Julakha JahanJui | Process Control | Best Researcher Award-6241

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Mrs. Julakha JahanJui, Computer , Best Researcher Award 

Doctor head at  Deakin University

Short bio

Julakha Jahan Jui is a dedicated professional with a background in [Field of Study], holding a Master’s degree from [University Name]. With a strong foundation in [Specific Areas], she has gained valuable experience in [Current Position or Field]. Her research interests include [Research Areas], and she is committed to advancing knowledge in [Field or Discipline]. Currently, she serves at [Organization/Institution], where she continues to contribute to [Field or Responsibilities].

professional profile

scopus

Educational Details

  • Master of Science (M.Sc.) in [Field of Study]:
    • University Name, Location
    • Year of Graduation
  • Bachelor of Science (B.Sc.) in [Field of Study]:
    • University Name, Location
    • Year of Graduation

Professional Experience

  • Current Position: Julakha Jahan Jui currently holds the position of [Current Position Title] at [Organization/Institution], where she is responsible for [Brief Description of Responsibilities].
  • Previous Roles: She has previously worked as [Previous Position Title] at [Previous Organization/Institution], contributing to [Key Responsibilities or Achievements].

Research Interest:

  • Julakha Jahan Jui’s research interests span [Research Areas], with a particular focus on [Specific Topics or Fields of Interest]. She has published and presented her work in [List Relevant Publications or Conferences].

Publications to Noted:

Optimal energy management strategies for hybrid electric vehicles: A recent survey of machine learning approaches

  •  Jui, J.J., Ahmad, M.A., Molla, M.M.I., Rashid, M.I.M.
  •  Journal of Engineering Research (Kuwait)

Robust PID tuning of AVR system based on Indirect Design Approach-2

  •  Ahmad, M.A., Tumari, M.Z.M., Ghazali, M.R., Suid, M.H., Jui, J.J.
  • Proceedings of 2023 International Conference on System Science and Engineering, ICSSE 2023

Metaheuristics algorithms to identify nonlinear Hammerstein model: a decade survey

  •  Jui, J.J., Ahmad, M.A., Rashid, M.I.M.
  •  Bulletin of Electrical Engineering and Informatics

Using Adaptive Safe Experimentation Dynamics Algorithm for Maximizing Wind Farm Power Production

  •  Ahmad, M.A., Jui, J.J., Ghazali, M.R.
  •  2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 – Proceedings.

Takayuki Sakai | Radiology| Best Researcher Award

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Dr.Takayuki Sakai | Radiology| Best Researcher Award

Chaohe Zheng is an Assistant Professor at the School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan, China. He earned his Ph.D. in Energy and Power Engineering from the same institution, following his Bachelor’s degree in the field. His research primarily focuses on computational dynamics, molecular dynamics, quantum dynamics, carbon capture and utilization, and chemical looping technologies.

Profiles

scopus

 

  🎓  Education and Academic Achievements

I pursued my academic journey at Kanazawa University, achieving significant milestones in health sciences. I completed a Doctoral Level Section of Integrated Course from the Graduate School of Medical Sciences (2018-2021), following a Master’s Level Section of Integrated Course (2016-2018). Prior to this, I laid the foundation for my career at Kitasato University School of Allied Health Sciences (1999-2003), where I began my studies in the field.

📝Original Papers

My research contributions are reflected in several notable publications. Among them, I authored papers such as “Functional Assessment of Lumbar Nerve Roots Using Direct Coronal Single-Shot Turbo Spin-Echo Diffusion Tensor Imaging” published in Magnetic Resonance in Medical Sciences (May 2020), and “Distortion-free diffusion tensor imaging for evaluation of lumbar nerve roots” in Magnetic Resonance Imaging (June 2018). These studies have advanced the field of medical imaging, particularly in evaluating spinal and nerve conditions.

🌐 Research, Innovations, and Extension Activities

My work focuses on pioneering quantitative evaluation and imaging innovations. This includes developing methods like 3D MIXTURE for assessing knee articular cartilage to prevent knee osteoarthritis, refining distortion-free diffusion tensor imaging for lumbar spinal nerve evaluation, and improving diffusion-weighted imaging SNR using SSGR techniques.

🏆 Awards and Recognition

I have been honored with a total of seven awards and have served as an invited speaker or resource person at 20 events. My research has made a cumulative impact, reflected in an impact factor of 15 over the last three years. Additionally, I hold editorial appointments in five journals and conferences, contributing actively to the academic community.

 🌐 Professional Affiliations and Contributions

As a member of various professional bodies, I have organized 10 research conferences and workshops and collaborated extensively on 24 joint publications and projects. These affiliations and contributions underscore my commitment to advancing medical sciences through research and collaborative efforts.

📚 Education and Research Highlights

In addition to his academic achievements, Chaohe Zheng has developed industrial-scale solutions for chemical looping combustion and waste treatment. His research innovations have significantly influenced the design and operational strategies of oxygen carriers, enhancing their performance and longevity while minimizing environmental impact.

Publications

Clinical evaluation of 3D high-resolution isotropic knee MRI using Multi-Interleaved X-prepared TSE with inTUitive RElaxometry (MIXTURE) for simultaneous morphology and T2 mapping

Authors: Sakai, T., Yoneyama, M., Zhang, S., Ochi, S., Miyati, T.

Journal: European Journal of Radiology, 2024

A novel simultaneous three-dimensional volumetric morphological imaging and T2-mapping method, multi-interleaved X-prepared turbo-spin echo with intuitive relaxometry provides more accurate quantification of cervical spinal nerves

Authors: Tokeshi, S., Eguchi, Y., Sakai, T., Takahashi, H., Ohtori, S.

Journal: Journal of Clinical Neuroscience, 2024

Relationship between assistant’s lens exposure and dose information during computed tomography examinations

Authors: Ito, H., Matsubara, K., Kobayashi, I., Yanagawa, N., Ochi, S.

Journal: Journal of Radiological Protection, 2024

Correction: Evaluation of x-ray effective focal spot size dependency on x-ray exposure settings using edge response analysis

Authors: Shimakawa, Y., Nishiki, M., Yanagita, S., Ochi, S., Yanagawa, N.

Journal: Radiological Physics and Technology, 2023

Evaluation of x-ray effective focal spot size dependency on x-ray exposure settings using edge response analysis

Authors: Shimakawa, Y., Nishiki, M., Yanagita, S., Ochi, S., Yanagawa, N.

Journal: Radiological Physics and Technology, 2023

Cardiac Magnetic Resonance Imaging Is Useful for Follow-up of Extremely Rare Pediatric COVID-19 Fulminant Myocarditis

Authors: Kinoshita, M., Higashi, K., Takaoka, H., Daimon, M., Kobayashi, Y.

Journal: Circulation Journal, 2023

Usefulness of a lead-acrylic shield for reducing lens dose of assistant in x-ray CT examination

Authors: Ito, H., Matsubara, K., Kobayashi, I., Yanagawa, N., Ochi, S.

Journal: Journal of Radiological Protection, 2022

Usefulness of Simultaneous Magnetic Resonance Neurography and Apparent T2 Mapping for the Diagnosis of Cervical Radiculopathy

Authors: Enomoto, K., Eguchi, Y., Sato, T., Takahashi, K., Ohtori, S.

Journal: Asian Spine Journal, 2022

An Injectable Hyaluronic Acid Hydrogel Promotes Intervertebral Disc Repair in a Rabbit Model

Authors: Inoue, M., Isa, I.L.M., Orita, S., Pandit, A., Ohtori, S.

Journal: Spine, 2021

Clinical feasibility of single-shot fluid-attenuated inversion recovery with wide inversion recovery pulse designed to reduce cerebrospinal fluid and motion artifacts for evaluation of uncooperative patients in acute stroke protocol

Authors: Kubota, Y., Yokota, H., Sakai, T., Ohira, K., Uno, T.

Journal: Journal of Magnetic Resonance Imaging, 2021

 

 

Sashikanta Prusty – Cancer Classification and Prediction- Best Researcher  – Award Winner 2023

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Congratulations to Sashikanta Prusty – Cancer Classification and Prediction- Best Researcher  – Award Winner 2023

Sashikanta Prusty

Sashikanta Prusty is dedicated to the development and implementation of new technology tools and applications in healthcare, with a focus on employing adaptive deep learning/machine learning strategies for effective disease diagnosis to prevent unnecessary deaths.

professional profile:

Professional Endeavors

  • Industry Experience: 03 years
  • Teaching Experience: 04 years
  • Research Experience: 3 years

Contributions and Research Focus

Sashikanta Prusty has an extensive research focus in the field of healthcare technology, particularly in the development and implementation of new technology tools and applications. His primary research areas include:

  • Deep Learning/Machine Learning Strategies: Applied for disease diagnosis to prevent unnecessary deaths.
  • Meta-Learning Algorithm: Used in the development of SEMeL-LR for breast cancer classification.
  • Integrated Machine Learning-Fuzzy and Dimension Reduction Techniques: Applied for breast cancer prediction.
  • Transfer Learning Techniques: Developed a novel technique for detecting breast cancer mammograms using VGG16 bottleneck features.

Accolades and Recognition

  • Published Papers in Journals:
    1. SEMeL-LR: An improvised modeling approach using a meta-learning algorithm to classify breast cancer.
    2. Prediction of Breast cancer using integrated machine learning-fuzzy and dimension reduction techniques.
    3. SKCV: Stratified K-fold Cross-Validation on ML Classifiers for Predicting Cervical Cancer.
    4. Comparative analysis and prediction of coronary heart disease.
    5. A Novel Transfer Learning Technique for Detecting Breast Cancer Mammograms Using VGG16 Bottleneck Feature.
    6. Smart City E-Governance Through Intelligent ICT Framework.
    7. Integrating Adam Optimizer to Enhance Efficiency of Transfer Learning Model for Diagnosing Cancers.
    8. Internet of Things brings Revolution in eHealth: Achievements and Challenges.
    9. Designing a Pipeline for Predicting Power Grid Stability with Artificial Neural Network (ANN).
    10. Impact of COVID-19 on Breast Cancer Screening Program (BCSP) in India.
    11. Application of Artificial Intelligence in Fuzzy Logic for Crop Management in Agriculture.
  • Conference Contributions:
    • Presented papers in various international conferences, such as APSIT, DASA, ICIDeA, MLCSS, and ODICON.

Impact and Influence

Sashikanta Prusty has contributed significantly to the field of healthcare technology, specifically in the development of machine learning models for disease diagnosis. His work has been published in reputable journals and presented at international conferences, indicating a positive impact on the academic and research community.

Legacy and Future Contributions

While the provided information does not explicitly mention the legacy, Sashikanta Prusty’s continuous involvement in research and academia suggests a commitment to making further contributions in the field of healthcare technology, machine learning, and data analysis.

Notable Publication

Smart City E-Governance Through Intelligent ICT Framework. 31 January 2022

Time Series Analysis of SAR-Cov-2 Virus in India Using Facebook’s Prophet. 

SEMeL-LR: An improvised modeling approach using a meta-learning algorithm to classify breast cancer.  March 2024, 107630

Prediction of Breast cancer using integrated machine learning-fuzzy and dimension reduction techniques. 02 July 2023 1633-1652, 2023

Akshay Agarwal-  Best Researcher – Award Winner 2023

Posted on by Academic achievements
Congratulations to Akshay Agarwal-  Best Researcher - Award Winner 2023
Akshay Agarwal:

Akshay Agarwal is an accomplished professional and academician, currently serving as an Assistant Professor in the Department of Data Science and Engineering at IISER Bhopal, India. With a robust background in computer science and a focus on cutting-edge research in artificial intelligence (AI) and computer vision, Akshay has made significant contributions to the field.

 

professional profile:

Early Academic Pursuits

Akshay Agarwal embarked on his academic journey with a Bachelor's in Computer Science from Invertis, Bareilly, UP, India, where he excelled with an overall percentage of 77.24%. This laid the foundation for his subsequent academic achievements.

His pursuit of knowledge continued with a Master's in Information Technology from IIIT-Allahabad, UP, India, where he achieved a CGPA of 8.85. This early academic rigor set the stage for his passion for technology and research.

Professional Endeavors

Venturing into the realm of academia, Akshay pursued a Ph.D. in Image Analysis and Biometrics from IIIT-Delhi, New Delhi, India. His doctoral research, titled "Panoptic Defenses for Secure Computer Vision," showcased his commitment to advancing the field. This dedication earned him the prestigious Visvesvaraya PhD Fellowship from the Government of India.

Post-Ph.D., Akshay expanded his horizons as a Research Assistant Professor at Texas A&M University, Kingsville, Texas, USA, delving deeper into the intricacies of his research interests.

Contributions and Research Focus

Akshay's research focus revolves around AI Security, Trustworthy Computer Vision, ML/DL, Presentation Attacks, Adversarial Robustness, Deepfakes, and Bias. His work has notably contributed to the development of robust defenses in computer vision, addressing critical challenges in the ever-evolving landscape of artificial intelligence.

During his visiting researcher experience at West Virginia University, USA, Akshay worked on pioneering projects related to face presentation attack detection and morphing attack detection, resulting in impactful publications in prestigious conferences.

Accolades and Recognition

The accolades bestowed upon Akshay Agarwal stand as a testament to his excellence. Notably, he received the IEEE Biometrics Council 'Best Doctoral Dissertation' Award in 2021 for his outstanding Ph.D. work. His recognition extended to winning the second place in the Agrusa Student Innovation competition at the University at Buffalo in 2021.

Impact and Influence

Akshay's influence is evident not only through awards but also through his selection for the 7th Heidelberg Laureate Forum, where only the 200 most qualified young researchers are invited. His work has left a lasting impact on the academic community, shaping discussions and developments in the fields of AI Security and Computer Vision.

Legacy and Future Contributions

As an Assistant Professor at IISER Bhopal, Akshay Agarwal continues to shape the next generation of researchers. His legacy lies in inspiring students and colleagues alike, fostering a culture of innovation and excellence.

Looking ahead, Akshay envisions contributing further to the field, addressing emerging challenges in AI security, and leaving an indelible mark on the landscape of computer vision and machine learning.

Notable Publications:

G. Goswami, A. Agarwal, N. K. Ratha, R. Singh, M. Vatsa, “Federated Learning for Local
and Global Data Distribution”, ICLR TinyPapers, 2023

U. Rathore, A. Agarwal, “Is DFR for Soft Biometrics Prediction in Unconstrained
Images Fair and Effective?”, ICLR TinyPapers, 2023

A. Agarwal, M. Vatsa, and R. Singh, “Role of Optimizer on Network Fine-tuning for
Adversarial Robustness (Student Abstract)”, In AAAI Conference on Artificial Intelligence
(AAAI), 2021, vol. 35, no. 18, pp. 15745-15746.

A. Mehra, A. Agarwal, M. Vatsa, and R. Singh, “Detection of Digital Manipulation in
Facial Images (Student Abstract)”, In AAAI Conference on Artificial Intelligence (AAAI),
2021, vol. 35, no. 18, pp. 15845-15846.

R. Singh, A. Agarwal, M. Singh, S. Nagpal and M. Vatsa, “On the Robustness of Face
Recognition Algorithms Against Attacks and Bias”, AAAI Conference on Artificial Intelligence, 2020, vol. 34, no. 09, pp. 13583-13589. (ORAL)

G. Goswami, N. Ratha, A. Agarwal, R. Singh, and M. Vatsa, “Unravelling Robustness of
Deep Learning based Face Recognition Against Adversarial Attacks”, In Association
for the Advancement of Artificial Intelligence (AAAI), 2018, pp. 6829-6836. (ORAL)