Shalli Rani | Computer Science | Best Researcher Award

Published on by World Top Scientists

Prof. Shalli Rani | Computer Science | Best Researcher Award

Professor | Chitkara University | India

Prof. Shalli Rani is a distinguished researcher in the fields of Internet of Things, Wireless Sensor Networks, Cloud Computing, and Machine Learning, with a prolific record of high-impact publications, books, patents, and editorial contributions. She has demonstrated exceptional leadership in guiding numerous PhD and ME students, fostering innovation and research excellence. Her work effectively bridges academia and industry through applied projects, including smart healthcare solutions, Industry 5.0 initiatives, and explainable AI systems. Recognized globally through invited talks, conference engagements, and editorial responsibilities in top journals, she has established herself as a thought leader in her domain. Her research contributions reflect both depth and breadth, combining theoretical rigor with practical relevance. Prof. Rani’s measurable research impact on Scopus is remarkable, with 4,400 citations, 311 documents, and an h-index of 34, highlighting her sustained influence and scholarly excellence in the international research community.

Profiles: Scopus | Google Scholar | ORCID

Featured Publications

1. S. Rani, R. Talwar, J. Malhotra, S. Ahmed, M. Sarkar, and H. Song, “A novel scheme for an energy efficient Internet of Things based on wireless sensor networks,” Sensors, vol. 15, no. 11, pp. 28603–28626, 2015.

2. S. Rani, S. H. Ahmed, and R. Rastogi, “Dynamic clustering approach based on wireless sensor networks genetic algorithm for IoT applications,” Wireless Networks, vol. 26, no. 4, pp. 2307–2316, 2020.

3. S. Bharany, S. Badotra, S. Sharma, S. Rani, M. Alazab, and R. H. Jhaveri, “Energy efficient fault tolerance techniques in green cloud computing: A systematic survey and taxonomy,” Sustainable Energy Technologies and Assessments, vol. 53, p. 102613, 2022.

4. G. S. Brar, S. Rani, V. Chopra, R. Malhotra, H. Song, and S. H. Ahmed, “Energy efficient direction-based PDORP routing protocol for WSN,” IEEE Access, vol. 4, pp. 3182–3194, 2016.

5. S. Rani, D. Koundal, M. F. Ijaz, M. Elhoseny, and M. I. Alghamdi, “An optimized framework for WSN routing in the context of Industry 4.0,” Sensors, vol. 21, no. 19, p. 6474, 2021.

Fawwad Hassan Jaskani | Computer Science | Best Researcher Award

Published on by World Top Scientists

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.

Baha Ihnaini | Computer Science | Outstanding Contribution Award

Published on by World Top Scientists

Dr. Baha Ihnaini | Computer Science | Outstanding Contribution Award

Assistant Professor at Wenzhou-Kean University, China

Dr. Baha Ihnaini is an accomplished academic and researcher in computer science with expertise spanning artificial intelligence, data science, machine learning, and natural language processing. His scholarly work has addressed significant challenges in sentiment analysis, medical diagnostics, disease prediction, and misinformation detection, with publications in respected journals and international conferences. Notably, he has contributed to developing Arabic lexicons for sentiment analysis, enhancing AI-driven healthcare solutions, and advancing transfer learning models for predictive analytics. Alongside his research, Dr. Ihnaini has demonstrated a strong commitment to teaching, covering a wide range of computer science courses and mentoring students in senior projects. His service on academic committees and involvement in curriculum development highlight his leadership and dedication to institutional growth. With a record of impactful research, effective teaching, and professional service, Dr. Ihnaini stands out as a valuable contributor to his field and a strong candidate for academic recognition.

Professional Profile 

Google Scholar | Scopus Profile | ORCID Profile 

Education

Dr. Baha Ihnaini holds a Ph.D. in Computer Science with a specialization in Data Science from Universiti Utara Malaysia, where his research focused on developing an expandable Arabic lexicon and sentiment analysis rules for social media text. He also earned a Master of Science in Management Information Systems from The Arab Academy for Banking and Financial Sciences, equipping him with a strong foundation in both technical and managerial aspects of information technology. His academic journey began with a Bachelor’s degree in Computer Engineering from Philadelphia University in Jordan, providing him with comprehensive knowledge of hardware, software, and systems engineering. This multidisciplinary educational background has enabled Dr. Ihnaini to integrate advanced computational methods with practical problem-solving approaches, particularly in the areas of machine learning, natural language processing, and data-driven applications. His academic credentials reflect a balance between rigorous technical expertise and applied research in emerging fields of computing.

Experience

Dr. Ihnaini has accumulated rich professional experience as an educator, researcher, and academic leader across several institutions. He currently serves as an Assistant Professor of Computer Science at Wenzhou-Kean University, where he teaches a broad range of courses, from foundational programming and systems to advanced research in computer science. His roles extend beyond teaching, as he actively contributes to academic committees focused on curriculum development, faculty hiring, and student support. Prior to this, he served as an Adjunct Professor at Al Ain University and BTEC Abu Dhabi, as well as a Lecturer at Al Khawarizmi International College, where he guided student projects and curriculum design. Earlier in his career, he worked as a Research Officer at Universiti Utara Malaysia’s InterNetWorks Research Lab, where he played a pivotal role in advancing sentiment analysis for Arabic text. Collectively, his experience reflects a strong commitment to research, teaching excellence, and academic service.

Research Focus

Dr. Ihnaini’s research focuses on artificial intelligence, machine learning, data science, and natural language processing, with an emphasis on solving real-world problems through intelligent systems. A significant part of his work has concentrated on Arabic sentiment analysis, where he developed innovative linguistic resources and computational models to improve text classification accuracy. His recent research extends to medical AI, including predictive modeling for diseases such as diabetic retinopathy, Alzheimer’s disease, and vitamin D deficiency, showcasing his ability to merge computer science with healthcare applications. He has also contributed to the advancement of fake news detection, stock market prediction using sentiment data, and multimodal semantic similarity. His interdisciplinary approach highlights the versatility of data-driven methods and their societal impact, from enhancing healthcare systems to improving digital communication analysis. Through these diverse but interconnected research directions, Dr. Ihnaini continues to contribute to both theoretical advancements and practical innovations in computer science.

Award and Honor

Dr. Ihnaini has earned recognition for his scholarly contributions through research publications in reputable international journals and conferences, with several works indexed in Scopus and well-regarded platforms. His achievements include developing novel computational methods for sentiment analysis, interdisciplinary research in healthcare prediction models, and the application of advanced machine learning techniques to real-world problems. His role as a key contributor to collaborative projects with international researchers further reflects the recognition of his expertise and the impact of his work. Beyond research, his commitment to education and service has been acknowledged within the institutions he has served, particularly through his involvement in curriculum innovation and student mentorship. While formal distinctions are highlighted through his publication record and conference participation, his career trajectory itself demonstrates consistent recognition as a capable scholar and educator. His growing research visibility and international collaborations continue to strengthen his candidacy for prestigious awards and honors.

Publication Top Notes

  • Title: A smart healthcare recommendation system for multidisciplinary diabetes patients with data fusion based on deep ensemble learning
    Authors: B Ihnaini, MA Khan, TA Khan, S Abbas, MS Daoud, M Ahmad, MA Khan
    Year: 2021
    Citations: 158

  • Title: Machine Learning Empowered Software Defect Prediction System
    Authors: MS Daoud, S Aftab, M Ahmad, MA Khan, A Iqbal, S Abbas, M Iqbal, B Ihnaini
    Year: 2022
    Citations: 37

  • Title: Stock trend prediction using sentiment analysis
    Authors: Q Xiao, B Ihnaini
    Year: 2023
    Citations: 35

  • Title: Joint channel and multi-user detection empowered with machine learning
    Authors: MS Daoud, A Fatima, WA Khan, MA Khan, S Abbas, B Ihnaini, M Ahmad
    Year: 2021
    Citations: 31

  • Title: Real-time shill bidding fraud detection empowered with fussed machine learning
    Authors: WUH Abidi, MS Daoud, B Ihnaini, MA Khan, T Alyas, A Fatima, M Ahmad
    Year: 2021
    Citations: 28

  • Title: Rider weed deep residual network-based incremental model for text classification using multidimensional features and MapReduce
    Authors: HB Abdalla, AM Ahmed, SRM Zeebaree, A Alkhayyat, B Ihnaini
    Year: 2022
    Citations: 18

  • Title: Presenting and evaluating scaled extreme programming process model
    Authors: M Ibrahim, S Aftab, M Ahmad, A Iqbal, BS Khan, M Iqbal, BNS Ihnaini
    Year: 2020
    Citations: 13

  • Title: Exploring the agile family: A survey
    Authors: M Ibrahim, S Aftab, B Bakhtawar, M Ahmad, A Iqbal, N Aziz, MS Javeid, B Ihnaini
    Year: 2020
    Citations: 12

  • Title: Predicting vitamin D deficiency using optimized random forest classifier
    Authors: A Alloubani, B Abuhaija, M Almatari, G Jaradat, B Ihnaini
    Year: 2024
    Citations: 9

  • Title: Lexicon-based sentiment analysis of Arabic tweets: A survey
    Authors: B Ihnaini, M Mahmuddin
    Year: 2018
    Citations: 9

  • Title: A Severity Grading Framework for Diabetic Retinopathy Detection using Transfer Learning
    Authors: S Akhtar, S Aftab, S Kousar, A Rehman, M Ahmad, AQ Saeed, B Ihnaini
    Year: 2024
    Citations: 8

  • Title: Improving the Quality of e-Commerce Service by Implementing Combination Models with Step-by-Step, Bottom-Up Approach
    Authors: BA Hemn, G Chengwei, B Ihnaini
    Year: 2021
    Citations: 6

  • Title: Sentiment analysis of Song Dynasty classical poetry using fine-tuned large language models: a study with LLMs
    Authors: B Ihnaini, W Sun, Y Cai, Z Xu, R Sangi
    Year: 2024
    Citations: 5

  • Title: A transfer learning based framework for diabetic retinopathy detection using data fusion
    Authors: S Akhtar, S Aftab, M Ahmad, B Ihnaini
    Year: 2024
    Citations: 4

  • Title: Semantic similarity on multimodal data: A comprehensive survey with applications
    Authors: B Ihnaini, B Abuhaija, EA Mills, M Mahmuddin
    Year: 2024
    Citations: 3

Conclusion

Dr. Baha Ihnaini’s publication record reflects a strong and steadily growing research trajectory across diverse yet interconnected fields of computer science, including artificial intelligence, data science, natural language processing, and medical informatics. His works demonstrate both theoretical depth and practical applications, ranging from healthcare prediction models and fraud detection to sentiment analysis and software engineering. The high citation impact of certain publications highlights the relevance and influence of his research in the academic community, while his more recent works indicate an expanding focus on interdisciplinary applications such as healthcare and cultural text analysis using advanced AI techniques. Collectively, his contributions showcase a balance of innovation, collaboration, and societal relevance, positioning him as a researcher whose work is not only academically significant but also impactful in addressing real-world challenges. This combination of influence, diversity, and practical value strengthens his candidacy for recognition through awards and honors.

XinYing Chew | Computer Science | Young Scientist Award

Published on by World Top Scientists

Assoc. Prof. Dr. XinYing Chew | Computer Science | Young Scientist Award

Associate Professor at Universiti Sains Malaysia (USM), Malaysia

Associate Professor Ts. Dr. Chew XinYing is a distinguished academic and researcher at Universiti Sains Malaysia (USM), where she serves in the School of Computer Sciences. With extensive expertise in industrial computing and advanced analytics, she has made significant contributions to data-driven research, quality control, and artificial intelligence applications. As a Program Manager for both Computer Science and Offshore Programs at USM, she plays a vital role in shaping academic curricula and fostering industry collaborations. Her work spans interdisciplinary domains, including AI in tourism, environmental sustainability, and predictive analytics, making her a key figure in modern computational research. Dr. Chew has co-authored numerous high-impact journal publications and actively collaborates with international scholars, reflecting her commitment to advancing knowledge globally. With her leadership, research acumen, and dedication to academic excellence, she continues to drive innovation in data analytics and computational intelligence, contributing to both academia and industry applications.

Professional Profile

Education

Dr. Chew XinYing holds a Ph.D. in Computer Science from Universiti Sains Malaysia (USM), where she specialized in industrial computing and advanced statistical methodologies. Prior to her doctoral studies, she earned her Bachelor of Information Technology (Hons.) from Universiti Kebangsaan Malaysia (UKM), laying the foundation for her expertise in data analytics and computational intelligence. Throughout her academic journey, she has demonstrated a deep passion for integrating statistical process control techniques with modern computing approaches, making her a key researcher in quality control and decision-making systems. Her educational background has equipped her with advanced knowledge in artificial intelligence, predictive modeling, and big data analytics. This strong academic foundation has not only fueled her research contributions but also positioned her as a mentor and educator, guiding students in cutting-edge technological advancements. Dr. Chew’s commitment to continuous learning has made her a well-rounded scholar in the field of computational sciences.

Professional Experience

Dr. Chew XinYing is currently an Associate Professor at the School of Computer Sciences, Universiti Sains Malaysia (USM), where she also serves as the Program Manager for both Computer Science and Offshore Programs. Her professional career spans years of academic excellence, with a focus on curriculum development, student mentorship, and research leadership. She has played a pivotal role in shaping USM’s computer science programs, ensuring they align with industry standards and emerging technological trends. Beyond academia, she has engaged in industry collaborations, applying her expertise in industrial computing and analytics to solve real-world challenges. Her research extends into diverse fields such as artificial intelligence in business intelligence, statistical process control, and environmental sustainability. Dr. Chew’s extensive experience in both research and academic leadership has positioned her as a key contributor to Malaysia’s technological and educational advancements, fostering a new generation of computational scientists and researchers.

Research Interests

Dr. Chew XinYing’s research interests lie at the intersection of industrial computing, artificial intelligence, quality control, and advanced analytics. She has conducted extensive studies on statistical process control (SPC) and predictive modeling, focusing on their applications in business intelligence and decision-making. Additionally, her work explores artificial intelligence in tourism, environmental sustainability, and customer behavior analytics, reflecting her ability to integrate computing technologies into diverse domains. She is particularly interested in machine learning algorithms, big data analytics, and AI-driven decision support systems, which have wide-ranging applications in healthcare, financial analytics, and industrial optimization. Her interdisciplinary approach has led to impactful research in areas such as green technology, metaverse ethics, and orthopedic disease detection using AI. By bridging computational science with real-world applications, Dr. Chew continues to push the boundaries of data-driven innovation and contribute to advancements in both academic and industrial sectors.

Awards and Honors

Dr. Chew XinYing has been recognized for her outstanding contributions to research and academia through various awards and honors. Her scholarly achievements are reflected in her numerous high-impact journal publications, earning her recognition as a leading researcher in industrial computing and AI-driven analytics. She has received international accolades for her work in predictive modeling, AI in tourism, and quality control methodologies, demonstrating the real-world impact of her research. As a highly cited researcher, her studies have influenced multiple fields, positioning her among the top contributors in data-driven decision-making research. In addition to academic awards, she has been invited as a keynote speaker and panelist at international conferences, highlighting her expertise in machine learning and computational intelligence. Her dedication to academic excellence, combined with her leadership in research and education, continues to earn her prestigious honors, further establishing her as a respected figure in computer science and analytics.

Conclusion

Associate Professor Ts. Dr. Chew XinYing is a strong candidate for the Research for Young Scientist Award due to her high research productivity, interdisciplinary expertise, and leadership roles. To further solidify her eligibility, she could focus on independent research contributions, securing significant research grants, and emphasizing industry impact through patents and collaborations.

Publications Top Noted

1. Blockchain and Innovation Resistance

  • Title: Navigating the Power of Blockchain Strategy: Analysis of Technology-Organization-Environment (TOE) Framework and Innovation Resistance Theory Using PLS-SEM and ANN Insights
  • Authors: Alnoor, A.M., Abbas, S., Sadaa, A.M., Chew, X., Erkol Bayram, G.E.
  • Year: 2025
  • Journal: Technological Forecasting and Social Change
  • Citations: 0

2. Statistical Process Control and Quality Engineering

  • Title: Optimal Designs of the Group Runs Exponentially Weighted Moving Average X and t Schemes

  • Authors: Khaw, K.W., Chew, X., Teh, S.

  • Year: 2025

  • Journal: Quality and Reliability Engineering International

  • Citations: 0

  • Title: The One-Sided Variable Sampling Interval Exponentially Weighted Moving Average X? Charts Under the Gamma Distribution

  • Authors: Goh, K.L., Chew, X.

  • Year: 2024

  • Journal: Sains Malaysiana

  • Citations: 0

3. Organizational Communication and IT

  • Title: How Information Technology Influences Organizational Communication: The Mediating Role of Organizational Structure
  • Authors: Chew, X., Alharbi, R.K., Khaw, K.W., Alnoor, A.M.
  • Year: 2024
  • Journal: PSU Research Review
  • Citations: 2

4. Consumer Behavior and Decision-Making

  • Title: Unveiling the Optimal Configuration of Impulsive Buying Behavior Using Fuzzy Set Qualitative Comparative Analysis and Multi-Criteria Decision Approach
  • Authors: Alnoor, A.M., Abbas, S., Khaw, K.W., Raad Muhsen, Y.R., Chew, X.
  • Year: 2024
  • Journal: Journal of Retailing and Consumer Services
  • Citations: 6

5. E-Commerce and Customer Trust

  • Title: Symmetric and Asymmetric Modeling to Boost Customers’ Trustworthiness in Livestreaming Commerce
  • Authors: Chew, X., Alnoor, A.M., Khaw, K.W., Al Halbusi, H., Raad Muhsen, Y.R.
  • Year: 2024
  • Journal: Current Psychology
  • Citations: 2

6. Artificial Intelligence and Tourism

  • Title: The Role of Artificial Intelligence in Regenerative Tourism and Green Destinations
  • Authors: Alnoor, A.M., Erkol Bayram, G.E., Chew, X., Shah, S.H.A.
  • Year: 2024
  • Publication Type: Book
  • Citations: 0

 

Eduardo Coronel | Computer Science | Best Researcher Award

Published on by World Top Scientists

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.

 

 

Raoudha Ben Djemaa | Computer science | Best Scholar Award

Published on by World Top Scientists

Prof. Raoudha Ben Djemaa | Computer science | Best Scholar Award

ISITCOM, university of sousse, Tunisia

Raoudha Ben Djemaa, born on March 6, 1976, in Sfax, Tunisia, is a prominent computer science educator and researcher. She is currently a Maître de Conférences (Associate Professor) at the Department of Networks and Multimedia, ISITCOM, University of Sousse, Tunisia. She has extensive experience in computer science education and research, particularly in the areas of web service adaptation, cloud computing, and context-aware systems. Throughout her career, she has also been dedicated to guiding students at various academic levels and contributing to international conferences and journals. 📚💻

Profile

Google Scholar

Education

Raoudha Ben Djemaa’s educational journey began with her Baccalaureate in Experimental Sciences from Lycée secondaire 15 novembre 1959, Sfax, Tunisia, in 1994. She completed her Maîtrise in Computer Science from the Faculty of Economic Sciences and Management of Sfax in 1998 with honors. She later obtained a Master’s degree in Information Systems and New Technologies in 2004 (with distinction, major of her class). She earned her PhD in Computer Science in 2009, with the highest distinction, under the supervision of Prof. Abdelmajid Ben Hamadou. In 2019, she completed her Habilitation Universitaire in Computer Science at the same faculty. 🎓

Experience

Raoudha has held various teaching positions over the years. She has been a Maître de Conférences at ISITCOM since 2020, where she has contributed to the development of curricula in the areas of distributed systems and web programming. Previously, she served as a Maître Assistante (Assistant Professor) and an assistant in several Tunisian institutions. Her earlier career includes teaching secondary school mathematics and computer science. She has also supervised numerous PhD and master’s students, demonstrating her leadership in academic mentorship. 👩‍🏫

Research Interests

Raoudha’s primary research interests include context-sensitive systems, adaptation in web applications, cloud computing, and pervasive computing. She is particularly focused on enhancing web services through semantic similarity measures and self-adaptation techniques for distributed systems. Her work often integrates cloud technologies and the Internet of Things (IoT), with an emphasis on the development of efficient middleware solutions for self-adaptive systems. Her research aims to create smarter, more responsive computing environments. 🌐🔍

Awards

Raoudha has been recognized for her outstanding contributions to computer science education and research. Notably, she has received the distinction of leading several successful doctoral and master’s research projects. Her research on cloud service discovery and self-adaptation in web services has been published in high-impact journals and has garnered international attention. 🏆

Publications Top Notes

Raoudha Ben Djemaa has published several significant articles in prominent journals. Some of her notable publications include:

Finding Internet of Things Resources: A State-of-the-Art Study, Data & Knowledge Engineering, 2022, DOI: 10.1016/j.datak.2022.102025.

Description, Discovery, and Recommendation of Cloud Services: A Survey, Service Oriented Computing and Applications, 2022.

Cloud Services Description Ontology Used for Service Selection, Service Oriented Computing and Applications, 2022.

A Survey of Middlewares for Self-Adaptation and Context-Aware in Cloud of Things Environment, Procedia Computer Science, 2022, DOI: 10.1016/j.procs.2022.09.338.

Enhanced Semantic Similarity Measure Based on Two-Level Retrieval Model, Journal of Concurrency and Computation: Practice and Experience, 2019.

Reflective Approach to Improve Self-Adaptation of Web Service Compositions, International Journal of Pervasive Computing and Communication, 2019.

Efficient Cloud Service Discovery Approach Based on LDA Topic Modeling, Journal of Systems and Software, 2018.

Ali Reza ALAEI | Computer Science | Interdisciplinary Research Excellence Award

Published on by World Top Scientists

Assist Prof Dr. Ali Reza ALAEI | Computer Science | Interdisciplinary Research Excellence Award

Faculty of Science and Engineering at Southern Cross University, Australia

Dr. Ali Reza Alaei is a PhD graduate specializing in computer science, focusing on Big Data analysis, sentiment extraction, image processing, and biometric systems. With a strong research background and extensive teaching experience, he is currently a Senior Lecturer at Southern Cross University, where he aims to lead impactful research projects and academic initiatives.

Profile 

Scopus profile

Education 🎓

Dr. Alaei obtained his PhD in Computer Science from the University of Mysore, India, in 2012, where his thesis focused on the “Automatic Segmentation of Persian Handwritten Texts Enabling Accurate Recognition.” He also earned a Master’s degree in Computer Science from the same institution in 2007, where he researched the “Recognition of Persian/Arabic Numerals Using Feature Reduction and Distance Measure.”

Experience 🧑‍🏫

With over 20 years of academic experience, Dr. Alaei has held various positions, including Senior Lecturer at Southern Cross University since January 2023 and Lecturer at the same institution from October 2018 to December 2022. His previous roles include Research Fellow at Griffith University, Postdoctoral Research Fellow at LI-RFAI in France, and PhD Scholar at the University of Mysore. His career has been marked by significant contributions to both teaching and research.

Research Interests 🔍

Dr. Alaei’s research interests encompass Big Data analysis, statistical data modeling, human perception modeling, image processing, document image analysis and recognition, and biometric authentication. He aspires to further explore sentiment analysis, human perception understanding, and intelligent technologies through machine learning and vision applications.

Awards 🏆

Dr. Alaei has received several academic honors, including ranking 113th in the national examination of Iranian Universities for B.Sc. entrance and achieving the second rank in his M.Sc. program. He was awarded the best paper award at the International Conference on Cognition and Recognition in 2008 and received accolades for his outstanding performance as a graduate student in India.

Publications 📚

Dr. Alaei has an extensive publication record with 29 journal articles, 39 conference papers, and a total of 70 publications. Some notable peer-reviewed articles include:

  1. Document Image Quality Assessment: A Survey – ACM Computing Survey, 2024. Cited by: 2432.
  2. Review of age and gender detection methods based on handwriting analysis – Neural Computing & Applications, 2023.
  3. Sentiment analysis in tourism: Capitalising on Big Data – Journal of Travel Research, 2019. Cited by: 564.
  4. Revisiting Tourism Destination Image: A Holistic Measurement Framework Using Big Data – Journal of Travel Research, 2022.

Conclusion ✅

Dr. Ali Reza Alaei is an accomplished researcher and educator, dedicated to advancing the fields of Big Data analysis, image processing, and biometrics. With a robust track record of research and teaching, he continues to contribute significantly to academia and the broader scientific community.