The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Machine Learning Algorithms - A Review
Open accessAbstract
Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without being explicitly programmed. Learning algorithms in many applications that's we make use of daily. Every time a web search engine like Google is used to search the internet, one of the reasons that work so well is because a learning algorithm thathas learned how to rank web pages.These algorithms are used for various purposes like data mining, image processing, predictive analytics, etc. to name a few.The main advantage of using machine learning is that, once an algorithm learns what to do with data, it can do its work automatically.In this paper, a brief review and future prospect of the vast applications of machine learning algorithms has been made.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A Survey on Machine-Learning Techniques in Cognitive Radios
Bkassiny, M., Li, Y., & Jayaweera, S. K. (2013) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2012.100412.00017 · Cited by 580
Cited by
Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24750 · Cited by 919
Recent Advances in Electrochemical Biosensors: Applications, Challenges, and Future Scope
Singh, A., Sharma, A., Ahmed, A., Sundramoorthy, A. K., Furukawa, H., Arya, S., & Khosla, A. (2021) · Biosensors · doi:10.3390/bios11090336 · Cited by 579
A Survey on Evolutionary Constrained Multiobjective Optimization
Liang, J., Ban, X., Yu, K., Qu, B., Qiao, K., Yue, C., Chen, K., & Tan, K. C. (2023) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2022.3155533 · Cited by 438
Machine Learning Applications in Agriculture: Current Trends, Challenges, and Future Perspectives
Araújo, S. O., Peres, R. S., Ramalho, J. C., Lidon, F., & Barata, J. (2023) · Agronomy · doi:10.3390/agronomy13122976 · Cited by 259
A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition
Neupane, D., & Seok, J. (2020) · Electronics · doi:10.3390/electronics9111972 · Cited by 192
Metadata
- Authors
- Mahesh, B.
- Venue
- International Journal of Science and Research (IJSR)
- Published
- 2020-01-05
- Volume
- 9
- Issue
- 1
- Pages
- 381-386
- ISSN
- 2319-7064 (ISSN-L)
- OpenAlex
- W4400134761
- Cited by
- 2,510
- References
- 1
- FWCI
- 223.39
- Type
- journal article
- Language
- en
- Open access
- diamond
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Machine Learning Algorithms - A Review. https://app.cochat.ai/papers/10.21275/art20203995
- The paper
- Mahesh, B. (2020). Machine Learning Algorithms - A Review. International Journal of Science and Research (IJSR). https://doi.org/10.21275/art20203995
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
