Muhammad Asif Munir | Machine Learning | Best Researcher Award

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Mr. Muhammad Asif Munir | Machine Learning | Best Researcher Award

Assistant Professor| Swedish College of Engineering and Technology | Pakistan

Dr. Muhammad Asif Munir is an accomplished researcher and academic in the field of Electrical Engineering, currently serving as an Assistant Professor at the Swedish College of Engineering and Technology, District Rahim Yar Khan, Punjab, Pakistan, and pursuing his Ph.D. at The Islamia University of Bahawalpur. His research primarily focuses on machine learning and deep learning applications in biomedical image analysis, with a particular emphasis on addressing the challenges of small and imbalanced radiomics datasets. With six peer-reviewed publications indexed in SCI and Scopus journals, including IEEE Access and Future Internet (MDPI), and a growing citation record of 56 citations (h-index: 4, i10-index: 2), Dr. Munir has demonstrated consistent academic excellence and research innovation. His notable contribution, the GSRA-KL framework, introduces a novel sparse regularized autoencoder–based methodology that significantly enhances synthetic data generation and improves the predictive accuracy of gene mutation analysis in lung cancer radiomics. This work not only contributes to the evolution of precision oncology but also exemplifies the integration of AI-driven data synthesis with clinical applications. His ongoing research explores the incorporation of explainable artificial intelligence (XAI) into radiomics for more interpretable, transparent, and reliable predictive modeling, fostering clinically explainable AI systems in healthcare. Dr. Munir’s interdisciplinary approach bridges data science, medical imaging, and clinical decision support, aiming to make AI tools both scientifically robust and ethically transparent. A member of professional organizations such as IEEE and IAENG, he remains actively engaged in promoting research collaboration and advancing the global discourse on intelligent healthcare systems. Through his scholarly contributions, Dr. Munir is significantly impacting the development of data-efficient, interpretable, and patient-centered AI frameworks, reinforcing the global transition toward smart healthcare technologies and next-generation precision medicine. His commitment to research excellence and translational impact continues to position him as a promising figure in the convergence of engineering and medical AI research.

Featured Publication

Aslam, M. A., Munir, M. A., & Cui, D. (2020). Noise removal from medical images using hybrid filters of technique. Journal of Physics: Conference Series, 1518(1), 012061.

Aslam, M. A., Xue, C., Wang, K., Chen, Y., Zhang, A., Cai, W., Ma, L., Yang, Y., Sun, X., & Munir, M. A. (2020). SVM based classification and prediction system for gastric cancer using dominant features of saliva. Nano Biomedicine and Engineering, 12(1), 1–13.

Munir, M. A., Aslam, M. A., Shafique, M., Ahmed, R., & Mehmood, Z. (2022). Deep stacked sparse autoencoders – A breast cancer classifier. Mehran University Research Journal of Engineering and Technology, 41(1), 41–52.

Aslam, M. A., Munir, M. A., Ahmad, R., Samiullah, M., Hassan, N. M., & Mahnoor, S. (2022). Deep neural networks for prediction of cardiovascular diseases. Nano Biomedicine and Engineering, 14(1).

Munir, M. A., Shah, R. A., Ali, M., Laghari, A. A., Almadhor, A., & Gadekallu, T. R. (2024). Enhancing gene mutation prediction with sparse regularized autoencoders in lung cancer radiomics analysis. IEEE Access.

Dr. Muhammad Asif Munir’s research advances intelligent healthcare by integrating machine learning and explainable AI to enhance diagnostic accuracy and transparency in medical imaging. His innovations in radiomics and synthetic data generation foster data-efficient, interpretable, and globally applicable solutions that strengthen precision oncology and next-generation healthcare systems.

Hawazin Elani | Machine Learning | Best Researcher Award

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Dr. Hawazin Elani | Machine Learning | Best Researcher Award

Harvard University | United States

Dr. Hawazin W. Elani, Ph.D., is an accomplished scholar and academic leader whose research integrates dentistry, epidemiology, and health policy to advance oral health equity through data-driven, interdisciplinary approaches. She serves as an Associate Professor in the Department of Health Policy and Management at the Harvard T.H. Chan School of Public Health and in the Department of Oral Health Policy and Epidemiology at the Harvard School of Dental Medicine, with additional affiliations at the Harvard Data Science Initiative and the Kempner Institute for the Study of Natural and Artificial Intelligence. Dr. Elani earned her Ph.D. in Dental Sciences with a concentration in Epidemiology and Population Health and an M.Sc. from McGill University, as well as an MMSc in Oral Biology and a Clinical Certificate in Prosthodontics from Harvard. Her research explores health disparities, oral health policy, and the application of artificial intelligence and machine learning in predicting oral health outcomes. She has authored over 30 peer-reviewed publications in high-impact journals such as Health Services Research, JAMA Network Open, and Journal of Dental Research, with her work cited widely for shaping discussions on healthcare access and reform. As principal investigator on multiple NIH and foundation-funded projects, including R01 and K-series grants, she has led innovative studies assessing the effects of Medicaid expansion and socioeconomic factors on dental care utilization. Recognized with Harvard’s Young Mentor Award and Distinguished Junior Faculty Award in 2024, Dr. Elani also contributes to national and international committees, including the NIH, the National Academies of Sciences, and the Medicaid Policy Research Advisory Group. Through her leadership, global collaborations, and dedication to mentoring, she continues to advance the intersection of artificial intelligence, population health, and oral health policy, driving forward equitable and sustainable improvements in healthcare delivery worldwide.

Profiles: Scopus | ORCID
Featured Publication

lani, H. W., Kawachi, I., & Sommers, B. D. (2020). Changes in emergency department dental visits after Medicaid expansion. Health Services Research, 55(1), 76–84.

Elani, H. W., Simon, L., Ticku, S., Bain, P. A., Barrow, J., & Riedy, C. A. (2018). Does providing dental services reduce overall health care costs? A systematic review of the literature. Journal of the American Dental Association (1939), 149(6), 430–438.e10.

Elani, H. W., Starr, J. R., Da Silva, J. D., & Gallucci, G. O. (2018). Trends in dental implant use in the U.S., 1999–2016, and projections to 2026. Journal of Dental Research, 97(13), 1424–1430.

Gil, M. S., Ishikawa-Nagai, S., Elani, H. W., Da Silva, J. D., Kim, D. M., Tarnow, D., Schulze-Späte, U., Cleber, S., & Bittner, N. (2019). Comparison of the color appearance of peri-implant soft tissue with natural gingiva using anodized pink-neck implants and pink abutments: A prospective clinical trial. The International Journal of Oral & Maxillofacial Implants, 34(1), 168–175.

Gil, M. S., Ishikawa-Nagai, S., Elani, H. W., Da Silva, J. D., Kim, D. M., Tarnow, D., Schulze-Späte, U., & Bittner, N. (2017). A prospective clinical trial to assess the optical efficacy of pink neck implants and pink abutments on soft tissue esthetics. Journal of Esthetic and Restorative Dentistry, 29(3), 213–219.

Christian Peluso | Artificial Intelligence | Excellence in Ethical AI Development Award

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Dr. Christian Peluso | Artificial Intelligence | Excellence in Ethical AI Development Award

Libero professionista | Consiglio Nazionale delle RicercheThis link is disabled | Italy

Dr. Christian Peluso is a researcher specializing in artificial intelligence with expertise in federated learning, deep learning, and cybersecurity, focusing on privacy-preserving systems for mobile and distributed environments. His research aims to develop intelligent models capable of processing complex and varied data while safeguarding user privacy and ensuring compliance with data protection regulations. Christian earned his Master’s degree in Artificial Intelligence from the University of Pisa with the highest distinction, presenting a thesis titled PrivNet: Advancing Mobile Security through Privacy-Preserving Federated Learning for Malware Detection, which introduced an innovative federated learning approach for mobile malware analysis using convolutional neural networks optimized for image-based data. He has actively collaborated with the Consiglio Nazionale delle Ricerche (CNR) and several academic and research institutions, contributing to projects that merge AI, cybersecurity, and data privacy. His publications, including “PrivNet: Advancing Mobile Security through Privacy-Preserving Federated Learning for Malware Detection” and “An Approach for Privacy-Preserving Mobile Malware Detection Through Federated Machine Learning,” reflect his deep involvement in advancing secure and decentralized AI solutions. He has also contributed to research on explainability-driven malware analysis using deep learning, aimed at improving model interpretability and aiding analysts in identifying malicious software components efficiently. Christian’s technical proficiency covers Python, machine learning frameworks, and reverse engineering methodologies, enabling him to design intelligent systems with strong analytical and practical impact. His academic achievements and professional experiences in software engineering, mobile application security, and AI-driven analysis demonstrate a consistent pursuit of excellence and innovation. His work not only strengthens theoretical understanding in federated machine learning but also delivers practical tools for protecting digital ecosystems. Through his commitment to research, collaboration, and ethical AI development, he continues to contribute meaningfully to the evolving landscape of artificial intelligence and data security. 17 Citations 3 Documents 2 h-index View h-index

Profiles: Google Scholar | Scopus | ORCID
Featured Publication

Iadarola, G., Casolare, R., Martinelli, F., Mercaldo, F., Peluso, C., & Santone, A. (2021). A semi-automated explainability-driven approach for malware analysis through deep learning. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE. Cited by: 19

Ciaramella, G., Martinelli, F., Mercaldo, F., Peluso, C., & Santone, A. (2024). An approach for privacy-preserving mobile malware detection through federated machine learning. In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024). SciTePress.
Cited by: 5

Peluso, C., Ciaramella, G., Mercaldo, F., Santone, A., & Martinelli, F. (2024). A federated learning-based Android malware detector through differential privacy. In International Conference on Computer Aided Systems Theory (EUROCAST 2024) (pp. 307–319).

Marco Capogni | Data Science | Best Researcher Award

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Prof. Dr. Marco Capogni | Data Science | Best Researcher Award

Researcher | ENEA – National Institute for Ionizing Radiation Metrology | Italy

Prof. Dr. Marco Capogni’s research focuses on the precise measurement and standardization of radionuclides, with a strong emphasis on ionizing radiation metrology and its applications in medicine, industry, and environmental monitoring. He has developed and maintained primary national standards for radioactivity, collaborating with international institutions such as the Bureau International des Poids et Mesures (BIPM) and contributing to interlaboratory comparisons to ensure global consistency in radionuclide measurements. His work includes the implementation of absolute measurement techniques and computational codes such as GEANT, MCNP, Penelope, and Fluka for both direct and indirect activity determination. Marco has been actively involved in projects producing medical radionuclides like Mo-99 and Cu-64, utilizing neutron activation and absolute or relative measurement systems, and has contributed to the development of innovative sources of fusion neutrons for radioisotope production under the Sorgentina-RF project. His expertise spans gamma spectrometry, liquid scintillation counting, and coincidence counting methods, and he has served as a member of international working groups including the International Committee for Radionuclide Metrology (ICRM) and the European Metrology Network for Radiation Protection (EURAMET). Marco has led and coordinated numerous European research projects funded by EMRP and EMPIR, focusing on robust production chains for medical radionuclides, radiological early warning networks, and metrology for decommissioning nuclear facilities. He has also contributed to the training of students at the master’s and doctoral levels in physics, engineering, and medical physics, supervising multiple theses on radionuclide metrology and measurement techniques. His work has resulted in significant publications, patents, and participation in international conferences, reflecting his leadership in metrological science and nuclear applications. Marco Capogni’s contributions demonstrate a blend of experimental expertise, computational proficiency, and collaborative engagement with international metrology and research networks, addressing challenges in nuclear measurement, radioprotection, and medical isotope production. He has achieved 1,882citations, authored 133 documents, and holds an h-index of 21.

Profiles: Scopus | ORCID
Featured Publication

Capogni, M., … (2024). Assessment of impurity production upon 14 MeV fusion neutron irradiation of both natural and isotopically enriched 100Mo samples. European Physical Journal Plus.
Citations: 1

Capogni, M., … (2024). Measurements of the absolute gamma-ray emission intensities from the decay of 166Ho. Applied Radiation and Isotopes.
Citations: 2

Capogni, M., … (2024). Future of 99Mo reactor-independent supply. Nature Reviews Physics.
Citations: 3

Capogni, M., … (2023). Analytical study of low energy proton interactions in the SORGENTINA’s fusion ion source-Part II: beam-gas: SORGENTINA ion beam interactions. European Physical Journal Plus.
Citations: 2

Capogni, M., … (2023). The international reference system for beta-particle emitting radionuclides: Validation through the pilot study CCRI(II)-P1.Co-60. Applied Radiation and Isotopes.
Citations: 5

Capogni, M., … (2023). The importance of uncertainty analysis and traceable measurements in routine quantitative 90Y-PET molecular radiotherapy: A multicenter experience. Pharmaceuticals.
Citations: 1

Capogni, M., … (2023). Experimental campaign on ordinary and baritic concrete samples for the SORGENTINA-RF plant: The SRF-bioshield tests. European Physical Journal Plus.
Citations: 3

Miao Cui | Artificial Intelligence | Best Researcher Award

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Prof. Miao Cui | Artificial Intelligence | Best Researcher Award

Professor |Dalian University of Technology| China

Professor Miao Cui focuses on the fields of digital transformation, innovation management, and data-driven business strategy, with extensive exploration in enterprise and community digitalization practices. Her research emphasizes how organizations orchestrate resources to adapt to digital economies, manage transformation, and foster innovation across various sectors, including state-owned enterprises, traditional manufacturing, high-tech firms, service industries, and non-profit community organizations. She has conducted in-depth case studies on more than 50 enterprises such as Haier, P&G, Inspur, and BBMW, as well as over 30 rural communities across China, providing valuable insights into digital capability development and data-oriented strategic renewal. Through her work, Miao Cui examines the interconnection between big data strategy and organizational growth, focusing on how data analysis informs decision-making, enhances resilience, and drives innovation in dynamic environments. Her studies extend to the role of information systems in enabling business transformation, ecosystem governance, and e-commerce-based social innovation, contributing significantly to both theory and practice in management sciences. Miao Cui’s research achievements include numerous high-impact publications in leading international journals such as the International Journal of Information Management, Information Systems Journal, and Journal of Strategic Information Systems, recognized as top-ranked in their field. Her scholarly contributions have been repeatedly highlighted through ESI highly cited and hot papers, reflecting the global relevance and influence of her work. Additionally, she has authored and edited multiple academic monographs, developed widely adopted management cases for Ivey Publishing, and received several awards for excellence in research and social science innovation. Her work has been cited extensively and applied in organizational and policy contexts, contributing to global discussions on digital transformation and innovation leadership. Miao Cui has 625 Citations, 26 Documents, and an h-index of 9. View h-index.

Profile: Scopus 
Featured Publication

Author(s) unknown. (2025). Collaborative innovation network embeddedness and a firm’s technological impact: Does prior networking experience matter? Journal of Technology Transfer. Cited by 1

Author(s) unknown. (2025). An integrated approach to modeling the influence of critical factors in low-carbon technology adoption by chemical enterprises in China. Journal of Environmental Management. Cited by 2

Khaista Rahman | Artificial Intelligence| Best Paper Award

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Dr. Khaista Rahman | Artificial Intelligence| Best Paper Award

Assistant Professor | Shaheed Benazir Bhutto University Sheringal | Pakistan 

Dr. Khaista Rahman is a distinguished researcher specializing in fuzzy set theory, fuzzy logic, aggregation operators, and artificial intelligence-based decision support systems, with a strong focus on solving decision-making problems under uncertainty. His work explores advanced mathematical structures like Pythagorean fuzzy numbers, interval-valued fuzzy models, and complex fuzzy systems to create robust solutions for multi-attribute group decision-making processes. Dr. Rahman has published extensively on generalized and induced aggregation operators, developing new models that enhance decision accuracy and reliability in diverse applications such as plant location selection, hospital siting during COVID-19, vaccine selection, and railway optimization problems. His research integrates t-norm and t-conorm-based approaches, Einstein hybrid operators, and logarithmic intuitionistic fuzzy techniques to handle complex decision environments. He has also supervised several M.Phil., M.Sc., and BS scholars, contributing significantly to academic mentorship and knowledge dissemination. Recognized among the top 2% scientists worldwide by Stanford University from 2022 to 2025, he has made substantial contributions to granular computing, soft computing, and intelligent systems literature. His work during the COVID-19 pandemic stands out for developing emergency response models using complex fuzzy information to predict and manage disease spread in Pakistan. As Principal Investigator of a funded project on complex intelligent decision support models, Dr. Rahman has bridged theoretical advancements with practical implementations, making his research highly impactful. With an H-index of 26 and over 1900 citations, his scholarly influence spans mathematics, operations research, and computational intelligence, providing frameworks that empower policymakers and industries to make optimal decisions in uncertain and dynamic scenarios. Dr. Khaista Rahman has achieved 776 citations across 532 documents with an impressive h-index of 16.

Profile:  Scopus | ORCID
Featured Publication

  1. Rahman, K., & Khishe, M. (2024). Confidence level based complex polytopic fuzzy Einstein aggregation operators and their application to decision-making process [Retracted]. Scientific Reports, 14(1), 15253.

  2. Rahman, K., & Khishe, M. (2024). Retraction Note: Confidence level based complex polytopic fuzzy Einstein aggregation operators and their application to decision-making process. Scientific Reports, 14(1).

  3. Rahman, K., et al. (2025). Unraveling vegetation diversity and environmental influences in the Sultan Kha Valley, Dir Upper, Pakistan: An advanced multivariate analysis approach. Polish Journal of Environmental Studies.

  4. Rahman, K. (2024). Some new types induced complex intuitionistic fuzzy Einstein geometric aggregation operators and their application to decision-making problem. Neural Computing and Applications.

Prof. Dr. Mushtaq Ahmed | Natural Language Processing | Best Researcher Award

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Prof. Dr. Mushtaq Ahmed | Natural Language Processing | Best Researcher Award

Professor | University of Science and Technology | Pakistan

Prof. Dr. Mushtaq Ahmed (Ph.D., UFSM-Brazil; Post-doc, Lund University, Sweden) is a distinguished researcher, HEC-approved Ph.D. supervisor, and two-time HEC Best University Teacher Awardee (2008 & 2020), currently serving as Professor & Chairman, Department of Biotechnology, and Dean, Faculty of Arts & Humanities at the University of Science and Technology, Bannu, Pakistan. With over two decades of academic and research experience, he has made impactful contributions to biochemistry, toxicology, enzymology, nanotechnology, and drug discovery, with a special focus on snake venom acetylcholinesterase characterization, neuroprotection, oxidative stress, diabetes management, and green synthesis of nanoparticles for biomedical applications. Dr. Ahmed has authored more than 100 international ISI-indexed publications in high-impact journals including Chemico-Biological Interactions, Journal of Enzyme Inhibition & Medicinal Chemistry, and Applied Organometallic Chemistry, advancing knowledge in drug design and therapeutic innovation. He has successfully supervised 8 Ph.D. and 30 M.Phil scholars, established state-of-the-art research facilities such as an animal house and enzymology laboratory, and led several nationally funded projects focused on novel drug design and neuroprotective strategies. In addition, he serves as a reviewer for multiple international journals and organizes scientific symposia, continuing to mentor future researchers and strengthen Pakistan’s scientific ecosystem.

Profile:  Google Scholar | ORCID

Featured Publications

1. Khan, R. A., Khan, M. R., Sahreen, S., & Ahmed, M. (2012). Evaluation of phenolic contents and antioxidant activity of various solvent extracts of Sonchus asper (L.) Hill. Chemistry Central Journal, 6(1), 12. Citations: 325

2. Khan, R. A., Khan, M. R., Sahreen, S., & Ahmed, M. (2012). Assessment of flavonoids contents and in vitro antioxidant activity of Launaea procumbens. Chemistry Central Journal, 6(1), 43. Citations: 292

3. Abbasi, A. M., Khan, M. A., Ahmed, M., & Zafar, M. (2010). Herbal medicines used to cure various ailments by the inhabitants of Abbottabad district, North West Frontier Province, Pakistan. Indian Journal of Traditional Knowledge, 9(1), 175–183. Citations: 162

4. Bagatini, M. D., Martins, C. C., Battisti, V., Gasparetto, D., Da Rosa, C. S., … & Ahmed, M. (2011). Oxidative stress versus antioxidant defenses in patients with acute myocardial infarction. Heart and Vessels, 26(1), 55–63. Citations: 155

5. Ahmed, M., Rocha, J. B. T., Corrêa, M., Mazzanti, C. M., Zanin, R. F., Morsch, A. L. B., … & Schetinger, M. R. C. (2006). Inhibition of two different cholinesterases by tacrine. Chemico-Biological Interactions, 162(2), 165–171. Citations: 80

Ahsan Ali | Machine Learning | Best Researcher Award

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Mr. Ahsan Ali | Machine Learning | Best Researcher Award

PhD Student at Tianjin University | Pakistan

Overall, Ahsan Ali emerges as a promising young researcher whose academic journey reflects both excellence and commitment to advancing the field of electrical power engineering. With a strong foundation laid through his master’s and bachelor’s degrees, he has already demonstrated the ability to translate theoretical knowledge into practical solutions. His expertise covers deep learning-based power quality disturbance classification, fault diagnosis in converters, power system protection, and renewable energy integration—areas that are of great importance in the current era of smart grids and sustainable power technologies. Beyond his academic pursuits, Ahsan has also gained valuable industrial exposure in sugar mills, cement factories, and large-scale power plants, which has enriched his applied perspective and problem-solving abilities. Furthermore, his active participation in IEEE activities, seminars, and conferences highlights his growing leadership potential. With sustained research productivity, strong collaborations, and a focus on impactful publications, Ahsan is well-prepared to become a leading figure in his domain.

Professional Profile

 Scopus 

Education

Ahsan Ali completed his Master’s degree in Electrical Power Engineering from Quaid-e-Awam University of Engineering, Science and Technology, Pakistan, with a strong academic record His master’s research was focused on the classification of power quality disturbances using advanced deep learning methods. The study addressed the increasing importance of reliable power system operation in modern electrical networks and explored the integration of Discrete Wavelet Transform and Multi-Resolution Analysis with one-dimensional convolutional neural networks. This work aimed to improve the accuracy of identifying and classifying disturbances such as sags, swells, harmonics, and transients that affect system reliability. He also earned a Bachelor of Electrical Engineering degree from the same institution. His undergraduate project involved modeling and simulating under-frequency relays for generator protection using MATLAB and Simulink, providing him with practical expertise in system reliability.

Experience

Ahsan Ali has developed a professional career in the field of electrical power systems through roles that combined technical responsibilities and applied industry learning. He worked as an Assistant Electrical Engineer at Khairpur Sugar Mills, where he supported the engineering team in resolving power disturbances, implementing protection schemes, and managing distribution systems. In a similar role at Rohri Cement Factory, he assisted in project planning and power management activities while ensuring smooth plant operations. He also gained valuable industrial training during internships at Zorlu Enerji Pakistan, where he observed wind turbine operations and grid station management, TNB Liberty Power Plant, where he studied combined cycle operations and turbine performance, and Jamshoro Power Company, where he familiarized himself with the functioning of large-scale thermal units. These experiences helped him build a strong foundation in energy production, distribution, and system reliability, combining both theoretical and practical aspects of electrical engineering in real environments.

Skills

Ahsan Ali possesses a wide range of technical and analytical skills that complement his academic and professional background in electrical engineering. He has advanced proficiency in MATLAB and Simulink for modeling, simulation, and analysis of power systems, as well as strong competence in programmable logic controller programming for industrial automation and protective arrangements. His expertise covers power system analysis, electrical distribution engineering, fault protection, renewable energy integration, and the design and control of electrical machines and drives. He has applied these skills in both academic research and industrial practice, focusing on optimizing system performance and ensuring reliability. Ahsan has also acquired certifications in advanced courses, including power system analysis, electrical distribution system engineering, and MATLAB applications. He completed specialized training in Typhoon HIL, gaining experience in power quality testing and power flow modeling. In addition, he has explored fields such as freelancing, WordPress, and graphic design to diversify his professional capabilities.

Research Focus

Ahsan Ali’s research focus centers on power system reliability and advanced diagnostic methods for modern electrical networks. His interests include fault diagnosis of high-power electronic converters, stability analysis, and the integration of renewable energy systems into existing grids. He has also worked extensively on the classification of power quality disturbances through the application of deep learning algorithms, which represents a significant contribution to intelligent power system monitoring. His publications highlight his dedication to advancing the field, with studies on PQD detection techniques, microgrid design for seaport operations, and classification models for system optimization. His research reflects a balance between theoretical development and applied engineering, addressing the challenges posed by distributed generation, energy transitions, and increasing demand for sustainable technologies. Through his projects, Ahsan has emphasized the importance of integrating artificial intelligence and machine learning into power systems to enhance fault detection, predictive maintenance, and operational decision-making.

Awards 

Ahsan Ali has earned recognition for his academic excellence, research contributions, and active participation in professional activities. He has received certificates of appreciation for organizing technical events and webinars, including recognition for his performance during the COVID-19 period, when he contributed to academic engagement through virtual platforms. He participated in poster competitions on power system fault diagnosis and was acknowledged by the IEEE QUEST Chapter for his contributions. His involvement in seminars and workshops includes presenting research on power quality disturbances classification and generator protection at national and institutional conferences, where he shared findings with peers and faculty. He has also attended multiple training programs and short courses related to industrial safety, renewable progress, technical writing, and research management. These experiences have strengthened his academic and professional profile. As an associate member of IEEE, Ahsan has demonstrated his commitment to professional growth and engagement with the global engineering community.

Publication Top Notes

Title: Comprehensive review of power quality disturbance detection and classification techniques
Journal: Computers and Electrical Engineering, Vol. 126, Article 110512

Title: Design and Analysis of Seaport Microgrid with Ship Loads
Journal: Proceedings of IEEE China International Youth Conference on Electrical Engineering (CIYCEE), Wuhan, China

Title: Power Quality Disturbances (PQDs) Classification Analyzed Based on Deep Learning Technique
Journal: Journal of Computing and Biomedical Informatics, Vol. 4, Issue 1

Title: Comparative Analysis of the PWM and SPWM on Three-Phase Inverter through Different Loads and Frequencies
Journal: Journal of Computing and Biomedical Informatics, Vol. 4, Issue 2, pp. 204–220

Conclusion

Ahsan Ali is a highly suitable and deserving candidate for the Best Researcher Award in Electrical Power Engineering, given the scope and relevance of his contributions. His research consistently bridges theoretical frameworks with real-world applications, particularly in areas such as power system reliability, renewable energy, and advanced control methods. These contributions underscore his ability to design innovative solutions that can enhance system stability and sustainability. Although there remains room for growth in terms of expanding his global research impact, securing patents, and publishing in more high-impact journals, his current record already reflects a blend of academic excellence and professional dedication. His consistent engagement with international conferences and reputed journals highlights his growing presence in the research community. With his career trajectory, it is evident that he embodies the qualities of an emerging researcher whose work contributes not only to scientific advancement but also to practical technological development, making him an ideal award recipient.