ÿ† Her efforts in academic and industrial projects further solidify her reputation as a leading researcher.

Publication Top Notes

Inmaculada Mohรญno Herranz has authored various impactful papers. She has published nine journal papers, six of which are indexed in the Journal Citation Report. ๐Ÿ“„ 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 โ€“ 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 โ€“ Published in Expert Systems with Applications (2024), this article has no citations yet.

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

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

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

Ladislav Karrach | Computer Vision | Best Researcher Award

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Dr. Ladislav Karrach | Computer Vision | Best Researcher Award

Post student, Technical University in Zvolen, Slovakia

Ladislav Karrach is a seasoned computer programmer and systems analyst from Kremnica, Slovakia. With a robust background in computer network administration and ERP systems, he has contributed significantly to the field of applied informatics since 1995. His dedication to technology and innovation has positioned him as a key player in developing internal information systems and enhancing client-server applications. ๐Ÿ–ฅ๏ธ

Publication Profile

ORCID

Education

Ladislav holds a Ph.D. in Environmental and Manufacturing Technology from the Technical University in Zvolen, where he focused on text recognition in images and its applications in manufacturing processes. He also earned his Ing. (MSc) degree in Applied Informatics from the University of ลฝilina, specializing in information and control systems. ๐ŸŽ“

Experience

Since 1995, Ladislav has been working as a computer programmer and systems designer at Mint Kremnica, where he manages database servers, designs information systems, and develops client-server applications. His extensive experience includes web programming and administration of ERP systems, making him a versatile professional in the tech industry. ๐Ÿ’ป

Research Focus

Ladislavโ€™s research interests lie in the fields of image processing, particularly focusing on text recognition methods, data matrix codes, and character recognition technologies. He is dedicated to optimizing production processes through innovative technological solutions and is involved in various research projects that explore the applications of image recognition in manufacturing. ๐Ÿ”

Awards and Honours

Ladislav has been recognized for his contributions to the field of informatics and manufacturing technology through various publications and collaborative projects. His work is highly regarded in academic circles, showcasing his commitment to advancing technology in practical applications. ๐Ÿ…

Publication Top Notes

 Data Matrix Code Location Marked with Laser on Surface of Metal Tools. Acta Facultatis Technicae, XXII, 2017 (2), 29โ€“38. โ€“ Cited by 1

 Data matrix code location in images acquired by camera. In Manufacturing and automation technology: book of abstracts, 15. โ€“ Cited by 0

The analysis of various methods for location of Data matrix codes in images. In ELEKTRO 2018: conference proceedings. โ€“ Cited by 2

 Comparing the impact of different cameras and image resolution to recognize the data matrix codes. Journal of Electrical Engineering, 286-292. โ€“ Cited by 4

 Optimizatio of manipulation logistics using data matrix codes. Advances in Science and Technology Research Journal, 173-180. โ€“ Cited by 3

 Recognition of Data Matrix Codes in Images and their Applications in Production Processes. Management Systems in Production Engineering, 154-161. โ€“ Cited by 5

 Using Different Types of Artificial Neural Networks to Classify 2D Matrix Codes and Their Rotations โ€” A Comparative Study. J. Imaging, 188. โ€“ Cited by 1

Mubarak Albathan | AI | Best Researcher Award

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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. Albathanโ€™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 Masterโ€™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 Bachelorโ€™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. Albathanโ€™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 Albathanโ€™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. Albathanโ€™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. ๐Ÿฉบ
  • Leveraging Ethereum Platform for Development of Efficient Tractability System in Pharmaceutical Supply Chain. ๐Ÿ’Š
  • EfficientPNetโ€”An Optimized and Efficient Deep Learning Approach for Classifying Disease of Potato Plant Leaves. ๐ŸŒพ
  • Computer-Aided Diagnosis of COVID-19 from Chest X-ray Images Using Hybrid-Features and Random Forest Classifier. ๐Ÿฆ 
  • A Supervised Method to Enhance Distance-based Neural Networksโ€™ Clustering Performance by Discovering Perfect Representative Neurons. ๐Ÿง 
  • Effective 20 Newsgroups Dataset Cleaning. ๐Ÿ“Š
  • Relevance Feature Discovery for Text Mining. ๐Ÿ“–
  • Using Extended Random Set to Find Specific Patterns. ๐Ÿ”
  • Interpreting Discovered Patterns in Terms of Ontology Concepts. ๐Ÿ“š
  • Enhanced N-gram Extraction Using Relevance Feature Discovery. ๐ŸŒ
  • Using Patterns Co-occurrence Matrix for Cleaning Closed Sequential Patterns for Text Mining. ๐Ÿ“ˆ
  • A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women. ๐ŸŽ—๏ธ
  • Optimized Deep Learning Techniques for Disease Detection in Rice Crop Using Merged Datasets. ๐ŸŒฑ
  • Detection of Depression Trends in Literary Cyber Writers Using Sentiment Analysis and Machine Learning. ๐Ÿ“–
  • Deep-Ocular: Improved Transfer Learning Architecture Using Self-Attention and Dense Layers for Recognition of Ocular Diseases. ๐Ÿ‘๏ธ
  • ROAST-IoT: A Novel Range-Optimized Attention Convolutional Scattered Technique for Intrusion Detection in IoT Networks. ๐Ÿ”’
  • Enhancing Cloud-Based Security: A Novel Approach for Efficient Cyber-Threat Detection Using GSCSO-IHNN Model. โ˜๏ธ

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

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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 candidateโ€™s research concentrates on machine learning, with a particular emphasis on multi-label learning and explainable machine learningโ€”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 candidateโ€™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 researchโ€™s global reach and interdisciplinary impact.

 

๐ŸŽ“ 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.

๐Ÿ† 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.

๐Ÿ” Research Focus

Jing Wangโ€™s research delves into machine learning, with a particular emphasis on multi-label learning and explainable machine learning. Jingโ€™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.

๐Ÿ… Awards and Honors

Lead Guest Editor for IEEE Transactions on Consumer Electronics on โ€œWhen Consumer Electronics Meet Large Models: Opportunities and Challenges.โ€ Guest Editor for the International Journal of Machine Learning and Cybernetics on โ€œReliable and Interpretable Machine Learning: Theory, Methodologies, Applications, and Beyond.โ€ 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).

๐Ÿ“š 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. Jingโ€™s research on โ€œDriver Distraction Detection Using Semi-supervised Lightweight Vision Transformerโ€ has been recognized for its innovative application in Engineering Applications of Artificial Intelligence.

Jing Wang, Fu Feng, Jianhui Lv, and Xin Geng. โ€œResidual k-Nearest Neighbors Label Distribution Learning.โ€ Pattern Recognition (PR), 2024, in press.

Zhiyun Zhang, Jing Wangโ€ , and Xin Geng. โ€œLabel Distribution Learning by Utilizing Common and Label-Specific Feature Fusion Space.โ€ International Journal of Machine Learning and Cybernetics, 2024, in press.

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

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

Jing Wang and Xin Geng. โ€œExplaining the Better Generalization of Label Distribution Learning for Classification.โ€ 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. Rituโ€™s academic journey and teaching roles underline her commitment to advancing both theoretical and practical aspects of her research.

Profile

Scopus

Research for โ€œBest Researcher Awardโ€ 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 focusโ€”stress and emotion recognition through physiological signalsโ€”highlights her deep engagement with cutting-edge technology and data analysis. Rituโ€™s work utilizes advanced techniques in deep learning and machine learning to address significant challenges in affective state recognition.

Innovative Research Contributions: Rituโ€™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: Rituโ€™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. Tanwarโ€™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 ๐ŸŽ“

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 ๐Ÿ’ผ

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 ๐Ÿ”ฌ

Ms. Tanwarโ€™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 ๐Ÿ†

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 Tanwarโ€™s research on stress and emotion recognition using physiological signals is both innovative and impactful, making her a strong candidate for the โ€œBest Researcher Award.โ€ 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

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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 Karadedeโ€™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. Karadedeโ€™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. Karadedeโ€™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:

  • Bachelorโ€™s Degree: Mathematics, Suleyman Demirel University, 2006-2010
  • Masterโ€™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:

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

Publications Top Notes:

Karadede, Y., ร–zdemir, 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., Biรงil, M., Karadede, Y., ร–zbek, F. E., ร–zdemir, 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 Karadedeโ€™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 TรœBฤฐTAK, 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

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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

๐ŸŽ“ Doctorate
Bennett University, Greater Noida (July 2023 โ€“ Present)
Department: Computer Science & Engineering

๐ŸŽ“ Master of Technology
Madan Mohan Malviya University of Technology, Gorakhpur (Aug 2018 โ€“ July 2020)
Department: Information Technology and Computer Application
Percentage: 69% (First Division)

๐ŸŽ“ Bachelor of Technology
Sir Chootu Ram Institute of Engineering & Technology, Meerut (July 2015 โ€“ June 2018)
University: Chaudhary Charan Singh University, Meerut, U.P
Course: Information Technology
Percentage: 74% (First Division)

๐ŸŽ“ Diploma in Engineering
Government Girls Polytechnic, Lucknow (July 2010 โ€“ December 2013)
University: Board of Technical Education, Lucknow UP
Course: Information Technology
Percentage: 70% (First Division)

๐ŸŽ“ High School
Soni Pariya Inter College, Farrukhabad (Apr 2009 โ€“ Mar 2010)
Board: Board of High School and Intermediate Education, U.P.
Percentage: 58% (Second Division)

Experience

๐Ÿ’ผ Assistant Professor
Noida Institute of Technology, Greater Noida (11 April 2022 โ€“ 17 May 2023)
Responsibilities included evaluation of copies, research work, academic work, and preparation of question banks and presentations.

๐Ÿ’ผ Academic Associate
Indian Institute of Management, Rohtak (22 July 2021 โ€“ 7 April 2022)
Assisted faculties, conducted empirical research, managed conferences, and evaluated copies.

๐Ÿ’ผ Research Assistant
Ajay Kumar Garg Engineering College, Ghaziabad (12 Oct 2020 โ€“ 16 Jul 2021)
Worked on a project titled โ€œCompressed parallel wavelet tree based on semantic searchโ€ funded by the Council of Science And Technology, Uttar Pradesh (UPCST).

Research Interest

๐Ÿ”ฌ Nehaโ€™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

๐Ÿ“š 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 โ€“ Published in the 1st International Conference on Recent Trends in Computer Science and Information Technology (ICRCSIT-20) at St. Martinโ€™s Engineering College Secunderabad Telangana, on 17-18 June 2020.

A review: Target Based Sentiment Analysis using Machine Learning โ€“ 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 โ€“ 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 โ€“ Published in 2021 at Sreyas Institute of Engineering and Technology, Hyderabad, on 9-10 July 2021.

A perspective towards 6G Networks โ€“ 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 โ€“ Published in Taylor and Francis Group Journal on 3 May 2023.