Kanika | Machine Learning | Best Researcher Award

Posted on by ScienceFather

Kanika | Machine Learning | Best Researcher Award

Ms. Kanika, National institute of technology Agartala, India.

Ms. Kanika, hailing from Hasanpur, Haryana, is an enthusiastic researcher with a strong passion for applied mathematics 🧮 and advanced computing technologies 💻. Her expertise spans optimization, uncertainty theory, numerical analysis, graph theory, artificial intelligence 🤖, and machine learning. With an M.Sc. in Mathematics and Computing 🎓 from NIT Agartala, where she ranked 6th, and a B.Sc. in Mathematics, Physics, and Computer Science 🎓 from Banasthali Vidyapith, she has consistently demonstrated academic excellence. Kanika is driven to solve real-life problems 🌍 through mathematics and is currently working on a machine-learning research paper while aspiring to contribute to computational imaging and AI.

Publication Profiles 

Googlescholar

Education and Experience

Education 🎓
  • M.Sc. in Mathematics and Computing (2021–2023), NIT Agartala: 89.5%, 8.95/10, Rank: 6️⃣
  • B.Sc. in Mathematics, Physics, and Computer Science (2017–2020), Banasthali Vidyapith: 85.8%, 8.58/10 🧮
  • Senior Secondary Examination (2016–2017), Board of School Education Haryana: 85.0% 🧑‍🎓
  • Secondary Examination (2014–2015), Board of School Education Haryana: 91.4% 🌟
Experience 🧑‍🔬
  • M.Sc. Thesis (2022–2023) at NIT Agartala: Focused on portfolio optimization under uncertainty 🌐.

Suitability For The Award

Ms. Kanika is an exceptional candidate for the Best Researcher Award, showcasing a strong academic foundation, innovative research contributions, and a deep commitment to advancing applied mathematics, machine learning, and artificial intelligence. Her dedication to leveraging mathematical and computational tools for solving real-world problems highlights her potential to make a significant impact in her field.

Professional Development

Kanika’s professional journey showcases her dedication to research and continuous learning 📚. She has gained expertise in machine learning 🤖, MATLAB 🧪, and scientific computing 🖥️. Her technical skills extend to programming languages like C/C++ and database management systems 💾. As a mathematics enthusiast, she has completed rigorous training programs like the Mathematics Training and Talent Research (MTTS) and the National Mathematics Talent Contest 🏅. She actively participates in workshops and online programs, enhancing her skills in cutting-edge mathematical technologies 🌟. Kanika is also a certified karateka 🥋, showcasing her versatile interests beyond academics.

Research Focus

Ms. Kanika’s research interests lie at the intersection of applied mathematics and emerging technologies 🌐. Her focus areas include optimization 📈, uncertainty theory, numerical analysis, graph theory, machine learning 🤖, and artificial intelligence. She aims to bridge theoretical mathematics with practical computing applications 💻, contributing to fields like computational imaging and decision-making under uncertainty. Currently working on a machine-learning research paper 📝, Kanika aspires to tackle real-life problems 🌍 using her expertise in applied mathematics and AI. Her passion for solving complex problems drives her to explore innovative solutions in these interdisciplinary domains.

Awards and Honors

  • IIT JAM 2021 🎓: All India Rank 2169 (Mathematical Sciences).
  • MTTS Level 1 🏅: Selected in the top 20 students, IISER Thiruvananthapuram (2020).
  • Banaras Hindu University Entrance Exam 🎓: All India Rank 363 (Mathematical Sciences, 2020).
  • Common Entrance Exam (CEE) by NCERT 🏆: State Rank 63 (General), NCERT (2017).
  • National Mathematics Talent Contest 🥇: Top 10%ile, Junior Level Screening Test, AMTI (2014).
  • Certified Karateka 🥋: 8th, 7th, and 6th Kyu (Blue Belt), JKMO (2018).
  • Olympic Value Education Program Ambassador 🏅: Honored by Banasthali Vidyapith (2017).

Publication Top Notes 

  • 📚 Tools and techniques for teaching computer programming: A review – Journal of Educational Technology Systems, 2020, Cited by: 88
  • 🤝 Effect of different grouping arrangements on students’ achievement in collaborative learning – Interactive Learning Environments, 2023, Cited by: 12
  • 🧬 Genetic algorithm‐based approach for making pairs and assigning exercises in programming – Computer Applications in Engineering Education, 2020, Cited by: 8
  • 📖 Enriching WordNet with subject-specific out-of-vocabulary terms using ontology – Data Engineering for Smart Systems, 2022, Cited by: 6
  • 🎓 KELDEC: A recommendation system for extending classroom learning with visual cues – Proceedings of SSIC, 2019, Cited by: 6
  • 🎯 VISTA: A teaching aid to enhance contextual teaching – Computer Applications in Engineering Education, 2021, Cited by: 3
  • 🌐 Linking classroom studies with dynamic environment – International Conference on Computing, Power and Communication, 2019, Cited by: 2
  • 🔄 Effect of varying the size of the initial parent pool in genetic algorithm – International Conference on Contemporary Computing and Informatics, 2014, Cited by: 2
  • 🌍 A review of English to Indian language translator: Anusaaraka – International Conference on Advances in Computer Engineering & Applications, 2014, Cited by: 2

Assoc. Prof. Dr. Mohammed Farag | Machine Learning Awards | Best Researcher Award

Posted on by ScienceFather

Assoc. Prof. Dr. Mohammed Farag | Machine Learning Awards | Best Researcher Award 

Assoc. Prof. Dr. Mohammed Farag, Alexandria University, Egypt

Dr. Mohammed M. Farag is an accomplished Associate Professor of Electrical Engineering with extensive academic experience spanning over two decades. Currently affiliated with King Faisal University, Saudi Arabia, and Alexandria University, Egypt, he specializes in the fields of machine learning, signal processing, and cybersecurity. His research is particularly focused on the development of innovative solutions for edge computing and cyber-physical systems. Dr. Farag holds a Ph.D. in Computer Engineering from Virginia Tech, where he conducted groundbreaking research on enhancing trust in cyber-physical systems. His academic journey also includes a Master’s and Bachelor’s degree in Electrical Engineering from Alexandria University, both achieved with distinction. A prolific researcher, he has an impressive publication record in high-impact journals and has secured numerous research grants. Beyond his research contributions, Dr. Farag is dedicated to advancing the field through excellence in teaching, mentorship, and quality assurance, actively contributing to program development and accreditation processes.

Professional Profile:

SCOPUS

ORCID

GOOGLE SCHOLAR

Summary of Suitability for Best Researcher Award: Dr. Mohammed M. Farag

Dr. Mohammed M. Farag’s academic and professional profile reflects significant accomplishments in research, teaching, and academic leadership. Based on his qualifications and achievements, he is a strong candidate for the Best Researcher Award for the following reasons.

🧑‍🎓 Education

🎓 Ph.D. in Computer Engineering (GPA: 4.00/4.00) – Virginia Tech, USA (2009-2012)
Dissertation: “Architectural Enhancements to Increase Trust in Cyber-Physical Systems Containing Untrusted Software and Hardware”

🎓 M.Sc. in Electrical Engineering (GPA: 4.00/4.00) – Alexandria University, Egypt (2003-2007)
Thesis: “Hardware Implementation of The Advanced Encryption Standard on Field Programmable Gate Arrays”

🎓 B.Sc. in Electrical Engineering, Distinction with Honor (GPA: 3.89/4.00) – Alexandria University, Egypt (1998-2003)
Project: “VLSI Design of Cryptographic Algorithms”

📚 Research Interests

🔍 Machine Learning for Signal Processing & Edge Computing
🔐 Cybersecurity and Hardware Security
💾 VLSI Design and Embedded Systems
🤖 AI Applications in Electrical Engineering
🌐 Cyber-Physical Systems

🏆 Key Achievements

📝 Citations: 411 | h-index: 11 | i10-index: 11 (As of October 2024)
📖 Published in IEEE Access, Sensors, and top-tier journals.
💰 Secured multiple research grants from King Faisal University, totaling over 100,000 SAR.

💻 Technical Expertise

💡 Programming: Python, C++, MATLAB
🖥️ Hardware Design: VHDL, Verilog
📊 Machine Learning: TensorFlow, PyTorch, Keras
🔧 CAD Tools: Synopsys, Cadence, Xilinx

🎓 Teaching Experience

🎓 Electrical Circuits, Signal Processing, Digital Logic, VLSI Design, Embedded Systems, and more!
🎯 Special focus on fostering practical skills in Semiconductor Devices and Cybersecurity.

Publication Top Notes

Wearable sensors based on artificial intelligence models for human activity recognition

A Tiny Matched Filter-Based CNN for Inter-Patient ECG Classification and Arrhythmia Detection at the Edge

Design and Analysis of Convolutional Neural Layers: A Signal Processing Perspective

Matched Filter Interpretation of CNN Classifiers with Application to HAR

A Self-Contained STFT CNN for ECG Classification and Arrhythmia Detection at the Edge

Aggregated CDMA Crossbar With Hybrid ARQ for NoCs

Overloaded CDMA crossbar for network-on-chip

Prof Dr. Ersin Elbasi | Machine learning Award | Excellence in Research

Posted on by ScienceFather

Prof Dr. Ersin Elbasi | Machine learning Award | Excellence in Research

Prof Dr. Ersin Elbasi, American University of the Middle East, Kuwait 

Ersin Elbasi, Ph.D., is a distinguished professor specializing in Computer Science and Engineering, currently serving at the American University of the Middle East in Kuwait. He earned his Ph.D. in Computer Science from the Graduate Center, CUNY, with a dissertation on robust video watermarking schemes, following a Master of Philosophy in the same field from the same institution. He holds a Master of Science in Electrical Engineering and Computer Science from Syracuse University and a Bachelor of Science in Industrial Engineering from Sakarya University, Turkey. Dr. Elbasi’s extensive academic career includes previous positions as Associate Professor at the American University of the Middle East and faculty roles at the Higher Colleges of Technology in the UAE and Çankaya University in Turkey. His research focuses on machine learning, multimedia security, and data mining, with notable projects in digital image and video watermarking and event mining in video sequences. He has also held significant roles in research and development at TÜBİTAK, contributed to SQL application development in New York City, and engaged in various international research activities. Dr. Elbasi’s technical expertise spans Visual C++, SQL programming, and Java, with a notable scholarship record and recognition for his contributions to the field.

Professional Profile:

Summary of Suitability for Excellence in Research 

Dr. Ersin Elbasi holds a Ph.D. in Computer Science from the Graduate Center, CUNY, with a specialization in robust video watermarking schemes in transform domains. His advanced degrees in computer science, electrical engineering, and industrial engineering demonstrate a strong interdisciplinary foundation.

Education

  • Ph.D. in Computer Science
    Graduate Center, CUNY, New York City, NY
    Graduated: April 2007
    Dissertation: “Robust Video Watermarking Scheme in Transform Domains”
  • Master of Philosophy in Computer Science
    Graduate Center, CUNY, New York City, NY
    Graduated: May 2006
  • Master of Science in Electrical Engineering & Computer Science
    Syracuse University, Syracuse, NY
    Graduated: May 2001
  • Bachelor of Science in Industrial Engineering
    Sakarya University, Sakarya, Turkey
    Graduated: June 1997

Work Experience

  • Professor
    American University of the Middle East (QS ranking 500-600), Kuwait
    June 2022 – Current
  • Associate Professor
    American University of the Middle East (QS ranking 500-600), Kuwait
    October 2016 – June 2022

    • Taught courses including CNIT 180, CNIT 280, CNIT 380, CNIT 315, CS 159, CNIT 480, CNIT 372, CNIT 399/499, TECH 330, and TECH 320.
  • Faculty Member
    Computer and Information Science, Higher Colleges of Technology, Al Ain, Abu Dhabi, UAE
    August 2015 – July 2016

    • Taught courses including Introduction to Multimedia, Research Methods in Emerging Technologies, Statistics and Probability, and Information Systems in Organizations and Society.
  • Instructor/Associate Professor
    Çankaya University (400-500 by Times ranking), Department of Computer Engineering, Ankara
    September 2007 – June 2015

    • Taught courses including Data Mining, Multimedia Security, Object-Oriented Languages, Database Management, Multimedia and Internet, Data Management and File Structures, and Formal Languages and Automata.
  • Expert/Chief Expert of Scientific Programs
    TÜBİTAK, Ankara, Turkey
    August 2007 – July 2014

    • Served as Executive Secretary to the Electrical, Electronics, and Informatics Research Grant Committee, National Scientific Expert in COST Information and Communication Domain, and National Delegate in COST (FP 7) Trans Domain Proposals.
  • SQL Application Developer
    Bureau of Revenue Enhancement and Automation, Finance Office, New York City Government
    November 2004 – July 2007

    • Developed SQL applications, performed ad-hoc queries, and managed staff training in SQL and related software tools.
  • Instructor
    The City University of New York (CUNY)
    September 2004 – May 2007

    • Taught courses at Brooklyn College, Borough Manhattan Community College, and Lehman College, including Operations Management, Introduction to Computer Applications, Database Management, Discrete Structures, and GMAT Math.
  • Research Assistant
    Electrical Engineering and Computer Science, Syracuse University
    January 2003 – May 2004

    • Worked on Automated Scenario Recognition in Video Sequences and implemented data mining and machine learning techniques.
  • Engineer
    Calik Textile, Istanbul, Turkey
    January 1999 – August 1999

    • Focused on Production Planning.
  • Engineer
    HES Machine, Kayseri, Turkey
    June 1997 – March 1998

    • Focused on Production Planning and Quality Control.

Publication top Notes:

Transformer Based Hierarchical Model for Non-Small Cell Lung Cancer Detection and Classification

Anticipate Movie Theme from Subtitle: A Deep Learning Approach

Robust and Secure Watermarking Algorithm Based on High Frequencies of Integer Wavelet Transform

Fortifying Integrity and Privacy in Medical Imaging: Discrete Shearlet and Radon Transform-Based Watermarking Approach

Machine Learning-Based Analysis and Prediction of Liver Cirrhosis

 

 

 

Mr. CHENGYONG JIANG | Machine Learning Award | Best Researcher Award

Posted on by ScienceFather

Mr. CHENGYONG JIANG | Machine Learning Award | Best Researcher Award 

Mr. CHENGYONG JIANG, Fudan university, China

Chengyong Jiang is a promising neurobiology Ph.D. candidate at Fudan University, China, with an outstanding academic record and significant research experience. He earned his Master’s degree in Biotechnology from Minzu University of China, graduating in the top 5% of his class, and is currently pursuing his doctoral studies at Fudan University, where he is ranked in the top 10% of his cohort. Jiang’s research focuses on the regulation of sleep and eye movement by cholinergic neurons in the oculomotor nerve nucleus. Jiang has demonstrated a strong commitment to academic and practical excellence through various roles, including as a teaching assistant at Beijing Foreign Studies University and a high school biology tutor at Hangzhou Zhipeng Network Technology Co., Ltd. His involvement in innovative projects, such as studying the therapeutic effects of Polygonum multiflorum on stress-induced depression and leading a social practice team analyzing undergraduate education in biology, highlights his leadership and research capabilities.

Professional Profile:

Summary of Suitability for Best Researcher Award:

Chengyong Jiang has demonstrated a strong academic background and research capability in neurobiology and biotechnology. His work, including his master’s research on stress-induced depression and his ongoing doctoral research on sleep and eye movement regulation, reflects a deep understanding of complex biological processes. His publications in reputable journals like Frontiers in Neuroscience and Advanced Science underscore his ability to conduct impactful and high-quality research.

Education

Fudan University, Shanghai, China
Neurobiology Doctor
September 2020 – June 2026
Top 10%

Minzu University of China, Beijing, China
Master of Biotechnology
September 2015 – June 2019
Top 5%

Work Experience

Institutes of Brain Science, Fudan University
Researcher
September 2020 – Present

  • Conducting research on “Regulation of sleep and eye movement by cholinergic neurons in the nucleus of the oculomotor nerve.”

Hangzhou Zhipeng Network Technology Co., Ltd., Hangzhou, China
High School Biology Tutor (Part-time)
September 2017

  • Provided online tutoring in biology to middle and high school students.

Beijing Foreign Studies University, Beijing, China
Teaching Assistant
July 2017 – September 2017

  • Participated in and organized the “E PLUS Beiwai Yijia Study Tour” summer camp, served as homeroom teacher, and assisted in English teaching activities.

Publication top Notes:

MLS-Net: An Automatic Sleep Stage Classifier Utilizing Multimodal Physiological Signals in Mice

Exosomes Derived from M2 Microglial Cells Modulated by 1070-nm Light Improve Cognition in an Alzheimer’s Disease Mouse Model.

Tracking Eye Movements During Sleep in Mice.

2,3,5,4′-Tetrahydroxystilbene-2-O-beta-D-glucoside Reverses Stress-Induced Depression via Inflammatory and Oxidative Stress Pathways.

Mr. Heng Luo | Machine Learning Awards | Young Scientist Award

Posted on by ScienceFather

Mr. Heng Luo | Machine Learning Awards | Young Scientist Award 

Mr. Heng Luo, The Hong Kong Polytechnic University, Hong Kong

Heng Luo is a distinguished researcher and PhD candidate at The Hong Kong Polytechnic University, specializing in the Institute of Textiles and Clothing since January 2021. His academic journey is marked by diverse and rich experiences across several prestigious institutions. Heng holds a Master’s degree in Electronic Engineering from the University of Electronic Science and Technology of China, completed in 2013, followed by another Master’s degree from the same institution in 2016, focusing on the Department of Industrial and Systems Engineering. Additionally, he earned an MSc from the University of Warwick’s Manufacturing Group. Heng’s research interests span across smart hardware, artificial intelligence, flexible devices, robotics, signal processing, cloud computing, and edge computing. His dedication to advancing technology is reflected in his active memberships with the Institution of Engineering and Technology and the IEEE, where he also contributes as a member of the Young Professionals group. His contributions to the field are recognized on platforms such as SciProfiles and ORCID, showcasing his commitment to connecting research and researchers worldwide. Heng Luo’s work exemplifies the integration of interdisciplinary knowledge and innovative thinking, driving forward the frontiers of technology and engineering

Professional Profile:

ORCID

Education:

  • 🎓 PhD, The Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Kowloon, Hong Kong (2021 – Present)
  • 🎓 MSc, Warwick Manufacturing Group, The University of Warwick, Coventry, West Midlands, UK (2013 – 2016)
  • 🎓 MSc, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong (2013 – 2016)
  • 🎓 Master Degree, Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China (2012 – 2013)
  • 🎓 Bachelor Degree, Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China (2008 – 2012)

Membership and Service:

  • 🏛️ Member, Institution of Engineering and Technology, Hong Kong, UK (2021 – Present)
  • 🌐 Member, IEEE, Hong Kong, NY, US (2021 – Present)
  • 👨‍💻 Young Professionals, IEEE, Hong Kong, NY, US (2021 – Present)

Work Experience

Note: The original information provided did not include details about work experience. If there is specific information about Heng Luo’s work experience that needs to be included, please provide those details.

Publication top Notes:

Integrated Wearable System for Monitoring Skeletal Muscle Force of Lower Extremities

Evaluating and Modeling the Degradation of PLA/PHB Fabrics in Marine Water

Ionic Hydrogel for Efficient and Scalable Moisture‐Electric Generation

Article identification method and device based on machine learning

Observer-based control of discrete-time fuzzy positive systems with time delays

Observer-based control of discrete-time fuzzy positive systems with time delays

Stability analysis of discrete-time fuzzy positive systems with time delays

Method for generating multi-input multi-output over-horizon (MIMO-OTH) radar waveforms based on digital signal processor (DSP) sequences