Mr. Ahmad Faraz Hussain | Machine learning | Best Scholar Award

Mr. Ahmad Faraz Hussain | Machine learning | Best Scholar Award

PhD student, Zhejiang university, China

Ahmad Faraz Hussain is an accomplished researcher and engineer specializing in audio signal processing, speaker recognition, and wireless sensor networks. With a strong academic background and extensive technical experience, he has contributed significantly to the field of electronics and information engineering. His work spans research, teaching, and industry, reflecting his passion for innovation and education.

Publication Profile

Scopus

🎓 Education:

Ahmad Faraz Hussain earned his Master of Science in Electronics & Information Engineering from the South China University of Technology, China (2017–2019), achieving an impressive 90%. His thesis focused on “Speaker Recognition with Emotional Speech,” showcasing his expertise in audio processing. He completed his Bachelor of Science in Electrical Engineering from the University of Engineering and Technology, Peshawar, Pakistan (2009–2014), with a thesis on “ZigBee-Based Wireless Sensor Network for Building Safety Monitoring.”

💼 Professional Experience:

Ahmad has a diverse professional journey, beginning as a Research Assistant at the South China University of Technology (2017–2019), where he worked on cutting-edge projects in speech recognition. Before that, he served as a Lecturer at Polytechnical College Kohat (2016–2017), imparting knowledge to aspiring engineers. His technical expertise was further honed during his two-year tenure as a Technical Engineer at PTCL, Pakistan, where he worked on telecommunications and networking solutions.

🏆 Awards and Honors:

Ahmad was a recipient of the prestigious CSC Scholarship, which enabled him to pursue his master’s degree in China. His academic excellence and dedication to research have earned him recognition in both academic and professional circles.

🔬 Research Focus:

Ahmad’s research interests lie in audio signal processing, speaker recognition, speech recognition, and wireless sensor networks. His work focuses on developing advanced methodologies for improving speech-based systems and enhancing security through smart sensor networks. His contributions to these fields are evident in his multiple publications and research projects.

🔚 Conclusion:

Ahmad Faraz Hussain is a dedicated researcher and engineer with a strong foundation in speech and wireless sensor technologies. His academic achievements, professional experience, and research contributions highlight his commitment to innovation and education. With a passion for higher learning and community service, he continues to make impactful contributions to the field of electronics and information engineering. 🚀

📚 Publications:

Three-Dimensional Dynamic Positioning Using a Novel Lyapunov-Based Model Predictive Control for Small Autonomous Surface/Underwater Vehicles

Fish Detection and Classification Based on Improved ViT

ZigBee-Based Wireless Sensor Network for Building Safety Monitoring – Published in the Journal of TWASP. Read here.

Speaker Recognition with Emotional Speech – Published in GSJ. Read here.

Speech Emotion Recognition – Under review.

ZigBee and GSM-Based Security System for Business Places– Accepted for publication.

Internet of Things-Based Information System for Smart Wireless Sensor Healthcare Applications – Submitted for review.

Hsiu Hsia Lin | Machine learning | Best Researcher Award

Prof. Hsiu Hsia Lin | Machine learning | Best Researcher Award

Research Fellow, Chang Gung Memorial Hospital, Taiwan

Dr. Hsiu-Hsia Lin is a dedicated Research Fellow at the Craniofacial Research Center, Chang Gung Memorial Hospital, Taiwan, and an Adjunct Assistant Professor at the Graduate Institute of Dental and Craniofacial Science, Chang Gung University. With a strong foundation in AI and 3D craniofacial image processing, her research contributes significantly to advancements in orthognathic surgery. Dr. Lin’s expertise in surgical navigation and CAD/CAM-assisted surgery is pivotal in improving craniofacial surgical outcomes. 🌟

Publication Profile

Education:

Dr. Lin earned her Ph.D. in Computer Science and Engineering from National Chung Hsing University, Taiwan, following a Master’s in Computer Science from Tunghai University. Her academic journey is deeply rooted in computer science, blending AI with craniofacial research. 🎓📚

Experience:

Dr. Lin has held key research positions, including Assistant Research Fellow and Postdoctoral Fellow at the Craniofacial Research Center, Chang Gung Memorial Hospital. Her postdoctoral work also extended to the Department of Computer Science and Engineering at National Chung Hsing University. Her extensive experience has helped bridge the gap between AI technology and clinical applications. 💼🔬

Research Focus:

Dr. Lin’s research revolves around Pattern Recognition, Artificial Intelligence, and 3D Craniofacial Image Processing. She specializes in computer-aided surgical simulation for orthognathic surgery, surgical navigation, and CAD/CAM-assisted procedures, aiming to optimize outcomes in facial surgery. 🧠💻

Awards and Honors:

Dr. Lin has received multiple recognitions for her contributions to craniofacial research and AI in surgery. Her work continues to shape modern surgical approaches, particularly in orthognathic surgery, enhancing patient outcomes. 🏆👏

Publication Top Notes:

Dr. Lin’s publications focus on integrating AI with medical applications, particularly in 3D craniofacial analysis and orthognathic surgery. Her studies offer novel methods for surgical planning, facial attractiveness assessment, and facial symmetry evaluation.

Quantification of facial symmetry in orthognathic surgery (Dec. 2024) in Comput Biol Med., cited by 5 articles. DOI

Average 3D virtual sk

eletofacial model for surgery planning (Feb. 2024) in Plast Reconstr Surg., cited by 3 articles. DOI

Facial attractiveness assessment using transfer learning (Jan. 2024) in Pattern Recognit., cited by 4 articles. DOI

Optimizing Orthognathic Surgery (Nov. 2023) in J. Clin. Med., cited by 6 articles. DOI

Single-Splint, 2-Jaw Orthognathic Surgery (Nov. 2023) in J Craniofac Surg., cited by 2 articles. DOI

Applications of 3D imaging in craniomaxillofacial surgery (Aug. 2023) in Biomed J., cited by 7 articles. DOI

Facial Beauty Assessment using Attention Mechanism (Mar. 2023) in Diagnostics, cited by 8 articles. DOI

 

MUNMI DUTTA | Machine Learning | Best Researcher Award

Mrs. MUNMI DUTTA | Machine Learning | Best Researcher Award

Research Scholar, Assam Engineering College, India

🔬 Munmi Dutta is a dedicated academic and researcher with expertise in Artificial Intelligence and Machine Learning. Her research focuses on speaker identification, product categorization, and generative AI for online education systems. Currently pursuing her Ph.D. at Gauhati University, she has contributed significantly to AI-driven applications in e-commerce and speech processing.

Publication Profile

Scopus

Strengths for the Award

  1. Academic Excellence: Munmi Dutta’s academic journey, including a Ph.D. in progress and an M.Tech in Electronics and Communication Technology, demonstrates her commitment to research and knowledge advancement.
  2. Project Experience: She has completed several significant projects, such as developing a fire alarm system, a remote-controlled fan regulator, and a pitch determination system using neural networks. These projects showcase her practical and research skills in both hardware and software domains.
  3. Research in AI and Machine Learning: Dutta’s work in speaker identification using Artificial Neural Networks (ANN) and product categorization in e-commerce using machine learning reflects her proficiency in cutting-edge technologies, especially Artificial Intelligence. Her research also addresses real-world problems, adding practical relevance.
  4. Publications: She has multiple journal publications, including in the prestigious Applied Soft Computing Journal, which demonstrates her research output in emerging technologies like machine learning and neural networks. The acceptance of a book chapter on AI and IoT in online education further highlights her versatility.
  5. Collaborative Research: The variety of co-authors in her publications suggests that Dutta is capable of working in teams and contributes effectively to collaborative research, which is a valuable quality in any researcher.

Areas for Improvement

  1. Broader Research Impact: Although her work in machine learning and AI is commendable, the scope of her research could be expanded to other interdisciplinary areas to broaden the impact. This would also enhance her chances of being recognized as a top researcher in her field.
  2. PhD Completion: As she is still pursuing her PhD, completing this degree could further strengthen her candidacy for the Best Researcher Award, as a completed doctoral degree adds academic credibility.
  3. Leadership and Mentorship: While her publications and research experience are impressive, demonstrating leadership in research groups or mentorship roles would help solidify her position as a leading researcher.
  4. International Exposure: Although she has participated in conferences and published research, gaining more international exposure by attending or presenting at global conferences could help elevate her recognition and contribution to the global research community.

Education

Munmi Dutta holds an M.Tech in Electronics and Communication Technology from IST, Gauhati University, with a CGPA of 7.13. She completed her B.E. in Applied Electronics and Instrumentation Engineering from GIMT, also under Gauhati University, achieving a percentage of 67.67%. Her academic journey began at Don Bosco High School, followed by J. B. College for higher secondary education. 🎓💡

Experience

💼 Munmi Dutta has extensive experience in academic research, with a focus on AI applications in speech processing, product categorization, and e-commerce. She has presented at national and international conferences and co-authored several notable publications. Her work includes building speaker identification systems and applying neural networks for speech recognition.

Research Focus

🧠 Munmi Dutta’s research interests include speaker identification using artificial neural networks, machine learning for product categorization in e-commerce, and generative AI in education systems. She has worked on innovative projects such as pitch determination for speaker identification, remote-controlled fan regulators, and fire alarms using temperature sensors.

Awards and Honors

🏆 Munmi Dutta has earned recognition for her contributions to AI and technology, including presenting at prestigious conferences like the International Conference on Recent Developments in Science, Technology, Engineering, and Management (ICRDSTEM-2022). Her work in the fields of AI and e-commerce has garnered respect within academic circles.

Publication Top Notes

📝 “Closed-Set Text Independent Speaker Identification System Using Multiple ANN Classifiers” – Advances in Intelligent Systems and Computing, 2014. Cited by several researchers, this paper focuses on the application of ANN for speaker identification Link.

📝 “Product Categorization in Fashion and Lifestyle Commerce using Machine Learning” – Journal of Emerging Technologies and Innovation Research, 2022. This study explores the use of machine learning in e-commerce product categorization Link.

📝 “Incremental-based YoloV3 model with Hyper-parameter Optimization for Product Image Classification in E-commerce Sector” – Applied Soft Computing Journal, 2024. A detailed examination of YoloV3 model optimization for product image classification Link.

Conclusion

Munmi Dutta has demonstrated strong potential as a researcher with her contributions in AI, machine learning, and electronics. Her numerous publications, research projects, and continued pursuit of a Ph.D. make her a promising candidate for the Best Researcher Award. However, achieving more interdisciplinary impact, completing her PhD, and gaining further international exposure will significantly bolster her qualifications for this award.