Amani Benamor | Physical Layer Security | Best Researcher Award

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Dr. Amani Benamor | Physical Layer Security | Best Researcher Award

Post-doctoral research fellow at University of Limoges, France

Dr. Amani Benamor is a Postdoctoral Researcher at the University of Limoges, specializing in next-generation wireless networks. She earned her PhD in Computer Systems Engineering from the University of Limoges, in collaboration with the University of Sfax, where her research focused on Multi-user Wireless Access Techniques for Machine-Type Communications. Her expertise includes advanced technologies like Non-Orthogonal Multiple Access (NOMA), Internet of Things (IoT), 5G/6G, and massive Machine-Type Communications (mMTC). Dr. Benamor has also contributed to the field of physical layer security and has published extensively in leading IEEE conferences and journals. Fluent in Arabic, French, and English, she is passionate about technological innovation, research, and development.

Profile:

Education:

Dr. Amani Benamor pursued her PhD in Computer Systems Engineering from the University of Limoges (France) in collaboration with the University of Sfax (Tunisia) between 2019 and 2023. Her field of study was Information and Communication Science and Technology. Her thesis focused on Multi-user Wireless Access Techniques for Machine-Type Communications, exploring advanced technologies such as Non-Orthogonal Multiple Access (NOMA), Internet of Things (IoT), massive Machine-Type Communications (mMTC), and future 5G/6G networks. The research was conducted within the XLIM Laboratory in Limoges, France, and the Electronics and Information Technology Laboratory in Sfax, Tunisia. Dr. Benamor utilized a variety of technical tools including game theory, machine learning, and Matlab for her research.

Professional Experience:

Dr. Amani Benamor is currently a Postdoctoral Researcher at the University of Limoges, where she focuses on Physical Layer Security for Next-Generation Wireless Networks. Her research explores advanced technologies such as Non-Orthogonal Multiple Access (NOMA), 5G, Internet of Things (IoT), and massive MIMO, utilizing tools like Matlab, Python, and network coding. Prior to this, Dr. Benamor completed a research engineer internship at the Laboratory of Information Processing Systems Teams in Cergy, France, where she conducted performance studies on 1-bit Quantized Linear Precoder for Massive MIMO Systems. She also gained hands-on experience as a Web Developer Assistant at the Advanced Technology Center in Gabes, Tunisia, developing a web application for human resource management, and as a Network Engineer Assistant at Tunisia Telecom Operator, where she analyzed urban telephone network structures. Dr. Benamor’s professional background reflects her expertise in wireless communications, system security, and network engineering, combined with a solid foundation in software development and applied research.

Research Interests:

Dr. Amani Benamor’s research interests lie at the intersection of information and communication technology, with a particular focus on wireless communication systems. Her current work explores Physical Layer Security for next-generation wireless networks, emphasizing the integration of Non-Orthogonal Multiple Access (NOMA) in 5G and 6G environments. Dr. Benamor is particularly interested in the application of game theory and machine learning to enhance resource allocation and optimize performance in massive Machine-Type Communications (mMTC) and the Internet of Things (IoT). Her innovative approach includes leveraging advanced mathematical techniques and modeling tools to address challenges in security and efficiency within wireless networks. Additionally, she is keen on exploring emerging technologies and their implications for improving connectivity and data privacy in increasingly complex communication landscapes.

Skills:

Dr. Amani Benamor possesses a diverse skill set that encompasses various domains within computer science and engineering. Proficient in operating systems such as Windows, Linux, and Android Studio, she excels in project analysis and design methodologies, including UML and Merise. Her technical expertise extends to web technologies, where she is adept in HTML5, CSS3, XML, PHP, JavaScript, and JSP. Dr. Benamor is also skilled in several programming languages, including Java, JavaEE, Python, C, and Matlab, allowing her to effectively tackle a wide range of programming and development tasks. Furthermore, she is experienced in utilizing tools for mathematics, game theory, GNS3, GitHub, Jenkins, Docker, and machine learning, enhancing her ability to conduct complex research and implement innovative solutions in her field. Her strong foundation in these areas equips her to contribute significantly to advancements in wireless communication and network security.

Conclusion:

Dr. Amani Benamor’s exceptional research on multi-user wireless access techniques and her contributions to advanced communication technologies, alongside her impressive academic credentials and diverse technical skills, make her a strong candidate for the Best Researcher Award. Her groundbreaking work in NOMA, IoT, and security for next-generation wireless networks reflects her commitment to innovation and excellence in the field of Information and Communication Science and Technology.

Publication Top Noted:

Multi-Armed Bandit Approach for Mean Field Game-Based Resource Allocation in NOMA Networks

Mean Field Game-Theoretic Framework for Distributed Power Control in Hybrid NOMA

Hayelom Gebrye | DDoS Attack Detection | Best Researcher Award

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Mr. Hayelom Gebrye | DDoS Attack Detection | Best Researcher Award

Ph.D. student at UESTC, China, Ethiopia

Mr. Hayelom Gebrye is a dedicated researcher and educator currently pursuing a Ph.D. in Computer Science and Technology at the University of Electronic Science and Technology of China (UESTC). He holds a Master of Science in Information Technology from Aksum University and a Bachelor of Science in Information Technology from Hawassa University. With extensive teaching experience at various institutions, including Harambee University and Adama Science and Technology University, he has lectured on topics such as programming, data structures, and emerging technologies. Mr. Gebrye is also actively involved in community training initiatives, providing workshops on digital technologies and effective communication. His research interests include machine learning, deep learning, and network security, with a focus on enhancing IoT security through innovative solutions. Proficient in multiple programming languages and fluent in English, Tigrigna, and Amharic, Mr. Gebrye is committed to advancing knowledge and technology in his field.

Profile:

Education:

Mr. Hayelom Gebrye is currently pursuing a Ph.D. in Computer Science and Technology at the University of Electronic Science and Technology of China (UESTC), a program he commenced in September 2019. He holds a Master of Science degree in Information Technology from Aksum University, which he completed between January 2015 and March 2017. His foundational education includes a Bachelor of Science degree in Information Technology from Hawassa University, where he studied from October 2008 to July 2012. Additionally, he obtained his Secondary School Certificates from Tadagiwa, Ethiopia, between 2004 and 2008, and an Elementary School Certificate from Facha Elementary School from 1996 to 2004. This comprehensive educational journey has equipped Mr. Gebrye with a strong theoretical and practical understanding of information technology and computer science.

Professional Experience:

Mr. Hayelom Gebrye has extensive professional experience in academia and training, reflecting his commitment to education and community development. He served as a lecturer at Harambee University from September 2022 to April 2023, where he taught courses such as Fundamentals of Programming, Introduction to Emerging Technologies, and Research Methods in Computer Science. Additionally, he was a lecturer at Adama Science and Technology University (ASTU) and held part-time lecturer positions at various institutions, including Unity University and Raya University, covering subjects like System Simulation and Modeling. Mr. Gebrye has also contributed to community training programs with LIVE ADDIS, where he provided workshops on digital technology and effective communication for youth. His role as a Data Manager at TZG General Development Research involved leading data collection initiatives and ensuring data quality standards for research projects. This diverse experience underscores his expertise in teaching, training, and data management within the information technology field.

Research Interests:

Mr. Hayelom Gebrye is deeply passionate about advancing knowledge in the fields of machine learning, deep learning, and computer vision. His research interests also encompass network security and intrusion detection, where he seeks to develop innovative solutions to enhance security protocols in modern technological environments. Additionally, he is focused on expert systems and general information technology, aiming to explore the intersections of these areas to improve data analysis and decision-making processes. Mr. Gebrye has actively contributed to research initiatives, including his recent publication on traffic data extraction and labeling for machine learning-based attack detection in IoT networks, which reflects his commitment to addressing critical challenges in cybersecurity and emerging technologies.

Skills:

Mr. Hayelom Gebrye possesses a diverse skill set that underpins his expertise in information technology and computer science. He is proficient in several programming languages, including Python, C++, Java, and PHP, allowing him to develop innovative software solutions and applications. His strong background in database management is demonstrated through his proficiency with SQL Server 2012, as well as his experience in MYSQL server administration. Additionally, Mr. Gebrye has a solid foundation in networking, having managed LAN networks and supervised ICT systems in various roles. His analytical skills are further enhanced by his capabilities in qualitative and quantitative data analysis, visualization, and interpretation using tools such as Python and other software applications. Furthermore, he is skilled in training and mentoring, having conducted numerous workshops and training sessions aimed at empowering youth and enhancing their digital competencies.

Conclusion:

Mr. Hayelom Gebrye’s robust educational background, extensive teaching and training experience, active research contributions, and personal skills position him as an excellent candidate for the Best Researcher Award. His dedication to advancing knowledge in computer science, particularly in machine learning and network security, reflects his potential to make significant impacts in the field.

Publication Top Noted:

Computer vision-based distributed denial of service attack detection for resource-limited devices

  • Authors: H. Gebrye, Y. Wang, F. Li
  • Journal: Computers and Electrical Engineering
  • Volume: 120, Article ID: 109716
  • Year: 2024

Traffic data extraction and labeling for machine learning-based attack detection in IoT networks

  • Authors: H. Gebrye, Y. Wang, F. Li
  • Journal: International Journal of Machine Learning and Cybernetics
  • Volume: 14, Issue 7, Pages 2317-2332
  • Year: 2023
  • Citations: 11

Deep Reinforcement Learning for Computation Offloading and Resource Allocation in Blockchain-Based Multi-UAV-Enabled Mobile Edge Computing

  • Authors: A. Mohammed, H. Nahom, A. Tewodros, Y. Habtamu, G. Hayelom
  • Conference: 2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)
  • Pages: 295-299
  • Year: 2020
  • Citations: 28

S M A K Azad | Internet of Things | Best Researcher Award

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Dr. S M A K Azad | Internet of Things | Best Researcher Award

Professor at SV College of Engineering, India

Dr. S M A K Azad is an accomplished educator and researcher with over 17 years of experience in academia and industry. He holds a Ph.D. from NIT Tiruchirappalli, specializing in Industry 4.0, Industrial IoT, and Industrial Automation and Control. Dr. Azad has a strong foundation in Embedded Systems, Data Science, Artificial Intelligence (AI), and Machine Learning (ML), complemented by certifications from IIT Roorkee and NPTEL. His professional career spans roles in both industry and academia, including positions at VIT-AP University and ABB GISL, where he contributed to industrial automation and control systems. Dr. Azad has published extensively in international journals, with numerous SCIE and Scopus-indexed papers, and holds two published patents. He is also an experienced academic leader, serving as Professor and Dean of Electrical Sciences at SV College of Engineering. His research interests include Cyber-Physical Systems, Data Analytics, Distributed Control Systems, and Industrial Communication Protocols, and he has mentored Ph.D. scholars while actively contributing to advancements in his field.

Profile:

Education:

Dr. S M A K Azad has a distinguished academic background. He completed his Ph.D. from the National Institute of Technology (NIT), Tiruchirappalli, Tamil Nadu, specializing in Industry 4.0, Industrial Internet of Things (IIoT), and Industrial Automation and Control, earning an impressive CGPA of 8.50 in September 2021. Prior to this, he obtained his M.Tech in Embedded Systems (ECE) from the National Institute of Science and Technology (NIST), Berhampur, Odisha, in April 2008, with a CGPA of 8.57. Dr. Azad’s undergraduate studies were completed at K.S.R.M. College of Engineering, Kadapa, Andhra Pradesh, where he earned a B.Tech in Electrical and Electronics Engineering (EEE) in March 2003, with a commendable score of 74.6%. Additionally, Dr. Azad has completed certifications from prestigious institutions like the Indian Institute of Technology (IIT) Roorkee in Artificial Intelligence (AI) and Deep Learning in June 2024, and from NPTEL in Data Analytics with Python and Industry 4.0, earning the Elite+Silver grade in 2024.

Professional Experience:

Dr. S M A K Azad has accumulated 17 years and 8 months of professional experience, with a significant focus on both academia and industry. His academic journey spans 14 years, where he has held prominent roles such as Professor and Dean of Electrical Sciences at SV College of Engineering, Tirupati, and Senior Assistant Professor at VIT-AP University, Amaravati. At VIT-AP, he also served as Assistant Director for the Career Development Center and the Entrepreneurship Cell. Additionally, Dr. Azad spent over five years as an Associate Professor at NIST, Berhampur, where he played a crucial role in managing research initiatives in industrial automation and control systems. Dr. Azad also brings over three years of valuable industry experience, having worked with ABB GISL and Yokogawa India Limited in Bangalore. At ABB GISL, he worked in the Managerial Cadre (INCRC), contributing to R&D in process automation and serving as a subject matter expert for training and product development. At Yokogawa India Limited, he held the position of Assistant Manager, overseeing customer service and handling international assignments such as deputations to Saudi Arabia. This extensive blend of academic and industry expertise enables Dr. Azad to bridge the gap between theoretical knowledge and real-world applications in fields such as industrial automation, embedded systems, and IIoT.

Research Interests:

Dr. S M A K Azad’s research interests encompass a broad range of cutting-edge topics in industrial automation, control systems, and data-driven technologies. His primary areas of focus include Cyber-Physical Systems, Data Analytics, and Distributed Control Systems. He is particularly interested in the integration of advanced technologies like Embedded Systems and Industrial IoT (IIoT) to create smarter and more efficient industrial environments, aligning with the principles of Industry 4.0. Additionally, Dr. Azad has deep expertise in Industrial Automation and Control, specializing in Industrial Communication Protocols and Networked Control Systems. His work also extends to Safety Systems (SIL) and their applications in complex industrial processes. This diverse and evolving portfolio of research highlights his commitment to exploring innovative solutions for real-world challenges in automation and intelligent system integration.

Skills:

Dr. S M A K Azad is skilled in industrial automation and control systems, with expertise in tools such as ABB AC 800M, Yokogawa CENTUM VP, and Siemens S7-300. He is proficient in industrial communication protocols like MODBUS and PROFIBUS, along with software platforms such as MATLAB, LabVIEW, and Power BI. His technical abilities extend to embedded systems, data analysis, and IIoT applications, making him highly versatile in both academic and industrial environments.

Conclusion:

Dr. S M A K Azad’s extensive academic and industrial experience, prolific research contributions, leadership roles, and technical expertise make him a highly suitable candidate for the Research for Best Researcher Award. His work in areas like Industry 4.0, IIoT, and AI aligns with the cutting-edge technologies shaping the future, and his commitment to both academic excellence and industrial innovation positions him as a deserving recipient of the award.

Publication Top Noted:

Fuzzy based controller for lidar sensor of an autonomous vehicle

  • Authors: A.K. Singh, A. Negi, S. Azad, S. Mudali
  • Journal: Energy Procedia
  • Volume: 117, Pages 1160-1164
  • Year: 2017
  • Cited by: 13
  • DOI: 10.1016/j.egypro.2017.05.155

Dynamic network scheduler for customized aperiodic communication in networked control system

  • Authors: S.M. Abdul Kalam Azad, K. Srinivasan
  • Journal: Automatic Control and Computer Sciences
  • Volume: 55, Pages 263-276
  • Year: 2021
  • Cited by: 6
  • DOI: 10.3103/S0146411621050015

Analysis of time delays in scheduled and unscheduled communication used in process automation

  • Authors: S. Azad, K. Srinivasan
  • Journal: Automatika: časopis za automatiku, mjerenje, elektroniku, računarstvo i komunikacije
  • Year: 2020
  • Cited by: 6
  • DOI: 10.1080/00051144.2020.1779777

Bandwidth assessment of scheduled and unscheduled communication in hybrid networked control system

  • Authors: S. Azad, S. Kannan
  • Journal: Cyber-Physical Systems
  • Volume: 8, Issue 4, Pages 321-346
  • Year: 2022
  • Cited by: 5
  • DOI: 10.1080/23335777.2022.2115066

A Case Study on the Multi-Hopping Performance of IoT Network Used for Farm Monitoring

  • Authors: S.M.A.K. Azad, S. Padhy, S. Dash
  • Journal: Automatic Control and Computer Sciences
  • Volume: 57, Issue 1, Pages 70-80
  • Year: 2023
  • Cited by: 4
  • DOI: 10.3103/S0146411623010034

Intelligent IoT-Based Healthcare System Using Blockchain

  • Authors: S. Dash, S. Padhy, S. Azad, M. Nayak
  • Conference: Ambient Intelligence in Health Care: Proceedings of ICAIHC 2022
  • Pages: 305-315
  • Year: 2022
  • Cited by: 4
  • DOI: 10.1007/978-981-19-5984-0_28

A computational scheme for data scheduling in industrial enterprise network using linear mixed model approach

  • Authors: S.M.A.K. Azad, K. Srinivasan
  • Journal: International Journal of Computer Integrated Manufacturing
  • Volume: 37, Issue 5, Pages 572-588
  • Year: 2024
  • Cited by: 3
  • DOI: 10.1080/0951192X.2024.1796735

Markov Chain Modelling of Standby Redundant Networked Control System

  • Authors: A. Raj, S. Azad
  • Conference: 2019 Fifth International Conference on Electrical Energy Systems (ICEES)
  • Year: 2019
  • Cited by: 2
  • DOI: 10.1109/ICEES.2019.8719310

Yuxing Yang | Detection and Prevention | Best Researcher Award

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Dr. Yuxing Yang | Detection and Prevention | Best Researcher Award

Postdoctoral at Xi’an Jiaotong-Liverpool University, China

Dr. Yuxing Yang is a dedicated researcher specializing in abnormal event detection, human action recognition, and multi-feature detection frameworks. He is currently pursuing his PhD in Engineering at Newcastle University, UK, focusing on developing novel frameworks for detecting abnormal events in video. Dr. Yang holds a Master’s degree in Electrical and Electronics Engineering from Newcastle University and a Bachelor’s degree in the same field from the University of Liverpool. His research experience spans signal processing, machine learning, and deep learning, with numerous publications in top-tier journals and conferences. Dr. Yang has also served as a Teaching Assistant and Research Assistant, mentoring students and contributing to key projects in multimodal security and video surveillance. His technical expertise, coupled with his leadership in academic and social initiatives, has earned him several accolades, including recognition for outstanding research presentations.

Profile:

Education:

Dr. Yuxing Yang has an extensive academic background in electrical and electronics engineering. He is currently pursuing a PhD in Engineering at Newcastle University, UK, where his research focuses on developing novel frameworks for multi-feature abnormal event detection in video. His PhD work addresses complex challenges in video anomaly detection, employing advanced signal processing and information fusion techniques under the supervision of Dr. Syed Mohsen Naqvi. Dr. Yang also holds a Master of Electrical and Electronics Engineering degree from Newcastle University, where he worked on a dissertation titled “PHD Filter for Multiple Human Tracking.” During his Master’s program, he studied modules such as Signal Processing, Modulation and Coding, Internet of Things, and Wireless Network Technologies. Dr. Yang earned his Bachelor’s degree in Electrical and Electronics Engineering from the University of Liverpool, where his dissertation explored the use of fluorescence for monitoring water quality. His academic journey has equipped him with deep expertise in embedded systems, digital and wireless communications, and engineering management.

Professional Experience:

Dr. Yuxing Yang has gained valuable professional experience through his research and teaching roles at Newcastle University, UK. From 2018 to 2023, he served as an MSc Research Assistant with the Intelligent Sensing and Communications (ISC) research group. In this role, he guided MSc students on projects related to signal processing, machine learning, and video anomaly detection. He played a crucial role in advising students, refining their research findings, and supporting the presentation of their work. Additionally, Dr. Yang worked as a Research Assistant on the 2021 EPSRC IAA project titled “Multimodal Human Security,” where he contributed to enhancing detection accuracy in security surveillance using multimodal data. His work on this project led to a publication at the IEEE International Conference on Information Fusion. Furthermore, Dr. Yang held a position as a Teaching Assistant and Lab Demonstrator at Newcastle University from 2018 to 2022, where he taught and mentored undergraduate and master’s students, providing instruction on theoretical concepts, lab equipment, and experiment conduction, while also assisting with grading and course evaluations.

Research Interests:

Dr. Yuxing Yang’s research interests lie at the intersection of video anomaly detection and human behavior analysis, with a particular focus on advanced techniques in machine learning and signal processing. His work includes multiple human tracking, image segmentation, human action recognition, and multi-feature abnormal event detection in video. Dr. Yang is passionate about developing novel frameworks that enhance the accuracy and efficiency of video surveillance systems, especially in detecting abnormal events in complex environments. His research contributes to advancements in security systems, where multi-modal data fusion and real-time anomaly detection are critical for improving public safety and monitoring.

Skills:

Dr. Yuxing Yang possesses a diverse skill set, with expertise in programming languages and tools critical to his research in video anomaly detection and signal processing. He is proficient in Python, particularly in machine learning frameworks such as Keras, TensorFlow, and PyTorch. Additionally, Dr. Yang is skilled in MATLAB, which he uses extensively for algorithm development and data analysis. His experience with Latex allows him to efficiently prepare academic papers, while his knowledge of Linux Basics ensures a strong foundation in system operations and scripting. These technical skills enable him to develop and implement advanced algorithms for human-related abnormal event detection and security surveillance.

Conclusion:

Dr. Yuxing Yang’s solid academic background, innovative research in abnormal event detection, and extensive teaching experience, coupled with his leadership and contributions to community awareness, make him a suitable and competitive candidate for the Best Researcher Award. His dedication to advancing knowledge in video surveillance and anomaly detection is evident through his numerous publications, research contributions, and awards.

Publication Top Noted:

Abnormal event detection for video surveillance using an enhanced two-stream fusion method

  • Authors: Y. Yang, Z. Fu, S. M. Naqvi
  • Journal: Neurocomputing
  • Year: 2023
  • Volume: 553, Article 126561
  • Cited by: 15
  • DOI: 10.1016/j.neucom.2023.126561

Enhanced adversarial learning-based video anomaly detection with object confidence and position

  • Authors: Y. Yang, Z. Fu, S. M. Naqvi
  • Conference: 2019 13th International Conference on Signal Processing and Communication
  • Year: 2019
  • Cited by: 14
  • DOI: 10.1109/icspcc46631.2019.8962857

A two-stream information fusion approach to abnormal event detection in video

  • Authors: Y. Yang, Z. Fu, S. M. Naqvi
  • Conference: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing
  • Year: 2022
  • Cited by: 13
  • DOI: 10.1109/icassp43922.2022.9746515

Pose-driven human activity anomaly detection in a CCTV-like environment

  • Authors: Y. Yang, F. Angelini, S. M. Naqvi
  • Journal: IET Image Processing
  • Year: 2023
  • Volume: 17, Issue 3, Pages 674-686
  • Cited by: 10
  • DOI: 10.1049/ipr2.12876

Video anomaly detection for surveillance based on effective frame area

  • Authors: Y. Yang, Y. Xian, Z. Fu, S. M. Naqvi
  • Conference: 2021 IEEE 24th International Conference on Information Fusion (FUSION)
  • Year: 2021
  • Cited by: 8
  • DOI: 10.23919/fusion49465.2021.9626892

Skeleton-based fall events classification with data fusion

  • Authors: L. Xie, Y. Yang, F. Zeyu, S. M. Naqvi
  • Conference: 2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)
  • Year: 2021
  • Cited by: 5
  • DOI: 10.1109/MFI52462.2021.9604344

One-shot medical action recognition with a cross-attention mechanism and dynamic time warping

  • Authors: L. Xie, Y. Yang, Z. Fu, S. M. Naqvi
  • Conference: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing
  • Year: 2023
  • Cited by: 4
  • DOI: 10.1109/icassp49357.2023.10095933

Action-based ADHD diagnosis in video

  • Authors: Y. Li, Y. Yang, S. M. Naqvi
  • Platform: arXiv preprint
  • Year: 2024
  • Cited by: 2
  • DOI: arXiv:2409.02261

Position and Orientation-Aware One-Shot Learning for Medical Action Recognition from Signal Data

  • Authors: L. Xie, Y. Yang, Z. Fu, S. M. Naqvi
  • Platform: arXiv preprint
  • Year: 2023
  • Cited by: 1
  • DOI: arXiv:2309.15635

 

Swati Sharma | Microgrids | Best Researcher Award

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Ms. Swati Sharma | Microgrids | Best Researcher Award

Research Scholar at Jamia Millia Islamia New Delhi, India

Ms. Swati Sharma is a dedicated researcher and academic with over eight years of experience in teaching, research, and industry. She is currently pursuing her Ph.D. in Smart Grids (Electrical Engineering) at Jamia Millia Islamia, New Delhi, with her thesis submission expected in November 2024. Ms. Sharma holds an M.Tech. in Control and Instrumentation Engineering from Delhi Technological University and a B.Tech. in Electrical and Electronics Engineering from Maharshi Dayanand University, Rohtak. Throughout her career, she has gained significant experience, working as a Research Assistant, Assistant Professor, and Guest Faculty. Her research interests include smart grids, renewable energy integration, and power systems optimization, and she has received several accolades, including the Best Research Paper award at Jamia Millia Islamia in 2023. Ms. Sharma has also contributed to various research projects, supervised M.Tech and B.Tech thesis work, and published in international journals and conferences. She is an active member of professional organizations like IEEE and IETE, demonstrating her commitment to advancing knowledge in her field.

Profile:

Education:

Ms. Swati Sharma is currently pursuing her Ph.D. in Smart Grids (Electrical Engineering) at Jamia Millia Islamia, Central University, New Delhi, with her thesis submission expected by November 2024. She holds an M.Tech. in Control and Instrumentation Engineering from Delhi Technological University, New Delhi, where she graduated in 2018 with a CGPA of 7.68. Prior to this, she completed her B.Tech. in Electrical and Electronics Engineering from Maharshi Dayanand University, Rohtak, Haryana, in 2016, securing 81.90%. Ms. Sharma’s early education includes her XII Class from Blue Bells Model School, Gurugram, Haryana, with 76.80% in 2012, and X Class from Air Force School, Gurugram, where she scored an 8.6 CGPA in 2010.

Professional Experience:

Ms. Swati Sharma has amassed over eight years of professional experience across academia, research, and industry. Currently, she serves as a Research Assistant in the Department of Electrical Engineering at Jamia Millia Islamia, New Delhi, a role she has held since October 2021. Prior to this, she worked as an Assistant Professor in the Department of Electrical and Electronics Engineering at Dronacharya College of Engineering, Farrukhnagar, from July 2018 to December 2019. During her M.Tech studies, Ms. Sharma gained teaching experience as a Guest Faculty at Delhi Technological University from December 2016 to June 2018. Additionally, she worked as a Graduate Engineer Trainee in the Research and Development and Administration Department at Sunbeam Auto Pvt. Ltd., Gurugram, from January 2016 to June 2016. Her diverse roles across teaching, research, and industry have contributed to her expertise in smart grids, renewable energy, and power systems.

Research Interests:

Ms. Swati Sharma’s research interests are centered around smart grids, renewable energy systems, and control and instrumentation engineering. Her work focuses on optimizing electric vehicle charging and discharging with sporadic renewable energy sources, as well as exploring demand response mechanisms in user-centric markets integrated with electric vehicles. She has also contributed to research on photovoltaic systems, particularly in grid-tied solar power plants and maximum power point tracking algorithms for efficient energy management. Ms. Sharma is passionate about exploring advanced control techniques, such as adaptive control using algorithms like the Jaya Algorithm, and their applications in real-time digital simulations for sustainable energy solutions.

Skills:

Ms. Swati Sharma possesses a diverse set of technical and personal skills that enhance her research and professional capabilities. Her technical skills include proficiency in MATLAB simulation and programming, basic knowledge of AutoCAD for electrical designing, and expertise in deep learning (DL)-based programming. She is also skilled in C programming, Python, Microsoft Office, RSCAD software-based simulation, and has a foundational understanding of LABVIEW simulation. On a personal level, Ms. Sharma has strong leadership qualities, adaptability to changing work environments, and the ability to manage multiple tasks efficiently under pressure. These skills have been integral to her successful contributions in teaching, research, and industrial projects.

Conclusion:

Ms. Swati Sharma’s comprehensive academic, research, and professional profile, combined with her technical expertise and numerous accolades, makes her a suitable candidate for the Research for Best Researcher Award. Her work in smart grids, renewable energy, and electric vehicle technologies positions her at the forefront of cutting-edge research in electrical engineering.

Publication Top Noted:

Demand Response Mechanism in User-Centric Markets Integrated with Electric Vehicles

Optimized Electric vehicle Charging and discharging with sporadic Renewable energy source

  • Authors: S. Sharma, I. Ali
  • Conference: 2023 International Conference on Power, Instrumentation, Energy and Control
  • Year: 2023
  • Cited by: 3
  • DOI: 10.1109/PIECO58666.2023.10084151

Design and Implementation of Energy Efficiency Augmentation Using Renewable Energy Source for Small-Scaled Residential Micro-grid

  • Authors: I. Ali, S. Sharma
  • Book Chapter: Advances in Energy Technology: Select Proceedings of EMSME 2020
  • Year: 2022
  • Cited by: 1
  • Pages: 693-702
  • DOI: 10.1007/978-981-16-0058-9_69

Dynamic pricing strategy for efficient electric vehicle charging and discharging in microgrids using multi-objective jaya algorithm

  • Authors: S. Sharma, I. Ali
  • Journal: Engineering Research Express
  • Year: 2024
  • Volume: 6, Issue 3, Article 035315
  • DOI: 10.1088/2631-8695/ac9bf7

Efficient Energy Management and Cost Optimization Using Grey Wolf Optimization for EV Charging and Discharging in Microgrid

  • Authors: S. Sharma, I. Ali
  • Platform: SSRN preprint
  • Year: N/A
  • DOI: SSRN:4684289

Rouhollah Ahmadian | Internet of Things | Best Researcher Award

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Dr. Rouhollah Ahmadian | Internet of Things | Best Researcher Award

PhD Candidate at amirkabir university of technology, Iran

Dr. Rouhollah Ahmadian is a dedicated researcher and Ph.D. candidate in Computer Science at Amirkabir University of Technology in Tehran, Iran. He has demonstrated exceptional academic performance, achieving a perfect GPA of 4.0/4.0 in his doctoral studies and ranking in the top 1% of his class during both his Bachelor’s and Master’s programs. His research interests lie in artificial intelligence, data science, and machine learning, with notable contributions to driver identification technologies using advanced neural networks and data analytics. Dr. Ahmadian has extensive practical experience as a Data Scientist at NORC Amirkabir University, where he focuses on innovative projects such as License Plate Recognition. He has also worked as a freelance Android Developer, creating various applications that leverage IoT and automation technologies. In addition to his research and professional work, he has served as a Teaching Assistant at his university, enriching the academic experience for students in courses like Computational Data Mining and Artificial Intelligence. His commitment to advancing knowledge in computer science, combined with his strong technical skills, positions him as a prominent figure in his field.

Profile:

Education:

Dr. Rouhollah Ahmadian is currently pursuing a Ph.D. in Computer Science at Amirkabir University of Technology in Tehran, Iran, where he has achieved an impressive overall GPA of 4.0/4.0 (18.49/20). His academic journey began with a Bachelor of Science in Computer Science from the University of Tabriz, graduating in July 2015 with a GPA of 3.3/4.0 (16.77/20), where he ranked in the top 1% of his cohort. He then continued his studies at Amirkabir University, obtaining a Master of Science in Computer Science in October 2020, with a GPA of 3.61/4.0 (17.48/20) and ranking in the top 1% of his master’s program. His selected coursework throughout his education has included advanced topics such as Data Analytics, Data Mining, Machine Learning, Deep Learning, Computational Data Mining, and Advanced Nonlinear Optimization, highlighting his strong foundation in computer science and artificial intelligence.

Professional Experience:

Dr. Rouhollah Ahmadian has gained substantial professional experience in the field of computer science, particularly in data science and software development. Currently, he serves as a Data Scientist at the NORC Amirkabir University of Technology, where he has been involved in innovative projects such as License Plate Recognition, focusing on the application of advanced algorithms and data analytics. In addition to his academic role, Dr. Ahmadian has worked extensively as a freelance Android Developer since 2013, creating a diverse array of applications that include municipal automation tools, real estate management platforms, and messaging applications. His tenure at Noor Islamic Sciences Research Center and Al-Zahra Society further honed his skills in mobile app development, where he contributed to projects aimed at managing educational and religious resources. Additionally, he co-founded Mojafzar Company, serving as both a shareholder and developer, which underscores his entrepreneurial spirit and ability to lead technical initiatives. This blend of academic and practical experience equips Dr. Ahmadian with a comprehensive skill set that he applies to his research and development efforts.

Research Interests:

Dr. Rouhollah Ahmadian’s research interests lie at the intersection of artificial intelligence, data science, and machine learning. He is particularly focused on the development of advanced algorithms for data analytics, which includes exploring techniques such as deep learning and neural networks to solve complex problems in various domains. His work has prominently featured driver identification systems using sensor data, highlighting his commitment to leveraging technology for practical applications. Additionally, Dr. Ahmadian is interested in the integration of data mining techniques and computational models to enhance data processing and interpretation, especially in spatiotemporal data analysis. He is also passionate about investigating innovative approaches to anomaly detection and object recognition, utilizing frameworks like TensorFlow and PyTorch to develop robust and scalable solutions. Through his research, Dr. Ahmadian aims to contribute to the advancement of smart technologies that can improve decision-making processes and enhance user experiences across multiple industries.

Skills:

Dr. Rouhollah Ahmadian possesses a diverse and robust skill set that spans multiple domains within computer science and software development. He is proficient in several programming languages, including Python, C, Java, and Kotlin, which enables him to tackle various software engineering challenges effectively. His expertise extends to artificial intelligence and machine learning, where he has hands-on experience with frameworks such as TensorFlow, PyTorch, and Scikit-learn, particularly in areas like deep learning, neural networks, and data mining. Dr. Ahmadian is well-versed in modern software development methodologies, including Agile and Scrum, which he applies to enhance project management and collaboration. He is adept in database management, utilizing systems like MySQL and Cassandra to design and implement efficient data storage solutions. Additionally, Dr. Ahmadian has developed skills in version control with Git and GitHub, ensuring seamless code collaboration and tracking. His capabilities also include advanced data processing techniques, such as anomaly detection and natural language processing (NLP), making him a versatile asset in any technology-driven environment.

Conclusion:

Dr. Rouhollah Ahmadian exemplifies the qualities of a top researcher through his outstanding academic record, diverse professional experience, and impactful research contributions. His commitment to advancing the field of computer science, particularly in artificial intelligence and data analytics, makes him a deserving candidate for the Best Researcher Award.

Publication Top Noted:

Discrete wavelet transform for generative adversarial network to identify drivers using gyroscope and accelerometer sensors

  • Authors: R. Ahmadian, M. Ghatee, J. Wahlström
  • Journal: IEEE Sensors Journal
  • Year: 2022
  • Cited by: 12
  • Volume: 22, Issue 7, Pages 6879-6886
  • DOI: 10.1109/JSEN.2022.3156679

Driver Identification by an Ensemble of CNNs Obtained from Majority-Voting Model Selection

  • Authors: R. Ahmadian, M. Ghatee, J. Wahlström
  • Conference: International Conference on Artificial Intelligence and Smart Vehicles
  • Year: 2023
  • Cited by: 2
  • Pages: 120-136
  • DOI: N/A (check conference proceedings)

Superior scoring rules for probabilistic evaluation of single-label multi-class classification tasks

  • Authors: R. Ahmadian, M. Ghatee, J. Wahlström
  • Platform: arXiv preprint
  • Year: 2024
  • Cited by: 1
  • DOI: arXiv:2407.17697

Training of Neural Networks to Classify Spatiotemporal Data by Probabilistic Fusion on Hopping Windows: Theory and Experiments

  • Authors: R. Ahmadian, M. Ghatee, J. Wahlström
  • Platform: SSRN
  • Year: N/A
  • Cited by: 1
  • DOI: SSRN:4616995

Uncertainty Quantification to Enhance Probabilistic Fusion Based User Identification Using Smartphones

  • Authors: R. Ahmadian, M. Ghatee, J. Wahlström, H. Zare
  • Journal: IEEE Internet of Things Journal
  • Year: 2024
  • Cited by: N/A
  • DOI: N/A (article in press)

Calibrated SVM for Probabilistic Classification of In-Vehicle Voices into Vehicle Commands via Voice-to-Text LLM Transformation

  • Authors: M. Moeini, R. Ahmadian, M. Ghatee
  • Conference: 2024 8th International Conference on Smart Cities, Internet of Things and Applications
  • Year: 2024
  • Cited by: N/A
  • DOI: N/A (check conference proceedings)

Driver Identification by Neural Network on Extracted Statistical Features from Smartphone Data

  • Authors: R. Ahmadian, M. Ghatee
  • Platform: arXiv preprint
  • Year: 2020
  • Cited by: N/A
  • DOI: arXiv:2002.00764

Manish Kumar | Digital Twin | Excellence in Privacy-Preserving Technologies

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Assist Prof Dr. Manish Kumar | Digital Twin | Excellence in Privacy-Preserving Technologies

Research Professor at Seoul National University of Science and Technology, South Korea

Assist Prof. Dr. Manish Kumar is a distinguished academic and researcher specializing in neural networks, IoT security, and signal processing. He earned his Ph.D. in Electrical and Electronics Engineering from Birla Institute of Technology, Mesra, Ranchi, where he developed adaptive filters for denoising medical images using nature-inspired neural network models. Dr. Kumar has extensive teaching and research experience, having served as a Research Professor at Seoul National University of Science & Technology and an Assistant Professor at Mody University of Science & Technology in Rajasthan, India. His work focuses on IoT security, machine learning, and biomedical engineering, with numerous publications in high-impact journals. In addition, Dr. Kumar holds a granted patent for “Vitro: Virtual Trial Room” and has contributed to various international projects. His expertise spans across coding, sensor systems, signal acquisition, and advanced neural network applications.

Profile:

Education:

Assist Prof. Dr. Manish Kumar holds a Ph.D. in Electrical and Electronics Engineering from Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India. He completed his doctoral research between 2014 and 2018, focusing on the development of adaptive filters based on nature-inspired neural network models for denoising medical images, earning a CGPA of 7.25. His Ph.D. was officially awarded on October 11, 2018. Additionally, he has undergone various professional training and certifications in fields such as TensorFlow, machine learning, and deep learning, further complementing his academic expertise.

Professional Experience:

Assist Prof. Dr. Manish Kumar has accumulated extensive professional experience in academia and research. He is currently a Research Professor in the Department of Computer Science & Engineering at Seoul National University of Science & Technology, a role he has held since November 2022. In this position, he conducts research on IoT Security and High-Performance Computing (HPC) performance. From June 2019 to November 2022, he served as an Assistant Professor in the Department of Electronics & Biomedical Engineering at Mody University of Science & Technology, Rajasthan, where he taught various subjects and pursued research in the fields of artificial intelligence and biomedical engineering. Prior to this, Dr. Kumar worked at the Indian Institute of Technology, Patna, where he conducted research on machine learning-based fault prediction. His efforts were supported by a fellowship from the Department of Science & Technology, India. During his Ph.D. at Birla Institute of Technology, Mesra, Ranchi (2014-2018), he focused on the development of adaptive filters based on neural networks and nature-inspired techniques for medical image denoising, leading to multiple published research articles. Additionally, he has experience teaching as a guest faculty at the University Polytechnic, BIT Mesra, and served as an intern at the Central Scientific Instrument Organization (CSIR-CSIO), where he worked on the design of sensors for infant condition monitoring systems. Dr. Kumar’s professional journey reflects his expertise in IoT security, machine learning, artificial intelligence, and signal processing, along with a strong foundation in teaching and technical research.

Research Interests:

Assist Prof. Dr. Manish Kumar’s research interests lie at the intersection of advanced technologies and healthcare applications. His primary focus is on Internet of Things (IoT) security, particularly in developing robust mechanisms to safeguard connected devices in healthcare systems. He is also deeply engaged in high-performance computing (HPC) performance optimization, which is crucial for processing large datasets generated in medical imaging and IoT environments. Dr. Kumar is keen on exploring machine learning and artificial intelligence techniques, especially in predictive analytics for fault prediction and data-driven decision-making in medical applications. His work involves the development of adaptive filters based on nature-inspired neural network models for denoising medical images, contributing to enhanced diagnostic accuracy and patient outcomes. Additionally, Dr. Kumar is interested in sensor systems and signal acquisition, focusing on innovative solutions for real-time monitoring and analysis in biomedical contexts. Through his research, he aims to address critical challenges in healthcare technology, emphasizing the importance of privacy-preserving methods in data management and processing.

Skills:

Assist Prof. Dr. Manish Kumar possesses a diverse skill set that encompasses both technical and academic expertise. His strong foundation in research is complemented by his proficiency in coding and technical writing, which allows him to effectively communicate complex ideas and findings. Dr. Kumar is well-versed in a variety of programming languages, including C and C++, and has hands-on experience with several machine learning frameworks such as TensorFlow, PyTorch, and Keras. His technical skills extend to software and tools like LabView, OpenCV, Google Colab, Scikit-learn, and Latex, enabling him to implement and document his research effectively. In addition to his coding skills, Dr. Kumar has a robust understanding of IoT security, sensor systems, and signal acquisition and processing. He is also experienced in the field of accreditation work, specifically with the Institution of Engineering and Technology (IET). His teaching experience includes subjects like Digital Signal Processing, Image Processing, and Artificial Neural Networks, showcasing his ability to convey complex concepts to students at various academic levels. Overall, Dr. Kumar’s blend of technical acumen, research capabilities, and teaching proficiency positions him as a valuable asset in the field of computer science and engineering.

Conclusion:

With his deep technical expertise in neural networks, IoT security, and machine learning, combined with his prolific research output and practical experience in securing sensitive data, Dr. Manish Kumar is well-positioned to excel in research for privacy-preserving technologies. His innovative work and forward-thinking approach align perfectly with the needs of this cutting-edge field.

Publication Top Noted:

Comparative Analysis of Classification Methods with PCA and LDA for Diabetes

  • Authors: V.K.D. Choubey, M. Kumar, V. Shukla, S. Tripathi
  • Journal: Current Diabetes Review
  • Year: 2020
  • Citations: 95

Cat swarm optimization based functional link artificial neural network filter for Gaussian noise removal from computed tomography images

  • Authors: M. Kumar, S.K. Mishra, S.S. Sahu
  • Journal: Applied Computational Intelligence and Soft Computing
  • Year: 2016
  • Article ID: 6304915
  • Citations: 26

Functional Link Convolutional Neural Network for the Classification of Diabetes Mellitus

  • Authors: S.K. Jangir, M. Kumar, D.K. Choubey, M. Verma
  • Journal: International Journal of Numerical Methods in Biomedical Engineering
  • Year: 2021
  • Citations: 25

A comprehensive review on nature inspired neural network based adaptive filter for eliminating noise in medical images

  • Authors: M. Kumar, S.K. Mishra
  • Journal: Current Medical Imaging
  • Year: 2020
  • Volume: 16(4)
  • Pages: 278-287
  • Citations: 17

Teaching learning based optimization-functional link artificial neural network filter for mixed noise reduction from magnetic resonance image

  • Authors: M. Kumar, S.K. Mishra
  • Journal: Bio-Medical Materials and Engineering
  • Year: 2017
  • Volume: 28(6)
  • Pages: 643-654
  • Citations: 17

GRU-based Digital Twin Framework for Data Allocation and Storage in IoT-enabled Smart Home Networks

  • Authors: S.K. Singh, M. Kumar, S. Tanwar, J.H. Park
  • Journal: Future Generation Computer Systems
  • Year: 2023
  • Citations: 16

Feature importance score-based functional link artificial neural networks for breast cancer classification

  • Authors: S. Singh, S.K. Jangir, M. Kumar, M. Verma, S. Kumar, T.S. Walia, S.M.M. Kamal
  • Journal: BioMed Research International
  • Year: 2022
  • Citations: 13

Jaya based functional link multilayer perceptron adaptive filter for Poisson noise suppression from X-ray images

  • Authors: M. Kumar, S.K. Mishra
  • Journal: Multimedia Tools and Applications
  • Year: 2018
  • Volume: 77(18)
  • Pages: 24405-24425
  • Citations: 13

Jaya-FLANN based adaptive filter for mixed noise suppression from ultrasound images

  • Authors: Manish Kumar, Sudhansu Kumar Mishra
  • Journal: Biomedical Research
  • Year: 2017
  • Volume: 28(9)
  • Pages: 4159-4164
  • Citations: 13

 

Naeem Ahmed | Machine Learning | Best Innovation Award

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Mr. Naeem Ahmed | Machine Learning | Best Innovation Award

Phd. Scholor at Nanjing University of Information Science and Technology, China

Summary:

Mr. Naeem Ahmed is a dedicated academic currently pursuing a PhD in Computer Science at NUIST, China, following his completion of a Master’s degree from the University of Engineering and Technology, Taxila, Pakistan, where he focused on sentiment analysis in Urdu using supervised machine learning. He has extensive teaching experience as a lecturer in various institutions, including the Government Postgraduate College Haripur and Abbottabad University of Science and Technology, covering topics such as artificial intelligence, natural language processing, and mobile app development. Mr. Ahmed has contributed to significant research publications, particularly in the areas of sentiment analysis and medical imaging, and has developed various real-world projects leveraging machine learning and deep learning technologies. His expertise encompasses a wide range of skills, including machine learning, computer vision, and web development. Beyond his academic pursuits, he enjoys traveling, photography, and keeping up with advancements in technology and automation.

Profile:

Education:

Mr. Naeem Ahmed is currently pursuing a Doctor of Philosophy (PhD) in Computer Science at NUIST, China, which he began in September 2024. He completed his Master of Science in Computer Science from the Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan, in October 2022. His master’s research focused on “Sentiment Analysis of Urdu Language using Supervised Machine Learning.” Prior to this, he earned a Bachelor of Science in Computer Science from the Department of Information Technology, University of Haripur, Pakistan, in August 2019, laying a strong foundation in the field of computer science.

Professional Experience:

Mr. Naeem Ahmed has built a solid professional foundation in academia, serving as a lecturer in the Department of Computer Science at various institutions in Pakistan. Currently, at the Government Postgraduate College Haripur, he instructs courses in artificial intelligence, operating systems, and data structures, where he emphasizes the importance of aligning course materials with industry trends and fostering student engagement through interactive projects. His previous role at Abbottabad University of Science and Technology involved teaching mobile app development and natural language processing, where he initiated projects that enhanced real-world problem-solving skills among students. Mr. Ahmed also held teaching positions at Government Postgraduate College Khalabat Township and the University of Haripur, covering subjects such as assembly language, software development, and information security. In addition to his teaching roles, he worked as a research assistant, where he instructed students in Python development and machine learning, and as a C#/.NET developer, where he developed applications to improve retail business operations. His diverse experience in both teaching and practical application in the technology sector underscores his commitment to education and innovation in computer science.

Research Interests:

Mr. Naeem Ahmed’s research interests are centered around the fields of machine learning, natural language processing, and computer vision. His work focuses on the development and application of innovative algorithms and models for sentiment analysis, particularly in low-resource languages, as evidenced by his publication on a novel approach for Urdu sentiment analysis using deep learning models. He is also engaged in exploring the intersection of technology and healthcare, demonstrated through his research on knee osteoarthritis detection and classification using transfer learning, as well as brain tumor detection using deep learning techniques. Additionally, Mr. Ahmed is interested in addressing real-world challenges through interdisciplinary projects, such as developing systems for emotion detection and COVID-19 detection from medical imaging. His commitment to advancing the fields of artificial intelligence and data analysis is reflected in his hands-on project experience, including applications in medical imaging, mobile app development, and deep fake detection. Through his research endeavors, Mr. Ahmed aims to contribute to the ongoing advancements in technology and its practical applications in various domains.

Skills:

Mr. Naeem Ahmed possesses a diverse skill set that encompasses various areas of computer science, particularly in machine learning, deep learning, and natural language processing. His expertise in machine learning algorithms and model development allows him to tackle complex problems, including sentiment analysis, emotion recognition, and medical imaging applications. Proficient in programming languages such as Python, R, C++, and C#, he effectively utilizes frameworks and libraries like TensorFlow, PyTorch, and scikit-learn to build and deploy sophisticated models. Mr. Ahmed also has substantial experience in computer vision and image processing, which enables him to develop applications for tasks such as brain tumor detection and fall detection for elderly individuals. His background in web and desktop application development further enhances his ability to create user-friendly solutions. Additionally, Mr. Ahmed’s skills in data preprocessing, exploratory data analysis, and cloud services empower him to analyze and interpret data efficiently, ensuring that his projects are aligned with industry trends and real-world needs. Through continuous learning and hands-on experience, Mr. Ahmed is well-equipped to contribute to the evolving landscape of technology and innovation.

Concution:

Naeem Ahmed’s strong academic credentials, innovative research in machine learning and AI, and practical applications in fields like healthcare and automation make him a highly suitable candidate for the Research for Best Innovation Award. His contributions to AI research and education reflect his dedication to advancing the field.

Publication Top Noted:

Machine learning techniques for spam detection in email and IoT platforms: analysis and research challenges

  • Authors: N. Ahmed, R. Amin, H. Aldabbas, D. Koundal, B. Alouffi, T. Shah
  • Journal: Security and Communication Networks
  • Year: 2022
  • Volume: 2022
  • Article ID: 1862888
  • Citations: 103

Trust management technique using blockchain in smart building

  • Authors: M. Saeed, R. Amin, M. Aftab, N. Ahmed
  • Journal: Engineering Proceedings
  • Year: 2022
  • Volume: 20(1)
  • Article: 24
  • Citations: 6

Sentiment analysis for covid-19 vaccine popularity

  • Authors: M. Saeed, N. Ahmed, A. Mehmood, M. Aftab, R. Amin, S. Kamal
  • Journal: KSII Transactions on Internet and Information Systems (TIIS)
  • Year: 2023
  • Volume: 17(5)
  • Pages: 1377-1393
  • Citations: 3

Intrusion detection systems for software-defined networks: a comprehensive study on machine learning-based techniques

  • Authors: Z. Mustafa, R. Amin, H. Aldabbas, N. Ahmed
  • Journal: Cluster Computing
  • Year: 2024
  • Pages: 1-27
  • Citations: 2

Urdu Sentiment Analysis Using Deep Attention-Based Technique

  • Authors: N. Ahmed, R. Amin, H. Ayub, M.M. Iqbal, M. Saeed, M. Hussain
  • Journal: Foundation University Journal of Engineering and Applied Sciences
  • Year: 2022
  • Citations: 1

Detection of Face Emotion and Music Recommendation System using Machine Learning

  • Authors: D. Ali, M.T. Huque, J.J. Godhuli, N. Ahmed
  • Journal: International Journal of Research and Innovation in Applied Science
  • Year: 2022
  • Volume: 7(11)
  • Citations: 1

DEVELOPMENT OF A SYSTEM FOR FIRE DETECTION AND ALARM USING MACHINE LEARNING AND COMPUTER VISION

  • Authors: D. Ali, J.J. Godhuli, B. Khan, M.T. Huque, N. Ahmed
  • Citations: 1

Jiaying Wu | Sharding Blockchain | Best Researcher Award

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Ms. Jiaying Wu | Sharding Blockchain | Best Researcher Award

Master’s Degree at Yunnan Normal University, China

Summary:

Ms. Jiaying Wu is a dedicated researcher currently pursuing a Master’s Degree in Computer Science at Yunnan Normal University, maintaining a GPA of 3.89/4.0. She holds a Bachelor’s Degree in Software Engineering from Hunan University of Humanities, Science and Technology, where she graduated with a GPA of 3.78/4.0. Her research focuses on blockchain scalability, sharding techniques, and cross-shard transaction security mechanisms. Ms. Wu has published several papers in renowned journals, contributed to key blockchain projects, and earned numerous academic and competition awards, showcasing her expertise and commitment to technological innovation.

Profile:

Education:

Ms. Jiaying Wu is currently pursuing a Master’s Degree in Computer Science at Yunnan Normal University, where she maintains an outstanding GPA of 3.89/4.0, having started her program in August 2022. Prior to this, she completed her Bachelor’s Degree in Software Engineering at Hunan University of Humanities, Science and Technology from September 2018 to June 2022, graduating with a GPA of 3.78/4.0. Her solid academic foundation is complemented by her focus on advanced topics such as blockchain scalability, sharding techniques, and cross-shard transaction security mechanisms.

Professional Experience:

Ms. Jiaying Wu has gained significant professional experience through her research work in blockchain and Internet of Things (IoT) security. Since 2022, she has focused on addressing performance bottlenecks in traditional blockchains within IoT scenarios by developing a high-performance dynamic sharding model. This model enhances blockchain scalability and cross-domain data access. From August 2022 to May 2024, she designed an attribute-based access control model aimed at improving flexibility in data decryption and public search functionalities. Additionally, she has contributed to major scientific projects in Yunnan Province and played a key role in establishing the Yunnan Provincial Key Laboratory of Blockchain and IoT Security.

Research Interests:

Ms. Jiaying Wu’s research interests lie in the areas of blockchain technology and its scalability, with a particular focus on sharding techniques and cross-shard transaction security mechanisms. She is also deeply interested in optimizing blockchain performance for Internet of Things (IoT) applications, working on solutions to improve scalability and efficiency in edge computing scenarios. Her work explores dynamic sharding models, secure cross-domain access, and blockchain-based access control systems, aiming to enhance both security and flexibility in decentralized networks. These research interests highlight her commitment to advancing blockchain technology in real-world applications.

Skills:

Ms. Jiaying Wu possesses a diverse set of professional skills that are highly relevant to her research in blockchain and computer science. She is proficient in programming languages such as C, Python, and Go, and has extensive experience with blockchain platforms like Hyperledger Fabric and Ethereum. Ms. Wu is skilled in writing blockchain smart contracts using Go and Solidity, allowing her to implement complex functionalities within decentralized systems. Additionally, she has a strong background in blockchain project development, particularly in system design and performance optimization. Her technical expertise extends to network technology, as evidenced by her Computer Level Certificate – Level 3 Network Technology.

Concution:

Considering Ms. Jiaying Wu’s academic performance, groundbreaking research contributions, and numerous awards, she is a highly deserving candidate for the Research for Best Researcher Award. Her work in blockchain scalability and IoT security, coupled with her technical expertise, places her at the forefront of innovation in her field.

Publication Top Noted:

A sharding blockchain protocol for enhanced scalability and performance optimization through account transaction reconfiguration

  • Authors: J. Wu, L. Yuan, T. Xie, H. Dai
  • Journal: Journal of King Saud University – Computer and Information Sciences
  • Year: 2024
  • Volume: 36(8)
  • Article: 102184
  • Status: In Press

Ciphertext Fuzzy Retrieval Mechanism with Bidirectional Verification and Privacy Protection

  • Authors: T. Xie, L. Yuan, Q. Zhang, J. Wu, F. Ren
  • Journal: IEEE Internet of Things Journal
  • Year: 2024
  • Status: In Press

Bogdan-Constantin Neagu | Power Systems | Best Researcher Award

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Assoc Prof Dr. Bogdan-Constantin Neagu | Power Systems | Best Researcher Award

Director of CEREM Department at Gheorghe Asachi Technical University of Iasi, Romania

Summary:

Assoc. Prof. Dr. Bogdan-Constantin Neagu is an accomplished academic and researcher in the field of electrical engineering. Currently an Associate Professor at “Gheorghe Asachi” Technical University of Iasi, Romania, he specializes in power transmission and distribution, energy systems planning, and smart grid technologies. Dr. Neagu holds a PhD in Electrical Engineering from the same university, where his research focused on optimizing electric energy distribution systems. With extensive teaching experience since 2009, he has guided numerous students through courses, dissertations, and research projects. His work includes contributions to academic research through grants, contracts, and publications, further establishing his expertise in energy systems.

Profile:

Education:

Assoc. Prof. Dr. Bogdan-Constantin Neagu has a strong academic foundation in electrical engineering. He earned his PhD in Electrical Engineering from “Gheorghe Asachi” Technical University of Iasi, Romania, with a thesis focused on optimizing the structure and steady-state of electric energy repartition and distribution systems. Dr. Neagu also holds a Master of Science in Power System Management from the same institution, where he explored power flow optimization in power distribution systems. His academic journey began with a Bachelor’s degree in Power Engineering from “Gheorghe Asachi” Technical University, where he gained in-depth knowledge of power distribution network analysis. His education has equipped him with advanced expertise in transmission, distribution, and optimization of electrical systems.

Professional Experience:

Assoc. Prof. Dr. Bogdan-Constantin Neagu has a distinguished professional background in electrical engineering, with over a decade of academic and research experience. He began his career as a University Tutor at the “Gheorghe Asachi” Technical University of Iasi, Romania, in 2009, where he contributed to teaching and research in power distribution and energy systems. He advanced to Assistant Professor in 2011 and later to Senior Lecturer in 2014, focusing on courses such as Transmission and Distribution of Electric Energy and Distribution Systems Planning Strategy. Since 2022, he has been serving as an Associate Professor, continuing to teach, supervise student research, coordinate theses, and contribute to academic committees. Dr. Neagu’s professional experience is marked by significant involvement in academic research, supported by grants, contracts, and publications, as well as his active participation in the academic and research community through student mentorship and innovative research in energy systems.

Research Interests:

Assoc. Prof. Dr. Bogdan-Constantin Neagu’s research interests are focused on optimizing electrical energy systems, with particular attention to the transmission and distribution of electric energy. His research has explored areas such as smart metering implementation, the optimization of power distribution networks, and the steady-state analysis of energy systems. He has contributed to developing innovative strategies for the integration of real-time data from SCADA systems and smart metering into the optimization of distribution network configurations. Additionally, Dr. Neagu is involved in research related to energy market policies, protection and automation systems, and the monitoring and diagnostics of electrical equipment. His work bridges theoretical advancements and practical applications, aiming to enhance the efficiency and reliability of power systems.

Skills:

Assoc. Prof. Dr. Bogdan-Constantin Neagu possesses a wide range of skills that contribute to his expertise in electrical engineering and academia. His technical skills include advanced proficiency in analyzing and designing electric energy transmission and distribution systems, as well as the steady-state optimization of power networks. He has extensive experience with specialized software like DIGSilent Power Factory, Neplan, and EDSA, and has developed his own software for power system analysis. In addition, he is skilled in programming languages such as C++ and Matlab. Dr. Neagu’s organizational and managerial abilities are reflected in his capacity for innovation, time management, multitasking, and budget management. He demonstrates strong leadership in team coordination and critical thinking. His communication skills are exemplary, enabling effective teaching, research collaboration, and student mentorship. Furthermore, Dr. Neagu is proficient in English and French, and he possesses high adaptability to new environments and technologies, critical for his work in research contracts and scientific article reviews for international ISI journals.

Concution:

Assoc. Prof. Dr. Bogdan-Constantin Neagu’s extensive teaching experience, strong educational background, significant research contributions, and exceptional communication and organizational skills make him a highly suitable candidate for the Best Researcher Award. His commitment to advancing electrical engineering through education and research positions him as an exemplary figure in the academic community.

Publication Top Noted:

Face spoofing, age, gender and facial expression recognition using advance neural network architecture-based biometric system

  • Authors: S. Kumar, S. Rani, A. Jain, C. Verma, M.S. Raboaca, Z. Illés, B.C. Neagu
  • Journal: Sensors
  • Year: 2022
  • Volume: 22(14)
  • Article: 5160
  • Citations: 83

Phase load balancing in low voltage distribution networks using metaheuristic algorithms

  • Authors: O. Ivanov, B.C. Neagu, M. Gavrilas, G. Grigoras, C.V. Sfintes
  • Conference: 2019 International Conference on Electromechanical and Energy Systems (SIELMEN)
  • Year: 2019
  • Pages: 1-6
  • Citations: 34

Optimal phase load balancing in low voltage distribution networks using a smart meter data-based algorithm

  • Authors: G. Grigoras, B.C. Neagu, M. Gavrilas, I. Tristiu, C. Bulac
  • Journal: Mathematics
  • Year: 2020
  • Volume: 8(4)
  • Article: 549
  • Citations: 32

A New Vision on the Prosumers Energy Surplus Trading Considering Smart Peer-to-Peer Contracts

  • Authors: B.C. Neagu, O. Ivanov, G. Grigoras, M. Gavrilas
  • Journal: Mathematics
  • Year: 2020
  • Volume: 8(2)
  • Article: 235
  • Citations: 32

Optimized sizing of energy management system for off-grid hybrid solar/wind/battery/biogasifier/diesel microgrid system

  • Authors: A.M. Jasim, B.H. Jasim, F.C. Baiceanu, B.C. Neagu
  • Journal: Mathematics
  • Year: 2023
  • Volume: 11(5)
  • Article: 1248
  • Citations: 31

Smart Meter Data-based three-stage algorithm to calculate power and energy losses in low voltage distribution networks

  • Authors: G. Grigoras, B.C. Neagu
  • Journal: Energies
  • Year: 2019
  • Volume: 12(15)
  • Article: 3008
  • Citations: 31

An efficient peer-to-peer based blockchain approach for prosumers energy trading in microgrids

  • Authors: B.C. Neagu, G. Grigoras, O. Ivanov
  • Conference: 2019 8th International Conference on Modern Power Systems (MPS)
  • Year: 2019
  • Pages: 1-4
  • Citations: 31

Efficient optimization algorithm-based demand-side management program for smart grid residential load

  • Authors: A.M. Jasim, B.H. Jasim, B.C. Neagu, B.N. Alhasnawi
  • Journal: Axioms
  • Year: 2022
  • Volume: 12(1)
  • Article: 33
  • Citations: 28

GeFL: Gradient Encryption-Aided Privacy Preserved Federated Learning for Autonomous Vehicles

  • Authors: R. Parekh, N. Patel, R. Gupta, N.K. Jadav, S. Tanwar, A. Alharbi, A. Tolba, B.C. Neagu
  • Journal: IEEE Access
  • Year: 2023
  • Volume: 11
  • Pages: 1825-1839
  • Citations: 26