Nadeem Khanday | Computer Science | Best Researcher Award

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Assist. Prof. Dr. Nadeem Khanday | Computer Science | Best Researcher Award

Assistant Professor from National Institute of Technology Srinagar, India

Dr. Nadeem Yousuf Khanday is an accomplished academic and researcher in Computer Science & Engineering, currently serving as an Assistant Professor at the School of Computer Science, UPES, Dehradun, India. With a strong academic foundation and a passion for advanced computing technologies, he has contributed extensively to the fields of artificial intelligence, machine learning, and deep visual learning. His research outputs include high-impact journal publications, international conference presentations, patents, and book chapters with globally recognized publishers. Dr. Khanday is deeply involved in exploring innovative AI techniques that address real-world challenges, including healthcare diagnostics, crop disease detection, cloud computing, and smart environments. He is also a certified GATE, UGC-NET, and JK-SET qualifier, emphasizing his academic excellence. Throughout his career, he has taught a variety of technical subjects and mentored students in core areas of computer science. He brings a balanced combination of research, teaching, and applied innovation to the academic domain. With a growing body of interdisciplinary work, Dr. Khanday continues to build his reputation as a future-oriented researcher contributing to both academia and industry. His deep commitment to scholarly excellence and emerging technologies positions him as a deserving candidate for recognition in prestigious research awards.

Professional Profile

Education

Dr. Nadeem Yousuf Khanday has pursued a rigorous academic trajectory in Computer Science & Engineering. He earned his Doctor of Philosophy (Ph.D.) from the prestigious National Institute of Technology (NIT), Srinagar, focusing on advanced computing technologies and artificial intelligence. Prior to his doctorate, he completed his Master of Technology (M.Tech) from Vivekananda Global University, Jaipur, where he achieved an outstanding CGPA of 9.69 in Computer Science & Engineering, demonstrating his academic strength and subject mastery. His undergraduate studies were conducted at Visvesvaraya Technological University (VTU), Belgaum, where he obtained a Bachelor of Engineering (B.E.) degree in Computer Science & Engineering with a commendable academic record. Dr. Khanday has also qualified national-level competitive exams including the Graduate Aptitude Test in Engineering (GATE) and University Grants Commission National Eligibility Test (UGC-NET), as well as JK-SET, qualifying him for Assistant Professorship roles in Indian universities. These qualifications reflect his high-level proficiency in the domain and commitment to continued academic growth. His academic background provides a strong foundation for his research endeavors, enabling him to tackle complex computing problems and advance the frontier of knowledge in artificial intelligence, machine learning, and computer vision.

Professional Experience

Dr. Nadeem Yousuf Khanday possesses diverse and dynamic professional experience across some of India’s reputed institutions. He is currently employed as a Regular Assistant Professor at the School of Computer Science (SoCS), UPES Dehradun since June 2023. Before this, he served as a Lecturer at the University of Kashmir, J&K, where he taught undergraduate and postgraduate computer science courses from March to June 2023. His earlier appointments include his tenure as an Assistant Professor (Contract) at NIT Srinagar from April 2017 to July 2018, and later as a Teaching Assistant (Research Scholar) from July 2018 to February 2023 at the same institute. These roles have helped him accumulate extensive experience in teaching core computer science courses such as Artificial Intelligence, Operating Systems, Data Structures, and Computer Architecture. Throughout his career, Dr. Khanday has skillfully blended teaching with hands-on research, working on projects related to visual learning, deep learning, and intelligent systems. His progressive journey from contract roles to full-time professorship demonstrates his steady academic development and increasing responsibilities. With significant academic leadership and research roles, he is well-positioned to lead innovative educational and research initiatives in AI and computing.

Research Interests

Dr. Nadeem Yousuf Khanday’s research interests lie at the intersection of Artificial Intelligence, Machine Learning, and Computer Vision, with a particular focus on deep visual learning and few-shot learning models. He explores innovative solutions to computational challenges involving limited data samples, aiming to improve learning accuracy and cross-domain generalization. His research extends into practical domains such as healthcare diagnostics, agricultural disease prevention, cloud computing optimization, and smart IoT-based systems. Dr. Khanday has investigated topics including convolutional neural networks for COVID-19 prognosis, metric learning models for classification, and AI-driven smart farming using 5G networks. His recent work has integrated Large Language Models (LLMs) and Generative AI to enhance decision-making systems in medical and industrial contexts. His interdisciplinary approach combines theoretical models with real-world applications, contributing to sustainable development through intelligent computing. Dr. Khanday’s research aims not only to push academic boundaries but also to provide practical, scalable solutions for modern societal challenges. His continuous engagement with cutting-edge technologies and publication in top-tier journals solidify his status as a thought leader in visual intelligence and machine learning systems.

Research Skills

Dr. Nadeem Yousuf Khanday possesses a strong portfolio of research skills that span multiple domains in computing. He is proficient in developing machine learning algorithms, deep learning architectures, and advanced image processing models for varied applications. His expertise includes designing few-shot learning frameworks, enhancing cross-domain classification performance, and deploying convolutional neural networks for medical image analysis and smart diagnostics. He has hands-on experience with AI-based anomaly detection, visual segmentation systems, and cloud environment optimization using hybrid fuzzy and swarm intelligence methods. Dr. Khanday is also skilled in patent writing, having developed innovative systems for crop disease detection and motorcycle safety. His publication record reflects his ability to effectively communicate complex methodologies, backed by data-driven validation and practical implementation. Additionally, his collaboration in multi-author projects and book chapters indicates strong academic teamwork and interdisciplinary engagement. His teaching and research experiences across different institutions have also honed his ability to mentor students and lead academic discussions. Equipped with technical, analytical, and conceptual research skills, Dr. Khanday continues to contribute impactful and scalable innovations across emerging fields like generative AI, IoT systems, and smart computing.

Awards and Honors

Dr. Nadeem Yousuf Khanday has received various forms of recognition for his scholarly achievements and research excellence. Notably, he has qualified multiple national-level eligibility exams, such as GATE, UGC-NET, and JK-SET, highlighting his academic distinction and competency to teach at the university level. In 2023, he was awarded recognition for his impactful contributions to AI-driven visual understanding and applications, as reflected in his high-impact publications and patents. His patent work, including an apparatus for auto-detection of crop diseases and motorcycle safety systems, has been acknowledged for its potential technological and societal value. Dr. Khanday’s research has also gained visibility through SCOPUS- and SCI-indexed publications with top journals like Computer Science Review and Neural Computing and Applications. His invited book chapters published by Taylor and Francis, Springer Nature, and Cambridge University Press underline his reputation among international academic publishers. Furthermore, he has presented at international conferences in Europe and Asia, receiving acclaim for his work on machine vision, fuzzy systems, and cloud intelligence. These accolades reflect both his individual excellence and collaborative impact within the research community.

Conclusion

Dr. Nadeem Yousuf Khanday exemplifies the profile of a high-caliber academician and innovative researcher with notable achievements in the fields of artificial intelligence, deep learning, and computer vision. Through a strong foundation in computer science education and a wealth of research experience, he has consistently contributed to advancing both theory and practice. His multidisciplinary research in healthcare, smart agriculture, and intelligent systems, along with a growing list of high-impact publications, patents, and book contributions, sets him apart as a forward-thinking scholar. His teaching experience across reputed Indian institutions and his ability to combine pedagogy with practical applications further enhance his value to academia. Dr. Khanday’s commitment to solving real-world problems using machine learning and AI tools not only enhances academic discourse but also promotes sustainable innovation. His emerging collaborations, international conference participation, and national recognitions affirm his credibility and future potential. In light of his qualifications, scholarly output, and research relevance, he stands as a highly deserving candidate for the Best Researcher Award, with the capacity to influence the global research community and contribute significantly to technological advancement

  1. Covariance-based Metric Model for Cross-domain Few-shot Classification and Learning-to-generalization
    📘 Journal: Applied Intelligence, 2023
    👥 Authors: Khanday, N.Y.; Sofi, S.A.

  2. Learned Gaussian ProtoNet for Improved Cross-domain Few-shot Classification and Generalization
    📘 Journal: Neural Computing and Applications, 2023
    👥 Authors: Khanday, N.Y.; Sofi, S.A.

  3. Deep Insight: Convolutional Neural Network and Its Applications for COVID-19 Prognosis
    📘 Journal: Biomedical Signal Processing and Control, 2021
    👥 Authors: Khanday, N.Y.; Sofi, S.A.

  4. Taxonomy, State-of-the-art, Challenges and Applications of Visual Understanding: A Review
    📘 Journal: Computer Science Review, 2021
    👥 Authors: Khanday, N.Y.; Sofi, S.A.

Eric Nizeyimana | Computer Science | Best Researcher Award

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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

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