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

Munirah Sarhan AlQahtani | Cybersecurity | Women Researcher Award

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Dr. Munirah Sarhan AlQahtani | Cybersecurity | Women Researcher Award

Associate professor at Imam Mohammed bin Saud university, Saudi Arabia

Summary:

Dr. Munirah Sarhan AlQahtani is an accomplished academic and researcher specializing in business management and human resource management. She earned her PhD in Business Management from Aston University, UK, and holds a Master of Science in Human Resource Management from the University of Stirling, UK. In addition to her academic qualifications, she is a Certified Management Consultant (CMC) and has a Postgraduate Certificate in Teaching and Learning in Higher Education. Currently an Assistant Professor at Imam Mohammed bin Saud University, Dr. AlQahtani is deeply engaged in research, teaching, and e-learning supervision. She is a member of various professional bodies, including the Academy of Management and the Higher Education Academy (UK), and is dedicated to advancing the fields of management science and organizational psychology through her research and teaching.

Profile:

Education:

Dr. Munirah Sarhan AlQahtani has a strong academic foundation in business and management. She began her higher education journey at the University of Stirling, UK, where she completed a Pre-Master in Business and Management (Postgraduate Diploma) in 2010, credited with 144 SCQF points. She then pursued a Master of Science in Human Resource Management from the same university in 2011, earning 180 SCQF points. Committed to furthering her academic expertise, Dr. AlQahtani completed her PhD in Business Management from Aston University, UK, in 2017. In addition to her degrees, she has obtained a Postgraduate Certificate (PGCert) in Teaching and Learning in Higher Education and a Certified Management Consultant Award (CMC) from the Institute of Management Consulting, further strengthening her professional qualifications.

Professional Experience:

Dr. Munirah Sarhan AlQahtani has a diverse and rich professional background that spans academia, administration, and community engagement. She began her career as a full-time Arabic language and literature teacher with the Ministry of Education in Riyadh, Saudi Arabia, from 2004 to 2008. She later transitioned into an administrative assistant role within the same ministry, where she has served since 2011. During her time in the UK, Dr. AlQahtani was a full-time doctoral researcher and teaching assistant at Aston University from 2013 to 2017. Currently, she is an Assistant Professor at Imam Mohammed bin Saud University, a role she has held since 2019. Additionally, she has been actively involved as an e-learning supervisor since 2020, contributing to the advancement of digital education platforms. Dr. AlQahtani’s wide-ranging experience highlights her dedication to education, research, and student development.

Research Interests:

Dr. Munirah Sarhan AlQahtani’s research interests lie at the intersection of management science, organizational psychology, and human resource management. She is particularly focused on exploring the latest advancements in management consultancy, leadership, and sustainability within business organizations. Dr. AlQahtani is also passionate about enhancing teaching and learning methodologies in higher education, with a special emphasis on improving student skills and developing innovative learning environments. Her research also extends to examining productivity, performance improvement, and strategic planning within organizations. Throughout her career, she has remained committed to contributing to the growing body of knowledge in management and organizational studies, with a focus on practical applications that benefit both academic and professional fields.

Skills:

Dr. Munirah Sarhan AlQahtani possesses a diverse set of skills that span both academic and professional domains. She has strong expertise in teaching and learning methodologies, particularly in higher education, where she excels in curriculum development, student mentoring, and academic supervision. Her skills in management consulting are backed by her Certified Management Consultant (CMC) certification, enabling her to provide strategic guidance on business operations, leadership, and organizational performance improvement. Dr. AlQahtani is also adept in research writing, with extensive experience in conducting and publishing academic research, particularly in management science and organizational psychology. Additionally, she has a solid command of e-learning platforms and digital education, contributing to her role as an e-learning supervisor. Her communication and presentation skills are evident from her active participation in conferences and workshops, where she shares her insights on management, education, and research strategies.

Concution:

Dr. Munirah Sarhan AlQahtani’s distinguished academic background, extensive professional experience, and dedication to research and education make her a strong candidate for the Women Researcher Award. Her commitment to advancing knowledge in business management, organizational psychology, and higher education is evident throughout her career.

Publication Top Noted:

  • The Role of Leaders’ Regulatory Focus Towards Creativity and Safety Ambidextrous Behavior: A Conceptual View
    • Author: Munirah Sarhan Alqahtani
    • Published: February 2023
    • Article Type: Full-text available
  • The Gender Gap in Senior Management: What Is Holding Saudi Women Back?
    • Author: Munirah Sarhan Alqahtani
    • Published: January 2023
    • Article Type: Full-text available
  • Winner Asia Case Comp 2022
    • Author: Munirah Sarhan Alqahtani
    • Published: December 2022
    • Article Type: Case study
  • Gender: The Moderator Role Between Materialism, Customer Value, and Customer Loyalty
    • Authors: Eman Abdulhmeed Hasnin, Munirah Sarhan Alqahtani, Somia Abdulkader Othman
    • Published: April 2022
    • Article Type: Full-text available
  • The Impact of Interactional Justice on Employees’ Job Performance and Assisting Behaviour
    • Author: Munirah Sarhan Alqahtani
    • Published: March 2022
    • Article Type: Full-text available
  • Effects of Working from Home on Job Performance: Empirical Evidence in the Saudi Context during the COVID-19 Pandemic
    • Authors: Jamel Choukir, Munirah Sarhan Alqahtani, Essam Khalil, Elsayed Mohamed
    • Published: March 2022

Mostafa Khater | Applied Mathematics | Best Researcher Award

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Prof Dr. Mostafa Khater | Applied Mathematics | Best Researcher Award

Professor at School of Medical Informatics and Engineering, Xuzhou Medical University, China

Summary:

Prof. Dr. Mostafa Khater is a prominent figure in applied mathematics and medical informatics, currently serving as a Professor at Xuzhou Medical University, China. He completed his Postdoctoral studies at Jiangsu University in 2022, focusing on computational and numerical simulations of nonlinear evolution equations, and earned his Ph.D. in Mathematics from the same institution in 2020, where he explored traveling wave solutions and their stability. With extensive experience as a lecturer and assistant lecturer at El Obour Higher Institute in Egypt, he has also contributed significantly as an editor for various academic journals and a reviewer for over 75 SCI journals. Prof. Khater has received several prestigious awards, including the Rising Star of Science Award and the Mathematics in Egypt Leader Award in 2023. Fluent in Arabic, English, Chinese, and French, he is skilled in various mathematical software, and his research interests focus on developing innovative numerical techniques for nonlinear partial differential equations.

Profile:

Education:

Prof. Dr. Mostafa Khater possesses a robust educational background in mathematics. He completed his Postdoctoral studies at the Department of Mathematics, Faculty of Science, Jiangsu University, China, in September 2022. His postdoctoral research focused on computational and numerical simulations of nonlinear evolution equations. Prior to this, he earned his Ph.D. in Mathematics from Jiangsu University in September 2020, where his thesis addressed explicit traveling wave solutions and stability properties for a class of nonlinear evolution equations. He also holds a Master of Science Degree (MSD) in Mathematics from Mansoura University, Egypt, obtained in May 2016, with a thesis centered on nonlinear partial differential equations. Prof. Khater’s academic journey began with a Bachelor of Science in Mathematics from Zagazig University, Egypt, which he completed in May 2011. This comprehensive educational foundation has equipped him with the knowledge and expertise necessary for his distinguished career in applied mathematics and medical informatics.

Professional Experience:

Prof. Dr. Mostafa Khater has amassed significant professional experience in the field of applied mathematics and education. He currently holds the position of Professor of Applied Mathematics, Medical Informatics, and Engineering at Xuzhou Medical University in China, a role he began in September 2022 and will continue through September 2025. Prior to this, he completed a postdoctoral fellowship at the School of Science, Jiangsu University, from September 2020 to September 2022. In addition to his academic appointments, Prof. Khater has served as a lecturer in the Department of Mathematics at El Obour Higher Institute in Egypt since September 2020, following his tenure as an assistant lecturer from September 2017 to September 2020. His contributions to the academic community extend to editorial responsibilities for five different journals since March 2018 and reviewing for over 75 SCI journals since May 2017. Prof. Khater’s career also includes teaching mathematics at the secondary school level in Egypt from September 2011 to August 2014, showcasing his commitment to mathematics education across various academic levels.

Research Interests:

Prof. Dr. Mostafa Khater’s research interests lie primarily in the areas of applied mathematics, with a particular focus on nonlinear partial differential equations and their applications in medical informatics and engineering. His research involves the development of innovative computational and numerical techniques for solving complex nonlinear evolution equations, emphasizing explicit traveling wave solutions and their stability properties. Additionally, Prof. Khater is interested in numerical analysis, calculus, basic statistics, linear algebra, and differential equations, which are foundational to his work. His commitment to advancing knowledge in these fields is demonstrated through his extensive publications and active engagement in editorial and review processes for various academic journals. By exploring new methodologies and approaches, he aims to contribute significantly to both theoretical advancements and practical applications in mathematics and its interdisciplinary connections.

Skills:

Prof. Dr. Mostafa Khater possesses a diverse skill set that enhances his capabilities as a researcher and educator in applied mathematics. He is proficient in several languages, including Arabic, English, Chinese, and French, enabling him to communicate effectively in international academic environments. His technical skills are extensive, with expertise in mathematical software such as Maple, Mathematica, MATLAB, and Origin, which he utilizes for computational modeling and simulations. Prof. Khater is also adept in using Photoshop for graphical representations and presentations. Additionally, his strong background in mathematics encompasses areas like calculus, statistics, linear algebra, and numerical analysis, equipping him to tackle complex mathematical problems and contribute to innovative research. His experience as an editor for multiple academic journals and as a reviewer for over 75 SCI journals further highlights his analytical and evaluative skills in the field, underscoring his commitment to advancing mathematical research and education.

Concution:

In light of his extensive experience, notable awards, substantial publication record, and commitment to advancing mathematical research, Prof. Dr. Mostafa Khater stands out as an exceptional candidate for the Best Researcher Award. His contributions not only enrich the field of applied mathematics but also foster international collaboration and innovation.

Publication Top Noted:

  • High Accuracy Solutions for the Pochhammer–Chree Equation in Elastic Media
    • Authors: M.M.A. Khater, S.H. Alfalqi
    • Year: 2024
    • Journal: Scientific Reports
    • Volume: 14(1), Article: 17562
  • Pfaffian Solutions and Nonlinear Dynamics of Surface Waves in Two Horizontal and One Vertical Directions with Dispersion, Dissipation and Nonlinearity Effects
    • Author: M.M.A. Khater
    • Year: 2024
    • Journal: Alexandria Engineering Journal
    • Volume: 108, Pages: 232–243
  • Exploring Plasma Phenomena with the Nizhnik-Novikov-Veselov Formula: Analyzing Ion-Acoustic Waves, Solitons, and Shocks
    • Authors: R. Altuijri, A.-H. Abdel-Aty, K.S. Nisar, M.M.A. Khater
    • Year: 2024
    • Journal: Modern Physics Letters B
    • Volume: 38(32), Article: 2450332
    • Citations: 0
  • Exploring Solitary Waves and Nonlinear Dynamics in the Fractional Chaffee–Infante Equation: A Study Beyond Conventional Diffusion Models
    • Authors: X. Zhang, T.A. Nofal, A. Vokhmintsev, M.M.A. Khater
    • Year: 2024
    • Journal: Qualitative Theory of Dynamical Systems
    • Volume: 23(Suppl 1), Article: 270
    • Citations: 0
  • Wave Propagation Analysis in the Modified Nonlinear Time Fractional Harry Dym Equation: Insights from Khater II Method and B-Spline Schemes
    • Author: M.M.A. Khater
    • Year: 2024
    • Journal: Modern Physics Letters B
    • Volume: 38(29), Article: 2450288
    • Citations: 11
  • Exploring Plasma Dynamics: Analytical and Numerical Insights into Generalized Nonlinear Time Fractional Harry Dym Equation
    • Authors: R. Altuijri, A.-H. Abdel-Aty, K.S. Nisar, M.M.A. Khater
    • Year: 2024
    • Journal: Modern Physics Letters B
    • Volume: 38(28), Article: 2450264
    • Citations: 1
  • Advancing Mathematical Physics: Insights into Solving Nonlinear Time-Fractional Equations
    • Authors: M. Li, W. Zhang, R.A.M. Attia, J.F. Alzaidi, M.M.A. Khater
    • Year: 2024
    • Journal: Qualitative Theory of Dynamical Systems
    • Volume: 23(4), Article: 172
    • Citations: 1
  • Innovative Insights into Wave Phenomena: Computational Exploration of Nonlinear Complex Fractional Generalized-Zakharov System
    • Authors: J. Liu, F. Wang, R.A.M. Attia, J.F. Alzaidi, M.M.A. Khater
    • Year: 2024
    • Journal: Qualitative Theory of Dynamical Systems
    • Volume: 23(4), Article: 170
    • Citations: 1

Md Abu Taher | Cybersecurity | Best Researcher Award

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Mr. Md Abu Taher | Cybersecurity | Best Researcher Award

PhD Student at Florida International University, United States

Summary:

Mr. Md Abu Taher is a dedicated researcher and professional in the field of electrical and computer engineering, currently pursuing his Ph.D. at Florida International University, Miami, with a focus on microgrid distributed control and cybersecurity. His research contributes to enhancing the resilience and security of modern energy systems. He holds a Bachelor’s degree in Electrical and Electronics Engineering from Bangladesh University of Engineering and Technology (BUET). With over a decade of experience, including roles as a Senior Lead Engineer at Grameenphone BD Ltd and Associate Engineer at Orascom BD Ltd, Mr. Taher has worked on energy optimization, power supply resilience, and cybersecurity measures in distributed energy systems. His research has been widely published in leading IEEE journals, emphasizing his expertise in renewable energy, smart grids, and cyber-attack mitigation.

Profile:

Education:

Mr. Md Abu Taher is currently pursuing a Ph.D. in Electrical and Computer Engineering at Florida International University, Miami, FL, USA, with a perfect CGPA of 4.0. His doctoral research focuses on microgrid distributed control and cybersecurity, areas critical to advancing energy security and grid resilience. He is expected to graduate in December 2025. Prior to this, Mr. Taher earned his Bachelor of Science (B.Sc.) degree in Electrical and Electronics Engineering from Bangladesh University of Engineering and Technology (BUET) in June 2008, where he graduated with a CGPA of 3.52 out of 4. His undergraduate studies concentrated on power systems, laying the foundation for his future work in energy systems and renewable energy solutions.

Professional Experience:

Mr. Md Abu Taher has extensive professional experience in the field of electrical engineering and energy systems. From January 2015 to April 2022, he served as a Senior Lead Engineer at Grameenphone BD Ltd, where he played a pivotal role in reducing energy consumption through various optimization techniques. His responsibilities included projecting the annual budget for network expansion and improving power supply resilience by adopting PV-based islanded power supply solutions. Prior to this, he worked as an Associate Engineer at Orascom BD Ltd from June 2008 to December 2014, where he was responsible for estimating total power consumption across the radio network and implementing optimized source selection strategies to reduce energy costs. His professional experience, combined with his academic expertise, has enabled him to make significant contributions to the energy sector, particularly in the areas of power system optimization and renewable energy solutions.

Research Interests:

Mr. Md Abu Taher’s research interests lie at the intersection of renewable energy, microgrid distributed control, and cybersecurity. His Ph.D. work focuses on developing resilient microgrid control systems, addressing the critical issue of energy security. He is particularly interested in analyzing the impact of various cyber-attacks on energy systems, including Distributed Denial-of-Service (DDoS) and False Data Injection (FDI) attacks, and exploring mitigation techniques through advanced machine learning algorithms. Additionally, Mr. Taher is passionate about enhancing the stability and resilience of the U.S. electrical grid, using artificial intelligence (AI) and innovative control strategies to protect distributed energy resources. His research contributes to advancing sustainable energy solutions and fortifying energy infrastructure against disruptions caused by both natural disasters and intentional cyber threats.

Skills:

Mr. Md Abu Taher possesses a diverse skill set that encompasses various aspects of electrical engineering and renewable energy systems. He excels in system design, operation, maintenance, and testing, demonstrating expertise in developing efficient and reliable power delivery systems. Proficient in simulation and modeling, he utilizes advanced software and tools for circuit design and analysis, including MATLAB, ETAP, and OPAL-RT. Mr. Taher also has strong programming skills in Python and expertise in using artificial intelligence techniques for cybersecurity applications. His analytical and problem-solving abilities are complemented by robust project management and organizational skills, enabling him to effectively allocate resources and lead projects. Additionally, he has a proven track record in collaborating with teams, showcasing his leadership and management capabilities in dynamic work environments. This multifaceted skill set positions him well to contribute significantly to innovative energy solutions and research initiatives.

Concution:

Mr. Md Abu Taher’s exceptional combination of academic achievements, publication record, research experience, technical expertise, and industry contributions make him a highly suitable candidate for the Best Researcher Award. His work in renewable energy, microgrid control, and cybersecurity addresses critical challenges in modern power systems, and his innovations have the potential to make a lasting impact on global energy sustainability.

Publication Top Noted:

Reliability analysis of wireless power transfer for electric vehicle charging based on continuous Markov process

  • Authors: M. Behnamfar, M.A. Taher, A. Polowsky, S. Roy, M. Tariq, A. Sarwat
  • Published In: 2023 Fourth International Symposium on 3D Power Electronics Integration and …
  • Citations: 14
  • Year: 2023

Analyzing replay attack impact in DC microgrid consensus control: Detection and mitigation by Kalman-filter-based observer

  • Authors: M.A. Taher, M. Tariq, M. Behnamfar, A.I. Sarwat
  • Published In: IEEE Access
  • Citations: 13
  • Year: 2023

Analyzing the effects of interference and packet loss on consensus-based secondary control in islanded AC microgrid

  • Authors: M.A. Taher, M. Tariq, A.I. Sarwat
  • Published In: 2023 IEEE Design Methodologies Conference (DMC), 1-6
  • Citations: 7
  • Year: 2023

Disruptive effects of denial-of-service (DoS) attacks on microgrid distributed control: Altered communication topology, voltage stability, and accurate power allocation

  • Authors: M.A. Taher, H. Iqbal, M. Tariq, A.I. Sarwat
  • Published In: 2023 IEEE International Conference on Energy Technologies for Future Grids …
  • Citations: 5
  • Year: 2023

Long short term memory utilized photovoltaic inverter humidity controller for capacitor reliability enhancement

  • Authors: S. Roy, A.S. Khan, M.A. Taher, M. Tariq, A. Sarwat
  • Published In: 2023 IEEE International Conference on Energy Technologies for Future Grids …
  • Citations: 5
  • Year: 2023

False Data Injection Attack Detection and Mitigation using Non-linear Autoregressive Exogenous Input-Based Observers in Distributed Control for DC Microgrid

  • Authors: M.A. Taher, M. Behnamfar, A.I. Sarwat, M. Tariq
  • Published In: IEEE Open Journal of the Industrial Electronics Society
  • Citations: 4
  • Year: 2024

Recurrent neural network-based sensor data attacks identification in distributed renewable energy-based DC microgrid

  • Authors: M.A. Taher, H. Iqbal, M. Tariq, A.I. Sarwat
  • Published In: 2024 IEEE Texas Power and Energy Conference (TPEC), 1-6
  • Citations: 4
  • Year: 2024

Enhancing security in islanded AC microgrid: Detecting and mitigating FDI attacks in secondary consensus control through AI-based method

  • Authors: M.A. Taher, M. Tariq, A.I. Sarwat
  • Published In: 2023 IEEE International Conference on Energy Technologies for Future Grids …
  • Citations: 4
  • Year: 2023

Wavelet and signal analyzer based high-frequency ripple extraction in the context of MPPT algorithm in solar PV systems

  • Authors: M.A. Taher, M. Behnamfar, A.I. Sarwat, M. Tariq
  • Published In: IEEE Access
  • Citations: 3
  • Year: 2024