Dr. Mohassin Ahmad | Deep Learning | Best Researcher Award

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Dr. Mohassin Ahmad | Deep Learning | Best Researcher Award 

Dr. Mohassin Ahmad, Guru Nanak Institutions, India

Dr. Mohassin Ahmad is an accomplished academic and researcher currently serving as an Assistant Professor in the Department of Electronics and Communication Engineering at Guru Nanak Institutions, Hyderabad, since September 2023. He earned his Ph.D. in Image Forensics from the National Institute of Technology Srinagar in 2024, following an M.Tech in Communication and Information Technology from the same institute and a Bachelor of Engineering degree in Electronics and Communication from the University of Kashmir. Dr. Ahmad has extensive teaching and research experience, including a previous tenure as Assistant Professor at NIT Jammu and Kashmir from 2013 to 2017. His research interests focus on digital image forensics, image tampering detection, and communication systems, with multiple publications in reputed international journals. He has contributed significantly to curriculum development and laboratory setup and is known for his dedication to student mentorship and academic excellence. Dr. Ahmad is also recognized for his Young Researcher Award for work in copy-move forgery detection algorithms. Fluent in English, Urdu, and Kashmiri, he combines strong technical expertise with effective communication and leadership skills.

Professional Profile:

GOOGLE SCHOLAR

ORCID

SCOPUS

Research and Academic Profile

Dr. Mohassin Ahmad has recently completed his PhD in Image Forensics (Electronics & Communication) from NIT Srinagar in 2024. His academic background is solid with a Master’s in Communication & Information Technology and a Bachelor’s in Electronics and Communication, showing a focused trajectory in communication technologies and electronics.

🎓 Education

  • PhD (2024) in Image Forensics (Electronics & Communication) — NIT Srinagar

  • M.Tech (2013) in Communication & Information Technology — NIT Srinagar (77.16%)

  • B.E (2010) in Electronics and Communication — University of Kashmir (79.3%)

💼 Work Experience

  • Assistant Professor, Guru Nanak Institutions, Hyderabad (ECE Dept.) — Since Sept 2023

  • Assistant Professor, Electronics & Communication Department, NIT Jammu & Kashmir — Sept 2013 to Aug 2017

    • Delivered lectures & coordinated courses

    • Established new labs & designed curriculum

    • Guided B.Tech & M.Tech research projects

    • Played key role in framing B.Tech & M.Tech curriculum

    • Mentored students with academic & personal support

🏆 Achievements & Awards

  • Young Researcher Award for paper:
    “A comparative analysis of Copy-Move forgery detection algorithms” — International Journal of Electronic Security and Digital Forensics, 2022

    • RSquarel score of 84, Award ID: RSL014

📚 Selected Research Publications

  • Detection and localization of image tampering with fused features — 2022

  • Comparative analysis of Copy-Move forgery detection algorithms — 2022

  • Novel image tamper detection using optimized CNN and firefly algorithm — 2021

  • Review on Digital Image Forgery Detection Approaches — 2021

  • FPGA implementation of convolution algorithms for image processing — 2019

Publication Top Notes:

Threats to medical diagnosis systems: analyzing targeted adversarial attacks in deep learning-based COVID-19 diagnosis

DS‐Net: Dual supervision neural network for image manipulation localization

A comparative analysis of copy-move forgery detection algorithms

Detection and localization of image tampering in digital images with fused features

A Comparative Analysis of Copy-Move Forgery Detection Algorithms

A novel image tamper detection approach by blending forensic tools and optimized CNN: Sealion customized firefly algorithm

Digital Image Forgery Detection Approaches: A Review

Prof. Yanlong Tai | Machine Learning | Best Researcher Award

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Prof. Yanlong Tai | Machine Learning | Best Researcher Award

Prof. Yanlong Tai, shenzhen institute of science and technology, China academic of science, China

Prof. Dr. Yanlong Tai is a distinguished researcher and professor in the field of smart sensing and flexible electronics. He is the Principal Investigator of the Smart-Sensing-Lab (SM-SE Lab.-SIAT) and serves as the Head of both the SIAT-UAEU International Smart Sensing & Energy Joint Lab and the SIAT-Fudan University (Zhuhai) Joint Innovation Center. Currently, he is a Full Professor at the Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), China, and a Joint Professor at the University of Science & Technology Shenzhen. Dr. Tai earned his Ph.D. from Fudan University, China (2009-2012), and was a visiting student at OHM University, Germany (2011-2012). He also holds Bachelor’s and Master’s degrees from Anhui University (2001-2008). His professional journey includes extensive research experience across multiple international institutions. He served as a Postdoctoral Researcher at University of California, Davis, USA (2012-2013), Fraunhofer ENAS, Chemnitz, Germany (2013-2014), and KAUST, Saudi Arabia (2014-2017). He later worked as a Research Scientist at Masdar Institute, UAE (2017-2019) before joining SIAT as a Professor in 2019.

Professional Profile:

GOOGLE SCHOLAR

Summary of Suitability for Best Researcher Award – Prof. Dr. Yanlong Tai

Prof. Dr. Yanlong Tai is an outstanding researcher and innovator, making him a highly suitable candidate for the Best Researcher Award. His extensive experience, leadership roles, and impactful research in smart materials, energy harvesting, and wearable electronics position him as a global leader in advanced sensing technologies.

🎓 Education

  • Ph.D. (2009 – 2012) – Fudan University, China

  • Visiting Student (2011 – 2012) – OHM University, Germany

  • Bachelor & Master Degree (2001 – 2008) – Anhui University, China

💼 Work Experience

  • Professor (2019 – Present) – Shenzhen Institutes of Advanced Technology (SIAT), CAS, China

  • Research Scientist (2017 – 2019) – Masdar Institute, United Arab Emirates

  • Postdoc Researcher (2014 – 2017) – King Abdullah University of Science and Technology (KAUST), Saudi Arabia

  • Postdoc Researcher (2013 – 2014) – Fraunhofer ENAS, Chemnitz, Germany

  • Postdoc Researcher (2012 – 2013) – University of California, Davis, USA

🏆 Achievements, Awards & Honors

  • 📌 Principal Investigator of Smart-Sensing-Lab (SM-SE Lab.-SIAT)

  • 🏅 Head of SIAT-UAEU International Smart Sensing & Energy Joint Lab

  • 🏅 Head of SIAT-Fudan University (Zhuhai) Joint Innovation Center

  • 🎖️ Full Professor at SIAT, CAS, Shenzhen, China

  • 🎖️ Joint Professor at the University of Science & Technology, Shenzhen

Publication Top Notes:

CITED:663
CITED:154
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CITED:92
CITED:78

Mr. Seyed matin malakouti | Deep learning Awards | Best Researcher Award

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Mr. Seyed matin malakouti | Deep learning Awards | Best Researcher Award

Mr. Seyed matin malakouti, University of Rijeka, Croatia

Seyed Matin Malakouti is an accomplished electrical engineer and researcher specializing in control systems engineering and machine learning. He completed his Master of Science in Electrical Engineering from the University of Tabriz, Iran, after earning his Bachelor’s degree from Isfahan University of Technology. His research spans various applications of machine learning, including wind power generation prediction, heart disease classification using ECG data, and solar farm power generation forecasting. Seyed’s work has resulted in several high-impact publications in prestigious journals, with his research on wind energy and machine learning techniques receiving significant citations. He has also been involved in cutting-edge projects such as predicting global temperature change and advancing renewable energy solutions. In recognition of his contributions, Seyed has received multiple awards, including the Best Researcher Award at the International Conference on Cardiology and Cardiovascular Medicine in 2023, and nominations for Best Paper and Best Researcher Awards in other international conferences. Additionally, he actively contributes to the scientific community as a peer reviewer for numerous journals in the fields of artificial intelligence, environmental sciences, and electrical engineering.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award

Seyed Matin Malakouti is a highly qualified and accomplished researcher in the field of Electrical Engineering, specializing in Control Systems, Machine Learning, and Data Science. His impressive academic background includes a Master’s degree in Electrical Engineering from the University of Tabriz and a Bachelor’s degree from Isfahan University of Technology.

Education & Training 🎓

  • 2020 – 2022: M.Sc. in Electrical Engineering – Control System Engineering, University of Tabriz, Iran
  • 2014 – 2019: B.Sc. in Electrical Engineering, Isfahan University of Technology, Iran

Awards & Honors 🏆

  • 2023: Best Researcher, International Conference on Cardiology and Cardiovascular Medicine
  • 2023: Nominated for Best Paper Award, International Research Awards on Mathematics and Optimization Methods
  • 2024: International Young Scientist Awards, Best Researcher Category

Technical Skills 🛠️

  • Machine Learning 🤖
  • Data Science 📊
  • Programming Languages: MATLAB, Python 💻

Peer Review Activities 🧐

Seyed has reviewed articles for prestigious journals, such as:

  • IEEE Access
  • Artificial Intelligence Review
  • BMC Public Health
  • Environmental Monitoring and Assessment 🌱

Publication top Notes:

Machine learning and transfer learning techniques for accurate brain tumor classification

ML: Early Breast Cancer Diagnosis

Advanced techniques for wind energy production forecasting: Leveraging multi-layer Perceptron + Bayesian optimization, ensemble learning, and CNN-LSTM models

Babysitting hyperparameter optimization and 10-fold-cross-validation to enhance the performance of ML methods in predicting wind speed and energy generation

Discriminate primary gammas (signal) from the images of hadronic showers by cosmic rays in the upper atmosphere (background) with machine learning

Estimating the output power and wind speed with ML methods: A case study in Texas