Huaiqu Feng | Agricultural Robotics | Best Researcher Award

Published on by Computer Vision Awards

Dr. Huaiqu Feng | Agricultural Robotics | Best Researcher Award

Huaiqu Feng | Zhejiang University | China

Huaiqu Feng is a skilled researcher with expertise in robotics and electromechanical intelligent equipment, focusing on computer vision, deep learning, and image processing for agricultural automation. He holds a Master of Engineering in Agricultural Mechanization Engineering from Northeast Agricultural University and a Bachelor of Engineering in Automation from Hubei Normal University. Throughout his academic and professional career, he has participated in multiple research projects, including provincial science and technology programs and industrial transformation initiatives, demonstrating strong capability in applying AI and robotics to practical agricultural problems. He has contributed to several high-impact publications, patents, and software developments, showcasing his innovative approach and technical proficiency. His professional experience includes leading research teams, mentoring students, and managing projects that integrate advanced technologies into real-world applications. His research interests span robotics, precision agriculture, intelligent equipment, and AI-based image analysis. He is proficient in Matlab for algorithm development, microcontroller programming with STM32, and 3D modeling and simulation using Creo and Pro/E. Huaiqu Feng also actively engages in community and leadership roles through student organizations, innovation competitions, and volunteer initiatives, highlighting his commitment to fostering collaboration and advancing the research community. 426 Citations, 20 Documents, 8 h-index.

Profiles: Google Scholar | Scopus | ORCID

Featured Publications

  1. Quan, L., Feng, H., Lv, Y., Wang, Q., Zhang, C., Liu, J., & Yuan, Z. (2019). Maize seedling detection under different growth stages and complex field environments based on an improved Faster R–CNN. Biosystems Engineering, 184, 1-23.

  2. Zhao, G., Quan, L., Li, H., Feng, H., Li, S., Zhang, S., & Liu, R. (2021). Real-time recognition system of soybean seed full-surface defects based on deep learning. Computers and Electronics in Agriculture, 187, 106230.

  3. Li, D., Li, B., Long, S., Feng, H., Xi, T., Kang, S., & Wang, J. (2023). Rice seedling row detection based on morphological anchor points of rice stems. Biosystems Engineering, 226, 71-85.

  4. Wei, C., Li, H., Shi, J., Zhao, G., Feng, H., & Quan, L. (2022). Row anchor selection classification method for early-stage crop row-following. Computers and Electronics in Agriculture, 192, 106577.

  5. Li, D., Li, B., Long, S., Feng, H., Wang, Y., & Wang, J. (2023). Robust detection of headland boundary in paddy fields from continuous RGB-D images using hybrid deep neural networks. Computers and Electronics in Agriculture, 207, 107713.

Tuğba Özge Onur | Image Reconstruction | Best Researcher Award

Published on by Computer Vision Awards

Assoc. Prof. Dr. Tuğba Özge Onur | Image Reconstruction | Best Researcher Award

Associate Professor | Zonguldak Bülent Ecevit University | Turkey

Assoc. Prof. Dr. Tuğba Özge Onur is a distinguished researcher specializing in signal processing, image reconstruction, and optimization. She earned her Ph.D. in electrical and electronics engineering from a leading university, where she developed a strong foundation in computational imaging and algorithm design. Her professional experience includes leading research projects, coordinating international collaborations, and mentoring students in both academic and applied research settings. Her research interests span computer vision, optimization techniques, and advanced signal processing methods, with a focus on developing innovative solutions for real-world challenges. She possesses a diverse set of research skills, including algorithm development, data analysis, experimental design, and implementation of complex computational models. She is actively engaged in the scientific community through professional memberships and collaborative initiatives. Her work has been widely recognized and published in reputed journals and conferences, demonstrating both the depth and impact of her contributions. Her commitment to advancing knowledge, mentoring emerging researchers, and participating in collaborative projects underscores her influence in the field. 98 Citations, 23 Documents, 6 h-index.

Profiles: Google Scholar | Scopus | ORCID | ResearchGate

Featured Publications

  1. Onur, T. Ö. (2022). Improved image denoising using wavelet edge detection based on Otsu’s thresholding. Acta Polytechnica Hungarica, 19(2), 79–92.

  2. Onur, Y. A., İmrak, C. E., & Onur, T. Ö. (2017). Investigation on bending over sheave fatigue life determination of rotation resistant steel wire rope. Experimental Techniques, 41(5), 475–482.

  3. Narin, D., & Onur, T. Ö. (2022). The effect of hyperparameters on the classification of lung cancer images using deep learning methods. Erzincan University Journal of Science and Technology, 15(1), 258–268.

  4. Kaya, G. U., & Onur, T. Ö. (2022). Genetic algorithm based image reconstruction applying the digital holography process with the Discrete Orthonormal Stockwell Transform technique for diagnosis of COVID-19. Computers in Biology and Medicine, 148, 105934.

  5. Onur, T. (2021). An application of filtered back projection method for computed tomography images. International Review of Applied Sciences and Engineering, 12(2), 194–200.

Akanksha Dwivedi | Parallel Computing | Excellence in Research Award

Published on by Computer Vision Awards

Ms. Akanksha Dwivedi | Parallel Computing | Excellence in Research Award

Research Scholar | Indian Institute of Technology Jodhpur | India

Ms. Akanksha Dwivedi is a doctoral research scholar in Computer Science and Engineering at the Indian Institute of Technology Jodhpur, where she works under the guidance of Dr. Dip Sankar Banerjee at the Systems for Performance, Analysis, and Data Engineering Lab. She holds a Master of Technology in Mechatronics, Robotics, and Automation from the Center for Advanced Studies, Lucknow, and a Bachelor of Technology in Electronics and Communication Engineering from Dr. APJ Abdul Kalam Technical University, Lucknow. She has served as a Teaching Assistant at IIT Jodhpur and RSVS Lucknow, as well as a Project Associate at the National Institute of Technology Uttarakhand, contributing to projects in deep learning for speech decoding and precision health technologies. Her research interests include high-performance computing, scalable parallel algorithms, data analytics, artificial intelligence for healthcare applications, robotics, and sensor technologies. She has published in reputed venues such as Future Generation Computer Systems and IEEE High Performance Extreme Computing, with additional contributions in AI-driven healthcare sensors and sustainable materials. Akanksha has received prestigious fellowships including the Anusandhan National Research Foundation project fellowship and the Ministry of Education doctoral fellowship. She has been honored with awards for innovative ideas, international travel grants, and recognition in hackathons and debate competitions, as well as achievements in sports at the state level. Her research skills span programming in C, Python, and CUDA, parallel computing with OpenMP, data analysis, robotics systems, and advanced tools such as MATLAB and Docker, reflecting her strong technical foundation and multidisciplinary expertise.

Profiles: ORCID | ResearchGate | LinkedIn

Featured Publications

  1. Dwivedi, A., & Banerjee, D. S. (2024, December 4). MST in incremental graphs through tree contractions. In Proceedings of the 28th IEEE High Performance Extreme Computing Conference (HPEC), Boston, USA.

  2. Dwivedi, A., Sharma, S., & Banerjee, D. S. (2023, March 3). Efficient parallel algorithms for large tree contraction. In Proceedings of the Student Research Symposium, International Conference on High Performance Computing (HiPC).

Vesna Skrbinjek | Social Robots | Best Researcher Award

Published on by Computer Vision Awards

Assist. Prof. Dr. Vesna Skrbinjek | Social Robots | Best Researcher Award

Vice-Dean | International School for Social and Business Studies | Slovenia

Dr. Vesna Skrbinjek is a dedicated academic and researcher specializing in higher education, economic sociology, quality assurance, and digital and employment skills. She earned her PhD in Economic Sociology from the School of Advanced Social Sciences in Nova Gorica, where her research focused on the effects of economic crises on higher education funding. Her professional experience spans teaching mathematics, statistics, and higher education topics, advancing from junior researcher to her current role as Vice Dean at the International School for Social and Business Studies in Celje, where she also serves as Associate Professor and Editor-in-Chief of the International Journal of Management in Education. She has contributed to international collaborations, including her role as an EACEA Expert for the European Commission in evaluating Erasmus+ projects, and has actively shaped academic quality processes, curriculum reforms, and student development initiatives. Her research interests include higher education policy, digital transformation in learning, and the sociology of education. She has been recognized for her leadership in academia and contributions to international research and publishing. Skilled in project management, quality assurance, digital platforms, and advanced data analysis tools such as SPSS and R, she combines research excellence with institutional leadership. She has achieved 102 citations, 11 documents and an h-index of 5.

Profiles: Google Scholar | Scopus | ORCID | ResearchGate | LinkedIn

Featured Publications

  1. Breznik, K., & Skrbinjek, V. (2020). Erasmus student mobility flows. European Journal of Education, 55(1), 105–117.

  2. Skrbinjek, V., & Dermol, V. (2019). Predicting students’ satisfaction using a decision tree. Tertiary Education and Management, 25(2), 101–113.

  3. Krabonja, M. V., Kustec, S., Skrbinjek, V., Aberšek, B., & Flogie, A. (2024). Innovative professional learning communities and sustainable education practices through digital transformation. Sustainability, 16(14), 1–19.

  4. Skrbinjek, V., Lesjak, D., & Šušteršič, J. (2018). Impact of the recent economic crisis on tertiary education funding: A comparative study. International Journal of Innovation and Learning, 23(2), 123–144.

  5. Skrbinjek, V., Vičič Krabonja, M., Aberšek, B., & Flogie, A. (2024). Enhancing teachers’ creativity with an innovative training model and knowledge management. Education Sciences, 14(12), 1381.

Shuangke Liu | Energy Conversion | Best Researcher Award

Published on by Computer Vision Awards

Mr. Shuangke Liu | Energy Conversion | Best Researcher Award

Associate Professor | National University of Defense Technology | China

Mr. Liu Shuangke is an associate professor and master’s supervisor at the National University of Defense Technology with extensive expertise in high-specific-energy lithium-sulfur and lithium-metal batteries as well as fiber-shaped batteries. He earned his Ph.D. from the National University of Defense Technology and has built a strong foundation in materials science and electrochemistry. Throughout his professional career, he has led numerous research projects, published over 40 papers in reputed journals such as Nature Communications, Energy & Environmental Science, Advanced Functional Materials, and Chem. Soc. Rev., and applied for 15 patents while authoring a monograph. His research interests focus on innovative battery materials, structural design of hollow carbon frameworks, gradient multicomponent solid electrolyte interphases, spin modulation, and orbital strengthening for high-voltage cathodes. He has mentored students who have won multiple awards, guided theses recognized at provincial and national levels, and contributed actively to the scientific community as a youth editorial board member for journals including Electrochemistry and Carbon Neutralization. Liu Shuangke has received various honors for teaching excellence and research achievements. His research skills include advanced electrochemical analysis, materials synthesis, battery design, and interphase engineering, reflecting both innovation and practical impact. His work has garnered 1,593 citations by 1,392 documents, with 47 documents and an h-index of 25, demonstrating high visibility and influence in the field of energy storage and battery research.

Profiles: Scopus | ORCID | ResearchGate        

Featured Publications

Liu, S., Yao, Z., Fu, T., Pan, T., Luo, C., Pang, M., Xiong, S., Guo, Q., Li, Y., Zheng, C., Sun, W., & Zhou, G. (2025). Dynamic doping and interphase stabilization for cobalt-free and high-voltage lithium metal batteries. Nature Communications.

Liu, S., [Authors as above]. (2025). In situ partial-cyclized polymerized acrylonitrile-coated NCM811 cathode for high-temperature ≥ 100 °C stable solid-state lithium metal batteries. Nano Micro Letters.

Liu, S., Pan, T., Li, Y., Yao, Z., Zhu, Y., Wang, X., Wang, J., Zheng, C., & Sun, W. (2025). Research advances on lithium-ion batteries calendar life prognostic models. Carbon Neutralization.

Integrated online identification of aerodynamic and thrust parameters for air-breathing aircraft. Conference Paper.

Mir Arman Mirzaaghaian Amiry | Human Lungs Health | Best Researcher Award

Published on by Computer Vision Awards

Mr. Mir Arman Mirzaaghaian Amiry | Human Lungs Health | Best Researcher Award

Researcher | Western Sydney University | Australia

Mr. Mir Arman Mirzaaghaian Amiry is a dedicated researcher in mechanical engineering with expertise in computational modeling, 3D design, medical device development, and biomedical applications. He is pursuing a PhD in Mechanical Engineering at Western Sydney University with a thesis focused on computational simulation of particle transport and deposition in a 3D human lung model. He also holds a master’s degree in mechanical engineering from Babol Noshirvani University of Technology and a bachelor’s degree from Mazandaran University of Science and Technology. His professional experience includes serving as a tutor, lab demonstrator, and technical officer at Western Sydney University, as well as contributing to an internship at Metabolic Health Solutions where he designed and prototyped an innovative breath-by-breath calorimeter leading to a patent on a metabolic measurement system with sensors, machine learning, and digital health integration. He has also been a STEM student ambassador mentoring high school students. His research interests include computational fluid dynamics, biomedical device design, medical device regulatory affairs, and additive manufacturing. Among his awards and honors are the Australian Government postgraduate research scholarship, excellence in reviewing recognition from the Thermal and Fluids Engineering Conference, and high academic ranking during his master’s studies. He has earned professional certifications in Lean Six Sigma, ISO 13485 for medical devices, and AI in project management. His research skills span SOLIDWORKS, Ansys Fluent, COMSOL Multiphysics, 3D printing, data analysis, and regulatory management, complemented by strong teamwork, communication, and project leadership abilities. Citations 90 and h-index 3.

Profiles: Google Scholar | ORCID | LinkedIn | ResearchGate 

Featured Publications

  1. Mirzaaghaian, A., & Ganji, D. D. (2016). Application of differential transformation method in micropolar fluid flow and heat transfer through permeable walls. Alexandria Engineering Journal, 55(3), 2183–2191.

  2. Mirzaaghaian, A., Ramiar, A., Ranjbar, A. A., & Warkiani, M. E. (2020). Application of level-set method in simulation of normal and cancer cells deformability within a microfluidic device. Journal of Biomechanics, 112, 110066.

  3. Mirzaaghaian, A., Zhao, M., Rahman, M. M., & Dong, K. (2024). Numerical simulation of targeted drug delivery to different regions of realistic human lung model under realistic aerosol breathing condition. Powder Technology, 444, 120039.

  4. Mirzaaghaian, A., Zhao, M., & Dong, K. (2025). Patient-specific release of the aerosols from the spacer and its effect on the drug delivery to the human lungs. ASTFE Digital Library.

  5. Cebis, M. J. P., Smith, T. L., Waheed, M., Amiry, M. A. M., & Lian, Y. S. J. J. (2024). Metabolic measurement system with sensors, machine learning, and digital health database. Australian Patent Application No. 2024900118.

Ewert Bengtsson | Quantitative Microscopy | Best Researcher Award

Published on by Computer Vision Awards

Prof. Ewert Bengtsson | Quantitative Microscopy | Best Researcher Award

Professor Emeritus | Uppsala University | Sweden

Prof. Ewert Bengtsson is a distinguished researcher in computerized image analysis and medical imaging, with current work on AI-based diagnostic tools for cancer detection. He earned his PhD in Physics from Uppsala University, where he developed pioneering methods for computer-aided analysis of microscopic images applied to early cancer screening. His professional experience spans research leadership, including Director of the Centre for Image Analysis, Vice Rector for IT at Uppsala University, and project leadership in both academic and industry settings. He has contributed to numerous international collaborations and led projects in medical imaging and IT-driven healthcare solutions. His research interests include AI-based medical diagnostics, computer vision, image processing, and automated cancer detection systems. He has a strong record of mentorship, guiding over 40 doctoral students, and has contributed to global research communities through program committees, editorial boards, and invited talks. His work has been recognized with fellowships, academy memberships, and distinguished awards for contributions to science, engineering, and medical imaging. He possesses advanced research skills in medical image analysis, AI, machine learning, microscopy, and software development for diagnostic tools. 3,627 citations by 2,993 documents, 137 documents, 31 h-index, view h-index button is disabled in preview mode, further highlight his global impact and recognition.

Profiles: Google Scholar | Scopus | ORCID

Featured Publications

  1. Wählby, C., Sintorn, I. M., Erlandsson, F., Borgefors, G., & Bengtsson, E. (2004). Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections. Journal of Microscopy, 215(1), 67–76.

  2. Rodenacker, K., & Bengtsson, E. (2003). A feature set for cytometry on digitized microscopic images. Analytical Cellular Pathology, 25(1), 1–36.

  3. Bengtsson, E., & Malm, P. (2014). Screening for cervical cancer using automated analysis of PAP‐smears. Computational and Mathematical Methods in Medicine, 2014, 842037.

  4. Wählby, C., Lindblad, J., Vondrus, M., Bengtsson, E., & Björkesten, L. (2002). Algorithms for cytoplasm segmentation of fluorescence labelled cells. Analytical Cellular Pathology: The Journal of the European Society for Analytical Cellular Pathology.

  5. Stenkvist, B., Bengtsson, E., Eriksson, O., Holmquist, J., Nordin, B., & others. (1979). Cardiac glycosides and breast cancer. The Lancet, 313(8115), 563.

Abdulwahid Al Abdulwahid | Cyber Security | Best Researcher Award

Published on by Computer Vision Awards

Assoc. Prof. Dr. Abdulwahid Al Abdulwahid | Cyber Security | Best Researcher Award

Associate Professor | Jubail Industrial College | Saudi Arabia

Dr. Abdulwahid Al Abdulwahid is an Associate Professor of Cybersecurity and Program Director at Jubail Industrial College, Royal Commission for Jubail and Yanbu, with extensive expertise in artificial intelligence for cybersecurity, IoT and Industry 4.0 security, cloud computing privacy, biometrics, and the human aspects of cybersecurity. He holds a PhD in Computing (Cyber Security) from the University of Plymouth, UK, an MSc in Management of Information Technology from the University of Nottingham, and a BSc in Computer and Information Systems from King Faisal University, along with a Graduate Teaching Associate certification from Plymouth University. Over two decades of professional academic and administrative experience, he has served in roles such as Deputy Director for Planning and Development, College Deputy for Student Affairs, and Department Chairperson, alongside delivering specialized lectures, workshops, and training programs locally, regionally, and internationally. His research interests focus on advancing secure and usable authentication systems, AI-driven cybersecurity solutions, and quality-driven approaches in computing education. He is highly skilled in academic accreditation, governance, quality management, and strategic leadership, in addition to contributing as a reviewer, auditor, and public speaker. As an active member of professional and community organizations including ACM, the Saudi Scientific Society for Cybersecurity, Hemaya, and the Saudi Society for Quality, he continues to foster collaboration between academia, industry, and society. His research impact is reflected through 118 citations by 113 documents, 14 publications, and an h-index of 6.

Profiles: Google Scholar | Scopus | ORCID

Featured Publications

  1. Al Abdulwahid, A., Clarke, N., Stengel, I., Furnell, S., & Reich, C. (2016). Continuous and transparent multimodal authentication: Reviewing the state of the art. Cluster Computing, 19(1), 455–474.

  2. Guo, Y., Wang, Y., Khan, F., Al-Atawi, A. A., Abdulwahid, A. A., Lee, Y., & Marapelli, B. (2023). Traffic management in IoT backbone networks using GNN and MAB with SDN orchestration. Sensors, 23(16), 7091.

  3. Al Abdulwahid, A. (2022). Detection of middlebox-based attacks in healthcare Internet of Things using multiple machine learning models. Computational Intelligence and Neuroscience, 2022, 2037954.

  4. Alassafi, M. O., AlGhamdi, R., Alshdadi, A. A., Abdulwahid, A. A., & Bakhsh, S. T. (2019). Determining factors pertaining to cloud security adoption framework in government organisations: An exploratory study. IEEE Access, 7, 136822–136835.

  5. Abdulwahid, A. A. (2023). Classification of ethnicity using efficient CNN models on MORPH and FERET datasets based on face biometrics. Applied Sciences, 13(12), 7288.

Mona Maze | Land Classification | Best Researcher Award

Published on by Computer Vision Awards

Assoc. Prof. Dr. Mona Maze | Land Classification | Best Researcher Award

Senior Researcher | Agricultural Research Center | Egypt

Dr. Mona Maze is a dedicated researcher specializing in agricultural climate, plant nutrition, and digital agriculture, with a strong focus on developing climate change adaptation strategies, precision farming approaches, and the use of remote sensing and machine learning in agriculture. She earned her PhD in Plant Nutrition from the Technical University of Munich, where her doctoral work addressed crop growth and yield modeling under water scarcity and changing climatic conditions. Over her professional career, she has actively contributed to national and international research projects in collaboration with institutions such as the European Commission, USAID, UNDP, and FAO, while also leading initiatives like the Digital Dynamic Agricultural Map of Egypt and Early Warning Systems for farmers. Her teaching experience and supervision of graduate students reflect her commitment to academic development, while her publication record in reputed journals such as Scientific Reports, ISPRS Journal of Photogrammetry and Remote Sensing, Agronomy, and Energies highlights her strong scientific contributions. Her research interests span climate-smart agriculture, soil fertility, plant nutrition, digital transformation in agriculture, and data-driven solutions for food security. She possesses advanced research skills in machine learning, deep learning, geospatial data analysis, crop modeling, and experimental design, complemented by professional certifications in business management, spatial data science, and AI-based systems. She has 54 citations by 54 documents, 11 publications, and an h-index of 4, reflecting her growing impact in the scientific community.

Profiles: Scopus | ORCID

Featured Publications

  1. Maze, M., Attaher, S., Taqi, M. O., Elsawy, R., Gad El-Moula, M. M. H., Hashem, F. A., & Moussa, A. S. (2025). Enhanced agricultural land use/land cover classification in the Nile Delta using Sentinel-1 and Sentinel-2 data and machine learning. ISPRS Journal of Photogrammetry and Remote Sensing.

  2. Salah, M., Maze, M., & Tonbol, K. (2024). Intersecting vulnerabilities: Climate justice, gender inequality, and COVID-19’s impact on rural women in Egypt. Multidisciplinary Adaptive Climate Insights.

  3. Maze, M., Taqi, M. O., Tolba, R., Abdel-Wareth, A. A. A., & Lohakare, J. (2024). Estimation of methane greenhouse gas emissions from livestock in Egypt during 1989 to 2021. Scientific Reports.

  4. El-Beltagi, H. S., Hashem, F. A., Maze, M., Shalaby, T. A., Shehata, W. F., & Taha, N. M. (2022). Control of gas emissions (N₂O and CO₂) associated with applied different rates of nitrogen and their influences on growth, productivity, and physio-biochemical attributes of green bean plants grown under different irrigation methods. Agronomy, 12(2), 249.

  5. Abd El-Fattah, D. A., Maze, M., Ali, B. A. A., & Awed, N. M. (2022). Role of mycorrhizae in enhancing the economic revenue of water and phosphorus use efficiency in sweet corn (Zea mays L. var. saccharata) plants. Journal of the Saudi Society of Agricultural Sciences.

Omid Hajipoor | Text Generation | Best Researcher Award

Published on by Computer Vision Awards

Mr. Omid Hajipoor | Text Generation | Best Researcher Award

Omid Hajipoor | Amirkabir University of Technology (Tehran Polytechnic) | Iran

Omid Hajipoor is a researcher in artificial intelligence with a strong focus on natural language processing, generative adversarial networks, and large language models. He is currently pursuing his PhD in Computer Engineering at Amirkabir University of Technology, Tehran, building on earlier academic training with a master’s in artificial intelligence and robotics from Malekashtar University and a bachelor’s in software engineering from Birjand University. His professional experience spans roles such as technical product manager, project manager, NLP team leader, and engineer, where he has contributed to the design and development of advanced NLP solutions, chatbots, social media text generation systems, error detection models, and sentiment lexicons. His research interests lie in text generation, adversarial learning, transformers, diffusion models, and applied AI systems for social media and multilingual contexts. He has been involved in impactful projects including railway optimization software, abusive language detection, and generative Persian text applications, and he has published in respected venues such as Neurocomputing and Scopus-indexed journals. In addition to his academic and industrial contributions, he has served as a teaching assistant and lecturer for undergraduate and postgraduate students, and he has mentored teams in innovation events that won recognition. His research skills include programming in Python, MATLAB, and C++, expertise in PyTorch, TensorFlow, and other machine learning frameworks, and strong experience in project management tools like Git and Docker. He has demonstrated leadership, creativity, and technical proficiency throughout his career. His research record shows citations by 2 documents from 1 publication with an h-index of 1.

Profile: Google Scholar | Scopus 

Featured Publications

Hajipoor, O., Nickabadi, A., & Homayounpour, M. M. (2025). GPTGAN: Utilizing the GPT language model and GAN to enhance adversarial text generation. Neurocomputing, 617, 128865.

Hajipoor, O., & Sadidpour, S. S. (2022). Automatic Persian text generation using rule-based models and word embedding. Electronic and Cyber Defense, 9(4), 43–54.

Hajipoor, O., & Sadidpour, S. S. (2020). Automatic keyword extraction from Persian short text using word2vec. Electronic and Cyber Defense, 8(2), 105–114.