Rajesh Kumar Samala | Engineering | Innovative Research Award

Published on by Forensic Scientist Awards

Innovative Research Award

Rajesh Kumar Samala
Visvesvaraya College of Engineering and Technology, India

Rajesh Kumar Samala
Affiliation Visvesvaraya College of Engineering and Technology
Country India
Scopus ID 57204032308
Documents 20
Citations 109
h-index 5
Subject Area Engineering
Event International Forensic Scientist Awards
ORCID 0000-0002-0324-4436

Rajesh Kumar Samala is an engineering researcher whose indexed scholarly work spans nanotechnology, additive manufacturing, machine learning, Internet of Things applications, and technology-assisted analysis. His Scopus record comprises 20 documents, 109 citations, and an h-index of 5. [1] The research profile demonstrates an interdisciplinary orientation in which computational methods and engineering systems are applied to contemporary technical and applied research questions.

Abstract

Rajesh Kumar Samala’s research record reflects work across engineering technologies with particular emphasis on computational intelligence, advanced manufacturing, nanotechnology, and connected systems. His recent publications address nanoparticle toxicology, deep-learning-based tool wear prediction, healthcare-related statistical assessment, and machine-learning-driven energy optimization in wireless sensor networks. [2] [3] These studies illustrate the application of quantitative and computational approaches to engineering and technology-oriented problems.

Keywords

  • Engineering research
  • Nanoparticle toxicology
  • Deep learning
  • Additive manufacturing
  • Internet of Things

Introduction

Contemporary engineering research increasingly combines experimental investigation with machine learning, networked technologies, and data-driven modelling. Samala’s indexed publications reflect this broader development through studies addressing both emerging materials and computational engineering applications. The documented research covers publications from 2024 and 2025, indicating recent engagement with interdisciplinary engineering topics. [1]

Research Profile

The research profile is characterized by the integration of engineering analysis and computational techniques. Work on additive manufacturing applies deep learning to tool wear prediction, while another study examines crossbreed clustering for energy optimization in wireless sensor networks. [3] Research concerning nanoparticle properties and toxicological effects extends the profile toward materials-related engineering and technology assessment. [2]

Research Contributions

  • Investigates relationships between nanoparticle characteristics and toxicological effects.
  • Applies deep learning to tool wear prediction in additive manufacturing.
  • Explores machine learning and IoT methods for energy optimization.
  • Contributes to interdisciplinary engineering research involving data-driven methodologies.

Publications

Selected indexed publications include research on nanoparticle toxicology, additive manufacturing, healthcare analysis, and wireless sensor networks. The 2025 article on nanoparticle properties and toxicological effects was published in Proceedings on Engineering Sciences. [2] The 2025 study on deep learning and tool wear prediction appeared in Progress in Additive Manufacturing. [3]

Research Impact

The available Scopus metrics record 20 documents, 109 citations, and an h-index of 5. [1] These bibliometric indicators provide a quantitative view of indexed research visibility and should be interpreted in relation to publication age, field-specific citation practices, and database coverage.

Award Suitability

The documented research themes correspond to an Innovative Research Award context through their emphasis on emerging technologies, computational approaches, and interdisciplinary engineering applications. The publication record provides identifiable evidence of research activity in areas including nanotechnology, additive manufacturing, machine learning, and IoT-enabled systems. [2] [3]

Conclusion

Rajesh Kumar Samala’s indexed research profile presents a multidisciplinary engineering portfolio combining computational intelligence, advanced manufacturing, nanotechnology, and connected systems. The documented publications and Scopus metrics provide a concise evidence base for recognizing his continuing contribution to contemporary engineering research.

References

  1. Elsevier. (n.d.). Scopus author details: Rajesh Kumar Samala, Author ID 57204032308. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57204032308
  2. Samala, R. K., et al. (2025). Exploring the Interrelationship Between Nanoparticle Properties and Their Toxicological Effects. Proceedings on Engineering Sciences.
    https://doi.org/10.24874/PES07.03A.009
  3. Samala, R. K., et al. (2025). Enhancing Tool Wear Prediction with Deep Learning Models in Additive Manufacturing Processes. Progress in Additive Manufacturing.
    https://doi.org/10.1007/s40964-025-01372-2
  4. Samala, R. K., et al. (2024). Assessment of Primary Lung Cancer Survival Rates in Relation to the Number of Thoracoscopic Lobectomies Performed in Hospitals. Onkologia I Radioterapia.
  5. Samala, R. K., et al. (2024). Machine Learning and Internet of Things Driven Energy Optimization in Wireless Sensor Networks through Crossbreed Clustering. Journal of Intelligent Systems and Internet of Things.
    https://doi.org/10.54216/JISIoT.130204
  6. Elsevier. (n.d.). Scopus indexed publication records associated with Author ID 57204032308. Scopus.

Elijah Gbenga Osunkentan | Engineering | Innovative Research Award

Published on by Forensic Scientist Awards

Innovative Research Award

Elijah Gbenga Osunkentan
Federal University of ABC

Elijah Gbenga Osunkentan
Affiliation Federal University of ABC
Country Brazil
Scopus ID 60381839900
Documents 1
Subject Area Engineering
Event International Forensic Scientist Awards
Google Scholar ID 8mrS_zkAAAAJ

Elijah Gbenga Osunkentan is associated with engineering research focused on electrical power systems, microgrids, fault detection, transmission-line analysis, and intelligent computational methods. The supplied publication record indicates work involving statistical voltage-based islanding detection and machine-learning approaches for identifying electrical faults. These themes connect conventional power-system analysis with data-driven techniques for improving the monitoring and diagnosis of modern electrical networks. [1]

Abstract

The research profile of Elijah Gbenga Osunkentan centers on engineering applications in electrical power systems. Documented work includes passive islanding detection in microgrids, transmission-line fault detection using Support Vector Machines (SVM) and Artificial Neural Networks (ANN), and fault-location methods using Long Short-Term Memory (LSTM) neural networks. These studies represent complementary approaches to system monitoring, classification, and intelligent fault diagnosis. [1] [2] [3]

Keywords

Microgrids; islanding detection; power systems; fault detection; transmission lines; SVM; ANN; LSTM; intelligent fault location; electrical engineering.

Introduction

Modern electrical networks require dependable methods for detecting abnormal operating conditions and locating faults. Microgrids introduce additional operational considerations because distributed generation can alter system behavior during islanded and grid-connected states. Machine-learning techniques can complement established analytical approaches by supporting classification and pattern recognition. The supplied research record addresses these engineering challenges through statistical and intelligent computational methods. [1] [2]

Research Profile

Osunkentan’s documented research combines statistical signal interpretation with machine-learning models for electrical-system diagnosis. The reported areas include passive islanding detection, transmission-line fault classification, and intelligent fault-location techniques. This combination reflects an interdisciplinary engineering approach in which computational models are applied to practical power-system monitoring problems. [1] [2] [3]

Research Contributions

  • Development of a statistical voltage-based approach for passive islanding detection in microgrids. [1]
  • Comparative investigation of SVM and ANN techniques for transmission-line fault detection. [2]
  • Application of LSTM neural networks to intelligent transmission-line fault-location analysis. [3]

Publications

The supplied publication information identifies three relevant research outputs. The 2026 article, “An efficient and cost-effective statistical voltage-based method for passive islanding detection in microgrids,” appears in Discover Electronics 3(1), article 132. A 2025 conference contribution examines SVM and ANN for transmission-line fault detection, while another work addresses transmission-line fault location using LSTM neural networks. [1] [2] [3]

Research Impact

The available record documents research activity across journal, conference, and preprint-oriented channels. The stated Scopus record contains one document; citation and h-index values were not supplied and are therefore not inferred here. The research themes have practical relevance to power-system monitoring because islanding detection and fault diagnosis address operational reliability and system protection requirements. [1]

Award Suitability

The documented research aligns with an Innovative Research Award category through its focus on statistical analysis, machine-learning methods, microgrid operation, and intelligent fault diagnosis. The supplied record provides identifiable research outputs and methods that can be considered in an academic recognition assessment. Final award decisions remain subject to the applicable evaluation criteria and review process of the International Forensic Scientist Awards. [4]

Conclusion

Elijah Gbenga Osunkentan’s supplied research record demonstrates a focused engineering interest in electrical-system diagnostics, microgrid islanding detection, transmission-line fault identification, and neural-network-based fault location. The combination of statistical and intelligent methods provides a coherent basis for documenting his research profile within an innovation-oriented academic recognition context.

References

  1. Osunkentan, E. G. (2026). An efficient and cost-effective statistical voltage-based method for passive islanding detection in microgrids. Discover Electronics, 3(1), 132.
  2. Osunkentan, E. G., dos Santos, R. C., & Da Silva, A. M. (2025). Comparative Analysis of SVM and ANN for Fault Detection in Transmission Lines. 2025 16th IEEE International Conference on Industry Applications (INDUSCON).
  3. Silva, A. M. (n.d.). An Effective Intelligent Method for Fault Location in Transmission Lines Using LSTM Neural Networks. SSRN 5928668.
  4. International Forensic Scientist Awards. (n.d.). International Forensic Scientist Awards.
    forensicscientist.org
  5. Elsevier. (n.d.). Scopus author details: Elijah Gbenga Osunkentan, Author ID 60381839900. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=60381839900
  6. Google Scholar. (n.d.). Elijah Gbenga Osunkentan publication and citation record.
    Google Scholar Profile