Citations Report

Global Journal of Technology and Optimization : Citations & Metrics Report

Articles published in Global Journal of Technology and Optimization have been cited by esteemed scholars and scientists all around the world.

Global Journal of Technology and Optimization has got h-index 12, which means every article in Global Journal of Technology and Optimization has got 12 average citations.

Following are the list of articles that have cited the articles published in Global Journal of Technology and Optimization.

	2022	2021	2020	2019	2018
Year wise published articles	46	43	2	1	9
Year wise citations received	69	80	70	89	106

Journal total citations count	664
Journal impact factor	1.5
Journal 5 years impact factor	2.48
Journal cite score	1.95
Journal h-index	12
Journal h-index since 2018	10

Important citations

Ma Y, Zhao K, Wang Q, Tian Y. Incremental Cost-Sensitive Support Vector Machine with Linear-Exponential Loss. IEEE Access. 2020 Aug 12;8:149899-914. View at Publisher View at Google Scholar View at Indexing
Cox J, Harper CA, de Waard A. Optimized machine learning methods predict discourse segment type in biological research articles. InSemantics, Analytics, Visualization 2017 Apr 3 (pp. 95-109). Springer, Cham. View at Publisher View at Google Scholar View at Indexing
Tewari S, Dwivedi UD, Biswas S. A Novel Application of Ensemble Methods with Data Resampling Techniques for Drill Bit Selection in the Oil and Gas Industry. Energies. 2021 Jan;14(2):432. View at Publisher View at Google Scholar View at Indexing
Bej S, Davtyan N, Wolfien M, Nassar M, Wolkenhauer O. LoRAS: An oversampling approach for imbalanced datasets. Machine Learning. 2021 Feb;110(2):279-301. View at Publisher View at Google Scholar View at Indexing
Nwe MM, Lynn KT. KNN-based overlapping samples filter approach for classification of imbalanced data. InInternational Conference on Software Engineering Research, Management and Applications 2019 May 29 (pp. 55-73). Springer, Cham. View at Publisher View at Google Scholar View at Indexing
Alhakbani H. Handling class imbalance using swarm intelligence techniques, hybrid data and algorithmic level solutions (Doctoral dissertation, Goldsmiths, University of London). View at Publisher View at Google Scholar View at Indexing
Al Majzoub H, Elgedawy I, Akayd?n Ö, Ulukök MK. HCAB-SMOTE: A hybrid clustered affinitive borderline SMOTE approach for imbalanced data binary classification. Arabian Journal for Science and Engineering. 2020 Apr;45(4):3205-22. View at Publisher View at Google Scholar View at Indexing
Liu C, Wu J, Mirador L, Song Y, Hou W. Classifying dna methylation imbalance data in cancer risk prediction using smote and tomek link methods. InInternational Conference of Pioneering Computer Scientists, Engineers and Educators 2018 Sep 21 (pp. 1-9). Springer, Singapore. View at Publisher View at Google Scholar View at Indexing
Karia V, Zhang W, Naeim A, Ramezani R. GenSample: A genetic algorithm for oversampling in imbalanced datasets. arXiv preprint arXiv:1910.10806. 2019 Oct 23. View at Publisher View at Google Scholar View at Indexing
Kabir MF, Ludwig S. Classification of breast cancer risk factors using several resampling approaches. In2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) 2018 Dec 17 (pp. 1243-1248). IEEE. View at Publisher View at Google Scholar View at Indexing
Muhsen IN, Elhassan T, Hashmi SK. Artificial intelligence approaches in hematopoietic cell transplantation: a review of the current status and future directions. Turkish Journal of Hematology. 2018 Sep;35(3):152. View at Publisher View at Google Scholar View at Indexing
Mduma N, Kalegele K, Machuve D. Machine learning approach for reducing students dropout rates. View at Publisher View at Google Scholar View at Indexing
Ghazal S, Sauthier M, Brossier D, Bouachir W, Jouvet PA, Noumeir R. Using machine learning models to predict oxygen saturation following ventilator support adjustment in critically ill children: A single center pilot study. PloS one. 2019 Feb 20;14(2):e0198921. View at Publisher View at Google Scholar View at Indexing
Ghorbani R, Ghousi R. Comparing different resampling methods in predicting Students’ performance using machine learning techniques. IEEE Access. 2020 Apr 13;8:67899-911. View at Publisher View at Google Scholar View at Indexing
Susan S, Kumar A. SSOMaj-SMOTE-SSOMin: Three-step intelligent pruning of majority and minority samples for learning from imbalanced datasets. Applied Soft Computing. 2019 May 1;78:141-9. View at Publisher View at Google Scholar View at Indexing
Junsomboon N, Phienthrakul T. Combining over-sampling and under-sampling techniques for imbalance dataset. InProceedings of the 9th International Conference on Machine Learning and Computing 2017 Feb 24 (pp. 243-247). View at Publisher View at Google Scholar View at Indexing
Kaur H, Pannu HS, Malhi AK. A systematic review on imbalanced data challenges in machine learning: Applications and solutions. ACM Computing Surveys (CSUR). 2019 Aug 30;52(4):1-36. View at Publisher View at Google Scholar View at Indexing
Soares JP. Explorar diferentes estratégias de data mining aplicadas a dois problemas no pré-processamento de dados (Doctoral dissertation, Universidade de Coimbra). View at Publisher View at Google Scholar View at Indexing
ÇET?N V, YILDIZ O. A comprehensive review on data preprocessing techniques in data analysis Veri analizinde veri ön i?leme teknikleri üzerine kapsaml? bir inceleme. View at Publisher View at Google Scholar View at Indexing
Thomas T, Rajabi E. A systematic review of machine learning-based missing value imputation techniques. Data Technologies and Applications. 2021 Apr 2. View at Publisher View at Google Scholar View at Indexing
Prev 1 2 3 4 5 6 7 8 9 10 11 12 13 ... 19 20 Next »

Relevant Topics

Awards & Nominations

50+ Million Readerbase

Journal Highlights

Google Scholar citation report

Citations: 664

Global Journal of Technology and Optimization received 664 citations as per Google Scholar report

Global Journal of Technology and Optimization

Citations Report

Global Journal of Technology and Optimization : Citations & Metrics Report

Important citations

Ma Y, Zhao K, Wang Q, Tian Y. Incremental Cost-Sensitive Support Vector Machine with Linear-Exponential Loss. IEEE Access. 2020 Aug 12;8:149899-914.

Cox J, Harper CA, de Waard A. Optimized machine learning methods predict discourse segment type in biological research articles. InSemantics, Analytics, Visualization 2017 Apr 3 (pp. 95-109). Springer, Cham.

Tewari S, Dwivedi UD, Biswas S. A Novel Application of Ensemble Methods with Data Resampling Techniques for Drill Bit Selection in the Oil and Gas Industry. Energies. 2021 Jan;14(2):432.

Bej S, Davtyan N, Wolfien M, Nassar M, Wolkenhauer O. LoRAS: An oversampling approach for imbalanced datasets. Machine Learning. 2021 Feb;110(2):279-301.

Nwe MM, Lynn KT. KNN-based overlapping samples filter approach for classification of imbalanced data. InInternational Conference on Software Engineering Research, Management and Applications 2019 May 29 (pp. 55-73). Springer, Cham.

Alhakbani H. Handling class imbalance using swarm intelligence techniques, hybrid data and algorithmic level solutions (Doctoral dissertation, Goldsmiths, University of London).

Al Majzoub H, Elgedawy I, Akayd?n Ö, Ulukök MK. HCAB-SMOTE: A hybrid clustered affinitive borderline SMOTE approach for imbalanced data binary classification. Arabian Journal for Science and Engineering. 2020 Apr;45(4):3205-22.

Liu C, Wu J, Mirador L, Song Y, Hou W. Classifying dna methylation imbalance data in cancer risk prediction using smote and tomek link methods. InInternational Conference of Pioneering Computer Scientists, Engineers and Educators 2018 Sep 21 (pp. 1-9). Springer, Singapore.

Karia V, Zhang W, Naeim A, Ramezani R. GenSample: A genetic algorithm for oversampling in imbalanced datasets. arXiv preprint arXiv:1910.10806. 2019 Oct 23.

Kabir MF, Ludwig S. Classification of breast cancer risk factors using several resampling approaches. In2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) 2018 Dec 17 (pp. 1243-1248). IEEE.

Muhsen IN, Elhassan T, Hashmi SK. Artificial intelligence approaches in hematopoietic cell transplantation: a review of the current status and future directions. Turkish Journal of Hematology. 2018 Sep;35(3):152.

Mduma N, Kalegele K, Machuve D. Machine learning approach for reducing students dropout rates.

Ghazal S, Sauthier M, Brossier D, Bouachir W, Jouvet PA, Noumeir R. Using machine learning models to predict oxygen saturation following ventilator support adjustment in critically ill children: A single center pilot study. PloS one. 2019 Feb 20;14(2):e0198921.

Ghorbani R, Ghousi R. Comparing different resampling methods in predicting Students’ performance using machine learning techniques. IEEE Access. 2020 Apr 13;8:67899-911.

Susan S, Kumar A. SSOMaj-SMOTE-SSOMin: Three-step intelligent pruning of majority and minority samples for learning from imbalanced datasets. Applied Soft Computing. 2019 May 1;78:141-9.

Junsomboon N, Phienthrakul T. Combining over-sampling and under-sampling techniques for imbalance dataset. InProceedings of the 9th International Conference on Machine Learning and Computing 2017 Feb 24 (pp. 243-247).

Kaur H, Pannu HS, Malhi AK. A systematic review on imbalanced data challenges in machine learning: Applications and solutions. ACM Computing Surveys (CSUR). 2019 Aug 30;52(4):1-36.

Soares JP. Explorar diferentes estratégias de data mining aplicadas a dois problemas no pré-processamento de dados (Doctoral dissertation, Universidade de Coimbra).

ÇET?N V, YILDIZ O. A comprehensive review on data preprocessing techniques in data analysis Veri analizinde veri ön i?leme teknikleri üzerine kapsaml? bir inceleme.

Thomas T, Rajabi E. A systematic review of machine learning-based missing value imputation techniques. Data Technologies and Applications. 2021 Apr 2.

Relevant Topics

Awards & Nominations

50+ Million Readerbase

Journal Highlights

Google Scholar citation report

Citations: 664

Global Journal of Technology and Optimization peer review process verified at publons

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