Bimal Kumar Dora | Engineering | Best Researcher Award

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Mr. Bimal Kumar Dora | Engineering | Best Researcher Award

PhD at Visvesvaraya National Institute of Technology Nagpur, India

Bimal Kumar Dora is an emerging researcher in electrical engineering with a strong focus on power system optimization and renewable energy integration. Currently pursuing a PhD at Visvesvaraya National Institute of Technology, his research addresses critical challenges in developing a global electricity grid by integrating advanced machine learning and meta-heuristic algorithms. He has contributed significantly to academic literature through multiple publications in reputed international journals and conferences, emphasizing innovative solutions such as hybrid optimization techniques and robust algorithmic frameworks. His work bridges traditional power systems with modern computational methods, demonstrating both theoretical insight and practical application. Alongside his research, he actively participates in workshops and seminars that further enhance his expertise in control, power, and electric drives. His dedication is evidenced by prestigious fellowships and continuous professional development. Dora’s interdisciplinary approach and commitment to solving complex energy problems make him a promising candidate for future advancements in sustainable power systems.

Professional Profile 

Education

Bimal Kumar Dora’s academic journey reflects a dedicated and robust foundation in electrical engineering and research methodologies. He began his studies with a Diploma in Electrical Engineering from Odisha School of Mining Engineering, where he built his fundamental technical skills. Subsequently, he earned a Bachelor’s degree in Electrical Engineering from Gandhi Institute for Technological Advancement, further solidifying his expertise in the field. His academic progression continued with a Master of Technology from the National Institute of Technology Sikkim, where he specialized in Control, Power, and Electric Drives, achieving an outstanding CGPA and engaging in research on power system optimization. Currently, he is pursuing a PhD at Visvesvaraya National Institute of Technology, focusing on the global electricity grid and integrating innovative approaches in machine learning and meta-heuristic algorithms. This comprehensive educational background has equipped him with both theoretical knowledge and practical skills necessary for addressing complex challenges in sustainable energy systems.

Professional Experience

Bimal Kumar Dora’s professional experience exemplifies high-level research and practical applications in the electrical engineering domain. As a dedicated researcher, he has contributed to multiple international journals and conference proceedings, showcasing his work on power system optimization, renewable energy integration, and advanced algorithm development. His work has earned international acclaim. His research projects involve developing innovative hybrid algorithms such as the Exchange Market based Butterfly Optimization Algorithm, tested on standard IEEE systems and real-world power grids. Dora’s experience extends to collaborating with renowned academic institutions and industry experts, enhancing the translational impact of his work. In addition, he has participated in various workshops and seminars focused on control systems, power electronics, and emerging technologies. His technical proficiency in programming tools like Python and MATLAB reinforces his ability to tackle complex engineering challenges. Through professional development and engagement in cutting-edge research, he remains a key asset to the sustainable energy sector.

Research Interest

Mr. Bimal Kumar Dora’s research interests lie at the intersection of power system optimization, renewable energy integration, and advanced computational techniques. His work primarily focuses on developing efficient algorithms to tackle complex challenges in modern electrical grids, including the integration of renewable energy sources into a global electricity grid. He employs machine learning, meta-heuristic, and soft computing methods to design and implement innovative solutions for generation expansion planning and optimal reactive power dispatch. His exploration of hybrid optimization techniques—such as the Exchange Market based Butterfly Optimization Algorithm—demonstrates his commitment to enhancing system reliability and efficiency. By integrating traditional power system engineering with contemporary data-driven methodologies, his research addresses both theoretical and practical aspects of sustainable energy systems. This multidisciplinary approach not only contributes to academic literature but also offers tangible benefits for industry applications, making his work highly relevant in the evolving landscape of energy management and smart grid technologies.

Award and Honor

Throughout his academic and research career, Mr. Dora has received significant recognition for his dedication and innovative contributions. Notably, he qualified in the GATE examination in 2018, which paved the way for prestigious scholarships and fellowships. His academic excellence was further acknowledged through the AICTE GATE Scholarship awarded by MHRD during his M. Tech studies, reinforcing his capability in rigorous research and problem-solving. Currently, his potential is recognized with the MHRD Research Fellowship, an honor that supports his PhD work at Visvesvaraya National Institute of Technology. These awards not only underscore his technical proficiency and research acumen but also reflect his commitment to pushing the boundaries of electrical engineering. Such accolades serve as a testament to his ability to innovate and lead in the field of sustainable energy solutions, earning him respect and admiration among his peers and mentors.

Research Skill

Mr. Bimal Kumar Dora exhibits a robust set of research skills that are fundamental to his success as an emerging scholar in electrical engineering. His technical expertise spans a wide array of programming and simulation tools, including Python, MATLAB, and Octave, which he adeptly applies to model complex power systems and optimize performance. Proficient in using specialized software such as QGIS, MiPower, and Power World, he efficiently conducts simulations and analyses that support his research hypotheses. Additionally, his familiarity with real-time simulators like Typhon HIL and the Real-Time Transmission Line Simulator enhances his ability to test theoretical models under practical conditions. His skills extend to academic writing and presentation, supported by his proficiency in LATEX and MS Office. This comprehensive toolkit not only allows him to conduct innovative experiments and develop hybrid algorithms but also ensures that his findings are communicated effectively through high-impact publications and presentations at international conferences.

Conclusion

Bimal Kumar Dora demonstrates significant promise as a researcher with a strong foundation in innovative and interdisciplinary work. His solid academic record, impactful publications, and technical proficiency make him a compelling candidate for the Best Researcher Award. Addressing areas such as leadership in collaborative projects and extending the societal impact of his research could further elevate his profile. Overall, his achievements and ongoing commitment to advancing power system optimization and renewable energy integration mark him as a deserving contender for this recognition.

Publications Top Noted

Inverse Thresholding to Spectrogram for the Detection of Broken Rotor Bar in Induction Motor
Title: Inverse Thresholding to Spectrogram for the Detection of Broken Rotor Bar in Induction Motor
Authors: S Halder, S Bhat, BK Dora
Year: 2022
Citation Count: 18

An Enhanced Pathfinder Algorithm Based MCSA for Rotor Breakage Detection of Induction Motor
Title: An Enhanced Pathfinder Algorithm Based MCSA for Rotor Breakage Detection of Induction Motor
Authors: S Halder, BK Dora, S Bhat
Year: 2022
Citation Count: 15

Optimal Reactive Power Dispatch Problem Using Exchange Market Based Butterfly Optimization Algorithm
Title: Optimal Reactive Power Dispatch Problem Using Exchange Market Based Butterfly Optimization Algorithm
Authors: BK Dora, A Rajan, S Mallick, S Halder
Year: 2023
Citation Count: 14

Start-up Transient Analysis Using CWT and Ridges for Broken Rotor Bar Fault Diagnosis
Title: Start-up Transient Analysis Using CWT and Ridges for Broken Rotor Bar Fault Diagnosis
Authors: S Halder, S Bhat, B Dora
Year: 2023
Citation Count: 14

Solution of Reactive Power Dispatch Problems Using Enhanced Dwarf Mongoose Optimization Algorithm
Title: Solution of Reactive Power Dispatch Problems Using Enhanced Dwarf Mongoose Optimization Algorithm
Authors: BK Dora, S Bhat, S Halder, M Sahoo
Year: 2023
Citation Count: 13

A Solution to the Techno-Economic Generation Expansion Planning Using Enhanced Dwarf Mongoose Optimization Algorithm
Title: A Solution to the Techno-Economic Generation Expansion Planning Using Enhanced Dwarf Mongoose Optimization Algorithm
Authors: BK Dora, S Bhat, S Halder, I Srivastava
Year: 2022
Citation Count: 8

A Solution to Multi Objective Stochastic Optimal Power Flow Problem Using Mutualism and Elite Strategy Based Pelican Optimization Algorithm
Title: A Solution to Multi Objective Stochastic Optimal Power Flow Problem Using Mutualism and Elite Strategy Based Pelican Optimization Algorithm
Authors: BK Dora, S Bhat, S Halder, I Srivastava
Year: 2024
Citation Count: 6

Optimum Scheduling and Dispatch of Power Systems with Renewable Integration
Title: Optimum Scheduling and Dispatch of Power Systems with Renewable Integration
Authors: A Rajan, BK Dora
Year: 2022
Citation Count: 5

Prediction of Broken Rotor Bar in Induction Motor Using Spectral Entropy Features and TLBO Optimized SVM
Title: Prediction of Broken Rotor Bar in Induction Motor Using Spectral Entropy Features and TLBO Optimized SVM
Authors: S Halder, S Bhat, B Dora
Year: 2022
Citation Count: 3

An Enhanced Path Finder Algorithm for the Estimation of the Stator Current Envelope to Detect Rotor Bar Breakage in an Induction Motor
Title: An Enhanced Path Finder Algorithm for the Estimation of the Stator Current Envelope to Detect Rotor Bar Breakage in an Induction Motor
Authors: S Halder, BK Dora, S Bhat
Year: 2024
Citation Count: 2

Jinsheng Liang | Engineering | Best Researcher Award

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Dr. Jinsheng Liang | Engineering | Best Researcher Award

PhD Candidate at Shenyang Institute of Automation, Chinese Academy of Science, China

Dr. Jinsheng Liang is a distinguished researcher specializing in ultra-precision machining and water jet-guided laser technology. He earned his Bachelor of Engineering from Wuhan University of Technology and is currently pursuing a Doctorate in Engineering at the Shenyang Institute of Automation, Chinese Academy of Sciences. His research focuses on fluid flow characteristics, laser transmission mechanisms, and high-efficiency milling techniques, contributing to advancements in precision manufacturing. Dr. Liang has played a key role in national research projects, particularly in enhancing the stability and efficiency of light-guiding liquid beams in laser processing. He has published five high-impact papers in The International Journal of Advanced Manufacturing Technology and Optics & Laser Technology, demonstrating expertise in fluid simulation and mechanical manufacturing. With strong technical skills and a commitment to innovation, Dr. Liang continues to push the boundaries of laser machining technology, aiming to bridge the gap between academic research and industrial applications.

Professional Profile 

Education

Dr. Jinsheng Liang has a strong academic background in mechanical engineering and ultra-precision machining. He is currently pursuing a Doctor of Engineering at the Shenyang Institute of Automation, Chinese Academy of Sciences, specializing in mechanical manufacturing and automation. His doctoral research focuses on water jet-guided laser technology, fluid flow simulation, and high-precision machining. Prior to this, he earned his Bachelor of Engineering in mechanical design, manufacturing, and automation from Wuhan University of Technology in 2019. Throughout his academic journey, Dr. Liang has gained extensive expertise in laser machining techniques, fluid dynamics, and numerical simulations, contributing to cutting-edge research in precision manufacturing. His educational background, combined with hands-on research experience, has positioned him as a promising expert in his field, bridging theoretical knowledge with practical applications to advance high-efficiency laser processing technologies.

Professional Experience

Dr. Jinsheng Liang has extensive research experience in ultra-precision machining and water jet-guided laser technology. Since 2019, he has been pursuing his Doctor of Engineering at the Shenyang Institute of Automation, Chinese Academy of Sciences, where he has been actively involved in national research projects. His key contributions include research on laser electrolysis composite high-efficiency milling technology and the stability of internal light-guiding liquid beams and laser transmission mechanisms. He has utilized Fluent software for fluid simulations, combining theoretical modeling with experimental validation to enhance laser machining precision. Dr. Liang has published five high-impact papers in renowned journals, solidifying his expertise in laser technology, fluid simulation, and mechanical manufacturing. His work significantly contributes to advancements in high-precision manufacturing, and his ability to integrate research findings with industrial applications underscores his potential as a leading researcher in laser machining and automation.

Research Interest

Dr. Jinsheng Liang’s research interests lie in the fields of laser technology, fluid simulation, and mechanical manufacturing, with a particular focus on ultra-precision machining and water jet-guided laser technology. His work explores fluid flow characteristics, laser transmission mechanisms, and high-efficiency milling techniques, aiming to improve the precision and stability of laser processing. He specializes in the numerical simulation of liquid-guided laser beams, using Fluent software to model fluid behavior and enhance machining accuracy. His research also extends to the development of advanced laser processing methods for complex materials, with potential applications in aerospace, electronics, and high-tech manufacturing. Through his studies, Dr. Liang seeks to bridge the gap between theoretical modeling and experimental validation, contributing to the advancement of next-generation laser machining technologies. His expertise in precision engineering and automation positions him as a key contributor to the future of high-precision manufacturing.

Award and Honor

Currently, there are no explicitly listed awards and honors for Dr. Jinsheng Liang. However, his significant contributions to ultra-precision machining and water jet-guided laser technology highlight his growing impact in the field of mechanical manufacturing and automation. As a doctoral researcher at the Shenyang Institute of Automation, Chinese Academy of Sciences, he has been actively involved in national research projects, demonstrating excellence in fluid simulation, laser transmission mechanisms, and high-efficiency milling techniques. His five high-impact publications in prestigious journals, such as The International Journal of Advanced Manufacturing Technology and Optics & Laser Technology, reflect the recognition of his work within the scientific community. Given his expertise and research accomplishments, Dr. Liang is a strong candidate for future academic awards, industry recognitions, and research grants. His contributions to precision laser machining and automation continue to position him as an emerging leader in the field.

Research Skill

Dr. Jinsheng Liang possesses advanced research skills in laser technology, fluid simulation, and mechanical manufacturing, with a strong focus on ultra-precision machining and water jet-guided laser technology. He is proficient in numerical simulation and computational fluid dynamics (CFD), utilizing Fluent software to analyze fluid flow characteristics and laser transmission mechanisms. His expertise extends to experimental validation, where he integrates simulation results with real-world laser machining processes to enhance precision and efficiency. Dr. Liang has a deep understanding of laser-material interactions, milling techniques, and high-efficiency processing methods, allowing him to contribute to cutting-edge manufacturing advancements. His ability to design and execute complex experiments, analyze large datasets, and optimize machining parameters makes him a valuable researcher in the field. With five high-impact journal publications, he demonstrates strong skills in technical writing, data interpretation, and problem-solving, essential for advancing high-precision laser processing technologies.

Conclusion

Jinsheng Liang is a strong candidate for the Best Researcher Award due to his specialized expertise, impactful research, and high-quality publications. His contributions to ultra-precision machining and laser technology are commendable, and his ability to conduct numerical simulations and experimental studies is impressive. Strengthening industry impact and international collaboration would further elevate his profile.

Publications Top Noted

Authors: Jinsheng Liang, Hongchao Qiao, Jibin Zhao, Yuting Zhang, Qing Zhang
Year: 2025
Journal: Optics and Laser Technology
Title: Simulation and experimental study on double staggered-axis air-assisted water jet-guided laser film cooling hole machining

Yi Sun | Engineering | Best Researcher Award

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Dr. Yi Sun | Engineering | Best Researcher Award

Southwest Jiaotong University, China

Dr. Yi Sun is a distinguished researcher specializing in equipment status monitoring, health indicator construction, and deep learning applications. Currently pursuing a Ph.D. in Mechanical and Electronic Engineering at Southwest Jiaotong University, he has an impressive academic track record with 12 published papers, including 7 SCI papers, 4 of which are in top-tier JCR Q1 journals. His research contributions include developing predictive maintenance algorithms, process parameter optimization, and aerodynamic identification models for hypersonic wind tunnels. He has also led industry projects in predictive maintenance systems and multi-source aerodynamic data fusion. Recognized with multiple National Scholarships and industry accolades such as Huawei’s “Rising Star” award, Dr. Sun demonstrates exceptional expertise in both academic research and practical applications. His work bridges the gap between theoretical advancements and industrial innovation, positioning him as a leading figure in mechanical engineering and deep learning-based monitoring systems.

Professional Profile 

Education

Dr. Yi Sun has a strong educational background in mechanical engineering and electronic systems. He earned his Bachelor’s degree in Mechanical Engineering and Automation from Zhengzhou University (2012-2016), where he built a solid foundation in engineering principles. He then pursued a Master’s degree in Mechanical Engineering at Southwest Jiaotong University (2017-2020), where he gained expertise in advanced manufacturing processes, equipment monitoring, and fault diagnosis. Currently, he is undertaking a Ph.D. in Mechanical and Electronic Engineering at Southwest Jiaotong University (2021-2025), focusing on deep learning applications, health indicator construction, and predictive maintenance for industrial systems. Throughout his academic journey, he has been recognized with prestigious honors, including National Scholarships and Outstanding Graduate Student awards. His education has provided him with a unique blend of theoretical knowledge and practical experience, enabling him to contribute significantly to both academia and industry in the fields of mechanical engineering and intelligent monitoring systems.

Professional Experience

Dr. Yi Sun has a diverse professional background spanning both academia and industry. He worked as an R&D Engineer at Huawei Technologies Co., Ltd. (2020-2021), where he contributed to cutting-edge research and development in predictive maintenance and equipment monitoring. His industry experience provided him with hands-on expertise in software and hardware integration, sensor selection, and algorithm development for real-world applications. As a Ph.D. researcher at Southwest Jiaotong University (2021-present), he has led multiple high-impact projects, including the development of predictive maintenance systems for CNC machine tools and multi-source aerodynamic data fusion models for the China Aerodynamics Research and Development Center. His research has resulted in 12 published papers, several in top-tier journals, and numerous awards for academic excellence. Dr. Sun’s professional journey demonstrates his ability to bridge the gap between theoretical research and industrial innovation, making significant contributions to mechanical engineering and deep learning-based monitoring technologies.

Research Interest

Dr. Yi Sun’s research interests lie at the intersection of mechanical engineering, deep learning, and intelligent monitoring systems. His work focuses on equipment status monitoring, health indicator construction, fault diagnosis, and predictive maintenance for industrial applications. He specializes in process parameter optimization, particularly in milling cutter status assessment, utilizing advanced signal analysis, noise reduction, and online monitoring techniques. His expertise extends to deep learning-based fault detection, including the development of aerodynamic force identification models and transfer learning techniques for aerodynamic data analysis in hypersonic wind tunnels. Dr. Sun is also engaged in multi-source data fusion, enhancing accuracy and consistency in industrial systems. His research aims to optimize mechanical performance, reduce downtime, and improve system reliability through AI-driven solutions. By integrating machine learning with mechanical systems, he contributes to advancing intelligent manufacturing, predictive maintenance, and next-generation industrial automation technologies.

Award and Honor

Dr. Yi Sun has received numerous prestigious awards and honors in recognition of his outstanding academic and research achievements. During his master’s and Ph.D. studies, he was awarded the National Scholarship, one of the highest academic honors in China, for his excellence in research and academics. He was also recognized as an Outstanding Graduate Student at both the university and provincial levels. His exceptional contributions to mechanical engineering and intelligent monitoring systems earned him the Mingcheng Award and the Comprehensive Quality A-Level Certificate during his postgraduate studies. In the corporate sector, he was honored as an Excellent Student in Huawei’s New Employee Training Camp and received the Huawei “Rising Star” Award for his innovative contributions. These accolades reflect his dedication, innovation, and leadership in academia and industry. Dr. Sun’s achievements highlight his remarkable research capabilities and his potential to drive advancements in intelligent manufacturing and predictive maintenance systems.

Research Skill

Dr. Yi Sun possesses exceptional research skills in mechanical engineering, deep learning, and intelligent monitoring systems. His expertise includes equipment status monitoring, fault diagnosis, health indicator construction, and predictive maintenance. He is proficient in signal processing, noise reduction, and multi-source data fusion, enabling accurate real-time monitoring and fault prediction for industrial systems. His strong foundation in deep learning and machine learning algorithms allows him to develop advanced models for aerodynamic force identification and process parameter optimization. Dr. Sun is skilled in software and hardware development, including sensor selection, data acquisition, edge computing, and algorithm integration for predictive maintenance systems. He also excels in scientific writing, publishing high-impact research in top-tier journals and presenting at international conferences. His ability to combine theoretical research with practical industrial applications demonstrates his versatility and innovation, making significant contributions to the advancement of intelligent manufacturing and mechanical system optimization.

Conclusion

Sun Yi is highly suitable for the Best Researcher Award due to his exceptional publication record, innovative contributions to equipment status monitoring and deep learning, industry experience, and leadership in research projects. While he could enhance his application with patents, tech commercialization, and broader collaborations, his current achievements make him a strong candidate for the award. 🚀

Publications Top Noted

  • L. Wei, Y. Sun, J. Zeng, S. Qu (2022). “Experimental and numerical investigation of fatigue failure for metro bogie cowcatchers due to modal vibration and stress induced by rail corrugation.” Engineering Failure Analysis, 142, 106810. Citations: 31

  • Y. Sun, L. Wei, C. Liu, H. Dai, S. Qu, W. Zhao (2022). “Dynamic stress analysis of a metro bogie due to wheel out-of-roundness based on multibody dynamics algorithm.” Engineering Failure Analysis, 134, 106051. Citations: 22

  • J. Mu, J. Zeng, C. Huang, Y. Sun, H. Sang (2022). “Experimental and numerical investigation into development mechanism of wheel polygonalization.” Engineering Failure Analysis, 136, 106152. Citations: 21

  • Y. Li, H. Dai, Y. Qi, S. Qu, Y. Sun (2023). “Experimental study of bogie instability monitoring and suppression measures for high-speed EMUs.” Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit. Citations: 6

  • Y. Sun, L. Wei, H. Dai, C. Liu, S. Qu, Y. Qi (2023). “Influence of rail weld irregularity on dynamic stress of bogie frame based on vehicle-track rigid-flexible coupled model.” Journal of Vibration and Control, 29 (17-18), 4172-4185. Citations: 5

  • Y. Sun, L. Wei, S. Qu, H. Dai (2024). “Fatigue stress estimation of metro bogie frame through frequency response functions by using limited sensors.” Structural Health Monitoring, 23 (1), 421-442. Citations: 2

  • Y. Sun, L. Wei, H. Dai (2024). “Indirect Dynamic Stress Measurement of Metro Bogie Using LSTM Network in Frequency Domain.” IEEE Sensors Journal.

Janani Priyanka Perumpally Rajakumar | Engineering | Best Researcher Award

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Ms. Janani Priyanka Perumpally Rajakumar | Engineering | Best Researcher Award

Student at Dong-A University, India

Janani Priyanka P.R. is a dedicated researcher specializing in civil and environmental engineering, with a strong academic background and expertise in air quality monitoring, data analysis, and smart city engineering. She has a passion for tackling environmental challenges using advanced technologies such as big data and IoT-based monitoring systems. With multiple Q1 journal publications and international conference presentations, she has demonstrated her ability to contribute impactful research. Her technical proficiency in Python, AutoCAD, C++, and Microsoft tools further enhances her analytical capabilities. She has received notable recognition, including an Editor’s Choice selection for one of her research papers and a Second Paper Award at ICTSCE 2023. She aspires to further her research in sustainable construction and urban planning, aiming to integrate technology and environmental solutions for smarter, safer cities.

Professional Profile

Education

Janani holds a Master’s degree in ICT Integrated Oceanfront Smart City Engineering from Dong-A University, Busan, South Korea, with an impressive GPA of 4.19/5. Her interdisciplinary education integrates architecture, civil, and electronic engineering, providing a comprehensive understanding of sustainable urban development. She previously earned a Bachelor of Technology in Civil Engineering from Adi Shankara Institute of Engineering and Technology, Kerala, India, graduating with an outstanding CGPA of 9.11/10. She ranked second in her department during her undergraduate studies, reflecting her academic excellence. Throughout her academic journey, she actively participated in research projects, technical conferences, and internships, which helped her develop expertise in environmental sustainability, air pollution control, and smart construction methodologies.

Professional Experience

Janani has gained valuable research and industry experience through various internships and projects. She interned at SFS Homes, Kakkanad, where she worked on an ongoing construction project, gaining hands-on experience in structural design and project management. Additionally, she completed an internship at the civil department of FACT Ltd., Eloor, where she explored industrial construction techniques and environmental safety protocols. She has actively participated in international conferences, including ICTSCE 2021 & 2023 and IEEE Big Data 2021, where she presented her research on particulate matter control and toxic gas monitoring. Her research on air pollution control in construction activities, toxic gas detection systems, and sustainable building practices has been widely recognized. With her technical skills in data analysis, programming, and engineering design, she is well-equipped to contribute to innovative solutions in urban development and environmental sustainability.

Research Interest

Janani Priyanka P.R.’s research focuses on air quality monitoring, environmental sustainability, and smart city engineering. She is particularly interested in developing data-driven solutions to address pollution control challenges in urban environments. Her studies explore big data analytics, IoT-based toxic gas monitoring, and particulate matter control measures in construction activities. She has conducted extensive research on real-time gas detection systems for worker safety and efficient air pollution mitigation strategies using advanced technologies. Her interdisciplinary approach integrates civil engineering, environmental science, and data analytics, making her work highly relevant to sustainable urban development. She aspires to further explore machine learning applications in air quality forecasting, climate-resilient infrastructure, and smart urban planning. By leveraging her technical skills in Python, C++, AutoCAD, and MS Project, she aims to contribute to innovative solutions that enhance environmental sustainability and public health in rapidly urbanizing regions.

Awards and Honors

Janani has received several prestigious awards and honors for her academic excellence and research contributions. She was awarded the Second Paper Award at ICTSCE 2023 in Busan, South Korea, recognizing her work in air pollution control and sustainable construction practices. Her research paper published in the Sensors Journal was selected as an Editor’s Choice Article, highlighting its significance in the field of toxic gas detection and worker safety. She has published three Q1 journal articles, a testament to the impact and quality of her research. Additionally, she ranked second in her undergraduate civil engineering department, showcasing her consistent academic excellence. Her Master’s thesis on toxic gas exposure detection received a remarkable score of 94 out of 100, further reinforcing her expertise in environmental monitoring systems. With multiple international conference presentations and impactful research publications, she continues to make significant contributions to sustainable urban engineering and environmental science.

Conclusion

Janani Priyanka P.R. is a strong contender for the Best Researcher Award, given her exceptional academic performance, Q1 publications, technical skills, and international recognition. However, further strengthening through a Ph.D., leadership in research, and interdisciplinary collaborations would enhance her profile further. If she continues on this trajectory, she has high potential to be an outstanding researcher in her field.

Publications Top Noted

  • Ham, Y.-B., Cheriyan, D., Kim, H.-U., Janani Priyanka, P.R., Choi, J.-H. (2024). “Particulate matter reduction efficiency analysis of sprinkler system as targeted control measures for construction activity.” Heliyon, 10(7), e27765.
    • Year: 2024
    • Citations: 0
  • Janani Priyanka, P.R., Cheriyan, D., Choi, J.-H. (2022). “A proactive approach to execute targeted particulate matter control measures for construction works.” Journal of Cleaner Production, 368, 133168.
    • Year: 2022
    • Citations: 5
  • Priyanka, J., Cheriyan, D., Choi, J.-H. (2021). “Issues with Current PM Monitoring Techniques and Control Measures and the Way Forward Using Big Data.” Proceedings – 2021 IEEE International Conference on Big Data (Big Data 2021), pp. 5997.
    • Year: 2021
    • Citations: 1

Dr Mahmood Al-Shareeda | Engineering | Best Researcher Award |

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🌟Dr. Mahmood Al-Shareeda, Engineering, Best Researcher Award🏆

Iraq University College, Iraq

Professional Profiles : 

Scopus Profile

Google Scholar Profile

Orcid Profile

👩‍🎓 Bio Summary:

Dr. Mahmood Al-Shareeda is a highly accomplished researcher specializing in cybersecurity and advanced networking. He holds a Bachelor’s Degree in Communication Engineering, a Master’s Degree in Computer & Communication Engineering, and a Ph.D. in Internet Infrastructure Security. With expertise in VANET and IoT security, wireless communication, and cryptography, Dr. Al-Shareeda’s work focuses on developing secure communication protocols for emerging technologies. He has held research positions at institutions such as Universiti Sains Malaysia and the University of Ha’il, contributing to projects ranging from quantum-resistant schemes to blockchain-based secure data sharing among vehicles.

🎓 Education:

Dr. Mahmood Al-Shareeda holds a Bachelor’s Degree in Communication Engineering from Iraq University College, a Master’s Degree in Computer & Communication Engineering from the Islamic University of Lebanon, and a Ph.D. in Internet Infrastructure Security from Universiti Sains Malaysia. Additionally, he completed speaking English courses at the English Language Institute of Symbiosis in India and pursued a Doctorate in Business Administration from the British Institute of Economics and Political Science in the UK.

👩‍💼 Professional Experience:

Dr. Al-Shareeda’s professional journey spans various research and academic roles. He served as a Postdoctoral Fellow at Universiti Sains Malaysia, focusing on authentication and privacy-preserving schemes for 5G-enabled vehicular fog computing. He also held research positions at the University of Ha’il in Saudi Arabia, contributing to projects such as quantum-resistant schemes and blockchain-based secure data sharing among vehicles.

🔬 Research Focus:

With expertise in VANET and IoT security, wireless communication, and classical and quantum cryptography, Dr. Al-Shareeda’s research interests lie at the intersection of cutting-edge technologies and cybersecurity. His work delves into developing efficient and secure communication protocols for emerging technologies.

🚀 Professional Journey:

Beginning his career with academic pursuits in communication engineering, Dr. Al-Shareeda’s trajectory evolved into the realm of cybersecurity and advanced networking. He transitioned from academia to research roles, actively contributing to projects aimed at enhancing the security and efficiency of communication networks, particularly in the context of emerging technologies like 5G and vehicular networks.

🏅 Honors & Awards:

Dr. Al-Shareeda’s contributions have been recognized with numerous honors and awards, reflecting his dedication and excellence in research. His achievements include notable distinctions such as research grants, scholarships, and accolades for his impactful publications and contributions to the field.

📚 Top Noted Publications & Contributions:

Survey of Authentication and Privacy Schemes in Vehicular Ad Hoc Networks

Authors: MA Al-shareeda, M Anbar, IH Hasbullah, S Manickam

Published in IEEE Sensors Journal in 2021

Citations: 126

VPPCS: VANET-based Privacy-Preserving Communication Scheme

Authors: MA Al-Shareeda, M Anbar, S Manickam, AA Yassin

Published in IEEE Access in 2020

Citations: 83

Efficient Conditional Privacy Preservation with Mutual Authentication in Vehicular Ad Hoc Networks

Authors: MA Al-Shareeda, M Anbar, IH Hasbullah, S Manickam, SM Hanshi

Published in IEEE Access in 2020

Citations: 51

SE-CPPA: A Secure and Efficient Conditional Privacy-Preserving Authentication Scheme in Vehicular Ad-Hoc Networks

Authors: MA Al-Shareeda, M Anbar, S Manickam, IH Hasbullah

Published in Sensors (Special Issue Recent Trends in Wireless Sensor and Actuator) in 2021

Citations: 48

Review of Prevention Schemes for Replay Attack in Vehicular Ad hoc Networks (VANETs)

Authors: MA Al-shareeda, M Anbar, IH Hasbullah, S Manickam, N Abdullah

Presented at the 2020 IEEE 3rd International Conference on Information Communication and Technology (ICICT)

Citations: 46

📊 Author Metrics:

Dr. Al-Shareeda’s impact in the academic community is reflected in his author metrics, with an impressive publication record, substantial citation counts, and a notable H-index across various platforms. His contributions to the field demonstrate both depth and breadth, underscoring his influence in advancing research in cybersecurity and communication engineering.

⏳ Research Timeline:

Dr. Al-Shareeda’s research journey has been characterized by a timeline marked with significant milestones and achievements. From his foundational education in communication engineering to his current role as a respected researcher specializing in cybersecurity and advanced networking, each phase of his career has contributed to his expertise and impact in the field.