Madhiarasan Manoharan | Data Science and Analytics | Innovative Research Award

Published on by Soil Scientist

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

DongHee Park | Data Science and Analytics | Innovative Research Award

Published on by Soil Scientist

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

Published on by Soil Scientist

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.