Ahmad Reza Naghsh Nilchi | Deep Learning | Best Researcher Award

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Prof. Ahmad Reza Naghsh Nilchi | Deep Learning | Best Researcher Award

Faculty Member | University of Isfahan | Iran

Prof. Ahmad Reza Naghsh-Nilchi is a distinguished researcher in computer vision, artificial intelligence, and medical image processing with a strong academic and professional background. He completed his PhD in Electrical and Computer Engineering at Michigan State University, where he specialized in digital image processing, and has since built an influential career in both academia and research. Over the years, he has served in multiple leadership positions including department chair, dean of research, and head of research laboratories, while also supervising numerous PhD and master’s students in advanced AI and imaging topics. His professional experience extends internationally through collaborations with leading institutions such as UC Irvine, University of Toronto, York University, and University of Ireland, contributing significantly to global research initiatives. His research interests span robust deep learning, adversarial defense, trustworthy AI, multimodal action recognition, image captioning, retinal analysis, and robot-camera pose estimation, reflecting both theoretical innovation and practical applications. He has published more than 70 papers in prestigious journals and conferences indexed by IEEE and Scopus, and his work has received more than 2,200 citations. His excellence has been recognized through multiple honors, including awards as University Researcher of the Year and Industrial Researcher of the Year. He possesses advanced research skills in AI model development, medical imaging, digital signal processing, and multimodal data analysis, complemented by editorial roles, conference organization, and active memberships in professional associations such as IEEE and ACM. His career demonstrates a commitment to advancing science, mentoring the next generation, and fostering impactful interdisciplinary collaborations. His Scopus output reflects international impact, with 1,319 citations by 1,214 documents, 65 published documents, and an h-index of 21.

Profile: Google Scholar | Scopus Profile | ORCID Profile

Featured Publications

Fathi, A., & Naghsh-Nilchi, A. R. (2012). Noise tolerant local binary pattern operator for efficient texture analysis. Pattern Recognition Letters, 33(9), 1093–1100.

Fathi, A., & Naghsh-Nilchi, A. R. (2012). Efficient image denoising method based on a new adaptive wavelet packet thresholding function. IEEE Transactions on Image Processing, 21(9), 3981–3990.

Fathi, A., & Naghsh-Nilchi, A. R. (2013). Automatic wavelet-based retinal blood vessels segmentation and vessel diameter estimation. Biomedical Signal Processing and Control, 8(1), 71–80.

Amirgholipour, S. K., & Ahmad, R. (2009). Robust digital image watermarking based on joint DWT-DCT. International Journal of Digital Content Technology and its Applications, 3(2), 42–48.*

Kasmani, S. A., & Naghsh-Nilchi, A. (2008). A new robust digital image watermarking technique based on joint DWT-DCT transformation. In 2008 Third International Conference on Convergence and Hybrid Information Technology (pp. 539–544). IEEE.

Osman Yildirim | Deep Learning | Best Researcher Award

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Prof. Osman Yildirim | Deep Learning | Best Researcher Award

Head of the Department | Istanbul Aydın University | Turkey 

Prof. Osman Yildirim is a distinguished academic and researcher recognized for his contributions at the intersection of engineering, business, sustainability, and biomedical applications. He holds dual doctoral degrees in Engineering and Business Administration, a unique combination that has enabled him to approach research challenges with a strong interdisciplinary perspective. Over the course of his career, he has taken on significant academic leadership roles, including serving as Head of Department at Istanbul Aydin University, while also guiding doctoral students and fostering collaborative research projects. His professional experience spans teaching across engineering and business disciplines, coordinating research initiatives, and contributing to institutional development through mentorship and administrative leadership. His primary research interests focus on green transformation, sustainable supply chains, carbon policy impacts, energy management systems in universities, and AI-based medical imaging applications for improved diagnostics. These areas reflect his commitment to aligning research with both technological advancements and societal needs, particularly in the context of sustainable development and healthcare innovation. He has published widely in reputed Q1 and Q2 indexed journals such as Scopus and SCI, showcasing the impact of his work in both technical and applied fields. His achievements have been recognized through awards and honors that acknowledge his contributions to advancing interdisciplinary research and education. In addition, he has built valuable collaborations with international teams, integrating expertise from engineering, business, and medicine to deliver impactful solutions with global relevance. His research skills include expertise in machine learning, AI-driven image analysis, sustainable system design, and computational modeling for optimization under carbon constraints. These technical strengths, combined with his leadership and mentorship, position him as a leading scholar dedicated to advancing academic excellence and addressing global challenges through innovative and socially relevant research.

Profile: Google Scholar | Scopus Profile | ORCID Profile

Featured Publications

Ozturk, A. I., Yıldırım, O., İdman, E., & İdman, E. (2025). A comparative study of hybrid decision tree–deep learning models in the detection of intracranial arachnoid cysts. Neuroscience Informatics, 100234.

Ozturk, A. I., Yildirim, O., Kaygusuz, K., Idman, E., & Idman, E. (2025). Brain cyst detection using deep learning models. International Journal of Innovative Research and Scientific Studies, 8(5), 8974.

Borhan Elmi, M. M., & Yıldırım, O. (2025). Improve MPPT in organic photovoltaics with chaos-based nonlinear MPC. Balkan Journal of Electrical and Computer Engineering, 13(1), 1418574.

Ozturk, A. I., Yıldırım, O., & Deryahanoglu, O. (2025). A comprehensive strategy for the identification of arachnoid cysts in the brain utilizing image processing segmentation methods. International Journal of Innovative Technology and Exploring Engineering, 14(2), 1031.

Borhan Elmi, M. M., & Yıldırım, O. (2024). Improve LVRT capability of organic solar arrays by using chaos-based NMPC. International Journal of Energy Studies, 4(3), 1449558.

Yildirim, O., Khaustova, V. Y., & Ilyash, O. I. (2023). Reliability and validity adaptation of the hospital safety climate scale. The Problems of Economy, 4(1), 207–216.

Yildirim, O. (2023). Multidimensional and strategic outlook in digital business transformation: Human resource and management recommendations for performance improvement. In Book chapter.

Yildirim, O. (2023). Health professionals’ perspective in the context of social media, paranoia, and working autonomy during the COVID-19 pandemic period. Archives of Health Science Research, 10(1), 30–37.

Yildirim, O. (2023). The personified model for supply chain management. In Multidimensional and strategic outlook in digital business transformation: Human resource and management recommendations for performance improvement.

Yildirim, O., Ilyash, O. I., Khaustova, V. Y., & Celiksular, A. (2022). The effect of emotional intelligence and work-related strain on the employee’s organizational behavior factors. The Problems of Economy, 2(1), 124–131.

Yildirim, O. (2022). Investigation of the electrical conductivity of pernigranilin with carbon monoxide and nitrogen monoxide doping. Mathematical Statistician and Engineering Applications, 9(4).

Yildirim, O. (2022). Cyst segmentation using filtering technique in computed tomography abdominal kidney images. Mathematical Statistician and Engineering Applications, 9(4).

Yildirim, O. (2022). Design of flyback converter by obtaining the characteristics of polymer based R2R organic PV panels. International Journal of Renewable Energy Research, 12(4).

Avdullahi, A., & Yildirim, O. (2021). The mediating role of emotional stability between regulation of emotion and overwork. In Book chapter.

Tunç, P., Yıldırım, O., Göktepe, E. A., & Çapuk, S. (2021). Investigation of the relationship between personality, organizational identification and turnover in competitive flight model. TroyAcademy, 6(1), 894141.

Tunç, P., Yıldırım, O., Göktepe, E. A., & Çapuk, S. (2021). Investigation of the relationship between personality, organizational identification and turnover in competitive flight model. Çanakkale Onsekiz Mart Üniversitesi Uluslararası Sosyal Bilimler Dergisi, 4(1), 804959.

Zahra Yahyaoui | Deep Learning | Women Researcher Award

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Dr. Zahra Yahyaoui | Deep Learning | Women Researcher Award

Teacher-Researcher at Higher Institute of Applied Sciences and Technology of Kasserine, Kairouan University | Tunisia

Dr. Zahra Yahyaoui is a dedicated researcher and educator with expertise in electronics, microelectronics, renewable energy systems, and artificial intelligence. She has established herself as an active contributor to the advancement of intelligent fault detection and diagnosis methods for photovoltaic and wind energy conversion systems. Her work bridges theory and practice, combining advanced machine learning techniques with embedded hardware implementation, ensuring her research is both academically rigorous and industrially relevant. Alongside her research activities, she has been deeply involved in teaching, supervision, and mentoring, helping to shape the academic and professional development of students in electronics and applied sciences. Her publications in high-impact journals and participation in international conferences highlight her growing recognition in the global research community. With technical versatility, adaptability, and strong teamwork skills, she continues to contribute to sustainable solutions in energy systems while promoting innovation, academic excellence, and interdisciplinary collaboration.

Professional Profiles 

Scopus Profile | ORCID Profile 

Education

Dr. Zahra Yahyaoui pursued her academic path in Tunisia, beginning with a bachelor’s degree in industrial computing with a specialization in embedded systems. She then advanced to a master’s research degree in nanomaterials and embedded electronics, where she specialized in embedded electronics and conducted important research on fault detection and diagnosis in wind energy systems using machine learning. Building on this foundation, she completed her doctoral studies in electronics and microelectronics at the Higher Institute of Applied Sciences and Technology of Kasserine, Kairouan University. Her PhD research focused on developing enhanced intelligent data-driven paradigms for fault detection and diagnosis in power systems, with practical applications on embedded architectures. She carried out her doctoral work within the Research Unit of Advanced Materials and Nanotechnologies, furthering her expertise at the intersection of artificial intelligence, renewable energy, and electronic systems. This strong academic background reflects her commitment to innovative, multidisciplinary research.

Professional Experience

Dr. Zahra Yahyaoui has built a solid academic and professional career through her teaching and research activities. She started as a part-time teacher at the Higher Institute of Applied Sciences and Technology of Kasserine, where she gained experience delivering courses and tutorials in electronics, microprocessor and microcontroller architectures, and embedded systems. Her role expanded to contractual teacher at the same institute under Kairouan University, where she was responsible for teaching system-on-chip design, combinational and sequential logic circuits, and analog signal processing, covering both theoretical and practical sessions. In addition to her teaching duties, she has co-supervised master’s theses on advanced topics such as interval-valued machine learning, deep learning for fault detection in renewable systems, and photovoltaic installation design. Through her academic contributions, she has combined teaching excellence with mentoring, ensuring students receive both theoretical knowledge and practical insights. Her professional journey highlights her commitment to education, innovation, and applied research.

Research Interest

Dr. Zahra Yahyaoui’s research interests lie at the intersection of electronics, artificial intelligence, and renewable energy systems. She focuses on developing intelligent data-driven approaches for fault detection and diagnosis, aiming to enhance the reliability and efficiency of power systems such as photovoltaic and wind energy converters. Her work emphasizes the use of advanced machine learning and deep learning techniques, including BiLSTM, GRU, and optimization algorithms, to address uncertainty in renewable energy conversion and monitoring. She is also interested in the implementation of these algorithms on embedded architectures, integrating software with hardware platforms like FPGA, Raspberry Pi, and microcontrollers for real-world applications. Beyond fault diagnosis, she explores forecasting methods for solar irradiance and power output, contributing to the broader field of sustainable energy management. By combining theoretical modeling, algorithm development, and embedded system integration, her research supports innovation in intelligent renewable energy technologies.

Research Skill

Dr. Zahra Yahyaoui has developed a diverse set of research skills that enable her to carry out impactful and interdisciplinary work. She is proficient in programming languages such as MATLAB and Python, which she uses extensively for data analysis, machine learning model development, and algorithm implementation. She is skilled in simulation tools like ISE and Simplorer, supporting her expertise in circuit and system design. Her hardware-related skills include working with Siemens S7-1200, FPGA boards, Raspberry Pi, and Arduino microcontrollers, allowing her to translate theoretical models into practical embedded system solutions. She has strong problem-solving abilities, adaptability, and teamwork skills, which contribute to successful research collaborations and academic projects. Her research methodology combines theoretical analysis with experimental validation, ensuring robust and application-oriented results. With certifications in artificial intelligence and embedded systems, she brings an advanced skillset for developing intelligent monitoring and diagnostic systems, particularly for renewable energy applications.

Publications Top Notes

Title: Fault detection and diagnosis in grid-connected PV systems under irradiance variations
Authors: Hajji, M.; Yahyaoui, Z.; Mansouri, M.; Nounou, H.; Nounou, M.
Year: 2023

Title: One-Class Machine Learning Classifiers-Based Multivariate Feature Extraction for Grid-Connected PV Systems Monitoring under Irradiance Variations
Authors: Yahyaoui, Z.; Hajji, M.; Mansouri, M.; Bouzrara, K.
Year: 2023

Title: Effective Fault Detection and Diagnosis for Power Converters in Wind Turbine Systems Using KPCA-Based BiLSTM
Authors: Yahyaoui, Z.; Hajji, M.; Mansouri, M.; Abodayeh, K.; Bouzrara, K.; Nounou, H.
Year: 2022

Title: Kernel PCA based BiLSTM for Fault Detection and Diagnosis for Wind Energy Converter Systems
Authors: Yahyaoui, Z.; Hajji, M.; Mansouri, M.; Bouzrara, K.; Nounou, H.; Nounou, M.
Year: 2022

Title: Efficient fault detection and diagnosis of wind energy converter systems
Authors: Yahyaoui, Z.; Hajji, M.; Mansouri, M.; Harkat, M.-F.; Kouadri, A.; Nounou, H.; Nounou, M.
Year: 2020

Conclusion

Dr. Zahra Yahyaoui is a deserving candidate for the Best Researcher Award due to her significant contributions in advancing intelligent data-driven techniques for renewable energy systems, fault detection, and embedded architectures. Her research has produced valuable publications in reputed international journals and conferences, with practical applications that support sustainable energy and technological innovation. Through her teaching, mentorship, and active participation in the academic community, she has demonstrated a strong commitment to knowledge sharing and capacity building. With her proven expertise, dedication, and potential for future leadership, she is well positioned to continue making impactful contributions to both research and society.

Sivanagireddy Kalli | Deep Learning for Computer Vision | Academic Excellence Distinction Award

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Dr. Sivanagireddy Kalli | Deep Learning for Computer Vision | Academic Excellence Distinction Award

Professor at Sridevi Women’s Engineering College, India

Dr. K. Sivanagireddy is a seasoned academician and researcher with over 20 years of experience in teaching, research, and administration. He has served in key academic leadership roles including Dean Academics, Head of Department, and Principal across reputed engineering institutions in Telangana and Andhra Pradesh. His extensive contributions include the publication of more than 60 research papers in SCI, Scopus, and UGC CARE-listed journals, along with participation in over 20 international conferences. He has been a driving force in innovation, holding eight patents—both national and international—and authoring nine technical books. He recently completed a Postdoctoral Fellowship at the University of South Florida (2024) and earned a Ph.D. in Electronics and Communication Engineering from JNTU Hyderabad (2019). His expertise spans areas like VLSI Design, IoT, AI, Embedded Systems, and Medical Image Processing. Recognized nationally and internationally, Dr. Sivanagireddy is also an active member of professional bodies such as IEEE, IAENG, and IAOE.

Professional Profile 

Education🎓

Dr. K. Sivanagireddy has a strong academic foundation rooted in electronics, communication, and embedded systems. He earned his Ph.D. in Electronics and Communication Engineering from Jawaharlal Nehru Technological University, Hyderabad, in 2019. Recently, in 2024, he completed his Postdoctoral Fellowship at the University of South Florida, USA, further enriching his research exposure and global academic outlook. His earlier postgraduate education includes an M.Tech in Embedded Systems from JNTUK, Kakinada (2014), and an M.E in VLSI Design from Vinayaka Missions University, Tamil Nadu (2006). He began his academic journey with a B.Tech in Electronics and Communication Engineering from Bharathidasan University, Tiruchirappalli, in 2002. His education reflects a clear emphasis on digital design, embedded computing, and system optimization, which laid the groundwork for his multifaceted contributions in academia and research. He has also pursued various NPTEL and FDP certifications from top IITs, demonstrating his commitment to lifelong learning and skill enhancement.

Professional Experience📝

Dr. K. Sivanagireddy brings over two decades of professional academic experience, with an emphasis on leadership, research, and teaching. Currently serving as Dean Academics and Professor at Sridevi Women’s Engineering College, Hyderabad since 2019, he previously held the positions of Head of Department and Associate Professor at the same institute. Earlier in his career, he worked at Arjun College of Technology and Science and LITAM, Guntur, where he mentored undergraduate and postgraduate students and handled administrative responsibilities. His contributions extend to coordinating academic accreditations like NAAC and NBA, overseeing student projects, counseling, and organizing technical paper contests. His strategic leadership has helped align institutional goals with academic excellence and research development. With a deep understanding of educational systems, faculty management, and curriculum design, Dr. Sivanagireddy has played a pivotal role in shaping the academic structure of the institutions he served. His professionalism and experience continue to influence engineering education in India.

Research Interest🔎

Dr. Sivanagireddy’s research interests are broad, multidisciplinary, and highly application-oriented. His primary focus lies in Medical Image Processing, Artificial Intelligence, Deep Learning, and IoT-enabled systems, especially for healthcare diagnostics and smart surveillance. He has conducted advanced research in brain tumor detection, cancer classification, heart disease prediction, and autonomous medical devices, often leveraging CNN, LSTM, and hybrid deep learning models. Additionally, his work spans VLSI Design, Embedded Systems, Cybersecurity, Video Surveillance, and Signal Processing, reflecting his versatility. His contributions also extend to developing IoT-integrated intelligent systems, machine learning-based prediction models, and hardware optimization techniques. Many of his projects are focused on societal needs, such as fall detection for the elderly, counterfeit currency detection, and remote health monitoring. His research is rooted in real-world impact, bridging engineering with life sciences and computing. This interdisciplinary approach allows him to explore innovative solutions across both theoretical and applied research domains.

Award and Honor🏆

Dr. K. Sivanagireddy’s scholarly achievements have been widely recognized through multiple national and international honors. He received the International Academic Excellence Award from I2OR in 2022, acknowledging his impactful global research footprint. In 2021, he was conferred with the National Faculty Excellency Award by the International Journal of MC Square Scientific Research, reflecting his outstanding contributions to teaching and innovation. He also earned the National Certificate of Excellence from the Telangana Engineering Colleges Faculty Association in 2020, further emphasizing his role in academic leadership. In addition to these awards, his editorial engagement with the Asian Council of Science Editors and professional memberships with IEEE, IAENG, and IAOE signify his active participation in international scholarly communities. His commitment to excellence, innovation, and quality research has made him a role model in engineering academia, and these accolades underscore his dedication to elevating academic standards at both institutional and national levels.

Research Skill🔬

Dr. Sivanagireddy possesses a diverse and robust set of research skills that span both theoretical modeling and practical application. He is adept in machine learning algorithms, deep learning frameworks, IoT development, and VLSI simulation tools. His proficiency in tools like MATLAB, Python, Verilog, and FPGA platforms has enabled him to develop and deploy intelligent systems for healthcare, security, and automation. He has expertise in image processing techniques, including segmentation, classification, and feature extraction using CNNs, Bi-LSTM, and hybrid models. Additionally, he demonstrates advanced knowledge in medical diagnostics, pattern recognition, and cloud computing integration. His research skillset is not only confined to software but extends to hardware optimization, including CMOS and ASIC design. Through his participation in over 20 conferences and completion of NPTEL certifications from IITs, he maintains up-to-date technical competence. These diverse skills allow him to drive interdisciplinary research, publish impactful papers, and mentor future innovators effectively.

Conclusion💡

Dr. K. Sivanagireddy is highly deserving and well-qualified for the Best Researcher Award. With a prolific publication record, leadership roles, multiple patents, academic books, and contributions to multiple domains in engineering and technology, he stands out as a multidisciplinary scholar and innovator. A stronger emphasis on research impact, international projects, and focused thematic expertise would further elevate his candidacy.

Publications Top Noted✍

  • Title: An effective motion object detection using adaptive background modeling mechanism in video surveillance system
    Authors: SNR Kalli
    Year: 2021
    Citations: 54

  • Title: Early lung cancer prediction using correlation and regression
    Authors: K Sivanagireddy, S Yerram, SSN Kowsalya, SS Sivasankari, J Surendiran, RG Vidhya
    Year: 2022
    Citations: 24

  • Title: Image Compression and reconstruction using a new approach by artificial neural network
    Authors: KSN Reddy, BR Vikram, LK Rao, BS Reddy
    Year: 2012
    Citations: 21

  • Title: A Fast Curvelet Transform Image Compression Algorithm using with Modified SPIHT
    Authors: KSN Reddy, BRS Reddy, G Rajasekhar, KC Rao
    Year: 2012
    Citations: 14

  • Title: A nanoplasmonic branchline coupler for subwavelength wireless networks
    Authors: K Thirupathaiah, KS Reddy, GRS Reddy
    Year: 2021
    Citations: 11

  • Title: Generative Adversarial Networks based Approach for Intrusion Detection System
    Authors: S Kalli, BN Kumar, S Jagadeesh
    Year: 2022
    Citations: 8

  • Title: IMPLEMENTATION OF OBJECT TRACKING AND VELOCITY DETERMINATION
    Authors: SNR Kalli
    Year: 2012
    Citations: 5

  • Title: Image compression by discrete curvelet wrapping technique with simplified SPIHT
    Authors: KSN Reddy, L Rao, P Ravikanth
    Year: 2012
    Citations: 4

  • Title: Identification of criminal & non-criminal faces using deep learning and optimization of image processing
    Authors: K Sivanagireddy, S Jagadeesh, A Narmada
    Year: 2024
    Citations: 3

  • Title: Low memory low complexity image compression using DWT and HS-SPIHT encoder
    Authors: K Sivanagireddy, M Saipravallika, PKC Tejaswini
    Year: 2012
    Citations: 3

  • Title: Reconstruction Using a New Approach By Artificial Neural Network
    Authors: SNRKI Compression
    Year: 2012
    Citations: 3

  • Title: Early Lung Cancer Prediction using Correlation and Regression
    Authors: K Sivanagireddy
    Year: 2022
    Citations: 2

  • Title: Smart Door Lock to Avoid Robberies in ATM
    Authors: VS Reddy, S Kalli, H Gebregziabher, BR Babu
    Year: 2021
    Citations: 2

  • Title: Image Segmentation by Using Modified Spatially Constrained Gaussian Mixture Model
    Authors: S Kalli, BM Bhaskara
    Year: 2016
    Citations: 2

  • Title: Efficient Memory and Low Complexity Image Compression Using DWT with Modified SPIHT Encoder
    Authors: KSN Reddy, VS Reddy, DBR Vikram
    Year: 2012
    Citations: 2

  • Title: Brain Tumor Detection through Image Fusion Using Cross Guided Filter and Convolutional Neural Network
    Authors: MV Srikanth, S Kethavath, S Yerram, SNR Kalli, JB Naik
    Year: 2024
    Citations: 1

  • Title: Autoencoder-based Deep Learning Approach for Intrusion Detection System using Firefly Optimization Algorithms
    Authors: N Kumar Bukka, S Jagadeesh, KS Reddy
    Year: 2024
    Citations: 1

Zhe Zhang | Deep Learning for Computer Vision | Best Researcher Award

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Dr. Zhe Zhang | Deep Learning for Computer Vision | Best Researcher Award

Lecturer at Henan University of Engineering, China

Zhe Zhang is a dedicated researcher specializing in deep learning and spatio-temporal forecasting, with a strong focus on meteorological applications such as tropical cyclone intensity prediction and typhoon cloud image analysis. His academic contributions demonstrate a solid grasp of advanced neural networks and remote sensing technologies, backed by an impressive publication record in high-impact SCI Q1 journals like Knowledge-Based Systems and IEEE Transactions on Geoscience and Remote Sensing. Zhang’s work integrates artificial intelligence with environmental monitoring, making significant strides in predictive modeling from satellite imagery. With a collaborative and interdisciplinary approach, his research contributes to both academic advancement and real-world disaster management. His innovative frameworks, such as spatiotemporal encoding modules and generative adversarial networks, exemplify technical excellence and societal relevance. Zhe Zhang stands out as a rising expert in AI-driven environmental systems and continues to push the frontiers of climate informatics through data-driven methodologies and scalable forecasting frameworks.

Professional Profile 

Education🎓 

Zhe Zhang holds a robust academic background in computer science and artificial intelligence, which has laid a strong foundation for his research in deep learning and remote sensing. He pursued his undergraduate studies in a computer science-related discipline, where he developed an early interest in data analytics and neural networks. Building on this foundation, he advanced to postgraduate education with a focus on machine learning, remote sensing applications, and environmental informatics. His graduate-level research emphasized deep learning-based forecasting models using satellite imagery, leading to early exposure to impactful interdisciplinary research. Throughout his academic journey, he has combined coursework in AI, image processing, and spatio-temporal modeling with practical lab experience and collaborative research projects. His educational trajectory has equipped him with both theoretical knowledge and technical skills, enabling him to develop innovative solutions to complex problems in climate and disaster prediction. Zhang’s educational background reflects a clear trajectory toward research leadership.

Professional Experience📝

Zhe Zhang has accumulated valuable professional experience through academic research positions, collaborative projects, and contributions to high-impact scientific publications. As a core member of multiple research groups focused on environmental AI and satellite image analysis, he has played a pivotal role in designing and developing deep learning frameworks for spatio-temporal prediction tasks. His collaborations span across disciplines, working with experts in meteorology, computer vision, and geospatial analysis. Zhang has contributed significantly to projects involving tropical cyclone intensity estimation, remote sensing super-resolution, and post-disaster damage assessment. In each role, he has demonstrated leadership in designing model architectures, implementing advanced training pipelines, and validating results with real-world data. His experience also includes CUDA-based optimization for remote sensing image processing, showcasing his computational and engineering proficiency. This combination of domain-specific and technical expertise has positioned him as a valuable contributor to AI-driven environmental applications in both academic and applied research environments.

Research Interest🔎

Zhe Zhang’s research interests center on deep learning, spatio-temporal forecasting, and remote sensing. He is particularly focused on developing neural network frameworks to predict and assess tropical cyclone intensity using satellite imagery, addressing critical challenges in climate-related disaster prediction. Zhang is passionate about enhancing model accuracy and generalizability in extreme weather forecasting through spatiotemporal encoding and generative adversarial networks. His work also extends to super-resolution of remote sensing images and object detection for damage assessment, demonstrating a strong interest in post-disaster management applications. He explores innovative ways to integrate multi-source data, such as infrared and visible satellite images, into unified prediction pipelines. Additionally, he is interested in scalable deep learning architectures optimized for high-performance computing environments like CUDA. Zhang’s overarching goal is to bridge the gap between artificial intelligence and environmental science, enabling more accurate, real-time, and actionable insights from complex geospatial datasets. His research continues to evolve toward intelligent Earth observation systems.

Award and Honor🏆

Zhe Zhang has earned academic recognition through his contributions to high-impact publications and collaborative research in deep learning and remote sensing. While specific awards and honors are not listed, his publication record in top-tier SCI Q1 journals such as Knowledge-Based Systems and IEEE Transactions on Geoscience and Remote Sensing attests to his research excellence and scholarly recognition. His first-author and co-authored papers have received commendations within the academic community for their novelty and real-world relevance, especially in the domains of environmental forecasting and image analysis. Additionally, Zhang’s involvement in multidisciplinary research projects indicates that he has likely contributed to grant-funded initiatives and may have been recognized through institutional acknowledgments or research excellence programs. With increasing citation counts and growing visibility in the AI for environmental science space, Zhang is well-positioned to earn future distinctions at national and international levels. His scholarly contributions lay a strong foundation for future honors.

Research Skill🔬

Zhe Zhang possesses a robust set of research skills that span deep learning, remote sensing, image processing, and high-performance computing. He is proficient in designing and implementing convolutional neural networks, spatiotemporal encoding architectures, and generative adversarial networks for geospatial data analysis. His ability to handle satellite imagery and extract meaningful patterns from complex datasets underlines his strengths in data preprocessing, feature engineering, and model optimization. Zhang is skilled in programming languages such as Python and frameworks like TensorFlow and PyTorch, and he is adept at deploying models on CUDA-based environments for accelerated processing. He has demonstrated expertise in both supervised and unsupervised learning, as well as in evaluating model performance using real-world datasets. His publication record reveals a deep understanding of domain-specific applications, including tropical cyclone intensity forecasting and damage detection. These skills enable him to bridge theory and application, making him a versatile and capable researcher in AI and environmental modeling.

Conclusion💡

Zhe Zhang presents a strong and competitive profile for the Best Researcher Award, especially in the fields of Deep Learning and Spatio-temporal Forecasting. The research is:

  • Technically sound (deep learning architectures),

  • Application-driven (cyclone prediction, disaster response),

  • And academically visible (SCI Q1 journal publications).

With slight enhancements in independent project leadership and wider domain application, Zhe Zhang would not only be a worthy recipient but could emerge as a leader in AI-driven environmental modeling.

Publications Top Noted✍

  • Title: Single Remote Sensing Image Super-Resolution via a Generative Adversarial Network With Stratified Dense Sampling and Chain Training
    Authors: Fanen Meng, Sensen Wu, Yadong Li, Zhe Zhang, Tian Feng, Renyi Liu, Zhenhong Du
    Year: 2024
    Citation: DOI: 10.1109/TGRS.2023.3344112
    (Published in IEEE Transactions on Geoscience and Remote Sensing)

  • Title: A Neural Network with Spatiotemporal Encoding Module for Tropical Cyclone Intensity Estimation from Infrared Satellite Image
    Authors: Zhe Zhang, Xuying Yang, Xin Wang, Bingbing Wang, Chao Wang, Zhenhong Du
    Year: 2022
    Citation: DOI: 10.1016/j.knosys.2022.110005
    (Published in Knowledge-Based Systems)

  • Title: A Neural Network Framework for Fine-grained Tropical Cyclone Intensity Prediction
    Authors: Zhe Zhang, Xuying Yang, Lingfei Shi, Bingbing Wang, Zhenhong Du, Feng Zhang, Renyi Liu
    Year: 2022
    Citation: DOI: 10.1016/j.knosys.2022.108195
    (Published in Knowledge-Based Systems)

Divya Mishra | Machine Learning | Best Researcher Award

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Assoc . Prof . Dr . Divya Mishra | Machine Learning | Best Researcher Award

Associate Professor at GL Bajaj Institute of Technology & Management, Greater Noida, India

Dr. Divya Mishra is a passionate and accomplished academician and AI researcher with over 13 years of cross-sectoral experience spanning academia, research, and industry. Currently serving as an Associate Professor in CSE-AIML at NIET and pursuing post-doctoral research remotely at Infrastructure University Kuala Lumpur (IUKL), her work centers on AI-driven sustainable e-governance. She brings deep expertise in machine learning, deep learning, and neural networks, underpinned by practical software development experience in Java and Python. Her PhD research addressed call drop prediction in mobile networks using an ANN-based model, resulting in near-perfect accuracy. Dr. Mishra is actively engaged in impactful research projects, including patents, edited books, and IEEE conference publications, while serving as a reviewer, session chair, and technical program committee member in prestigious forums. With a commitment to transparency, innovation, and sustainability in digital transformation, she is a leading voice in AI applications for public administration and smart solutions.

Professional Profile 

Education🎓

Dr. Divya Mishra holds a robust academic background in computer science and electronics. She earned her Ph.D. in Computer Science and Engineering from Noida International University in August 2021, with research focused on mitigating mobile network call drops using deep learning. She previously completed her M.Tech in Computer Science (Full-Time) from the same institution with a stellar CGPA of 9.2, securing a Gold Medal. Her postgraduate studies include an MCA from U.P. Technical University in 2011 with 77.4%, and a BCA from IGNOU, New Delhi. She also holds a Diploma in Electronics Engineering from B.T.E. Lucknow with 72.95%. Her academic journey reflects a consistent trajectory of academic excellence, technical competence, and multidisciplinary learning. Recognized for her honors during MCA by the Governor of Uttar Pradesh, Dr. Mishra’s educational path has equipped her with the theoretical and applied foundation required for her advanced research in AI, machine learning, sustainable systems, and digital governance.

Professional Experience📝

Dr. Divya Mishra boasts over 13 years of versatile professional experience across academia, industry, and research. She currently serves as an Associate Professor in the CSE-AIML Department at NIET, Greater Noida, since May 2025, while also pursuing post-doctoral research on AI-driven e-governance at IUKL, Malaysia. Her academic tenure includes Assistant Professor roles at GL Bajaj Institute and GNIOT, where she taught and mentored students in AI, ML, and data analytics. Previously, she was a Research Scholar at Noida International University, contributing significantly to AI-based telecom systems. Her industrial experience includes software development roles at Tripti e Solutions, Apex TG India Pvt. Ltd., and IIHT Ltd, where she also served as Center Head. She began her technical journey as a Diploma Trainee at Indian Telephone Industries Ltd. Her multifaceted experience enables her to seamlessly integrate theoretical concepts with practical applications in her teaching and research efforts.

Research Interest🔎

Dr. Divya Mishra’s research interests lie at the intersection of artificial intelligence, machine learning, deep learning, and sustainable governance systems. She is particularly passionate about developing intelligent, real-time AI-driven solutions for public administration, telecom, e-governance, and smart environmental monitoring. Her doctoral research focused on mitigating call drops in mobile networks through ANN-based models integrated into a real-time mobile application. Her post-doctoral focus extends into AI-powered sustainable e-governance frameworks, emphasizing transparency and accountability. She is also involved in multidisciplinary projects such as wildlife monitoring using deep learning, hand sign language recognition, waste classification, and emotion recognition from voice, reflecting her commitment to using AI for societal benefit. Dr. Mishra’s work spans practical AI implementations in healthcare, energy optimization, VANET security, and IoT systems. Through her edited books, patents, and publications, she continues to explore innovative intersections of AI with sustainability, data integrity, and policy, aligning her research with global digital transformation agendas.

Award and Honor🏆

Dr. Divya Mishra has received numerous accolades recognizing her academic excellence, impactful research, and leadership in AI. Notably, she was honored with the Shakti Award 2024 by Jansharnam NGO on Women’s Day for her outstanding contributions to technology and education. She also received the Gold Medal during her M.Tech, and her MCA degree was conferred by the Governor of Uttar Pradesh, recognizing her academic honors. She was appreciated for her contributions at international conferences like IICS 2021, and awarded the Quality Contribution Award by GNIOT, Greater Noida. Additionally, her leadership as an Innovation Ambassador at GL Bajaj’s Innovation Cell and roles as session chair and reviewer for multiple IEEE and Springer conferences further validate her active participation in shaping global research discourse. Her recognitions from institutional and national forums reflect her continuous drive toward academic excellence, innovative research, and meaningful community contributions in AI and governance.

Research Skill🔬

Dr. Divya Mishra possesses an extensive and dynamic research skill set across the AI landscape. She is proficient in programming languages like Python, Java, and C, and has a deep command over machine learning, deep learning, neural networks, and data analysis. Her expertise includes developing intelligent algorithms for real-time applications, evidenced by her ANN-based call drop prediction model and integration into the MyTelecomApp. She has published and reviewed numerous peer-reviewed papers, contributed to edited books, and filed multiple AI-driven patents across domains such as environment, health, and security. Dr. Mishra excels in research writing, patent drafting, project conceptualization, and conference management. She also has experience in hands-on technical training and mentoring, contributing to student development and curriculum design. Her interdisciplinary skills allow her to translate complex AI frameworks into socially impactful, sustainable solutions, making her a versatile and effective researcher in applied artificial intelligence and digital innovation ecosystems.

Conclusion💡

Dr. Divya Mishra demonstrates strong qualifications, multidisciplinary impact, and innovative leadership that make her a highly suitable candidate for the Best Researcher Award. Her ongoing postdoctoral work, numerous publications, patents, and reviewer engagements speak to her active and impactful research career. With minor enhancements in global collaborations, funding portfolios, and citation metrics, her candidacy would become even more compelling.

Publications Top Noted✍

  1. Title: Self-optimization in LTE: An approach to reduce call drops in mobile network
    Authors: D. Mishra, A. Mishra
    Year: 2018
    Citations: 8

  2. Title: Sentimental Voice Recognition: An Approach to Analyse the Emotion by Voice
    Authors: A. Gupta, D. Mishra
    Year: 2024
    Citations: 2

  3. Title: Neural Network: A Way to Know Consumer Satisfaction During Voice Call
    Authors: D. Mishra, S. Mishra
    Year: 2022
    Citations: 2

  4. Title: Performance Enhanced and Improvised Approach to Reduce Call Drops Using LTE-SON
    Authors: D. Mishra, A. Mishra
    Year: 2019
    Citations: 2

  5. Title: Drowsiness Alert System: An Approach To Save The Life
    Authors: A. Chandra, D. Mishra, B. Shaw, A. Gupta
    Year: 2023
    Citations: 1

  6. Title: Mobility Robustness Optimization Using ANN for Call Drop Prediction
    Authors: D. Mishra, S. Yadav
    Year: 2020
    Citations: 1

  7. Title: Fine tuning of MapReduce jobs using parallel K Map clustering
    Authors: D. Mishra, S. Yadav
    Year: 2019
    Citations: 1

  8. Title: Empowering Sustainable Waste Management: A Comparative Study of Machine Learning Models for Citizen Engagement
    Authors: D. Mishra, R. Kumar, A.B. bin Abdul Hamid
    Year: 2025

  9. Title: Machine Learning: A Self-Optimized Boon for Deaf and Mute to Recognize Real-Time Hand Sign Language
    Authors: P. Pandey, D. Mishra
    Year: 2025

  10. Title: Character Detection: An Approach to Clarify the Texts Using Machine Learning
    Authors: B. Shaw, D. Mishra
    Year: 2025

  11. Title: Intellicam: A Self-Optimizing Approach to Detect Burglary using Machine Learning
    Authors: A. Chandra, D. Mishra
    Year: 2025

  12. Title: Integrating Cryptographic Techniques with Machine Learning Algorithms for Enhanced Data Privacy and Information Security: A Mathematical Framework
    Authors: G. Merlin Florrence, D. Mishra, G. Ghule, P.K. Sahu, Singh
    Year: 2024

  13. Title: A Mathematical Framework for Enhancing IoT Security in VANETs: Optimizing Intrusion Detection Systems through Machine Learning Algorithms
    Authors: D. Mishra, S. Moudgi, D. Virmani, Y.P. Faniband, A.B. Nandyal, P.K. Sahu
    Year: 2024

  14. Title: YOLO: A way to identify gemstone and predict its relevant finger to wear
    Authors: D. Mishra, S. Mishra
    Year: 2023

  15. Title: Instant Energy Products: An Analysis
    Authors: D.M. Mohasin Haque, Irfan Ahamad
    Year: 2023

  16. Title: Mid–Point Sorting Algorithm: A New Way to Sort
    Authors: A. Garg, V. Patel, D. Mishra
    Year: 2022

  17. Title: A review on call drop
    Authors: D. Mishra, A. Mishra
    Year: 2016

Mr. Andrews Tang | Deep Learning | Best Researcher Award

Published on by Computer Vision Awards

Mr. Andrews Tang | Deep Learning | Best Researcher Award

Andrews Tang at Kwame Nkrumah University of Science and Technology, Ghana

👨‍🎓 Profiles

Scopus

Google Scholar

Publications

Assessing blockchain and IoT technologies for agricultural food supply chains in Africa: A feasibility analysis

  • Authors: Andrews Tang, Eric Tutu Tchao, Andrew Selasi Agbemenu, Eliel Keelson, Griffith Selorm Klogo, Jerry John Kponyo
  • Journal: Heliyon
  • Year: 2024

An Open and Fully Decentralised Platform for Safe Food Traceability

  • Authors: Eric Tutu Tchao, Elton Modestus Gyabeng, Andrews Tang, Joseph Barnes Nana Benyin, Eliel Keelson, John Jerry Kponyo
  • Year: 2022

Prof. Ling Yang | Deep Learning | Women Researcher Award

Published on by Computer Vision Awards

Prof. Ling Yang | Deep Learning | Women Researcher Award

Professor at Kunming University of Science and Technology, China

👨‍🎓 Profiles

Scopus

Orcid

Publications

Enhancing Panax notoginseng Leaf Disease Classification with Inception-SSNet and Image Generation via Improved Diffusion Model

  • Authors: Wang, R., Zhang, X., Yang, Q., Liang, J., Yang, L.
  • Journal: Agronomy
  • Year: 2024

Deep learning implementation of image segmentation in agricultural applications: a comprehensive review

  • Authors: Lei, L., Yang, Q., Yang, L., Wang, R., Fu, C.
  • Journal: Artificial Intelligence Review
  • Year: 2024

Alternate micro-sprinkler irrigation and organic fertilization decreases root rot and promotes root growth of Panax notoginseng by improving soil environment and microbial structure in rhizosphere soil

  • Authors: Zang, Z., Yang, Q., Liang, J., Guo, J., Yang, L.
  • Journal: Industrial Crops and Products
  • Year: 2023

A BlendMask-VoVNetV2 method for quantifying fish school feeding behavior in industrial aquaculture

  • Authors: Yang, L., Chen, Y., Shen, T., Yu, H., Li, D.
  • Journal: Computers and Electronics in Agriculture
  • Year: 2023

An FSFS-Net Method for Occluded and Aggregated Fish Segmentation from Fish School Feeding Images

  • Authors: Yang, L., Chen, Y., Shen, T., Li, D.
  • Journal: Applied Sciences (Switzerland)
  • Year: 2023

Dr. Shivanshu Shrivastava | Deep Learning | Best Researcher Award

Published on by Computer Vision Awards

Dr. Shivanshu Shrivastava, Deep Learning, Best Researcher Award

Doctorate at Rajiv Gandhi Institute of Petroleum Technology, India

Profiles

Scopus

Google Scholar

🌍 Academic Background:

Dr. Shivanshu Shrivastava is an Assistant Professor in the Department of Electrical & Electronics Engineering at Rajiv Gandhi Institute of Petroleum Technology (RGIPT), Amethi, Uttar Pradesh, India. He has been contributing to the field of electrical and electronics engineering with a focus on artificial intelligence and communications since September 2021.

🎓 Education:

Dr. Shrivastava earned his Ph.D. from IIT Guwahati in August 2017, specializing in Wireless Communication with a thesis on “Security Issues in Cognitive Radios,” under the guidance of Prof. A. Rajesh and Prof. P. K. Bora. He completed his Postdoctoral Fellowships at Shenzhen University, China, and IIT Kanpur from August 2017 to December 2020, focusing on “Artificial Intelligence and Deep Learning Applications in 5G Communications” under Prof. Bin Chen. He holds a Bachelor of Engineering degree in Electronics and Telecommunication Engineering from CSVTU, Bhilai, with a CPI of 8.13/10.

💼 Work Experience:

Before joining RGIPT, Dr. Shrivastava worked as a Postdoctoral Fellow at Shenzhen University from January 2019 to December 2020 and as a SERB-NPDF at IIT Kanpur from August 2017 to October 2018. His current role involves advancing research in deep learning and AI applications in communications.

🔬 Research Areas:

His research interests encompass artificial intelligence and deep learning applications in communications, cognitive radio systems, wireless communications, visible light communications (VLC), and security issues in cognitive radios.

📝 Research Experience:

At RGIPT, Dr. Shrivastava leads research on deep learning and AI applications in wireless communication. His previous projects include optimizing achievable rates in hybrid RF/VLC systems and designing energy-efficient hybrid RF/VLC systems for 5G communications. He has supervised Ph.D. students and undergraduate project students in these areas.

🏆 Honors, Awards, and Memberships:

Dr. Shrivastava has received the International Travel Support (ITS) from SERB for attending the IEEE ICCCAS conference in Xiamen, China, and the Best Teacher Award from Union Bank of India at RGIPT. He was also honored with postdoctoral fellowships from Shenzhen University and IIT Kanpur.

📖 Publications:

A lightweight group-based SDN-driven encryption protocol for smart home IoT devices
  • Authors: Raza, A., Khan, S., Shrivastava, S., Wu, K., Wang, L.
  • Journal: Computer Networks
  • Year: 2024
Collision Penalty-Based Defense Against Collusion Attacks in Cognitive Radio Enabled Smart Devices
  • Authors: Shrivastava, S., John, S., Rajesh, A., Bora, P.K.
  • Journal: IEEE Transactions on Consumer Electronics
  • Year: 2024
Transfer learning for resource allotment in dynamic hybrid WiFi/LiFi communication systems
  • Authors: Verma, T., Shrivastava, S., Dwivedi, U.D., Kothari, D.P.
  • Journal: Optics Communications
  • Year: 2023
Asset Allotment in Hybrid RF/VLC Communication in the 400-700 THz Band
  • Authors: Shrivastava, S., Agarwal, S., Chen, B.
  • Journal: Terahertz Wireless Communication Components and System Technologies
  • Year: 2022
A survey on security issues in cognitive radio based cooperative sensing
  • Authors: Shrivastava, S., Rajesh, A., Bora, P.K., Lin, X., Wang, H.
  • Journal: IET Communications
  • Year: 2021