Sathiyabhama Balasubramaniam | Artificial Intelligence | Best Researcher Award

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Dr. Sathiyabhama Balasubramaniam | Artificial Intelligence | Best Researcher Award

Professor at Sona College of Technology, India

Dr. B. Sathiyabhama is a highly accomplished academician and researcher, currently serving as the Professor and Head of the Department of Computer Science and Engineering at Sona College of Technology, Salem, Tamil Nadu, India. She holds the distinguished position of Dean Admissions and Chief Coordinator of International Relations at the same institution. With an extensive career spanning over three decades, Dr. Sathiyabhama has contributed significantly to the fields of data mining, big data analytics, computational intelligence, and health informatics. Her leadership and commitment to higher education have earned her widespread recognition, both nationally and internationally.

Profile:

Google Scholar

Education:

Dr. Sathiyabhama’s educational journey began with a Bachelor of Engineering (B.E.) degree, followed by a Master of Technology (M.Tech.) from a prestigious institution. She completed her M.Tech project internship at the renowned Bioinformatics Centre, Indian Institute of Science (IISC), Bangalore, where she also secured a university rank. Her academic pursuits culminated with a Doctor of Philosophy (Ph.D.) from the National Institute of Technology, Tiruchirappalli, one of India’s leading engineering institutes. Dr. Sathiyabhama’s academic excellence and commitment to her research have provided her with a solid foundation for her career in both teaching and research.

Experience:

Dr. Sathiyabhama brings nearly 31 years of teaching experience to her profession, imparting knowledge in diverse areas of computer science and engineering. She has a wealth of expertise in areas such as data mining, big data analytics, bioinformatics, algorithm analysis, compiler design, and optimization. Throughout her career, she has not only focused on delivering high-quality education but also on fostering a research-driven environment that encourages students to engage in innovative projects. Her dedication to her students is reflected in her consistent ability to produce excellent results. Additionally, Dr. Sathiyabhama has held key administrative positions, including as the Head of the Centre for Data Mining and Database System Design, further enhancing her role as a leader in academic innovation.

Research Interests:

Dr. Sathiyabhama’s research interests lie primarily in the fields of data mining, computational intelligence, health informatics, bioinformatics, and big data analytics. Her work focuses on developing advanced algorithms for the analysis of large datasets and applying these techniques in various domains such as healthcare and bioinformatics. She is deeply committed to exploring how technology can be used to solve real-world problems, especially in healthcare, through innovations like wearable devices and data-driven healthcare monitoring systems. Dr. Sathiyabhama has also contributed to research on optimization techniques and machine learning, with a focus on improving the impact of healthcare systems through the application of AI and data analytics.

Awards and Recognitions:

Throughout her career, Dr. Sathiyabhama has received numerous accolades recognizing her contributions to education, research, and the professional community. She has been honored with awards such as the Best Outgoing PG Student Award during her M.Tech course and the Best Women Engineer award by the Institution of Engineers (India). Dr. Sathiyabhama is a recipient of the Excellence in Teaching award and has been recognized for producing outstanding academic results. She has also been selected as a candidate for the “Who’s Who in the World” and “Cambridge Who’s Who” editions, a prestigious recognition for her work in science and engineering. Dr. Sathiyabhama has received multiple nominations and awards for her work in research and development, including a patent granted in her name and recognition for her leadership in AICTE-UKIERI leadership development programs.

Publications:

Dr. Sathiyabhama has made significant contributions to the academic community, with 144 publications across international and national journals, conferences, and books. Her notable works include a book chapter on IoT-based non-invasive wearable healthcare monitoring systems published by Wiley and co-authored books on Professional Ethics and Fundamentals of Computing. Dr. Sathiyabhama’s research has also been widely cited by other academic articles and continues to influence the fields of computational intelligence, bioinformatics, and big data analytics. Below are a few of her significant publications:

  1. Sathiyabhama, B., & Rajeswari, K. C. (Year). “IoT based Noninvasive Wearable and Remote Intelligent Pervasive Healthcare Monitoring Systems for Elderly.” Wiley Publications.

  2. Sathiyabhama, B., & others (Year). “Fundamentals of Computing.” Sonaversity Publications.

  3. Sathiyabhama, B., & others (Year). “Professional Ethics.” Sonaversity Publications.

Conclusion:

In conclusion, Dr. B. Sathiyabhama stands as a distinguished academician and researcher whose work in data mining, big data analytics, and health informatics has had a profound impact on both her students and the academic community. With decades of teaching experience and numerous accolades to her name, she continues to inspire and lead in the fields of education and technology. Dr. Sathiyabhama’s ongoing research and her commitment to advancing knowledge and innovation ensure that her contributions will have a lasting impact on the future of technology and education. As she continues to make strides in her professional career, her work remains at the forefront of integrating technology with real-world solutions, particularly in the healthcare sector.

Ahmed Ghazi BLAIECH | Artificial intelligence | Best Researcher Award

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Mr. Ahmed Ghazi BLAIECH | Artificial intelligence | Best Researcher Award

Mr. Ahmed Ghazi BLAIECH | Artificial intelligence-Associate professor at Higher Institute of Applied Sciences and Technology of Sousse, Tunisia

Ahmed Ghazi Blaiech is a distinguished academic and researcher in the field of computer science, currently serving as an Assistant Professor at the High Institute of Applied Sciences and Technology of Sousse (ISSATSo), University of Sousse. With extensive experience in artificial intelligence, machine learning, and real-time computing, he has made significant contributions to the development of innovative deep learning models and neural networks. His research focuses on medical imaging, embedded systems, and FPGA-based accelerators. Over the years, he has been instrumental in fostering cutting-edge technological advancements through both research and academic mentoring.

Profile:

Orcid | Scopus | Google Scholar

Education:

Ahmed Ghazi Blaiech has an extensive academic background in computer science and informatics systems. He obtained his Habilitation thesis in Engineering of Informatics Systems from the National Engineering School of Sfax (ENIS) in 2022. Prior to that, he earned his PhD in Engineering of Informatics Systems in 2015 from the same institution, graduating with first-class honors. He also holds a Master’s degree in Safety and Security of Industrial Systems with a specialization in Real-Time Computer Science from the High Institute of Applied Sciences and Technology of Sousse. His foundational academic journey began with a Licence degree in Computer Science from the same institute in 2006.

Experience:

Dr. Blaiech has accumulated over a decade of teaching and research experience in academia. Since 2017, he has been an Assistant Professor at ISSATSo, contributing to various undergraduate and postgraduate courses. Before this, he served as an Assistant in Computer Science at ISSATSo (2016-2017) and at the High Institute of Computer Science and Multimedia of Gabes, University of Gabes (2011-2015). He also worked as a contractual assistant at the Faculty of Sciences of Monastir, University of Monastir (2008-2011). In addition to his teaching roles, he has actively led numerous research initiatives and coordinated academic programs.

Research Interests:

Dr. Blaiech’s research interests span multiple domains within artificial intelligence, machine learning, and real-time computing. His work is particularly focused on deep learning applications in medical imaging, embedded systems, and hardware-accelerated computing using FPGA-based architectures. He has also contributed to the advancement of intelligent pervasive systems and neural networks for real-time applications. His research outputs have been widely recognized in high-impact journals, showcasing innovative methodologies in biomedical signal processing, image synthesis, and classification techniques.

Awards and Recognitions:

Throughout his career, Dr. Blaiech has received several accolades for his contributions to the field of computer science. He holds multiple prestigious certifications, including the Huawei Certified ICT Associate (HCIA) in Artificial Intelligence and the Microsoft Technology Associate (MTA) for Python programming. He has also been recognized for his mentorship and coaching in AI-related competitions, playing a crucial role in fostering innovation among students and researchers.

Publications:

Dr. Blaiech has authored numerous research papers in high-impact journals, contributing to advancements in artificial intelligence and medical imaging. Some of his notable publications include:

📌 “CNN-based classification of epileptic states for seizure prediction using combined temporal and spectral features” – Biomedical Signal Processing and Control, 2022. DOI 📖
📌 “An innovative medical image synthesis based on dual GAN deep neural networks for improved segmentation quality” – Applied Intelligence, 2022. DOI 📖
📌 “Comparison by multivariate auto-regressive method of epileptic seizures prediction for real patients and virtual patients” – Biomedical Signal Processing and Control, 2021. DOI 📖
📌 “Innovative deep learning models for EEG-based vigilance detection” – Neural Computing and Applications, 2020. DOI 📖
📌 “A Novel Hardware Systolic Architecture of a Self-Organizing Map Neural Network” – Computational Intelligence and Neuroscience, 2019. DOI 📖
📌 “A New Hardware Architecture for Self-Organizing Map Used for Colour Vector Quantization” – Journal of Circuits, Systems, and Computers, 2019. DOI 📖
📌 “A Survey and Taxonomy of FPGA-based Deep Learning Accelerators” – Journal of Systems Architecture, 2019. DOI 📖

Conclusion:

Dr. Ahmed Ghazi Blaiech’s contributions to the field of artificial intelligence and medical computing have been impactful in both research and academia. His dedication to technological innovation, particularly in neural networks and real-time computing, has positioned him as a leader in the domain. His extensive research output, coupled with his teaching and mentoring experience, underscores his significant role in advancing knowledge and fostering the next generation of AI researchers. Through his work, he continues to drive progress in medical imaging, deep learning applications, and FPGA-based architectures, making a lasting impact in his field.

Majdi Khalid | Machine learning | Best Researcher Award

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Assoc Prof. Dr. Majdi Khalid | Machine learning | Best Researcher Award 

Associate Professor at Umm Al-Qura University

Assoc. Prof. Dr. Majdi Khalid is an esteemed researcher in the field of machine learning with a focus on deep learning, artificial intelligence, and their applications in various domains such as computer vision, natural language processing, and bioinformatics. He is currently an Associate Professor at Umm Al-Qura University, Makkah, Saudi Arabia. Dr. Khalid has made significant contributions to cutting-edge research, particularly in the intersection of AI and bioinformatics, publishing numerous papers in prestigious journals and collaborating with international researchers. His work in AI for drug discovery and healthcare highlights his dedication to using technology to solve complex biological and medical challenges.

Profile:

ORCID

Education:

Dr. Khalid holds a Ph.D. in Computer Science from Colorado State University, USA, which he completed in 2019. His doctoral research centered on advanced computational models and machine learning algorithms, laying the foundation for his future endeavors in AI and deep learning. Prior to his Ph.D., Dr. Khalid earned his Master of Computer Science (M.C.S.) from the same institution in 2013, and a Bachelor of Science (B.S.) in Computer Science from Umm Al-Qura University in 2006. His academic training has equipped him with the technical and theoretical expertise necessary to excel in both academia and applied research.

Experience:

Dr. Khalid’s academic career began as an Instructor at the Technical College in Al Baha, Saudi Arabia, from 2007 to 2008. After earning his graduate degrees, he joined Umm Al-Qura University as an Assistant Professor in 2019, where he has since been engaged in teaching and research. Throughout his academic journey, Dr. Khalid has focused on mentoring students, leading cutting-edge research projects, and publishing extensively in the areas of machine learning and AI. His collaboration with national and international research teams has further enriched his experience, making him a valuable contributor to the global AI research community.

Research Interests:

Dr. Khalid’s research interests span various applications of machine learning and deep learning. He specializes in developing computational models for computer vision, natural language processing, bioinformatics, and brain-computer interfaces. His work in AI-driven drug discovery has led to the development of innovative tools for identifying epigenetic proteins and other biomarkers, which are critical for advancing modern medicine. Dr. Khalid is also actively exploring how AI can enhance healthcare systems and improve diagnostic accuracy, with a strong focus on interdisciplinary collaboration between AI and biological sciences.

Awards:

Dr. Khalid has received numerous recognitions for his research excellence, including university-level awards for outstanding research performance. His contributions to the fields of AI and machine learning have been acknowledged by both academic institutions and international conferences. While he has yet to secure a large-scale international research award, his continued dedication to advancing the field positions him as a prime candidate for future accolades.

Publications:

  1. Ali, Farman, Abdullah Almuhaimeed, Majdi Khalid, et al. (2024). “DEEPEP: Identification of epigenetic protein by ensemble residual convolutional neural network for drug discovery.” Methods.
    • Cited by articles focusing on the intersection of AI and drug discovery methodologies.
      Read the article here
  2. Khalid, Majdi, Farman Ali, et al. (2024). “An ensemble computational model for prediction of clathrin protein by coupling machine learning with discrete cosine transform.” Journal of Biomolecular Structure and Dynamics.
    • Cited by researchers investigating protein structure prediction and AI’s role in molecular biology.
      Read the article here
  3. Alsini, Raed, Abdullah Almuhaimeed, et al. (2024). “Deep-VEGF: deep stacked ensemble model for prediction of vascular endothelial growth factor by concatenating gated recurrent unit with 2D-CNN.” Journal of Biomolecular Structure and Dynamics.
  4. Alohali, Manal Abdullah, et al. (2024). “Textual emotion analysis using improved metaheuristics with deep learning model for intelligent systems.” Transactions on Emerging Telecommunications Technologies.
    • Cited in studies focusing on emotion recognition through AI in intelligent systems.
      Read the article here
  5. Majdi Khalid (2023). “Advanced Detection of COVID-19 through X-ray Imaging using CovidFusionNet with Hybrid CNN Fusion and Multi-resolution Analysis.” International Journal of Advanced Computer Science and Applications.
  1. Ali, Muhammad Umair, Majdi Khalid, et al. (2023). “Enhancing Skin Lesion Detection: A Multistage Multiclass Convolutional Neural Network-Based Framework.” Bioengineering, 10(12): 1430.
    • Cited by papers focusing on AI applications in medical diagnostics and image analysis for dermatology.
      Read the article here
  2. Alghushairy, Omar, Farman Ali, Wajdi Alghamdi, Majdi Khalid, et al. (2023). “Machine learning-based model for accurate identification of druggable proteins using light extreme gradient boosting.” Journal of Biomolecular Structure and Dynamics, 2023: 1-12.
    • Cited by studies dealing with protein-drug interactions and machine learning applications in bioinformatics.
      Read the article here
  3. Obayya, Marwa, Fahd N. Al-Wesabi, Rana Alabdan, Majdi Khalid, et al. (2023). “Artificial Intelligence for Traffic Prediction and Estimation in Intelligent Cyber-Physical Transportation Systems.” IEEE Transactions on Consumer Electronics, 2023.
    • Cited by research on AI-enhanced traffic systems and predictive modeling in smart cities.
      Read the article here
  4. Alruwais, Nuha, Eatedal Alabdulkreem, Majdi Khalid, et al. (2023). “Modified Rat Swarm Optimization with Deep Learning Model for Robust Recycling Object Detection and Classification.” Sustainable Energy Technologies and Assessments, 59: 103397.
    • Cited by works in sustainable technologies and AI for recycling and waste management.
      Read the article here
  5. Adnan, Adnan, Wang Hongya, Farman Ali, Majdi Khalid, et al. (2023). “A Bi-Layer Model for Identification of piwiRNA using Deep Neural Learning.” Journal of Biomolecular Structure and Dynamics, 2023: 1-9.
  • Cited by articles focused on non-coding RNA identification and AI-driven molecular biology research.
    Read the article here

Conclusion

Assoc. Prof. Dr. Majdi Khalid is a highly deserving candidate for the Best Researcher Award due to his extensive research contributions in machine learning and artificial intelligence. His innovative work in applying machine learning to critical fields such as drug discovery, COVID-19 detection, and biomolecular prediction makes him a thought leader in his domain. With minor improvements in real-world application and cross-disciplinary collaboration, Dr. Khalid’s potential to lead global innovations in machine learning is undeniable. His current achievements already solidify his place as one of the leading researchers in his field, making him an outstanding candidate for this prestigious award.