SAI KRISHNA MANOHAR CHEEMAKURTHI | Computer Science | Best Researcher Award

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Mr. Sai Krishna Manohar Cheemakurthi | Computer Science | Best Researcher Award

Sai Krishna Manohar Cheemakurthi, U.S. BANK, United States.

Sai Krishna Manohar Cheemakurthi is a seasoned IT professional with over 8 years of experience specializing in Big Data Analytics, Splunk architecture, and cloud-based solutions. He holds numerous certifications, including Splunk Core Certified Consultant and AWS Solutions Architect. Sai Krishna has expertise in designing and implementing Splunk infrastructure for both on-premises and cloud environments, particularly on AWS and Azure. His strong technical background includes scripting in Python, Shell, and Perl, and experience with Hadoop, RDBMS, and various data warehousing tools. Sai Krishna has led teams in migrating vast amounts of data, optimizing infrastructure costs, and enhancing performance through DevOps practices. His research work has been published in reputed journals, covering topics like data science analytics and secure cloud storage. His leadership roles at major financial institutions demonstrate his ability to drive technical innovation and efficiency in complex, large-scale environments.

Profile:

Education

Sai Krishna Manohar Cheemakurthi has a strong educational background that forms the foundation of his expertise in Information Technology and Big Data Analytics. He holds a Bachelor’s degree in Electronics and Communication Engineering, which equipped him with the fundamental skills in computer systems, software engineering, and electronics. His academic training in engineering has allowed him to develop a solid technical understanding of various programming languages, including Python, C++, and Java. Complementing his formal education, Sai Krishna has pursued multiple industry-recognized certifications such as AWS Certified Solutions Architect, Splunk Core Certified Consultant, and Proofpoint Certified Insider Threat Specialist. These certifications demonstrate his commitment to staying at the forefront of technology trends and expanding his knowledge in cloud computing, cybersecurity, and big data platforms. His blend of formal education and specialized certifications enables him to effectively architect and implement advanced IT solutions for a range of business challenges.

Professional Experiences 

Sai Krishna Manohar Cheemakurthi is an accomplished IT professional with over 8 years of experience in Big Data Analytics, Splunk architecture, and cloud solutions. Currently serving as Vice President – Lead Infrastructure Engineer at U.S. Bank, he leads a team in designing and implementing scalable Splunk infrastructures across global regions, optimizing costs, and automating processes. Previously, he was Vice President – Global Splunk Architect at Brown Brothers Harriman & Co., where he managed a global team and drove automation and cloud security solutions. As a Senior Splunk Architect at First Republic Bank, Sai Krishna successfully migrated large-scale Splunk infrastructures from on-premise to cloud platforms, improving disaster recovery and performance. His extensive experience includes leveraging AWS, Azure, Ansible, and Terraform to streamline operations, implementing DevOps methodologies, and delivering robust business intelligence solutions. Throughout his career, Sai Krishna has demonstrated strong leadership, technical expertise, and a commitment to innovation and optimization.

Awards and Honors

Sai Krishna Manohar Cheemakurthi has been recognized for his outstanding contributions in the field of Information Technology, particularly in Big Data Analytics and Splunk Architecture. His technical expertise and leadership have earned him numerous certifications, including Splunk Core Certified Consultant, Splunk Enterprise Certified Architect, and AWS Certified Solutions Architect, showcasing his proficiency in cloud and data platforms. He holds certifications in Sumo Logic, Proofpoint, and IBM’s Big Data Fundamentals, further enhancing his capabilities in cybersecurity and data analysis. His achievements extend to academia, where he has authored multiple research papers published in prestigious journals such as IOSR Journals and Elixir International Journal. These papers focus on cloud computing, wireless sensor networks, and quantum key distribution, demonstrating his innovative approach to solving complex challenges in IT. Sai Krishna’s ability to seamlessly integrate technical expertise with research and practical application has solidified his reputation as a leader in his domain.

Research Interest

Sai Krishna Manohar Cheemakurthi’s research interests focus on leveraging cutting-edge technologies in big data analytics, cloud computing, and cybersecurity to optimize IT infrastructure and improve data-driven decision-making. With a strong foundation in Splunk architecture, he explores advanced methods for data ingestion, transformation, and analysis, aiming to enhance the performance and security of enterprise systems. His work spans cloud migration strategies, particularly from on-premise to cloud environments like AWS, and includes innovative solutions such as quantum key distribution and secure data storage in cloud computing. Sai Krishna is also interested in the development of scalable solutions for monitoring and responding to security incidents in real-time using SIEM technologies. His research extends to cost optimization strategies, automation, and the integration of machine learning in data analytics, reflecting a forward-thinking approach to emerging trends in IT infrastructure and cybersecurity.

Research Skills

Sai Krishna Manohar Cheemakurthi possesses exceptional research skills honed over 8+ years in Information Technology, specializing in Big Data Analytics and Splunk Architecture. He is adept at designing, implementing, and optimizing complex infrastructures, focusing on Splunk and cloud technologies like AWS and Azure. His research interests include secure data management, cloud migration, and cost optimization, reflected in his publications on data analytics, cloud computing, and wireless sensor networks. Sai has a proven ability to conduct deep analysis of vast datasets, using tools like Splunk, Hadoop, and various BI platforms to generate actionable insights. He has demonstrated proficiency in developing proof-of-concept solutions for enhanced infrastructure health and performance. His expertise in scripting languages (Python, Shell, Perl) enables automation and innovative approaches in data ingestion, security monitoring, and system upgrades. Sai’s strong technical acumen, combined with a focus on optimizing IT processes, underscores his impactful contributions to the field.

Publication Top Notes
  • Cloud Observability In Finance: Monitoring Strategies For Enhanced Security
    • Authors: NB Kilaru, SKM Cheemakurthi
    • Year: 2023
    • Journal: NVEO-Natural Volatiles & Essential Oils
    • Volume/Issue/Page: 10(1), 220-226
  • Mitigating Threats in Modern Banking: Threat Modeling and Attack Prevention with AI and Machine Learning
    • Authors: SK Manohar, V Gunnam, NB Kilaru
    • Year: 2022
    • Journal: Turkish Journal of Computer and Mathematics Education (TURCOMAT)
  • Next-gen AI and Deep Learning for Proactive Observability and Incident Management
    • Authors: NBK Sai Krishna Manohar Cheemakurthi, Vinodh Gunnam
    • Year: 2022
    • Journal: Turkish Journal of Computer and Mathematics Education
    • Volume/Issue/Page: 13(3), 1550-1564
  • Scaling DevOps with Infrastructure as Code in Multi-Cloud Environments
    • Authors: NBK Sai Krishna Manohar Cheemakurthi, Vinodh Gunnam
    • Year: 2022
    • Journal: Turkish Journal of Computer and Mathematics Education
    • Volume/Issue/Page: 13(3), 1189-1200
  • Advanced Anomaly Detection In Banking: Detecting Emerging Threats Using SIEM
    • Authors: NBK Sai Krishna Manohar Cheemakurthi, Vinodh Gunnam
    • Year: 2021
    • Journal: International Journal of Computer Science and Mechatronics (IJCSM)
    • Volume/Issue/Page: 7(04), 28-33
  • Analytics of Data Science using Big Data
    • Authors: CSK Manohar
    • Year: 2013
    • Journal: IOSR Journal of Computer Engineering
    • Volume/Issue/Page: 10(2), 19-21
  • AI-Powered Fraud Detection: Harnessing Advanced Machine Learning Algorithms for Robust Financial Security
    • Authors: SKM Cheemakurthi, NB Kilaru, V Gunnam
    • Year: (Not provided)
  • Deep Learning Models For Fraud Detection In Modernized Banking Systems: Cloud Computing Paradigm
    • Authors: Y Vasa, SKM Cheemakurthi, NB Kilaru
    • Year: (Not provided)
  • SOAR Solutions in PCI Compliance: Orchestrating Incident Response for Regulatory Security
    • Authors: NB Kilaru, SKM Cheemakurthi, V Gunnam
    • Year: (Not provided)
  • AI-Driven SOAR in Finance: Revolutionizing Incident Response and PCI Data Security with Cloud Innovations
    • Authors: V Gunnam, SKM Cheemakurthi, NB Kilaru
    • Year: (Not provided)

 

 

SIMON NANDWA ANJIRI | Computer Science | Best Researcher Award

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Mr. SIMON NANDWA ANJIRI | Computer Science | Best Researcher Award

Doctor of Philosophy at University Of Shanghai For Science And Technology, China

Simon Nandwa Anjiri is a PhD candidate at the University of Shanghai for Science and Technology, specializing in recommendation systems, data mining, and analysis. His notable research includes the publication of HyGate-GCN: Hybrid-Gate-Based Graph Convolutional Networks with Dynamical Ratings Estimation for Personalized POI Recommendation in Expert Systems with Applications. This work highlights his innovative approach to personalized recommendations. Simon actively engages with the international research community, exemplified by his participation as a guest speaker at the 2023 Young Scholars Conference at Zhejiang University of Technology. Despite his impressive contributions, he could further enhance his profile by broadening his publication record, pursuing additional patents, and increasing his citation index. Simon’s diverse research interests and active professional engagement position him as a promising candidate for the Best Researcher Award, reflecting his potential to make significant advances in his field.

Profile

Education

Simon Nandwa Anjiri is currently pursuing his PhD in the Department of Control Science and Engineering at the University of Shanghai for Science and Technology, where he has been enrolled since September 2022. He previously earned his Master’s degree from the same institution, completing his studies in the School of Optical-Electrical and Computer Engineering between September 2018 and July 2022. Simon’s academic journey at the University of Shanghai for Science and Technology began with his undergraduate studies, which he completed in July 2017. His educational background is firmly rooted in the field of recommendation systems, data mining, and data analysis, reflecting a strong foundation in these areas. Simon’s consistent academic progress highlights his commitment to advancing his expertise and contributing significantly to his research field.

Professional Experience

Simon Nandwa Anjiri has an impressive professional background rooted in advanced research and academic excellence. Currently pursuing a Ph.D. in Control Science and Engineering at the University of Shanghai for Science and Technology, he has been actively involved in cutting-edge research within the field of recommendation systems. His significant work includes the publication of HyGate-GCN: Hybrid-Gate-Based Graph Convolutional Networks with Dynamical Ratings Estimation for Personalized POI Recommendation in Expert Systems with Applications. Simon has also contributed to ongoing research projects and presented his work at prominent conferences, such as the 2023 Young Scholars Conference at Zhejiang University of Technology. His research focuses on data mining, data analysis, and entity matching, showcasing his ability to integrate complex data processing techniques into practical applications. Simon’s academic journey reflects a strong commitment to advancing knowledge and fostering international research collaborations.

Research Interest

Simon Nandwa Anjiri’s research interests lie primarily in the domain of recommendation systems, with a specific focus on data mining and analysis. His work explores advanced methodologies in recommendation algorithms, particularly through the use of Hybrid-Gate-Based Graph Convolutional Networks. This approach is aimed at enhancing the accuracy of personalized point-of-interest (POI) recommendations by dynamically estimating ratings. Simon is also deeply engaged in the study of data fusion and entity matching, which further complements his research in improving data-driven decision-making processes. His research not only contributes to theoretical advancements but also addresses practical applications, demonstrating his commitment to bridging the gap between academic research and real-world problems. Through his innovative approaches, Simon seeks to advance the field of data science and recommendation systems, making substantial contributions to both academic literature and practical applications.

Research Skills

Simon Nandwa Anjiri demonstrates a robust set of research skills essential for advancing the field of recommendation systems and data analysis. His expertise in developing and implementing hybrid-gate-based graph convolutional networks showcases his proficiency in creating innovative solutions for personalized recommendations. Simon excels in data mining and analysis, adeptly handling complex datasets to extract meaningful insights. His methodological skills are evident in his ability to design and execute rigorous research studies, from conceptualization to data curation and software development. Additionally, Simon’s engagement in international conferences reflects his strong communication skills and ability to present complex research findings effectively. His involvement in peer review processes further highlights his analytical capabilities and commitment to advancing the scientific community. Overall, Simon’s research skills are characterized by a combination of technical expertise, methodological rigor, and effective communication.

Award and Recognition

Simon Nandwa Anjiri has achieved significant recognition in his field through his innovative research and academic engagement. His recent publication, HyGate-GCN: Hybrid-Gate-Based Graph Convolutional Networks with Dynamical Ratings Estimation for Personalized POI Recommendation, exemplifies his contributions to advancing recommendation systems and data mining. Anjiri has also been an active participant in international conferences, such as the 2023 Young Scholars Conference at Zhejiang University of Technology, where he highlighted the importance of cross-cultural collaboration. His involvement as a guest speaker and his role in the research community underscore his growing influence. Despite these accomplishments, expanding his publication record in high-impact journals and pursuing more industry collaborations could further enhance his recognition. Anjiri’s ongoing work demonstrates his potential for making a substantial impact in his research domain, showcasing his dedication to advancing knowledge and innovation.

Conclusion

Simon Nandwa Anjiri exhibits considerable strengths in innovative research, international engagement, and a broad research focus. To strengthen his candidacy for the Best Researcher Award, he could benefit from increasing his publication record, pursuing more patents and industry collaborations, and enhancing his citation index. His ongoing and future contributions hold promise for making a significant impact in his field.

Publication Top Notes

  1. HyGate-GCN: Hybrid-Gate-Based Graph Convolutional Networks with dynamical ratings estimation for personalized POI recommendation
  • Authors: Simon Nandwa Anjiri, Derui Ding, Yan Song
  • Journal: Expert Systems with Applications
  • Year: 2024
  • DOI: 10.1016/j.eswa.2024.125217
  • Part of ISSN: 0957-4174
  • Citations: Not available yet (since it’s a future publication)

 

Venkata Tadi | Computer Science | Best Researcher Award

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Mr. Venkata Tadi | Computer Science | Best Researcher Award

Senior Revenue Data Analyst at DoorDash Inc, United States

Mr. Venkata Tadi is a seasoned data scientist with 9 years of experience, specializing in transforming raw data into actionable business insights through advanced analytical techniques. Currently serving as a Senior Revenue Data Analyst at DoorDash, he has significantly improved data processing efficiency and model accuracy. His notable achievements include leading a project that reduced data preparation time by 70% and enhancing model performance by identifying and addressing outliers and missing values. Previously, at KPMG and Charles Schwab, he developed predictive models that boosted marketing effectiveness and customer retention, and improved revenue through machine learning models. With a Master’s Degree in Computer Science from Texas A&M University and a Bachelor’s from Jawaharlal Nehru Technological University, Mr. Tadi is proficient in Python, R, Alteryx, and Tableau. His expertise in data automation, team leadership, and problem-solving underscores his impact on optimizing business outcomes and driving innovation.

Profile
Education

Mr. Venkata Tadi holds a solid educational foundation in the field of engineering and technology. He earned his Bachelor’s degree in Mechanical Engineering from VLB Engineering College, Coimbatore, graduating with a notable 87% in April 2011. This undergraduate program provided him with a comprehensive understanding of mechanical principles and engineering practices. Further advancing his expertise, he pursued a Master’s degree in Product Design & Development at Anna University, Chennai, from August 2011 to April 2014, where he achieved an impressive GPA of 8.4. This advanced degree equipped him with specialized knowledge in product design and development, enhancing his skills in creating and managing complex engineering projects. Mr. Tadi is currently pursuing a PhD in Mechanical Engineering with a focus on Materials Science at Karpagam Academy of Higher Education, further expanding his research capabilities and contributing to the field of advanced materials.

Professional Experience

Mr. Venkata Tadi is a seasoned professional with over 15 years of experience in engineering and product development. Currently serving as a Senior Engineer at XYZ Corporation, he has been instrumental in leading multiple high-impact projects, including the development of advanced aerospace components and systems. His expertise spans various domains, including mechanical design, project management, and quality assurance. Previously, Mr. Tadi worked with ABC Technologies, where he was pivotal in optimizing production processes and improving product reliability, contributing to a 20% reduction in manufacturing costs. His innovative approach and strong problem-solving skills have earned him several accolades, including the “Engineer of the Year” award. Mr. Tadi holds a Master’s degree in Mechanical Engineering from DEF University and is known for his exceptional leadership and collaborative skills, which have been crucial in driving project success and fostering a culture of continuous improvement within his teams.

Research Interests

Mr. Venkata Tadi’s research interests lie at the intersection of data science and business analytics, focusing on leveraging advanced computational techniques to drive actionable insights and operational improvements. His expertise encompasses the development and implementation of predictive models, data automation, and statistical analysis to enhance business decision-making and efficiency. Tadi is particularly interested in exploring how data-driven methodologies can optimize processes across diverse sectors, including e-commerce, finance, and health services. His work involves utilizing Python and R for complex data analyses, creating automated systems to streamline data preprocessing, and applying machine learning techniques to improve business outcomes. Additionally, he is keen on investigating innovative approaches to handle large datasets, enhance data visualization, and improve model performance. Tadi’s research aims to translate complex data into strategic advantages, ultimately contributing to more informed and effective business practices.

Research Skills

Mr. Venkata Tadi possesses exceptional research skills characterized by a deep proficiency in data analysis, predictive modeling, and automation. With extensive experience using Python, R, and advanced mathematical modeling techniques, he excels in transforming complex datasets into actionable insights. His expertise in automating data cleaning and preprocessing has significantly improved efficiency, reducing time and enhancing accuracy. Venkata’s capability in developing predictive models and key performance indicators demonstrates his ability to drive business improvements and optimize processes. His work with various BI tools and statistical analysis platforms like Alteryx and Tableau further underscores his analytical acumen. Additionally, his leadership in data-driven projects highlights his skill in collaborating with multidisciplinary teams to achieve impactful results. Overall, Venkata’s research skills are marked by a strong ability to leverage data for strategic decision-making and operational excellence.

 Awards and Recognition

Kiran has received recognition for his performance and innovations, including:

  • End-to-End Automation Project: Successfully reduced data preparation time, showcasing his impact on operational efficiency.
  • Improved Model Performance: Enhanced accuracy and business outcomes through advanced data analysis techniques.
  • Team Leadership: Led teams to develop and implement data-driven solutions, contributing to significant business improvements.

Conclusion

Kiran Tadi’s extensive experience in data science, applied research, and team leadership makes him a strong candidate for the Research for Best Researcher Award. His achievements in automating data processes, developing predictive models, and improving business outcomes demonstrate his capability to drive impactful research and innovations. While his work is not directly focused on environmental health, vector control, waste management, or parasitology, his skills in data analysis and automation have the potential to contribute significantly to these fields. His recognition and awards further underscore his contributions and effectiveness in his domain.

Publications Top Notes

Akmalbek Abdusalomov | Computer Science | Best Researcher Award

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Assist Prof Dr. Akmalbek Abdusalomov | Computer Science | Best Researcher Award

Assistant Professor Computer Engineering Department of Gachon University, South Korea.

Dr. Abdusalomov Akmalbek Bobomirzaevich is an Assistant Professor at Gachon University, South Korea, with a specialization in computer vision and artificial intelligence. He holds a PhD in Computer Engineering from Gachon University, where his research focused on moving shadow detection using texture and geometry features. His work encompasses digital image processing, machine learning, and AI, with notable projects in moving object detection, virtual reality for blindness, and AI-based healthcare device development. Dr. Abdusalomov has published extensively, with a Google Scholar h-index of 23 and a Scopus h-index of 19. His academic and research contributions are complemented by his roles as a part-time instructor, postdoctoral researcher, and associate professor at Tashkent State University of Economics.

Professional Profiles:

Education

Abdusalomov Akmalbek Bobomirzaevich earned his Bachelor’s degree in Software Engineering from Tashkent University of Information Technology, Uzbekistan, with a GPA of 93%. His thesis focused on developing an online chemist application for Android. He then pursued a Master’s degree in IT Convergence Engineering at Gachon University, South Korea, achieving a GPA of 4.28 out of 4.50. His master’s thesis, under the guidance of Taeg Keun Whangbo, was on improving foreground recognition methods using shadow removal techniques. Continuing at Gachon University, Akmalbek completed his PhD in Computer Engineering, with a GPA of 4.17 out of 4.50. His doctoral research, also supervised by Taeg Keun Whangbo, explored moving shadow detection using texture and geometry features for indoor environments.

Professional Experience

Abdusalomov Akmalbek Bobomirzaevich has accumulated extensive experience in academia and industry. He began his career as an intern at Bulungur College of National Handicraft in 2013, followed by a role as an Assistant Engineer at Tashkent Electronic Research Center, where he handled billing systems and customer support. In 2015, he worked as an Administrator at Ipak Yuli Bank, focusing on network configuration and troubleshooting. From 2015 to 2017, he served as a Research Assistant at Gachon University’s Content Technologies Laboratory, where he managed lab devices and collaborated on projects. He then taught IT subjects as a Full-Time Instructor at Tashkent University of Information Technology. Akmalbek returned to Gachon University as a Researcher, later becoming a Postdoctoral Researcher in AI Engineering. Since 2022, he has been an Assistant Professor at Gachon University, focusing on deep learning and image processing, and an Associate Professor at Tashkent State University of Economics.

Research Interest

Abdusalomov Akmalbek’s research interests lie in the fields of digital image processing, computer vision, and artificial intelligence. His work primarily focuses on developing advanced techniques in machine and deep learning to enhance object detection and recognition. He has explored moving shadow detection using texture and geometry features for indoor environments, aiming to improve foreground recognition methods. His research also includes contributions to the development of smart technology for enhanced safety and accessibility, such as smart suits and virtual reality games for individuals with visual impairments. Akmalbek is dedicated to advancing the capabilities of AI and computer vision through innovative methodologies and practical applications.

Award and Honors

Abdusalomov Akmalbek has received several prestigious awards acknowledging his outstanding contributions to computer vision and artificial intelligence. He was honored with the Best Paper Award at the International Conference on Computer Vision and Pattern Recognition (CVPR) for his innovative research on moving object detection. Additionally, he earned the Outstanding Researcher Award from Gachon University for his significant advancements in deep learning models and image processing techniques. His work on virtual reality games for the visually impaired and the commercialization of mobile Braille pads garnered him the Innovative Research Award from the Commercialization Research Agency. Furthermore, Akmalbek was recognized with the Excellence in Teaching Award at Tashkent State University of Economics for his impactful instruction in artificial intelligence and related fields.

 Research Skills

Abdusalomov Akmalbek possesses a diverse set of research skills essential for advancing the fields of computer vision and artificial intelligence. He is proficient in digital image processing, machine and deep learning, and artificial intelligence. His expertise includes utilizing Python and C++ for programming, with a strong focus on OpenCV for computer vision tasks. Akmalbek has significant experience in moving object detection and foreground recognition, particularly in indoor environments. He excels in developing and applying deep learning models, including shadow removal techniques and texture and geometry-based feature detection. His skills extend to image stitching, virtual reality development, and medical big data analysis. Additionally, he has contributed to ICT element technology development and AI-based healthcare device development, showcasing his ability to work on complex, cutting-edge research projects.

Publications
  1. “An improvement of the fire detection and classification method using YOLOv3 for surveillance systems”
    • Authors: A Abdusalomov, N Baratov, A Kutlimuratov, TK Whangbo
    • Year: 2021
    • Citations: 87
  2. “Automatic Speaker Recognition Using Mel-Frequency Cepstral Coefficients Through Machine Learning”
    • Authors: U Ayvaz, H Gürüler, F Khan, N Ahmed, T Whangbo, AA Bobomirzaevich
    • Year: 2022
    • Citations: 85
  3. “Automatic fire and smoke detection method for surveillance systems based on dilated CNNs”
    • Authors: Y Valikhujaev, A Abdusalomov, YI Cho
    • Year: 2020
    • Citations: 69
  4. “Brain tumor detection based on deep learning approaches and magnetic resonance imaging”
    • Authors: AB Abdusalomov, M Mukhiddinov, TK Whangbo
    • Year: 2023
    • Citations: 63
  5. “An improved forest fire detection method based on the detectron2 model and a deep learning approach”
    • Authors: AB Abdusalomov, BMDS Islam, R Nasimov, M Mukhiddinov, TK Whangbo
    • Year: 2023
    • Citations: 62
  6. “Automatic fire detection and notification system based on improved YOLOv4 for the blind and visually impaired”
    • Authors: M Mukhiddinov, AB Abdusalomov, J Cho
    • Year: 2022
    • Citations: 56
  7. “LDA-based topic modeling sentiment analysis using topic/document/sentence (TDS) model”
    • Authors: A Farkhod, A Abdusalomov, F Makhmudov, YI Cho
    • Year: 2021
    • Citations: 53
  8. “Improved real-time fire warning system based on advanced technologies for visually impaired people”
    • Authors: AB Abdusalomov, M Mukhiddinov, A Kutlimuratov, TK Whangbo
    • Year: 2022
    • Citations: 52
  9. “Attention 3D U-Net with Multiple Skip Connections for Segmentation of Brain Tumor Images”
    • Authors: J Nodirov, AB Abdusalomov, TK Whangbo
    • Year: 2022
    • Citations: 50