Km Poonam | Computer Science | Best Researcher Award

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Ms. Km Poonam | Computer Science | Best Researcher Award

Ms. Km Poonam, National Institute of Technology Warangal, India.

🧑‍🎓 Ms. Km Poonam is a Ph.D. scholar at NIT Warangal specializing in Natural Language Processing and Deep Learning. She has published several papers in SCI and Scopus journals 📚, with a focus on stance detection models. Poonam is a GATE and UGC-NET qualifier 🎯 and an award-winning researcher 🏆. She also serves as a reviewer for Springer journals and contributes to FDPs. Her skills include Python, C++, and Data Mining 💻, driven by a passion for innovation and academic excellence.

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🎓 Early Academic Pursuits

Ms. Km Poonam began her academic journey with a consistent record of excellence. She secured 77.33% in her 10th standard and 83% in her 12th, both from the UP Board. Her inclination toward computer science led her to pursue B.Tech from GBTU Lucknow with 71.9%, followed by an M.Tech from Madan Mohan Malaviya University of Technology, Gorakhpur, where she achieved an impressive 86.9%. Her commitment to research brought her to the National Institute of Technology Warangal, where she is currently pursuing her Ph.D. in Computer Science and Engineering since August 2021. 🎓📘

💼 Professional Endeavors

As a self-driven and ambitious scholar, Poonam has actively participated in both teaching and research. She has qualified prestigious exams like GATE (2020, 2021) and UGC NET (2020) in Computer Science, showcasing her theoretical depth and academic rigor. She has also taken on mentorship and training roles, having delivered hands-on sessions during the NLP FDP by NITW and JNTU, Hyderabad (2024). 🧑‍🏫

🔬 Contributions and Research Focus On Computer Science

Poonam’s research is primarily centered on Stance Detection, Deep Learning, and Multimodal Machine Learning. Her work includes advanced techniques involving BiLSTM-GRU, Meta Learning, Fuzzy Logic, BERT, and ResNet, with applications in tweet classification and sentiment analysis. Her innovative methodologies aim to enhance accuracy and reduce bias in language models. 🤖📊

🌍 Impact and Influence

Her work has been recognized at national and international levels. She was awarded First Prize at the 18th National Frontiers of Engineering (NatFoE) Symposium (2024) for her breakthrough model on stance detection. She also contributes to the scholarly community as a reviewer for Springer publications and top-tier conferences. 🏅🌐

🧠 Research Skills

Poonam is skilled in deep learning, natural language processing, and multimodal data analysis. Her technical arsenal includes Python, NS2, SQL, and machine learning frameworks, with hands-on expertise in Jupyter Notebook, Linux, and Visual Studio Code. Her ability to model complex neural networks with optimization techniques highlights her strong analytical and algorithmic capabilities. 💻🧠

🏅 Awards and Honors

  • 🥇 First Prize at NatFoE Symposium 2024 for research on BiLSTM-GRU model.

  • 🔍 Reviewer for Springer journals and various international conferences.

  • 🧠 FDP Trainer on Natural Language Processing, NITW & JNTU, 2024.

🏛️ Legacy and Future Contributions

With a deep passion for research and teaching, Poonam is poised to make lasting contributions to the fields of Artificial Intelligence and Data Science. Her work not only pushes the boundaries of current computational linguistics but also sets the stage for ethical and bias-resilient AI systems. She aspires to lead multidisciplinary projects and inspire the next generation of researchers through academic service and mentorship. 🔮📘

Publications Top Notes

📄 “Dual Bi-LSTM-GRU based Stance Detection in Tweets Ordered Classes”
Journal: Neural Computing and Applications
Year: 2024
Authors: K Poonam, T Ramakrishnudu
🧠🤖🗣️ — Deep learning, NLP, Social Media Analysis

📄 “Bias-Resilient Multi-Label Deep Learning Hybrid Model for Stance Detection”
Journal: International Journal of Data Science and Analytics
Year: 2025
Authors: KMPT Ramakrishnudu
📊🧠🧾 — AI Bias Mitigation, Data Science, Multi-label Learning

📄 “Self-Supervised Multimodal Stance Detection with BERT and ResNet”
Journal: Advanced Computing and Communications: Responsible AI (ADCOM 2461)
Year: 2025
Authors: A. Harikesh, KM Poonam, Tene Ramakrishnudu
📷💬🤖 — Multimodal AI, Self-supervised Learning, Responsible AI

📄 “Proactive, Reactive, and Hybrid Routing Protocol Simulation-Based Results for MANET”
Journal: International Journal for Research in Applied Science & Engineering Technology (IJRASET)
Year: 2021
Authors: SS Poonam
📡🔁📶 — Network Protocols, MANET, Simulation-Based Analysis

📄 “An Object for Finding an Effective and Source Authentication Mechanism for Multicast Communication in the Hash Tree: Survey Paper”
Journal: International Journal for Research in Applied Science & Engineering Technology (IJRASET)
Year: 2021
Authors: SS Poonam
🔐🌐🌳 — Cybersecurity, Multicast Communication, Hash Trees

Haojin Tang | Artificial Intelligence | Innovative Research Award

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Dr. Haojin Tang | Artificial Intelligence | Innovative Research Award

Dr. Haojin Tang, Guangzhou University, China.

🧑‍🔬 Dr. Haojin Tang is a Lecturer at Guangzhou University, specializing in 🌐 Artificial Intelligence, Deep Learning, and 🛰️ Hyperspectral Image Processing. He holds a Ph.D. in Information and Communication Engineering and has published 20+ top-tier papers, secured national patents 🧾, and led major research projects. As an inspiring mentor, he guides students to achieve excellence in intelligent manufacturing and environmental sensing. His work is shaping the future of smart technologies and remote sensing innovation. 🚀📡

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🎓 Early Academic Pursuits

Dr. Haojin Tang’s academic excellence began at Shenzhen University, where he pursued a B.S. in Electronic Information Engineering (2014–2018) and was recommended for postgraduate study without examination. He later earned both his M.S. (2018–2020) and Ph.D. (2020–2023) in Information and Communication Engineering, supported by the National Scholarship and recognized among the Top 10 Doctoral Dissertations. His solid academic foundation laid the groundwork for a promising research career in artificial intelligence and remote sensing. 🎓📘

💼 Professional Endeavors

Since July 2023, Dr. Tang has served as a Lecturer at the School of Electronic and Communication Engineering, Guangzhou University. He actively mentors undergraduate and graduate students, encouraging them to explore cutting-edge AI techniques in agricultural, forestry, and intelligent manufacturing applications. Under his supervision, students have secured high-impact publications and received numerous provincial and university-level gold awards. 🏅📚

🔬 Contributions and Research Focus On Artificial Intelligence

Dr. Tang’s research is rooted in the integration of Artificial Intelligence, Deep Learning, and Hyperspectral Image Processing, with special attention to industrial fault detection and few-shot learning. His contributions include:

  • Publishing over 20 papers in top-tier journals (JCR Q1, CAS TOP) and CCF Class A conferences.

  • Developing innovative algorithms for hyperspectral image classification and zero-shot learning.

  • Leading projects on cross-domain image classification using large language models. 🧠🛰️

🌍 Impact and Influence

Dr. Tang’s influence extends across academia and industry:

  • He has been invited to review for top journals including IEEE TGRS, Remote Sensing, and J-STARS.

  • His interdisciplinary research addresses real-world challenges in environmental monitoring and intelligent manufacturing.

  • His work has contributed to the advancement of UAV-based hyperspectral sensing and fault detection systems. 📡🌱

🧠 Research Skills

Dr. Tang is adept at designing and implementing deep learning architectures for low-shot learning tasks, developing cross-domain classification algorithms, and leveraging large language models for image interpretation. His skills extend to UAV-based remote sensing systems, software development for big data analysis, and interdisciplinary innovation, making him a versatile researcher and practitioner. 🤖💻

🏅 Awards and Honors

  • National Scholarship (Master’s & Ph.D.)

  • Top 10 Doctoral Dissertations at Shenzhen University

  • Student mentees under his guidance have won numerous provincial and institutional gold medals for research excellence.
    These accolades underscore his academic distinction and mentorship capabilities. 🎖️🌟

🏛️ Legacy and Future Contributions

Dr. Tang is on a trajectory to become a leading innovator in AI-driven remote sensing and industrial diagnostics. His upcoming work on Large Language Model-driven image classification signals a bold move toward integrating generative AI into remote sensing. As a mentor and researcher, he is nurturing future scientists while paving the way for interpretable and scalable AI models in hyperspectral imaging and intelligent manufacturing. 🚀🌐

Publications Top Notes

  • 🛰️ A Spatial–Spectral Prototypical Network for Hyperspectral Remote Sensing Image
    Journal: IEEE Geoscience and Remote Sensing Letters
    Citations: 64
    Year: 2019
    ✨ Pioneer in spatial-spectral modeling for remote sensing

  • 🔍 Multidimensional Local Binary Pattern for Hyperspectral Image Classification
    Journal: IEEE Transactions on Geoscience and Remote Sensing
    Citations: 37
    Year: 2021
    🔬 Robust feature extraction in HSI

  • 🧠 Fusion of Multidimensional CNN and Handcrafted Features for Small-Sample Hyperspectral Image Classification
    Journal: Remote Sensing
    Citations: 13
    Year: 2022
    🤖 Hybrid deep learning for limited data

  • 📊 A Multiscale Spatial–Spectral Prototypical Network for Hyperspectral Image Few-Shot Classification
    Journal: IEEE Geoscience and Remote Sensing Letters
    Citations: 13
    Year: 2022
    🔁 Improved generalization with few-shot learning

  • ⚙️ HFC-SST: Improved Spatial-Spectral Transformer for Hyperspectral Few-Shot Classification
    Journal: Journal of Applied Remote Sensing
    Citations: 12
    Year: 2023
    🧭 Enhanced transformer model in HSI

  • 🛠️ Multi-Label Zero-Shot Learning for Industrial Fault Diagnosis
    Conference: 6th Int’l Conf. on Information Communication and Signal Processing
    Citations: 7
    Year: 2023
    🏭 AI for smart industry diagnostics

  • 🛰️ Multi-Scale Attention Adaptive Network for Object Detection in Remote Sensing Images
    Conference: 5th Int’l Conf. on Information Communication and Signal Processing
    Citations: 4
    Year: 2022
    🎯 Precision object detection framework

  • 🧠 Global-Local Attention-Aware Zero-Shot Learning for Industrial Fault Diagnosis
    Journal: IEEE Transactions on Instrumentation and Measurement
    Citations: 2
    Year: 2025
    💡 Breakthrough in industrial ZSL

  • 📐 TSSLBP: Tensor-Based Spatial–Spectral Local Binary Pattern
    Journal: Journal of Applied Remote Sensing
    Citations: 2
    Year: 2020
    🧮 Tensor-based HSI analysis

  • 🧬 AMHFN: Aggregation Multi-Hierarchical Feature Network for Hyperspectral Image Classification
    Journal: Remote Sensing
    Citations: 1
    Year: 2024
    🔗 Deep feature aggregation strategy

  • 🎯 Dense Convolution Siamese Network for Hyperspectral Image Target Detection
    Conference: 5th Int’l Conf. on Information Communication and Signal Processing
    Citations: 1
    Year: 2022
    🛸 High-precision target detection

 

Chengjiang Zhou | Artificial Intelligence | Best Researcher Award

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Assoc. Prof. Dr. Chengjiang Zhou | Artificial Intelligence | Best Researcher Award

Assoc. Prof. Dr. Chengjiang Zhou, Yunnan normal university, China.

Dr. Chengjiang Zhou 🎓, an Associate Professor at Yunnan Normal University, specializes in AI-driven fault diagnosis for industrial systems. With 10+ years of research experience, he has published 40+ SCI papers, secured 9 patents, and led national/provincial projects. 🛰️ His innovations aid real-time monitoring in wind turbines and pipelines. As a mentor, he has guided students to 16 national awards 🏆. Dr. Zhou actively serves as a guest editor and reviewer for top journals. 🧠🔧

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🎓 Early Academic Pursuits

Dr. Chengjiang Zhou began his academic journey with a Ph.D. in Control Engineering, laying a strong foundation in intelligent systems and automation. His passion for signal processing and fault diagnosis was cultivated early in his studies, which led him to engage in high-impact research projects during his graduate years. Collaborating on four National Natural Science Foundation projects and participating in three infrastructure-based research initiatives, Dr. Zhou gained critical experience that shaped his future research trajectory.

💼 Professional Endeavors

As an Associate Professor at Yunnan Normal University, Dr. Zhou has dedicated over a decade to academic excellence and research innovation. Beyond classroom teaching, he plays a pivotal role in project management, student mentorship, and experimental center operations. His leadership in academia is evidenced by his guidance of students to win over 16 national competition awards and his coordination of university-wide research collaborations that uplift institutional research capacity.

🔬 Contributions and Research Focus On Artificial Intelligence 

Dr. Zhou’s research revolves around AI-driven intelligent fault diagnosis under variable and noisy environments, particularly in high-altitude regions like Yunnan. He developed novel algorithms like FRLSTSVM and EMGGDFuRDE, which address overfitting and signal noise issues in wind turbine and bearing fault detection. His innovations bridge AI theory and real-world utility, enabling accurate, cross-domain fault diagnostics. His work has been implemented in industry, significantly improving pipeline monitoring and turbine health systems in local enterprises.

🌍 Impact and Influence

Dr. Zhou’s research has had far-reaching impacts in both academia and industry. His diagnostic solutions are actively employed by companies such as Yunnan Dahongshan Pipeline Co., improving system reliability and energy sustainability. His interdisciplinary innovations enhance operational safety and efficiency in green energy infrastructure, affirming his role as a catalyst for smart industrial transformation in southwestern China.

🧠 Research Skills

Dr. Zhou exhibits deep expertise in signal processing, intelligent fault diagnosis, machine learning, and entropy-based modeling. His ability to integrate AI algorithms into hardware systems demonstrates strong practical and theoretical skills. He is proficient in developing cross-domain transfer diagnosis methods and specializes in working with noisy, uncertain datasets, making his work both technically challenging and practically valuable.

🏅 Awards and Honors

Dr. Zhou has been honored with multiple accolades, including the prestigious Yunnan Natural Science Prize, in recognition of his scientific contributions. He has successfully secured five competitive grants as Principal Investigator, including a major project from the National Natural Science Foundation of China (NSFC). These accomplishments underscore his leadership in high-impact research and innovation.

📖 Editorial Appointments & Reviewer Roles

Dr. Zhou serves as a Guest Editor for Mathematical Problems in Engineering and a reviewer for journals such as Expert Systems with Applications (19 reviews), Information Fusion (18 reviews), and Measurement (7 reviews), among others. His rigorous peer-review contributions help uphold the quality of global scientific discourse.

🔧 Industry Projects & Consultancy

Dr. Zhou has successfully led multiple industry-academic collaborations, including:

  • Development of real-time detection systems for electric shock risks (Yunnan Power Grid).

  • Smart pipeline and vehicle terminal systems (Liancheng Technology).

  • AI-based “Transparent Kitchen” inspection platforms (Hunan KeChuang).

  • High-pressure pump diagnostics (Yunnan Dahongshan).

These projects highlight his role in transferring academic breakthroughs into scalable, real-world applications.

🤝 Collaborations

Dr. Zhou has collaborated with leading industrial and academic partners including:

  • Yunnan Power Grid Co., Ltd.

  • Hunan Skyleaf Information Technology Co.

  • Liancheng Technology Group

  • Yunnan Dahongshan Pipeline Co.

  • National and provincial government-funded research agencies

These collaborations have led to tangible products, publications, and enhanced research ecosystems.

🏛️ Legacy and Future Contributions

Dr. Zhou is poised to leave a lasting legacy through his continued dedication to AI for industrial resilience and safety. His upcoming research in wind turbine fault tracking under uncertain labels aims to push the boundaries of unsupervised learning. As a mentor, innovator, and leader, he is shaping the next generation of engineers while contributing to China’s green and intelligent infrastructure. His future work is set to expand internationally, enhancing global standards in fault diagnostics and smart systems.

Publications Top Notes

  1. A fault diagnosis approach using improved multiscale fluctuation Rényi dispersion entropy and optimized binary tree LSTSVM
    Expert Systems with Applications, 2025 🛠️🧠

  2. A fault diagnosis method using decomposition denoising improved multiscale weighted permutation entropy and one-versus-one least squares twin SVM
    Measurement, 2025 🔍📉

  3. A Novel Fault Diagnosis Method Using FCEEMD-Based Multi-Complexity Low-Dimensional Features and Directed Acyclic Graph LSTSVM
    Entropy, 2024 🌀📊

  4. Fuzzy regular least squares twin support vector machine and its application in fault diagnosis
    Expert Systems with Applications, 2023 🧩⚙️

  5. A Mechanical Part Fault Diagnosis Method Based on Improved Multiscale Weighted Permutation Entropy and Multiclass LSTSVM
    Measurement, 2023 ⚙️📈

  6. A Novel Fault Diagnosis Method Based on NEEEMD-RUSLP Feature Selection and BTLSTSVM
    IEEE Access, 2023 🛠️🖥️

  7. A Fault Feature Extraction Method Based on Improved VMD Multi-Scale Dispersion Entropy and TVD-CYCBD
    Entropy, 2023 🔧🧮

  8. A FCEEMD Energy Kurtosis Mean Filtering-Based Fault Feature Extraction Method
    Coatings, 2022 🛠️📐

  9. Fault Diagnosis of Check Valve Based on KPLS Optimal Feature Selection and Kernel Extreme Learning Machine
    Coatings, 2022 🔩📉

  10. Research on Twin Extreme Learning Fault Diagnosis Method Based on Multi-Scale Weighted Permutation Entropy
    Entropy, 2022 ⚙️🧠

  11. Research on Fault Diagnosis Method of Rolling Bearing Based on Feature Optimization and Self-Adaptive SVM
    Mathematical Problems in Engineering, 2022 🔍🔄

📷 Image Processing & Target Detection

  1. A noise-robust CNN architecture with global attention and gated convolutional Kernels for bearing fault detection
    Measurement Science and Technology, 2024 🤖🎯

  2. AER-Net: Adaptive Feature Enhancement and Hierarchical Refinement Network for Infrared Small Target Detection
    IEEE Transactions on Instrumentation and Measurement, 2024 🌌🎯

  3. AT-GAN: A generative adversarial network with attention and transition for infrared and visible image fusion
    Information Fusion, 2023 🌉🧠

  4. Boosting target-level infrared and visible image fusion with regional information coordination
    Information Fusion, 2023 🔦🤝

  5. DCFusion: A Dual-Frequency Cross-Enhanced Fusion Network for Infrared and Visible Image Fusion
    IEEE Transactions on Instrumentation and Measurement, 2023 🔁🖼️

🚁 UAV Systems & Remote Sensing

  1. A Dual UAV Cooperative Positioning System With Advanced Target Detection and Localization
    IEEE Access, 2024 🚁📡

  2. Scene-aware refinement network for unsupervised monocular depth estimation in ultra-low altitude oblique photography of UAV
    ISPRS Journal of Photogrammetry and Remote Sensing, 2023 🛰️🏞️

  3. A Transmission Line Defect Detection Method Based on YOLOv7 and Multi-UAV Collaboration Platform
    Journal of Electrical and Computer Engineering, 2023 ⚡🚁

  4. Research on Insulator Defect Detection Based on Improved YOLOv7 and Multi-UAV Cooperative System
    Coatings, 2023 ⚙️🔍

💡 Other Applications

  1. A New Hyperspectral Image Identification Method Based on LSDA and OSELM
    Modelling and Simulation in Engineering, 2024 🌈🧠

  2. Bearing Fault Prediction Based on Mixed Domain Features and GWO‐SVM
    Journal of Electrical and Computer Engineering, 2024 ⚙️📊

  3. Two-Branch Feature Interaction Fusion Method Based on Generative Adversarial Network
    Electronics, 2023 🔄🧠

  4. A Kitchen Standard Dress Detection Method Based on the YOLOv5s Embedded Model
    Applied Sciences, 2023 👕📷

  5. HELOP: Multi-target tracking based on heuristic empirical learning algorithm and occlusion processing
    Displays, 2023 👁️🎯