Supraja Ballari | Computer Science | Best Researcher Award

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Mrs. Supraja Ballari | Computer Science | Best Researcher Award

Assistant Professor from Guru Nanak Institutions Technical Campus, India

Smt. B. Supraja is an experienced academician and researcher in the field of Computer Science and Engineering. With over 15 years of teaching experience at various reputed technical institutions in India, she has consistently contributed to both pedagogy and applied research. Currently serving as an Assistant Professor at Guru Nanak Institutions Technical Campus, Telangana, she is also pursuing her Ph.D. in Computer Science from Dravidian University, Kuppam. Her academic journey is marked by a strong foundation in computer applications and engineering, with a focus on emerging areas such as machine learning, cybersecurity, blockchain, and data mining. She has authored several research papers in reputed journals and holds multiple patents reflecting her commitment to innovation. Her work spans interdisciplinary applications of computing in logistics, vehicular networks, and employee management systems. Known for her diligence and academic integrity, Smt. Supraja combines her teaching skills with active research, mentorship, and curriculum development. Her ability to blend theory with practical applications makes her a valuable asset in academia. Her academic contributions have positioned her as a researcher with great potential for national recognition, including eligibility for research excellence awards.

Professional Profile

Education

Smt. B. Supraja holds a rich academic background that lays the foundation for her current research pursuits. She is presently pursuing a Ph.D. in Computer Science from Dravidian University, Kuppam, with a focus on contemporary issues in cybersecurity, data analytics, and intelligent systems. She completed her M.Tech in Computer Science and Engineering from PBR Visvodaya Engineering College, Kavali (affiliated to JNTUA) between 2011 and 2014, where she deepened her technical knowledge in core computer engineering disciplines. Her postgraduate studies began with a Master of Computer Applications (M.C.A.) from Geethanjali College of PG Studies under Sri Venkateswara University, Nellore (2002–2005). Her academic credentials are well aligned with the technological demands of today’s dynamic research landscape. Her education spans foundational programming, software engineering principles, and advanced technologies, making her a capable researcher and instructor. Throughout her academic journey, she has remained focused on interdisciplinary applications of computer science in real-world contexts. Her continuous academic progression—culminating in her doctoral studies—underscores her lifelong commitment to education and research excellence.

Professional Experience

Smt. Supraja’s professional journey spans nearly two decades in the higher education sector, where she has served in various teaching capacities. She is currently employed as an Assistant Professor at Guru Nanak Institutions Technical Campus, Telangana (since February 2023), where she teaches undergraduate and postgraduate courses in Computer Science. Prior to this, she held the same role at Narayana Engineering College, Nellore from July 2021 to January 2023, and at Krishna Chaitanya Educational Institutions from December 2014 to July 2021, teaching a mix of B.Sc., BCA, and M.Sc. students. Her earlier roles included positions at S. Chaavan Institute of Science & Technology and S.V. Arts & Science College, Gudur, where she taught various computer science subjects to both undergraduate and postgraduate students. In each of these positions, she has contributed to academic instruction, student mentoring, and curriculum development. Her experience reflects a deep engagement with the academic process, ranging from foundational teaching to more research-oriented mentorship. This long-standing teaching career demonstrates not only her pedagogical strengths but also her dedication to shaping the next generation of computer scientists.

Research Interests

Smt. B. Supraja’s research interests span a wide range of cutting-edge domains in computer science. Her primary focus areas include machine learning, cybersecurity, blockchain applications, data mining and data warehousing, fog computing, and cloud-based control systems. Her work reflects a deep interest in the intersection of artificial intelligence with societal and industrial applications. She has conducted research on anomaly detection in software-defined networks, data sharing in vehicular social networks using blockchain, and logistics optimization through structural equation modeling. She also explores areas such as sentiment analysis using Naïve Bayes classifiers, encrypted control systems, and cyberattack prediction through machine learning techniques. These interests align closely with today’s technological priorities such as data protection, automation, and intelligent decision-making. Her work seeks to bridge the gap between academic research and industrial applicability. The diverse yet cohesive nature of her research interests indicates her adaptability and eagerness to explore interdisciplinary applications. These interests not only reflect technical competence but also her sensitivity to real-world challenges that require intelligent, scalable, and secure technological solutions.

Research Skills

Smt. B. Supraja brings a robust set of research skills honed through academic work, project collaborations, and innovation initiatives. She is proficient in programming languages such as Java, C, and C++, and has practical experience with databases like Oracle and MS Access, as well as web technologies like HTML, JavaScript, and XML. Her expertise includes operating within different development environments using tools like Eclipse and Editplus. These technical proficiencies support her capability in implementing machine learning models, simulation systems, and data analysis applications. She has successfully authored and co-authored peer-reviewed publications and book chapters, showing familiarity with scientific writing, research methodology, and collaborative scholarship. In addition, she has contributed to the innovation space through patent filings in areas such as employee churn prediction and cyberattack prevention systems using machine learning algorithms. Her ability to apply theoretical knowledge into practical systems design and her experience in real-world problem solving mark her as a capable and results-oriented researcher. Her academic and technological skills are further strengthened by her consistent teaching of core subjects, which reinforces her depth in fundamental computer science concepts.

Awards and Honors

While a formal list of awards and honors is not provided in her academic profile, Smt. B. Supraja’s achievements in publishing, patenting, and contributing to book chapters reflect strong professional recognition. Her patents—three of which are published between 2022 and 2024—indicate acknowledgment of her work’s novelty and utility in applied computer science. Her scholarly contributions to journals such as the Journal of Engineering Sciences and Design Engineering, alongside collaborative book chapters on contemporary issues like COVID-19’s digital impact, have been positively received in academic circles. These publications are indicative of her growing visibility in the research community. Furthermore, her inclusion in multidisciplinary anthologies and collaborations with senior academicians from diverse fields show a level of trust and professional respect. Although specific awards or titles are not yet documented, her research outputs and innovation track record position her as a strong candidate for future academic honors and distinctions. Her work is gaining momentum, and with further institutional and international engagement, she is well poised for formal recognition through research awards and academic fellowships.

Conclusion

In conclusion, Smt. B. Supraja is a dedicated academic professional and an emerging researcher in the field of computer science. Her profile reflects a balanced integration of long-standing teaching experience and active research engagement. She has demonstrated capability in producing impactful scholarly work through journal publications, book chapters, and patents. Her expertise spans across machine learning, blockchain, cloud systems, and cybersecurity—fields that are not only technologically significant but also socially relevant. While she is still progressing in her doctoral research, her current contributions are commendable and indicate strong future potential. Areas for growth include enhancing research impact through increased citation metrics, obtaining funded projects, and expanding global collaborations. However, the depth and diversity of her current academic efforts strongly support her candidacy for research awards. Smt. Supraja exemplifies the qualities of a modern researcher—technically skilled, pedagogically sound, and oriented towards practical applications. With continued dedication and strategic academic outreach, she is well-positioned to become a recognized contributor to India’s research and innovation landscape.

Publications Top Notes

  1. A vital neurodegenerative disorder detection using speech cues
    BS Jahnavi, BS Supraja, S Lalitha
    2020

  2. Simplified framework for diagnosis brain disease using functional connectivity
    T Swarnalatha, B Supraja, A Akula, R Alubady, K Saikumar, …
    2024

  3. DARL: Effectual deep adaptive reinforcement learning model enabled security and energy-efficient healthcare system in Internet of Things with the aid of modified manta ray
    B Supraja, V Kiran Kumar, N Krishna Kumar
    2025

  4. IoT based effective wearable healthcare monitoring system for remote areas
    S Tiwari, N Jain, N Devi, B Supraja, NT Chitra, A Sharma
    2024

  5. Securing IoT networks in healthcare for enhanced privacy in wearable patient monitoring devices
    V Tiwari, N Jharbade, P Chourasiya, B Supraja, PS Wani, R Maurya
    2024

  6. Machine learning-based prediction of cardiovascular diseases using Flask
    V Sagar Reddy, B Supraja, M Vamshi Kumar, C Krishna Chaitanya
    2023

  7. Real time complexities of research on machine learning algorithm: A descriptive research design
    GP Dr. N. Krishna Kumar, B. Supraja, B.S. Hemanth Kumar, U. Thirupalu
    2022

  8. IT employee job satisfaction survey during Covid-19
    GVMR Dr. N. Krishna Kumar, B. Supraja
    2022

  9. Covid-19 and digital era
    GVMR Dr. N. Krishna Kumar, B. Supraja
    2022

  10. Forwarding detection and identification anomaly in software defined network
    DNKK B. Supraja, A. Venkateswatlu
    2022

  11. Machine learning structural equation modeling algorithm on logistics and supply chain management
    UT B. Supraja, Dr. N. Krishna Kumar, B.S. Hemanth Kumar, B. Saranya, G …
    2022

  12. Sentiment analysis of customer feedback on restaurants using Naïve Bayes classifier
    DNKK A. Venkateswatlu, B. Supraja
    2021

  13. Design and implementation of fog-based encrypted control system in public clouds
    DNKK B. Supraja, A. Venkateswatlu
    2021

  14. Enhancing one to many data sharing using blockchain in vehicular social networks
    DNKK B. Supraja, A. Venkateswatlu
    2021

Ling Qin | Computer Science | Best Researcher Award

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Ms. Ling Qin | Computer Science | Best Researcher Award

Professor from Inner Mongolia University of Science &Technology, China

Dr. Ling Qin is a dedicated and accomplished professor in the Department of Information Engineering at Inner Mongolia University of Science and Technology, China. Born in August 1979, she has established a strong academic and research background in optical communication, particularly in the areas of visible light communication (VLC), indoor positioning systems, and atmospheric laser communication. Over more than two decades of academic service at her home institution, she has progressed from teaching assistant to professor, showcasing a steady and determined career development. Dr. Qin’s research has significantly contributed to the understanding and enhancement of VLC systems in complex environments, such as intelligent transportation systems and indoor positioning applications using LED lighting. Her publication record is extensive, with numerous articles published in well-recognized journals indexed in SCI and EI. She has also successfully led multiple nationally funded research projects and holds a Chinese patent related to optical signal reception. With her expertise, innovation, and dedication, Dr. Qin exemplifies the qualities of a leading academic researcher. Her work bridges the gap between theory and practical application, making her a suitable and promising candidate for recognition in advanced communication engineering fields.

Professional Profile

Education

Dr. Ling Qin holds an impressive academic background in engineering and communication technologies. She began her higher education journey in 1997, earning a Bachelor of Engineering in Communication Engineering from Chengdu University of Information Technology in 2001. She continued to deepen her specialization in optical communication by pursuing a Master’s degree in Engineering at Xi’an University of Technology, where she studied from 2004 to 2007. Demonstrating a strong commitment to academic growth and expertise, Dr. Qin earned her Ph.D. in Engineering from Chang’an University in Xi’an between 2011 and 2018. Her doctoral research aligned closely with her professional focus, examining advanced communication theories and systems including visible light and laser-based communication. The comprehensive progression of her academic qualifications reflects her long-standing dedication to mastering both the theoretical and technical aspects of her field. These qualifications have formed a solid foundation for her research career, allowing her to contribute meaningfully to high-impact areas such as LED-based indoor positioning systems and signal processing in complex environments. Her education has not only equipped her with the necessary knowledge but has also driven her to pursue innovation and advanced research in optical communication technologies.

Professional Experience

Dr. Ling Qin has built a robust academic and professional career spanning over two decades at Inner Mongolia University of Science and Technology in Baotou, China. She began her professional journey in 2001 as a teaching assistant and steadily rose through academic ranks due to her contributions to teaching and research. Between 2007 and 2012, she served as a lecturer, where she began to engage more actively in research and curriculum development. From 2012 to 2018, she was promoted to associate professor, during which she established her research presence in visible light communication and indoor positioning systems. Since 2019, Dr. Qin has held the title of full professor, where she continues to lead research initiatives and mentor students in cutting-edge communication technologies. Throughout her career, she has taught various specialized courses, including visible light communication theory, positioning systems, and atmospheric laser communications. Her long-term affiliation with a single institution reflects both stability and deep institutional commitment, while her advancement through all faculty ranks highlights her professional development. As a professor, she plays a vital role in advancing research, guiding graduate students, and contributing to scientific innovation through her projects and publications.

Research Interests

Dr. Ling Qin’s research interests focus on key innovations in the field of optical wireless communication, particularly visible light communication (VLC), indoor positioning systems, and atmospheric laser communications. One of her primary areas of study is the development and optimization of visible light communication systems, where she explores theoretical models and practical designs to enhance LED-based communication in complex traffic and indoor environments. Her work addresses challenges such as background light interference, signal modulation, and system performance under real-world conditions. Another important focus of her research is indoor positioning technologies using LED lighting. She investigates the integration of machine learning techniques, such as convolutional and recurrent neural networks, into positioning algorithms to improve accuracy and reliability. Additionally, Dr. Qin is engaged in the research of atmospheric laser communication systems, where she works on coding theory, modulation/demodulation methods, and performance enhancement strategies for data transmission in free-space environments. Her research is interdisciplinary, often overlapping with applications in intelligent transportation, aerospace signal processing, and biomedical engineering. These interests not only reflect her command over complex engineering concepts but also demonstrate her forward-thinking approach in developing communication technologies that serve modern infrastructure and industry demands.

Research Skills

Dr. Ling Qin possesses advanced research skills that make her a leading expert in optical communication and system development. Her technical expertise includes the modeling and implementation of visible light communication (VLC) systems in challenging environments, particularly for intelligent transportation and indoor positioning. She is proficient in applying modulation and demodulation techniques, signal coding, beamforming, and error suppression in complex signal environments. Her research integrates machine learning algorithms—including convolutional neural networks (CNNs), gated recurrent units (GRUs), and transformer-based models—into communication and positioning systems to enhance accuracy and system performance. Dr. Qin is also skilled in developing system architectures using hardware components like FPGA (Field Programmable Gate Arrays), contributing to the practical realization of her theoretical models. Additionally, she has experience with spread spectrum technologies and power inversion techniques for background light suppression. Her research has also extended into interdisciplinary domains, such as carbon nanoparticle applications in medical systems and satellite navigation under plasma interference. These wide-ranging skills have been applied in various research projects funded by national and regional science foundations, demonstrating her ability to execute complex research plans and produce tangible outcomes. Her scientific rigor and technical versatility position her as a valuable asset in the field.

Awards and Honors

While Dr. Ling Qin’s profile does not list specific individual awards or honors, her consistent track record of securing competitive research funding from prestigious agencies reflects significant academic recognition. She has been awarded multiple research grants by the National Natural Science Foundation of China, supporting her projects on visible light communication, satellite navigation under plasma conditions, and laser communication systems. These grants indicate high confidence from the scientific community in the relevance and impact of her research. Additionally, she has contributed to the development of a nationally recognized patent for an optical signal receiving system, which further showcases her innovation and contribution to applied research. Her position as a full professor at Inner Mongolia University of Science and Technology is itself a recognition of her professional achievements and academic standing. Her numerous publications in high-impact journals and conferences indexed by SCI and EI are further testament to her contributions. While formal honors such as best paper or teaching awards are not noted, the cumulative evidence of her leadership in research, ability to secure funding, and innovation through patents suggests she has achieved considerable peer recognition in her field.

Conclusion

Dr. Ling Qin stands out as a strong and capable academic professional with notable contributions to the field of optical communication. Her career reflects a steady ascent through academic ranks, backed by a solid foundation in education and a deep commitment to research excellence. With a focused interest in visible light communication, indoor positioning systems, and laser-based communication technologies, she has contributed significantly to both theoretical advancements and real-world applications. Her skills in modeling complex communication systems, integrating artificial intelligence techniques, and implementing hardware-based solutions place her at the intersection of innovation and practicality. Although not heavily decorated with formal awards, her success in securing national-level research grants and her involvement in patent development speak volumes about her scientific impact. She has authored an extensive list of peer-reviewed publications that enhance her reputation and contribute to global scientific knowledge. Overall, Dr. Qin exemplifies the qualities of a modern researcher—technically skilled, innovative, and committed to advancing engineering solutions for real-world problems. Her profile makes her a highly suitable candidate for the Best Researcher Award, and recognition of her work would be well-deserved within the scientific community.

Publications Top Notes

  1. Title: CirnetamorNet: An ultrasonic temperature measurement network for microwave hyperthermia based on deep learning
    Authors: F. Cui, Y. Du, L. Qin, C. Li, X. Meng
    Year: 2025

  2. Title: Visible light channel modeling and application in underground mines based on transformer point clouds optimization
    Authors: J. Yu, X. Hu, Q. Wang, F. Wang, X. Kou
    Year: 2025

  3. Title: Fractional OAM Vortex SAR Imaging Based on Chirp Scaling Algorithm
    Authors: L. Yu, D. Yongxing Du, L. Baoshan Li, L. Qin, L. Chenlu Li
    Year: 2025

  4. Title: Indoor visible light positioning system based on memristive convolutional neural network
    Authors: Q. Chen, F. Wang, B. Deng, L. Qin, X. Hu
    Year: 2025
    Citations: 2

  5. Title: Visible light visual indoor positioning system for based on residual convolutional networks and image restoration
    Authors: D. Chen, L. Qin, L. Cui, Y. Du
    Year: 2025

Said Boumaraf | Computer Science | Environmental Engineering Impact Award

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Dr. Said Boumaraf | Computer Science | Environmental Engineering Impact Award

Researcher and AI scientist from Khalifa University, UAE

Dr. Said Boumaraf is a distinguished researcher specializing in artificial intelligence (AI), computer vision, and medical imaging. Currently serving as a Postdoctoral Fellow at Khalifa University, his work primarily focuses on developing advanced AI methodologies to address complex challenges in visual recognition and healthcare diagnostics. Dr. Boumaraf has contributed significantly to the field through his involvement in projects that enhance remote sensing of gas flares and improve face parsing techniques under occlusion conditions. His research has been published in reputable journals and conferences, reflecting his commitment to advancing technological solutions for real-world problems. Collaborating with international teams, he continues to push the boundaries of AI applications, particularly in areas that intersect with environmental monitoring and medical diagnostics. Dr. Boumaraf’s dedication to research excellence positions him as a leading figure in the integration of AI technologies into practical applications.

Professional Profile

Education

Dr. Boumaraf’s academic journey is marked by a strong foundation in computer science and engineering. He earned his Ph.D. in Computer Science, where his research focused on the development of AI algorithms for medical image analysis. His doctoral studies provided him with in-depth knowledge of machine learning, deep learning, and their applications in healthcare. Prior to his Ph.D., Dr. Boumaraf completed his Master’s degree in Computer Engineering, during which he explored various aspects of computer vision and pattern recognition. His academic pursuits have equipped him with a robust skill set that bridges theoretical understanding and practical implementation of AI technologies. Throughout his education, Dr. Boumaraf has demonstrated a commitment to interdisciplinary research, integrating principles from computer science, engineering, and healthcare to develop innovative solutions. His educational background lays the groundwork for his ongoing contributions to the field of AI and its applications in critical domains.

Professional Experience

Dr. Boumaraf’s professional experience encompasses a range of roles that highlight his expertise in AI and its applications. As a Postdoctoral Fellow at Khalifa University, he has been instrumental in leading research projects that apply deep learning techniques to environmental and medical challenges. His work includes developing AI-enhanced methods for remote sensing of gas flares and creating robust face parsing algorithms capable of handling occlusions. Prior to his current role, Dr. Boumaraf collaborated with various research institutions and industry partners, contributing to projects that required the integration of AI into practical solutions. His experience extends to developing computer-aided diagnosis systems for breast cancer detection, showcasing his ability to apply AI in critical healthcare settings. Dr. Boumaraf’s professional journey reflects a consistent focus on leveraging AI to address real-world problems, underscoring his role as a key contributor to the advancement of intelligent systems in diverse applications.

Research Interests

Dr. Boumaraf’s research interests lie at the intersection of artificial intelligence, computer vision, and medical imaging. He is particularly focused on developing deep learning models that enhance the accuracy and efficiency of image analysis in complex scenarios. His work on occlusion-aware face parsing addresses challenges in visual recognition where parts of the face are obscured, improving the reliability of facial analysis systems. In the medical domain, Dr. Boumaraf has contributed to creating AI-driven diagnostic tools that assist in the early detection of diseases such as breast cancer. His research also explores the application of AI in environmental monitoring, specifically in the remote sensing of gas flares, which has implications for energy management and environmental protection. Dr. Boumaraf’s interdisciplinary approach combines theoretical research with practical applications, aiming to develop AI solutions that can be effectively integrated into various sectors.

Research Skills

Dr. Boumaraf possesses a comprehensive set of research skills that enable him to tackle complex problems in AI and its applications. His proficiency in deep learning frameworks such as TensorFlow and PyTorch allows him to design and implement sophisticated neural network architectures. He is skilled in image processing techniques, including segmentation, feature extraction, and classification, which are essential for medical image analysis and computer vision tasks. Dr. Boumaraf is adept at handling large datasets, employing data augmentation and preprocessing methods to enhance model performance. His experience with algorithm optimization and model evaluation ensures the development of efficient and accurate AI systems. Additionally, his collaborative work with multidisciplinary teams demonstrates his ability to integrate AI solutions into broader technological and scientific contexts. Dr. Boumaraf’s research skills are instrumental in advancing AI applications across various domains.

Awards and Honors

Throughout his career, Dr. Boumaraf has received recognition for his contributions to the field of artificial intelligence. His research publications in esteemed journals and conferences have garnered attention from the academic community, reflecting the impact of his work. While specific awards and honors are not detailed in the available information, his role as a Postdoctoral Fellow at a leading institution like Khalifa University signifies a level of esteem and acknowledgment of his expertise. Dr. Boumaraf’s ongoing collaborations and research endeavors continue to position him as a respected figure in the AI research community.

Conclusion

Dr. Said Boumaraf stands out as a dedicated researcher whose work bridges the gap between artificial intelligence theory and practical application. His contributions to computer vision and medical imaging demonstrate a commitment to developing AI solutions that address real-world challenges. Through his role at Khalifa University, Dr. Boumaraf continues to engage in cutting-edge research, collaborating with international teams to push the boundaries of what AI can achieve. His interdisciplinary approach and robust research skills make him a valuable asset to the scientific community, and his work holds promise for significant advancements in both environmental monitoring and healthcare diagnostics. As AI continues to evolve, researchers like Dr. Boumaraf play a crucial role in ensuring that these technologies are harnessed effectively for the betterment of society.

Publications Top Notes

  • Title: A new transfer learning based approach to magnification dependent and independent classification of breast cancer in histopathological images
    Authors: S. Boumaraf, X. Liu, Z. Zheng, X. Ma, C. Ferkous
    Year: 2021
    Citations: 169

  • Title: Conventional machine learning versus deep learning for magnification dependent histopathological breast cancer image classification: A comparative study with visual explanation
    Authors: S. Boumaraf, X. Liu, Y. Wan, Z. Zheng, C. Ferkous, X. Ma, Z. Li, D. Bardou
    Year: 2021
    Citations: 83

  • Title: A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms
    Authors: S. Boumaraf, X. Liu, C. Ferkous, X. Ma
    Year: 2020
    Citations: 80

  • Title: A new three-stage curriculum learning approach for deep network based liver tumor segmentation
    Authors: H. Li, X. Liu, S. Boumaraf, W. Liu, X. Gong, X. Ma
    Year: 2020
    Citations: 12

  • Title: Deep distance map regression network with shape-aware loss for imbalanced medical image segmentation
    Authors: H. Li, X. Liu, S. Boumaraf, X. Gong, D. Liao, X. Ma
    Year: 2020
    Citations: 11

  • Title: A multi-scale and multi-level fusion approach for deep learning-based liver lesion diagnosis in magnetic resonance images with visual explanation
    Authors: Y. Wan, Z. Zheng, R. Liu, Z. Zhu, H. Zhou, X. Zhang, S. Boumaraf
    Year: 2021
    Citations: 10

  • Title: AI-enhanced gas flares remote sensing and visual inspection: Trends and challenges
    Authors: M. Al Radi, P. Li, S. Boumaraf, J. Dias, N. Werghi, H. Karki, S. Javed
    Year: 2024
    Citations: 6

  • Title: Web3-enabled metaverse: the internet of digital twins in a decentralised metaverse
    Authors: N. Aung, S. Dhelim, H. Ning, A. Kerrache, S. Boumaraf, L. Chen, M.T. Kechadi
    Year: 2024
    Citations: 6

  • Title: U-SDRC: a novel deep learning-based method for lesion enhancement in liver CT images
    Authors: Z. Zheng, L. Ma, S. Yang, S. Boumaraf, X. Liu, X. Ma
    Year: 2021
    Citations: 5

  • Title: Bi-Directional LSTM Model For Classification Of Vegetation From Satellite Time Series
    Authors: K. Bakhti, M.E.A. Arabi, S. Chaib, K. Djerriri, M.S. Karoui, S. Boumaraf
    Year: 2020
    Citations: 5

Elavarasi Kesavan | Computer Science | Best Industrial Research Award

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Mrs. Elavarasi Kesavan | Computer Science | Best Industrial Research Award

Full-Stack QA Architect from Cognizant, India

Mrs. Elavarasi Kesavan is an accomplished Full Stack QA Architect with over 18 years of extensive experience in software quality assurance and automation testing. She has built a robust career with a strong specialization in Salesforce platforms, web-based applications, and various automated testing tools and methodologies. Her in-depth knowledge spans end-to-end software testing processes, mobile and web service testing, ETL validation, and automation using industry-standard tools like Selenium WebDriver, TestNG, Rest Assured, and Tricentis TOSCA. She is particularly proficient in test management, having implemented seamless integrations between tools like Jira and QTest. Elavarasi has consistently demonstrated excellence in designing testing frameworks, managing offshore teams, and ensuring quality compliance throughout the Software Development Life Cycle (SDLC). Additionally, she is well-versed in Agile, Waterfall, and V-Model methodologies and excels in accessibility testing using tools like JAWS Reader. She brings technical expertise in Java, JavaScript, and Ruby to her QA automation efforts. Through her leadership roles at Cognizant and other firms, she has led teams to deliver high-quality software solutions with a focus on automation, innovation, and efficiency. Her strong communication and client engagement skills have further enhanced her value in the industrial and research sectors.

Professional Profile

Education

Mrs. Elavarasi Kesavan holds a Bachelor of Technology (B.Tech) degree in Information Technology from Anjali Ammal Mahalingam Engineering College, affiliated with Anna University, which she completed in 2006. To complement her technical foundation, she pursued and successfully earned a Master of Business Administration (MBA) in General Management from SRM Easwari Engineering College, Anna University in 2011. Her academic journey reflects a unique blend of technical proficiency and managerial acumen, which has significantly contributed to her effectiveness in leading QA initiatives and managing cross-functional teams. Her academic training in Information Technology provided a solid grounding in programming languages, databases, and web technologies, while her MBA developed her capabilities in project management, strategic planning, and team leadership. This combination has been instrumental in her ability to bridge technical expertise with business-oriented decision-making. Additionally, her continuous pursuit of professional development through various certifications in AI testing, cloud technologies, and test automation tools demonstrates her commitment to lifelong learning and staying ahead in the rapidly evolving tech industry. Her education has laid the foundation for her successful career and her capacity to contribute meaningfully to industrial research and QA architecture.

Professional Experience

Mrs. Elavarasi Kesavan brings over 18 years of progressive experience in the IT industry, primarily focusing on software quality assurance, automation, and test architecture. She currently serves as an Engineer Manager and Full Stack QA Architect at Cognizant, a role she has held since November 2022. Prior to this, she worked at Concentrix as a Technology Lead for Full Stack QA Engineering from October 2021 to November 2022. Her earlier tenure at Cognizant (2010–2021) as a Senior Associate included responsibilities such as developing and maintaining automated test frameworks, integrating QA tools with defect tracking systems, and leading cross-functional teams. She began her professional journey as a Software Developer at IBM, followed by a stint at Vayana India Pvt Ltd. Elavarasi’s hands-on experience with a variety of test management and automation tools such as Selenium, TOSCA, Postman, Jira, and QTest highlights her adaptability and technical depth. She has effectively driven the QA strategy in complex project environments, aligning quality goals with business objectives. She is recognized for her innovative solutions, strong client interactions, and mentoring capabilities. Her ability to handle diverse tools, technologies, and methodologies has cemented her as a valuable leader in the QA domain across multiple industries.

Research Interests

Mrs. Elavarasi Kesavan’s research interests lie at the intersection of software quality assurance, automation engineering, AI-driven testing, and compliance-focused application validation. She is particularly focused on developing frameworks and methodologies for efficient and scalable automation testing of web, mobile, and enterprise applications, including CRM platforms like Salesforce. Her work emphasizes scriptless automation using tools like Tricentis TOSCA and integration of AI-based testing approaches to enhance test coverage, reliability, and efficiency. She is keenly interested in security and compliance testing, aligning quality assurance practices with international standards such as GDPR, HIPAA, and PCI-DSS. Elavarasi’s exploration of testing tools that support DevOps and Agile frameworks demonstrates her commitment to continuous delivery and integration practices. Moreover, she is enthusiastic about advancing quality engineering through research on defect prediction models, test data management, and automation in cloud-native environments. Her engagement in multidisciplinary forums and conferences reveals a strong inclination toward applied industrial research. She aspires to contribute to the future of QA through intelligent automation frameworks, optimization of test cycles using AI, and expanding automation in AI/ML-based systems. These interests align with the goals of the Best Industrial Research Award by showcasing innovation and impact on real-world software engineering challenges.

Research Skills

Mrs. Elavarasi Kesavan is equipped with a comprehensive set of research and technical skills that support her contributions to industrial software testing and automation research. She is adept in using a wide array of automation tools such as Selenium WebDriver, Tricentis TOSCA, Postman, and SOAP UI. Her proficiency in developing and implementing test strategies spans data-driven and behavior-driven frameworks, including TestNG, Cucumber, Jasmine, and Rest Assured. Elavarasi has advanced capabilities in API testing, cross-browser testing, accessibility validation (JAWS), and end-to-end test management using tools like Jira and QTest. Her programming expertise includes Java, JavaScript, and Ruby, which she employs for custom test scripts and automation logic. She is skilled in web service validation, database verification (SQL, Oracle, MySQL), and cloud environment testing, complemented by hands-on experience in CI/CD tools like Jenkins and Maven. Her analytical and documentation capabilities are evident in her creation of test plans, traceability matrices, and compliance validation reports. In AI testing, she applies certified methodologies for testing machine learning models and intelligent systems. Her research-oriented approach, combined with practical application and tool proficiency, positions her as a technically strong candidate capable of innovating in industrial software quality research.

Awards and Honors

Mrs. Elavarasi Kesavan has received numerous prestigious awards and honors that reflect her excellence in technology innovation, industrial research, and leadership in software quality assurance. Notably, she was the recipient of the Distinguished Technology Award at the Dubai Dynamic Ultimate Business & Academic Iconic Awards in 2025. Her innovative contributions to IoT were recognized through the Best Patent Award for the design and development of an IoT-based multifunction agriculture robot, presented by the Scientific International Publishing House. Elavarasi also received the Best Paper Award for her work on cloud computing in Industry 4.0 at the UAE International Conference on Multidisciplinary Research and Innovation (ICMRI-2025). Additionally, she was honored with the Best Woman Researcher Award at the International Conference on Computational Science, Engineering & Technology (ICCSET-2025). Her editorial contributions were acknowledged with a Certificate of Excellence for her role as Chief Editor in Contemporary Research in Engineering, Management, and Science. Furthermore, she was recognized with a Digital Excellence Award by the CAPE Forum and a Certificate of Emerging Leader in Technology Innovation by RCS International Awards. These accolades not only highlight her technical prowess but also her impact on industrial innovation and collaborative research.

Conclusion

Mrs. Elavarasi Kesavan presents a strong and compelling case for the Best Industrial Research Award. With nearly two decades of experience in software quality assurance and a consistent record of innovation in test automation and QA strategy, she stands out as a leader who bridges technical execution with strategic foresight. Her deep expertise in automation tools, QA methodologies, compliance testing, and AI testing frameworks positions her at the forefront of industrial QA research. The recognition she has received through multiple awards and her contributions in patent development and conference presentations further reinforce her role as a pioneering professional in the field. Elavarasi’s research-oriented mindset, hands-on technical proficiency, and proven ability to lead teams and deliver enterprise-grade solutions make her a strong candidate whose work aligns with the goals of industrial research excellence. While she could benefit from further academic publications in peer-reviewed journals to bolster her academic research credentials, her real-world impact, technical acumen, and award-winning innovations clearly demonstrate her merit. Overall, Mrs. Elavarasi Kesavan exemplifies the ideal qualities of an industrial researcher whose work drives both technological advancement and practical value in the software engineering domain.

Publication Top Notes

  • Title: The Impact of Cloud Computing on Software Development: A Review
    Author: E. Kesavan
    Journal: International Journal of Innovations in Science, Engineering and Management
    Year: 2025
    Citations: 3

  • Title: AI Adapt Digital Learning in Education
    Author: E. Kesavan
    Conference: International Conference Proceeding on Innovation and Sustainable Strategies
    Year: 2025

  • Title: Explore How Digital Infrastructure Has Shaped Startup Growth
    Author: E. Kesavan
    Conference: International Conference on the Role of Innovation Policies
    Year: 2025

  • Title: Artificial Intelligence in Commerce: How Businesses Can Leverage Artificial Intelligence to Gain a Competitive Edge in the Global Marketplace
    Author: E. Kesavan
    Publication: Thiagarajar College of Preceptors, Edu Spectra
    Year: 2025

  • Title: The Evolution of Software Design Patterns: An In-Depth Review
    Author: E. Kesavan
    Journal: International Journal of Innovations in Science, Engineering and Management
    Year: 2025

  • Title: Impact of Artificial Intelligence on Software Development Processes
    Authors: SMSA Cuddapah Anitha, Nirmal Kumar Gupta, Balaji Chintala, Daniel Pilli, E. Kesavan
    Journal: Journal of Information Systems Engineering and Management
    Volume/Issue: 10 (25s), Pages 431–437
    Year: 2025

  • Title: Information and Communication Technology Development in Emerging Countries
    Author: E. Kesavan
    Journal: Journal on Electronic and Automation Engineering
    Volume/Issue: 3 (1), Pages 60–68
    Year: 2024

  • Title: Comprehensive Evaluation of Electric Motorcycle Models: A Data-Driven Analysis
    Author: E. Kesavan
    Journal: REST Journal on Data Analytics and Artificial Intelligence
    Year: 2023
    ISSN: 2583-… (incomplete in original text)

  • Title: Assessing Laptop Performance: A Comprehensive Evaluation and Analysis
    Author: E. Kesavan
    Journal: Recent Trends in Management and Commerce
    Volume: 4, Pages 175–185
    Year: 2023

Eric Nizeyimana | Computer Science | Best Researcher Award

Posted on by Shen Awards

Dr. Eric Nizeyimana | Computer Science | Best Researcher Award

Lecturer from University of Rwanda, Rwanda

Dr. Eric Nizeyimana is a Rwandan researcher and academic specializing in Internet of Things (IoT) and embedded systems. He has built a career grounded in advanced technological solutions for environmental and infrastructural challenges, particularly in air pollution monitoring and data-driven IoT applications. His recent work includes developing decentralized, predictive frameworks using blockchain, machine learning, and IoT technologies to track pollution spikes in real time. With extensive research and teaching experience across African and Asian academic institutions, including the University of Rwanda and Seoul National University, he brings a global perspective to technological development. Dr. Nizeyimana is known for integrating practical and scalable systems with academic rigor, earning recognition for his innovative and impactful work. His contributions have been published in several reputable journals, and he continues to influence the next generation of engineers and scientists through both classroom teaching and research mentorship. Fluent in English, French, Kinyarwanda, and Swahili, and having held leadership roles in academic committees and church communities, he blends technical excellence with interpersonal and organizational strengths. As a proactive researcher and educator, Dr. Nizeyimana continues to push the boundaries of IoT systems in addressing societal issues, especially in transportation, environmental sustainability, and smart infrastructure.

Professional Profile

Education

Dr. Eric Nizeyimana has pursued a progressive academic path centered on engineering, mathematical sciences, and emerging technologies. He earned his Ph.D. in Internet of Things (IoT) with a specialization in Embedded Systems from the University of Rwanda – College of Science and Technology (UR-CST), under the African Center of Excellence in Internet of Things (ACEIoT), in collaboration with Seoul National University (SNU), South Korea, from 2020 to 2024. His doctoral research focused on environmental monitoring systems using IoT and edge computing technologies, particularly addressing air pollution monitoring and predictive analytics. Prior to this, he completed a master’s program in Mathematical Sciences at the African Institute for Mathematical Sciences (AIMS-Cameroon) in 2015. His academic foundation was laid through a bachelor’s degree in Computer Engineering from the Kigali Institute of Science and Technology (KIST), which he completed in 2012. This strong foundation in both engineering and mathematics positioned him well for his advanced research in smart systems and applied technologies. His educational journey reflects a consistent focus on interdisciplinary innovation, bridging computational science, real-world data systems, and environmental sustainability. Through scholarships and competitive academic grants, Dr. Nizeyimana has demonstrated academic excellence and international competitiveness.

Professional Experience

Dr. Eric Nizeyimana has accumulated rich professional experience in academia and research-focused technical roles. As of October 2024, he serves as a Lecturer at the University of Rwanda – College of Science and Technology, where he also previously held the role of Assistant Lecturer between August 2015 and May 2017. In this capacity, he has taught diverse subjects, including Embedded Computer Systems, Artificial Intelligence, Java Programming, and Computer Programming. He has also supervised undergraduate and graduate research projects and contributed to proposal writing and curriculum development. From April to October 2023, Dr. Nizeyimana was a researcher at Seoul National University, where he developed IoT-based systems for environmental monitoring, optimized embedded systems, and analyzed complex data. Between 2019 and 2023, he worked as an IT Analyst and Training Officer at the African Institute for Mathematical Science (AIMS), coordinating IT infrastructure, providing technical training, and managing secure digital environments. Earlier, from 2017 to 2018, he held the role of IT Officer and System Administrator at AIMS in both Rwanda and Cameroon. These roles highlight his hybrid expertise in teaching, systems design, network security, and capacity building, establishing him as a technically proficient and educationally driven professional.

Research Interests

Dr. Eric Nizeyimana’s research interests lie at the intersection of the Internet of Things (IoT), embedded systems, edge computing, and environmental monitoring. He focuses on developing intelligent, decentralized systems to address real-world challenges such as air pollution, particularly in urban transportation networks. His work explores the integration of edge devices, machine learning algorithms, and blockchain technologies to design predictive and real-time monitoring solutions. Another key interest involves leveraging IoT infrastructures for smart city applications, including traffic management, public health monitoring, and resource optimization. Dr. Nizeyimana is particularly interested in how embedded systems can be adapted to constrained environments to achieve high accuracy with low power consumption and minimal latency. In addition to technical development, he investigates the ethical and infrastructural implications of deploying such technologies in developing countries. His research also includes data analytics for IoT devices, remote sensing systems, and system interoperability within distributed computing frameworks. Through his multidisciplinary approach, he seeks to expand the boundaries of scalable, secure, and sustainable technology for societal benefit. These interests reflect his commitment to using engineering innovation to improve public services, infrastructure management, and environmental stewardship in both local and global contexts.

Research Skills

Dr. Eric Nizeyimana possesses advanced research skills in embedded systems design, IoT application development, and edge computing architecture. He is proficient in integrating IoT sensors and communication protocols with real-time data processing systems to monitor and analyze environmental data, especially for detecting air pollution peaks. His work involves embedded system programming, circuit design, microcontroller deployment, and the use of platforms such as Arduino and Raspberry Pi. He also has experience in machine learning model development for predictive analytics, including supervised learning techniques applied to transportation and pollution datasets. Dr. Nizeyimana demonstrates expertise in decentralized systems using blockchain for data immutability and enhanced security. Additionally, he has strong skills in scientific writing, proposal development, and collaborative project implementation. His ability to design end-to-end solutions—from hardware development to software implementation and data interpretation—sets him apart in the IoT research space. Furthermore, he is skilled in academic dissemination, having presented at multiple international seminars and conferences. His competence in working across multicultural teams, both locally and internationally, further enhances his collaborative research capabilities. These skills are underpinned by a solid background in programming languages such as Python, Java, and C++, along with system administration and IT infrastructure management.

Awards and Honors

Dr. Eric Nizeyimana has been recognized for his academic excellence and research contributions through various prestigious awards. In 2023, he received the Mobility Research Grant from Rwanda’s National Council of Science and Technology (NCST), which enabled him to conduct critical experimental work at an international research institution. This grant, valued at approximately 8 million Rwandan francs, supported his living and research expenses during a two-month exchange, reflecting the national confidence in his research potential. In 2020, he was awarded a full four-year Ph.D. scholarship through the Partnership for skills in Applied Sciences, Engineering and Technology (PASET), a competitive regional initiative aimed at promoting advanced STEM education in Africa. His leadership and service have also been acknowledged through appointments such as PhD student representative and Master’s student representative, demonstrating trust in his leadership within academic communities. In addition, his consistent presence at international conferences and seminars, along with publications in respected peer-reviewed journals, underscores his active engagement in the global research community. These honors not only validate his academic achievements but also highlight his capability to drive impactful, solution-oriented research with both national and international relevance.

Conclusion

Dr. Eric Nizeyimana embodies the qualities of an outstanding researcher through his technical innovation, academic leadership, and commitment to solving real-world problems using emerging technologies. His focused research in IoT, embedded systems, and air pollution monitoring has generated valuable insights into how smart systems can be leveraged for environmental and urban challenges. His publication record in high-quality journals and active participation in global research exchanges reflect a strong orientation toward scholarly excellence and international collaboration. With a foundation in mathematics and engineering, his interdisciplinary approach allows him to bridge theory and application effectively. His work with institutions like Seoul National University and AIMS demonstrates adaptability, technical depth, and professional maturity. As an educator, he contributes to capacity building through teaching, mentorship, and curriculum development. Recognized with competitive grants and scholarships, he has proven his potential to lead transformative research in both academic and industrial contexts. While there remains room for broader global engagement and interdisciplinary outreach, Dr. Nizeyimana has established himself as a valuable contributor to the research community. His profile makes him a highly suitable candidate for recognition under a Best Researcher Award, affirming both his achievements and future promise.

Publications Top Notes

  1. Prototype of monitoring transportation pollution spikes through the internet of things edge networks

    • Authors: E. Nizeyimana, D. Hanyurwimfura, J. Hwang, J. Nsenga, D. Regassa

    • Year: 2023

    • Citations: 7

    • Journal: Sensors, 23(21), 8941

  1. Integration of Vision IoT, AI-based OCR and Blockchain Ledger for Immutable Tracking of Vehicle’s Departure and Arrival Times

    • Authors: M. Sichinga, J. Nsenga, E. Nizeyimana

    • Year: 2023

    • Citations: Not listed

    • Conference: 2023 8th Int. Conf. on Machine Learning Technologies

  1. Miniaturized Ultrawideband Microstrip Antenna for IoT‐Based Wireless Body Area Network Applications

    • Authors: U. Pandey, P. Singh, R. Singh, N.P. Gupta, S.K. Arora, E. Nizeyimana

    • Year: 2023

    • Citations: 15

    • Journal: Wireless Communications and Mobile Computing, 2023(1), 3950769

  1. IOT‐Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms

    • Authors: A. Rokade, M. Singh, S.K. Arora, E. Nizeyimana

    • Year: 2022

    • Citations: 20

    • Journal: Computational and Mathematical Methods in Medicine, 2022(1), 8434966

  1. Design of smart IoT device for monitoring short-term exposure to air pollution peaks

    • Authors: E. Nizeyimana, J. Nsenga, R. Shibasaki, D. Hanyurwimfura, J.S. Hwang

    • Year: 2022

    • Citations: 7

    • Journal: International Journal of Advanced Computer Science and Applications (IJACSA)

  1. Design of a decentralized and predictive real-time framework for air pollution spikes monitoring

    • Authors: E. Nizeyimana, D. Hanyurwimfura, R. Shibasaki, J. Nsenga

    • Year: 2021

    • Citations: 9

    • Conference: 2021 IEEE 6th Int. Conf. on Cloud Computing and Big Data Analysis

  1. Effect of Window Size on PAPR Reduction in 4G LTE Network Using Peak Windowing Algorithm in Presence of Non-linear HPA

    • Authors: M. Fidele, H. Damien, N. Eric

    • Year: 2020

    • Citations: 10

    • Conference: 2020 IEEE 5th Int. Conf. on Signal and Image Processing (ICSIP)

  1. Monitoring system to strive against fall armyworm in crops: case study on maize in Rwanda

    • Authors: D. Hanyurwimfura, E. Nizeyimana, F. Ndikumana, D. Mukanyiligira, …

    • Year: 2018

    • Citations: 7

    • Conference: 2018 IEEE SmartWorld/Ubiquitous Intelligence & Computing

  1. Comparative study on performance of High Performance Computing under OpenMP and MPI on Image Segmentation

    • Authors: E. Hitimana, E. Nizeyimana, G. Bajpai

    • Year: 2016

    • Citations: 1

    • Conference: Third International Conference on Advances in Computing, Communication and Informatics

  1. Development of an encrypted patient database including a doctor user interface

  • Author: E. Nizeyimana

  • Year: 2015

  • Citations: Not listed

  • Institution: African Institute for Mathematical Sciences Tanzania

Saurabh Kumar | Computer Science | Best Researcher Award

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Mr. Saurabh Kumar | Computer Science | Best Researcher Award

Shri Ramswaroop Memorial University, India

Saurabh Kumar is a passionate and driven Computer Science Engineering student with a strong focus on Artificial Intelligence, Machine Learning, and Natural Language Processing (NLP). With a deep interest in solving complex real-world challenges, Saurabh has worked extensively on AI-driven projects, including fine-tuning state-of-the-art models, developing computer vision applications, and enhancing NLP systems. His expertise spans multiple domains, including deep learning, speech synthesis, and autonomous systems. Saurabh actively contributes to the tech community through open-source projects and research-driven initiatives. His commitment to continuous learning, innovation, and collaboration sets him apart as a dedicated researcher in AI.

Professional Profile

Education

Saurabh Kumar is currently pursuing a degree in Computer Science Engineering, specializing in Artificial Intelligence and Machine Learning. Throughout his academic journey, he has developed a strong foundation in data science, deep learning, and cloud computing. His coursework includes advanced machine learning algorithms, computer vision, NLP, and big data analysis. In addition to academic learning, he has actively participated in AI-focused bootcamps, hackathons, and online certifications to enhance his technical knowledge. His commitment to education is evident through his consistent efforts to bridge theoretical knowledge with practical applications in AI-driven research.

Professional Experience

Saurabh has gained hands-on experience through various AI-based projects and internships. His work includes developing a Vehicle Classification Model using deep learning and computer vision, creating an advanced Text-to-Speech (TTS) model, and building multiple real-time computer vision applications. Additionally, he has experience working with cloud platforms like IBM Cloud and using tools such as SQL, Tableau, and Docker for AI deployment. His ability to work with cutting-edge AI models and optimize them for real-world use cases highlights his technical acumen. Saurabh’s professional experience reflects a strong ability to innovate, research, and implement AI solutions effectively.

Research Interests

Saurabh Kumar’s research interests lie at the intersection of Artificial Intelligence, Machine Learning, and Natural Language Processing. He is particularly passionate about Conversational AI, Reinforcement Learning, Explainable AI, and Generative AI. His work focuses on optimizing AI models for practical applications, enhancing NLP-based speech synthesis, and improving AI-driven automation. He is also interested in exploring AI ethics, fairness in machine learning, and the development of AI-driven assistive technologies. His continuous learning in AI research methodologies and practical deployment strategies showcases his commitment to pushing the boundaries of AI innovation.

Research Skills

Saurabh possesses a strong set of research skills, including data analysis, deep learning model optimization, and AI-driven problem-solving. He is proficient in Python, PyTorch, TensorFlow, OpenCV, and NLP frameworks such as Hugging Face. His expertise in AI extends to cloud computing, SQL-based data management, and deployment of machine learning models. He has hands-on experience with real-world AI challenges, including speech synthesis, computer vision applications, and text-based AI solutions. His ability to develop, fine-tune, and deploy AI models efficiently highlights his strong research-oriented approach.

Awards and Honors

Saurabh Kumar has been recognized for his contributions to AI and research. He has successfully completed the OpenCV Bootcamp, demonstrating expertise in Computer Vision and Deep Learning. His AI-driven projects have received recognition within the tech community, and his work in fine-tuning AI models has been acknowledged on various platforms. His commitment to advancing AI research is evident through his achievements in open-source contributions and AI development. These accolades showcase his dedication to continuous learning and impactful research in Artificial Intelligence.

Conclusion

Saurabh Kumar is a dedicated AI researcher and technology enthusiast committed to innovation, research, and problem-solving. His expertise in Artificial Intelligence, Machine Learning, and NLP, combined with his passion for AI-driven solutions, makes him a strong candidate for the Best Researcher Award. His extensive work in AI model development, contributions to open-source projects, and commitment to continuous learning set him apart as a future leader in AI research. By further expanding his research publications and collaborative efforts, he is well-positioned to make significant contributions to the field of AI.

Publications Top Notes

  1. Title: Real Time Vehicle Classification Using Deep Learning—Smart Traffic Management
    Authors: T Maurya, S Kumar, M Rai, AK Saxena, N Goel, G Gupta
    Year: 2025

 

Navid Ghaffarzadeh | Engineering | Best Researcher Award

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Assoc Prof Dr. Navid Ghaffarzadeh | Engineering | Best Researcher Award

Assoc Prof Dr. Navid Ghaffarzadeh, Imam Khomeini International University, Iran

Assoc Prof Dr. Navid Ghaffarzadeh is an accomplished engineer recognized for his innovative contributions to the field of engineering. With a focus on [specific area of expertise], he has been instrumental in advancing research and development initiatives. His dedication and impactful work earned him the prestigious Best Researcher Award, highlighting his commitment to excellence and collaboration. Navid continues to inspire through his research, aiming to drive advancements that benefit both industry and society.

 

Profile:

Education

Navid Ghaffarzadeh earned his PhD in Electrical Engineering from Iran University of Science and Technology in Tehran, completing his studies from September 2007 to April 2011. Prior to that, he obtained his Master of Science in Electrical Engineering from Amirkabir University of Technology (Tehran Polytechnic) between September 2005 and August 2007. He also holds a Bachelor of Science in Electrical Engineering from Zanjan University, where he studied from September 2001 to June 2005.

Professional Activities

Navid Ghaffarzadeh is actively engaged in the academic community as a reviewer for numerous prestigious journals in the field of electrical engineering. His reviewing contributions span a wide array of publications, including Renewable and Sustainable Energy Reviews, Applied Energy, Journal of Energy Storage, and IEEE Transactions on Power Systems, among others, with impact factors ranging from 1.276 to 16.799. With over 100 reviewed journal papers, Navid plays a vital role in advancing research quality and integrity in the field. His extensive experience demonstrates his commitment to fostering innovation and excellence in engineering research.

Research Interests

Navid Ghaffarzadeh’s research interests encompass a wide range of cutting-edge topics in electrical engineering. He focuses on renewable energy, exploring innovative solutions in battery energy storage systems and electric vehicles. His work in microgrid and smart grid design aims to enhance the efficiency and reliability of power systems. Navid is particularly interested in the application of artificial intelligence in renewable energy systems, as well as power systems protection and transients. Additionally, he investigates intelligent systems and optimization techniques to improve power systems, with a strong emphasis on ensuring power quality.

Honors and Awards: ‌

Navid Ghaffarzadeh has received numerous honors and awards throughout his academic and professional career. In 2012, he was honored with the IET Science, Measurement and Technology Premium Award for his outstanding paper on power quality disturbances, recognized as one of the best published in the journal. He has been named Outstanding Researcher at I.K International University multiple times, in 2013, 2014, 2016, and 2020, and has also received the Outstanding Professor award in 2017, 2019, 2020, 2021, and 2023. Additionally, he was awarded the Best Iranian PhD Dissertation in power system protection, highlighting his significant contributions to the field. Navid achieved top rankings in his studies, finishing first among PhD electrical power engineering students at Iran University of Science and Technology with a GPA of 18.72 out of 20, first among M.Sc. students at Amirkabir University of Technology with a GPA of 19.18 out of 20, and first among B.Sc. students at Zanjan University with a GPA of 18.36 out of 20.

 

Publication Top Note

A. Bamshad, N. Ghaffarzadeh, “A novel smart overcurrent protection scheme for renewables-dominated distribution feeders based on quadratic-level multi-agent system (Q-MAS),” Electrical Engineering, vol. 105, pp. 1497–1539, February 2023.

S. Ansari, N. Ghaffarzadeh, “A Novel Superimposed Component-Based Protection Method for Multi Terminal Transmission Lines Using Phaselet Transform,” IET Generation, Transmission & Distribution, vol. 17, no. 1, pp. 469–485, January 2023.

A. HN. Tajani, A. Bamshad, N. Ghaffarzadeh, “A novel differential protection scheme for AC microgrids based on discrete wavelet transform,” Electric Power Systems Research, vol. 220, pp. 1-12, July 2023.

A. Zarei, N. Ghaffarzadeh, “Optimal Demand Response-based AC OPF Over Smart Grid Platform Considering Solar and Wind Power Plants and ESSs with Short-term Load Forecasts using LSTM,” Journal of Solar Energy Research, vol. 8, no. 2, pp. 1367-1379, April 2023.

M. Dodangeh, N. Ghaffarzadeh, “A New Protection Method for MTDC Solar Microgrids using on-line Phaselet, Mathematical Morphology, and Signal Energy Analysis,” Energy Engineering & Management, vol. 13, no. 1, pp. 40-53, March 2023 (in Persian).

M. Dodangeh, N. Ghaffarzadeh, “An Intelligent Protection Method for Multi-terminal DC Microgrids Using On-line Phaselet, Mathematical Morphology, and Fuzzy Inference Systems,” Energy Engineering & Management, vol. 12, no. 2, pp. 12-25, August 2022 (in Persian).

M. Dodangeh, N. Ghaffarzadeh, “Optimal Location of HTS-FCLs Considering Security, Stability, and Coordination of Overcurrent Relays and Intelligent Selection of Overcurrent Relay Characteristics in DFIG Connected Networks Using Differential Evolution Algorithm,” Energy Engineering & Management, vol. 10, no. 2, pp. 14-25, May 2020 (in Persian).

A. Inanloo Salehi, N. Ghaffarzadeh, “Fault detection and classification of VSC-HVDC transmission lines using a deep intelligent algorithm,” International Journal of Research and Technology in Electricity Industry, vol. 1, no. 2, pp. 161-170, September 2022.

N. Ghaffarzadeh, H. Faramarzi, “Optimal Solar plant placement using holomorphic embedded power flow considering the clustering technique in uncertainty analysis,” Journal of Solar Energy Research, vol. 7, no. 1, pp. 997-1007, Winter 2022.

N. Ghaffarzadeh, A. Bamshad, “A new approach to AC microgrids protection using a bi-level multi-agent system,” International Journal of Research and Technology in Electricity Industry, vol. 1, no. 1, pp. 66-74, March 2022.

Amel SAHLI | Computer Science | Best Researcher Award

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MS. Amel SAHLI | Computer Science | Best Researcher Award

École Nationale des Sciences de l’Informatique , Tunisia

Amel Sahli is a dedicated researcher pursuing her PhD in computer science at the École Nationale des Sciences de l’Informatique in Tunisia, focusing on optimizing e-learning processes through AI and key performance indicators. She holds a Master’s degree in information systems and has published significant work on performance measurement in education. Sahli’s diverse professional background includes roles as a contract lecturer and various internships, providing her with practical insights and teaching experience. Her technical skills in programming and web development, coupled with her proficiency in Arabic, French, and English, enhance her ability to engage with the international research community. Amel Sahli’s commitment to advancing educational methodologies through her research makes her a strong candidate for the Best Researcher Award, highlighting her potential to contribute meaningfully to the field of education technology.

 

Profile:

Education

Amel Sahli is currently pursuing her PhD in computer science at the École Nationale des Sciences de l’Informatique (ENSI) in Tunisia. Her doctoral research focuses on developing an integrated approach that leverages artificial intelligence (AI) and key performance indicators (KPIs) to optimize e-learning processes. Prior to her PhD, she earned a Master’s degree in information systems and web technologies, where she studied performance measurement in educational settings. This followed her Bachelor’s degree in computer science, during which she designed and implemented web applications for educational management. Sahli’s academic journey has been marked by consistent excellence, earning distinctions in her studies and developing a strong foundation in both theoretical and practical aspects of computer science. Her educational background not only highlights her technical competencies but also underscores her commitment to advancing the field of education through innovative research.

Professional Experiences

Amel Sahli has gained diverse professional experience that enriches her academic pursuits. She began her career as a bank intern and a counter agent, where she honed her customer service and operational skills. Following these roles, she interned at the Institut Supérieur d’Informatique du Kef, further deepening her understanding of information technology in educational contexts. In 2023, she transitioned into academia as a part-time lecturer, sharing her expertise in computer science with students. Currently, Sahli is engaged in research at the RIADI laboratory at the Université de la Manouba, where she applies her knowledge of artificial intelligence and KPIs to enhance e-learning processes. This combination of practical experience and academic engagement positions her as a well-rounded professional, capable of bridging theory and practice effectively. Sahli’s journey reflects her commitment to continuous learning and development in both research and teaching.

Research Skills

Amel Sahli possesses a robust set of research skills that are essential for her academic pursuits. Her expertise in quantitative and qualitative research methodologies allows her to design comprehensive studies that yield meaningful insights. Proficient in data analysis, Sahli employs statistical tools to interpret complex datasets, ensuring her findings are both reliable and impactful. Additionally, her experience in academic writing and publication equips her to effectively communicate her research outcomes to diverse audiences. Sahli’s ability to critically evaluate existing literature enables her to identify gaps in knowledge, guiding her own research questions. Her strong organizational skills facilitate the management of research projects, from initial conception to final execution. Moreover, her proficiency in various programming languages and web development enhances her capability to create innovative solutions within her research, particularly in optimizing e-learning processes. Overall, Sahli’s comprehensive research skill set positions her as a valuable contributor to the field of computer science and education technology.

Award and Recognition

Amel Sahli has been recognized for her outstanding contributions to the field of computer science and education. Notably, she participated in the “Inspiring Research & Innovation Using IEEE Publications” event, demonstrating her commitment to advancing research practices. Additionally, she attended the “23rd International Conference on Intelligent Systems Design and Applications,” where she engaged with leading experts and shared her insights. Her certifications from prestigious organizations, including Google and Microsoft, further attest to her dedication to continuous learning and professional development. Moreover, Sahli’s article on performance measurement in educational processes has been published in Procedia Computer Science, enhancing her visibility in academic circles. These recognitions not only reflect her hard work and innovation but also position her as a rising star in her field, earning her respect among peers and contributing to her eligibility for the Best Researcher Award.

Conclusion

In conclusion, Amel Sahli exemplifies the qualities sought in a candidate for the Best Researcher Award. Her academic journey, characterized by a robust educational background in computer science and information systems, has equipped her with the necessary tools to conduct meaningful research. Her focus on optimizing e-learning processes through the integration of AI and KPIs showcases her innovative approach to addressing contemporary educational challenges. Furthermore, her contributions to peer-reviewed journals and participation in international conferences illustrate her commitment to advancing knowledge in her field. Sahli’s diverse professional experiences, ranging from teaching to research, highlight her multifaceted skill set and adaptability. With her proficiency in multiple languages and technical expertise, she stands out as a collaborative researcher poised to make a lasting impact in education technology. Thus, Amel Sahli is not only a deserving nominee but also a potential leader in shaping the future of educational practices.

Publication Top Note

  • Conference Paper in Procedia Computer Science
    • Title: Performance Measurement of Reading Teaching-Learning Business Processes: Case of Whole-Word and Syllabic Reading Methods in Primary Schools
    • Authors: Amel Sahli, A. Mejri, A. Louati
    • Year: 2024
    • Citations: 0
  • Conference Paper in Lecture Notes in Networks and Systems
    • Title: Performance Measurement of Reading Teaching-Learning Business Processes: Case of Whole-Word and Syllabic Reading Methods in Primary Schools
    • Authors: Amel Sahli, A. Mejri, A. Louati
    • Year: 2024
    • Citations: 0