Fawwad Hassan Jaskani | Computer Science | Best Researcher Award

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Dr. Fawwad Hassan Jaskani | Computer Science | Best Researcher Award

Doctor at The Islamia University of Bahawalpur | Pakistan

Dr. Fawwad Hassan Jaskani is a distinguished researcher and leader specializing in machine learning, robotics, and advanced data-driven applications. As the Chief Executive Officer of FHJ Complex Infinite Solutions, he has guided teams in delivering high-quality research assistance and innovative technical solutions tailored to the needs of scholars and professionals. His expertise spans Microsoft Azure, Power BI, and Robotic Process Automation, which he effectively integrates into projects to enhance efficiency and impact. With an academic foundation rooted in The Islamia University of Bahawalpur and Universiti Tun Hussein Onn Malaysia, Dr. Jaskani has produced influential publications addressing diverse fields, including artificial neural networks, digital protection systems, Internet of Things, and medical data analysis. His contributions as a peer reviewer for international journals further underscore his dedication to advancing knowledge and ensuring quality in research. Combining academic rigor with practical application, he continues to shape the research landscape with innovation and leadership.

Professional Profile 

Google Scholar | Scopus Profile

Education

Dr. Fawwad Hassan Jaskani holds a strong academic background in machine learning and robotics, having pursued advanced studies at The Islamia University of Bahawalpur, where he completed his Master of Engineering with a focus on machine learning and robotics. He further enhanced his expertise by earning a Doctor of Philosophy in Machine Learning from Universiti Tun Hussein Onn Malaysia. His educational journey has provided him with a deep understanding of artificial intelligence, data analysis, and automation technologies, which he has effectively applied in his professional and research career. Through his academic training, he has developed a robust foundation in both theoretical concepts and practical implementations, enabling him to bridge the gap between innovation and application. His educational achievements have not only fueled his research pursuits but also established his credibility as a thought leader in the domains of artificial intelligence, data-driven technologies, and computational research methodologies.

Experience

Dr. Fawwad Hassan Jaskani brings extensive professional experience spanning leadership, research, and technical consultancy. As Chief Executive Officer of FHJ Complex Infinite Solutions, he has successfully led teams in providing tailored research assistance, technical simulations, and high-quality solutions for academic and professional clients. His experience as a peer reviewer for international journals with TechScience Press reflects his role in maintaining scholarly standards and contributing to the global research community. Over the years, he has also worked as a professional freelancer, collaborating with diverse clients on projects requiring specialized expertise in artificial intelligence, automation, and data science. These experiences have honed his project management, communication, and problem-solving skills, positioning him as both a leader and an innovator. His diverse career reflects a unique ability to merge academic insights with industry requirements, demonstrating his effectiveness in driving impactful outcomes while fostering research excellence and applied technological advancements.

Research Focus

Dr. Fawwad Hassan Jaskani’s research primarily focuses on machine learning, artificial intelligence, robotics, and their applications across interdisciplinary fields. His publications showcase a wide array of studies, including neural networks, digital differential protection schemes, operating systems for the Internet of Things, and predictive modeling for healthcare, particularly early detection of diseases. He also explores visualization techniques for complex biological datasets and comparative analyses of classification models, reflecting his commitment to advancing both theoretical and applied dimensions of research. His work emphasizes the integration of AI-driven solutions into real-world challenges, bridging the gap between academia and practical implementation. By combining algorithmic efficiency with innovation, Dr. Jaskani’s research contributes to fields as diverse as bioinformatics, automation, energy systems, and digital security. His ability to explore multiple disciplines through the lens of machine learning makes his research not only impactful but also forward-looking, contributing to global technological and scientific progress.

Award and Honor

Dr. Fawwad Hassan Jaskani has earned recognition for his contributions as a researcher, innovator, and academic leader. His role as a peer reviewer for international journals highlights the trust placed in his expertise and his influence within the scholarly community. His academic achievements, including successful completion of advanced degrees in machine learning and robotics, further underscore his dedication and excellence. In his professional career, he has been acknowledged for leading FHJ Complex Infinite Solutions, where his efforts in transforming research assistance into high-impact, customized solutions have been highly valued. Additionally, his publications across diverse areas of artificial intelligence and automation demonstrate his contribution to knowledge creation, which is itself a mark of distinction. While his recognitions are rooted in his academic and professional excellence, his ongoing commitment to innovation, mentorship, and applied research continues to elevate his profile as an accomplished researcher deserving of honors and awards.

Publication Top Notes

  • Title: Time-Series Prediction of Cryptocurrency Market using Machine Learning Techniques
    Year: 2021
    Citations: 73

  • Title: Comparison of classification models for early prediction of breast cancer
    Year: 2019
    Citations: 73

  • Title: ICC T20 Cricket World Cup 2020 winner prediction using machine learning techniques
    Year: 2020
    Citations: 38

  • Title: Prediction of Cardiovascular Disease on Self‐Augmented Datasets of Heart Patients Using Multiple Machine Learning Models
    Year: 2022
    Citations: 37

  • Title: IOTA‐Based Mobile Crowd Sensing: Detection of Fake Sensing Using Logit‐Boosted Machine Learning Algorithms
    Year: 2022
    Citations: 21

  • Title: An Investigation on Several Operating Systems for Internet of Things
    Year: 2019
    Citations: 17

  • Title: Lungs nodule cancer detection using statistical techniques
    Year: 2020
    Citations: 16

  • Title: Convolutional Autoencoder‐Based Deep Learning Approach for Aerosol Emission Detection Using LiDAR Dataset
    Year: 2022
    Citations: 15

  • Title: Urbanization Detection Using LiDAR‐Based Remote Sensing Images of Azad Kashmir Using Novel 3D CNNs
    Year: 2022
    Citations: 15

  • Title: Short-Term Prediction Model for Multi-Currency Exchange Using Artificial Neural Network
    Year: 2020
    Citations: 12

  • Title: Detection of Uterine Fibroids in Medical Images Using Deep Neural Networks
    Year: 2022
    Citations: 11

  • Title: Hybrid machine learning techniques to detect real time human activity using UCI dataset
    Year: 2021
    Citations: 9

  • Title: Detection of anomaly in videos using convolutional autoencoder and generative adversarial network model
    Year: 2020
    Citations: 9

  • Title: Comparative Analysis of Face Detection Using Linear Binary Techniques and Neural Network Approaches
    Year: 2018
    Citations: 7

  • Title: Karachi Stock Exchange Price Prediction using Machine Learning Regression Techniques
    Year: 2021
    Citations: 6

Conclusion

Dr. Fawwad Hassan Jaskani has established himself as a prolific researcher with impactful contributions across diverse domains, including machine learning, healthcare analytics, IoT systems, financial forecasting, and computer vision. His publications reflect a consistent effort to bridge academic theory with real-world applications, often addressing socially and technologically significant challenges such as disease prediction, urbanization monitoring, and market forecasting. The steady citation record of his work demonstrates both relevance and influence within the global research community. His ability to collaborate across disciplines, produce high-quality research outputs, and contribute to advancing modern computational techniques highlights his position as a strong candidate for recognition. With continued focus on interdisciplinary innovation and global engagement, he is well-poised to make even greater contributions to the fields of artificial intelligence and applied research.

Eduardo Coronel | Computer Science | Best Researcher Award

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Dr. Eduardo Coronel | Computer Science | Best Researcher Award

M.Sc. Eng. at Facultad Politécnica,  Paraguay

Eduardo Damián Coronel Torales, born on March 5, 1991, in Asunción, Paraguay, is a distinguished researcher and engineer specializing in electrical engineering, automation, and artificial intelligence applications. He has actively contributed to academia, industry, and international conferences, earning recognition for his innovative work in energy distribution and automation systems. His professional journey has taken him from academic research to practical implementations in one of the world’s largest hydroelectric plants, Itaipu Binacional. With a strong foundation in engineering and computational intelligence, Coronel Torales has made significant contributions to optimizing power distribution and developing automation solutions. His research extends beyond Paraguay, reaching international platforms and collaborations. He continues to push the boundaries of technology by integrating advanced optimization techniques, machine learning, and smart grid systems, positioning himself as a leader in his field.

Professional Profile

Education

Coronel Torales holds a Master’s degree in Electrical Engineering with an emphasis on Energy Systems Planning from the Facultad Politécnica of the Universidad Nacional del Este, obtained in 2021. His postgraduate research focused on optimizing power distribution using computational intelligence. He completed his undergraduate degree in Electronics Engineering with a specialization in Mechatronics at the Universidad Nacional de Asunción in 2017. During his academic career, he demonstrated exceptional analytical and problem-solving skills, engaging in multiple research projects related to automation, robotics, and energy systems. His academic journey reflects a strong commitment to technological advancements and interdisciplinary research. The combination of these degrees has provided him with a robust foundation in both theoretical and practical aspects of energy optimization, artificial intelligence, and industrial automation, equipping him with the expertise to tackle complex engineering challenges at both research and industrial levels.

Professional Experience

With extensive experience in academia and industry, Coronel Torales has worked as a research engineer at Itaipu Binacional, contributing to the modernization of automation systems. His expertise in failure analysis using PI tools and machine learning models has been instrumental in enhancing the reliability of large-scale energy infrastructure. He has also served as a postgraduate lecturer at the Universidad Nacional del Este, teaching heuristic optimization methods. Additionally, he has worked as an instructor at the Paraguay-Korea Advanced Technology Center (SNPP-KOICA), where he trained professionals in digital electronics and industrial automation. His work experience blends research, teaching, and industry applications, allowing him to bridge the gap between theory and practice. Through his diverse roles, he has been actively involved in developing intelligent systems, optimizing automation processes, and mentoring students and professionals in engineering disciplines.

Research Interests

Coronel Torales’ research interests lie at the intersection of power systems optimization, automation, and artificial intelligence. He has extensively explored the use of metaheuristic and multi-objective optimization techniques for enhancing the efficiency of electrical power distribution systems. His research also focuses on computer vision, machine learning, and control systems, particularly for applications in autonomous vehicles, industrial automation, and smart grids. Additionally, he is interested in the integration of AI-driven fault detection and predictive maintenance in large-scale energy infrastructures. His work contributes to improving the reliability and efficiency of energy management systems through data-driven solutions. By combining engineering principles with computational intelligence, he aims to develop sustainable and intelligent solutions for modern energy challenges. His forward-thinking research aligns with global trends in smart energy systems, IoT-enabled automation, and digital transformation in power distribution networks.

Awards and Honors

Coronel Torales has received international recognition for his research contributions, including multiple conference presentations at IEEE and other prestigious platforms. His work on remote-controlled switch optimization in power distribution systems has been published in IEEE Latin America Transactions and presented at international computing and engineering conferences such as CLEI, ICDIM, and INTERCON. He has been acknowledged for his contributions to automation failure analysis at Itaipu Binacional, influencing modernization decisions in one of the world’s largest hydroelectric plants. Additionally, his early research in autonomous vehicle navigation and fuzzy logic control earned him invitations to research symposiums in Argentina, Peru, South Korea, and the United States. His ability to translate research into practical applications has cemented his reputation as an emerging leader in electrical engineering and computational intelligence. His continued contributions are setting a benchmark for innovation in energy systems and industrial automation.

Conclusion

Eduardo Damián Coronel Torales has a strong research background with impactful contributions in energy systems optimization, automation, and AI applications. His publications, international recognition, and industry collaboration make him a strong candidate for the Best Researcher Award. However, to further strengthen his candidacy, he should aim for higher-impact journal publications, more independent research leadership, and broader contributions in emerging fields.

Publications Top Noted

  • Coronel, E., Barán, B., & Gardel, P. (2025). A Survey on Data Mining for Data-Driven Industrial Assets Maintenance Technologies. Journal article. DOI: 10.3390/technologies13020067.
  • Coronel Torales, E. D. (2024). Leveraging Machine Learning for Multi-Step Failure Forecasting in RTU Analog Modules and Estimating Key Performance Indicators to Support Management Decision-Making. CIGRE Paris Session 2024, Conference poster.
  • Coronel, E., Barán, B., & Gardel, P. (2022). Optimal Placement of Remote Controlled Switches in Electric Power Distribution Systems with a Meta-heuristic Approach. IEEE Latin America Transactions. DOI: 10.1109/TLA.2022.9675464.
  • Coronel Torales, E. D. (2021). Optimal Placement of Remote Controlled Switches in Electric Power Distribution Systems with a Multi-Objective Approach. 2021 XLVII Latin American Computing Conference (CLEI). DOI: 10.1109/clei53233.2021.9639970.
  • Coronel Torales, E. D. (2020). Optimización en la Ubicación de Seccionadores Tele-comandados en Sistemas de Distribución de Energía Eléctrica con enfoque meta-heurístico y soporte de decisión multi-criterio. Edited book. DOI: 10.13140/RG.2.2.32305.92002.
  • Coronel Torales, E. D. (2017). Estimación de disponibilidad de energía eléctrica de la Central Hidroeléctrica Itaipú y del crecimiento de la energía cedida al Paraguay hasta el 2023. Facultad Politécnica – Universidad Nacional del Este. DOI: 10.13140/RG.2.2.11838.79685.
  • Coronel Torales, E. D. (2015). Reliable navigation-path extraction system for an autonomous mobile vehicle. 2015 Tenth International Conference on Digital Information Management (ICDIM). DOI: 10.1109/icdim.2015.7381882.
  • Coronel Torales, E. D. (2015). PROTOTIPO DE VEHÍCULO AUTÓNOMO CON RNA Y VISIÓN POR COMPUTADORA. Simposio Argentino de Sistema Embebidos (SASE), Conference poster.
  • Coronel Torales, E. D. (2015). SISTEMA DE ALGORITMOS DE VISIÓN POR COMPUTADOR, APRENDIZAJE DE MÁQUINA, LOCALIZACIÓN Y NAVEGACIÓN DESARROLLADOS EN MATLAB, CON IMPLEMENTACIÓN EN VEHÍCULOS TERRESTRES PARA AUTO-CONDUCCIÓN. XXII Congreso Internacional de Ingeniería Eléctrica, Electrónica, Computación y Afines INTERCON 2015, Conference paper.
  • Coronel Torales, E. D. (2014). STABILITY COMMAND OF A TILT-ROTOR VEHICLE WITH A FUZZY LOGIC CONTROLLER. 3rd Conference of Computational Interdisciplinary Sciences – CCIS 2014, Conference poster. ISBN: 978-85-68888-00-1.
  • Coronel Torales, E. D. (2014). BALANCEADOR AERODINÁMICO CON LÓGICA DIFUSA. XXI Congreso Internacional de Ingeniería Electrónica, Eléctrica y Computación INTERCON 2014, Conference poster.