Frnaz Akbar | Neuroscience | Best Researcher Award

Dr. Frnaz Akbar | Neuroscience | Best Researcher Award

Dr. Frnaz Akbar, National University Of Modern Languages, Pakistan

Dr. Frnaz Akbar is a dedicated computer science and software engineering educationist, researcher, and web developer with over a decade of experience in teaching and web development. She has consistently contributed to building student knowledge in computer science and is committed to high-quality education. Her expertise spans artificial intelligence, data mining, precision agriculture, IoT, and blockchain. Dr. Akbar is currently pursuing a PhD in Artificial Intelligence at Air University, Islamabad, and has a strong publication record, including studies on deep learning, pattern recognition, and EEG-based Alzheimer’s research. She has worked as a lecturer at NUML University and held teaching positions at prestigious institutions. Her technical proficiency includes MERN, Python, C++, and ASP.Net. Recognized for academic excellence, she has received multiple honors, including a Best Teacher Trophy and High Achiever Scholarships. Passionate about research and innovation, she continues to contribute significantly to computer science and AI.

Publication Profile

Scopus

Education 🎓

Dr. Frnaz Akbar is currently pursuing a PhD in Artificial Intelligence at Air University, Islamabad, focusing on machine learning and deep learning applications. She holds an MS in Software Engineering (2018-2020) from Bahria University, Islamabad, and a Master’s in Information Technology (2015-2017) from PMAS ARID University, Rawalpindi, both with first-division honors. Her Bachelor’s in Computer Science (2013-2015) was completed at Punjab University, Lahore, where she received a Role of Honor Certificate. Additionally, she earned a B.Ed. (2020-2022) from Sarhad University, Islamabad Campus, further strengthening her educational expertise. Her academic journey began with HSSC and SSC from Islamabad College F-6/2, where she maintained outstanding academic performance. Throughout her education, she has demonstrated excellence, securing High Achiever Scholarships and academic awards. With expertise in AI, IoT, and software development, her education reflects a strong foundation in research, programming, and innovative computing technologies.

Experience 👩‍🏫

Dr. Frnaz Akbar has extensive experience in academia and industry, specializing in computer science and software engineering. She is currently a Visiting Lecturer at NUML University, Islamabad, in the Department of Software Engineering. Previously, she served as a Senior Computer Science Teacher at IMCG F-10/2 (2017-2023) and ISS, G-13/1, Islamabad (2013-2017), where she mentored students and designed curricula. She has also been a Computer Tutor at Allama Iqbal Open University since 2020, providing part-time instruction. In addition to teaching, she worked as a Software Developer at K-Soft, Ministry of Defense, focusing on MIS applications. With expertise in AI, IoT, and software development, she has contributed significantly to research and education. Her teaching philosophy emphasizes innovation and hands-on learning, ensuring students acquire both theoretical and practical skills. She is passionate about integrating emerging technologies into education and has a proven track record of academic excellence.

Awards and Honors 🏆

Dr. Frnaz Akbar has been recognized for her outstanding contributions to education and research. She received the Best Teacher Trophy from IMCG F-10/2, Islamabad, for her exemplary performance in student development and academic excellence. During her MS at Bahria University, she was appointed as a Teacher Assistant, reflecting her strong command over software engineering concepts. She secured a High Achiever Scholarship in every semester of her MSc IT at PMAS ARID University, Rawalpindi, showcasing her dedication to academic excellence. Her bachelor’s degree at Punjab University earned her a Role of Honor Certificate, highlighting her exceptional academic performance. In her early education, she was awarded a certificate for high attendance at IMCG F-7/2 and secured the second position in SSC. These accolades demonstrate her commitment to education, mentorship, and research, reinforcing her position as a leader in computer science and software engineering.

Research Focus 🔬

Dr. Frnaz Akbar’s research interests revolve around cutting-edge technologies in artificial intelligence and data science. She specializes in AI-driven applications, including deep learning, data mining, and pattern recognition. Her work extends to precision agriculture, where she leverages AI for optimizing crop health analysis. She is also actively engaged in research on the Internet of Things (IoT) and edge computing, focusing on real-time data processing. Blockchain technology and its integration with AI for secure computing systems form another critical aspect of her research. One of her notable contributions is in healthcare AI, particularly Alzheimer’s disease detection using EEG signal analysis. She has published and submitted multiple research papers on AI applications in medical diagnostics and agricultural automation. Her research is highly interdisciplinary, combining machine learning, neural networks, and computational modeling to solve real-world problems, making significant contributions to AI and software engineering domains. 🚀

Publication Top Notes

📖 Optimized Approach in Requirements Change Management in Geographically Dispersed Environment (GDE), International Journal of Foundations of Computer Science, 2020

🌿 Identifying Lesions in Cotton Leaves Unconstrained Images using Deep Neural Network, Computers in Biology and Medicine, 2023 (Under Review)

🧠 Unlocking the Potential of EEG in Alzheimer’s Disease Research: Current Status and Pathways to Precision Detection, Foundations and Trends in Machine Learning, 2024 (Under Review)

📊 Assessing the Effects of Alzheimer Disease on EEG Signals using the Entropy Measure: A Meta-analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024 (Under Review)

Luca Faes | Computational neuroscience | Best Researcher Award

Prof. Luca Faes | Computational neuroscience | Best Researcher Award

Professor, University of Palermo, Italy

Prof. Luca Faes is a Full Professor at the Department of Engineering, University of Palermo, Italy. He earned his MSc in Electronic Engineering (1998) from the University of Padova and a PhD in Electronic Devices (2003) from the University of Trento. His research focuses on biomedical signal processing, information dynamics, and physiological networks. He has held positions at institutions such as the Bruno Kessler Foundation, University of Trento, and has been a visiting researcher in the USA, Belgium, and Brazil. A recipient of multiple awards, including the IEEE Senior Member recognition, Prof. Faes has been named among the World’s Top 2% Scientists by Stanford University. He actively contributes to academic programs, Erasmus+ initiatives, and editorial boards in biomedical engineering.

Publication Profile

Scopus

Orcid

Education

Prof. Luca Faes earned his Master’s degree (Italian Laurea) in Electronic Engineering cum laude from the University of Padova, Italy, in 1998 ⚙️. He then pursued a PhD in Electronic Devices at the University of Trento, Italy, in 2003 🏅. His academic journey laid the foundation for his expertise in biomedical engineering, signal processing, and complex system analysis 📡. With a strong background in electronic systems and bioengineering applications, he has made significant contributions to research and academia, shaping the field of biomedical signal processing and physiological network dynamics 🔬📊.

Professional Experience

Prof. Luca Faes has had a distinguished career in biomedical engineering and bioinformatics. He began as a Research Fellow (1999-2000) at ITC-irst, Trento, followed by a PhD at the University of Trento (2000-2003) 🎓. He held postdoctoral positions (2003-2013) at Trento’s Biophysics and BIOtech labs 🧬. Later, he became a Researcher (2014-2017) at FBK and Professor at the University of Palermo (2018-present) 🏅. His global research collaborations span USA, Belgium, and Brazil 🌍. Recognized as an IEEE Senior Member (2019) and among the World’s Top 2% Scientists (2021-2022), he is an esteemed expert in bioengineering and complex systems ⚙️📊.

Academic Contributions

Prof. Luca Faes has played a pivotal role in academic development and international collaborations. He has been a Doctorate Committee Member at CiMeC, University of Trento (2016-2017) and later at Palermo’s ICT Department (2018-present) 📚. He co-organized the Biomedical Engineering Master’s Program and serves as Vice Coordinator & Tutor for the Bachelor’s Program 🎓. As an Erasmus+ Coordinator, he has fostered partnerships with universities across Europe 🌍. He also leads the Biomedical Information Technologies Curriculum (2021-present) and organizes seminars with global scholars, strengthening biomedical and cybernetics engineering education 📡📊.

Teaching Contributions

Prof. Luca Faes has been actively involved in teaching since 1999, starting as a Teaching Assistant in General Physics II and Signal & Image Processing at the University of Trento 🎓. He has delivered specialized courses internationally, including Brazil 🇧🇷 (2015), IEEE MOOC (2016), and Portugal 🇵🇹 (2017). Since 2018, he has been a Tenured Professor at the University of Palermo, teaching Sensors, Biomedical Devices, and Statistical Analysis of Biomedical Signals 📊. His expertise in biomedical signal processing and sensor technology continues to shape future engineers in cybernetics and biomedical fields 🤖🔬.

🧠 Research Focus

Prof. Luca Faes specializes in computational neuroscience, biomedical engineering, and physiological signal processing. His research focuses on brain-heart interactions ❤️🧠, nonlinear dynamics 🔄, entropy measures 📊, and causal inference in physiological systems. He applies Granger causality and transfer entropy to analyze cardiovascular variability, cerebrovascular function, and neural synchronization. His work contributes to wearable health technology ⌚, autonomic regulation studies, and physiological stress assessment ⚡. He collaborates on machine learning 🤖 and high-order statistical modeling for biomedical applications. His interdisciplinary approach bridges neuroscience, physics, and AI-driven healthcare solutions. 🌍✨

Publication Top Notes

📄 Measures and Models of Brain-Heart InteractionsIEEE Reviews in Biomedical Engineering, 2025  🧠❤️

📄 Chaotic Dynamics and Synchronization under Tripartite CouplingsChaos, Solitons and Fractals, 2024  🔄⚡

📄 Comparison of Feature Selection Methods for Physiological Stress ClassificationPhysiological Measurement, 2024  📊📈

📄 Disentangling High-Order Effects in Transfer EntropyPhysical Review Research, 2024 | 3 citations 🔗📡

📄 Assessing Granger Causality in Cerebrovascular VariabilityIEEE Transactions on Biomedical Engineering, 2024 | 2 citations 🏥🧠

📄 Entropy Rate Measures for Time Series ComplexityBiocybernetics and Biomedical Engineering, 2024 | 4 citations 🔢📉

📄 Wearable Ring-Shaped Biomedical Device for Physiological MonitoringBiosensors, 2024 | 3 citations ⌚📡

📄 Describing Cerebral Autoregulation via State Space MethodsConference Paper, 2024 🏥📈

📄 Decomposing Transfer Entropy in Cardiovascular InteractionsConference Paper, 2024 ❤️📊

📄 Gender Differences in Cardiovascular Variability EntropyConference Paper, 2024  🚻🫀