Mustaqeem Khan | Deep learning | Best Researcher Award

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Assist. Prof. Dr. Mustaqeem Khan | Deep learning | Best Researcher Award

Dr. Mustaqeem Khan is an accomplished researcher and educator specializing in speech and video signal processing, with a keen focus on emotion recognition using deep learning. Recognized among the Top 2% Scientists globally (2023–2024), he currently serves as an Assistant Professor at the United Arab Emirates University (UAEU). He earned his Ph.D. in Software Convergence from Sejong University, South Korea, and has authored over 40 high-impact publications in IEEE, Elsevier, Springer, and ACM. His contributions span multimodal systems, computer vision, and intelligent surveillance. With extensive experience in academia and research labs, Dr. Khan has also served as a lab coordinator, team leader, and guest editor. He actively collaborates internationally and mentors graduate students. His technical expertise includes TensorFlow, PyTorch, MATLAB, and computer vision frameworks, making him a key contributor to projects involving emotion detection, UAV surveillance, and medical imaging. He brings innovation, leadership, and academic excellence to his roles.

Profile

🎓 Education

Dr. Mustaqeem Khan holds a Ph.D. in Software Convergence (2022) from Sejong University, Seoul, South Korea, where he achieved an outstanding CGPA of 4.44/4.5 (98%) and earned the Outstanding Research Award. His doctoral dissertation focused on advanced studies in speech-based emotion recognition using deep learning. He completed his MS in Computer Science (2018) at Islamia College Peshawar with a Gold Medal, securing a CGPA of 3.94/4.00, and specialized in video-based human action recognition. His undergraduate degree (BSCS, 2015) was from the Institute of Business and Management Sciences, AUP Peshawar, where he developed a web-based design project. His academic background laid the foundation for his research in multimodal deep learning, AI, and signal processing. Throughout his education, Dr. Khan combined rigorous coursework with impactful research, leading to numerous publications and international recognition.

🧪 Experience

Dr. Mustaqeem Khan is currently serving as an Assistant Professor at UAEU (2025–Present), focusing on teaching, research, and student supervision. From 2022 to 2024, he was a Postdoctoral Fellow and Lab Coordinator at MBZUAI, where he led AI projects like drone surveillance and collaborated with the Technical Innovation Institute. At Sejong University (2019–2022), he worked as a Research Assistant and IT Lab Coordinator, guiding projects and mentoring graduate students in speech processing and energy informatics. Prior to this, he was a Lecturer (2018–2019) and Research Assistant (2016–2018) at Islamia College Peshawar, where he taught courses in programming, image processing, and AI. He also led computer vision and speech analytics projects. His international collaborations span institutes in South Korea, France, Saudi Arabia, and India, highlighting his global academic footprint. Dr. Khan is deeply involved in editorial roles and research supervision, embodying academic excellence and research leadership.

🏅 Awards and Honors

Dr. Mustaqeem Khan has been recognized as one of the Top 2% Scientists in the world (2023–2024), a testament to his research impact. He received the Outstanding Research Award from Sejong University in 2022 and was a Gold Medalist during his MS in Computer Science at Islamia College Peshawar (2016–2018). His work has earned multiple Best Paper Awards, including from the Korea Information Processing Society (2021) and Mathematics Journal (2020). He was also granted a fully funded Ph.D. scholarship at Sejong University. Dr. Khan has reviewed for over 35 reputed international journals and serves as an editor and guest editor for several leading publications, including MDPI, IEEE, and Springer journals. His patents in speech-based emotion recognition further validate his innovation. These accolades underscore his academic rigor, global recognition, and leadership in signal processing, AI, and intelligent systems.

🔬 Research Focus

Dr. Mustaqeem Khan’s research lies at the intersection of speech signal processing, multimodal emotion recognition, and computer vision. His Ph.D. work established a foundation for deep learning-based systems capable of understanding human emotions through speech. He has since expanded his research to include age/gender detection, action recognition, violence detection, and medical image analysis using AI. His deep learning models—ranging from CNNs to transformers—have been applied across audio, video, text, and sensor-based data. Dr. Khan is particularly interested in cross-modal transformer-based architectures, edge-AI surveillance systems, and emotion recognition for smart cities. He is also exploring medical AI for fetal, retinal, and Parkinson’s disease diagnostics. His work is published in top-tier venues like IEEE Transactions, Nature Scientific Reports, and ACM. Ongoing collaborations with MBZUAI, TII, and Korean institutions focus on real-time AI applications in UAV systems, smart healthcare, and metaverse content generation.

✅ Conclusion

Dr. Mustaqeem Khan is a globally recognized AI researcher and educator specializing in multimodal emotion recognition and computer vision, whose impactful contributions, international collaborations, and innovative deep learning applications continue to shape the fields of signal processing, smart surveillance, and healthcare technologies.

Publications

Melania Ruggiero | Neuroinflammation | Best Researcher Award

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Dr. Melania Ruggiero | Neuroinflammation | Best Researcher Award

Dr. Melania Ruggiero is a postdoctoral researcher at the University of Bari Aldo Moro, specializing in neuroinflammation and neurodegenerative diseases. She earned her Master’s degree in Medical Biotechnology and Nanobiotechnology from the University of Salento in 2020, followed by a PhD in Functional and Applied Genomics and Proteomics from the University of Bari Aldo Moro in 2024. Throughout her academic journey, she has attended specialized training in neuroscience and advanced fluorescence microscopy. Her research focuses on exploring bioactive compounds for neuroprotection, reprogramming astrocytes into neurons, and mitigating neuroinflammatory processes. She has contributed extensively to scientific literature, authoring multiple publications in high-impact journals, and currently serves as a reviewer for journals such as the International Journal of Molecular Sciences and Cells. Dr. Ruggiero actively collaborates with national and international research groups, contributing to innovative approaches in the treatment of neurodegenerative disorders.

Profile

🎓 Education

Dr. Melania Ruggiero’s academic journey began with her Master’s degree in Medical Biotechnology and Nanobiotechnology at the University of Salento (2020), where she gained a strong foundation in molecular biology, biotechnology, and nanomedicine. She subsequently pursued a PhD in Functional and Applied Genomics and Proteomics at the University of Bari Aldo Moro (2024), during which she engaged in extensive research focused on neurodegenerative disorders. Her doctoral studies were enriched by attending specialized courses, including the “Cellular, Behavioural and Cognitive Neuroscience” school and the “Fluomicro@ICGEB-ICGEB Practical Course of Fluorescence Microscopy and High Throughput Imaging.” These programs equipped her with advanced skills in cellular neuroscience, imaging, and data analysis. Her academic training provided a strong interdisciplinary foundation that bridges biotechnology, neuroscience, and clinical research, supporting her ongoing contributions to innovative treatments for neurodegenerative diseases.

🧪 Experience

Dr. Ruggiero’s professional experience encompasses advanced academic research and international collaboration. Currently a postdoctoral researcher at the Department of Biosciences, Biotechnology and Environment at the University of Bari Aldo Moro, she actively investigates neurodegeneration, neuroinflammation, and potential therapeutic interventions. Her research has led to seven completed and five ongoing research projects. Dr. Ruggiero’s expertise includes the study of bioactive compounds for neuroprotection, demonstrating their ability to reduce neuroinflammatory processes and promote astrocyte reprogramming. She has authored numerous peer-reviewed publications and is actively involved in peer-review activities for journals such as International Journal of Molecular Sciences and Cells. Her collaborative network extends across various esteemed institutions, including universities in Italy and Saudi Arabia. Dr. Ruggiero’s experience reflects a commitment to cutting-edge neuroscience research, fostering translational approaches to combat neurodegenerative disorders.

🏅 Awards and Honors

While specific awards are not listed, Dr. Ruggiero’s scholarly contributions demonstrate substantial recognition in the scientific community. Her numerous publications in internationally indexed journals, with a current h-index of 6, signify her growing influence and impact in the field of neuroscience. Serving as a peer reviewer for prestigious journals such as International Journal of Molecular Sciences and Cells highlights her standing as a respected expert. Additionally, her collaborations with multiple international institutions reflect her role as a valued scientific partner in global research networks. Her research findings, particularly on the neuroprotective roles of bioactive compounds and astrocyte reprogramming, position her as a strong candidate for the Best Researcher Award, acknowledging her innovative contributions to neuroscience and neurodegenerative disease research.

🔬 Research Focus

Dr. Ruggiero’s research centers on neuroinflammation and neurodegenerative diseases, with a particular emphasis on bioactive compounds as potential therapeutic agents. Her innovative work has demonstrated that compounds such as resveratrol, vitamin C, irisin, and lactoferrin can reduce astrogliosis and microgliosis, mitigating neuroinflammatory responses that underlie neurodegeneration. Significantly, she has pioneered findings showing that lactoferrin not only attenuates astrocyte reactivity but also reprograms astrocytes into neuronal precursor cells, promoting neurogenesis and counteracting neuronal loss. This groundbreaking research contributes to developing safer, more effective therapies for neurodegenerative disorders, minimizing side effects compared to conventional treatments. Her work integrates cellular biology, molecular neuroscience, and translational medicine, advancing novel therapeutic strategies for conditions such as Alzheimer’s and Parkinson’s disease. Her collaborations across national and international institutions further enhance the multidisciplinary nature and clinical relevance of her research.

✅ Conclusion

Dr. Melania Ruggiero is an emerging leader in neurodegenerative research, whose pioneering work on bioactive compounds and astrocyte reprogramming offers innovative therapeutic avenues, demonstrated through extensive publications, international collaborations, and impactful scientific contributions, making her a strong candidate for the Best Researcher Award.

Publications

Aymane Edder | Ai and Iot in Healthcare | Best Researcher Award

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Mr. Aymane Edder | Ai and Iot in Healthcare | Best Researcher Award

Edder Aymane is a highly motivated third-year PhD student specializing in Tiny Machine Learning (TinyML) for vital signs monitoring. Based in Casablanca, Morocco, his research focuses on developing efficient machine learning models deployable on embedded systems to analyze and interpret biomedical data in real-time, ultimately enhancing health monitoring and diagnostics. His technical expertise spans TinyML, embedded systems, IoT, biomedical signal processing, model optimization, and programming languages such as Python, C/C++, and MATLAB. Edder has gained hands-on experience through internships at ERAMEDIC, CHU Ibn Rochd, MEDICINA, and BRET LAB, contributing to various healthcare technology projects including the development of remote monitoring prototypes for COVID-19 patients. His academic journey reflects a strong foundation in biomedical engineering, industrial science physics, and medical analysis, complemented by extensive practical skills. Fluent in Arabic and English, with intermediate proficiency in French, Edder Aymane is committed to advancing real-time healthcare solutions through innovative machine learning applications.

Profile

🎓 Education

Edder Aymane’s educational path demonstrates a strong interdisciplinary foundation. He is currently pursuing his PhD at UM6SS, Casablanca (2023–present), focusing on TinyML applications for healthcare monitoring. He earned his Engineering Degree in Biomedical Sciences from ENSAM Rabat (2019–2022), where he worked on practical projects such as remote vital parameters monitoring for COVID-19 patients. Prior to that, he completed preparatory classes in Industrial Science Physics (PSI) (2017–2019), which equipped him with a strong base in physics and engineering principles. Throughout his academic training, Edder engaged in various internships at leading healthcare and research institutions, including ERAMEDIC, CHU Ibn Rochd, and MEDICINA, where he gained real-world experience in laboratory analysis, medical device installation, and healthcare informatics. His academic career combines theoretical learning with hands-on practice, positioning him well for advanced research in biomedical machine learning and embedded systems.

🧪 Experience

Edder Aymane has developed extensive professional experience through diverse internships and research projects related to healthcare technology and biomedical engineering. At ERAMEDIC (July–Sept 2021), he contributed to the installation of Neuro-Navigation Surgery Software and pre-installation of radiology rooms. During his time at MEDICINA (July–Aug 2020), he performed internal and external quality control of biochemistry, hematology, and serology automat systems. At CHU Ibn Rochd, he was involved in setting up COVID-19 services and developing remote monitoring prototypes for vital signs during the pandemic. His end-of-studies internship at BRET LAB (2023–present) further strengthened his research expertise in biomedical signal processing and TinyML model development. These experiences allowed him to apply his technical skills in embedded systems, IoT integration, and machine learning, giving him a well-rounded profile in both research and applied biomedical technologies.

🏅 Awards and Honors

While specific formal awards and honors are not listed, Edder Aymane’s consistent selection for highly technical internships and research projects at reputable healthcare institutions demonstrates recognition of his expertise and potential. His involvement in cutting-edge projects such as the development of a prototype remote vital parameters monitor for COVID-19, installation of complex neuro-navigation systems, and leadership roles during his internships indicate a high level of trust from supervisors and collaborators. His acceptance into the PhD program at UM6SS to work on emerging fields like TinyML reflects both academic and professional acknowledgment of his abilities. Additionally, his multidisciplinary skillset in machine learning, embedded systems, and biomedical signal processing showcases his outstanding technical competency, positioning him as a promising researcher poised for future honors as his academic career progresses.

🔬 Research Focus

Edder Aymane’s research focuses on leveraging Tiny Machine Learning (TinyML) for real-time health monitoring and diagnostics. His work involves developing highly efficient machine learning models optimized for deployment on embedded systems with limited computational resources. Specifically, he focuses on analyzing biomedical signals such as ECG data, enabling continuous monitoring of vital signs directly from wearable or portable devices. His research integrates advanced signal processing techniques, noise filtering, IoT protocols (MQTT, CoAP, BLE), and real-time data interpretation, contributing to more accessible, scalable healthcare solutions. By combining biomedical engineering with embedded AI, Edder aims to bridge the gap between sophisticated machine learning models and practical, low-power medical devices. His work has significant implications for early diagnostics, remote patient monitoring, and scalable healthcare delivery, particularly in resource-limited settings. This research contributes to the growing field of personalized, preventive healthcare powered by intelligent, real-time monitoring systems.

✅ Conclusion

Edder Aymane is an emerging biomedical researcher specializing in TinyML for vital signs monitoring, with a strong foundation in biomedical engineering, embedded systems, IoT, and machine learning; his hands-on experience across leading healthcare institutions and advanced research in real-time healthcare monitoring position him as a promising innovator poised to advance scalable, efficient, and accessible healthcare solutions through cutting-edge embedded AI technologies.

Publications

Ahmad Muhammad | Medical Image Analysis | Best Researcher Award

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Mr. Ahmad Muhammad | Medical Image Analysis | Best Researcher Award

Muhammad Ahmad is a passionate AI researcher and software engineer currently pursuing a Master’s in Information and Communication Engineering at the University of Electronic Science and Technology of China (UESTC). With a Bachelor’s in Computer Science from the University of South Asia, Lahore, he has gained extensive experience in generative AI, LLMs, deep learning, and medical image analysis. He has served as a Software Engineer at E-teleQuote Inc. (USA), where he led projects involving LLaMA 3.1, sentiment analysis, and real-time chatbot systems. His academic contributions include first-author publications on Alzheimer’s disease and brain tumor diagnosis using hybrid deep learning models. Recognized with multiple awards and scholarships, including a fully funded Master’s scholarship, Ahmad brings together strong programming skills, leadership experience, and a commitment to innovation in healthcare AI. His work reflects a deep interest in combining machine learning with medical imaging to solve real-world challenges through intelligent systems.

Profile

🎓 Education

Muhammad Ahmad holds a Master’s degree in Information and Communication Engineering from UESTC, Chengdu, China, where he maintains a GPA of 3.54/4.0 and focuses on generative AI, LLMs, deep learning, and medical image analysis. Previously, he earned a BS in Computer Science from the University of South Asia, Lahore, Pakistan, graduating with a CGPA of 3.16/4.0. His final year project—Walmart Weekly Sales Prediction—reflected his early commitment to machine learning. His academic journey has been bolstered by self-motivated learning, with certifications from Stanford University, IBM, and DeepLearning.AI in TensorFlow, machine learning with Python, and data analysis. Alongside his formal education, Ahmad has organized machine learning workshops and led ACM and IEEE student chapters, showcasing a combination of technical proficiency and community leadership. His educational background lays a strong foundation for interdisciplinary AI research, especially in biomedical applications.

🧪 Experience

Muhammad Ahmad has valuable industry experience as a Software Engineer in AI at E-teleQuote Inc. (Florida, USA), where he led projects utilizing LLaMA 3.1 for document processing and chatbot development. He developed robust NLP solutions, including sentiment analysis and speech recognition systems, while deploying and optimizing AI models for production environments. Earlier, during his internship at Quid Sol (Lahore), he worked on deep learning-based object detection, segmentation, and noise reduction, applying feature engineering and model optimization techniques. Beyond technical roles, he held leadership positions, including Vice-Chair of the ACM Society and event organizer for IEEE, fostering innovation within academic communities. Ahmad’s experience combines hands-on coding with strategic project leadership in AI, making him adept at translating theoretical machine learning concepts into real-world applications, particularly in healthcare and image analysis domains.

🏅 Awards and Honors

Muhammad Ahmad’s academic excellence and leadership have earned him multiple awards. He received a fully funded scholarship from the University of Electronic Science and Technology of China (UESTC) to pursue his Master’s studies in AI. In 2020, he was awarded a semester scholarship for conducting a high-impact workshop on machine learning at the University of South Asia. His community engagement was recognized by the Rooh Foundation and the Government of Pakistan for volunteer work with the Humanity Welfare Foundation. In technical competitions, he secured 1st place at COMSATS University’s Web Development Competition (April 2018) and 2nd place at Superior University (September 2018), demonstrating his early programming excellence. Additionally, Ahmad has earned respected certifications in machine learning, deep learning, and data analysis from Stanford, IBM, and CognitiveClass.ai, highlighting his continuous pursuit of technical mastery in the field of artificial intelligence and data science.

🔬 Research Focus

Muhammad Ahmad’s research focuses on deep learning, generative AI, and large language models (LLMs), particularly applied to medical image analysis. He is committed to enhancing diagnostic accuracy in complex medical conditions using AI. His notable work includes developing a hybrid deep learning architecture with adaptive feature fusion for multi-stage Alzheimer’s disease classification, published in Brain Sciences. Another study, submitted to the International Journal of Machine Learning and Cybernetics, proposes a dynamic fusion model for brain tumor diagnosis. His academic pursuits aim to integrate LLMs and computer vision for robust, intelligent medical systems. Ahmad’s goal is to bridge gaps between artificial intelligence and clinical practice, focusing on real-time, explainable, and scalable AI systems for healthcare. His research embodies a combination of theoretical rigor and practical implementation, striving to deliver solutions that are both impactful and clinically relevant.

✅ Conclusion

Muhammad Ahmad is a driven AI researcher and engineer specializing in generative AI, LLMs, and deep learning for medical imaging, with proven academic, research, and industry experience, recognized through prestigious awards and impactful publications, currently contributing to advanced healthcare technologies at UESTC

Publications

  • Hybrid Deep Learning Architecture with Adaptive Feature Fusion for Multi-Stage Alzheimer’s Disease Classification

    Brain Sciences
    2025-06-06 | Journal article
    DOI: 10.3390/brainsci15060612
    CONTRIBUTORS: Ahmad Muhammad; Qi Jin; Osman Elwasila; Yonis Gulzar
  • Dynamic Fusion of Local and Global Features for Superior Brain Tumor Diagnosis Submitted as First Author
    to International Journal of Machine Learning and Cybernetics. Submission ID: 6a1c905c-080c-44f9-98e8-
    4f23727a5dc7.

Ibrahim Akinjobi Aromoye | Computer Vision | Best Researcher Awards

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Mr.Ibrahim Akinjobi Aromoye | Computer Vision | Best Researcher Awards

Aromoye Akinjobi Ibrahim is a dedicated researcher in Electrical and Electronic Engineering, currently pursuing an MSc (Research) at Universiti Teknologi PETRONAS, Malaysia. His research focuses on hybrid drones for pipeline inspection, integrating machine learning to enhance surveillance capabilities. With a B.Eng. in Computer Engineering from the University of Ilorin, Nigeria, he has excelled in robotics, artificial intelligence, and digital systems. Aromoye has extensive experience as a research assistant, STEM educator, and university teaching assistant, contributing to 5G technology, UAV development, and machine learning applications. He has authored multiple research papers in reputable journals and conferences. A proactive leader, he has held executive roles in student associations and led innovative projects. His expertise spans embedded systems, IoT, and cybersecurity, complemented by certifications in Python, OpenCV, and AI-driven vision systems. He actively contributes to academic peer review and professional development, demonstrating a commitment to technological advancements and education.

Profile

Education 🎓

Aromoye Akinjobi Ibrahim is pursuing an MSc (Research) in Electrical and Electronic Engineering at Universiti Teknologi PETRONAS (2023-2025), focusing on hybrid drones for pipeline inspection under the supervision of Lo Hai Hiung and Patrick Sebastian. His research integrates machine learning with air buoyancy technology to enhance UAV flight time. He holds a B.Eng. in Computer Engineering from the University of Ilorin, Nigeria (2015-2021), graduating with a Second Class Honors (Upper) and a CGPA of 4.41/5.0. His undergraduate thesis involved developing a smart bidirectional digital counter with a light control system for energy-efficient automation. Excelling in digital signal processing, AI applications, robotics, and software engineering, he has consistently demonstrated technical excellence. His academic journey is enriched with top grades in core engineering courses and hands-on experience in embedded systems, IoT, and AI-driven automation, making him a skilled researcher and developer in advanced engineering technologies.

Experience 👨‍🏫

Aromoye has diverse experience spanning research, teaching, and industry. As a Graduate Research Assistant at Universiti Teknologi PETRONAS (2023-present), he specializes in hybrid drone development, 5G technologies, and machine learning for UAVs. His contributions include designing autonomous systems and presenting research at international conferences. Previously, he was an Undergraduate Research Assistant at the University of Ilorin (2018-2021), where he worked on digital automation and AI-driven projects. In academia, he has been a Teaching Assistant at UTP, instructing courses in computer architecture, digital systems, and electronics. His industry roles include STEM Educator at STEMCafe (2022-2023), where he taught Python, robotics, and electronics, and a Mobile Games Development Instructor at Center4Tech (2019-2021), guiding students in game design. He also worked as a Network Support Engineer at the University of Ilorin (2018). His expertise spans AI, IoT, and automation, making him a versatile engineer and educator.

Awards & Recognitions 🏅

Aromoye has received prestigious scholarships and leadership recognitions. He is a recipient of the Yayasan Universiti Teknologi PETRONAS (YUTP-FRG) Grant (2023-2025), a fully funded scholarship supporting his MSc research in hybrid drones. As an undergraduate, he demonstrated leadership by serving as President of the Oyun Students’ Association at the University of Ilorin (2019-2021) and previously as its Public Relations Officer (2018-2019). He led several undergraduate research projects, including developing a smart bidirectional digital counter with a light controller system, earning accolades for innovation in automation. His contributions extend to professional peer review for IEEE Access and Results in Engineering. Additionally, he has attained multiple certifications in cybersecurity (MITRE ATT&CK), IoT, and AI applications, reinforcing his technical expertise. His dedication to academic excellence, leadership, and research impact continues to shape his career in engineering and technology.

Research Interests 🔬

Aromoye’s research revolves around hybrid UAVs, AI-driven automation, and 5G-enabled surveillance systems. His MSc thesis at Universiti Teknologi PETRONAS explores the development of a Pipeline Inspection Air Buoyancy Hybrid Drone, enhancing flight efficiency through a combination of lighter-than-air and heavier-than-air technologies. His work integrates deep learning-based object detection algorithms for real-time pipeline monitoring. He has contributed to multiple research publications in IEEE Access, Neurocomputing, and Elsevier journals, covering UAV reconnaissance, transformer-based pipeline detection, and swarm intelligence. His research interests extend to AI-driven control systems, autonomous robotics, and IoT-based energy-efficient automation. Additionally, he investigates cybersecurity applications in UAVs and smart embedded systems. His interdisciplinary expertise enables him to develop innovative solutions for industrial surveillance, automation, and smart infrastructure, positioning him as a leading researcher in AI-integrated engineering technologies.

Publications 

  • Significant Advancements in UAV Technology for Reliable Oil and Gas Pipeline Monitoring

    Computer Modeling in Engineering & Sciences
    2025-01-27 | Journal article
    DOI: 10.32604/cmes.2025.058598
    Part ofISSN: 1526-1506
    CONTRIBUTORS: Ibrahim Akinjobi Aromoye; Hai Hiung Lo; Patrick Sebastian; Shehu Lukman Ayinla; Ghulam E Mustafa Abro

  • Real-Time Pipeline Tracking System on a RISC-V Embedded System Platform

    14th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2024
    2024 | Conference paper
    DOI: 10.1109/ISCAIE61308.2024.10576323
    EID:

    2-s2.0-85198901224

    Part of ISBN: 9798350348798
    CONTRIBUTORS: Wei, E.S.S.; Aromoye, I.A.; Hiung, L.H.

 

Chunyu Liu | Cognitive Computing | Best Researcher Award

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Dr. Chunyu Liu | Cognitive Computing | Best Researcher Award

Chunyu Liu is a Lecturer at North China Electric Power University, specializing in machine learning, neural decoding, and visual attention. 📚 She earned her B.S. in Mathematics and Applied Mathematics from Henan Normal University, an M.S. in Applied Mathematics from Northwest A&F University, and a Ph.D. in Computer Application Technology from Beijing Normal University. 🎓 She completed postdoctoral training at Peking University. 🔬 Her research integrates AI methodologies with cognitive neuroscience, focusing on neural encoding, decoding, and attention mechanisms. 🧠 She has published over 10 research papers, including six SCI-indexed publications as the first author. 📝 Her work aims to bridge artificial intelligence with human cognitive function understanding, contributing significantly to computational neuroscience. 🌍 Liu has also been involved in several major research projects, furthering advancements in neural signal analysis and cognitive computing. 🚀

Profile

Education 🎓

Chunyu Liu holds a strong academic background in mathematics and computational sciences. She obtained her B.S. degree in Mathematics and Applied Mathematics from Henan Normal University. ➕ She pursued her M.S. in Applied Mathematics at Northwest A&F University, where she deepened her expertise in mathematical modeling. 🔢 Continuing her academic journey, she earned a Ph.D. in Computer Application Technology from Beijing Normal University. 🖥️ Her doctoral research explored advanced AI techniques applied to neural decoding and cognitive processing. 🧠 To further refine her skills, she completed postdoctoral training at Peking University, focusing on integrating artificial intelligence with neural mechanisms. 🔬 Her academic pathway reflects a multidisciplinary approach, merging mathematics, computer science, and cognitive neuroscience to address complex challenges in brain science and AI. 📊 Liu’s education laid the foundation for her contributions to machine learning, visual attention studies, and neural encoding research.

Experience 👨‍🏫

Dr. Chunyu Liu is currently a Lecturer at North China Electric Power University, where she teaches and conducts research in cognitive computing and machine learning. 🎓 She has led and collaborated on multiple projects related to neural encoding and decoding, investigating how the brain processes object recognition, emotions, and attention. 🧠 Prior to her current role, she completed postdoctoral research at Peking University, where she worked on advanced AI-driven models for neural signal analysis. 🔍 Over the years, Liu has gained extensive experience in analyzing multimodal neural signals, including magnetoencephalography (MEG) and functional MRI (fMRI). 📡 She has also served as a reviewer for esteemed scientific journals and collaborated with interdisciplinary research teams on AI and brain science projects. 🔬 Her expertise extends to both academia and industry, where she has contributed to the development of novel computational models for decoding brain activity. 🚀

Research Interests 🔬

Dr. Chunyu Liu’s research integrates artificial intelligence and brain science to understand cognitive functions through neural decoding. 🧠 She employs multi-modal neural signals such as magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) to analyze brain activity. 📡 Her work explores neural encoding and decoding, focusing on object recognition, emotion processing, and multiple-object attention. 🎯 She develops AI-based models to extract human brain features and gain insights into cognitive mechanisms. 🤖 By integrating psychological experimental paradigms with AI, Liu aims to advance computational neuroscience. 🏆 Her research also inspires the development of new AI theories and algorithms based on principles of brain function. 📊 She has led major projects in cognitive computing, contributing significantly to both theoretical advancements and practical applications in neural signal processing. 🚀 Through her work, she bridges the gap between human cognition and artificial intelligence, driving innovations in brain-computer interface research. 🏅

 

Awards & Recognitions 🏅

Dr. Chunyu Liu has received recognition for her outstanding contributions to cognitive computing and AI-driven neuroscience research. 🏅 She has been nominated for the prestigious International Cognitive Scientist Award for her pioneering work in neural decoding and visual attention mechanisms. 🎖️ Liu’s research publications have been featured in high-impact journals, earning her accolades from the scientific community. 📜 Her first-author papers in IEEE Transactions on Neural Systems and Rehabilitation Engineering, Science China Life Sciences, and IEEE Journal of Biomedical and Health Informatics have been widely cited. 📝 She has also been honored with research grants and funding for AI-driven cognitive studies. 🔬 Her innovative work in decoding brain signals has been recognized in international AI and neuroscience conferences. 🌍 Liu’s academic excellence and contributions continue to shape the field of computational neuroscience and machine learning applications in cognitive science. 🚀

Publications 📚

Mudassar Raza | Machine Learning | Best Researcher Award

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Prof. Mudassar Raza | Machine Learning | Best Researcher Award

Prof. Dr. Mudassar Raza is a leading AI researcher and academician, serving as a Professor at Namal University, Mianwali, Pakistan. He is a Senior IEEE Member, Chair Publications of IEEE Islamabad Section, and an Academic Editor for PLOS ONE. With 20+ years of teaching and research experience, he has worked at HITEC University Taxila and COMSATS University Islamabad. His research spans AI, deep learning, image processing, and cybersecurity. He has published 135+ research papers with a cumulative impact factor of 215+, 6066+ citations, an H-index of 44, and an I-10 index of 93. He was listed in Elsevier’s World’s Top 2% Scientists (2023) and ranked #11 in Computer Science in Pakistan. Dr. Raza has supervised 3 PhDs, co-supervising 6 more, and mentored 100+ undergraduate R&D projects. He actively contributes to academia, industry collaborations, and curriculum development while serving as a reviewer for prestigious journals. 🌍📖

Profile

Education 🎓

  • Ph.D. in Control Science & Engineering (2014-2017) – University of Science & Technology of China (USTC), China 🇨🇳
    • Specialization: Pattern Recognition & Intelligent Systems
  • MS (Computer Science) (2009-2010) – Iqra University, Islamabad, Pakistan 🇵🇰
    • CGPA: 3.64 | Specialization: Image Processing
  • MCS (Master of Computer Science) (2004-2006) – COMSATS Institute of Information Technology, Pakistan
    • CGPA: 3.24 | 80% Marks
  • BCS (Bachelor in Computer Science) (1999-2003) – Punjab University, Lahore, Pakistan
    • CGPA: 3.28 | 64.25% Marks
  • Higher Secondary (Pre-Engineering) – Islamabad College for Boys
  • Matriculation (Science) – Islamabad College for Boys
    Dr. Raza’s academic journey is marked by top-tier universities and a strong focus on AI, pattern recognition, and cybersecurity. 🎓📚

Experience 👨‍🏫

  • Professor (2024-Present) – Namal University, Mianwali
    • Teaching AI, Cybersecurity, and Research Supervision
  • Associate Professor/Head AI & Cybersecurity Program (2023-2024) – HITEC University, Taxila
    • Led AI & Cybersecurity programs, supervised PhDs, and organized industry-academic collaborations
  • Associate Professor (2023) – COMSATS University, Islamabad
  • Assistant Professor (2012-2023) – COMSATS University, Islamabad
  • Lecturer (2008-2012) – COMSATS University, Islamabad
  • Research Associate (2006-2008) – COMSATS University, Islamabad
    Dr. Raza has 20+ years of experience in academia, R&D, and industry collaborations, contributing significantly to AI, deep learning, and cybersecurity. 🏫📊

Research Interests 🔬

Prof. Dr. Mudassar Raza’s research revolves around Artificial Intelligence, Deep Learning, Computer Vision, Image Processing, Cybersecurity, and Parallel Programming. His work includes pattern recognition, intelligent systems, visual robotics, and AI-driven cybersecurity solutions. With 135+ international publications, he has significantly contributed to AI’s real-world applications. His research impact includes 6066+ citations, an H-index of 44, and an I-10 index of 93. He leads multiple AI research groups, supervises PhD/MS students, and actively collaborates with industry and academia. His work is frequently cited, placing him among the top AI researchers globally. As an IEEE Senior Member and a PLOS ONE Academic Editor, he is a key figure in AI-driven innovations and technology advancements. 🧠📊

  • National Youth Award 2008 by the Prime Minister of Pakistan for contributions to Computer Science 🎖️
  • Listed in World’s Top 2% Scientists (2023) by Elsevier 🌍
  • Ranked #11 in Computer Science in Pakistan by AD Scientific Index 📊
  • Senior IEEE Member (ID: 91289691) 🔬
  • HEC Approved PhD Supervisor 🎓
  • Best Research Productivity Awardee at COMSATS University multiple times 🏆
  • Recognized by ResearchGate with a Research Interest Score higher than 97% of members 📈
  • Reviewer & Editor for prestigious journals including PLOS ONE 📝
    Dr. Raza has received numerous accolades for his contributions to AI, research excellence, and academia. 🌟

Publications 📚

Yangyang Huang | Object detection | Excellence in Innovation

Posted on by Cognitive Scientist Awards

Dr. Yangyang Huang | Object detection | Excellence in Innovation

Yangyang Huang is a Ph.D. student at the School of Computer Science and Engineering, South China University of Technology (SCUT), Guangzhou, China. His research focuses on artificial intelligence, computer vision, and large models. He previously graduated from Wuhan University, where he developed a strong foundation in AI and computational sciences. Yangyang has contributed to significant research projects, including the Collaborative Innovation Major Project for Industry, University, and Research. His work, “LVMUM: Toward Open-World Object Detection with Large Vision Models and Unsupervised Modeling,” has gained notable citations. Passionate about AI advancements, he actively participates in academic collaborations and professional memberships, contributing to AI-driven innovations.

Profile

Education 🎓

Yangyang Huang completed his undergraduate studies at Wuhan University, where he gained expertise in artificial intelligence and computational sciences. Currently, he is pursuing his Ph.D. at the School of Computer Science and Engineering, South China University of Technology (SCUT), Guangzhou, China. His doctoral research focuses on large vision models, unsupervised modeling, and object detection. He has been involved in cutting-edge AI research, particularly in deep learning and computer vision. His academic journey has been marked by significant contributions to AI-driven innovations, leading to multiple publications in high-impact journals. Yangyang actively collaborates with researchers in academia and industry, further strengthening his expertise in AI and machine learning applications.

Experience 👨‍🏫

Yangyang Huang has extensive research experience in artificial intelligence, computer vision, and large models. As a Ph.D. student at SCUT, he has been involved in the Collaborative Innovation Major Project for Industry, University, and Research. His research contributions include developing large vision models for open-world object detection, leading to highly cited publications. Yangyang has also participated in consultancy and industry projects, applying AI techniques to real-world problems. He has authored several journal articles indexed in SCI and Scopus and has contributed to the academic community through editorial roles. His collaborative research efforts have led to impactful AI advancements, making him a rising scholar in the field of AI and machine learning.

Research Interests 🔬

Yangyang Huang’s research primarily focuses on artificial intelligence, computer vision, and large models. His recent work, “LVMUM: Toward Open-World Object Detection with Large Vision Models and Unsupervised Modeling,” explores novel AI techniques for enhancing object detection capabilities. He specializes in deep learning, unsupervised learning, and AI-driven automation. His research interests include developing robust AI models for real-world applications, advancing AI ethics, and improving AI interpretability. Yangyang actively collaborates with academia and industry to bridge the gap between theoretical AI research and practical applications. His contributions extend to consultancy projects, AI innovation, and scholarly publications, making him a key contributor to AI advancements. 🚀

Awards & Recognitions 🏅

Yangyang Huang has received recognition for his outstanding contributions to artificial intelligence and computer vision. His research on large vision models and open-world object detection has been widely cited, earning him academic recognition. He has been nominated for prestigious research awards, including Best Researcher Award and Excellence in Research. His work in AI has been acknowledged through various grants and funding for industry-academic collaborative projects. Yangyang’s active participation in international conferences has led to best paper nominations and accolades for his innovative contributions. He is a member of esteemed professional organizations, further cementing his reputation as an emerging AI researcher.

Publications 📚

Vikas Palekar | Machine Leaning | Best Researcher Award

Posted on by Cognitive Scientist Awards

Mr. Vikas Palekar | Machine Leaning | Best Researcher Award

 

Profile

Education

He is currently pursuing a Ph.D. in Computer Science and Engineering at Vellore Institute of Technology, Bhopal, Madhya Pradesh, since December 2018. His research focuses on developing an Adaptive Optimized Residual Convolutional Image Annotation Model with a Bionic Feature Selection Strategy. He holds a Master of Engineering (M.E.) in Information Technology from Prof. Ram Meghe College of Engineering Technology and Research, Badnera (SGBAU Amravati), which he completed in December 2012 with an impressive 88.00%, securing the first merit position in the university for the summer 2012 examination. Prior to that, he earned a Bachelor of Engineering (B.E.) in Computer Science and Engineering from Shri Guru Gobind Singhji Institute of Engineering Technology and Research, Nanded (SRTMNU, Nanded), in June 2007, achieving a commendable 74.40%.

Work experience

He is currently working as an Assistant Professor in the Department of Computer Engineering at Bajaj Institute of Technology, Wardha, since July 31, 2023. In addition to his teaching responsibilities, he serves as the Academic Coordinator of the department and has worked as a Senior Supervisor for the DBATY Winter-23 Exam at Government College of Engineering, Yavatmal.

Previously, he worked as an Assistant Professor (UGC Approved, RTMNU, Nagpur) in the Department of Computer Science and Engineering at Datta Meghe Institute of Engineering, Technology & Research, Wardha, from June 14, 2011, to June 30, 2023. During this tenure, he held the position of Head of the Department from April 21, 2016, to June 30, 2023. He taught various subjects, including Distributed Operating Systems, TCP/IP, System Programming, Data Warehousing and Mining, Artificial Intelligence, and Computer Architecture and Organization. Additionally, he contributed to university examinations as the Chief Supervisor in the Winter-2015 Examination and a committee member for the Summer-2013, Summer-2015, and Summer-2018 Examinations. He also played a key role in institutional development as a member of the Admission Committee, NBA & NAAC core committees at the department level, and as the convener of the National Level Technical Symposium “POCKET 16” organized by the CSE Department on March 16, 2016.

Earlier in his career, he served as an Assistant Professor in the Department of Computer Engineering at Bapurao Deshmukh College of Engineering, Wardha, from November 26, 2008, to April 30, 2011. He taught subjects such as Unix and Shell Programming, Object-Oriented Programming, and Operating Systems while also serving as a Department Exam Committee Member.

Achievement

He was the first university topper (merit) in M.Tech (Information Technology) and received the Best Paper Award at the 2021 International Conference on Computational Performance Evaluation (ComPE), organized by the Department of Biomedical Engineering, North Eastern Hill University (NEHU), Shillong, Meghalaya, India, from December 1st to 3rd, 2023. He has actively participated in various conferences, including presenting the paper “Label Dependency Classifier using Multi-Feature Graph Convolution Networks for Automatic Image Annotation” at ComPE 2021 in Shillong, India. He also presented his research on “Visual-Based Page Segmentation for Deep Web Data Extraction” at the International Conference on Soft Computing for Problem Solving (SocProS 2011) held from December 20-22, 2011. Additionally, he contributed to the Computer Science & Engineering Department at Sardar Vallabhbhai National Institute of Technology, Surat, by presenting “A Critical Analysis of Learning Approaches for Image Annotation Based on Semantic Correlation” from December 13-15, 2022. His work on “A Survey on Assisting Document Annotation” was featured at the 19th International Conference on Hybrid Intelligent Systems (HIS) at VIT Bhopal University, India, from December 10-12, 2022. Furthermore, he co-authored a study titled “Review on Improving Lifetime of Network Using Energy and Density Control Cluster Algorithm,” which was presented at the 2018 IEEE International Students’ Conference on Electrical, Electronics, and Computer Science (SCEECS) in Bhopal, India.

 

Publication

Mahmoud Alimoradi | Machine Learning | Best Researcher Award

Posted on by Cognitive Scientist Awards

Mr. Mahmoud Alimoradi | Machine Learning | Best Researcher Award

Lahijan Azad ,Iran

He understands the growing need for Machine Learning and has a keen interest in the field, which he considers a blessing. Recognizing the importance of managing large and complex computations to control various aspects of the human environment has led him into this vast world. He is particularly fascinated by machine learning, especially reinforcement learning, supervised learning, semi-supervised learning, outliers, and basic data challenges. Furthermore, optimization, an area of artificial intelligence that requires fundamental studies and a change in approach, is another of his key research interests.

Profile

Education

He holds a Master’s degree in Artificial Intelligence Engineering from the University of Shafagh, completed in 2020. His thesis was titled “Trees Social Relations Optimization Algorithm: A New Swarm-Based Metaheuristic Technique to Solve Continuous and Discrete Optimization Problems.” He also earned a Bachelor’s degree in Software Engineering from Azad Lahijan University, which he attended from 2007 to 2011.

Research Interests

Theory: Reinforcement Learning (high-dimensional problems, regularized algorithms, model
learning,
representation learning and deep RL, learning from demonstration, inverse optimal control, deep
Reinforcement Learning); Machine Learning (statistical learning theory, nonparametric
algorithms, time series. processes, manifold learning, online learning); Large-scale Optimization;
Evolutionary Computation, Metaheuristic Algorithm, Deep Learning, Healthcare Machine
learning, Big Data, Data Problems (Imbalanced), Signal Analysis
Applications: Automated control, space affairs, robotic control, medicine and health, asymmetric
data, data science, scheduling, proposing systems, self-enhancing systems

Work Experience

He is a freelance programmer with expertise in various operating systems, including Microsoft Windows and Linux (Arch, Ubuntu, Fedora). He is proficient in software tools such as Microsoft Office, Anaconda, Jupyter, PyCharm, Visual Studio, Tableau, RapidMiner, MATLAB, and Visual Studio. His programming skills include Matlab, Python, C++, Scala, Java, and Julia, with a focus on data mining, data science, computer vision, and machine learning. He is experienced with Python libraries like Pandas, Numpy, Matplotlib, Seaborn, PyCV, TensorFlow, Time Series Analysis, Spark, Hadoop, and Cassandra. Additionally, he is skilled in using Github, Docker, and MySQL. His expertise spans machine learning, deep learning, imbalanced data, missing data, semi-supervised learning, healthcare machine learning, algorithm design, and metaheuristic algorithms. He is fluent in English and Persian.

Publications