A. Joshuva

656 total citations
33 papers, 455 citations indexed

About

A. Joshuva is a scholar working on Control and Systems Engineering, Mechanical Engineering and Mechanics of Materials. According to data from OpenAlex, A. Joshuva has authored 33 papers receiving a total of 455 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Control and Systems Engineering, 18 papers in Mechanical Engineering and 10 papers in Mechanics of Materials. Recurrent topics in A. Joshuva's work include Machine Fault Diagnosis Techniques (19 papers), Gear and Bearing Dynamics Analysis (13 papers) and Engineering Diagnostics and Reliability (8 papers). A. Joshuva is often cited by papers focused on Machine Fault Diagnosis Techniques (19 papers), Gear and Bearing Dynamics Analysis (13 papers) and Engineering Diagnostics and Reliability (8 papers). A. Joshuva collaborates with scholars based in India, Spain and United States. A. Joshuva's co-authors include V. Sugumaran, S. Sivakumar, R. Sathish Kumar, Mohankumar Subramaniam, Jenoris Muthiya Solomon, Sang‐Kwon Lee, Bilal Akash, D. Dinakaran, Sathishkumar Ramalingam and D. Nagarajan and has published in prestigious journals such as SHILAP Revista de lepidopterología, Energy and SAE technical papers on CD-ROM/SAE technical paper series.

In The Last Decade

A. Joshuva

31 papers receiving 422 citations

Peers

Countries citing papers authored by A. Joshuva

Since Specialization

Citations

This map shows the geographic impact of A. Joshuva's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by A. Joshuva with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites A. Joshuva more than expected).

Fields of papers citing papers by A. Joshuva

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by A. Joshuva. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by A. Joshuva. The network helps show where A. Joshuva may publish in the future.

Co-authorship network of co-authors of A. Joshuva

This figure shows the co-authorship network connecting the top 25 collaborators of A. Joshuva. A scholar is included among the top collaborators of A. Joshuva based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with A. Joshuva. A. Joshuva is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Cameraless sensor fusion: developing a cost-effective driver assistance system using radar and ultrasonic sensor 2024 ·Sensor Review ·(unknown), (unknown), A. Joshuva, D. Nagarajan	0
2	Split-Point and Attribute-Reduced Classifier Approach for Fault Diagnosis of Wind Turbine Blade through Vibration Signals 2020 ·IOP Conference Series Materials Science and Engineering ·A. Joshuva, (unknown), (unknown), Bilal Akash, (unknown)	4
3	An insight on VMD for diagnosing wind turbine blade faults using C4.5 as feature selection and discriminating through multilayer perceptron 2020 ·Alexandria Engineering Journal ·A. Joshuva, (unknown), S. Sivakumar, (unknown), (unknown)	51
4	A Machine Learning Approach for Vibration Signal Based Fault Classification on Hydraulic Braking System through C4.5 Decision Tree Classifier and Logistic Model Tree Classifier 2020 ·SAE technical papers on CD-ROM/SAE technical paper series ·A. Joshuva, (unknown), (unknown), Jenoris Muthiya Solomon, Mohankumar Subramaniam	4
5	Research on Hyper pipes and Voting Feature Intervals Classifier for Condition Monitoring of Wind Turbine Blades using Vibration Signals 2019 ·International Journal of Recent Technology and Engineering (IJRTE) ·A. Joshuva, S. Sivakumar, (unknown), (unknown), Sathishkumar Ramalingam, (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown)	1
6	Fault Diagnosis of Wind Turbine Blades using Histogram Features through Nested Dichotomy Classifiers 2019 ·International Journal of Recent Technology and Engineering (IJRTE) ·A. Joshuva, S. Sivakumar, Sathishkumar Ramalingam, (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown)	4
7	Logistic Model Tree Classifier for Condition Monitoring of Wind Turbine Blades 2019 ·International Journal of Recent Technology and Engineering (IJRTE) ·A. Joshuva, (unknown), S. Sivakumar, Sathishkumar Ramalingam, (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown)	5
8	Implementation of Rule based Classifiers for Wind Turbine Blade Fault Diagnosis using Vibration Signals 2019 ·International Journal of Recent Technology and Engineering (IJRTE) ·A. Joshuva, (unknown), (unknown), Sathishkumar Ramalingam, S. Sivakumar, (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown)	5
9	Data set on optimization of ethyl ester production from sapota seed oil 2019 ·SHILAP Revista de lepidopterología ·R. Sathish Kumar, S. Sivakumar, A. Joshuva, (unknown), (unknown)	7
10	A lazy learning approach for condition monitoring of wind turbine blade using vibration signals and histogram features 2019 ·Measurement ·A. Joshuva, V. Sugumaran	65
11	A Contemporary Research on Perceptual Expert System for Medical Diagnosis through Neural Dust Sensor 2019 ·Procedia Computer Science ·A. Joshuva, (unknown), (unknown), (unknown), (unknown)	2
12	Selection of a meta classifier-data model for classifying wind turbine blade fault conditions using histogram features and vibration signals: a data-mining study 2019 ·Progress in Industrial Ecology An International Journal ·A. Joshuva, V. Sugumaran	10
13	A STUDY OF VARIOUS BLADE FAULT CONDITIONS ON A WIND TURBINE USING VIBRATION SIGNALS THROUGH HISTOGRAM FEATURES 2018 ·SHILAP Revista de lepidopterología ·A. Joshuva, V. Sugumaran	14
14	A machine learning approach for condition monitoring of wind turbine blade using autoregressive moving average (ARMA) features through vibration signals: a comparative study 2018 ·Progress in Industrial Ecology An International Journal ·A. Joshuva, V. Sugumaran	15
15	A Comparative Study for Condition Monitoring on Wind Turbine Blade using Vibration Signals through Statistical Features: a Lazy Learning Approach 2018 ·International Journal of Engineering & Technology ·A. Joshuva, V. Sugumaran	7
16	A machine learning approach for condition monitoring of wind turbine blade using autoregressive moving average (ARMA) features through vibration signals: a comparative study 2018 ·Progress in Industrial Ecology An International Journal ·A. Joshuva, V. Sugumaran	9
17	A data driven approach for condition monitoring of wind turbine blade using vibration signals through best-first tree algorithm and functional trees algorithm: A comparative study 2017 ·ISA Transactions ·A. Joshuva, V. Sugumaran	82
18	Remaining Life-Time Assessment of Gear Box using Regression Model 2016 ·Indian Journal of Science and Technology ·A. Joshuva	13
19	Remaining Life-Time Assessment of Gear Box Using Regression Model 2016 ·Indian Journal of Science and Technology ·A. Joshuva, V. Sugumaran, (unknown), Sang‐Kwon Lee	6
20	Speech Recognition For Humanoid Robot 2015 ·A. Joshuva, V. Sugumaran	2

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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