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

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