Muhammad Kashif Jabbar | Artificial Intelligence | Best Researcher Award

Mr. Muhammad Kashif Jabbar | Artificial Intelligence | Best Researcher Award

Doctor Student at Shenzhen University, China

Muhammad Kashif Jabbar is a research-focused professional specializing in medical image processing. With a strong foundation in Electronics and Information Engineering, he has contributed significantly to research, particularly in developing transfer learning-based models for diabetic retinopathy diagnosis. Muhammad Kashif is multilingual, skilled in technical domains, and experienced in international collaborations.

Publication Profileย 

Scopus

Educational Background ๐ŸŽ“

  1. Shenzhen University
    • Degree: Ph.D. in Electronics and Information Engineering
    • Specialization: Medical Image Processing
    • Session: September 2018 โ€“ June 2022
  2. Beijing University of Technology (BJUT)
    • Degree: Masterโ€™s in Information and Communication Engineering
    • Specialization: Medical Image Processing
    • Session: September 2018 โ€“ June 2022
  3. Superior University of Lahore
    • Degree: Masterโ€™s in Information Technology (MIT)
    • Session: 2014 โ€“ 2016

Professional Experience ๐Ÿ’ผ

  • Worked extensively on developing advanced methodologies in medical image processing.
  • Conducted research focusing on diabetic retinopathy diagnosis, utilizing transfer learning techniques.
  • Developed applications in web development and database management.

Research Interests ๐Ÿ”ฌ

  • Medical Image Processing
  • Transfer Learning for Disease Diagnosis
  • Data Security in Medical Imaging (Steganography and Cryptography)
  • Artificial Intelligence and Optimization Algorithms in Healthcare Applications

Awards and Honors๐Ÿ†โœจ

  • Passed HSK4 Chinese Language Proficiency Exam (2018).
  • Performed at the 14th BJUT International Day opening ceremony.
  • Recognized for successful completion of the 2019 International Students Exploring Haidian program.

Certifications

  1. HSK4 Chinese Language Certification โ€“ Beijing University of Technology
  2. Graphic Design โ€“ ARENA Multimedia, Islamabad Campus (2015)

Conclusion๐ŸŒŸ

Muhammad Kashif Jabbar is a highly skilled researcher with a passion for advancing medical technologies using artificial intelligence and image processing techniques. His education and expertise make him a valuable asset to organizations focused on cutting-edge medical research and innovation.

Publications ๐Ÿ“š

๐Ÿ“ก Radar and Engineering

  1. Enhancing Radar Tracking Accuracy Using Combined Hilbert Transform and Proximal Gradient Methods
    • Authors: Jabbar, A., Jabbar, M.K., Jabbar, A., Mahmood, T., Rehman, A.
    • Journal: Results in Engineering, 2024, 24, 103479.
    • ๐ŸŒ Type: Article (Open Access)
    • ๐Ÿ“Š Citations: 0

๐Ÿ‘๏ธ Ophthalmology and AI

  1. A Retinal Detachment Based Strabismus Detection Through FEDCNN
    • Authors: Jabbar, A., Jabbar, M.K., Mahmood, T., Nobanee, H., Rehman, A.
    • Journal: Scientific Reports, 2024, 14(1), 23255.
    • ๐ŸŒ Type: Article (Open Access)
    • ๐Ÿ“Š Citations: 0

๐Ÿ”„ Errata and Corrections

  1. Correction to: Deep Transfer Learning-Based Automated Diabetic Retinopathy Detection Using Retinal Fundus Images in Remote Areas
    • Authors: Jabbar, A., Naseem, S., Li, J., Rehman, A., Saba, T.
    • Journal: International Journal of Computational Intelligence Systems, 2024, 17(1), 145.
    • ๐ŸŒ Type: Erratum (Open Access)
    • ๐Ÿ“Š Citations: 1

  2. Correction to: Transfer Learning-Based Model for Diabetic Retinopathy Diagnosis Using Retinal Images

    • Authors: Jabbar, M.K., Yan, J., Xu, H., Ur Rehman, Z., Jabbar, A.
    • Journal: Brain Sciences, 2024, 14(8), 777.
    • ๐ŸŒ Type: Erratum (Open Access)
    • ๐Ÿ“Š Citations: 0

๐Ÿง  Diabetic Retinopathy and AI Models

  1. Transfer Learning-Based Model for Diabetic Retinopathy Diagnosis Using Retinal Images
    • Authors: Jabbar, M.K., Yan, J., Xu, H., Rehman, Z.U., Jabbar, A.
    • Journal: Brain Sciences, 2022, 12(5), 535.
    • ๐ŸŒ Type: Article (Open Access)
    • ๐Ÿ“Š Citations: 49

 

 

 

Swati Jaiswal | Deep Learning | Women Researcher Award

Mrs. Swati Jaiswal | Deep Learning | Women Researcher Award

Assistant Professor at DES Pune University, Pune, India

Swati Jaiswal, Ph.D. candidate at VIT Vellore, is an experienced Assistant Professor in Computer Engineering with over 14 years of academic and research expertise. Currently, she is serving at the School of Computer Engineering & Technology, DES Pune University. She has held various teaching and administrative roles across esteemed institutions like PCCOE, ZCOER, and SKNSITS, contributing significantly to academic development and research. Swati’s contributions span diverse fields like Machine Learning, Cybersecurity, Autonomous Vehicles, AI, and IoT, reflected in her numerous publications, patents, and book chapters ๐Ÿ“š๐Ÿ”. Swatiโ€™s dedication to research and teaching is complemented by a passion for developing innovative solutions to real-world problems ๐Ÿค–๐Ÿ’ก.

Publication Profile :ย 

Google Scholar

Education๐ŸŽ“

Swati holds a Masterโ€™s in Computer Science & Engineering with 86% from RGPV, Bhopal (2012), and a BE in the same discipline with 80% (2009). She is currently pursuing a Ph.D. in the field of AI and Machine Learning at VIT Vellore, under the guidance of Dr. Chandra Mohan B. Her academic journey also includes certifications in various fields like Data Science, Machine Learning, and Software Testing ๐ŸŽ“๐Ÿ“œ.

Professional Experience๐Ÿ’ผ

Swati began her career as an Assistant Professor at SAMCET Bhopal in 2009, where she coordinated seminars and workshops. Over the years, she worked at several prestigious institutions, including SKNSITS, ZCOER, and PCCOE, contributing to curriculum development, departmental coordination, and research activities. Since June 2024, she has been with DES Pune University, where she continues her academic journey while nurturing the next generation of engineers and researchers. Along with teaching, she has overseen various academic and administrative responsibilities, including time-table coordination, research guidance, and university exams ๐Ÿซ๐Ÿ“Š.

Research Interests๐Ÿ”ฌ

Her research primarily focuses on Machine Learning, Artificial Intelligence, Cybersecurity, Autonomous Systems, and Internet of Things (IoT). She has explored deep learning models for real-time systems, especially in autonomous driving, vehicle communication systems, and intelligent robotics. Additionally, Swati is passionate about the application of AI and ML in solving complex real-world problems such as fraud detection, data security, and predictive analytics ๐Ÿ’ป๐Ÿ”๐Ÿš—.

Publications Top Notes๐Ÿ“š

  1. Jha, R. K., Kumar, A., Prakash, S., Jaiswal, S., Bertoluzzo, M., Kumar, A., Joshi, B. P., & … (2022). Modeling of the resonant inverter for wireless power transfer systems using the novel MVLT method. Vehicles, 4(4), 1277-1287. [34 citations]
  2. Kachhoria, R., Jaiswal, S., Khairnar, S., Rajeswari, K., Pede, S., Kharat, R., … (2023). Lie group deep learning technique to identify the precision errors by map geometry functions in smart manufacturing. The International Journal of Advanced Manufacturing Technology, 1-12. [12 citations]
  3. Kachhoria, R., Jaiswal, S., Lokhande, M., & Rodge, J. (2023). Lane detection and path prediction in autonomous vehicle using deep learning. In Intelligent edge computing for cyber physical applications (pp. 111-127). [11 citations]
  4. Swati Jaiswal, D. C. M. B. (2017). A survey: Privacy and security to Internet of Things with cloud computing. International Journal of Control Theory and Applications, 10(1), 487-500. [7 citations]
  5. Jaiswal, S., & Rodge, J. (2019). Comprehensive overview of neural networks and its applications in autonomous vehicles. In Computational Intelligence in the Internet of Things (pp. 159-173). [6 citations]
  6. Kati, S., Ove, A., Gotipamul, B., Kodche, M., & Jaiswal, S. (2022). Comprehensive overview of DDOS attack in cloud computing environment using different machine learning techniques. In Proceedings of the International Conference on Innovative Computing. [5 citations]
  7. Raut, R., Jadhav, A., Jaiswal, S., & Pathak, P. (2022). IoT-assisted smart device for blind people. In Intelligent Systems for Rehabilitation Engineering (pp. 129-150). [4 citations]
  8. Jaiswal, S., & Desai, M. (2019). Importance of information security and strategies to prevent data breaches in mobile devices. In Improving Business Performance Through Innovation in Digital Economy (pp. 215-225). [4 citations]
  9. Jaiswal, S., & Chandra, M. B. (2023). An efficient real-time decision-making system for autonomous vehicle using timber chased wolf optimization-based ensemble classifier. Journal of Engineering Science and Technology Review, 16(1), 75-84. [3 citations]
  10. Jaiswal, S., & Balasubramanian, C. M. (2023). An advanced deep learning model for maneuver prediction in real-time systems using alarming-based hunting optimization. International Journal of Advances in Intelligent Informatics, 9(2). [2 citations]
  11. Sorde, C., Jadhav, A., Jaiswal, S., Padwad, H., & Raut, R. (2023). Generative adversarial networks and its use cases. In Generative Adversarial Networks and Deep Learning (pp. 1-11). [2 citations]
  12. Rajeswari, K., Vispute, S., Maitre, A., Kharat, R., Aher, N., Vivekanandan, N., … (2023). Time series analysis with systematic survey on COVID-19 based predictive studies during pandemic period using enhanced machine learning techniques. iJOE, 19(07), 161. [2 citations]
  13. Jadhav, A., Raut, R., Jhaveri, R., Patil, S., Jaiswal, S., Katole, A., … (2021). A device for child safety and security. [2 citations]
  14. Jaiswal, S., Prakash, S., Gupta, N., & Rewadikar, D. (n.d.). Performance optimization in ad-hoc networks. International Journal of Computer Technology and Electronics Engineering. [2 citations]
  15. Jaiswal, S., & Mohan, B. C. (2024). Deep learning-based path tracking control using lane detection and traffic sign detection for autonomous driving. Web Intelligence, 22(2), 185-207. [1 citation]
  16. Raut, R., Jadhav, A., Jaiswal, S., Kathole, A., & Patil, S. (2023). Intelligent information system for detection of COVID-19 based on AI. In Proceedings of 3rd International Conference on Recent Trends in Machine Learning and Artificial Intelligence. [1 citation]
  17. Jaiswal, S., Sarkar, S., & Mohan, C. (2017). COT: Evaluation and analysis of various applications with security for cloud and IoT. In Examining Cloud Computing Technologies through Internet of Things (pp. 251-263). [1 citation]
  18. Prakash, S., Saxena, V., & Jaiswal, S. (2016). Smart grid: Optimized power sharing and energy storage system framework with recent trends and future ahead. In Handbook of Research on Emerging Technologies for Electrical Power Planning and Analysis (pp. 1-12). [1 citation]
  19. Jaiswal, S., Gupta, N., & Shrivastava, H. (2012). Enhancing the features of intrusion detection system by using machine learning approaches. International Journal of Scientific and Research Publications, 166. [1 citation]
  20. Kharat, R. S., Kalos, P. S., Kachhoria, R., Kadam, V. E., Jaiswal, S., Birari, D., … (2023). Thermal analysis of fuel cells in renewable energy systems using generative adversarial networks (GANs) and reinforcement learning. [No citation count]

 

 

 

Rafael Natalio Fontana Crespo | Artificial Intelligence | Young Scientist Award

Mr. Rafael Natalio Fontana Crespo | Artificial Intelligence | Young Scientist Award

PhD Student at Politecnico di Torino, Italy

Rafael Natalio Fontana Crespo is a dedicated and sociable Ph.D. student specializing in Computer and Control Engineering at Politecnico di Torino. With a strong academic background in mechatronics and practical experience in electrical energy analysis, he is passionate about tackling complex challenges through innovative solutions. ๐ŸŒ๐Ÿ’ก

Publication Profile :ย 

Orcid

 

๐ŸŽ“ Educational Background :

Rafael is currently pursuing a Ph.D. in Computer and Control Engineering at Politecnico di Torino, Italy, since May 2022. He previously obtained a Masterโ€™s Degree in Mechatronic Engineering from the same institution, graduating with 110/110 cum laude in July 2022. His master’s thesis focused on designing and developing a distributed software platform for additive manufacturing. Rafael studied Electromechanical Engineering at the Universidad Nacional de Cรณrdoba, Argentina, where he also completed a double degree program.

๐Ÿ’ผ Professional Experience :

Rafael gained practical experience during his internship at EPEC (Empresa Provincial de Energรญa de Cรณrdoba) in Argentina, where he worked in the Statistics and Technical Department from May 2020 to May 2021. He was involved in analyzing thermal images of electrical components to prevent failures, contributing to the overall safety and efficiency of electrical systems.

๐Ÿ“š Research Interests :ย 

Rafaelโ€™s research interests lie at the intersection of computer engineering, control systems, and mechatronics, particularly focusing on additive manufacturing, machine learning applications in energy systems, and the optimization of neural networks.

๐Ÿ“ Publication Top Notes :

      1. Fontana Crespo, R.N., E. Patti, S. Di Cataldo, D. Cannizzaro. (2022). Design and Development of a Distributed Software Platform for Additive Manufacturing. Master’s Thesis, Politecnico di Torino.
      2. Fontana Crespo, R.N. (2023). Machine Learning in Energy Applications. Course Exam Paper, Politecnico di Torino.
      3. Fontana Crespo, R.N. (2023). IoT Platforms for Spatial Analytics in Smart Energy Systems. Course Exam Paper, Politecnico di Torino.
      4. Fontana Crespo, R.N. (2023). Optimized Execution of Neural Networks at the Edge. Course Exam Paper, Politecnico di Torino.
      5. Fontana Crespo, R.N. (2023). Adversarial Training of Neural Networks. Course Exam Paper, Politecnico di Torino.