Maryam Kheirabadi | Computer Science | Women Researcher Award

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Women Researcher Award

Maryam Kheirabadi
Affiliation Islamic Azad University
Country Iran
Scopus ID 57210153054
Documents 16
Citations 710
h-index 9
Subject Area Computer Science
Event International Academic Achievements & Awards
ORCID 0000-0001-8980-4299

Maryam Kheirabadi
Islamic Azad University, Iran

The Women Researcher Award recognizes distinguished scholarly achievement, research excellence, scientific innovation, and sustained academic contributions by women researchers. Maryam Kheirabadi has established a research profile in computer science, artificial intelligence, medical image analysis, and intelligent healthcare systems through peer-reviewed publications indexed in Scopus. Her work demonstrates interdisciplinary applications of deep learning, convolutional neural networks, recurrent neural networks, and medical decision-support systems, contributing to diagnostic technologies and intelligent recommendation systems.[1]

Abstract

Maryam Kheirabadi’s scholarly work focuses on applying artificial intelligence techniques to healthcare, medical image processing, pattern recognition, and educational recommendation systems. Her publications emphasize convolutional neural networks, deep learning architectures, image classification, recurrent neural networks with attention mechanisms, and computer-assisted diagnosis of gastric diseases. These contributions illustrate the integration of computer science methodologies into practical healthcare applications while advancing intelligent computational models for diagnostic accuracy and automated decision support.[2]

Keywords

Artificial Intelligence, Deep Learning, Computer Science, Medical Image Analysis, Convolutional Neural Networks, Healthcare Informatics, Machine Learning, Clinical Decision Support

Introduction

Artificial intelligence has become an increasingly influential discipline for solving complex healthcare challenges. The combination of machine learning, deep neural networks, and biomedical image analysis has improved diagnostic accuracy, automated disease detection, and clinical decision support. Within this evolving research landscape, Maryam Kheirabadi has contributed to studies addressing intelligent diagnostic systems, neural network optimization, and educational recommender systems, demonstrating interdisciplinary collaboration between computer science and healthcare research.[2]

Research Profile

According to the supplied Scopus metrics, Maryam Kheirabadi has authored sixteen indexed publications, received more than seven hundred citations, and achieved an h-index of nine. Her research portfolio centers on artificial intelligence, image processing, neural networks, biomedical signal analysis, and intelligent software systems. Her publications demonstrate continued engagement with computational approaches that support clinical diagnosis and intelligent educational technologies.[1]

Research Contributions

  • Development of deep convolutional neural network models for gastric cancer diagnosis using tongue image analysis.
  • Research on intelligent educational recommender systems employing recurrent neural networks and attention mechanisms.
  • Advancement of MRI brain tumor classification through convolutional neural network architectures.
  • Application of biomedical image processing techniques for healthcare diagnostics.
  • Contribution to explainable and data-driven medical decision-support technologies.

Publications

  • Diagnosis of Gastric Cancer via Classification of the Tongue Images using Deep Convolutional Networks (2021). Journal of Information Systems and Telecommunication.
  • Dynamic Educational Recommender System Based on Improved Recurrent Neural Networks Using Attention Technique (2021). Applied Artificial Intelligence. DOI: https://doi.org/10.1080/08839514.2021.2005298
  • Increasing the Accuracy in the Diagnosis of Stomach Cancer Based on Color and Lint Features of Tongue (2021). Biomedical Signal Processing and Control. DOI: https://doi.org/10.1016/j.bspc.2021.102782
  • Y-net: A Reducing Gaussian Noise Convolutional Neural Network for MRI Brain Tumor Classification with NADE Concatenation (2021). Biomedical Physics and Engineering Express.
    DOI: https://doi.org/10.1088/2057-1976/ac107b

Research Impact

The reported citation record reflects meaningful scholarly visibility within computer science and healthcare-related artificial intelligence research. Her studies contribute to the development of intelligent diagnostic systems, automated disease classification, medical image interpretation, and educational recommendation technologies. These interdisciplinary contributions support ongoing research into machine learning applications that improve analytical performance and healthcare decision-making.[3]

Award Suitability

Maryam Kheirabadi demonstrates qualifications consistent with the objectives of the Women Researcher Award through sustained scientific publication, interdisciplinary innovation, measurable citation performance, and contributions to artificial intelligence applications in healthcare. Her research profile reflects active engagement in internationally indexed scholarship and supports recognition for advancing computational methods with practical societal applications.[1]

Conclusion

Maryam Kheirabadi’s publication portfolio illustrates the integration of artificial intelligence with healthcare and intelligent information systems. Her contributions to deep learning, neural network design, biomedical imaging, and recommendation systems have strengthened interdisciplinary research in computer science. The documented publication record, citation performance, and research focus collectively support recognition within academic achievement and research excellence programs.

References

  1. Elsevier. (n.d.). Scopus Author Details: Maryam Kheirabadi, Author ID 57210153054. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57210153054
  2. Kheirabadi, M., et al. (2021). Diagnosis of Gastric Cancer via Classification of the Tongue Images using Deep Convolutional Networks. Journal of Information Systems and Telecommunication.
    http://www.scopus.com/inward/record.url?eid=2-s2.0-85111735812&partnerID=MN8TOARS
  3. Kheirabadi, M., et al. (2021). Dynamic Educational Recommender System Based on Improved Recurrent Neural Networks Using Attention Technique. Applied Artificial Intelligence. DOI: https://doi.org/10.1080/08839514.2021.2005298
  4. Kheirabadi, M., et al. (2021). Increasing the Accuracy in the Diagnosis of Stomach Cancer Based on Color and Lint Features of Tongue. Biomedical Signal Processing and Control. DOI: https://doi.org/10.1016/j.bspc.2021.102782
  5. Kheirabadi, M., et al. (2021). Y-net: A Reducing Gaussian Noise Convolutional Neural Network for MRI Brain Tumor Classification with NADE Concatenation. Biomedical Physics and Engineering Express. DOI: https://doi.org/10.1088/2057-1976/ac107b

Divya Gautam | Computer Science | Innovative Research Award

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Innovative Research Award

Divya Gautam
Affiliation STME, SVKM’s NMIMS, Indore
Country India
Scopus ID 57315556700
Documents 17
Citations 81
h-index 4
Subject Area Computer Science
Event International Academic Achievements & Awards
ORCID 0000-0002-1395-997X

Divya Gautam
STME, SVKM’s NMIMS, Indore, India

The Innovative Research Award recognizes researchers whose scholarly contributions demonstrate originality, scientific rigor, and meaningful impact within their respective disciplines. Divya Gautam has established a research portfolio in computer science with particular emphasis on mobile ad hoc networks (MANET), denial-of-service attack detection, machine learning-based network security, and optimization techniques. Her publications have contributed to understanding secure communication mechanisms and intelligent intrusion detection methods for decentralized wireless environments.[1]

Abstract

Divya Gautam’s research primarily investigates cybersecurity challenges within mobile ad hoc networks, focusing on intelligent techniques for detecting and mitigating denial-of-service attacks. Her work integrates support vector machines, particle swarm optimization, and pattern-based analytical methods to improve network resilience and communication reliability. The resulting publications collectively contribute toward advancing practical security frameworks for decentralized wireless systems while supporting broader developments in computer science research.[2]

Keywords

Computer Science, Cybersecurity, Mobile Ad Hoc Networks, DoS Detection, Machine Learning, Network Security, Support Vector Machine, Optimization Algorithms

Introduction

Modern wireless communication environments require effective mechanisms for detecting malicious activities while maintaining reliable network performance. Mobile ad hoc networks remain particularly vulnerable because of their decentralized architecture and dynamic topology. Research conducted by Divya Gautam addresses these challenges through computational intelligence techniques capable of improving attack detection accuracy while reducing network disruption.[3]

Research Profile

According to Scopus, Divya Gautam has authored seventeen indexed publications with eighty-one citations and an h-index of four. Her scholarly activities are concentrated within computer science, particularly network security, intrusion detection, wireless communication, intelligent optimization algorithms, and secure distributed networking. These metrics indicate an active and developing research profile with contributions appearing in journals, conference proceedings, and scholarly book chapters.[1]

Research Contributions

  • Development of pattern-based techniques for detecting DoS and DDoS attacks.
  • Application of Support Vector Machine methodologies for intelligent intrusion detection.
  • Integration of Particle Swarm Optimization with machine learning for improved classification.
  • Comparative evaluation of attack mitigation techniques in MANET environments.
  • Contribution to secure wireless networking research through conference and journal publications.

Publications

  1. A Comparative Study of DoS Attack Detection and Mitigation Techniques in MANET (2020), Lecture Notes in Networks and Systems.DOI:
    10.1007/978-981-15-2071-6_50
  2. Fixed points results in b-metric spaces (2020), Materials Today: Proceedings.DOI:
    10.1016/j.matpr.2021.05.147
  3. Pattern Based Detection and Mitigation of DoS Attacks in MANET Using SVM-PSO (2020).DOI:
    10.1007/978-3-030-44758-8_16
  4. Detection of DoS attacks in MANET using LIBSVM (2019), International Journal of Engineering and Advanced Technology.
  5. Pattern based detection of DDoS attacks in MANET (2019), International Journal of Innovative Technology and Exploring Engineering.

Research Impact

The available publication record demonstrates continued engagement with contemporary cybersecurity challenges affecting wireless communication networks. Citation activity reflects scholarly visibility, while the combination of journal articles, conference papers, and book chapters indicates dissemination across multiple academic publishing platforms. The research has practical relevance for secure communication systems, intelligent network monitoring, and machine learning-based threat detection.[4]

Award Suitability

Based on the documented publication record, indexed research output, citation performance, and focus on innovative cybersecurity methodologies, Divya Gautam demonstrates characteristics commonly associated with recognition through an Innovative Research Award. Her research emphasizes methodological development, practical applicability, interdisciplinary integration, and contribution to secure wireless networking technologies.[5]

Conclusion

Divya Gautam’s academic portfolio reflects sustained contributions to computer science, particularly in network security and intelligent attack detection for mobile ad hoc networks. Through the integration of machine learning and optimization techniques, her publications support ongoing developments in cybersecurity research while contributing to the broader understanding of resilient wireless communication systems. The documented scholarly record provides a solid foundation for academic recognition within innovation-oriented research awards.

References

  1. Elsevier. (n.d.). Scopus Author Details: Divya Gautam, Author ID 57315556700. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57315556700
  2. Gautam, D., & Tokekar, V. (2020). A Comparative Study of DoS Attack Detection and Mitigation Techniques in MANET. Lecture Notes in Networks and Systems, Springer. DOI: https://doi.org/10.1007/978-981-15-2071-6_50
  3. Gautam, D., & Tokekar, V. (2020). Pattern Based Detection and Mitigation of DoS Attacks in MANET Using SVM-PSO. Intelligent Computing Applications for Sustainable Real-World Systems.DOI: https://doi.org/10.1007/978-3-030-44758-8_16
  4. Gautam, D., & Tokekar, V. (2020). Fixed Points Results in b-Metric Spaces. Materials Today: Proceedings.
    https://doi.org/10.1016/j.matpr.2021.05.147
  5. SVKM’s NMIMS. (n.d.). Faculty Profile: Dr. Divya Gautam, Assistant Professor, Computer Engineering.
    https://indore.nmims.edu/faculty/dr-divya-gautam/

Quang Minh Tran | Computer Science | Innovative Research Award

Published on by Academic Achievements

Innovative Research Award

Quang Minh Tran
Affiliation University of Wollongong
Country Australia
ORCID 0009-0007-9413-2600
Documents 2
Subject Area Computer Science
Event International Academic Achievements & Awards

Quang Minh Tran
Institution: University of Wollongong,

Quang Minh Tran is a researcher in the field of Computer Science whose recent work focuses on trustworthy artificial intelligence, deepfake audio detection, adversarial machine learning, and multimedia security. His research investigates the robustness of deep learning systems against sophisticated adversarial attacks while contributing to the development of reliable forensic methods for synthetic audio detection. These studies address important challenges in AI security, digital trust, and the protection of multimedia systems against manipulation.[1]

Abstract

This article presents an academic profile of Quang Minh Tran in recognition of research contributions to Computer Science, particularly in adversarial machine learning and deepfake audio detection. His work examines the resilience of artificial intelligence systems under universal adversarial perturbations while advancing forensic methods capable of identifying manipulated synthetic speech. The research contributes to improving the security, reliability, and robustness of AI-enabled multimedia technologies.[2]

Keywords

Computer Science, Artificial Intelligence, Deepfake Audio Detection, Adversarial Machine Learning, Multimedia Security, Universal Adversarial Perturbations, AI Robustness, Audio Forensics, Digital Trust, Machine Learning Security.

Introduction

The increasing adoption of artificial intelligence has intensified concerns regarding the misuse of generative technologies, including deepfake audio. Detecting synthetic speech while maintaining robustness against adversarial attacks represents a significant challenge in AI security. Quang Minh Tran’s research explores these issues through systematic evaluation of deepfake detectors and vocoder fingerprint detectors, supporting the development of trustworthy AI systems suitable for practical deployment.[2]

Research Profile

  • Research field: Computer Science.
  • Primary interests include AI security and multimedia forensics.
  • Research emphasizes adversarial robustness of deep learning systems.
  • Investigates deepfake audio detection and vocoder fingerprint analysis.
  • Contributes to trustworthy artificial intelligence and secure multimedia applications.

Research Contributions

Quang Minh Tran has contributed to the evaluation of adversarial robustness in deepfake audio detection systems through comprehensive analysis of universal adversarial perturbations. His work investigates vulnerabilities in deep learning-based forensic models while identifying approaches that improve detector resilience. These contributions are relevant to cybersecurity, digital media authentication, trustworthy AI, and the broader development of reliable machine learning systems capable of operating under adversarial conditions.[2]

Publications

  • Evaluating Adversarial Robustness of Deepfake Audio Detectors and Vocoder Fingerprint Detectors Against Universal Adversarial Perturbations. Future Internet, 2026. DOI:10.3390/fi18070344
  • Evaluating Adversarial Robustness of Deepfake Audio Detectors and Vocoder Fingerprint Detectors Against Universal Adversarial Perturbations. Preprint, 2026. DOI:10.20944/preprints202606.0272.v1

Research Impact

The research addresses an increasingly important area of artificial intelligence by strengthening the understanding of adversarial vulnerabilities affecting deepfake detection technologies. The findings provide valuable insights for researchers, cybersecurity practitioners, and developers seeking to improve the resilience of AI-based forensic systems. This work contributes to ongoing efforts aimed at enhancing digital trust, secure communication, and responsible deployment of artificial intelligence.[2]

Award Suitability

Based on the available scholarly publications, Quang Minh Tran demonstrates emerging research contributions in artificial intelligence security, adversarial machine learning, and multimedia forensics. His work addresses contemporary challenges involving deepfake detection and AI robustness using rigorous scientific methodology. These contributions provide a sound academic basis for consideration within the Innovative Research Award category of the International Academic Achievements & Awards program.[1]

Conclusion

Quang Minh Tran’s research advances the field of Computer Science by addressing the robustness and security of artificial intelligence systems against adversarial manipulation. His investigations into deepfake audio detection and multimedia forensics contribute to the growing body of knowledge supporting trustworthy AI technologies. The combination of technical innovation, practical relevance, and scientific rigor reflects meaningful scholarly progress within the rapidly evolving domain of AI security.

References

  1. ORCID. (n.d.). Quang Minh Tran ORCID Record.
    https://orcid.org/0009-0007-9413-2600
  2. Future Internet. (2026). Evaluating Adversarial Robustness of Deepfake Audio Detectors and Vocoder Fingerprint Detectors Against Universal Adversarial Perturbations.
    https://doi.org/10.3390/fi18070344
  3. Preprints.org. (2026). Evaluating Adversarial Robustness of Deepfake Audio Detectors and Vocoder Fingerprint Detectors Against Universal Adversarial Perturbations.
    https://doi.org/10.20944/preprints202606.0272.v1

Md Minhajul Amin | Computer Science | Best Innovator Award

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Best Innovator Award

Md Minhajul Amin
Affiliation Intento Analytics
Country United States
Google Scholar 8Cc4X70AAAAJ
Documents 11
Citations 86
h-index 4
Subject Area Computer Science
Event International Academic Achievements & Awards

Md Minhajul Amin
Intento Analytics, United States

Md Minhajul Amin is a data analyst and researcher specializing in business analytics, artificial intelligence, healthcare informatics, project management, and big data applications. His multidisciplinary research integrates predictive analytics, machine learning, digital healthcare, and operational decision-making. Through academic publications, IEEE conference papers, industrial projects, and editorial activities, he has contributed to advancing evidence-based management and intelligent data-driven systems.[1]

Abstract

Md Minhajul Amin has established an interdisciplinary research portfolio connecting artificial intelligence, predictive analytics, healthcare systems, fraud detection, digital communication, and project management. His work emphasizes practical analytical solutions that improve organizational efficiency and support evidence-based decision-making across healthcare and business environments.[2]

Keywords

Business Analytics, Artificial Intelligence, Machine Learning, Healthcare Analytics, Telemedicine, Big Data, Project Management, Data Visualization, Predictive Analytics, Digital Transformation.

Introduction

Holding an M.S. in Information Systems from Central Michigan University, Md Minhajul Amin combines academic research with industry experience in data analytics. His research focuses on solving real-world organizational challenges through statistical analysis, visualization, artificial intelligence, and business intelligence technologies.[3]

Research Profile

  • IEEE conference contributor.
  • Associate Editor at IFR Discovery.
  • Editorial Board Member of The Science Post Journal.

Research Contributions

His publications cover ethical business analytics, AI-powered project management, fraud detection, cancer classification using machine learning, telemedicine implementation, customer segmentation, healthcare dashboards, RF communication systems, and network management technologies. His research demonstrates the practical integration of advanced analytics into healthcare, engineering, and organizational management.[4]

Publications

  • Ethical Challenges in Business Analytics.
  • Developing a Project Management Dashboard for Telehealth.
  • Business Analytics in the Era of Big Data.
  • AI-Powered Personalized Marketing.
  • Classification of Cancer Stages Using Machine Learning.

Research Impact

His scholarly output has attracted citations across business analytics, healthcare informatics, artificial intelligence, and project management disciplines. His professional activities further include patented AI-driven analytical devices and collaborative research addressing practical industry challenges.

Award Suitability

Considering his multidisciplinary research portfolio, peer-reviewed publications, IEEE conference participation, editorial responsibilities, industrial analytics experience, and measurable research impact, Md Minhajul Amin demonstrates qualifications aligned with recognition under an Innovative Research Award category.

Conclusion

Md Minhajul Amin continues contributing to applied research that bridges artificial intelligence, business analytics, healthcare systems, and project management. His academic achievements and industry experience illustrate the growing role of data-driven methodologies in addressing modern organizational and societal challenges.

External Links

References

    1. Amin, M. M., Munmun, Z. S., Atayeva, J., Ahmed, S. W., Shamim, I., & Akter, M. H. (2025).
      Developing a Project Management Dashboard for Telehealth Implementation.
      https://www.researchgate.net/publication/392591167_Developing_a_Project_Management_Dashboard_for_Telehealth_Implementation
    2. Google Scholar. (2026).
      Md Minhajul Amin – Google Scholar Profile.
      https://scholar.google.com/citations?user=8Cc4X70AAAAJ&hl=en
    3. ResearchGate. (2026).
      Md Minhajul Amin – Research Profile.
      https://www.researchgate.net/profile/Md-Minhajul-Amin
    4. LinkedIn. (2026).
      Md Minhajul Amin – Professional Profile.
      https://www.linkedin.com/in/md-minhajul-amin-cmu

Getachew Getu Enyew | Computer Science | Research Excellence Award

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Mr. Getachew Getu Enyew | Computer Science | Research Excellence Award

Information Network Security Administration (INSA) & Addis Ababa Science and Technology University | Ethiopia

Getachew Getu Enyew is an AI/ML Engineer and emerging researcher specializing in artificial intelligence, machine learning, and autonomous systems. He holds an M.Sc. in Electrical and Computer Engineering from Addis Ababa Science and Technology University, Ethiopia. His research focuses on intelligent decision-making systems, robotics perception, computer vision, and anomaly detection for real-world applications, particularly in cybersecurity and critical infrastructure. Currently working at the Information Network Security Administration (INSA), he develops AI-driven solutions for threat detection and leads MLOps integration for scalable deployment of machine learning models. He has authored multiple research papers on topics such as traffic accident prediction, industrial fault diagnosis, and AI-based intrusion detection, and has presented his work at national conferences. His contributions aim to advance safe, adaptive, and trustworthy AI systems with strong societal and industrial impact.

Featured Publications

Artificial Intelligence in Fault Diagnosis of Industrial Machinery: A Comprehensive Review
– Structural Control and Health Monitoring (2025) | Citations: 1

 

Jie Li | Artificial Intelligence | Best Researcher Award

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Assoc Prof Dr. Jie Li | Artificial Intelligence | Best Researcher Award 

Assoc Prof Dr. Jie Li, Chongqing University of Science & Technology, China

Profile

scopus

Dr. Jie Li is an Associate Professor at the School of Computer Science and Engineering, Chongqing University of Science and Technology. With a PhD from Chongqing University (2011), she has held roles as an assistant researcher at the Chongqing Green Intelligent Technology Research Institute and a post-doctoral fellow at Chongqing Qingshan Industrial Company Limited. Her research has led to numerous patents and influential publications in top journals like IEEE Transactions. Dr. Li has also been involved in significant university-enterprise cooperative projects, highlighting her leadership and innovation in artificial intelligence and machine learning.

Strengths for the Award:

  1. Significant Research Contributions: Dr. Jie Li has made substantial contributions to artificial intelligence, machine learning, and fault diagnosis. Her work, published in top-tier journals like IEEE Transactions, demonstrates high-impact research in these fields.
  2. Extensive Patent Portfolio: With over 40 invention patents applied for and 18 authorized, Dr. Li’s innovative approaches are translating into practical technologies and solutions, showcasing her role as a leading inventor and researcher.
  3. Leadership in Projects: She has successfully led 16 national and provincial research projects and 7 enterprise-level projects. Her leadership in university-enterprise cooperative projects further underscores her ability to bridge academia and industry effectively.
  4. Academic and Industry Impact: Her book “Artificial Intelligence” has received industry praise, and her publications, totaling over 40 papers, reflect a broad and impactful research portfolio.

Areas for Improvement:

  1. Broader Citation Metrics: While Dr. Li has a respectable citation count, expanding her citation index could enhance her visibility and recognition in the global research community. Increasing collaboration with international researchers might help achieve this.
  2. Research Dissemination: Although Dr. Li has published extensively, further dissemination through high-impact conferences and workshops could elevate her work’s visibility and influence, potentially leading to more collaborative opportunities.
  3. Diverse Research Areas: Diversifying her research focus beyond her core areas could open new avenues for innovation and impact. Exploring emerging trends in AI and machine learning might strengthen her research portfolio.

Education🎓

Dr. Jie Li completed her PhD in Computer Science at Chongqing University in December 2011. Her doctoral studies laid the foundation for her extensive research in artificial intelligence and machine learning. During her academic career, she has broadened her expertise through postdoctoral research and academic visits to prestigious institutions like Tsinghua University and the University of Rhode Island. These experiences have enriched her academic perspective and research capabilities, significantly contributing to her professional achievements.

Experience💼

Dr. Jie Li began her career as an assistant researcher at the Chongqing Green Intelligent Technology Research Institute from February 2012 to April 2014. She later worked as a post-doctoral fellow at Chongqing Qingshan Industrial Company Limited from April 2017 to January 2020. Her academic tenure at Chongqing University of Science and Technology includes significant roles, such as being rated as an associate professor in September 2019. Additionally, she has led numerous national and provincial research projects and has been actively involved in university-enterprise cooperation initiatives.

Research Focus🔬

Dr. Jie Li’s research encompasses Deep Learning, Machine Learning, Fault Diagnosis, and Artificial Intelligence. Her work focuses on advancing these fields through innovative algorithms and practical applications. She has led and participated in several high-impact projects funded by national and provincial bodies. Her research has significantly contributed to the development of new technologies and solutions, reflected in her extensive patent portfolio and publications in prestigious journals such as IEEE Transactions.

Publications Top Notes

Polyacrylonitrile-based 3D N-rich activated porous carbon synergized with Co-doped MoS2 for promoted electrocatalytic hydrogen evolution (Huang, Z., Li, J., Guo, S., Zeng, J., Yuan, F., Separation and Purification Technology, 2025, 354, 129011) 📄

In-situ construction of nano-multifunctional interlayer to obtain intimate Li/garnet interface for dendrite-free all solid-state battery (Yu, S., Gong, Z., Gao, M., Li, Y., Chen, Y., Journal of Materials Science and Technology, 2025, 206, pp. 248–256) 📄

Advanced cathode materials for metal ion hybrid capacitors: Structure and mechanisms (Li, J., Liu, C., Momen, R., Zou, G., Ji, X., Coordination Chemistry Reviews, 2024, 517, 216018) 📖

Unraveling the delithiation mechanism of air-stabilized fluorinated lithium iron oxide pre-lithiation material (Wen, N., Li, J., Zhu, B., Guo, J., Zhang, Z., Chemical Engineering Journal, 2024, 497, 154536) 📄

Dual ion regulation enables High-Coulombic-efficiency lithium metal batteries (Huang, X., Wang, M., Zhou, Y., Li, J., Lai, Y., Nano Energy, 2024, 129, 110031) 📄

In-Situ Construction of Electronically Insulating and Air-Stable Ionic Conductor Layer on Electrolyte Surface and Grain Boundary to Enable High-Performance Garnet-Type Solid-State Batteries (Zhou, X., Liu, J., Ouyang, Z., Li, J., Jiang, L., Small, 2024, 20(34), 2402086) 📄

Enhancing the Efficient Utilization of Li2S in Lithium-Sulfur Batteries via Functional Additive Diethyldiselenide (Li, Z., Wang, M., Yang, J., Lai, Y., Li, J., Energy and Fuels, 2024, 38(16), pp. 15762–15770) 📄

Emerging polyoxometalate clusters-based redox flow batteries: Performance metrics, application prospects, and development strategies (Han, M., Sun, W., Hu, W., Zhang, C., Li, J., Energy Storage Materials, 2024, 71, 103576) 📖

Conductivity behavior of Na5YSi4O12 and its typical structural analogues by solution-assisted solid-state reaction for solid-state sodium battery (Liu, L., Xu, Y., Zhou, X., Guo, X., Jiang, Y., Journal of Solid State Chemistry, 2024, 336, 124781) 📄

Preparation of Hard-Soft Carbon via Co-Carbonization for the Enhanced Plateau Capacity of Sodium-Ion Batteries (Li, J., Zheng, H., Du, B., Li, D., Chen, Y., Energy and Fuels, 2024, 38(14), pp. 13398–13406) 📄

Conclusion:

Dr. Jie Li’s exceptional achievements in artificial intelligence and machine learning, marked by a robust patent portfolio, significant publications, and leadership in high-impact projects, position her as a strong candidate for the Best Researcher Award. Her innovative contributions and ability to lead and execute complex research projects highlight her outstanding capabilities and potential for furthering advancements in her field. Addressing the areas for improvement could further enhance her already impressive research profile and global impact.

Moumita Chanda | Deep Learning | Best Researcher Award

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Ms.Moumita Chanda | Deep Learning | Best Researcher Award

Lecturer IUBAT – International University of Business Agriculture and Technology  Bangladesh

Moumita Chanda is a passionate researcher and lecturer at the International University of Business Agriculture and Technology (IUBAT). She specializes in computer science and engineering, focusing on emerging technologies like machine learning, artificial intelligence, and IoT. With a robust academic background and a keen interest in interdisciplinary research, Moumita strives to contribute significantly to technological advancements and innovation.

Profile

Google Scholar

Education

🎓 Moumita Chanda earned her M.Sc. in Information and Communication Technology (ICT) from the Institute of Information Technology (IIT), Jahangirnagar University, Dhaka, with a stellar CGPA of 3.71/4.00, securing the 1st position among her peers in 2022-2023. She also holds a B.Sc. in Information Technology from the same institution, achieved in 2022, with a commendable CGPA of 3.53/4.00. Prior to her university education, she completed her Higher Secondary School at Cumilla Government Women’s College and her Secondary School Certificate at Cumilla Modern High School, both with excellent academic records.

Experience

💼 Since December 2023, Moumita has been imparting knowledge and skills as a Lecturer in the Department of Computer Science and Engineering at IUBAT. Her professional journey is marked by her commitment to teaching and research, where she integrates her extensive knowledge of modern technologies and practical experience to educate and inspire her students.

Research Interest

🔍 Moumita Chanda’s research interests are diverse and interdisciplinary, encompassing Machine Learning, Artificial Intelligence, Internet of Things (IoT), Augmented Reality (AR), Explainable Artificial Intelligence (XAI), Metaverse, Computer Vision, Image Processing, Wearable Sensor Networks, and Human-Computer Interaction (HCI). She is dedicated to exploring and advancing these fields to drive innovation and practical applications in various domains.

Awards and Achievements

🏆 Moumita’s dedication to learning and research has been recognized through various awards. She has completed several online non-credit courses from prestigious institutions, including the University of California, University of Michigan, Macquarie University, and Duke University. Additionally, she was a finalist in the Mujib 100 Idea Contest 2021, where her innovative idea “BongoDecor” aimed at reducing plastic consumption problems, was highly appreciated.

Publications

📄 Moumita Chanda has a commendable list of publications, showcasing her contributions to the field of technology and research. Some of her notable works include:

  • “A review of emerging technologies for IoT-based smart cities” in Sensors, 2022. Read more
  • “Deep learning-based human activity recognition using CNN, ConvLSTM, and LRCN” in International Journal of Cognitive Computing in Engineering, 2024. Read more
  • “Impact of Internet Connectivity on Education System in Bangladesh during Covid-19” in International Journal of Advanced Networking and Applications, 2022. Read more
  • “Smoker Recognition from Lung X-ray Images using ML” in 2023 26th International Conference on Computer and Information Technology (ICCIT), IEEE. Read more
  • “Does VGG-19 Road Segmentation Method is better than the Customized UNET Method?” Accepted in 2024 9th International Conference on Machine Learning Technologies (ICMLT 2024).

 

 

ANUJA BHARGAVA | Artificial Intelligence | Most Cited Paper Award

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Assist Prof Dr. ANUJA BHARGAVA | Artificial Intelligence | Most Cited Paper Award

Assistant Professor GLA University India

Dr. Anuja Bhargava is an accomplished academic and researcher, currently serving as an Assistant Professor at GLA University, Mathura. With a Ph.D. in Electronics and Communication Engineering, she specializes in Digital Signal Processing, VLSI, and Artificial Intelligence. Dr. Bhargava has a wealth of teaching experience and has published extensively in renowned journals and conferences. Her dedication to education and research has earned her a prominent place in her field.

Profile

Scopus

Education 🎓

Dr. Anuja Bhargava earned her Ph.D. in Electronics and Communication Engineering from GLA University, Mathura, where she conducted groundbreaking research on “Quality Evaluation of Fruits using Image Processing.” She holds a Master of Technology in Digital Communication from Uttrakhand Technical University and a Bachelor of Engineering in Electronics and Communication Engineering from Modi Institute of Technology, Kota, both with first-class honors.

Experience 🏫

Dr. Bhargava’s academic journey includes roles as Assistant Professor at GLA University since October 2021, and previously at Gurukul Institute of Engineering & Technology and Maharishi Arvind International Institute of Technology. Her extensive teaching experience spans over a decade, focusing on various aspects of electronics and communication engineering.

Research Interests 🔍

Dr. Bhargava’s research interests are diverse and include Digital Signal Processing, Very Large Scale Integration (VLSI), Control Systems, Signal and Systems, Electromagnetic Field Theory, Microprocessors, and Basic Electrical and Electronics. She is particularly interested in the application of Artificial Intelligence in these domains.

Awards 🏆

Dr. Anuja Bhargava has been recognized for her contributions to academia and research with various awards and nominations. She has served as a keynote speaker at international conferences and received accolades for her innovative research and teaching methodologies.

Publications Top Notes 📚

Gupta D, Bhargava A, et al. “Deep Learning-Based Truthful and Deceptive Hotel Reviews.” Sustainability, 2024, link, cited by articles.

Bhargava A, et al. “Plant Leaf Disease Detection, Classification and Diagnosis using Computer Vision and AI: A Review.” IEEE Access, 2024, link, cited by articles.

Sachdeva A, Bhargava A, et al. “A CNTFET based stable, single ended 7T SRAM cell with improved write operation.” Physica Scripta, 2024, link, cited by articles.

Bhargava A, et al. “Machine learning & computer vision-based optimum black tea fermentation detection.” Multimed Tools Appl, 2023, link, cited by articles.

Sharma A, Bhargava A, et al. “Multi-level Segmentation of Fruits Using Modified Firefly Algorithm.” Food Anal. Methods, 2022, link, cited by articles.