Madhiarasan Manoharan | Data Science and Analytics | Innovative Research Award

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Innovative Research Award

Madhiarasan Manoharan
Independent Researcher, India

Madhiarasan Manoharan
Affiliation Independent Researcher
Country India
Scopus ID 56997519900
Documents 31
Citations 624
h-index 10
Subject Area Data Science and Analytics
Event International Soil Scientist Awards

Madhiarasan Manoharan is an independent researcher in the subject area of Data Science and Analytics. His research profile in the context of the Innovative Research Award, presented at the International Academic Achievements & Awards event [4].

Abstract

This article presents a scholarly overview of Madhiarasan Manoharan, an independent researcher working in Data Science and Analytics. It summarises his bibliometric indicators as recorded in Scopus, outlines the scope of his research activity, and considers his recognition through the Innovative Research Award [1][4].

Keywords

Data science; analytics; independent research; bibliometrics; Scopus; academic recognition; innovative research.

Introduction

Data science and analytics draw on statistics, computing and domain knowledge to extract useful information from data. Researchers in this field often contribute across several application areas. Madhiarasan Manoharan conducts his research as an independent researcher, and his publication record is indexed in the Scopus database [1][5].

Research Profile

The Scopus profile, identified by Author ID 56997519900, records the following indicators [1]:

  • Documents indexed: 31
  • Total citations: 624
  • h-index: 10
  • Subject area: Data Science and Analytics

The researcher is also identifiable through an ORCID record, a persistent digital identifier that links a researcher to his or her scholarly outputs [2].

Research Contributions

His work falls within Data Science and Analytics, with emphasis on the analytical and computational treatment of data. The indexed output shows sustained scholarly activity, and the citation count indicates that parts of this work have been referenced by other researchers [1]. Detailed descriptions of individual studies are available through the publication records listed on the Scopus profile and in the DOI-registered articles [3].

Publications

The Scopus database lists 31 documents under this author profile [1]. Representative publications can be verified through their Digital Object Identifiers (DOIs), which provide permanent links to the published versions [3]. Readers are encouraged to consult the full list on the profile for complete bibliographic details.

Research Impact

Citation-based indicators offer one measure of research visibility. A total of 624 citations and an h-index of 10 indicate that at least ten of his documents have each received ten or more citations [1]. Such metrics are descriptive and should be read alongside the quality and context of the work itself.

Award Suitability

The Innovative Research Award, presented at the International Academic Achievements & Awards event, recognises researchers whose work shows originality and scholarly merit [4]. His suitability may be considered on the following grounds:

  • A body of 31 indexed documents in a recognised database [1]
  • Citation evidence of 624 citations and an h-index of 10 [1]
  • Independent research activity within Data Science and Analytics

Conclusion

Madhiarasan Manoharan has built a documented research record in Data Science and Analytics as an independent researcher. His Scopus indicators support his consideration for the Innovative Research Award [1][4].

References

  1. Madhiarasan, M., & Louzazni, M. (2022). Analysis of artificial neural network: Architecture, types, and forecasting applications. Journal of Electrical and Computer Engineering, 2022(1), 5416722.
    https://doi.org/10.1155/2022/5416722
  2. Madhiarasan, M., & Deepa, S. N. (2016). Comparative analysis on hidden neurons estimation in multi layer perceptron neural networks for wind speed forecasting. Artificial Intelligence Review, 1–23.
    https://doi.org/10.1007/s10462-016-9506-6
  3. Al-Dahidi, S., Madhiarasan, M., Al-Ghussain, L., Abubaker, A. M., Ahmad, A. D., et al. (2024). Forecasting solar photovoltaic power production: A comprehensive review and innovative data-driven modeling framework. Energies, 17(16), 4145.
    https://doi.org/10.3390/en17164145
  4. Madhiarasan, M., & Roy, P. P. (2022). A comprehensive review of sign language recognition: Different types, modalities, and datasets. arXiv preprint arXiv:2204.03328.
    https://arxiv.org/abs/2204.03328
  5. Madhiarasan, M., & Deepa, S. N. (2016). A novel criterion to select hidden neuron numbers in improved back propagation networks for wind speed forecasting. Applied Intelligence, 44(4), 878–893.
    https://doi.org/10.1007/s10489-015-0737-z

Takeshi Tsuda | Data Science | International Soil Scientist Awards

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International Soil Scientist Awards

Takeshi Tsuda
Nemours Children’s Hospital Delaware

Takeshi Tsuda
Affiliation Nemours Children’s Hospital Delaware
Country United States
Scopus ID 7402162750
Documents 70
Citations 1,810
h-index 22
Subject Area Data Science and Analytics
Event International Soil Scientist Awards

Takeshi Tsuda is a physician-researcher affiliated with Nemours Children’s Hospital Delaware whose indexed scholarly record includes work in pediatric cardiology, cardiovascular physiology, exercise performance, cardiomyopathy, and related clinical research. Institutional and bibliographic sources identify his affiliation with Nemours Children’s Health Delaware and associate his publications with the ORCID identifier 0000-0001-8110-1388. [1] [2]

Abstract

This academic recognition profile presents the research record of Takeshi Tsuda in the context of the International Soil Scientist Awards. The supplied bibliometric profile records 70 documents, 1,810 citations, and an h-index of 22. Independent institutional and bibliographic sources document a broader research program centered on pediatric cardiovascular medicine and quantitative clinical investigation. [1] [3]

Keywords

  • Takeshi Tsuda
  • Research Analytics
  • Data Science and Analytics
  • Pediatric Cardiology
  • Cardiovascular Research
  • International Soil Scientist Awards

Introduction

Takeshi Tsuda’s documented academic activity spans clinical research and quantitative investigation in pediatric cardiovascular medicine. Nemours identifies him as a physician at its Delaware children’s hospital, while its researcher profile records research interests involving exercise physiology, cardiac rehabilitation, cardio-oncology, and dystrophic heart research. [2] These areas provide a substantial evidence base for examining how structured research data, clinical measurements, and analytical methods can contribute to biomedical investigation.

Research Profile

The supplied Scopus-based profile lists 70 documents, 1,810 citations, and an h-index of 22. Scopus-associated bibliographic information independently identifies author ID 7402162750 and reports publications across multiple biomedical journals. [1] The profile should therefore be interpreted as a bibliometric snapshot rather than a complete measure of research quality or significance.

Research Contributions

Published studies associated with Tsuda include investigations of cardiovascular abnormalities, exercise performance, cardiomyopathy, and pediatric cardiac conditions. For example, research has examined subclinical coronary abnormalities following arterial switch procedures, while later work addressed cardiovascular changes associated with Duchenne muscular dystrophy. [4] [5] Such publications illustrate the use of clinical observations, physiological measurements, and quantitative analysis in pediatric cardiovascular research.

Publications

  • Tsuda T. Transient Ischemic Attack and Ischemic Stroke in Danon Disease with Formation of Left Ventricular Apical Thrombus despite Normal Systolic Function. doi:10.1155/2017/6576382. [3]
  • Tsuda T. et al. Identifying Subclinical Coronary Abnormalities and Silent Myocardial Ischemia After Arterial Switch Operation. doi:10.1007/s00246-019-02085-4. [4]
  • Tsuda T. et al. Sinus Tachycardia and Unrelieved Wall Stress Precede Left Ventricular Systolic Dysfunction During Preclinical Cardiomyopathic Changes in Duchenne Muscular Dystrophy. doi:10.3390/jcdd12080280. [5]

Research Impact

The supplied citation metrics indicate a substantial indexed publication and citation record. Bibliographic sources also show continuing publications through 2026, including research on exercise performance and cardiovascular conditions. [1] [6] Citation counts and h-index values are useful indicators of scholarly visibility, but they do not independently establish the methodological quality, clinical importance, or originality of individual studies.

Award Suitability

For an award consideration process, the documented publication record, citation indicators, institutional affiliation, and identifiable research outputs provide material that can be evaluated against the award’s published criteria. The available evidence is primarily biomedical rather than soil-science-specific; consequently, any assessment should distinguish general research achievements from contributions directly relevant to soil science, environmental systems, or agricultural applications. The award committee may use the complete submitted record for its formal evaluation.

Conclusion

Takeshi Tsuda’s documented research profile combines an established bibliometric record with continuing scholarly activity in pediatric cardiovascular and quantitative clinical research. Institutional records and indexed publications provide verifiable evidence of his affiliation and research outputs, while the supplied Scopus metrics offer a concise representation of citation-based visibility. [1] [2]

References

  1. Elsevier. (n.d.). Scopus author details: Takeshi Tsuda, Author ID 7402162750. Scopus/ScienceDirect author record.
    https://www.sciencedirect.com/author/7402162750/takeshi-tsuda
  2. Nemours Children’s Health. (n.d.). Takeshi Tsuda, MD — Researcher Profile.
    https://www.nemours.org/find-a-researcher/4660-takeshi-tsuda-wilmington.html
  3. Tsuda, T. et al. (2017). Transient Ischemic Attack and Ischemic Stroke in Danon Disease with Formation of Left Ventricular Apical Thrombus despite Normal Systolic Function. Case Reports in Pediatrics. DOI: https://doi.org/10.1155/2017/6576382
  4. Tsuda, T. et al. (2019). Identifying Subclinical Coronary Abnormalities and Silent Myocardial Ischemia After Arterial Switch Operation. Pediatric Cardiology. DOI: https://doi.org/10.1007/s00246-019-02085-4
  5. Tsuda, T., Walczak, A., & O’Neil, K. (2025). Sinus Tachycardia and Unrelieved Wall Stress Precede Left Ventricular Systolic Dysfunction During Preclinical Cardiomyopathic Changes in Duchenne Muscular Dystrophy. Journal of Cardiovascular Development and Disease. DOI: https://doi.org/10.3390/jcdd12080280

DongHee Park | Data Science and Analytics | Innovative Research Award

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Innovative Research Award

DongHee Park
DAVISS, South Korea

DongHee Park
Affiliation DAVISS
Country South Korea
Documents 8
Subject Area Data Science and Analytics
Event International Soil Scientist Awards
ORCID 0009-0008-0010-1756

DongHee Park is a researcher affiliated with DAVISS in South Korea whose documented scholarly work applies data science and machine-learning methods to industrial fault diagnosis. The available record contains eight publications addressing condition-index representations, diagnostic automation, convolutional neural networks and feature classification for machinery faults. [1]

Abstract

Park’s research focuses on computational approaches for identifying and classifying faults in industrial machinery. Recent work compares conventional feature-based representations with physics-guided condition-index representations for motor fault diagnosis, while related studies investigate automated elevator diagnosis, rotor-system classification and machine-learning methods for blade-rubbing defects. [1] [2] The research demonstrates a consistent interest in translating operational data into diagnostic information.

Keywords

Data science; machine learning; fault diagnosis; condition monitoring; industrial motors; diagnostic automation; convolutional neural networks; rotor systems; blade rubbing; feature classification; predictive maintenance.

Introduction

Modern industrial systems generate large quantities of operational data that can be analysed to detect abnormal behaviour. Machine-learning algorithms and engineered diagnostic features provide methods for distinguishing normal operation from specific fault conditions. Park’s publication record addresses these challenges across several machinery categories, linking data analytics with practical condition-monitoring problems. [3]

Research Profile

The supplied profile records eight documents in the subject area of Data Science and Analytics. Citation and h-index values were not provided and therefore are not inferred. The publication sequence from 2024 to 2026 indicates sustained investigation of machine-learning feature development, classification methods, automated diagnosis and physics-guided representations.

Research Contributions

  • Evaluation of conventional and physics-guided representations for industrial motor fault diagnosis. [1]
  • Development of rule-based automation technology for elevator fault diagnosis. [2]
  • Use of combination images of feature vectors with convolutional neural networks for rotor fault classification. [3]
  • Development and classification of machine-learning features for early blade-rubbing diagnosis. [4] [5]

Publications

Comparative Evaluation of Conventional Feature-Based and Physics-Guided Condition-Index Representations for Industrial Motor Fault Diagnosis (2026), Machines. [1]

Development of Rule-Based Diagnostic Automation Technology for Elevator Fault Diagnosis (2025), Sensors. [2]

CNN-based fault classification using combination image of feature vectors in rotor systems (2024), Journal of Mechanical Science and Technology. [3]

A Study on Machine Learning-Based Feature Classification for the Early Diagnosis of Blade Rubbing (2024), Sensors.[4]

Development of features for blade rubbing defect classification in machine learning (2024), Journal of Mechanical Science and Technology.[5]

Research Impact

Because citation and h-index values were not supplied, quantitative scholarly impact cannot be assessed from the available profile alone. The publication record nevertheless demonstrates application of data-driven techniques to multiple industrial systems, including motors, elevators, rotor systems and machinery affected by blade rubbing. Publication across Machines, Sensors and the Journal of Mechanical Science and Technology provides a documented scholarly context for these contributions. [1] [3]

Award Suitability

The Innovative Research Award profile is supported by a coherent body of research applying machine learning and analytical representations to industrial fault diagnosis. The progression from feature development and classification toward automated diagnostic systems and physics-guided condition indices indicates methodological continuity. [2] [5] Final recognition should be determined using the complete scholarly record, originality, methodological quality, practical relevance, reproducibility and broader research contributions.

Conclusion

DongHee Park’s documented research is centred on data science and analytics for industrial condition monitoring and fault diagnosis. Publications from 2024 through 2026 cover machine-learning features, convolutional neural networks, rule-based automation and physics-guided diagnostic representations. This focused research trajectory provides a substantive basis for consideration for the Innovative Research Award within the International Soil Scientist Awards framework.

References

  1. Park, DongHee. (2026). Comparative Evaluation of Conventional Feature-Based and Physics-Guided Condition-Index Representations for Industrial Motor Fault Diagnosis. Machines.
    https://doi.org/10.3390/machines14091024
  2. Park, DongHee. (2025). Development of Rule-Based Diagnostic Automation Technology for Elevator Fault Diagnosis. Sensors.
    https://doi.org/10.3390/s26010223
  3. Park, DongHee. (2024). CNN-based fault classification using combination image of feature vectors in rotor systems. Journal of Mechanical Science and Technology.
    https://doi.org/10.1007/s12206-024-1006-z
  4. Park, DongHee. (2024). A Study on Machine Learning-Based Feature Classification for the Early Diagnosis of Blade Rubbing. Sensors.
    https://doi.org/10.3390/s24186013
  5. Park, DongHee. (2024). Development of features for blade rubbing defect classification in machine learning. Journal of Mechanical Science and Technology.
    https://doi.org/10.1007/s12206-023-1201-3

Rui Wang | Data Science | Research Excellence Award

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Dr. Rui Wang | Data Science | Research Excellence Award

Ningbo University | China 

Rui Wang’s research advances intelligent energy systems with a strong emphasis on battery state estimation, data-driven diagnostics, and optimization frameworks for sustainable power applications. His work integrates artificial intelligence, probabilistic modeling, optimal control, and cyber-physical system design to enhance the reliability, safety, and efficiency of lithium-ion batteries and broader electrical infrastructures. Through contributions spanning battery State of Health estimation, extremum-seeking control for battery pack equalization, model-data fusion domain adaptation, and ensemble learning for confidence-aware diagnostics, he addresses critical challenges in renewable energy storage and intelligent transportation systems. His involvement in developing reinforcement learning architectures further strengthens the adaptability and optimization capacity of modern energy networks. With 4 research documents, 38 citations, and an h-index of 3, his published work reflects growing recognition within the energy systems, artificial intelligence, and control engineering communities. Collaborative projects with industrial partners highlight the practical relevance of his methods, particularly in implementing early-warning mechanisms for battery degradation and improving the reliability of next-generation electric mobility technologies. His research contributions collectively support the advancement of intelligent, sustainable, and data-driven energy ecosystems aligned with global technological and environmental priorities.

Profile : Scopus 

Featured Publications

Wang, Q., Xie, M., Wang, R., & Mo, H. (2025). FESD: Feature-Enhanced Structured-State-Space Diffusion Model for Battery SOH Prediction and Imputation. IEEE Transactions on Instrumentation and Measurement.

Habte Tadesse LIKASSA | Data Science | Best Academic Researcher Award

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Dr. Habte Tadesse LIKASSA | Data Science | Best Academic Researcher Award

Dr. Habte Tadesse Likassa, Arizona State University, United States

Dr. Habte Tadesse Likassa is a versatile and accomplished academic researcher whose work bridges critical areas of biostatistics, medical imaging, and econometrics. He has made notable contributions to methodological innovation and has demonstrated leadership in education and research policy. His career is marked by a commitment to excellence, interdisciplinary collaboration, and applied impact.

Profile

Scopus

Google Scholar

Summary:

Dr. Habte Tadesse Likassa is an accomplished statistician and researcher with strong credentials in applied and theoretical domains. His work is interdisciplinary, spanning healthcare, economics, and statistical methodologies, and is marked by real-world relevance and academic rigor. His leadership roles, technical skills, and international collaborations reinforce his profile as a high-potential researcher. However, strategic focus on publishing in higher-impact journals and increasing citation metrics would strengthen his global scholarly presence.

🎓 Education

Habte T. Likassa holds a PhD in Statistics from the National Taiwan University of Science and Technology, Taiwan, awarded in July 2019. His doctoral research focused on developing new robust methods for outlier and heavy sparse noise detection in high-dimensional images, earning him an A+ grade. He also holds an MSc in Statistics from Addis Ababa University, Ethiopia, where his thesis involved spatial and count regression models for analyzing tuberculosis incidence in North Shoa Zone. He earned his BSc in Statistics from the University of Gondar, Ethiopia, with a research focus on time series modeling for traffic data forecasting. Additionally, he obtained a Higher Diploma in Teaching at Higher Education from Debre Birhan University in 2013.

💼Experience

Dr. Likassa is currently serving as a Postdoctoral Research Scholar at the Department of Biostatistics, College of Health Solutions, Arizona State University, USA, since 2024. His work involves both methodological advancements in biostatistics and applied analysis using high-dimensional datasets. Previously, he held academic appointments across several Ethiopian universities including Addis Ababa University, Ambo University, and Debre Birhan University, serving in various capacities such as Assistant Professor, Lecturer, and Graduate Assistant between 2008 and 2023.

🔬Research Focus

Dr. Likassa’s research interests are diverse and interdisciplinary. His work primarily focuses on the development of robust statistical methods for medical imaging and anomaly detection in high-dimensional data. He is also engaged in survival analysis for heart failure, COVID-19, and cancer patients, as well as econometric modeling of inflation volatility using multivariate GARCH techniques. His research interests include machine learning, deep robust methods, low-rank sparse decomposition such as RPCA and Tensor RPCA, optimization techniques, artificial intelligence, and applied statistical modeling including logistic regression, multivariate analysis, spatial analysis, medical image analysis, and time series forecasting.

🛠️Skills

Dr. Likassa has extensive expertise in data management and advanced statistical modeling. His skills span big data analytics, management of economic and survey claim data, spatial exploratory data analysis, and high-dimensional health and image data analysis. He is proficient in various statistical software including LaTeX, SPSS, SAS, Stata, R, GIS, MATLAB, and Python. He has also delivered numerous training sessions to researchers, postgraduate students, and government professionals on research ethics, statistical methods, and data analysis techniques.

🏆Awards

Dr. Likassa has received several awards for his contributions to research and presentations. In 2024, he delivered a talk at the Arizona Alzheimer’s Consortium. He has received multiple Best Presenter Certificates at international conferences, including a Best Presenter Award in Taiwan, Republic of China.

📚 Publications

  • Title: A Multivariate GARCH Model with Time-Varying Correlations: What Do Inflation Data Show in Ethiopia
    Authors: Likassa, Habte Tadesse; Chen, Dinggeng; Nadarajah, Saralees; Temesgen, Shibru; Gotu, Butte
    Year: 2025
    Journal: Computational Economics

  • Title: Building a Sustainable Future: Investigating the Role and Contributions of Higher Education Institutions Instructors in Promoting Social Sustainability—Empirical Evidence from Ethiopia
    Authors: Tafese, Mestawot Beyene; Kopp, Erika; Likassa, Habte Tadesse
    Year: 2024
    Journal: Education Sciences

Conclusion:

Dr. Likassa is highly suitable for the Best Academic Researcher Award. His breadth of expertise, research innovation, and dedication to academic service make him a commendable choice. Addressing the noted areas for improvement could further enhance his candidacy, but his current achievements already position him as an impactful and deserving researcher on an international scale

Hongchao Li | Data Science | Best Researcher Award

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Mr. Hongchao Li | Data Science | Best Researcher Award

Mr. Hongchao Li, Kunming University of Science and Technology, China

Mr. Hongchao Li is an Associate Professor at Kunming University of Science and Technology, China. He holds a Ph.D. in Rock Engineering from China University of Mining and Technology (Beijing). His research focuses on rock blasting theory and technology, including the dynamic performance of rocks and concrete, numerical simulation of blasting fragmentation damage, and controlled blasting techniques. With experience in both academia and industry, he has worked as a Project Deputy Manager at Beijing Zhongda Explosion Engineering Co., Ltd. and as an Assistant Lecturer at Kunming Industrial Vocational and Technical College. His expertise includes numerical simulation, rock mechanics, and blasting engineering.

Profile

Scopus

Summary:

Mr. Hongchao Li is a dedicated researcher with expertise in rock engineering and blasting technology. His ability to combine numerical modeling with experimental research makes him a strong candidate for the Best Researcher Award. His contributions to machine learning applications in engineering also highlight his forward-thinking approach.

🎓 Education

Li Hongchao obtained his Bachelor of Engineering degree in Mining Engineering from Kunming University of Science and Technology in 2008. He then pursued a Master of Engineering in Mining Engineering at the same institution, completing it in 2010. Continuing his academic journey, he earned a Ph.D. in Rock Engineering from China University of Mining and Technology (Beijing) in 2016.

💼Experience

Li Hongchao began his professional career as a Project Deputy Manager at Beijing Zhongda Explosion Engineering Co., Ltd. from 2010 to 2012. He then transitioned into academia, serving as an Assistant Lecturer in Mining Engineering at Kunming Industrial Vocational and Technical College from 2012 to 2013. Since 2016, he has been an Associate Professor at Kunming University of Science and Technology, School of Urban Construction, where he focuses on research and teaching related to rock blasting and engineering.

🔬Research Focus

His research primarily revolves around rock blasting theory and technology, with an emphasis on the dynamic performance and structural relationship of rocks and concrete. His work includes numerical simulation research on rock blasting fragmentation damage models and advancements in controlled blasting theory and technology.

🛠️Skills

Li Hongchao specializes in numerical simulation, rock blasting analysis, structural damage modeling, and controlled blasting techniques. His expertise extends to practical applications in the mining and construction industries, as well as academic research in rock mechanics and engineering.

🏆Awards

Li Hongchao has made significant contributions to the field of rock engineering and blasting, earning recognition for his research and professional achievements. His work has been acknowledged in both academic and industrial settings.

📚 Publications

Title: Dimensionless Machine Learning: Dimensional Analysis to Improve LSSVM and ANN Models and Predict Bearing Capacity of Circular Foundations
Author: H. Li, S.H. Hosseini, B. Gordan, J. Zhou, S. Ullah
Year: 2025
Journal: Artificial Intelligence Review

Conclusion:

Mr. Hongchao Li is a strong contender for the Best Researcher Award. However, increasing international collaborations and citation impact could further solidify his position as a leading researcher in his field.