Sneha Mandal | Chemistry | Best Researcher Award

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Ms. Sneha Mandal | Chemistry | Best Researcher Award

Int.-PhD at Indian Institute of Science Education and Research (IISER), Tirupati, India

Ms. Sneha Mandal is a dedicated researcher currently pursuing an integrated Ph.D. in Chemistry at the Indian Institute of Science Education and Research (IISER), Tirupati, India. Her research focuses on nano-composite design and van der Waals gap modulation for advanced energy applications. She holds an M.S. in Chemistry from IISER Tirupati and a B.Sc. in Chemistry (Hons.) from P.K. Roy Memorial College, Dhanbad, where she graduated with first-class distinction. Her expertise spans electrochemistry, materials science, and advanced energy storage systems, with hands-on experience in cutting-edge techniques and instruments. Ms. Mandal has co-authored multiple research publications and actively participates in international conferences and workshops, contributing to advancements in energy storage technologies.

 

Profile

SCOPUS

ORCID

Education

Ms. Sneha Mandal is currently pursuing an integrated Ph.D. in Chemistry at the Indian Institute of Science Education and Research (IISER), Tirupati, India, since 2019. Her doctoral research focuses on “Nano-Composite Design: Van der Waals Gap Modulation for Advanced Energy Applications” under the supervision of Prof. Vijayamohanan K. Pillai. As part of this program, she has also completed an M.S. in Chemistry at IISER Tirupati with a CGPA of 7.2. Prior to this, she earned her Bachelor of Science in Chemistry (Hons.) with first-class distinction from P. K. Roy Memorial College, affiliated with Binod Bihari Mahato Koylanchal University, Dhanbad, Jharkhand, India, in 2019. Her progressive academic journey reflects her commitment to excellence and her strong foundation in the field of chemistry.

 Experience

Ms. Sneha Mandal is an accomplished researcher with extensive experience in advanced energy applications and materials science. Currently, she is pursuing an integrated Ph.D. at the Indian Institute of Science Education and Research (IISER), Tirupati, under the guidance of Prof. Vijayamohanan K. Pillai. Her doctoral research focuses on nano-composite design and van der Waals gap modulation for energy storage systems, particularly solid-state and Na-/Li-ion batteries. Ms. Mandal has honed her skills through hands-on experience with state-of-the-art instruments such as battery testers, electrochemical workstations, Raman spectroscopy, XRD, and many others. She has demonstrated expertise in electrochemical and galvanostatic measurements, coin-cell fabrication, and data analysis for cutting-edge research. Additionally, her active participation in workshops and training sessions on electrochemistry and cryo-electron microscopy highlights her commitment to professional development. Her contributions include co-authoring high-impact research publications in journals like Scientific Reports and Energy Advances and presenting her findings at esteemed international conferences. These experiences collectively reflect her deep engagement with advancing technologies in energy storage and materials science.

Research Interests

Ms. Sneha Mandal’s research interests lie at the intersection of electrochemistry, materials science, and advanced energy storage systems. She is particularly focused on the development and optimization of solid-state batteries, including Na-ion, Li-ion, Li-S, and Na-S batteries. Her expertise extends to the study and application of 2D materials, solid electrolytes, and advanced nanomaterials for enhancing battery performance. Ms. Mandal is deeply engaged in exploring electrocatalysis and the modulation of van der Waals gaps in nano-composites to advance energy applications. Her work also involves designing novel materials and improving the efficiency and stability of energy storage devices. Through her research, she aims to contribute to sustainable and high-performance energy solutions.

Skills

Ms. Sneha Mandal possesses hands-on expertise in advanced instrumentation and techniques essential for materials science and electrochemistry research. She is proficient in operating battery testers (BCS 805), electrochemical workstations (Solartron 1470E, Biologic SP 200), and conducting cyclic voltammetry, impedance spectroscopy, and galvanostatic measurements. Her skillset includes advanced spectroscopy and imaging techniques such as RAMAN, XRD, AFM, UV-Visible spectroscopy, and HR-TEM, along with coin-cell fabrication and glove box handling. Ms. Mandal is adept at data analysis and interpretation across a range of tools, making her well-equipped to address complex challenges in energy storage and materials development.

 

Publication

Van der Waals Gap Modulation of Graphene Oxide through Mono-Boc Ethylenediamine Anchoring for Superior Li-Ion Batteries

  • Authors: Mandal, S., Pillai, V.K., Ranjana Ponraj, M., Thavasi, V., Renugopalakrishnan, V.
  • Journal: Energy Advances
  • Volume: 3, Issue 8, Pages 1977–1991
  • Publication Year: 2024
  • Access: Open Access
  • Citations: 0

Interlayer, Gallery-Engineered Graphene Oxide Using Selective Protection of Mono-Boc-Ethylenediamine as Anode for Sodium Ion Batteries

  • Authors: Thushara, K.M., Ponraj, M.R., Mandal, S., Pillai, V.K., Bhagavathsingh, J.
  • Journal: Journal of Energy Storage
  • Volume: 73, Article 109237
  • Publication Year: 2023
  • Citations: 1

Effect of Hetero-Atom Doping on the Electrocatalytic Properties of Graphene Quantum Dots for Oxygen Reduction Reaction

  • Authors: Goswami, M., Mandal, S., Pillai, V.K.
  • Journal: Scientific Reports
  • Volume: 13, Article 5182
  • Publication Year: 2023
  • Citations: 12
  • Access: Open Access

Conclusion

Ms. Sneha Mandal’s robust academic background, cutting-edge research, technical proficiency, and impactful publications make her an exemplary candidate for the Best Researcher Award. Her dedication to advancing knowledge in energy materials and nanotechnology positions her as a promising scientist and deserving recipient of this honor.

FAN Bo | Data Security Management | Best Researcher Award

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Dr. FAN Bo | Data Security Management | Best Researcher Award

Senior Engineer at Southwest Jiaotong University, China

Dr. Fan Bo is a Senior Engineer and Chief Technical Specialist at the National Engineering Laboratory for Industrial Big Data Application Technology in China. With a Ph.D. and extensive experience in industrial innovation, he also serves as a project manager for the Ministry of Science and Technology’s Key Field Innovation Team and a technical expert for Chongqing Iron & Steel Electronic Co., Ltd. Dr. Fan has participated in over ten national R&D programs, published more than ten research papers, filed ten invention patents, and contributed to national and industrial standards. His groundbreaking work in data governance, business data modeling, and multi-value-chain distributed data spaces has benefited thousands of enterprises, earning him prestigious awards and international recognition.

 

Profile

ORCID

Education

Dr. Fan Bo has a strong academic foundation that underpins his expertise in industrial big data and digital transformation technologies. He earned his Ph.D. in a specialized field, equipping him with advanced knowledge and research skills crucial for tackling complex engineering and technological challenges. His academic journey reflects a commitment to excellence and a focus on integrating theoretical insights with practical applications, which has significantly contributed to his professional accomplishments in industrial innovation and data science.

 Experience

Dr. Fan Bo has a distinguished career marked by leadership and innovation in industrial big data and digital transformation. As Chief Technical Specialist at the National Engineering Laboratory for Industrial Big Data Application Technology, he oversees key technological advancements. He has participated in over ten national R&D programs, including leading a sub-project, and directed four key R&D initiatives in Sichuan Province. Additionally, Dr. Fan managed two horizontal digital transformation projects for China’s National Pipeline Group during the 13th and 14th Five-Year Plans. His concurrent roles as project manager for the Ministry of Science and Technology’s Key Field Innovation Team and technical expert for Chongqing Iron & Steel Electronic Co., Ltd. highlight his multifaceted expertise and ability to drive impactful projects in the field.

Research Interests

Dr. Fan Bo’s research interests focus on the application of industrial big data, data governance, and digital transformation technologies. He is particularly interested in the development and implementation of data governance platforms, business data models, and multi-value-chain distributed data spaces. His work aims to optimize industrial processes through innovative data solutions, particularly in the manufacturing and automotive sectors. Dr. Fan’s research also explores the integration of cloud service technologies and scenario-oriented business models to enhance efficiency and drive economic benefits for industrial enterprises. His contributions continue to shape the future of industrial data applications and digital innovation.

Skills

Dr. Fan Bo possesses a wide range of technical and leadership skills that have contributed to his success in industrial big data and digital transformation. He is highly skilled in data governance, designing and implementing complex data platforms, and developing business data models tailored to specific industrial scenarios. His expertise also extends to multi-value-chain distributed data spaces, enabling optimized data utilization across various industries, particularly in manufacturing and automotive sectors. Additionally, Dr. Fan has strong project management abilities, having led multiple high-impact initiatives under national R&D programs and major industrial projects. His proficiency in cloud service technologies, technical standard development, and innovation in digital transformation underscores his versatility and ability to drive technological advancements.

 

Publication

Title: Optimal Selection Technology of Business Data Resources for Multi-Value Chain Data Space—Optimizing Future Data Management Methods

  • Authors: Bo Fan, Linfu Sun, Dong Tan, Meng Pan
  • Journal: Electronics
  • Volume: 13
  • Issue: 23
  • Article Number: 4690
  • DOI: 10.3390/electronics13234690
  • Year: 2024

Conclusion

Dr. Fan Bo’s substantial research, innovation, and leadership in advancing industrial big data applications make him a deserving candidate for the Research for Best Researcher Award. His contributions not only address critical challenges in digital transformation but also generate tangible economic and industrial benefits, solidifying his position as a leading figure in his field.

Hui-Ling Yang | Inventory Control | Best Researcher Award

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Prof. Hui-Ling Yang | Inventory Control | Best Researcher Award

Scholar at HungKuang University, Taiwan

Prof. Hui-Ling Yang is a distinguished academic with extensive expertise in mathematics, statistics, and operations research. She holds a Ph.D. in Industrial Engineering from National Tsing Hua University, along with a Master’s and Bachelor’s degree in Mathematics from National Taiwan Normal University, Taiwan, ROC. Currently a Professor at HungKuang University, she has been a part of the institution since 1986, progressing through various roles. Her research focuses on inventory control, optimization strategies, and supply chain analytics. Prof. Yang has authored numerous journal articles and has been recognized in the Who’s Who in the World 25th Silver Anniversary Edition for her academic contributions.

 

Profile

Scopus

ORCID

Education

Prof. Hui-Ling Yang has a strong academic foundation, with a Bachelor of Science degree in Mathematics and a Master of Science degree in Graduate Mathematics from National Taiwan Normal University, Taiwan, ROC. She further advanced her studies by earning a Ph.D. in Industrial Engineering from National Tsing Hua University, Taiwan, ROC. Her diverse educational background equips her with interdisciplinary expertise, which she applies effectively in her teaching and research in mathematics, statistics, and operations research.

 Experience

Prof. Hui-Ling Yang has had a distinguished academic career spanning several decades at HungKuang University. She has served as a Professor since August 2004, following her tenure as an Associate Professor from February 2003 to July 2004. Prior to that, she held the position of Associate Professor at HungKuang Technology College from February 1998 to January 2003, and Lecturer from August 1997 to January 1998. Prof. Yang’s journey began as a Lecturer at HungKuang Junior College, where she contributed from August 1986 to July 1997. Her extensive teaching and research experience reflect her dedication to academia and her profound impact on her field.

Research Interests

Prof. Hui-Ling Yang’s research interests encompass a diverse range of topics in applied mathematics and operations research. Her expertise includes Mathematics, Statistics, and Operations Research, with a specialized focus on Inventory Control. She is particularly interested in developing optimal replenishment strategies and inventory models for deteriorating items, addressing fluctuating demand patterns, and exploring the impact of financial considerations such as advance cash-credit payment schemes and supplier credits. Her work integrates theoretical rigor with practical applications, offering innovative solutions to complex supply chain and inventory management challenges.

Skills

Prof. Hui-Ling Yang possesses a comprehensive skill set that spans Mathematics, Statistics, and Operations Research. Her expertise includes advanced analytical and problem-solving abilities, particularly in designing and optimizing inventory control models. She is proficient in formulating and solving complex mathematical models that address practical challenges in supply chain management. Additionally, Prof. Yang is skilled in conducting discounted cash-flow analyses, evaluating replenishment strategies, and integrating financial and operational decision-making. Her ability to bridge theoretical research with real-world applications highlights her strength in innovative and impactful academic contributions.

 

Publications

Optimal Replenishment Strategy for a High-Tech Product Demand with Non-Instantaneous Deterioration under an Advance-Cash-Credit Payment Scheme by a Discounted Cash-Flow Analysis

  • Authors: H.-L. Yang, C.-T. Chang, Y.-T. Tseng
  • Journal: Mathematics
  • Volume: 12 (19), Article: 3160
  • Year: 2024
  • Citations: 0

An Optimal Replenishment Cycle and Order Quantity Inventory Model for Deteriorating Items with Fluctuating Demand

  • Author: H.-L. Yang
  • Journal: Supply Chain Analytics
  • Volume: 3, Article: 100021
  • Year: 2023
  • Citations: 10

A Note on ‘A Two-Warehouse Partial Backlogging Inventory Model for Deteriorating Items with Permissible Delay in Payment under Inflation’

  • Author: H.-L. Yang
  • Journal: International Journal of Operational Research
  • Volume: 45 (2), Pages: 141–160
  • Year: 2022
  • Citations: 2

EOQ-Based Pricing and Customer Credit Decisions under General Supplier Payments

  • Authors: R. Li, H.-L. Yang, Y. Shi, J.-T. Teng, K.-K. Lai
  • Journal: European Journal of Operational Research
  • Volume: 289 (2), Pages: 652–665
  • Year: 2021
  • Citations: 25

Retailer’s Ordering Policy for Demand Depending on the Expiration Date with Limited Storage Capacity under Supplier Credits Linked to Order Quantity and Discounted Cash Flow

  • Author: H.-L. Yang
  • Journal: International Journal of Systems Science: Operations and Logistics
  • Volume: 8 (2), Pages: 136–153
  • Year: 2021
  • Citations: 6

An Optimal Ordering Policy for Deteriorating Items with Partial Backlogging and Time Varying Selling Price and Purchasing Cost under Inflation

  • Author: H.-L. Yang
  • Journal: International Journal of Operational Research
  • Volume: 31 (3), Pages: 403–419
  • Year: 2018
  • Citations: 5

An Inventory Model for Increasing Demand under Two Levels of Trade Credit Linked to Order Quantity

  • Authors: J.-T. Teng, H.-L. Yang, M.-S. Chern
  • Journal: Applied Mathematical Modelling
  • Volume: 37 (14-15), Pages: 7624–7632
  • Year: 2013
  • Citations: 75

A Two-Warehouse Partial Backlogging Inventory Model for Deteriorating Items with Permissible Delay in Payment under Inflation

  • Authors: H.-L. Yang, C.-T. Chang
  • Journal: Applied Mathematical Modelling
  • Volume: 37 (5), Pages: 2717–2726
  • Year: 2013
  • Citations: 90

Two-Warehouse Partial Backlogging Inventory Models with Three-Parameter Weibull Distribution Deterioration under Inflation

  • Author: H.-L. Yang
  • Journal: International Journal of Production Economics
  • Volume: 138 (1), Pages: 107–116
  • Year: 2012
  • Citations: 82

Conclusion

Prof. Hui-Ling Yang’s extensive contributions to research, coupled with her impactful publications and recognition, make her a strong candidate for the Research for Best Researcher Award. Her work bridges theoretical advancements and practical applications in inventory and operations management, aligning perfectly with the award’s criteria.

Xiangyun Tang | Artificial Intelligence Security | Best Researcher Award

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Prof. Xiangyun Tang | Artificial Intelligence Security | Best Researcher Award

Associate Professor at Minzu University of China, China

Prof. Xiangyun Tang is a distinguished researcher specializing in machine learning security, data privacy preservation, and applied cryptography. He earned his Ph.D. in Cyberspace Security from the Beijing Institute of Technology, where his research focused on secure and trustworthy distributed machine learning. He also holds a Bachelor of Engineering in Computer Science from Minzu University of China. Prof. Tang has contributed significantly to privacy-preserving systems, including federated learning and blockchain-based secure systems, with numerous publications in prestigious journals and conferences. His work experience includes roles in industry, such as a Machine Learning Engineer at Tencent, and he has been recognized with multiple awards and honors for his academic and professional achievements.

 

Profile

Google Scholar

ORCID

Education

Prof. Xiangyun Tang holds a Ph.D. in Cyberspace Security from the Beijing Institute of Technology, Beijing, China, where he studied from September 2016 to June 2022. His doctoral research focused on “Secure and Trustworthy Distributed Machine Learning,” supervised by Prof. Liehuang Zhu and Prof. Meng Shen, and he achieved an impressive GPA of 3.83/4.00. He also holds a Bachelor of Engineering in Computer Science from Minzu University of China, completed in June 2016, with a GPA of 3.68/4.00. His strong academic foundation underscores his expertise in cybersecurity and distributed systems.

 Experience

Prof. Xiangyun Tang has accumulated extensive experience in academia, research, and industry. As a Ph.D. candidate at the Beijing Institute of Technology, he conducted groundbreaking research in secure and trustworthy distributed machine learning under the guidance of Profs. Liehuang Zhu and Meng Shen. During his tenure as a Machine Learning Engineer at Tencent Technology Inc., he developed real-time forwarding prediction models for cascade graphs and sentiment quantification solutions using Graph Neural Networks. Additionally, he has participated in various funded projects, including privacy-preserving federated learning systems and blockchain-based collaborative learning mechanisms, where he contributed to system design and implementation. Prof. Tang has also provided professional services as a reviewer for top-tier journals like the IEEE Internet of Things J

Research Interests

Prof. Xiangyun Tang’s research interests lie at the intersection of machine learning security, data privacy preservation, and applied cryptography. He focuses on designing robust privacy-preserving verifiability systems to enhance the security of federated learning, effectively defending against malicious participants. His work leverages advanced cryptographic techniques, including zero-knowledge proofs, homomorphic encryption, and secret sharing, to ensure data privacy and system integrity. Prof. Tang’s research aims to address critical challenges in secure collaborative learning and develop innovative solutions that integrate privacy and performance in distributed machine learning environments.

Skills

Prof. Xiangyun Tang possesses a diverse skill set that bridges advanced cryptographic techniques and practical applications in machine learning security. His expertise includes designing privacy-preserving systems utilizing zero-knowledge proofs, homomorphic encryption, and secret sharing to ensure secure and trustworthy distributed machine learning. He is proficient in developing robust algorithms for federated learning, enabling secure data sharing and collaborative training across decentralized environments. Additionally, Prof. Tang has extensive experience in sentiment quantification, real-time data analysis on cascade graphs, and implementing secure systems for software-defined networks and blockchain-based applications. His technical acumen is complemented by strong problem-solving skills and a commitment to advancing data privacy and cybersecurity.

 

Publications

Privacy-Preserving Support Vector Machine Training over Blockchain-Based Encrypted IoT Data in Smart Cities

  • Authors: M. Shen, X. Tang, L. Zhu, X. Du, M. Guizani
  • Journal: IEEE Internet of Things Journal
  • Volume: 6 (5), Pages: 7702-7712
  • Cited By: 465
  • Year: 2019

Privacy-Preserving DDoS Attack Detection Using Cross-Domain Traffic in Software Defined Networks

  • Authors: L. Zhu, X. Tang, M. Shen, X. Du, M. Guizani
  • Journal: IEEE Journal on Selected Areas in Communications
  • Volume: 36 (3), Pages: 628-643
  • Cited By: 121
  • Year: 2018

Secure SVM Training over Vertically-Partitioned Datasets Using Consortium Blockchain for Vehicular Social Networks

  • Authors: M. Shen, J. Zhang, L. Zhu, K. Xu, X. Tang
  • Journal: IEEE Transactions on Vehicular Technology
  • Volume: 69 (6), Pages: 5773-5783
  • Cited By: 87
  • Year: 2019

Privacy-Preserving Machine Learning Training in IoT Aggregation Scenarios

  • Authors: L. Zhu, X. Tang, M. Shen, F. Gao, J. Zhang, X. Du
  • Journal: IEEE Internet of Things Journal
  • Volume: 8 (15), Pages: 12106-12118
  • Cited By: 33
  • Year: 2021

Pile: Robust Privacy-Preserving Federated Learning via Verifiable Perturbations

  • Authors: X. Tang, M. Shen, Q. Li, L. Zhu, T. Xue, Q. Qu
  • Journal: IEEE Transactions on Dependable and Secure Computing
  • Volume: 20 (6), Pages: 5005-5023
  • Cited By: 28
  • Year: 2023

Blockchains for Artificial Intelligence of Things: A Comprehensive Survey

  • Authors: M. Shen, A. Gu, J. Kang, X. Tang, X. Lin, L. Zhu, D. Niyato
  • Journal: IEEE Internet of Things Journal
  • Volume: 10 (16), Pages: 14483-14506
  • Cited By: 27
  • Year: 2023

Secure and Trusted Collaborative Learning Based on Blockchain for Artificial Intelligence of Things

  • Authors: X. Tang, L. Zhu, M. Shen, J. Peng, J. Kang, D. Niyato, A. A. Abd El-Latif
  • Journal: IEEE Wireless Communications
  • Volume: 29 (3), Pages: 14-22
  • Cited By: 24
  • Year: 2022

Secure Semantic Communications: Challenges, Approaches, and Opportunities

  • Authors: M. Shen, J. Wang, H. Du, D. Niyato, X. Tang, J. Kang, Y. Ding, L. Zhu
  • Journal: IEEE Network
  • Cited By: 15
  • Year: 2023

Fully Exploiting Cascade Graphs for Real-Time Forwarding Prediction

  • Authors: X. Tang, D. Liao, W. Huang, L. Zhu, M. Shen, J. Xu
  • Conference: AAAI 2021 Conference
  • Cited By: 36
  • Year: 2021

When Homomorphic Cryptosystem Meets Differential Privacy: Training Machine Learning Classifier with Privacy Protection

  • Authors: X. Tang, L. Zhu, M. Shen, X. Du
  • Platform: arXiv preprint
  • arXiv ID: arXiv:1812.02292
  • Cited By: 11
  • Year: 2018

Conclusion

Prof. Xiangyun Tang’s extensive contributions to privacy-preserving machine learning, applied cryptography, and data security establish him as an exceptional candidate for the Research for Best Researcher Award. His innovative research, industry experience, academic service, and numerous accolades collectively make him highly deserving of this recognition.

A S M Ahsanul Sarkar Akib | Cyber Threat | Best Researcher Award

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Mr. A S M Ahsanul Sarkar Akib | Cyber Threat | Best Researcher Award

Managing Director at Robo Tech Valley, Bangladesh

Mr. A.S.M. Ahsanul Sarkar Akib is a highly accomplished robotics and technology expert based in Bangladesh. He holds a Bachelor of Science in Engineering from the Bangladesh University of Business and Technology, specializing in Computer Science and Engineering. With a strong passion for robotics, IoT, and drone development, he has gained extensive experience in the field through roles such as Technical Director at the Japan-Bangladesh Robotics and Advanced Technology Research Centre, and Robotics Trainer at various universities. Additionally, he is the Founder & CEO of Dhopa Elo Online Laundry Service and Robo Tech Valley & Shop. Mr. Akib has also earned several accolades, including multiple championships in robotics competitions such as Ureckon 19 in India and Technocracy 2019 in Bangladesh. His innovative projects include humanoid robots, self-learning AI educational robots, and IoT-based solutions for water turbines and smart door systems.

 

Profile

Google Scholar

ORCID

Education

Mr. A.S.M. Ahsanul Sarkar Akib has a robust academic foundation, highlighted by his Bachelor of Science (B.Sc.) in Engineering from the Department of Computer Science and Engineering at Bangladesh University of Business and Technology. His academic journey is marked by exceptional achievements, including a GPA of 4.94 in his Higher Secondary Certificate (HSC) examinations from Cantonment Public School and College (BUSMS) under the Dinajpur Board, specializing in science. He also excelled in his Secondary School Certificate (SSC) examinations, attaining a GPA of 4.75 from the same institution. These accomplishments reflect his strong analytical abilities and dedication to academic excellence.

 Experience

Mr. A.S.M. Ahsanul Sarkar Akib has a diverse and extensive professional background in robotics, technology, and entrepreneurship. He has been serving as the Technical Director at the Japan-Bangladesh Robotics and Advanced Technology Research Centre since May 2018, where he plays a key role in advancing research and development in robotics. Additionally, he worked as a Robotics Trainer at BGC Trust University (Chattogram) and the National Academy for Computer Training and Research (NACTAR) in 2020. Mr. Akib also served as a Project Coordinator and Researcher at the Bangladesh Advance Robotics Research Center in 2018 and worked as a Robotics Engineer at Rafusoft Software and Robotics Firm between 2017 and 2018. Throughout his career, he has contributed to various robotics workshops across leading universities in Bangladesh, including Dhaka University, NSU, and Brac University. As an entrepreneur, Mr. Akib is the Founder & CEO of Dhopa Elo Online Laundry Service and the Founder & Managing Director of Robo Tech Valley & Shop, where he continues to drive innovation in both technology and business.

Research Interests

Mr. A.S.M. Ahsanul Sarkar Akib’s research interests lie in the fields of robotics, Internet of Things (IoT), and drone development. He focuses on developing cutting-edge technologies such as self-learning AI systems, humanoid robots, and IoT-based automation solutions. His work includes designing innovative projects like the Humanoid Bangla AI Robot “Bongo,” a self-learning educational robot, and various robotics systems for renewable energy applications, including hydro power water turbines and automated fish farming solutions. Mr. Akib is also passionate about developing IoT-based systems for smart home automation, water monitoring, and waste management. Through his research, he aims to push the boundaries of robotics and IoT technologies to improve daily life and contribute to sustainable development.

Skills

Mr. A.S.M. Ahsanul Sarkar Akib possesses a diverse set of technical skills, with expertise in robotics, IoT, and drone development. He is proficient in programming languages such as C, C++, Java, and Python, and has hands-on experience with Robotics Operating Systems (ROS). His technical skills also include working with Latex for documentation, and he is adept at designing and developing complex robotics systems. Additionally, Mr. Akib has specialized knowledge in the creation of self-learning AI models and the development of IoT-based solutions for smart homes, water monitoring systems, and renewable energy projects. His skills in project coordination and leadership, coupled with his deep understanding of cutting-edge technology, have made him a valuable asset in various robotics and technology-driven initiatives.

 

Publications

Artificial Intelligence Humanoid Bongo Robot in Bangladesh

  • Conference: 2019 1st International Conference on Advances in Science, Engineering, and Technology
  • Cited By: 22
  • Year: 2019

Future Micro Hydro Power: Generation of Hydroelectricity and IoT-Based Monitoring System

  • Conference: 2021 International Conference on Innovation and Intelligence for Informatics
  • Cited By: 4
  • Year: 2021

Precision Fish Farming to Mitigate Pond Water Quality Through IoT

  • Conference: 2024 IEEE 3rd International Conference on Computing and Machine Intelligence
  • Cited By: 1
  • Year: 2024

Computer Vision-Based IoT Architecture for Post-COVID-19 Preventive Measures

  • Journal: Journal of Advances in Information Technology
  • Volume: 14 (1), Pages: 7-19
  • Cited By: 5
  • Year: 2023

IoT-Based Smart Remote Door Lock and Monitoring System Using an Android Application

  • Journal: Engineering Proceedings
  • Volume: 76 (1), Pages: 85
  • Year: 2024

Conclusion

Mr. A.S.M. Ahsanul Sarkar Akib’s academic excellence, innovative research, and diverse professional achievements make him a strong candidate for the Best Researcher Award. His groundbreaking projects, technical expertise, and dedication to advancing robotics and IoT position him as a leader in his field, deserving of recognition for his contributions to science and technology.

Yanpeng Song | Museum Studies | Best Researcher Award

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Dr. Yanpeng Song | Museum Studies | Best Researcher Award

Director at Information Center of Dunhuang Research Academy, China

Dr. Yanpeng Song is a distinguished researcher and academic in the field of museum studies and grotto arts. He earned his Ph.D. in Museum Studies from the University of Leicester, UK, in 2023, with a focus on the role of museums in promoting moral education for middle school children in China. Dr. Song has been a key staff member at the Dunhuang Research Academy since 2012, where he currently serves as the Director of the Information Center for Dunhuang Studies. He has published over ten academic papers on topics related to Dunhuang grottoes and museum education. Additionally, he has led significant research projects, including a literature review on the Silk Road and Dunhuang, supported by the Dunhuang Research Academy. Dr. Song has also been involved in several national research projects funded by China’s National Social Science Foundation and Ministry of Education. As a respected expert, he has given numerous lectures in museums and universities and actively participates in international academic exchanges, earning recognition in the global academic community.

 

Profile

ORCID

Education

Dr. Yanpeng Song holds a Ph.D. in Museum Studies from the University of Leicester, UK, where he completed his studies from January 2019 to January 2023. His doctoral dissertation, titled “From Moral Indoctrination to Moral Experience: An Experiential Model for Promoting Middle School Children’s Moral Education in China’s Museums,” reflects his innovative approach to museum education. Prior to his Ph.D., Dr. Song earned a Master of Business Administration (MBA) from Lanzhou Jiaotong University between September 2010 and December 2012. He also holds a Bachelor of Arts in English Language and Literature from Anhui Normal University, which he completed from September 2003 to July 2007, and passed the Test for English Majors (TEM-8), further demonstrating his proficiency in the English language. Dr. Song’s diverse educational background has equipped him with a strong foundation in both humanities and business, enabling him to contribute significantly to the fields of museum studies and Dunhuang research.

 Experience

Dr. Yanpeng Song has an extensive professional background in museum studies and cultural heritage research. Since 2012, he has been a key staff member at the Dunhuang Research Academy, where his career has advanced significantly. He served as the Deputy Director of the Information Center for Dunhuang Studies from 2021 to 2024, and in 2024, he was promoted to Director of the Information Center. In addition to his leadership role, Dr. Song has contributed to various research projects, including those supported by the National Social Science Foundation of China and the Ministry of Education of China. He has also worked as a Master’s supervisor at Lanzhou University, mentoring the next generation of scholars. Throughout his career, Dr. Song has been involved in numerous academic seminars, conferences, and international exchange activities, enhancing his reputation in the field of museum studies and grotto art research. His professional work focuses on the intersection of cultural heritage, museum education, and the preservation of Dunhuang’s artistic legacy.

Research Interests

Dr. Yanpeng Song’s research interests lie at the intersection of cultural heritage, museum studies, and grotto art, with a particular focus on the rich legacy of the Dunhuang region. He explores innovative approaches to moral education in museums, as exemplified by his doctoral research on experiential models for middle school children’s moral development. His work delves into the artistic and historical significance of Dunhuang Grottoes, examining topics such as iconography, mural analysis, and thematic representations. Additionally, Dr. Song is dedicated to advancing the understanding of the relationship between the Silk Road and Dunhuang’s cultural history. His academic pursuits extend to enhancing museum learning experiences and fostering global discussions on cultural preservation and education. Through his research, Dr. Song contributes to the preservation and promotion of invaluable cultural assets, bridging historical insights with contemporary educational practices.

Skills

Dr. Yanpeng Song possesses strong analytical and research skills, particularly in cultural heritage and museum studies, with expertise in iconography and historical interpretation. He is proficient in project management, academic writing, and publishing, demonstrated through successful research projects and numerous scholarly articles. As a skilled educator and public speaker, he supervises master’s students and delivers lectures at universities and museums. His technological proficiency and multilingual abilities enhance his leadership in managing digital tools and facilitating international collaborations, solidifying his global academic reputation.

 

Publications

Moral Feeling in a Museum Learning Through an Exemplar-Based Thematic Exhibition

  • Journal: Curator: The Museum Journal
  • Date: 2024-11-28
  • Type: Journal Article

A Preliminary Study on the Relationship between the Paintings on the South Wall and the Votive Text on the North Wall in Mogao Cave 285

  • Journal: Dunhuang Studies
  • Date: 2024-11-26
  • Type: Journal Article (Writing – Original Draft)

Research on the Cock Images in Mogao Cave 285

  • Publication: Proceedings of Research Seminar on Mogao Cave 285
  • Date: 2024-06-11
  • Type: Conference Paper

Research on the Images of Dragon-Shaped Pendant in Dunhuang and Yungang Grottoes

  • Journal: Journal of Hongde
  • Date: 2024-06-01
  • Type: Journal Article (Writing – Original Draft)

Research on the Images and Source of the Three-Pronged Crown Seen in Dunhuang Grottoes

  • Journal: Image Historical Studies
  • Date: 2023-06-01
  • Type: Journal Article

From Moral Indoctrination to Moral Experience: An Experiential Model for Promoting Middle School Children’s Moral Education in China’s Museums

  • Type: Dissertation/Thesis
  • Date: 2023-01-16

Summary of the 2021 Academic Seminar on Dunhuang Studies from the Perspective of the ‘One Belt and One Road’ Initiative & the Council Meeting of the Chinese Association of Dunhuang and Turfan Studies

  • Journal: Dunhuang Studies
  • Date: 2022-01-02
  • Type: Journal Article

Conclusion

Dr. Yanpeng Song’s exceptional research output, leadership in Dunhuang studies, and his influential role in advancing museum education make him a remarkable candidate for the Best Researcher Award. His multidisciplinary expertise, combined with a commitment to academic excellence and international collaboration, positions him as a highly deserving recipient of this recognition.

Swati Tyagi | Mathematical Modelling | Women Researcher Award

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Assist. Prof. Dr. Swati Tyagi | Mathematical Modelling | Women Researcher Award

Assistant Professor at Amity University Punjab, India

Assist. Prof. Dr. Swati Tyagi is an accomplished mathematician and educator, currently serving as an Assistant Professor at Amity University, Mohali. She holds a Ph.D. in Mathematics from IIT Mandi, where she specialized in neural network models and differential equations. With a strong academic background, including an M.Sc. in Mathematics (H) from Panjab University and a B.Sc. in Computer Science, she has held teaching and research positions at prestigious institutions such as Punjab Engineering College, Chandigarh University, and Shoolini University. Her research focuses on mathematical modeling, epidemiology, delay differential equations, fractional differential equations, and stability analysis. Dr. Tyagi has received several accolades, including the SERB National Postdoctoral Fellowship and recognition for her highly cited research. A prolific scholar, she has published extensively in reputed journals, presented at numerous international conferences, and contributed actively to academic administration and journal reviewing.

 

Profile

Google Scholar

Education

Assist. Prof. Dr. Swati Tyagi has an impressive academic trajectory, reflecting her dedication to the field of mathematics. She completed her Ph.D. in Mathematics at IIT Mandi in 2016, earning a remarkable CGPA of 8. Prior to this, she pursued her M.Sc. in Mathematics (Hons.) from Panjab University, graduating in 2011 with a commendable score of 69.55%. Her foundational studies include a B.Sc. in Computer Science from Panjab University, where she excelled with a score of 78.75% in 2009. Dr. Tyagi’s secondary education also highlights her academic excellence, with 81% in her Senior Secondary (Non-Medical) from CBSE in 2006 and an outstanding 86.8% in her High School from PSEB in 2004. Her consistent academic performance was recognized through various scholarships and merit certificates, underscoring her strong academic foundation.

Experience

Assist. Prof. Dr. Swati Tyagi has extensive academic and research experience, having contributed to various prestigious institutions. Currently an Assistant Professor at Amity University, Mohali, she previously served as an Assistant Professor in Mathematics at Chandigarh University and Punjab Engineering College (Deemed to be University), Chandigarh. She gained valuable postdoctoral research experience as a SERB National Postdoctoral Fellow at IIT Ropar, where she worked on advanced neural network models under the mentorship of Dr. S.C. Martha. Earlier in her career, Dr. Tyagi held teaching positions at Shoolini University, Solan, and Chandigarh Group of Colleges, Landran. Her teaching portfolio includes a wide range of undergraduate and postgraduate courses, and she has also served as a teaching assistant for mathematics courses during her tenure at IIT Mandi and IIT Ropar. Dr. Tyagi has been actively involved in academic administration, event coordination, and mentoring students throughout her career.

Research Interests

Assist. Prof. Dr. Swati Tyagi’s research interests lie at the intersection of applied mathematics and computational sciences, with a focus on mathematical modeling and epidemiology. Her work encompasses the study of delay differential equations, fractional differential equations, and neural networks, delving into their stability analysis and solution approximations. Dr. Tyagi is particularly interested in exploring the dynamics of infectious diseases, developing advanced models for neural networks, and analyzing their implications in real-world applications. Her contributions also extend to numerical methods and theoretical frameworks that enhance the understanding of complex systems in her field.

Skills

Assist. Prof. Dr. Swati Tyagi possesses a diverse set of skills that bridge theoretical mathematics and practical computational applications. She is proficient in advanced mathematical techniques, including stability analysis, approximation methods, and modeling complex systems such as neural networks and infectious disease dynamics. Her expertise in computational tools like MATLAB, Maple, and LaTeX enhances her ability to conduct simulations and present research findings effectively. Additionally, her skills in teaching and mentoring students are demonstrated through her extensive experience in delivering undergraduate and postgraduate courses in mathematics and related subjects. Dr. Tyagi is also skilled in academic writing, peer review, and conference presentations, which complement her research and academic endeavors.

 

Publications

Global analysis of a delayed density dependent predator–prey model with Crowley–Martin functional response

  • Authors: JP Tripathi, S Tyagi, S Abbas
  • Journal: Communications in Nonlinear Science and Numerical Simulation
  • Year: 2016
  • Volume: 30, Issues: 1-3, Pages: 45-69
  • DOI: 10.1016/j.cnsns.2015.06.002

Finite-time stability for a class of fractional-order fuzzy neural networks with proportional delay

  • Authors: S Tyagi, SC Martha
  • Journal: Fuzzy Sets and Systems
  • Year: 2019
  • DOI: 10.1016/j.fss.2018.04.011

Discrete fractional‐order BAM neural networks with leakage delay: existence and stability results

  • Authors: J Alzabut, S Tyagi, S Abbas
  • Journal: Asian Journal of Control
  • Year: 2020
  • Volume: 22, Issue: 1, Pages: 143-155
  • DOI: 10.1002/asjc.1973

Mathematical modeling and analysis for controlling the spread of infectious diseases

  • Authors: S Tyagi, SC Martha, S Abbas, A Debbouche
  • Journal: Chaos, Solitons & Fractals
  • Year: 2021
  • Volume: 144, Article: 110707
  • DOI: 10.1016/j.chaos.2021.110707

Global exponential stability of fractional‐order impulsive neural network with time‐varying and distributed delay

  • Authors: HM Srivastava, S Abbas, S Tyagi, D Lassoued
  • Journal: Mathematical Methods in the Applied Sciences
  • Year: 2018
  • Volume: 41, Issue: 5, Pages: 2095-2104
  • DOI: 10.1002/mma.4734

Global Mittag–Leffler stability of complex valued fractional-order neural network with discrete and distributed delays

  • Authors: S Tyagi, S Abbas, M Hafayed
  • Journal: Rendiconti del Circolo Matematico di Palermo Series 2
  • Year: 2016
  • Volume: 65, Pages: 485-505
  • DOI: 10.1007/s12215-015-0190-1

Stability and bifurcation analysis of a fractional‐order model of cell‐to‐cell spread of HIV‐1 with a discrete time delay

  • Authors: S Abbas, S Tyagi, P Kumar, VS Ertürk, S Momani
  • Journal: Mathematical Methods in the Applied Sciences
  • Year: 2022
  • Volume: 45, Issue: 11, Pages: 7081-7095
  • DOI: 10.1002/mma.8071

Conclusion

Dr. Swati Tyagi’s impressive academic achievements, pioneering research, and contributions to the mathematical community make her a deserving candidate for the Research for Women Researcher Award. Her dedication to advancing knowledge and mentoring future generations further underscores her exceptional profile.

Nagamaniammai Govindarajan | Food Technology | Best Researcher Award

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Assoc. Prof. Dr. Nagamaniammai Govindarajan | Food Technology | Best Researcher Award

Associate Professor at SRM Institute of Science and Technology, India

Assoc. Prof. Dr. Nagamaniammai Govindarajan is a distinguished academic and researcher with over 21 years of experience spanning academia, industry, and quality assurance. She holds a Ph.D. in Nutraceutical and Functional Foods from SRM Institute of Science and Technology, Chennai. Her academic background includes an M.Tech. in Food Technology and Biochemical Engineering from Jadavpur University and a B.E. in Food Processing and Preservation Technology from Avinashilingam Deemed University. Currently serving as an Associate Professor in the Department of Food Process Engineering at SRM Institute of Science and Technology, she is actively involved in teaching, mentoring, and research. Her expertise includes food processing, nutraceutical development, quality systems, and value-added food products. Dr. Govindarajan has also contributed significantly to her field with numerous publications in SCI-indexed journals, focusing on innovative approaches to food technology and nutrition.

 

Profile

SCOPUS

ORCID

Education

Assoc. Prof. Dr. Nagamaniammai Govindarajan holds a Ph.D. in Process Development, Optimization, and Quality Evaluation of Nutraceutical and Functional Foods from SRM Institute of Science and Technology (SRM University), Chennai, completed in June 2014. She earned her Master of Technology in Food Technology and Biochemical Engineering from Jadavpur University, Kolkata, graduating with 71% in 2004. Additionally, she holds a Bachelor of Engineering in Food Processing and Preservation Technology from Avinashilingam Deemed University, Coimbatore, where she graduated with 69% in 2001. Her diverse academic qualifications form a strong foundation for her expertise in food science and technology.

Experience

Assoc. Prof. Dr. Nagamaniammai Govindarajan brings over 21 years of extensive experience in academia, corporate sectors, and research and development. She currently serves as an Associate Professor in the Department of Food Process Engineering at SRM Institute of Science and Technology, where she teaches M.Tech. courses, mentors Ph.D. and M.Tech. students, establishes industry linkages, and spearheads academic research initiatives. Previously, she was Professor and Head of the Department of Food Processing Technology at AMET University, where she successfully implemented activity-based teaching methodologies, established undergraduate program infrastructure, and secured external funding for research. Her industry experience includes serving as Assistant General Manager and Head (QA – R&D) at Sri Varadaraja Food Exports Pvt. Ltd., where she established quality systems adhering to ISO 9001:2015, ISO 22000:2005, HACCP, and organic processing standards. Dr. Govindarajan’s career reflects her commitment to excellence in teaching, research, and the food processing industry.

Research Interests

Assoc. Prof. Dr. Nagamaniammai Govindarajan’s research interests are deeply rooted in the field of food science and technology, with a particular focus on the development of nutraceutical and functional foods. She is dedicated to optimizing processing techniques to enhance the nutritional quality of food products and mitigate anti-nutritional factors. Her work explores the utilization of underutilized legumes, plant-based protein alternatives, and advanced food processing methods to address global nutritional challenges. Additionally, she is keenly interested in food safety, quality assurance systems, and innovative approaches to sustainable food production, contributing to advancements in both academic and industrial food science domains.

Skills

Assoc. Prof. Dr. Nagamaniammai Govindarajan possesses extensive research skills in food science and technology, with expertise in developing innovative processes for nutraceuticals and functional foods. She is adept at process development, optimization, and quality evaluation, focusing on enhancing the nutritional value of food products while mitigating anti-nutritional factors. Her skills include advanced techniques in food processing, such as leveraging legumes and plant-based proteins for sustainable alternatives. She is proficient in quality assurance systems, including ISO and HACCP standards, ensuring safety and compliance in food production. Additionally, her analytical abilities extend to experimental design, data interpretation, and collaborative research, contributing to impactful publications in leading scientific journals.

 

Publications

Traditional weaning foods and processing methods with fortification for sustainable development of infants to combat zero hunger: a review

  • Authors: Kabeer, S., Mary, S.J., Govindarajan, N., Essa, M.M., Qoronfleh, M.W.
  • Journal: Journal of Food Science and Technology
  • Year: 2024
  • Volume: 61(12), Pages: 2263–2274

Unlocking the potential of underutilized legumes: nutritional benefits, anti-nutritional challenges, and mitigation strategies

  • Authors: Navin Venketeish, K.S., Govindarajan, N., Pandiselvam, R., Mousavi Khaneghah, A.
  • Journal: Journal of Food Measurement and Characterization
  • Year: 2024
  • Volume: 18(11), Pages: 9588–9602

Optimization and extraction of annatto pigments obtained from Bixa orellana L. using supercritical fluid extraction

  • Authors: Paramadhas, S., Selvi, P., Shridar, B., Govindarajan, N., Pandiselvam, R.
  • Journal: Microchemical Journal
  • Year: 2024
  • Volume: 206, Article: 111494

Effect of drying methods on the biochemical and functional qualities of ready-to-mix traditional pearl millet porridge (Pennisetum glaucum)

  • Authors: Sivasubramaniyan, V., Govindarajan, N., Kottur Senthilkumar, N.V., Kabeer, S.
  • Journal: International Journal of Nutrition, Pharmacology, Neurological Diseases
  • Year: 2024
  • Volume: 14(3), Pages: 337–342

Effect of partial replacement of whole milk khoa with groundnut (Arachis hypogaea) and sunflower seeds (Helianthus annuus) milk on biochemical and functional properties

  • Authors: Sivaraj, D., Govindarajan, N., Pandiselvam, R.
  • Journal: Journal of Food Science and Technology
  • Year: 2024

Impact of Different Processing Techniques on the Retention of Zinc in Foods

  • Authors: Govindarajan, N., Kabeer, S., Venketeish, N.
  • Book Chapter: Zinc: Early Development, Applications, and Emerging Trends
  • Year: 2024
  • Pages: 223–231

Effect of drying technique on physiochemical and nutritional properties of Eleusine coracana (finger millet) porridge powder

  • Authors: Kabeer, S., Govindarajan, N., Radhakrishnan, P., Essa, M.M., Qoronfleh, M.W.
  • Journal: Journal of Food Science and Technology
  • Year: 2023
  • Volume: 60(12), Pages: 3024–3034

Biosensors as freshness indicators for packed animal and marine products: A review

  • Authors: Milintha, T.P.M., Kumaravel, B., Nagamaniammai, G., Qoronfleh, M.W., Chacko, L.
  • Journal: International Food Research Journal
  • Year: 2023
  • Volume: 30(4), Pages: 848–854

Conclusion

Assoc. Prof. Dr. Nagamaniammai Govindarajan’s robust academic background, diverse professional experience, and impactful research outputs make her an excellent candidate for the “Best Researcher Award.” Her contributions to nutraceuticals, functional foods, and sustainable food processing reflect her commitment to advancing science and addressing global challenges.

Ms. Sana Naz | Blockchain Consensus | Best Researcher Award

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Ms. Sana Naz | Blockchain Consensus | Best Researcher Award

Ph.D. Scholar at Hanyang University, South Korea

Ms. Sana Naz, born in Karachi, Pakistan, in 1990, is a dedicated researcher and academic in the field of Computer Science and Engineering. She holds a B.S. in Computer Engineering from Sir Syed University of Engineering and Technology and an M.S. in Software Engineering from Hamdard University. Currently, she is pursuing her Ph.D. at Hanyang University, South Korea, with a focus on blockchain technology and consensus mechanisms. With prior experience as a faculty member at Dawood University of Engineering and Technology and as a Software Engineer, Ms. Naz has contributed to both academia and industry. Her research interests include blockchain networks, software design patterns, and code quality, complemented by her active participation in professional organizations like the Pakistan Engineering Council.

 

Profile

SCOPUS

GOOGLE SCHOLAR

ORCID

Education

Ms. Sana Naz was born in Karachi, Pakistan, in 1990. She completed her Bachelor of Science degree in Computer Engineering from Sir Syed University of Engineering and Technology, Pakistan, in 2012. She then pursued her Master of Science degree in Software Engineering at Hamdard University, Pakistan, graduating in 2016. Currently, she is advancing her academic journey by pursuing a Ph.D. in Computer Science and Engineering at Hanyang University, South Korea, where her research focuses on developing innovative consensus mechanisms for blockchain networks.

Experience

Ms. Sana Naz has a diverse professional background, blending academic and industry expertise. Before pursuing her Ph.D. in Computer Science and Engineering at Hanyang University, South Korea, she served as a faculty member in the Computer Engineering Department at Dawood University of Engineering and Technology, Pakistan. During her tenure, she contributed to curriculum development and academic mentorship. In addition to her academic roles, she gained significant industry experience as a Software Engineer, working with various software houses on projects involving software development and design. This combination of teaching and industry experience has equipped her with a strong foundation for conducting impactful research in blockchain technology and software engineering.

Research Interests

Ms. Sana Naz’s research interests lie at the intersection of blockchain technology and software engineering. She is particularly focused on developing advanced consensus mechanisms for blockchain networks to enhance their security and efficiency. Her work also delves into software design patterns, anti-patterns, and code smells, aiming to improve software quality and maintainability. With a keen interest in exploring innovative solutions, she is dedicated to advancing the fields of cybersecurity and cryptography through her research contributions.

Skills

Ms. Sana Naz possesses a versatile skill set that spans both technical and academic domains. She is proficient in blockchain technology, specializing in developing and optimizing consensus mechanisms. Her expertise extends to software engineering, including software design patterns, anti-patterns, and code quality analysis. Additionally, she holds certifications such as Microsoft Technology Associate (MTA) in Database and Software Development (C#) and Microsoft Office Specialist (MOS) in Word, Excel, and PowerPoint. With strong analytical and problem-solving abilities, Ms. Naz combines her technical expertise with a solid foundation in research and teaching, making her a well-rounded professional in her field.

 

Publications

Title: Why the New Consensus Mechanism is Needed in Blockchain Technology?

  • Authors: S. Naz, SUJ Lee
  • Conference: 2020 Second International Conference on Blockchain Computing and Applications
  • Year: 2020
  • Citations: 23

Title: Sea Shield: A Blockchain Technology Consensus to Improve Proof-of-Stake-Based Consensus Blockchain Safety

  • Authors: S. Naz, SUJ Lee
  • Journal: Mathematics
  • Year: 2024
  • Volume: 12
  • Issue: 6
  • Article ID: 833
  • Citations: 0

Title: S&Sem: A Secure and Speed-Up Election Mechanism for PoS-Based Blockchain Network

  • Authors: S. Naz, SUJ Lee
  • Status: Available at SSRN
  • Year: 2024

Conclusion

Ms. Sana Naz is a strong candidate for the Best Researcher Award in the category of Cybersecurity and Cryptography. Her innovative contributions to blockchain technology, combined with her academic and professional accomplishments, make her a deserving nominee. Additionally, she is equally qualified for recognition in categories such as the Women Research Award, Young Scientist Award, and Best Research Scholar Award. Her work is both relevant and forward-looking, contributing to the advancement of secure and efficient blockchain systems.

Sunzida Siddique | Physical Models with AI | Best Researcher Award

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Ms. Sunzida Siddique | Physical Models with AI | Best Researcher Award

Student at Daffodil International University, Bangladesh

Ms. Sunzida Siddique is a skilled software engineer and researcher with a strong academic foundation in Computer Science. She holds a Master of Science in Computer Science from Jahangirnagar University and a Bachelor of Science in Computer Science & Engineering from Daffodil International University. Currently working as a Jr. Software Engineer at Silicon Orchard Ltd., she specializes in AI, machine learning, deep learning, and cybersecurity. Ms. Siddique has contributed to several impactful projects, including Bengali speech-based gender classification and machine learning-based book reading level prediction. Her leadership experience includes roles as an organizer and research instructor in various tech-related clubs. With expertise in programming, web development, and advanced computational techniques, she is committed to advancing research and technology.

 

Profile

SCOPUS

GOOGLE SCHOLAR

ORCID

Education

Ms. Sunzida Siddique has an impressive academic background, which reflects her dedication and aptitude in the field of Computer Science and Engineering. She holds a Master of Science (M.Sc.) in Computer Science from Jahangirnagar University, where she graduated with a remarkable CGPA of 3.83. Her academic journey also includes a Bachelor of Science (B.Sc.) in Computer Science & Engineering from Daffodil International University, where she achieved a strong CGPA of 3.70. Prior to her university education, Ms. Siddique completed her Higher Secondary Certificate from Narsingdi Model College in 2015, securing a perfect 5.00 in the Science group. She also earned a Secondary School Certificate from Brahmondi Girl’s High School in 2013, where she similarly excelled with a perfect score of 5.00. Ms. Siddique’s consistent academic excellence throughout her educational journey highlights her commitment to mastering her field and her readiness to contribute to cutting-edge research and technological advancements.

Experience

Ms. Sunzida Siddique has gained valuable professional experience in both technical and administrative roles. Since February 2023, she has been working as a Jr. Software Engineer at Silicon Orchard Ltd., where she applies her skills in AI, machine learning, deep learning, cybersecurity, and generative AI to solve complex analytical problems. Her work focuses on leveraging cutting-edge technologies to address challenges in various domains. Prior to this, Ms. Siddique served as an Office Executive at Future Cloud Bangladesh Ltd. from April 2021 to January 2023, where she handled customer service, administrative duties, and maintained effective communication through email and phone. This role honed her multitasking and communication skills while allowing her to gain valuable experience in organizational operations. Her professional journey reflects a strong

Research Interests

Ms. Sunzida Siddique’s research interests lie at the intersection of Artificial Intelligence (AI), Machine Learning, Deep Learning, and Cybersecurity. She is particularly focused on exploring innovative solutions through AI-driven techniques, such as natural language processing and computer vision, to address real-world challenges. Her work includes projects like Bengali Continuous Speech Voice-Based Gender Classification and Machine Learning-based Book Reading Level Prediction, where she applies machine learning algorithms to enhance language processing and educational tools. Additionally, Ms. Siddique is interested in the integration of Physics Guided Neural Networks and Knowledge Graphs to solve complex scientific problems. Her passion for advancing Generative AI and Cybersecurity further drives her research, as she aims to create secure, efficient, and intelligent systems that can transform industries and improve everyday life.

Skills

Ms. Sunzida Siddique possesses a diverse skill set that combines technical proficiency with strong interpersonal capabilities. She is well-versed in programming languages such as C, Java, and Python, with a particular focus on AI and Machine Learning applications. Her expertise extends to utilizing frameworks and libraries to develop solutions in deep learning and cybersecurity. In addition to her programming skills, Ms. Siddique is proficient in web development technologies, including HTML5, CSS3, Bootstrap5, and JavaScript. She is also adept at using tools like Microsoft Word, Excel, and PowerPoint, which support her in documentation, data analysis, and presentation tasks. Her multitasking abilities, coupled with strong communication and leadership skills, enable her to collaborate effectively and manage projects efficiently. These skills, along with her commitment to continuous learning, position Ms. Siddique as a highly capable and adaptable professional.

 

Publication Top Noted

Title: Survey on machine learning biases and mitigation techniques

  • Authors: S Siddique, MA Haque, R George, KD Gupta, D Gupta, MJH Faruk
  • Journal: Digital
  • Volume: 4
  • Issue: 1
  • Pages: 1-68
  • Cited by: 9
  • Year: 2023

Title: Machine learning-based model for predicting stress level in online education due to coronavirus pandemic: a case study of Bangladeshi students

  • Authors: S Siddique, S Baidya, MS Rahman
  • Conference: 2021 5th International Conference on Electrical Information and Communication Technology (EICT)
  • Cited by: 8
  • Year: 2021

Title: Cyber Security issues in the industrial applications of digital twins

  • Authors: S Siddique, MA Haque, RH Rifat, R George, K Shujaee, KD Gupta
  • Conference: 2023 IEEE Symposium Series on Computational Intelligence (SSCI)
  • Pages: 873-878
  • Cited by: 4
  • Year: 2023

Title: Challenges and opportunities of computational intelligence in industrial control system (ICS)

  • Authors: S Siddique, MA Haque, RH Rifat, LR Das, S Talukder, SB Alam, KD Gupta
  • Conference: 2023 IEEE Symposium Series on Computational Intelligence (SSCI)
  • Pages: 1158-1163
  • Cited by: 2
  • Year: 2023

Title: Deep Learning Modeling for Gourd Species Recognition Using VGG-16

  • Authors: MM Hasan, K Alam, S Siddique, TA Topu, MT Habib, MS Uddin
  • Book: Computer Vision and Machine Learning in Agriculture, Volume 3
  • Pages: 19-35
  • Cited by: 2
  • Year: 2023

Title: Computer Vision and Machine Learning in Agriculture, Volume 3

  • Authors: JC Bansal, MS Uddin
  • Publisher: Springer Verlag, Singapore
  • Cited by: 2
  • Year: 2023

Title: People Thoughts Prediction using Machine Learning on Women’s Contribution in ICT in Bangladesh

  • Authors: S Akter, S Siddique, I Jahan, T Rabeya, MR Mim
  • Conference: 2021 IEEE 6th International Conference on Computing, Communication and Networking Technologies (ICCCNT)
  • Cited by: 1
  • Year: 2021

Conclusion

Ms. Sunzida Siddique’s strong academic foundation, her contribution to innovative research projects, and her leadership in organizing and mentoring within technical clubs make her an exceptional candidate for the Research for Best Researcher Award. Her work in AI, machine learning, and other emerging technologies, combined with her enthusiasm for continuous learning, underscores her potential to make significant strides in her field.