Forouzan Mirmasoudi | Decision Sciences | Best Researcher Award

Posted on by Academic achievements

Dr. Forouzan Mirmasoudi | Decision Sciences | Best Researcher Award

postdoctoral student | Sharif University | Iran

Forouzan Mirmasoudi is an Iranian theoretical physicist specializing in quantum information, condensed matter physics, and quantum chaos. He has held prominent academic positions, including a postdoctoral position at Sharif University of Technology, where he conducted advanced research on the nonlinear response of quantum systems using the Jordan-Wigner fermionization method. With a robust academic background that includes a Ph.D. in Theoretical Physics from Mohaghegh Ardabili University, his work explores quantum dynamics, entanglement, and information theory in complex systems. He is a faculty member at the University of Mohaghegh Ardabili, contributing both as a teacher and researcher. Mirmasoudi’s work aims to bridge the gap between quantum theory and its practical applications, particularly in quantum information processing and thermodynamics.

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Education

Mirmasoudi’s educational journey in physics began with a B.Sc. from Damghan University in 2009, followed by an M.Sc. in Physics from Mohaghegh Ardabili University, where he specialized in particle physics and quantum information. His research during his Master’s program focused on the investigation of symmetry and conservation laws in particle physics. Building upon his foundational knowledge, he pursued a Ph.D. in Theoretical Physics at Mohaghegh Ardabili University, completing his thesis on “Quantum information theory in chaotic dynamical systems and its applications.” His doctoral research explored quantum chaos and the behavior of quantum systems, laying the groundwork for his later work in quantum entanglement and quantum chaos models. He further expanded his academic horizons with a postdoctoral fellowship at Sharif University of Technology, specializing in condensed matter theory, particularly the study of nonlinear responses in quantum systems.

Experience

Mirmasoudi has accumulated significant teaching experience over the years, serving as a faculty member at multiple universities. He has taught basic and advanced physics courses at Mohaghegh Ardabili University, University of Caspian College of Engineering, University of Tehran, and the University of Guilan. In addition to his teaching roles, Mirmasoudi has actively contributed to research, particularly in the areas of quantum dynamics and information theory. His expertise in using computational tools like Fortran, Mathematica, Matlab, and Python complements his theoretical work, especially in many-body systems and exact diagonalization methods. Mirmasoudi has also participated in various research collaborations, including a visiting researcher role at the University of Guilan, where he worked on quantum teleportation and super dense coding models.

Research Interests

Mirmasoudi’s research interests span several areas of quantum physics, including quantum entanglement, quantum chaotic systems, many-body physics, and complex dynamic systems. His work often intersects with quantum information science, focusing on the roles of quantum correlations, such as quantum discord, in the dynamics of quantum systems. He is particularly interested in how quantum information can be manipulated and harnessed in various physical systems, including spin chains, superconductors, and low-dimensional quantum systems. His current research also extends to the investigation of quantum thermodynamics, especially in relation to quantum heat engines and energy transfers, and the role of quantum entanglement in these processes. Mirmasoudi’s projects continue to evolve, reflecting his interest in both theoretical studies and practical applications of quantum mechanics.

Awards

Throughout his academic career, Mirmasoudi has received recognition for his contributions to quantum physics, particularly in the areas of quantum information theory and quantum chaos. His work has been acknowledged through various awards and nominations, reflecting his growing influence in the field. His published papers have received significant attention, and his contributions to understanding quantum systems in both theoretical and applied contexts have positioned him as a notable figure in the physics community.

Publications

Mirmasoudi’s research has resulted in several impactful publications, which have contributed to the understanding of quantum dynamics, information, and entanglement. His most notable works include:

  1. Mirmasoudi, F., & Ahadpour, S. (2018). Dynamics of super quantum discord and optimal dense coding in quantum channels, Journal of Physics A: Mathematical and Theoretical.
  2. Mirmasoudi, F., & Ahadpour, S. (2019). Application quantum renormalization group to optimal dense coding in transverse Ising model, Physica A: Statistical Mechanics and its Applications.
  3. Mirmasoudi, F., & Ahadpour, S. (2017). Dynamics super quantum discord and quantum discord teleportation in the Jaynes–Cummings model, Journal of Modern Optics.
  4. Mirmasoudi, F., Ahadpour, S., Vahedi, J., & Mahdavifar, S. (2019). The Loschmidt-echo dynamics in a quantum chaos model, Physica Scripta.
  5. Mirzaei, S., Najarbashi, G., Fasihi, M. A., & Mirmasoudi, F. (2018). Entanglement of multipartite fermionic coherent states for pseudo-Hermitian Hamiltonians, Theoretical and Mathematical Physics.
  6. Mirmasoudi, F., & Ahadpour, S. (2018). Thermal Quantum Discord and Super Quantum Discord Teleportation via Two-Qubit Spin Squeezing Model, Theoretical and Mathematical Physics.
  7. Ahadpour, S., Nemati, A., Mirmasoudi, F., & Hematpour, N. (2018). Projective Synchronization of Piecewise Nonlinear Chaotic Maps, Theoretical and Mathematical Physics.

These papers, published in high-impact journals, are widely cited and have contributed to significant advancements in the fields of quantum information, thermodynamics, and chaos theory. His research has had a profound influence on understanding quantum systems, with many of his works cited in subsequent studies, furthering the knowledge in these areas.

Conclusion

Forouzan Mirmasoudi’s academic and research career highlights a commitment to advancing the understanding of quantum physics, particularly in the realms of information theory, chaos, and condensed matter systems. His extensive teaching experience, coupled with his active participation in cutting-edge research, has positioned him as a prominent researcher in his field. Through his work, Mirmasoudi continues to explore the intricate dynamics of quantum systems, contributing valuable insights into the role of quantum information in both theoretical and applied contexts. As he progresses in his research, Mirmasoudi remains dedicated to furthering the field of quantum physics and inspiring the next generation of researchers.

Rashmi S | Machine Learning Techniques | Best Researcher Award

Posted on by Academic achievements

Mrs. Rashmi S | Machine Learning Techniques | Best Researcher Award

Rashmi S – Machine Learning Techniques | Senior Research Fellow at JSS Science and Technology University, India

Rashmi S. is an accomplished Ph.D. research scholar specializing in Computer Vision and Machine Intelligence. Her academic focus is particularly on medical image analysis, with a concentration on radiographic image annotation using AI and deep learning techniques. With approximately five years of experience in the tech industry as a Core Java Developer, Rashmi brings a unique blend of software development expertise and advanced research skills. She is currently working at the Pattern Recognition & Image Processing Lab at JSS Science and Technology University, Mysuru. Rashmi is driven by the ambition to enhance healthcare systems through innovative AI solutions, and her research contributions aim to create more accurate, automated systems for interpreting medical imagery.

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Education

Rashmi S. completed her Bachelor of Engineering (B.E.) in Computer Science and Engineering from SJCE, Mysore, graduating with a CGPA of 9.05. She then pursued her Master’s degree in Computer Engineering (M.Tech) from the same institution, achieving an outstanding CGPA of 9.77. Currently, she is pursuing her Ph.D. in Computer Science and Engineering at JSS S&TU, where she is expected to submit her thesis in September 2024. Her academic journey has been marked by a strong commitment to research excellence, particularly in Machine Learning and Deep Learning, both of which she applies in her medical image analysis research.

Experience

Rashmi S. has held various roles in both academic and industry settings, which have enriched her research and technical skills. She began her career in software engineering, working with Cisco Video Technology in Bengaluru, where she was involved in the development of Java-based software for Set-Top Boxes. She later moved on to Oracle India Pvt. Ltd. as an Application Engineer, working on software maintenance and the development of Oracle Projects Fusion, a project management tool. Rashmi’s academic career includes positions as a Junior Research Fellow and Senior Research Fellow at JSS Science and Technology University, where she currently conducts her doctoral research. Her professional journey in both the software industry and academia gives her a unique edge in developing and implementing cutting-edge research in healthcare.

Research Interests

Rashmi S. is primarily focused on Machine Learning, Deep Learning, and Image Processing, especially in the context of medical image analysis. Her research interests revolve around improving diagnostic tools through AI-powered systems. Specifically, her work addresses cephalometric landmark annotation in radiographs using both traditional machine learning algorithms and deep learning techniques. Rashmi has explored applications of EEG signal processing and computer vision in healthcare, striving to develop solutions that can automate the annotation of medical images for more accurate diagnoses. Her research aims to bridge the gap between artificial intelligence and clinical practices, potentially revolutionizing medical imaging and diagnostic procedures.

Awards

Rashmi S. has received several prestigious awards throughout her academic and professional career. She was awarded the UGC-NET Junior Research Fellowship in November 2021, which has enabled her to pursue her doctoral research in depth. She was also recognized with the Senior Research Fellowship by the University Grants Commission in February 2024. Additionally, Rashmi has been the recipient of several scholarships, including the MHRD & GATE Scholarships during her undergraduate and postgraduate studies. Her commitment to research excellence has also earned her multiple accolades for her academic performance, including being recognized for her outstanding contributions to machine learning in the medical field.

Publications

“Cephalometric Skeletal Structure Classification Using Convolutional Neural Networks and Heatmap Regression“, co-authored with P. Murthy, V. Ashok, and S. Srinath, published in SN Computer Science (2022). This study leverages convolutional neural networks (CNNs) and heatmap regression for advanced skeletal structure classification in cephalometric radiographs, with a focus on enhancing the accuracy of diagnostic tools in orthodontics.

“Extended Template Matching Method for Region of Interest Extraction in Cephalometric Landmarks Annotation“, co-authored with S. Srinath, R. Rakshitha, and B.V. Poornima, presented at the 2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical… This paper introduces an extended template matching method aimed at improving the extraction of regions of interest (ROIs) in cephalometric image annotation, a crucial step for automatic landmark detection.

“Lateral Cephalometric Landmark Annotation Using Histogram Oriented Gradients Extracted from Region of Interest Patches“, co-authored with S. Srinath, K. Patil, P.S. Murthy, and S. Deshmukh, published in Journal of Maxillofacial and Oral Surgery (2023). This research presents a novel approach for lateral cephalometric landmark annotation by extracting histogram-oriented gradients from ROIs, advancing the methods for more precise orthodontic assessments.

“A Novel Method for Cephalometric Landmark Regression Using Convolutional Neural Networks and Local Binary Pattern“, co-authored with V. Ashok, presented at the 5th International Conference on Computer Vision and Image Processing (2021). This paper explores a novel technique for landmark regression in cephalometric images using a combination of CNNs and local binary patterns, enhancing the automation of cephalometric analysis.

“Landmark Annotation Through Feature Combinations: A Comparative Study on Cephalometric Images with In-depth Analysis of Model’s Explainability“, co-authored with S. Srinath, S. Murthy, and S. Deshmukh, published in Dentomaxillofacial Radiology (2024). This comparative study examines various feature combinations for landmark annotation and provides an explainability analysis of the models used, aiming to make machine learning-based medical imaging more transparent and understandable.

“Recognition of Indian Sign Language Alphanumeric Gestures Based on Global Features“, co-authored with B.V. Poornima, S. Srinath, and R. Rakshitha, presented at the 2023 IEEE International Conference on Distributed Computing, VLSI… This paper investigates the use of global features for recognizing Indian Sign Language gestures, contributing to the development of gesture recognition systems in communication technologies.

“ISL2022: A Novel Dataset Creation on Indian Sign Language“, co-authored with R. Rakshitha, S. Srinath, and S. Rashmi, presented at the 2023 10th International Conference on Signal Processing and Integrated…. This paper presents the creation of the ISL2022 dataset, a significant step toward improving machine learning models for Indian Sign Language recognition, highlighting the importance of datasets in advancing language recognition research.

“Cephalometric Landmark Annotation Using Transfer Learning: Detectron2 and YOLOv8 Baselines on a Diverse Cephalometric Image Dataset“, co-authored with S. Srinath, S. Deshmukh, S. Prashanth, and K. Patil, published in Computers in Biology and Medicine (2024). This work leverages transfer learning techniques, using Detectron2 and YOLOv8 models, to annotate cephalometric landmarks on a diverse dataset, pushing the envelope for automated medical image analysis.

“Crack SAM: Enhancing Crack Detection Utilizing Foundation Models and Detectron2 Architecture“, co-authored with R. Rakshitha, S. Srinath, N. Vinay Kumar, and B.V. Poornima, published in Journal of Infrastructure Preservation and Resilience (2024). This research explores advanced crack detection techniques, using foundation models and Detectron2, to improve the detection of cracks in infrastructure.

“Enhancing Crack Pixel Segmentation: Comparative Assessment of Feature Combinations and Model Interpretability”, co-authored with R. Rakshitha, S. Srinath, N. Vinay Kumar, and B.V. Poornima, published in Innovative Infrastructure Solutions (2024). This paper focuses on crack pixel segmentation, offering insights into the comparative performance of various feature combinations and the interpretability of machine learning models used in infrastructure monitoring.

Conclusion

Rashmi S. has demonstrated exceptional skill and dedication to the field of Computer Vision and Machine Intelligence. With her substantial industry experience and strong academic background, Rashmi has contributed significantly to AI research in healthcare. Her work has the potential to revolutionize medical image analysis, offering more efficient and accurate diagnostic tools. Through her awards, publications, and ongoing research, Rashmi S. stands as an exemplary candidate for the Best Researcher Award, with the promise of continuing to make groundbreaking advancements in her field.

Sara Bendjeddou | Statistics | Best Researcher Award

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Mrs. Sara Bendjeddou | Statistics | Best Researcher Award

Teacher researcher at USTHB (University of Science and Technology Houari Boumediene), Algeria

Dr. Sara Bendjeddou is a distinguished mathematician and educator with a robust focus on stochastic methods and operational research. Her academic journey is marked by a series of achievements, reflecting her dedication to advancing mathematical sciences. Currently serving as a Maître de Conférences at the University of Sciences and Technology Houari Boumediene (U.S.T.H.B.) in Algeria, Dr. Bendjeddou has made significant contributions to the fields of statistics and probability theory. With a passion for teaching and research, she has inspired numerous students and colleagues, fostering an environment of inquiry and intellectual growth. Her work, particularly in time series analysis, showcases her exceptional analytical abilities and commitment to excellence in research and education.

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Education

Dr. Bendjeddou holds an impressive array of academic credentials. She earned her Doctorate in Mathematics in April 2018 from U.S.T.H.B., specializing in Stochastic Methods in Operational Research. Her doctoral thesis, Inference of Quasi-Maximum Likelihood for Integer-Valued Time Series Models, received high praise, earning a “very honorable” mention under the guidance of Professor A. Aknouche. Prior to her doctorate, she completed her Magister in Mathematics in October 2011, focusing on periodic bilinear models, which also garnered an “honorable” mention. Dr. Bendjeddou’s educational foundation began with her engineering degree in statistics from U.S.T.H.B. in September 2008, where she achieved a “very honorable” distinction. Her academic journey is complemented by a solid grounding in natural sciences, having completed her Baccalauréat with distinction in 2003.

Experience

Dr. Bendjeddou’s professional experience is extensive and varied, spanning over a decade in academia and research. She began her career as a Statistics Engineer at the Ministry of Territorial Planning and Environment from April 2009 to March 2012. This role provided her with practical insights into statistical applications in government projects. Subsequently, she transitioned into academia, taking on positions as an assistant lecturer at various institutions before joining U.S.T.H.B. as a Maître de Conférences in 2018. Throughout her tenure, Dr. Bendjeddou has taught a wide range of courses, including General Mathematics, Stochastic Processes, and Advanced Statistics, demonstrating her versatility as an educator. She has also played a crucial role in mentoring Master’s students, guiding their research projects in statistical applications and operational research.

Research Interests

Dr. Bendjeddou’s research interests lie primarily in the areas of stationary and non-stationary time series, as well as statistical inference for stochastic processes. Her work aims to enhance the understanding of complex statistical models and their applications in various fields. Dr. Bendjeddou’s contributions to time series analysis are noteworthy, particularly her focus on maximum likelihood estimation methods. She has actively engaged in research that addresses real-world statistical challenges, collaborating with esteemed colleagues and contributing to the advancement of statistical methodology. Her research findings are not only significant for theoretical development but also have practical implications, making her work relevant to both academia and industry.

Awards

Throughout her career, Dr. Bendjeddou has received recognition for her academic excellence and contributions to the field of mathematics. Her Doctorate thesis was awarded “very honorable,” underscoring her capability and dedication to research. Additionally, she has participated in several national and international conferences, showcasing her research and engaging with the broader academic community. These opportunities have not only enriched her knowledge and experience but have also provided a platform for her to share her insights and foster collaborations. Dr. Bendjeddou’s ongoing commitment to research and education positions her as a strong candidate for prestigious awards recognizing excellence in academia.

Publications

Aknouche, A. & Bendjeddou, S. (2017). Estimateur du quasi-maximum de vraisemblance géométrique d’une classe générale de modèles de séries chronologiques à valeurs entières. C. R. Acad. Sci. Paris, Ser. I, 355, 99-104.

Aknouche, A., Bendjeddou, S. & Touche, N. (2018). Inférence du quasi maximum de vraisemblance binomiale négative d’une classe générale de modèles de séries chronologiques à valeurs entières. Journal of Time Series Analysis.

Bendjeddou, S. (2024). Preliminary Test Estimation for Parallel 2-Sampling in Autoregressive Model. Stats, 7(4), 1141-1158.

Conclusion

In conclusion, Dr. Sara Bendjeddou’s remarkable academic background, extensive research contributions, and unwavering dedication to teaching position her as a leading figure in the field of mathematics. Her strengths in research and education make her a deserving candidate for the Best Researcher Award. Dr. Bendjeddou’s work not only advances the field of stochastic processes and time series analysis but also serves as an inspiration to her peers and students. Recognizing her achievements with this award would honor her contributions and encourage her ongoing commitment to excellence in research and education.

James Dong | Statistical modeling | Best Researcher Award

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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:

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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.

Chandra Sekhar Kolli | Nanotechnology| Best Researcher Award

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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

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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.