Yunge Zou | Computer Science | Best Scholar Award

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Dr. Yunge Zou | Computer Science | Best Scholar Award

Dr. Yunge Zou, Chongqing University, China

Dr. Yunge Zou is a Ph.D. scholar at Chongqing University, specializing in hybrid powertrain design and battery degradation in the Department of Automotive Engineering. He is a talent under the Chongqing Excellence Program and a Shapingba Elite Talent (2023–2025). Dr. Zou has led key projects, including the National Key R&D Program, focusing on high-efficiency powertrain technologies. His contributions include innovative methods like Hyper-Rapid Dynamic Programming, which optimizes multi-mode hybrid powertrains. With multiple patents and high-impact publications, he collaborates with leading automotive firms like Chang’an New Energy, advancing sustainable transportation. 🚗🔋📚

 

Publication Profile

Orcid

Google Scholar

Academic and Professional Background 🔋

Dr. Yunge Zou earned his B.E. degree in Automotive Engineering from Chongqing University, China, in 2018. Currently, he is pursuing his Ph.D. in hybrid powertrain design and optimization at the Vehicle Power System Lab, Department of Automotive Engineering, Chongqing University. Recognized for his exceptional talent, Dr. Zou is part of the prestigious Chongqing Excellence Program and was honored as a Shapingba Elite Talent for 2023–2025. His research focuses on hybrid powertrain topology design, battery degradation, energy management systems (EMS), and enhancing battery life, contributing to sustainable transportation innovation. 📚🔧🌱

 

Research and Innovations 🚗

Dr. Yunge Zou is leading several groundbreaking research projects in the field of hybrid powertrain design and optimization. His work includes the National Key Research and Development Program of China on high-efficiency range extender assembly and electric vehicle integration (2022-2024), with a funding of 2.5 million yuan. He is also working on optimizing hybrid electric vehicle design through the National Science Fund for Excellent Young Scholars (2023-2025). Additionally, he contributes to various projects focusing on hybrid vehicle dynamics, energy efficiency, and low-emission technologies, backed by substantial funding from multiple prestigious organizations. 🛠️⚡

 

🛠️ Research Focus

Dr. Yunge Zou’s research primarily focuses on hybrid powertrain design and optimization for electric and range-extended vehicles. His work includes the development of control strategies and topology design for hybrid systems, aiming to improve fuel economy, efficiency, and reduce emissions. Dr. Zou has made significant advancements in aging-aware optimization and mode-switching mechanisms for multi-mode hybrid vehicles. His contributions also extend to battery degradation, energy management, and the computational efficiency of fuel economy assessment using innovative algorithms like Hyper Rapid Dynamic Programming (HR-DP). His work is instrumental in the evolution of transportation electrification. 🚗⚡

 

Publication Top Notes

  • “Design of all-wheel-drive power-split hybrid configuration schemes based on hierarchical topology graph theory” – Energy 242, 122944 (Cited by 14, 2022) 🔋
  • “Aging-aware co-optimization of topology, parameter and control for multi-mode input-and output-split hybrid electric powertrains” – Journal of Power Sources 624, 235564 (Cited by 1, 2024) ⚙️
  • “Design of optimal control strategy for range extended electric vehicles considering additional noise, vibration and harshness constraints” – Energy 310, 133287 (Cited by 1, 2024) 🚗
  • “Computationally efficient assessment of fuel economy of multi-modes and multi-gears hybrid electric vehicles: A Hyper Rapid Dynamic Programming Approach” – Energy, 133811 (Cited by 0, 2024) 🔧

Mr. Shofiqul Islam | Computer Science | Best Researcher Award

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Mr. Shofiqul Islam | Computer Science | Best Researcher Award

Mr Shafiqul Islam ,    Deakin University , Australia

Md. Shofiqul Islam, born in May 1989, is a resident of Highton, Geelong, Australia. Originally hailing from Chutipur, Notipota, Dhamuruda-7220, Chuadanga, Bangladesh, he has established himself in Australia while maintaining ties to his permanent address in Bangladesh. Shofiqul Islam is the son of Md. Abdul Khaleque. He stands 5 feet 7.5 inches tall and weighs 78 kilograms. As a male citizen of Bangladesh, he holds a National ID with the number 1813183458130.

Publication profile
Educational Background

Md. Shofiqul Islam is currently pursuing a Ph.D. in AI-Based Haptic Robotic Control in Medical Tele-Surgery at Deakin University, Australia, a program he began in May 2024. Prior to this, he completed a Master of Science (M.Sc) by Research at the Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), Pahang, Malaysia, in November 2022, achieving an impressive 80% mark. He earned his Bachelor of Science (B.Sc) in Computer Science and Engineering from Islamic University, Kushtia, Bangladesh, in 2014, where he graduated with a CGPA of 3.69 out of 4.00, securing 2nd position among 45 students. His academic journey began at Notipota High School in Dhamurhuda, Chuadanga, Bangladesh, where he obtained his Secondary School Certificate (SSC) in Science in 2005 with a GPA of 4.06 out of 5.00. He then completed his Higher Secondary Certificate (HSC) in Science at Chuadanga Govt. College in 2007, with a GPA of 4.70 out of 5.00.

Research and Work Experience

Md. Shofiqul Islam is currently pursuing a Ph.D. in AI-Based Haptic Robotic Control in Medical Tele-Surgery at Deakin University, Australia, a journey he began in May 2024. Before embarking on his Ph.D., he served as an Assistant Professor in the Department of Computer Science and Engineering at the Military Institute of Science and Technology (MIST) in Dhaka, Bangladesh, from March 2023 to April 15, 2024. Prior to this role, he was a Senior Lecturer at the World University of Bangladesh (WUB) for a brief period from January 2023 to April 2023. His academic career also includes a significant tenure as a Graduate Research Assistant at the Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), where he contributed to research from October 2018 to June 2022. Earlier, he held positions as a Lecturer in Computer Science and Engineering at Atish Dipankar University of Science and Technology from October 2016 to September 2018, and at First Capital University of Bangladesh (FCUB) from February 2015 to June 2016. His extensive experience in academia reflects his deep commitment to advancing knowledge in computer science and engineering.

 

Awards and Recognitions

Md. Shofiqul Islam has been awarded a prestigious fully funded Ph.D. scholarship from the Australian Government, supporting his research at Deakin University from May 2022 to May 2027. This scholarship enables him to pursue advanced studies in AI-Based Haptic Robotic Control in Medical Tele-Surgery. Prior to this, he received a Graduate Research Assistantship (GRA) at Universiti Malaysia Pahang, Malaysia, where he contributed to significant research projects from March 2018 to November 2022. His academic excellence was also recognized during his undergraduate studies, where he received a Merit Scholarship each year from 2009 to 2014 while pursuing a B.Sc. in Computer Science and Engineering at Islamic University, Bangladesh. These achievements reflect his consistent dedication and outstanding performance throughout his academic career.

Publication Top Notes

  • HARDC: A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention-based dual structured RNN with dilated CNN
    Authors: MS Islam, KF Hasan, S Uddin, JMW Quinn, MA Moni
    Journal: Neural Networks, Volume 162, Pages 271-287, 2023
    Citations: 30
  • A review on video classification with methods, findings, performance, challenges, limitations and future work
    Authors: MS Islam, U Kumar Roy, J Al Mahmud
    Journal: Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), Volume 6, Pages 47-57, 2020
    Citations: 30
  • New hybrid deep learning approach using BiGRU-BiLSTM and multilayered dilated CNN to detect arrhythmia
    Authors: MS Islam, MN Islam, N Hashim, M Rashid, BS Bari, F Al Farid
    Journal: IEEE Access, Volume 10, Pages 58081-58096, 2022
    Citations: 26
  • A review on recent advances in Deep learning for Sentiment Analysis: Performances, Challenges and Limitations
    Authors: MS Islam, N Ab Ghani, MM Ahmed
    Journal: Journal of COMPUSOFT, 2020
    Citations: 16
  • A Novel BiGRUBiLSTM Model for Multilevel Sentiment Analysis Using Deep Neural Network with BiGRU-BiLSTM
    Authors: MS Islam, NA Ghani
    Book Series: Lecture Notes in Electrical Engineering (LNEE), Springer, 2021
    Citations: 15
  • HARC-New Hybrid Method with Hierarchical Attention Based Bidirectional Recurrent Neural Network with Dilated Convolutional Neural Network to Recognize Multilabel Emotions from Text
    Author: MS Islam
    Journal: Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), Volume 7, April 2021
    Citations: 14
  • Machine Learning-Based Music Genre Classification with Pre-Processed Feature Analysis
    Authors: MS Islam, MM Hasan, MA Rahim, AM Hasan, M Mynuddin, I Khandokar, …
    Journal: Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), Volume 7, Issue 3, Pages 491-502, 2021
    Citations: 10
  • Applications of Artificial Neural Networks in Engine Cooling System
    Authors: MM Hasan, MS Islam, SA Bakar, MM Rahman, MN Kabir
    Conference: 2021 International Conference on Software Engineering & Computer Systems (ICSECS), IEEE
    Citations: 7
  • Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach
    Authors: MS Islam, MN Kabir, NA Ghani, KZ Zamli, NSA Zulkifli, MM Rahman, …
    Journal: Artificial Intelligence Review, Volume 57, Issue 3, Pages 62, 2024
    Citations: 6
  • Machine Learning Methods to Predict and Analyse Unconfined Compressive Strength of Stabilised Soft Soil with Polypropylene Columns
    Authors: HRS Ikramul Hoque, Muzamir Hasan, Shofiqul Islam, Moustafa Houda, Mirvat Houda
    Journal: Cogent Engineering, Volume 10, Issue 1, 2023
    Citations: 6
  • New Hybrid Deep Learning Method to Recognize Human Action from Video
    Authors: MS Islam, MJ Islam
    Journal: Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), Volume 7, Pages 306-313, 2021
    Citations: 4

 

Huilong Fan | Computer Science | Best Researcher Award

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Dr Huilong Fan |  Computer Science |  Best Researcher Award

assistant researcher at  University of Electronic Science and Technology of China

Huilong Fan is a research assistant at the University of Electronic Science and Technology of China, born in December 1992, and residing in Changsha, Hunan. He specializes in Edge Computing and Artificial Intelligence.

profile

Academic Background:

  • Ph.D. in Computer Science and Technology, Central South University (2019-2023)
    • Major: Satellite multi-intelligence collaborative computing, digital twins, swarm intelligence negotiation, multi-intelligence deep reinforcement learning, online scheduling, artificial intelligence, machine learning.
  • Master in Computer Science and Technology, Guizhou University (2015-2018)
    • Major: Medical big data, big data analysis and prediction, deep learning, multi-label data classification, natural language processing.
  • Bachelor in Network Engineering, Nanyang Institute of Technology (2010-2014)
    • Major: Computer Networks, Principles of Computer Composition, Operating Systems, Algorithm Design.

Professional Experience:

  • Data Analyst, Beijing Ark Hospital (June 2014-Sept 2015; Dec. 2023-Present)
    • Responsibilities: Data cleaning, analysis, and visualization, system development and maintenance, research on satellite networks, collaborative computing, and edge computing.
  • R&D Engineer, Hunan Lisen Data Technology Co Ltd (June 2018-Sept 2019)
    • Responsibilities: Algorithm design, multi-platform software architecture design, software development, database management, interface development and design.

Projects and Leadership:

  • Led projects on mixed integer programming for multi-process production scheduling, satellite and management software R&D, and real-time analysis methods for large-scale multi-source data based on supercomputing.
  • Participated in significant research such as intelligent analysis technology for TFDS images and resource allocation technology based on collaborative perception.

Awards and Patents:

  • Second prize in scientific and technological progress (2020)
  • First prize in the Guizhou Province Innovation and Entrepreneurship Competition (2016)
  • National third prize in the ‘Internet +’ College Students Innovation and Entrepreneurship Competition (2016)
  • Invention Patents: Multi-agent Space-based Information Network Task Scheduling Method (2021), Dynamic Reconfigurable Space-based Information Network Simulation and Computing System (2022).

Skills:

  • Proficient in software architecture design, Java, Python, C, and other programming languages.
  • Experienced in leading R&D teams and writing research project applications.

Research Focus in Computer Science:

Huilong Fan’s research in Computer Science spans several advanced and interdisciplinary areas, primarily focusing on:

  1. Satellite Multi-Intelligence Collaborative Computing:
    • Developing systems that allow multiple intelligent agents to work together effectively in satellite networks.
    • Utilizing collaborative algorithms to improve the efficiency and reliability of satellite communications and operations.
  2. Digital Twins:
    • Creating digital replicas of physical systems to simulate and analyze their real-world counterparts.
    • Applying digital twin technology to monitor, diagnose, and optimize satellite and network operations.
  3. Swarm Intelligence Negotiation:
    • Investigating algorithms that enable decentralized agents to coordinate and negotiate within a swarm.
    • Using swarm intelligence for tasks such as resource allocation and scheduling in dynamic environments.
  4. Multi-Intelligence Deep Reinforcement Learning:
    • Developing deep learning models that enable multiple intelligent agents to learn and adapt to complex environments.
    • Applying these models to solve problems in satellite networks and edge computing.
  5. Online Scheduling:
    • Researching methods for real-time scheduling of tasks and resources in dynamic and distributed systems.
    • Focusing on optimizing the allocation of contact windows in satellite communication networks.
  6. Artificial Intelligence and Machine Learning:
    • Applying AI and ML techniques to solve complex problems in big data analysis, prediction, and decision-making.
    • Emphasizing multi-label data classification and natural language processing for diverse applications.
  7. Medical Big Data:
    • Analyzing and predicting trends in medical data using big data technologies.
    • Developing models for deep learning and multi-label classification to enhance medical data interpretation and application.
  8. Graph-Driven Resource Allocation:
    • Utilizing graph theory and cooperative game theory to optimize resource allocation in Internet of Things (IoT) and satellite networks.
    • Developing adaptive scheduling algorithms for real-time and dynamic environments.

Through his extensive research, Huilong Fan aims to push the boundaries of what is possible in satellite communication, edge computing, and AI, contributing significantly to advancements in these fields.

Publication Top Notes:

  • Dynamic Network Resource Autonomy Management and Task Scheduling Method Li, X., Yang, J., Fan, H.
    Mathematics, 2023, 11(5), 1232. Citations: 6
  • A novel multi-satellite and multi-task scheduling method based on task network graph aggregation Fan, H., Yang, Z., Zhang, X., Long, J., Liu, L.
    Expert Systems with Applications, 2022, 205, 117565. Citations: 15
  • A Spatio-Temporal Graph Neural Network Approach for Traffic Flow Prediction Li, Y., Zhao, W., Fan, H.
    Mathematics, 2022, 10(10), 1754. Citations: 6
  • Quantum Digital Signature with Continuous-Variable Deng, X., Zhao, W., Shi, R., Ding, C., Fan, H.
    International Journal of Theoretical Physics, 2022, 61(5), 144. Citations: 3

 

Vipin Bansal | Computer Science Award | Academic Summit Impact Award

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Mr. Vipin Bansal | Computer Science Award | Academic Summit Impact Award

Mr. Vipin Bansal, Cognizant, India

Vipin Bansal is an accomplished Senior Engineering Manager specializing in AI and ML solutions. 📊 His expertise spans computer vision, anomaly detection, and AI-based healthcare innovations. He is proficient in deploying scalable AI models and cloud-based solutions using platforms like AWS and Azure. ☁️ Vipin’s work includes impactful projects in autonomous driving, healthcare, and commercial applications. 🚗 He is pursuing a PhD in Explainable AI and has authored significant research in the field. 📜 Passionate about leading teams and driving technological advancements, he continues to excel in the dynamic tech landscape. 💼

Publication Profile

Orcid

Education 🎓

Vipin is pursuing a PhD in Explainable AI from Chandigarh University and holds a Master’s in Computer Applications from Birla Institute of Technology, Ranchi. 🧑‍🎓

Work Experience 💼

Vipin has served as a Senior Engineering Manager at Cognizant, focusing on computer vision AI solutions and cloud infrastructure. He also worked at Molnlycke HealthCare on business applications and at Altran on autonomous driving technologies and data quality analysis. His earlier roles include leading mobile app development at Imagination Technology and architecting m-commerce solutions at Aricent. 🚗

Research Focus 📚🔬

Vipin Bansal’s research focuses on the application of generative AI techniques for medical imaging, specifically in detecting diabetic retinopathy. His work, showcased in a detailed review published in “Results in Optics,” emphasizes leveraging advanced AI models to improve diagnostic accuracy in ophthalmology. Collaborating with Amit Jain and Navpreet Kaur Walia, Bansal explores the potential of AI to revolutionize disease detection, highlighting the role of technology in enhancing healthcare outcomes. His research aligns with the domains of medical AI and computer vision, contributing significantly to the field of healthcare technology and artificial intelligence. 🧠👁️💡

Publication Top Notes

Diabetic retinopathy detection through generative AI techniques: A review