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

Bernal-de-Lázaro JM, Corona CC, Rocha ML, Neto AJ, Llanes-Santiago O. A Proposal of Condition Monitoring with Missing Data and Small-Magnitude Faults in Industrial Plants. InInternational Workshop on Artificial Intelligence and Pattern Recognition 2021 Oct 5 (pp. 167-176). Springer, Cham. View at Publisher View at Google Scholar View at Indexing
Sivaraj R, DeviPriya R. Memetic particle swarm optimisation for missing value imputation. International Journal of Data Analysis Techniques and Strategies. 2019;11(3):273-89. View at Publisher View at Google Scholar View at Indexing
Kampen C. Missing data imputation based on probabilistic data (Bachelor's thesis, University of Twente). View at Publisher View at Google Scholar View at Indexing
Nickolas S, Shobha K. Clustering Based Imputation Algorithm Using Unsupervised Neural Network for Enhancing the Quality of Healthcare Data. International Journal of Intelligent Systems & Applications. 2020 Aug 1;12(4). View at Publisher View at Google Scholar View at Indexing
Shobha K, Savarimuthu N. Clustering based imputation algorithm using unsupervised neural network for enhancing the quality of healthcare data. Journal of Ambient Intelligence and Humanized Computing. 2021 Feb;12(2):1771-81. View at Publisher View at Google Scholar View at Indexing
Das D, Nayak M, Pani SK. Missing Value Imputation-A Review. View at Publisher View at Google Scholar View at Indexing
Fernández A, García S, Galar M, Prati RC, Krawczyk B, Herrera F. Data intrinsic characteristics. InLearning from Imbalanced Data Sets 2018 (pp. 253-277). Springer, Cham. View at Publisher View at Google Scholar View at Indexing
Das S, Datta S, Chaudhuri BB. Handling data irregularities in classification: Foundations, trends, and future challenges. Pattern Recognition. 2018 Sep 1;81:674-93. View at Publisher View at Google Scholar View at Indexing
Apostolidis A, Pelt M, Stamoulis KP, Myklebust T, Stålhane T, Jenssen GD, Wærø I. Aviation data analytics in MRO operations (Doctoral dissertation, Hogeschool van Amsterdam). View at Publisher View at Google Scholar View at Indexing
Stadelmann T, Keuzenkamp J, Grabner H, Würsch C. The AI-Atlas: didactics for teaching AI and machine learning on-site, online, and hybrid. Education Sciences. 2021 Jul;11(7):318. View at Publisher View at Google Scholar View at Indexing
Stadelmann T, Würsch C. Maps for an uncertain future: teaching AI and machine learning using the ATLAS concept. View at Publisher View at Google Scholar View at Indexing
Apostolidis A, Pelt M, Stamoulis KP. Aviation data analytics in MRO operations: prospects and pitfalls. In2020 Annual Reliability and Maintainability Symposium (RAMS) 2020 Jan 27 (pp. 1-7). IEEE. View at Publisher View at Google Scholar View at Indexing
Stadelmann T, Braschler M, Stockinger K. Introduction to applied data science. InApplied Data Science 2019 (pp. 3-16). Springer, Cham. View at Publisher View at Google Scholar View at Indexing
Mehmood A, Naseer S, Ali A, Fatimah H, Rehman S, Kiani AK. Identification of novel vaccine candidates against carbapenem resistant Klebsiella pneumoniae: A systematic reverse proteomic approach. Computational Biology and Chemistry. 2020 Dec 1;89:107380. View at Publisher View at Google Scholar View at Indexing
Sadeghi S, Bandehpour M, Hoseini MH, Kazemi B. Designing and Expression of an Immunogenic Poly-Epitope Protein from Streptococcus pneumoniae. Trends in Peptide and Protein Sciences.;6:1-5. View at Publisher View at Google Scholar View at Indexing
Mahajan D, Verma GK. Data Mining Techniques in Imaging of Embryogenesis. InMachine Learning and IoT 2018 Jul 4 (pp. 133-143). CRC Press. View at Publisher View at Google Scholar View at Indexing
Tang J, Ma T, Luo Q. Trends Prediction of Big Data: A Case Study based on Fusion Data. Procedia Computer Science. 2020 Jan 1;174:181-90. View at Publisher View at Google Scholar View at Indexing
Parry J. Enabling large-scale dataset analysis in resource-constrained environments through application-aware preprocessing (Doctoral dissertation, Queensland University of Technology). View at Publisher View at Google Scholar View at Indexing
Chowdhury S, Sinha N, Ganguli P, Bhowmick R, Singh V, Nandi S, Sarkar RR. BIOPYDB: a dynamic human cell specific biochemical pathway database with advanced computational analyses platform. Journal of integrative bioinformatics. 2018 Sep 1;15(3). View at Publisher View at Google Scholar View at Indexing
Sen S, Datta L, Mitra S, editors. Machine Learning and IoT: A Biological Perspective. CRC Press; 2018 Jul 4. 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

Bernal-de-Lázaro JM, Corona CC, Rocha ML, Neto AJ, Llanes-Santiago O. A Proposal of Condition Monitoring with Missing Data and Small-Magnitude Faults in Industrial Plants. InInternational Workshop on Artificial Intelligence and Pattern Recognition 2021 Oct 5 (pp. 167-176). Springer, Cham.

Sivaraj R, DeviPriya R. Memetic particle swarm optimisation for missing value imputation. International Journal of Data Analysis Techniques and Strategies. 2019;11(3):273-89.

Kampen C. Missing data imputation based on probabilistic data (Bachelor's thesis, University of Twente).

Nickolas S, Shobha K. Clustering Based Imputation Algorithm Using Unsupervised Neural Network for Enhancing the Quality of Healthcare Data. International Journal of Intelligent Systems & Applications. 2020 Aug 1;12(4).

Shobha K, Savarimuthu N. Clustering based imputation algorithm using unsupervised neural network for enhancing the quality of healthcare data. Journal of Ambient Intelligence and Humanized Computing. 2021 Feb;12(2):1771-81.

Das D, Nayak M, Pani SK. Missing Value Imputation-A Review.

Fernández A, García S, Galar M, Prati RC, Krawczyk B, Herrera F. Data intrinsic characteristics. InLearning from Imbalanced Data Sets 2018 (pp. 253-277). Springer, Cham.

Das S, Datta S, Chaudhuri BB. Handling data irregularities in classification: Foundations, trends, and future challenges. Pattern Recognition. 2018 Sep 1;81:674-93.

Apostolidis A, Pelt M, Stamoulis KP, Myklebust T, Stålhane T, Jenssen GD, Wærø I. Aviation data analytics in MRO operations (Doctoral dissertation, Hogeschool van Amsterdam).

Stadelmann T, Keuzenkamp J, Grabner H, Würsch C. The AI-Atlas: didactics for teaching AI and machine learning on-site, online, and hybrid. Education Sciences. 2021 Jul;11(7):318.

Stadelmann T, Würsch C. Maps for an uncertain future: teaching AI and machine learning using the ATLAS concept.

Apostolidis A, Pelt M, Stamoulis KP. Aviation data analytics in MRO operations: prospects and pitfalls. In2020 Annual Reliability and Maintainability Symposium (RAMS) 2020 Jan 27 (pp. 1-7). IEEE.

Stadelmann T, Braschler M, Stockinger K. Introduction to applied data science. InApplied Data Science 2019 (pp. 3-16). Springer, Cham.

Mehmood A, Naseer S, Ali A, Fatimah H, Rehman S, Kiani AK. Identification of novel vaccine candidates against carbapenem resistant Klebsiella pneumoniae: A systematic reverse proteomic approach. Computational Biology and Chemistry. 2020 Dec 1;89:107380.

Sadeghi S, Bandehpour M, Hoseini MH, Kazemi B. Designing and Expression of an Immunogenic Poly-Epitope Protein from Streptococcus pneumoniae. Trends in Peptide and Protein Sciences.;6:1-5.

Mahajan D, Verma GK. Data Mining Techniques in Imaging of Embryogenesis. InMachine Learning and IoT 2018 Jul 4 (pp. 133-143). CRC Press.

Tang J, Ma T, Luo Q. Trends Prediction of Big Data: A Case Study based on Fusion Data. Procedia Computer Science. 2020 Jan 1;174:181-90.

Parry J. Enabling large-scale dataset analysis in resource-constrained environments through application-aware preprocessing (Doctoral dissertation, Queensland University of Technology).

Chowdhury S, Sinha N, Ganguli P, Bhowmick R, Singh V, Nandi S, Sarkar RR. BIOPYDB: a dynamic human cell specific biochemical pathway database with advanced computational analyses platform. Journal of integrative bioinformatics. 2018 Sep 1;15(3).

Sen S, Datta L, Mitra S, editors. Machine Learning and IoT: A Biological Perspective. CRC Press; 2018 Jul 4.

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

Indexed In

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