Zhihao Kang | Deep Learning | Best Researcher Award

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Ms. Zhihao Kang | Deep Learning | Best Researcher Award

Ms. Zhihao Kang | Deep Learning | Ph.D at Tianjin University | China

Ms. Zhihao Kang is an accomplished academic and researcher at Tianjin University, China, specializing in urban perception modeling, AI-driven landscape design, ecological sensitivity mapping, and social media-based urban analytics. She earned her Ph.D. in Environmental Science and Urban Planning from Tianjin University, where her doctoral work focused on integrating deep learning frameworks and spatial modeling to evaluate visual and ecological sensitivity across urban landscapes. Ms. Kang has developed extensive professional experience through her participation in multi-institutional and cross-border projects on urban heat island prediction, sustainable landscape design, and spatial data visualization, collaborating with international research teams across Asia and Europe. Her research interests span artificial intelligence applications in environmental studies, geospatial data analysis, climate resilience planning, and the use of social media data for real-time urban perception modeling. In terms of research skills, Ms. Kang demonstrates expertise in machine learning algorithms, remote sensing, GIS-based urban analysis, CA–Markov modeling, and Google Earth Engine-based predictive simulations. She has co-authored multiple peer-reviewed papers indexed in Scopus and IEEE, contributing to global discourse on sustainable urbanization and digital environmental mapping. Her publications have received over 130 citations, reflecting growing recognition within the academic community. Ms. Kang’s work has earned her institutional awards and research fellowships that acknowledge her excellence in applied geospatial analytics and AI innovation. She is also an active member of IEEE and ACM, engaging in initiatives promoting smart and sustainable urban environments. With a strong interdisciplinary foundation and a commitment to technological innovation, Ms. Zhihao Kang continues to advance the frontier of urban informatics research, contributing impactful insights that support ecological resilience and evidence-based urban policy design.

Academic Profile: Google Scholar

Featured Publications:

  1. Ullah, N., Khan, J., Saeed, I., Zada, S., Xin, S., Kang, Z., & Hu, Y. K. (2022). Gastronomic tourism and tourist motivation: Exploring northern areas of Pakistan. International Journal of Environmental Research and Public Health, 19(13), 7734. Citations: 84

  2. Ullah, N., Siddique, M. A., Ding, M., Grigoryan, S., Khan, I. A., Kang, Z., Tsou, S., et al. (2023). The impact of urbanization on urban heat island: Predictive approach using Google Earth Engine and CA-Markov modelling (2005–2050) of Tianjin City, China. International Journal of Environmental Research and Public Health, 20(3), 2642. Citations: 50

 

 

Pratiksha Chaudhari | Machine Learning | Best Researcher Award

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Ms. Pratiksha Chaudhari | Machine Learning | Best Researcher Award

Ms. Pratiksha Chaudhari | Machine Learning | Doctoral Candidate at The University of Alabama | United States

Ms. Pratiksha Chaudhari is a dedicated researcher and emerging academic in the field of Computer Science, specializing in Artificial Intelligence (AI), Machine Learning (ML), and Computer Vision. She is currently pursuing her Ph.D. in Computer Science at the University of Alabama, USA, where her work focuses on developing intelligent and data-driven systems for smart buildings and environmental monitoring. She holds a Master of Science in Computer Science and a Bachelor of Engineering in Computer Engineering from the University of Pune, India, both completed with distinction. Throughout her academic career, Ms. Pratiksha Chaudhari has demonstrated exceptional technical proficiency, combining theoretical depth with practical implementation in areas such as deep learning architectures, AI-based automation, and hydrological modeling. Professionally, she has gained valuable experience as a Graduate Research Assistant and Teaching Assistant at the University of Alabama, contributing to federally funded projects by the Cooperative Institute for Research to Operations in Hydrology (CIROH), U.S. Geological Survey (USGS), and the Great Lakes Protection Fund (GLPF). Her expertise spans Python, C++, PyTorch, TensorFlow, OpenCV, and QT Creator, alongside an ability to build and optimize large-scale AI frameworks for IoT and environmental data analysis. Her research interests include smart infrastructure, sustainable AI systems, microplastic detection, and federated learning-based IoT applications. Ms. Chaudhari has published multiple peer-reviewed papers in IEEE and Scopus-indexed journals, contributing to the advancement of applied AI research. She has been recognized for her academic excellence, innovative research contributions, and mentoring roles in interdisciplinary learning environments. With her growing portfolio of impactful publications and ongoing collaborations, Ms. Pratiksha Chaudhari continues to demonstrate strong potential as a future leader in AI research, committed to creating intelligent, ethical, and sustainable technologies for real-world applications.

Profile: ORCID | Google Scholar

Featured Publications 

  1. Chaudhari, P. (2025). Translution: A Hybrid Transformer–Convolutional Architecture with Adaptive Gating for Occupancy Detection in Smart Buildings. Electronics. 5 Citations.

  2. Chaudhari, P. (2024). Fundamentals, Algorithms, and Technologies of Occupancy Detection for Smart Buildings Using IoT Sensors. Sensors. 8 Citations.

  3. Chaudhari, P. (2024). Deep Learning-Based Streamflow Reconstruction Using Hydro-Transformer Models for Climate Data Analysis. Environmental Modelling & Software. 4 Citations.

  4. Chaudhari, P. (2023). Real-Time Detection and Classification of Microplastic Particles Using OpenCV and Raman Spectroscopy. Journal of Environmental Informatics. 6 Citations.

  5. Chaudhari, P. (2023). Federated Learning Models for Anomaly Detection in IoT-Enabled Smart Environments. IEEE Internet of Things Journal. 9 Citations.

  6. Chaudhari, P. (2022). AI-Powered Vocal Coaching System Using Wearable Sensors and Machine Learning Feedback Loops. Computers in Human Behavior. 3 Citations.

  7. Chaudhari, P. (2022). Developing an AI Framework for Smart Building Energy Optimization Using Transformer Networks. Applied Energy. 7 Citations.

 

Libo Huang | Deep Learning | Best Researcher Award

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Assist. Prof. Dr. Libo Huang | Deep Learning | Best Researcher Award

Assist. Prof. Dr. Libo Huang | Deep Learning – Assistant Researcher at Institute of Computing Technology, Chinese Academy of Sciences, China

Dr. Libo Huang is a dedicated and rapidly emerging research scientist specializing in machine learning, with a focus on continual, incremental, and lifelong learning. Currently serving as an Assistant Researcher at the Institute of Computing Technology, Chinese Academy of Sciences, he is actively contributing to the frontier of intelligent learning systems. Known for blending theoretical insight with practical innovation, Dr. Huang has become a key contributor in the areas of neural systems, generative modeling, and knowledge distillation. He plays an instrumental role in several national and provincial-level AI initiatives in China and internationally. With multiple cross-border academic experiences, he brings a global perspective to advancing adaptive intelligence for real-world challenges.

Academic Profile

Google Scholar  |  ORCID

Education

Dr. Huang’s academic journey began with a Bachelor’s degree in Information and Computational Mathematics, which he earned from Jiangxi Normal University. Building on this quantitative foundation, he completed his Master’s degree in Pattern Recognition from Guangdong University of Technology, where he delved deeper into data-driven pattern systems under expert mentorship. Pursuing excellence in machine learning, he obtained his Ph.D. at Guangdong University of Technology between 2018 and 2021, focusing on neural spike sorting and adaptive learning algorithms. Notably, he also undertook a prestigious joint Ph.D. program at Brunel University London (2019–2020), working on spike clustering techniques using sparse and low-rank representation frameworks, demonstrating his commitment to multidisciplinary and international research development.

Professional Experience

Dr. Huang joined the Institute of Computing Technology in July 2021 and has since led multiple key projects related to deep continual learning. He is currently working under the direction of Prof. Yongjun Xu, contributing significantly to practical machine learning deployments and system-level AI innovations. In addition to his postdoctoral and researcher responsibilities, he has served as project leader on government-funded programs, including a national project on causal structure models (2024–2025) and a youth fund project supported by the Beijing Natural Science Foundation. Dr. Huang’s work is well-regarded for bridging gaps between foundational research and deployable AI systems. His past experience also includes participation in a national joint training Ph.D. program, enhancing his cross-institutional capabilities.

Research Interests

His primary research interests revolve around continual learning, deep generative models, lifelong learning algorithms, and knowledge distillation. He has extensively investigated the problem of class-incremental learning and developed techniques to improve model plasticity and stability trade-offs. Dr. Huang also explores the design of efficient AI systems using low-rank and sparse representation methods, spike sorting frameworks for neurodata, and causal reinforcement learning. His current focus includes integrating feedback-driven reconstruction techniques and embedding distillation approaches to support dynamic learning environments for both supervised and unsupervised tasks. His interdisciplinary lens enables him to create robust models applicable to healthcare, robotics, and intelligent sensing.

Awards and Recognition

Dr. Huang has received multiple recognitions, including funding awards from both the National Natural Science Foundation and Beijing Municipal Science entities. Notable among these is the 2024–2025 National Project Grant on Causal Structures and the 2023–2025 national-level project on explainable knowledge-driven task planning. He was also selected under the 2019 National Joint Training Ph.D. Program for High-Level Universities. In terms of peer recognition, he holds membership in the IEEE, the Chinese Association for Artificial Intelligence, and the Chinese Computer Society. His active participation in elite academic circles positions him as a strong candidate for the Best Researcher Award.

Selected Publications

🔬 WMsorting: Wavelet packets’ decomposition and mutual information-based spike sorting method, IEEE TNB, 2019 – cited by over 80 articles; focuses on neural spike classification.
🧠 A unified optimization model of feature extraction and clustering for spike sorting, IEEE TNSRE, 2021 – cited in various neuroinformatics works.
🧪 KFC: Knowledge Reconstruction and Feedback Consolidation for Continual Generative Learning, ICLR Tiny Papers Track, 2024 – praised for improving memory retention in generative tasks.
🤖 eTag: Class-Incremental Learning with Embedding Distillation, AAAI, 2024 – addresses scalable AI learning, cited in domain adaptation literature.
🎯 Automatical Spike Sorting with Low-Rank and Sparse Representation, IEEE TBME, 2023 – improved processing accuracy, cited by biomedical systems papers.
🔄 Continual Learning in the Frequency Domain, NeurIPS, 2024 – explored spectral representations in lifelong learning models.
🎥 CLIP-KD: An Empirical Study of Distilling CLIP Models, CVPR, 2024 – applied to visual-language pretraining, influential in vision-language learning.

Conclusion

Dr. Libo Huang is a rising star in machine learning research with a solid blend of theoretical innovation and application-driven impact. His consistent publication record in IEEE and AAAI venues, project leadership in lifelong learning and AI causality, and collaborative work with international researchers reflect his promise as a future research leader. As AI continues to shape scientific and industrial landscapes, Dr. Huang’s work contributes to sustainable, adaptive, and interpretable intelligent systems. His active engagement in academic services, reviewer duties, and project mentorship makes him an ideal and deserving candidate for the Best Researcher Award.

Preeti Sharma | Deep Learning | Women Researcher Award

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Mrs . Preeti Sharma | Deep Learning | Women Researcher Award 

Assistant Professor , DIT University, Dehradun, Uttrakhand , India

Preeti Sharma is a dedicated researcher and educator currently pursuing a Ph.D. in Computer Science and Engineering at the University of Petroleum and Energy Studies, Dehradun. With a distinguished academic background including gold medals and high honors in her MTech and MCA degrees, Preeti has demonstrated excellence in her field. She is passionate about advancing the field of artificial intelligence and machine learning, focusing on generative adversarial networks (GANs) and deepfake detection.

Profile

Google Scholar

Education 

Preeti Sharma is pursuing a Ph.D. in Computer Science and Engineering at the University of Petroleum and Energy Studies, Dehradun, with her thesis submitted. She holds an MTech in Computer Science and Engineering from Uttarakhand Technical University, where she graduated as a gold medalist with an impressive 85%. Preeti completed her M.C.A. from M.D.U. (Campus), Rohtak, with a strong academic record of 82%.

Experience 

Preeti Sharma currently serves as a Junior Research Fellow and Teaching Assistant at the University of Petroleum and Energy Studies, Dehradun, where she has been contributing since April 2021. Prior to this, she was a Non-Teaching Staff member at the same university from September 2015 to March 2021. She also gained valuable experience as a Guest Lecturer at Arihant Institute of Technology, Haldwani, and an intern at the National Informatics Center (NIC).

Research Interests 

Preeti Sharma’s research interests include the application of Generative Adversarial Networks (GANs) in image and deepfake detection, robust CNN models, and advancements in digital forensics. Her work explores innovative methods for deepfake detection and image forgery using GAN-based models, contributing significantly to the field of multimedia tools and applications.

Awards 

Preeti Sharma has been recognized for her exceptional research and presentations. She received a certification for the best oral presentation at the International Young Researcher Conclave (IYRC-2024). Her paper on generative adversarial networks won first prize in the Research Conclave IYRC 2024 at UPES.

Publications 

  • Sharma, P., Kumar, M., Sharma, H.K. et al. Generative adversarial networks (GANs): Introduction, Taxonomy, Variants, Limitations, and Applications. Multimedia Tools and Applications (2024). Link
  • Sharma, P., Kumar, M., & Sharma, H.K. Robust GAN-Based CNN Model as Generative AI Application for Deepfake Detection, EAI Endorsed Trans IoT, vol. 10 (2024).
  • Sharma, P., Kumar, M., & Sharma, H.K. A generalized novel image forgery detection method using a generative adversarial network. Multimedia Tools and Applications (2023). Link
  • Sharma, P., Kumar, M., & Sharma, H.K. A GAN-based model of deepfake detection in social media. Procedia Computer Science, 218, 2153-2162 (2023).
  • Sharma, P., Kumar, M., & Sharma, H.K. Comprehensive analyses of image forgery detection methods from traditional to deep learning approaches: an evaluation. Multimedia Tools and Applications, 82(12), 18117-18150 (2023).
  • Sharma, P., Kumar, M., & Sharma, H.K. A Guide to Digital Forensic: Theoretical to Software-Based Investigations. Perspectives on Ethical Hacking and Penetration Testing, IGI Global (2023). Link
  • Sharma, P., Kumar, M., & Sharma, H.K. CNN-based Facial Expression Recognition System Using Deep Learning Approach. Conference on Computational Intelligence and Information Retrieval CIIR (2021).
  • Sharma, P. Real Time Tracking System for Object Tracking using the Internet of Things (IoT). Conference on Computational Intelligence and Information Retrieval CIIR (2021).
  • Sharma, P. Leach and Improved Leach: A Review. International Journal of Advanced Research in Computer Science, Vol 10 (2019).

Conclusion

Preeti Sharma’s profile shows a strong foundation in research and technical expertise, with notable contributions to GANs and deepfake detection. Her academic achievements, innovative patents, and recognition in the field underscore her qualifications. To strengthen her candidacy for the Research for Women Researcher Award, she could emphasize the broader impact of her research and highlight her leadership or mentorship roles. Overall, her qualifications and achievements make her a strong contender for the award.

Lijuan Zhang | Deep Learning | Best Researcher Award

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Prof. Dr. Lijuan Zhang | Deep Learning | Best Researcher Award 

Professor | College of Internet of Things Engineering, Wuxi University, Wuxi | China

Research for Best Researcher Award Evaluation

Strengths for the Award

  1. Academic Excellence and Educational Background: Lijuan Zhang has an impressive academic background with degrees from notable institutions, consistently ranking in the top percentile of her class. Her extensive education in engineering, particularly in the field of opto-electronic and computer sciences, provides a solid foundation for her research work.
  2. Diverse and Relevant Research Contributions: Dr. Zhang’s research spans several critical areas, including adaptive optics, image restoration, and advanced image processing techniques. Her work on blind deconvolution algorithms and high-accuracy image registration is highly relevant in the fields of optics and computer vision.
  3. High Impact Publications: Dr. Zhang has a significant number of publications in reputed journals, including several in high-impact SCI and EI-indexed journals. Notable papers include her recent work on Class-Incremental Learning and YOLO-based pest detection algorithms, reflecting her current focus on integrating advanced AI techniques with practical applications.
  4. Innovative Patents and Projects: She holds patents related to rangefinders and has led multiple research projects funded by prestigious institutions. These patents and projects demonstrate her capability to translate theoretical research into practical, impactful technologies.
  5. Recognition and Honors: Dr. Zhang has received multiple awards, including the third-level prize for her work on CCD ranging technology and an outstanding level prize for her rangefinder invention. These accolades underscore the significant impact of her contributions to her field.
  6. Teaching and Mentorship: Her role as a university teacher at Changchun University of Technology and recognition as an outstanding graduation design teacher indicate her commitment to education and her influence on the next generation of engineers.

Areas for Improvement

  1. Broader Research Dissemination: While Dr. Zhang has several publications, expanding her research into more interdisciplinary journals could increase the visibility and impact of her work across different fields.
  2. Collaborative Research: Engaging in more collaborative projects with international researchers could enhance the scope and impact of her research. Collaborative efforts often lead to more innovative solutions and broader application of findings.
  3. Funding and Grants: Securing more extensive and diverse funding sources, including international grants, could enable more ambitious projects and further innovations. Diversifying funding sources could also enhance the sustainability and reach of her research endeavors.
  4. Public Outreach and Engagement: Increasing engagement with the public and industry stakeholders through conferences, workshops, and outreach programs could help in translating her research into more widely adopted technologies and practices.
  5. Focus on Emerging Technologies: Staying updated with rapidly evolving technologies such as quantum computing, next-gen AI models, and their applications could provide new avenues for her research, ensuring its relevance in the future.

Short Bio

Dr. Lijuan Zhang is a distinguished researcher in the fields of image processing and adaptive optics, currently serving as a professor at the College of Internet of Things Engineering, Wuxi University, China. With a career spanning over two decades, Dr. Zhang has made significant contributions to the development of advanced algorithms and technologies for image restoration and object detection. Her work is characterized by a commitment to integrating theoretical research with practical applications, earning her recognition and accolades in her field.

Profile

ORCID

Education

Dr. Zhang earned her Bachelor of Engineering from Jilin Normal University in 2001, ranking in the top 10% of her class. She then completed her Master of Engineering at Changchun University of Science and Technology in 2004, where she was ranked in the top 5%. She achieved her Doctor of Engineering degree in 2015 from the same institution, also finishing in the top 5%. Her educational journey underscores a solid foundation in engineering and computer science.

Experience

Since 2004, Dr. Zhang has been a faculty member at Changchun University of Technology, where she has taught various courses in computer science and engineering. Her role as an educator extends to guiding students in their research projects and graduation designs. Additionally, she has been involved in leading and completing several research projects, contributing to advancements in image measurement and detection technologies.

Research Interest

Dr. Zhang’s research interests primarily focus on adaptive optics, image restoration, and advanced image processing techniques. Her work explores algorithms for blind deconvolution, high-accuracy image registration, and object detection using AI technologies. Recently, she has been involved in developing innovative solutions for agricultural pest detection and medical image segmentation.

Awards

Dr. Zhang has received notable recognition for her contributions to engineering and technology. She was awarded the third-level prize for her work on high precision CCD ranging technology in 2012 and the outstanding level prize for her binocular CCD rangefinder invention in 2013. She was also honored as an Outstanding Graduation Design Teacher at Changchun University of Technology in 2013.

Publications

Zhang, L., Li, D., Su, W., Yang, J., & Jiang, Y. (2014). Research on Adaptive Optics Image Restoration Algorithm by Improved Expectation Maximization Method. Abstract and Applied Analysis. DOI: 10.1155/2014/781607 (Cited by: 54)

Zhang, L., Yang, J., Su, W., et al. (2014). Based on improved Expectation Maximization of Multi-frame Iteration Blind Deconvolution Algorithm for Adaptive Optics Image Restoration. Acta Armanebtarii, 35(11) (in Chinese) (Cited by: 32)

Zhang, L., Yang, J., Su, W., Wang, X., & Jiang, Y. (2013). Research on Blind Deconvolution Algorithm of Multi-Frame Turbulence Degraded Images. Journal of Information and Computational Science, 10(12) (Cited by: 27)

Zhang, L., Yang, J., Jiang, Y., et al. (2014). Research on Target Image Matching Algorithm for Binocular CCD Ranging. Laser & Optoelectronics Progress, 51(9) (in Chinese) (Cited by: 21)

Zhang, L., Yang, J., & Jiang, C. (2012). Image Restoration Based on Cross-correlative Blur Length Estimation. Computer Engineering, 9(20) (in Chinese) (Cited by: 19)

Zhang, L., Li, D., et al. (2012). High-accuracy Image Registration Algorithm Using B-splines. ICCSNT 2012 (Cited by: 15)

Zhang, L., Yang, J., et al. (2011). An Image Mosaic Algorithm Taking into Account Speed and Robustness. ICMEAT 2011 (Cited by: 13)

Zhang, L., Yang, X., et al. (2023). Class-Incremental Learning Based on Anomaly Detection. IEEE ACCESS, 2023.7 (SCI, Q2) (Cited by: 7)

Zhang, L., Zhao, C., et al. (2023). Pests Identification of IP102 by YOLOv5 Embedded with the Novel Lightweight Module. Agronomy, 2023.6 (SCI, Q1) (Cited by: 5)

Li, D., Yin, S., Lei, Y., Zhang, L., et al. (2023). Segmentation of White Blood Cells Based on CBAM-DC-UNet. IEEE Access, 2023.1 (SCI, Q2) (Cited by: 9)

Zhang, L., Liu, J., et al. (2022). MSAA-Net: A Multi-Scale Attention-Aware U-Net for Liver Segmentation. Signal, Image and Video Processing, 2022.7 (SCI, Q4) (Cited by: 4)

Zhang, L., Ding, G., et al. (2023). DCF-Yolov8: An Improved Algorithm for Aggregating Low-Level Features to Detect Agricultural Pests and Diseases. Agronomy, 2023.8 (Cited by: 3)

Zhang, L., Cui, H., et al. (2023). CLT-YOLOX: Improved YOLOX Based on Cross-Layer Transformer for Object Detection Method Regarding Insect Pest. Agronomy, 2023.8 (Cited by: 2)

Conclusion

Lijuan Zhang is a highly qualified candidate for the Best Researcher Award due to her extensive academic background, significant research contributions, and recognized achievements. Her innovative work in image processing and adaptive optics, coupled with her leadership in research projects and educational contributions, highlight her exceptional capabilities as a researcher. Addressing the suggested areas for improvement could further enhance her impact and ensure her continued leadership in the field. Overall, Dr. Zhang’s achievements and potential make her a deserving nominee for the award.

 

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).