Tharindu Madhushanka | Engineering | Best Researcher Award

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Mr. Tharindu Madhushanka | Engineering | Best Researcher Award

Engineer at Browns Engineering and Construction, Sri Lanka

Mr. Tharindu Indunil Madhushanka is a promising researcher and civil engineering professional from the University of Moratuwa, Sri Lanka. He holds a Master of Science in Civil Engineering, with a focus on using artificial intelligence for flood forecasting, specifically in the Polonnaruwa region. His research integrates machine learning techniques such as LSTM, ANN, and Transformer models to predict water levels using meteorological and hydrological data. Tharindu has also contributed to sustainable construction through his undergraduate research on the thermal performance and embodied energy of precast panel buildings. His academic achievements include a GPA of 3.54 in Civil Engineering and notable publications, including a paper in the Journal of Hydrologic Engineering. He has gained hands-on experience in both teaching and industry, having worked as an instructor and research assistant at the University of Moratuwa and a trainee civil engineer. Tharindu is dedicated to advancing AI applications in civil engineering for disaster management and sustainability.

Professional Profile

Education

Mr. Tharindu Indunil Madhushanka has a strong educational background in civil engineering, having completed his Bachelor of Science in Civil Engineering (Honors) from the University of Moratuwa, Sri Lanka, where he graduated with a second-class upper division and a GPA of 3.54 out of 4.2. His undergraduate studies provided him with a solid foundation in engineering principles and practices. He further pursued a Master of Science at the same university, beginning in November 2022, with a research focus on utilizing artificial intelligence to forecast floods, particularly in the Polonnaruwa region of Sri Lanka. Under the guidance of Prof. M.T.R. Jayasinghe, his postgraduate research aims to develop machine learning models for predicting water levels using meteorological and hydrological data. This interdisciplinary approach bridges civil engineering and AI, reflecting his commitment to advancing both fields. His studies are set to culminate in July 2024, contributing valuable insights to flood risk management.

Professional Experience

Mr. Tharindu Indunil Madhushanka has gained valuable professional experience through both academic and industry roles. As a research assistant at the Department of Civil Engineering, University of Moratuwa, he contributed to various engineering modules, including Mechanics, Structural Mechanics, and the Design of Large Structures. His responsibilities included assisting in teaching and providing support for courses such as Building Construction & Materials and Design of Masonry and Timber Structures. Additionally, Tharindu worked as an instructor in the Department of Computer Science Engineering, teaching Programming Fundamentals from June to September 2024. His industry experience includes serving as a trainee civil engineer at RR Construction (Pvt) Ltd, where he was involved in significant projects such as the Mahaweli Water Security Investment Program. These projects, including the Minipe Left Bank Canal Rehabilitation and North-Western Province Canal Project, provided him with hands-on experience in large-scale civil engineering works, enhancing his practical skills.

Research Interest

Mr. Tharindu Indunil Madhushanka’s research interests lie at the intersection of civil engineering and artificial intelligence, with a focus on disaster risk management and sustainable construction. His primary research area is the use of machine learning techniques, particularly deep learning models like LSTM, ANN, and Transformer, to forecast floods and predict water levels in flood-prone regions, such as Polonnaruwa, Sri Lanka. By utilizing meteorological and hydrological data, Tharindu aims to enhance flood prediction systems, providing valuable insights for mitigating the impacts of natural disasters. Additionally, he is interested in sustainable building practices, as demonstrated by his undergraduate research on the thermal performance and embodied energy of precast panel buildings. Tharindu’s work seeks to improve the environmental efficiency of construction materials and methods, making buildings more energy-efficient over their life cycles. His research reflects his commitment to advancing both AI applications and sustainability within the civil engineering field.

Award and Honor

Mr. Tharindu Indunil Madhushanka has achieved notable academic recognition throughout his educational journey. He graduated with a second-class upper division in his Bachelor of Science in Civil Engineering (Honors) from the University of Moratuwa, Sri Lanka, with a commendable GPA of 3.54 out of 4.2. This achievement underscores his strong academic performance and dedication to his studies. Tharindu has also earned recognition for his research contributions, particularly in the field of flood forecasting using artificial intelligence. His publication, “Multiple-Day-Ahead Flood Prediction in the South Asian Tropical Zone Using Deep Learning,” in the Journal of Hydrologic Engineering, demonstrates the impact of his work on flood management. Although his H-index is currently 1, it reflects his emerging influence in the research community. Tharindu’s research on sustainable building practices, including the thermal performance of precast panel buildings, has been presented at international conferences, further highlighting his growing recognition within the civil engineering and AI research communities.

Conclusion

Tharindu Indunil Madhushanka demonstrates a strong foundation in innovative, interdisciplinary research, particularly in leveraging artificial intelligence for flood forecasting and sustainable building practices. His academic achievements, technical expertise, and impactful research in disaster management are highly commendable.

Publications Top Noted

  • Title: Multi Day Ahead Flood Prediction in South Asian Tropical Zone Using Deep Learning
    Authors: T Madhushanka, T Jayasinghe, R Rajapakse
    Year: 2024
    Cited by: 1
  • Title: Multiple-Day-Ahead Flood Prediction in the South Asian Tropical Zone Using Deep Learning
    Authors: G Madhushanka, MTR Jayasinghe, RA Rajapakse
    Journal: Journal of Hydrologic Engineering 30 (1), 04024054
    Year: 2025
    Cited by: Not available
  • Title: Behavior of LSTM and Transformer Deep Learning Models in Flood Simulation Considering South Asian Tropical Climate
    Authors: G Madhushanka, MTR Jayasinghe, RA Rajapakse
    Year: 2024
    Cited by: Not available
  • Title: Transformer & LSTM Based Models for Multi-Day Ahead Flood Prediction in Tropical Climates
    Authors: T Madhushanka, T Jayasinghe, R Rajapakse
    Year: 2024
    Cited by: Not available
    Available at: SSRN 4746297
  • Title: Flood Prediction for Tropical Climates Using LSTM and Transformer Machine Learning Models
    Authors: T Madhushanka, T Jayasinghe, R Rajapakse
    Year: 2024
    Cited by: Not available
    Available at: SSRN 4736261
  • Title: LONG SHORT-TERM MEMORY (LSTM) & FEEDFORWARD ARTIFICIAL NEURAL NETWORK (ANN) FOR FLOOD PREDICTION
    Authors: G.W.T.I. Madhushanka, M.T.R. Jayasinghe, R.A. Rajapakse
    Event: Proceedings of the 14th International Conference on Sustainable Built …
    Year: 2023
    Cited by: Not available
  • Title: Thermal Performance of Precast Panel Buildings
    Authors: G Madhushanka, SS Bandaranayaka, MTR Jayasinghe, H Herath
    Event: University of Ruhuna
    Year: 2023
    Cited by: Not available

Daniel Mmereki | Engineering | Best Researcher Award

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Dr. Daniel Mmereki | Engineering | Best Researcher Award

Research Coordinator at University of the Witwatersrand, South Africa

Daniel Mmereki is a Postdoctoral Research Fellow at the School of Public Health in Johannesburg, South Africa, with an academic background rooted in environmental science and exposure science. He holds a PhD in Engineering (Exposure Science) from Chongqing University, China, and an MSc in Environmental Science from the University of Botswana. Daniel has garnered considerable international recognition, as evidenced by his NRF C2 rating, and has experience working across several countries, including Botswana, China, Vietnam, and South Africa. His work bridges multiple disciplines, integrating environmental science, public health, and advanced technologies such as AI and machine learning. Daniel is passionate about addressing global challenges, particularly those related to environmental health and public health. His expertise includes environmental exposure assessments, statistical methods, research project management, and postgraduate student supervision.

Professional Profile

Education

Daniel Mmereki’s academic journey is distinguished by degrees and training in environmental science and exposure science. He earned his PhD in Engineering (Exposure Science) from Chongqing University in China, a program that allowed him to gain expertise in assessing environmental exposures and their implications on public health. Prior to his PhD, he completed his MSc in Environmental Science and a Bachelor’s degree in Environmental Science, both at the University of Botswana. His education is complemented by additional specialized training in statistical analysis, research project management, and machine learning. These qualifications equip him with a multidisciplinary approach to tackling complex research challenges. Throughout his academic career, Daniel has been committed to gaining in-depth knowledge and improving his technical skills in environmental and public health research.

Professional Experience

Daniel has accumulated extensive professional experience in academic and research settings, both in South Africa and internationally. As a Postdoctoral Research Fellow at the School of Public Health in Johannesburg, he is involved in advanced public health research, focusing on environmental exposures and their impact on human health. His earlier work includes conducting research across Botswana, China, and Vietnam, where he applied his expertise in exposure science to a variety of environmental challenges. Additionally, Daniel has supervised postgraduate students, contributing to the academic development of emerging researchers. His professional experience also includes training in specialized areas such as project budgeting, writing research proposals, and applying statistical methods using software like STATA, STATISTICA, and SPSS. These roles have honed his skills in research leadership, project management, and mentorship.

Research Interests

Daniel Mmereki’s research interests lie at the intersection of environmental science, exposure science, and public health. He focuses on understanding the environmental factors that influence human health, particularly in the context of global environmental changes. His work involves assessing exposure to environmental pollutants and their health effects, with a special interest in how these factors contribute to chronic diseases. Daniel’s recent interests also include exploring the application of machine learning and AI techniques in public health research, aiming to identify patterns and trends in environmental health data. He is particularly passionate about addressing global public health challenges through data-driven approaches and interdisciplinary solutions. His research contributes to a better understanding of environmental health risks and aims to inform policy and public health strategies aimed at reducing these risks.

Awards and Honors

Daniel Mmereki has been recognized for his significant contributions to environmental science and public health research. His notable recognition includes the NRF C2 rating, which signifies considerable international recognition for his research impact and influence. This prestigious rating is a testament to his contributions to the field, particularly in the areas of exposure science and environmental health. Daniel has also received various honors and awards throughout his academic and professional career for his innovative research projects and academic leadership. His ability to merge advanced statistical methods with environmental science has positioned him as a respected researcher in his field. As he continues his career, Daniel’s work is expected to inspire further recognition for its global impact on public health and environmental policy.

Conclusion

Daniel Mmereki has a solid academic and research foundation with significant international experience and recognition in his field. His achievements in environmental science, exposure science, and public health demonstrate his capability for the Best Researcher Award. However, to further solidify his candidacy, he could focus on enhancing the public health impact of his work, increasing his publication presence, and applying machine learning in novel ways within his research areas. Overall, he has the potential to be a standout researcher with further refinement of these aspects.

Publications Top Noted

  • Bladder cancer: a retrospective audit at a single radiation oncology unit of an academic hospital in Johannesburg, South Africa
    📝 Authors: Oliver, T., Ramiah, D., Mmereki, D., Hugo, M., Ayeni, O.A.
    📅 Year: 2024
    📚 Citations: 0
  • Phthalates on indoor surfaces and associated exposure via surface touch behavior: Observation in university dormitories and its implications
    📝 Authors: Yuan, F., Sun, Y., Li, N., Xu, Y., Bu, Z.
    📅 Year: 2024
    📚 Citations: 0
  • The management and prevention of food losses and waste in low- and middle-income countries: A mini-review in the Africa region
    📝 Authors: Mmereki, D., David, V.E., Wreh Brownell, A.H.
    📅 Year: 2024
    📚 Citations: 6
  • Reducing children’s exposure to di(2-ethylhexyl) phthalate in homes and kindergartens in China: Impact on lifetime cancer risks and burden of disease
    📝 Authors: Tao, D., Sun, W., Mo, D., Dong, C., Bu, Z.
    📅 Year: 2024
    📚 Citations: 1
  • Status of health care waste management plans and practices in public health care facilities in Gauteng Province, South Africa
    📝 Authors: Ramodipa, T., Engelbrecht, K., Mokgobu, I., Mmereki, D.
    📅 Year: 2023
    📚 Citations: 1
  • Application of Innovative Materials and Methods in Green Buildings and Associated Occupational Exposure and Health of Construction Workers: A Systematic Literature Review
    📝 Authors: Mmereki, D., Brouwer, D.
    📅 Year: 2022
    📚 Citations: 1
  • Waste-to-energy in a developing country: The state of landfill gas to energy in the Republic of South Africa
    📝 Authors: Mbazima, S.J., Masekameni, M.D., Mmereki, D.
    📅 Year: 2022
    📚 Citations: 14
  • Exposure to phthalates in the sleeping microenvironment of university dormitories: A preliminary estimate based on skin wipe and dust sampling
    📝 Authors: Yao, J., Hu, M., Yuan, F., Zheng, Y., Bu, Z.
    📅 Year: 2022
    📚 Citations: 8
  • Modeled exposure to phthalates via inhalation and dermal pathway in children’s sleeping environment: A preliminary study and its implications
    📝 Authors: Bu, Z., Dong, C., Mmereki, D., Ye, Y., Cheng, Z.
    📅 Year: 2021
    📚 Citations: 13
  • Phthalates in Chinese vehicular environments: Source emissions, concentrations, and human exposure
    📝 Authors: Bu, Z., Hu, M., Yuan, F., Cao, J., Zheng, Y.
    📅 Year: 2021
    📚 Citations: 8

Xiaotian Wang | Engineering Award | Excellence in Research

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Mr. Xiaotian Wang | Engineering Award | Excellence in Research

Associate Professor at Northwestern Polytechnical University, China

Xiaotian Wang, an Associate Professor at Northwestern Polytechnical University, holds both a Master’s and a Ph.D. degree, earned in 2016 and 2020, respectively, from the same institution. His academic background and current position within a renowned university are strong indicators of his expertise and commitment to research. His specialized focus on computer vision and remote sensing image processing, particularly in object detection and tracking, aligns with cutting-edge technological advancements in these fields. Wang’s contributions to unmanned systems research further highlight his alignment with contemporary research trends and his potential to lead innovative projects.

Professional Profile

Education

Dr. Xiaotian Wang completed his Master’s degree and Ph.D. in the field of Computer Vision and Remote Sensing from Northwestern Polytechnical University, Xi’an, China. He earned his Master’s in 2016 and his Ph.D. in 2020, both from the same prestigious institution. Throughout his education, Dr. Wang developed a deep understanding of machine learning, artificial intelligence, and their applications in unmanned systems. His academic journey involved rigorous research in object detection and tracking algorithms, which he continued to develop through various academic and practical projects. His research contributions were shaped by the rigorous training and mentorship he received during his graduate studies. Dr. Wang’s education provided him with a strong theoretical foundation and the technical expertise necessary to conduct pioneering research in remote sensing image processing and computer vision, making him a recognized expert in his field.

Experience

Dr. Xiaotian Wang is currently an Associate Professor at Northwestern Polytechnical University, where he has made significant contributions to research and development in unmanned systems and remote sensing. His primary focus is on computer vision, particularly object detection and tracking technologies that have important applications in surveillance, robotics, and unmanned vehicles. Prior to his current position, Dr. Wang has been actively involved in several key research projects, collaborating with national and international researchers in the development of cutting-edge technologies for unmanned systems. His expertise in integrating computer vision algorithms with remote sensing has led to several innovative solutions in the field. Additionally, he actively mentors graduate students and early-career researchers, guiding them in advancing their knowledge and research skills in these high-tech domains. His academic and research experience provides a foundation for developing practical, scalable solutions in remote sensing and unmanned technologies.

Research Focus

Dr. Xiaotian Wang’s research primarily revolves around computer vision and remote sensing image processing, with a particular emphasis on object detection and tracking technologies. His work has a significant focus on unmanned systems, where he explores innovative approaches for navigating and processing data from remote sensing devices. Dr. Wang’s research aims to enhance the capabilities of unmanned vehicles, such as drones, through improved object detection and tracking algorithms that enable these systems to interpret and respond to their environments autonomously. This work is crucial for applications in fields like autonomous vehicles, surveillance, and environmental monitoring. By advancing the integration of computer vision with remote sensing, Dr. Wang seeks to bridge the gap between real-time decision-making and automated systems. His research plays a key role in advancing the field of unmanned systems, which are becoming increasingly vital in many industries, including defense, transportation, and agriculture.

Conclusion

Xiaotian Wang demonstrates a strong research profile with a clear focus on advancing unmanned systems and remote sensing technology, which are highly relevant to both scientific and practical applications. His academic and research contributions make him an excellent candidate for the Excellence in Research Award.

Publications Top Noted

Cross-Attention-Driven Adaptive Graph Relational Network for Multilabel Remote Sensing Scene Classification”

Authors: Bi, H., Chang, H., Wang, X., Hong, D.

Citations: 0

Year: 2024

Journal: IEEE Transactions on Geoscience and Remote Sensing

Volume: 62

Article ID: 5224414

“Complexity Evaluation of Aerial Infrared Countermeasure Scenes”

Authors: Xie, F., Dong, M., Wang, X., Yang, D., Yan, J.

Citations: 0

Year: 2024

Journal: IEEE Transactions on Aerospace and Electronic Systems

“Can Rumor Detection Enhance Fact Verification? Unraveling Cross-Task Synergies Between Rumor Detection and Fact Verification”

Authors: Jin, W., Jiang, M., Tao, T., Zhao, B., Yang, G.

Citations: 0

Year: 2024

Journal: IEEE Transactions on Big Data

“A Research on Rapid Assessment of Cross-Domain Perceptual Fidelity for Practical Applications”

Authors: Tao, W., Wang, X., Yan, T., Zeng, Q., Lu, R.

Citations: 0

Year: 2024

Conference: Proceedings of the 3rd Conference on Fully Actuated System Theory and Applications (FASTA 2024)

“An Improved Small Infrared Target Detection Algorithm Based on Yolov5”

Authors: Wang, X., Yang, Z., Sun, Y., Qian, C., Zhao, Y.

Citations: 0

Year: 2024

Conference: Lecture Notes in Electrical Engineering (LNEE), 1175, pp. 405–413

“Detection of Occlusion-Resistant Based on Improved YOLOv7”

Authors: Tao, W., Wang, K., Li, Y., Yan, T., Wang, X.

Citations: 0

Year: 2024

Conference: Lecture Notes in Electrical Engineering (LNEE), 1173, pp. 430–439

“ESF-YOLO: an accurate and universal object detector based on neural networks”

Authors: Tao, W., Wang, X., Yan, T., Liu, Z., Wan, S.

Citations: 0

Year: 2024

Journal: Frontiers in Neuroscience

Volume: 18

Article ID: 1371418

“An Infrared Small Target Detection Method Based on Attention Mechanism”

Authors: Wang, X., Lu, R., Bi, H., Li, Y.

Citations: 3

Year: 2023

Journal: Sensors (Basel, Switzerland)

Volume: 23

Issue: 20

“SiamCAR-Kal: anti-occlusion tracking algorithm for infrared ground targets based on SiamCAR and Kalman filter”

Authors: Fu, G., Zhang, K., Yang, X., Tian, X., Wang, X.T.

Citations: 0

Year: 2023

Journal: Machine Vision and Applications

Volume: 34

Issue: 3

Article ID: 43

“Robust small infrared target detection using multi-scale contrast fuzzy discriminant segmentation”

Authors: Wang, X., Xie, F., Liu, W., Tang, S., Yan, J.

Citations: 5

Year: 2023

Journal: Expert Systems with Applications

Volume: 212

Article ID: 118813

 

 

Masoud Yaghini | Engineering | Best Researcher Award

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Assoc Prof Dr Masoud Yaghini | Engineering | Best Researcher Award

Faculty Member at Iran University of Science and Technology, Iran

Dr. Masoud Yaghini is a distinguished faculty member in the Department of Rail Transportation at the Iran University of Science and Technology. Born on December 8, 1966, he holds an extensive academic and professional background in rail transportation planning and optimization techniques. With over two decades of experience, Dr. Yaghini has made substantial contributions to the fields of transportation logistics, network design, and data mining, particularly within the railway industry. His innovative approaches to complex rail transportation problems have earned him a reputation as a leading researcher in the field. Dr. Yaghini is widely published and continues to shape the future of transportation with cutting-edge research.

Professional Profile

Education

Dr. Yaghini received his Ph.D. in Rail Transportation Planning and Engineering from Northern Jiaotong University, Beijing, China, in 2003, with a focus on dynamic service network design. He also holds an MSc and BSc in Industrial Management from Islamic Azad University, Tehran. His master’s thesis on resource assignment optimization in preventive maintenance laid the foundation for his interest in large-scale optimization problems. Additionally, he furthered his knowledge with specialized training in Ergonomics and Human Factors for Railways from the University of Birmingham, UK, in 2005. This diverse educational background has equipped Dr. Yaghini with both theoretical and practical expertise in optimizing transportation systems.

Professional Experience

Dr. Yaghini has over 20 years of professional experience, primarily as a faculty member at the Iran University of Science and Technology. He teaches a wide range of courses, from advanced computer programming to railway operations management and data mining in transportation. His professional experience extends beyond academia into consultancy work in optimization and transportation planning. Dr. Yaghini has also conducted numerous short courses and workshops in data mining, information management, and metaheuristic algorithms for both academic institutions and private companies. His role as an educator and consultant has allowed him to bridge the gap between academic research and real-world transportation challenges.

Research Interests

Dr. Yaghini’s research primarily focuses on optimization problems in rail transportation, including train scheduling, fleet sizing, and locomotive scheduling. He has a strong interest in metaheuristics such as Genetic Algorithms, Tabu Search, and Ant Colony Optimization, as well as exact solution methods like Column Generation and Branch and Cut. His work also explores data mining techniques applied to railway systems, such as the prediction of train delays and analysis of accident data. His research is driven by the need to optimize and improve efficiency in transportation systems, particularly in large-scale rail networks. His work has significant practical implications for enhancing railway operations and minimizing costs.

Awards and Honors

Dr. Yaghini’s contributions to transportation research have earned him multiple accolades, though his recognition mainly stems from his published works in high-impact journals such as Applied Mathematical Modelling and Journal of Transportation Engineering. He has been recognized for his work on solving complex railway optimization problems through innovative algorithms like Ant Colony Optimization and Simulated Annealing. In addition to his publications, Dr. Yaghini has been invited to present his findings at numerous international conferences. While he has not widely publicized any specific awards, his ongoing research contributions have earned him a solid reputation in the global transportation research community, marking him as a key figure in rail transportation planning and optimization.

Conclusion

Dr. Masoud Yaghini’s research portfolio is impressive, with a strong emphasis on rail transportation and optimization problems. His consistent contributions to both academic knowledge and practical railway systems demonstrate his potential for recognition as a top researcher. By broadening his collaborative network and impact beyond academia, he could further strengthen his candidacy for prestigious awards like the Best Researcher Award.

Publication top noted

  1. Online prediction of arrival and departure times in each station for passenger trains using machine learning methods
    • Vafaei, S., Yaghini, M.
    • Transportation Engineering, 2024
    • 📖 0 citations
  2. Analysis of the relationship between geometric parameters of railway track and twist failure by using data mining techniques
    • Izadi Yazdan Abadi, E., Khadem Sameni, M., Yaghini, M.
    • Engineering Failure Analysis, 2023
    • 📖 2 citations
  3. A mathematical formulation and an LP-based neighborhood search matheuristic solution method for the integrated train blocking and shipment path problem
    • Yaghini, M., Mirghavami, M., Zare Andaryan, A.
    • Networks, 2021
    • 📖 5 citations
  4. Efficient algorithms to minimize makespan of the unrelated parallel batch-processing machines scheduling problem with unequal job ready times
    • Zarook, Y., Rezaeian, J., Mahdavi, I., Yaghini, M.
    • RAIRO – Operations Research, 2021
    • 📖 10 citations
  5. An adaptive structure on a new local branching algorithm using instantaneous dimensions and convergence speed: a case study for multi-commodity network design problems
    • Hajiyan, H., Yaghini, M.
    • SN Applied Sciences, 2020
    • 📖 1 citation
  6. Optimization of embedded rail slab track with respect to environmental vibrations
    • Esmaeili, M., Yaghini, M., Moslemipour, S.
    • Scientia Iranica, 2020
    • 📖 0 citations
  7. An Effective Improvement to Main Non-periodic Train Scheduling Models by a New Headway Definition
    • Jafarian-Moghaddam, A.R., Yaghini, M.
    • Iranian Journal of Science and Technology – Transactions of Civil Engineering, 2019
    • 📖 2 citations
  8. Optimizing headways for urban rail transit services using adaptive particle swarm algorithms
    • Hassannayebi, E., Zegordi, S.H., Amin-Naseri, M.R., Yaghini, M.
    • Public Transport, 2018
    • 📖 26 citations
  9. Train timetabling at rapid rail transit lines: a robust multi-objective stochastic programming approach
    • Hassannayebi, E., Zegordi, S.H., Amin-Naseri, M.R., Yaghini, M.
    • Operational Research, 2017
    • 📖 48 citations
  10. Timetable optimization models and methods for minimizing passenger waiting time at public transit terminals
  • Hassannayebi, E., Zegordi, S.H., Yaghini, M., Amin-Naseri, M.R.
  • Transportation Planning and Technology, 2017
  • 📖 35 citations

Govind Rai Goyal | Engineering | Best Researcher Award

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Dr. Govind Rai Goyal | Engineering | Best Researcher Award

Associate Professor of University of Engineering and Management Jaipur, India

Dr. Govind Rai Goyal is a distinguished academic and researcher in Electrical Engineering, specializing in Power Systems. With a Ph.D. from Kurukshetra University 🎓 and an M.E. from L.D. College of Engineering ⚡, he has a robust educational foundation. His journey in academia spans over 12 years 💼, marked by significant contributions at esteemed institutions such as ARYA Institute of Engineering & Technology, Vivekananda Global University, and University of Engineering & Management 🌍. Dr. Goyal is not only adept in teaching subjects like Circuit Analysis and Control Systems 📚 but also excels in research and technical skills, including MATLAB Programming and SPSS Statistics 💻. His dedication to excellence is reflected in his successful roles in NBA and NAAC accreditation processes 🌟.

Professional profile
Education📚

Dr. Govind Rai Goyal has a commendable educational background in Electrical Engineering 📚. He earned his Ph.D. in Electrical Engineering, specializing in Power Systems ⚡, from Kurukshetra University, completing his studies from April 2013 to December 2015 🎓. Prior to this, he obtained his M.E. in Electrical Engineering with a focus on Power Systems from L.D. College of Engineering, Ahmedabad, in 2012, achieving a remarkable 82% and securing the 2nd rank in the university 🏆. He also holds a B.E. in Electrical Engineering from the University of Rajasthan, Jaipur, where he graduated in 2008 with an impressive 88%, earning the 1st rank in his department 🥇. His academic journey is further distinguished by qualifying the GATE exam and receiving a scholarship from MHRD 🎖️.

Professional Experience🏛️

Dr. Govind Rai Goyal boasts a robust professional experience spanning over 12 years in the field of Electrical Engineering ⚙️. He began his career as a Lecturer at ARYA Institute of Engineering & Technology in Jaipur, where he served from July 2009 to June 2013, accumulating 4 years of teaching expertise 🎓. He then advanced to the role of Assistant Professor at Vivekananda Global University in Jaipur, contributing significantly from February 2016 to January 2021, a period of 5 years 📚. Following this, he held the position of Assistant Professor (Sr.) at the College of Engineering Roorkee from January 2021 to March 2022, gaining valuable experience over the course of a year 🏫. Most recently, he has been an Assistant Professor-III at the University of Engineering & Management in Jaipur from March 2022 to February 2024, before being promoted to Associate Professor in March 2024, where he continues to make impactful contributions 📘.

Research Interest🌐

Dr. Govind Rai Goyal’s research interests are rooted deeply in Electrical Engineering ⚡. His primary focus areas include Power Systems, Control Systems, and Circuit Analysis 🔌. He is particularly intrigued by the applications of Artificial Intelligence in Power Systems, aiming to integrate modern AI techniques to enhance the efficiency and reliability of electrical networks 🤖. Dr. Goyal also explores advanced simulation techniques and their practical applications in power system modeling, striving to push the boundaries of current technological capabilities 🌐. His dedication to these research domains is evident in his extensive academic and professional endeavors 📚.

Awards and Honors🏆

Dr. Govind Rai Goyal has received numerous awards and honors throughout his illustrious career 🌟. He was awarded his Ph.D. in Electrical Engineering with a specialization in Power Systems from Kurukshetra University in December 2016 🎓. Notably, he ranked 2nd in his university during his M.E. in Electrical Engineering at L.D. College of Engineering, Ahmedabad 🏅. Additionally, he qualified for the GATE exam and received a prestigious scholarship from MHRD 🏆. His consistent academic excellence was marked by achieving 1st rank in his department during his B.E. at the University of Rajasthan, Jaipur 🥇.

Research skill🔬

Dr. Govind Rai Goyal possesses a robust set of research skills that distinguish him in the field of Electrical Engineering, particularly in Power Systems ⚡. He is proficient in Microsoft Office, MATLAB Programming, SPSS Statistics, and LaTeX, enabling him to analyze data and present his findings effectively 📊. His expertise extends to Circuit Analysis & Synthesis, Control Systems, and Power System Analysis, both at the undergraduate and postgraduate levels 📚. Additionally, Dr. Goyal has demonstrated advanced simulation skills, making significant contributions to modern control systems and AI applications in power systems 🤖. His dedication to research and academic excellence is evident in his ability to handle complex technical challenges and deliver innovative solutions 💡.

Achievements🏅
  • 🎓 Ph.D. in Electrical Engineering (Power Systems) from Kurukshetra University (2024)
  • 🏅 Awarded M.E. in Electrical Engineering (Power System) from L.D. College of Engineering, Ahmedabad (2016) with 2nd rank in university
  • 📜 Qualified GATE Exam and received scholarship from MHRD
  • 🎓 B.E. in Electrical Engineering from the University of Rajasthan, Jaipur (2010) with 1st rank in department
  • 🏅 Successfully completed NBA accreditation as a core team member with A+ grade at AIET, Jaipur
  • 🏅 Worked in NAAC team and successfully completed accreditation with A+ grade at VGU, Jaipur
  • 🏅 Contributed to NAAC accreditation with B+ grade at COER, Roorkee
  • 💻 Proficient in Microsoft Office, MATLAB Programming, SPSS Statistics, and LaTeX
  • 📘 Published multiple research papers in renowned journals and conferences
  • 🏆 Recognized for outstanding contributions in teaching and research in the field of Electrical Engineering
Projects🛠️
    • 🔋 Energy Efficiency Project (2021-2022)
      • Developed a model for energy efficiency improvements in power systems.
      • Implemented advanced simulation techniques for optimizing energy use.
    • 🖥️ AI in Power Systems (2020-2021)
      • Explored applications of artificial intelligence in modern control systems.
      • Created algorithms for predictive maintenance and fault detection.
    • ⚡ Power System Analysis (2018-2019)
      • Conducted extensive circuit analysis and synthesis for power systems.
      • Led a team in modeling and simulation of complex electrical networks.
    • 🏭 Industrial Control Systems (2016-2017)
      • Developed control systems for industrial automation.
      • Integrated modern control theories with practical applications in industry.
    • 🧪 Advanced Simulation Lab (2015-2016)
      • Established and managed a state-of-the-art simulation lab for postgraduate students.
      • Provided hands-on training in MATLAB and other simulation software.
    • 📈 Circuit and Network Analysis (2014-2015)
      • Implemented teaching modules and laboratory setups for undergraduate courses.
      • Enhanced practical learning through innovative lab experiments and projects.
    • 🛠️ NBA Accreditation Project (2013-2014)
      • Core team member for NBA accreditation at AIET, Jaipur.
      • Achieved A+ grade by implementing stringent academic and administrative standards.
Publications📜
  • A Multi-objective PSO (MOPSO) Algorithm for Optimal Active Power Dispatch with Pollution Control
    • Authors: GD Sen, J Sharma, GR Goyal, AK Singh
    • Year: 2017
    • Citation: 30 📈
    • Published in: MATHEMATICAL MODELLING OF ENGINEERING PROBLEMS 4 (3), 132-137
  • Solution of Combined Economic Emission Dispatch with Demand Side Management Using Meta-heuristic Algorithms
    • Authors: GR Goyal, S Vadhera
    • Year: 2019
    • Citation: 20 📈
    • Published in: Journal Européen des Systèmes Automatisés 52 (2), 143-148
  • Multi-objective Optimal Active Power Dispatch Using Swarm Optimization Techniques
    • Authors: GR Goyal, HD Mehta
    • Year: 2015
    • Citation: 17 📈
    • Published in: Engineering (NUiCONE), 2015 5th Nirma University International Conference on …
  • Optimal Dispatch of Active and Reactive Power Using Cuckoo Search Method
    • Authors: GR Goyal, HD Mehta
    • Year: 2015
    • Citation: 16 📈
    • Published in: INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN ELECTRICAL, ELECTRONICS …
  • Multi-interval Programming Based Scheduling of Appliances with User Preferences and Dynamic Pricing in Residential Area
    • Authors: GR Goyal, S Vadhera
    • Year: 2021
    • Citation: 13 📈
    • Published in: Sustainable Energy, Grids and Networks 27, 100511
  • Challenges of Implementing Demand Side Management in Developing Countries
    • Authors: GR Goyal, S Vadhera
    • Year: 2020
    • Citation: 13 📈
    • Published in: Journal of Power Technologies 100 (1), 43
  • Contingency Ranking for Voltage Stability in Power System
    • Authors: F Hussian, GR Goyal, AK Arya, BP Soni
    • Year: 2021
    • Citation: 9 📈
    • Published in: 2021 IEEE International Conference on Electronics, Computing and …
  • Performance Study of Recent Swarm Optimization Techniques with Standard Test Functions
    • Authors: A Sharma, G Rai, M Jain, P Kumawat
    • Year: 2019
    • Citation: 9 📈
    • Published in: National Conference on Recent Advancements in Computational Mathematics and …
  • Comparative Study of Power Consumption Minimization in Analog Electronic Circuit Using AI Techniques
    • Authors: K Singhal, GR Goyal
    • Year: 2018
    • Citation: 8 📈
    • Published in: European Journal of Electrical Engineering 20 (4), 427
  • Optimal Setting of Parameters of Power System Controllers Using PSO Technique for Economic Load Dispatch
    • Authors: A Sharma, G Rai, M Jain, P Kumawat
    • Year: 2021
    • Citation: 6 📈
    • Published in: International Conference on Science and Computing (ICSC 2021)