Supritha Nagendra | Big data analytics | Best Researcher Award

Mrs. Supritha Nagendra | Big data analytics | Best Researcher Award

Mrs. Supritha Nagendra | Big data analytics- Research scholar at BMSIT&M, India

Supritha N is a dedicated academic professional with extensive experience in the field of Computer Science and Engineering (CSE). With a career spanning over 8.6 years in teaching and research, she has worked across several reputed institutions, contributing immensely to the academic and research community. Currently pursuing her Ph.D. at BMSIT&M, she has established herself as an innovative researcher with a focus on Big Earth Analytics, remote sensing, and active learning models. Supritha’s dedication to her work and her ability to blend academic and research excellence make her a deserving candidate for various accolades in the field of research and education.

Profile:

Orcid

Education:

Supritha holds a robust academic background, with a Ph.D. in Computer Science and Engineering from BMSIT&M. She completed her M.Tech. in the same field from A.P.S.C.E, securing an impressive 76%. Prior to this, she earned her B.E. from JVIT, with a 71% score. Her academic journey also includes a strong foundation in basic sciences, as evidenced by her outstanding performance during her P.U.C. and SSLC years, where she achieved high marks in her respective board examinations. Supritha’s consistent academic performance highlights her strong analytical skills, determination, and commitment to continuous learning.

Experience:

Supritha N brings over 8.6 years of professional experience, primarily in teaching and research roles. Her career began as a Lecturer at JVIT, where she worked from 2009 to 2011, before transitioning to a more extensive role as an Assistant Professor. She served at East West Institute of Technology from 2017 to 2021 and at RNS Institute of Technology from 2021 to 2024. Throughout her tenure in these positions, Supritha has mentored students, taught various undergraduate and postgraduate courses, and played a pivotal role in guiding student projects. Her comprehensive teaching portfolio includes subjects such as Digital Design and Computer Organization, Big Data Analytics, Microprocessors and Embedded Systems, and Cryptography, among others. Her experience in teaching has been marked by her innovative approach to complex subjects, enabling students to grasp critical concepts with ease.

Research Interests:

Supritha’s research interests lie at the intersection of Big Data Analytics, Remote Sensing, and Artificial Intelligence. She is particularly focused on developing resource-efficient models for Big Earth Spatial Data Management and Analytics (BESDMA). Her Ph.D. research aims to design sparse image representation models to improve remote sensing applications and resource efficiency in Earth observation systems. Additionally, Supritha has worked on various projects related to machine learning and active learning, emphasizing the importance of quality-sensitive solutions in data processing and analysis. Her innovative work in these fields has led to the publication of several impactful papers, furthering knowledge in her areas of research.

Awards:

Supritha N has earned numerous accolades throughout her career, underscoring her dedication and excellence in both research and teaching. She was awarded the Best Paper Award for her paper on “Object Placement Using Augmented Reality” at the 2nd National Conference on Computing Technology in 2022. Additionally, her work on “Communication for Motor Neuron Disease Patients via Eye Blink to Voice Recognition” received the Best Paper Award at the 14th National Conference in 2019. Her contributions to research have been recognized not only in academic circles but also through her active participation in national and international conferences and her ability to translate her research into real-world applications.

Publications:

Supritha N has authored and co-authored several research papers, many of which have been published in esteemed journals and conferences. Some of her key publications include:

  1. “Deep Spatio-textural feature driven multi-constraints pool-based active learning model for resource-efficient big earth observations” (2025), International Journal of Remote Sensing 🌍📚 [Cited by: 15 articles]
  2. “A Comprehensive Study on Satellite based Data Communication for Big Earth Observation Systems” (2024), IEEE International Conference on Knowledge Engineering and Communication Systems 🌐📈 [Cited by: 7 articles]
  3. “A Comparative Study of The CNN Based Models Used for Remote Sensing Image Classification” (2023), IJEER Journal 🛰️📄 [Cited by: 10 articles]
  4. “The Scope of Artificial Intelligence in Agriculture and Healthcare Sectors – A Comprehensive Review” (2022), Advanced Engineering Sciences 🤖🌾 [Cited by: 5 articles]

Conclusion:

Supritha N’s journey in academia and research reflects a high level of commitment, excellence, and passion for innovation. Her career trajectory, marked by substantial teaching experience and groundbreaking research, places her in a strong position for recognition in the field of Computer Science and Engineering. Her research work, particularly in Big Earth Analytics, remote sensing, and AI-driven models, has not only advanced academic understanding but also shown promise for real-world applications. With multiple publications, awards, and a demonstrated commitment to mentoring students, Supritha N is a deserving nominee for the Best Researcher Award. Her work continues to inspire peers and students alike, and she remains focused on pushing the boundaries of knowledge in her field.

James Dong | Statistical modeling | Best Researcher Award

Dr. James Dong | Statistical modeling | Best Researcher Award 

Professor at University of Nebraska Medical Center, United States

Dr. Jianghu (James) Dong is a distinguished researcher and professor in the Department of Biostatistics at the College of Public Health, University of Nebraska Medical Center. His expertise lies in developing advanced statistical models for biomedical data and chronic disease research, with a strong focus on functional data analysis, survival analysis, and statistical genetics. With an extensive academic background and a wealth of experience in interdisciplinary collaborations, Dr. Dong has made significant contributions to the fields of public health, organ transplant studies, and COVID-19 research. His work has been widely published in peer-reviewed journals, making a profound impact on the statistical and medical research communities.

Profile:

SCOPUS

Education:

Dr. Dong’s academic journey began with a B.Sc. in Mathematics from Beijing Normal University in 1997, which laid the foundation for his career in statistics and biostatistics. He earned two M.Sc. degrees in Statistics: one from Renmin University of China in 2003 and another from the University of Alberta in 2005, where he honed his skills in advanced statistical modeling. Dr. Dong completed his Ph.D. in Statistics from Simon Fraser University in 2018, focusing on functional data analysis and survival models, particularly applied to biomedical data. His educational background reflects his dedication to developing statistical methods that have real-world applications in health sciences.

Experience:

Dr. Dong has built a robust career in academia and research, starting from his postdoctoral work and progressing to his current position as a professor in biostatistics. His interdisciplinary approach has led him to collaborate with professionals in medicine, public health, and engineering, working on critical healthcare problems. Throughout his career, he has worked on projects involving the analysis of complex longitudinal health data, organ transplantation outcomes, and decision-making models in chronic disease management. He has also contributed to research addressing global health challenges, such as the COVID-19 pandemic, applying his statistical expertise to develop predictive models and joint analyses.

Research Interests:

Dr. Dong’s research interests are broad and encompass several important areas of biostatistics. He specializes in functional data analysis, which allows for the analysis of data that vary over time, such as biomedical signals or patient outcomes. His work in longitudinal and survival analysis has led to the development of new methods for predicting patient outcomes in organ transplant studies and chronic diseases. In addition, Dr. Dong has a strong interest in statistical machine learning and its applications in healthcare, particularly for analyzing biomarkers and genetic data. His research extends to cost-effectiveness analysis and the creation of decision trees for health policy, making his contributions relevant to both theoretical and applied statistics.

Awards:

Dr. Dong’s research excellence has been recognized through various academic awards and grants throughout his career. While specific awards may not be listed here, his contributions to statistical modeling and health research have earned him respect and recognition within the academic and medical communities. His interdisciplinary research collaborations and impactful publications have consistently placed him at the forefront of public health research and biostatistics.

Publications:

Dr. Dong has authored numerous peer-reviewed articles, reflecting his extensive research contributions. Notable publications include:

Merani S, Urban M, Westphal S, Dong J, et al. (2023). Improved Early Post-Transplant Outcomes and Organ Use in Kidney Transplant Using Normothermic Regional Perfusion for Donation after Circulatory Death. J Am Coll Surg. Link.

Kyuhak O, Dong J, et al. (2023). Initial experience with an electron FLASH research extension (FLEX) for the Clinac system. Radiation Oncology Physics. Link.

Nyandemoh A, Anzalone J, Dong J, et al. (2023). What Risk Factors Cause Long COVID and Its Impact on Patient Survival Outcomes. arXiv. Link.

Dong J, et al. (2021). Jointly modeling multiple transplant outcomes by a competing risk model via functional principal component analysis. Journal of Applied Statistics. Link.

Du Y, Su D, Dong J, et al. (2023). Factors Associated with Awareness and Knowledge of Nonalcoholic Fatty Liver Disease. Journal of Cancer Education. Link.

Conclusion:

Dr. Jianghu Dong is an exceptional candidate for the “Research for Best Researcher Award” in biostatistics and public health. His academic background, innovative research, and contributions to the analysis of chronic diseases, transplantation outcomes, and the COVID-19 pandemic exhibit the high-level scholarship and practical impact that this award aims to recognize. His growing portfolio of applied statistical research in critical areas of healthcare showcases his potential to continue advancing the field of biostatistics, making him a fitting choice for this prestigious award.

Majdi Khalid | Machine learning | Best Researcher Award

Assoc Prof. Dr. Majdi Khalid | Machine learning | Best Researcher Award 

Associate Professor at Umm Al-Qura University

Assoc. Prof. Dr. Majdi Khalid is an esteemed researcher in the field of machine learning with a focus on deep learning, artificial intelligence, and their applications in various domains such as computer vision, natural language processing, and bioinformatics. He is currently an Associate Professor at Umm Al-Qura University, Makkah, Saudi Arabia. Dr. Khalid has made significant contributions to cutting-edge research, particularly in the intersection of AI and bioinformatics, publishing numerous papers in prestigious journals and collaborating with international researchers. His work in AI for drug discovery and healthcare highlights his dedication to using technology to solve complex biological and medical challenges.

Profile:

ORCID

Education:

Dr. Khalid holds a Ph.D. in Computer Science from Colorado State University, USA, which he completed in 2019. His doctoral research centered on advanced computational models and machine learning algorithms, laying the foundation for his future endeavors in AI and deep learning. Prior to his Ph.D., Dr. Khalid earned his Master of Computer Science (M.C.S.) from the same institution in 2013, and a Bachelor of Science (B.S.) in Computer Science from Umm Al-Qura University in 2006. His academic training has equipped him with the technical and theoretical expertise necessary to excel in both academia and applied research.

Experience:

Dr. Khalid’s academic career began as an Instructor at the Technical College in Al Baha, Saudi Arabia, from 2007 to 2008. After earning his graduate degrees, he joined Umm Al-Qura University as an Assistant Professor in 2019, where he has since been engaged in teaching and research. Throughout his academic journey, Dr. Khalid has focused on mentoring students, leading cutting-edge research projects, and publishing extensively in the areas of machine learning and AI. His collaboration with national and international research teams has further enriched his experience, making him a valuable contributor to the global AI research community.

Research Interests:

Dr. Khalid’s research interests span various applications of machine learning and deep learning. He specializes in developing computational models for computer vision, natural language processing, bioinformatics, and brain-computer interfaces. His work in AI-driven drug discovery has led to the development of innovative tools for identifying epigenetic proteins and other biomarkers, which are critical for advancing modern medicine. Dr. Khalid is also actively exploring how AI can enhance healthcare systems and improve diagnostic accuracy, with a strong focus on interdisciplinary collaboration between AI and biological sciences.

Awards:

Dr. Khalid has received numerous recognitions for his research excellence, including university-level awards for outstanding research performance. His contributions to the fields of AI and machine learning have been acknowledged by both academic institutions and international conferences. While he has yet to secure a large-scale international research award, his continued dedication to advancing the field positions him as a prime candidate for future accolades.

Publications:

  1. Ali, Farman, Abdullah Almuhaimeed, Majdi Khalid, et al. (2024). “DEEPEP: Identification of epigenetic protein by ensemble residual convolutional neural network for drug discovery.” Methods.
    • Cited by articles focusing on the intersection of AI and drug discovery methodologies.
      Read the article here
  2. Khalid, Majdi, Farman Ali, et al. (2024). “An ensemble computational model for prediction of clathrin protein by coupling machine learning with discrete cosine transform.” Journal of Biomolecular Structure and Dynamics.
    • Cited by researchers investigating protein structure prediction and AI’s role in molecular biology.
      Read the article here
  3. Alsini, Raed, Abdullah Almuhaimeed, et al. (2024). “Deep-VEGF: deep stacked ensemble model for prediction of vascular endothelial growth factor by concatenating gated recurrent unit with 2D-CNN.” Journal of Biomolecular Structure and Dynamics.
  4. Alohali, Manal Abdullah, et al. (2024). “Textual emotion analysis using improved metaheuristics with deep learning model for intelligent systems.” Transactions on Emerging Telecommunications Technologies.
    • Cited in studies focusing on emotion recognition through AI in intelligent systems.
      Read the article here
  5. Majdi Khalid (2023). “Advanced Detection of COVID-19 through X-ray Imaging using CovidFusionNet with Hybrid CNN Fusion and Multi-resolution Analysis.” International Journal of Advanced Computer Science and Applications.
  1. Ali, Muhammad Umair, Majdi Khalid, et al. (2023). “Enhancing Skin Lesion Detection: A Multistage Multiclass Convolutional Neural Network-Based Framework.” Bioengineering, 10(12): 1430.
    • Cited by papers focusing on AI applications in medical diagnostics and image analysis for dermatology.
      Read the article here
  2. Alghushairy, Omar, Farman Ali, Wajdi Alghamdi, Majdi Khalid, et al. (2023). “Machine learning-based model for accurate identification of druggable proteins using light extreme gradient boosting.” Journal of Biomolecular Structure and Dynamics, 2023: 1-12.
    • Cited by studies dealing with protein-drug interactions and machine learning applications in bioinformatics.
      Read the article here
  3. Obayya, Marwa, Fahd N. Al-Wesabi, Rana Alabdan, Majdi Khalid, et al. (2023). “Artificial Intelligence for Traffic Prediction and Estimation in Intelligent Cyber-Physical Transportation Systems.” IEEE Transactions on Consumer Electronics, 2023.
    • Cited by research on AI-enhanced traffic systems and predictive modeling in smart cities.
      Read the article here
  4. Alruwais, Nuha, Eatedal Alabdulkreem, Majdi Khalid, et al. (2023). “Modified Rat Swarm Optimization with Deep Learning Model for Robust Recycling Object Detection and Classification.” Sustainable Energy Technologies and Assessments, 59: 103397.
    • Cited by works in sustainable technologies and AI for recycling and waste management.
      Read the article here
  5. Adnan, Adnan, Wang Hongya, Farman Ali, Majdi Khalid, et al. (2023). “A Bi-Layer Model for Identification of piwiRNA using Deep Neural Learning.” Journal of Biomolecular Structure and Dynamics, 2023: 1-9.
  • Cited by articles focused on non-coding RNA identification and AI-driven molecular biology research.
    Read the article here

Conclusion

Assoc. Prof. Dr. Majdi Khalid is a highly deserving candidate for the Best Researcher Award due to his extensive research contributions in machine learning and artificial intelligence. His innovative work in applying machine learning to critical fields such as drug discovery, COVID-19 detection, and biomolecular prediction makes him a thought leader in his domain. With minor improvements in real-world application and cross-disciplinary collaboration, Dr. Khalid’s potential to lead global innovations in machine learning is undeniable. His current achievements already solidify his place as one of the leading researchers in his field, making him an outstanding candidate for this prestigious award.

Chandra Sekhar Kolli | Nanotechnology| Best Researcher Award

Dr.Chandra Sekhar Kolli| Data Science | Best Researcher Award

Dr. Chandra Sekhar Kolli ,Shri Vishnu Engineering College for Women, India

Dr. Chandra Sekhar Kolli is associated with Shri Vishnu Engineering College for Women in India. He holds expertise in [mention his area of expertise, e.g., computer science/engineering]. Dr. Kolli has contributed significantly to academia through research, publications, and academic leadership roles. His professional journey is marked by a commitment to education and innovation in [mention specific field if

Profile

Scopus

Education:

Ph.D. in Computer Science and Engineering from GITAM University, Visakhapatnam, 2021.,M.Tech in Computer Science and Engineering from Hindustan University, Chennai, 2011.,MCA from Andhra University, Visakhapatnam, 2008.,B.Sc in Computer Science from Andhra University, Visakhapatnam, 2005.,Intermediate (MPC) from Govt Junior College, West Godavari District, 2002.,SSC from ZPH School, West Godavari District, 2000.

Experience:

Associate Professor at Shri Vishnu Engineering College for Women, Bhimavaram, West Godavari since June 2023.,Assistant Professor at GITAM (Deemed to be University), Visakhapatnam from July 2022 to June 2023.,Assistant Professor at Aditya College of Engineering & Technology, East Godavari from July 2021 to June 2022.,Assistant Professor at KL University, Guntur District from October 2017 to June 2021.,Assistant Professor at Madanapalle Institute of Technology & Science, Chittoor District from December 2010 to September 2017.

Skills:

  • Programming Languages: Python, Java, C++, C.
  • Database: NoSQL (Mongo DB), Oracle SQL.
  • Algorithms: Data Structures using C, C++, Java, Python, and Design and Analysis of Algorithms (DAA).
  • Core Courses: Machine Learning, DBMS, Operating Systems, Computer Networks.

Awards:

Best Teacher Award for the Academic Year 2019-20, CSE Department, KL University, Vijayawada.,WIPRO Certified Faculty – Qualified in Wipro Talent Next Global Certification in October 2020.,Automation Anywhere Certified in Advanced Level.,Ratified as an Assistant Professor by JNTU Kakinada and JNTU Anantapur.

Research Focus:

Dr. Chandra Sekhar Kolli’s research focuses on several areas including:,Deep Learning and Neural Networks.,IoT Integration and Applications.,Fraud Detection in Banking Transactions.,Machine Learning Applications in Healthcare.,Image Processing and Computer Vision.

 publications:

    • Dr. Kolli has a substantial number of research publications including:
      • Journal Articles: SCI and SCOPUS indexed articles.
      • Conference Papers: Presented at international conferences on topics ranging from IoT to AI and cybersecurity.
      • Book Chapters: Contributions in books related to Robotics, IoT, and Deep Learning.