Challenges of Big Data analysis

Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482

Convex Optimization

Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601

A new look at the statistical model identification

Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879

Regression shrinkage and selection via the lasso

Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482

Matrix Computations

Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505

The Elements of Statistical Learning

Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970

The Elements of Statistical Learning

Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162

Compressed sensing

Donoho, D. L. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.871582 · Cited by 23,994

Longitudinal data analysis using generalized linear models

LIANG, K.-Y., & ZEGER, S. L. (1986) · Biometrika · doi:10.1093/biomet/73.1.13 · Cited by 18,550

Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers

Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000016 · Cited by 17,075

Large-deviation theory for diluted Wishart random matrices

Castillo, I. P., & Metz, F. L. (2018) · Physical review. E · doi:10.1103/physreve.97.032124 · Cited by 17

HAFNI-enabled largescale platform for neuroimaging informatics (HELPNI)

Makkie, M., Zhao, S., Jiang, X., Lv, J., Zhao, Y., Ge, B., Li, X., Han, J., & Liu, T. (2015) · Brain Informatics · doi:10.1007/s40708-015-0024-0 · Cited by 15

Monitoring e-commerce adoption from online data

Blazquez-Soriano, A., Domènech, J., Gil, J. A., & Pont, A. (2018) · Knowledge and Information Systems · doi:10.1007/s10115-018-1233-7 · Cited by 15

Teaching the Future: The Vision of AI/ChatGPT in Education

Bahrami, M. R., Bahrami, B., Behboodi, F., & Pourrafie, S. (2023) · Smart innovation, systems and technologies · doi:10.1007/978-981-99-3068-5_37 · Cited by 15

Toward monolithic growth integration of nanowire electronics in 3D architecture: a review

Liang, L., Hu, R., & Yu, L. (2023) · Science China Information Sciences · doi:10.1007/s11432-023-3774-y · Cited by 14

Cognitive Learning and Neuromorphic Systems Using Resistive Switching Random-Access Memory

Noh, M., Park, H., & Kim, S. (2025) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.5c00131 · Cited by 15

Harnessing the biological complexity of Big Data from LINCS gene expression signatures

Musa, A., Tripathi, S., Kandhavelu, M., Dehmer, M., & Emmert‐Streib, F. (2018) · PLoS ONE · doi:10.1371/journal.pone.0201937 · Cited by 18

Variational Bayesian weighted complex network reconstruction

Xu, S., Zhang, C., Wang, P., & Zhang, J.-S. (2020) · Information Sciences · doi:10.1016/j.ins.2020.02.050 · Cited by 15

Improve correlation matrix of Discrete Fourier Transformation technique for finding the missing values of MRI images

Saeed, S., Haron, H., Jhanjhi, N. Z., Naqvi, M., Alhumyani, H. A., & Masud, M. (2022) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2022420 · Cited by 15

Analysis and prediction of second-hand house price based on random forest

Zhang, Y., Huang, J., Zhang, J., Liu, S., & Shorman, S. M. (2022) · Applied Mathematics and Nonlinear Sciences · doi:10.2478/amns.2022.1.00052 · Cited by 15

Spherical Cap Packing Asymptotics and Rank-Extreme Detection

Zhang, K. (2017) · IEEE Transactions on Information Theory · doi:10.1109/tit.2017.2700202 · Cited by 15

An Information Retrieval and Recommendation System for Astronomical Observatories

Mukund, N., Thakur, S., Abraham, S., Aniyan, A. K., Mitra, S., Philip, N. S., Vaghmare, K., & Acharjya, D. P. (2018) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/aaadb2 · Cited by 15

Big data analytics in medical imaging using deep learning

Tahmassebi, A., Ehtemami, A., Mohebali, B., Gandomi, A. H., Pinker, K., & Meyer‐Baese, A. (2019) · Big Data: Learning, Analytics, and Applications · doi:10.1117/12.2516014 · Cited by 15

High‐dimensional variable selection and prediction under competing risks with application to SEER‐Medicare linked data

Hou, J., Paravati, A. J., Hou, J., Xu, R., & Murphy, J. D. (2018) · Statistics in Medicine · doi:10.1002/sim.7822 · Cited by 15

Sparse transition matrix estimation for high-dimensional and locally stationary vector autoregressive models

Ding, X., Qiu, Z., & Chen, X. (2017) · Electronic Journal of Statistics · doi:10.1214/17-ejs1325 · Cited by 15

Using big data and mobile health to manage diarrhoeal disease in children in low-income and middle-income countries: societal barriers and ethical implications

Keddy, K. H., Saha, S., Kariuki, S., Kalule, J. B., Qamar, F. N., Haq, Z., & Okeke, I. N. (2021) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(21)00585-5 · Cited by 15

Big Data

Baker, R. (2017) · doi:10.1002/9781119041702.ch3 · Cited by 15

An evolving Takagi-Sugeno model based on aggregated trapezium clouds for anomaly detection in large datasets

Wang, M.-X., & Wang, J. (2017) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-16254 · Cited by 14

Testing equality of several distributions in separable metric spaces: A maximum mean discrepancy based approach

Zhang, J., Guo, J., & Zhou, B. (2022) · Journal of Econometrics · doi:10.1016/j.jeconom.2022.03.007 · Cited by 14

DeepCGP: A Deep Learning Method to Compress Genome-Wide Polymorphisms for Predicting Phenotype of Rice

Islam, T., Kim, C. H., Iwata, H., Shimono, H., & Kimura, A. (2023) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3231466 · Cited by 13

Reproducibility and Scientific Integrity of Big Data Research in Urban Public Health and Digital Epidemiology: A Call to Action

Gutiérrez, A. C. Q., Lindegger, D. J., Heravi, A. T., Stojanov, T., Sykora, M. D., Elayan, S., Mooney, S. J., Naslund, J. A., Fadda, M., & Gruebner, O. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20021473 · Cited by 17

Building Resilient Digital Forensic Frameworks for NoSQL Database: Harnessing the Blockchain and Quantum Technology

Rahman, R. U., Singh, K., Tomar, D. S., & Musheer, R. (2024) · Contributions to environmental sciences & innovative business technology · doi:10.1007/978-981-97-0088-2_11 · Cited by 14

Identical Twins Doubly Exchanged at Birth: A Case Report of Genetic and Environmental Influences on the Adult Epigenome

Segal, N. L., Montoya, Y. S., Loke, Y. J., & Craig, J. M. (2016) · Epigenomics · doi:10.2217/epi-2016-0104 · Cited by 14

Uncovering Patterns of Product Co-consideration: A Case Study of Online Vehicle Price Quote Request Data

Damangir, S., Du, R. Y., & Hu, Y. (2018) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2017.11.002 · Cited by 14

Genetic Programming Based on Error Decomposition: A Big Data Approach

Tahmassebi, A., & Gandomi, A. H. (2018) · Genetic and evolutionary computation · doi:10.1007/978-3-319-90512-9_9 · Cited by 14

Network-based feature screening with applications to genome data

Wu, M., Zhu, L., & Feng, X. (2018) · The Annals of Applied Statistics · doi:10.1214/17-aoas1097 · Cited by 14

Fast and Positive Definite Estimation of Large Covariance Matrix for High-Dimensional Data Analysis

Wen, F., Chu, L., Ying, R., & Liu, P. (2019) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2019.2937785 · Cited by 14

Quantifying the structure of strong gravitational lens potentials with uncertainty-aware deep neural networks

Vernardos, G., Tsagkatakis, G., & Pantazis, Y. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa3201 · Cited by 14

CLEP: a hybrid data- and knowledge-driven framework for generating patient representations

Bharadhwaj, V. S., Ali, M., Birkenbihl, C., Mubeen, S., Lehmann, J., Hofmann‐Apitius, M., Hoyt, C. T., & Domingo‐Fernándéz, D. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab340 · Cited by 14

Data analytics diffusion in the UK renewable energy sector: an innovation perspective

Kava, H., Spanaki, K., Παπαδόπουλος, Θ., Despoudi, S., Rodríguez-Espíndola, O., & Fakhimi, M. (2021) · Annals of Operations Research · doi:10.1007/s10479-021-04263-1 · Cited by 16

Analyzing spatiotemporal trends in social media data via smoothing spline analysis of variance

Helwig, N. E., Gao, Y., Wang, S., & Ma, P. (2015) · Spatial Statistics · doi:10.1016/j.spasta.2015.09.002 · Cited by 14

Big data, better energy management and control decisions for distribution systems in smart grid

Refaat, S. S., Abu‐Rub, H., & Mohamed, A. (2016) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2016.7840966 · Cited by 14

Comparative Genomic Analysis of Two Clonally Related Multidrug Resistant Mycobacterium tuberculosis by Single Molecule Real Time Sequencing

Leung, K. S.-S., Siu, G. G., Tam, K. K.-G., To, S. W.-C., Rajwani, R., Ho, P., Wong, S. S. Y., Zhao, W., Ma, O. C. K., & Yam, W.-C. (2017) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2017.00478 · Cited by 14

Digitalisation of maintenance

Jantunen, E., Campos, J., Sharma, P., & Baglee, D. (2017) · International Conference on System Reliability and Safety (ICSRS) · doi:10.1109/icsrs.2017.8272846 · Cited by 14

Compressive Big Data Analytics: An ensemble meta-algorithm for high-dimensional multisource datasets

Marino, S., Zhao, Y., Zhou, N., Zhou, Y., Toga, A. W., Zhao, L., Jian, Y., Yang, Y., Chen, Y., Wu, Q., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0228520 · Cited by 14

Subtype Classification and Heterogeneous Prognosis Model Construction in Precision Medicine

You, N., He, S., Wang, X., Zhu, J., & Heping Zhang, D. (2018) · Biometrics · doi:10.1111/biom.12843 · Cited by 13

Data-driven track geometry fault localisation using unsupervised machine learning

Popov, K., Bold, R. D., Chai, H., Forde, M. C., Ho, C. L., Hyslip, J. P., Kashani, H. F., Kelly, R., Hsu, S. S., & Rippin, M. (2023) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2023.131141 · Cited by 13

Geographical big data and data mining: A new opportunity for “water-energy-food” nexus analysis

Yang, J., Cao, X., Yao, J. P., Kang, Z., Chang, J., & Wang, Y. (2024) · Journal of Geographical Sciences · doi:10.1007/s11442-024-2202-6 · Cited by 13

DEFM: Delay-embedding-based forecast machine for time series forecasting by spatiotemporal information transformation

Peng, H., Wang, W., Chen, P., & Liu, R. (2024) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0181791 · Cited by 14

Improving Likert scale big data analysis in psychometric health economics: reliability of the new compositional data approach

Lehmann, R., & Vogt, B. (2024) · Brain Informatics · doi:10.1186/s40708-024-00232-z · Cited by 13

Implementation of Nephrology Subspecialty Curricular Milestones

Yuan, C. M., Prince, L. K., Oliver, J. D., Abbott, K. C., & Nee, R. (2015) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2015.01.020 · Cited by 13

Evidence amalgamation, plausibility, and cancer research

Bertolaso, M., & Sterpetti, F. (2017) · Synthese · doi:10.1007/s11229-017-1591-9 · Cited by 13

Applying machine learning to big data streams : An overview of challenges

Augenstein, C., Spangenberg, N., & Franczyk, B. (2017) · International Conference on Soft Computing & Machine Intelligence (ISCMI) · doi:10.1109/iscmi.2017.8279592 · Cited by 13

A Multi-Step Nonlinear Dimension-Reduction Approach with Applications to Big Data

Krishnan, R., Samaranayake, V. A., & Jagannathan, S. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2876848 · Cited by 12

Reliable object recognition system for cloud video data based on LDP features

Nayagam, M. G., & Ramar, K. (2019) · Computer Communications · doi:10.1016/j.comcom.2019.10.027 · Cited by 13

Correcting Biases in Online Social Media Data Based on Target Distributions in the Physical World

Wang, Z., Yu, Z., Fan, R., & Guo, B. (2020) · IEEE Access · doi:10.1109/access.2020.2966790 · Cited by 13

L1-Norm Higher-Order Orthogonal Iterations for Robust Tensor Analysis

Chachlakis, D. G., Prater-Bennette, A., & Markopoulos, P. P. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053701 · Cited by 13

eHealth solutions to fight against COVID-19: A scoping review of applications

Eslami, P., Kalhori, S. R. N., & Taheriyan, M. (2021) · Medical Journal of the Islamic Republic of Iran · doi:10.47176/mjiri.35.43 · Cited by 16

Link travel time inference using entry/exit information of trips on a network

Yin, K., Wang, W., Wang, X. B., & Adams, T. M. (2015) · Transportation Research Part B Methodological · doi:10.1016/j.trb.2015.07.007 · Cited by 13

Supervised clustering of high-dimensional data using regularized mixture modeling

Chang, W., Wan, C., Zang, Y., Zhang, C., & Cao, S. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa291 · Cited by 13

Combining multiple imputation and control function methods to deal with missing data and endogeneity in discrete-choice models

Gopalakrishnan, R., Guevara, C. Á., & Ben-Akiva, M. E. (2020) · Transportation Research Part B Methodological · doi:10.1016/j.trb.2020.10.002 · Cited by 13

Evaluating performance and determining optimum sample size for regression tree and automatic linear modeling

Genç, S., & Mendeş, M. (2021) · Arquivo Brasileiro de Medicina Veterinária e Zootecnia · doi:10.1590/1678-4162-12413 · Cited by 13

A Survey of Statistical Methods and Computing for Big DataPreprint

Wang, C., Chen, M., Schifano, E. D., Wu, J., & Yan, J. (2015) · arXiv (Cornell University) · Cited by 15

Big Data in Official Statistics

Braaksma, B., Zeelenberg, K., & Broe, S. D. (2020) · doi:10.1002/9781118976357.ch10 · Cited by 13

When we talk about Big Data, What do we really mean? Toward a more precise definition of Big Data

Han, X., Gstrein, O. J., & Andrikopoulos, V. (2024) · Frontiers in Big Data · doi:10.3389/fdata.2024.1441869 · Cited by 15

Artificial intelligence and big data-driven evaluation research and practices: A systematic literature review

Bouyousfi, S. E., & Ouedraogo, M. (2024) · Evaluation · doi:10.1177/13563890241289937 · Cited by 31

Nexus between green innovation, policy stability, and environmental sustainability on sustainable economic growth: the case of China

Ullah, H., Diao, X., Shahzad, U., & Iqbal, F. (2025) · Clean Technologies and Environmental Policy · doi:10.1007/s10098-025-03140-w · Cited by 12

Against Dataism and for Data Sharing of Big Biomedical and Clinical Data with Research Parasites

Emmert‐Streib, F., Dehmer, M., & Yli-Harja, O. P. (2016) · Frontiers in Genetics · doi:10.3389/fgene.2016.00154 · Cited by 13

Reproducibility: A Primer on Semantics and Implications for Research

Pellizzari, E. D., Lohr, K. N., Blatecky, A. R., & Creel, D. V. (2017) · doi:10.3768/rtipress.2017.bk.0020.1708 · Cited by 12

Challenges of automated machine learning on causal impact analytics for policy evaluation

Hu, Y.-J., & Huang, S.-W. (2017) · 2017 2nd International Conference on Telecommunication and Networks (TEL-NET) · doi:10.1109/tel-net.2017.8343571 · Cited by 12

Knowledge Management in PCS-enabled ports: an assessment of the barriers

Zerbino, P., Aloini, D., Dulmin, R., & Mininno, V. (2018) · Knowledge Management Research & Practice · doi:10.1080/14778238.2018.1473830 · Cited by 15

Low latency and energy efficient cluster based routing design for wireless sensor network

Basavaraj, G. N., & Jaidhar, C. D. (2019) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v13.i2.pp615-625 · Cited by 12

Bridging open source tools and Geoportals for interactive spatial data analytics

She, B., Hu, T., Zhu, X., & Bao, S. (2019) · Geo-spatial Information Science · doi:10.1080/10095020.2019.1645497 · Cited by 12

High dimensional data challenges in estimating multiple linear regression

Hussain, J. N. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1591/1/012035 · Cited by 12

The Effect of Big Data Analytic Capabilities Upon Bank Performance via FinTech Innovation

Al-Dmour, A., Al-Dmour, R., Al-Dmour, H. H., & Ahmadamin, E. B. (2021) · International Journal of Information Systems in the Service Sector · doi:10.4018/ijisss.2021100104 · Cited by 12

Data Visualization and Statistical Graphics in big data analysis by Google Data Studio – Sales Case Study

Allaymoun, M. H., Khaled, M., Saleh, F., & Merza, F. (2022) · IEEE Technology and Engineering Management Conference (TEMSCON) · doi:10.1109/temsconeurope54743.2022.9801964 · Cited by 12

Big Data Analysis Using Hybrid Meta-Heuristic Optimization Algorithm and MapReduce Framework

Bashabsheh, M. Q., Abualigah, L., & Alshinwan, M. (2022) · Studies in computational intelligence · doi:10.1007/978-3-030-99079-4_8 · Cited by 12

Seizing opportunities in Europe: a roadmap for efficient big data implementation in Spanish SMEs

Sanchez-Hughet, C., Aramendia-Muneta, M. E., & Erro‐Garcés, A. (2022) · Digital Policy Regulation and Governance · doi:10.1108/dprg-02-2022-0019 · Cited by 12

Opportunities and Challenges in Relation to Big Data Analytics for the Shipping and Port Industries

Dalaklis, D., Nikitakos, N. V., Papachristos, D., & Dalaklis, A. (2023) · Studies in national governance and emerging technologies · doi:10.1007/978-3-031-25296-9_14 · Cited by 12

High Performance Computing and Big Data Analytics Paradigms and Challenges

Tulasi, B., Wagh, R. S., & Balaji, S. (2015) · International Journal of Computer Applications · doi:10.5120/20311-2356 · Cited by 12

Towards an automatic analyze and standardization of unstructured data in the context of big and linked data

Fadili, H., & Jouis, C. (2016) · International Conference on Management of Emergent Digital EcoSystems (MEDES) · doi:10.1145/3012071.3012103 · Cited by 12

Access methods for Big Data: current status and future directions

Rashid, A. (2017) · ICST Transactions on Scalable Information Systems · doi:10.4108/eai.28-12-2017.153520 · Cited by 12

Direct Error-Driven Learning for Deep Neural Networks With Applications to Big Data

Krishnan, R., Jagannathan, S., & Samaranayake, V. A. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2920964 · Cited by 12

Energy efficient clustering and routing optimization model for maximizing lifetime of wireless sensor network

Savitha, S., Lingareddy, S. C., & Chitnis, S. R. (2020) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v10i5.pp4798-4808 · Cited by 12

Tangible mtDNA

Manshaei, R., Baig, N., DeLong, S., Khayyer, S., East, B. G., & Mazalek, A. (2017) · International Conference on Tangible, Embedded, and Embodied Interaction (TEI) · doi:10.1145/3024969.3025005 · Cited by 12

A constrained $$\ell $$1 minimization approach for estimating multiple sparse Gaussian or nonparanormal graphical models

Wang, B., Singh, R., & Qi, Y. (2017) · Machine Learning · doi:10.1007/s10994-017-5635-7 · Cited by 12

Optimal subsampling for least absolute relative error estimators with massive data

Ren, M., Zhao, S., & Wang, M. (2022) · Journal of Complexity · doi:10.1016/j.jco.2022.101694 · Cited by 11

Robust optimal subsampling based on weighted asymmetric least squares

Ren, M., Zhao, S., Wang, M., & Zhu, X. (2023) · Statistical Papers · doi:10.1007/s00362-023-01480-7 · Cited by 11

Ensemble Learning Approach for Developing Performance Models of Flexible Pavement

Taheri, A., & Sobanjo, J. O. (2024) · Infrastructures · doi:10.3390/infrastructures9050078 · Cited by 11

Systematic comparison of unsupervised feature extraction methods for enhancing discrimination accuracy of brown rice flour adulteration in LIBS

Zhang, D., Yang, X., & Guo, L. (2024) · Microchemical Journal · doi:10.1016/j.microc.2024.110664 · Cited by 11

Detecting and characterizing high-frequency oscillations in epilepsy: a case study of big data analysis

Huang, L., Ni, X., Ditto, W. L., Spano, M. L., Carney, P. R., & Lai, Y. (2017) · Royal Society Open Science · doi:10.1098/rsos.160741 · Cited by 11

Brainsmatics—bridging the brain science and brain-inspired artificial intelligence

Luo, Q. (2017) · Scientia Sinica Vitae · doi:10.1360/n052017-00094 · Cited by 11

State-of-the-Art in Applying Machine Learning to Electronic Trading

Rabhi, F., Mehandjiev, N., & Baghdadi, A. (2020) · Lecture notes in business information processing · doi:10.1007/978-3-030-64466-6_1 · Cited by 11

Exploratory analysis of multivariate data: Applications of parallel coordinates in ecology

Alminagorta, O., Loewen, C. J. G., de Kerckhove, D. T., Jackson, D. A., & Chu, C. (2021) · Ecological Informatics · doi:10.1016/j.ecoinf.2021.101361 · Cited by 11

Big Data Analytics and Data Mining for Healthcare Informatics (HCI)

Varshney, M., Bhushan, B., & Haque, A. B. (2022) · Studies in big data · doi:10.1007/978-981-19-0924-5_11 · Cited by 11

Anomaly Detection in Time Series: Current Focus and Future Challenges

Arslan, F., Javaid, A., Awan, M. D. Z., & Ebad-ur-Rehman (2023) · Artificial intelligence · doi:10.5772/intechopen.111886 · Cited by 13

Business Performance Measurements in Asset Management with the Support of Big Data Technologies

Campos, J., Sharma, P., Jantunen, E., Baglee, D., & Fumagalli, L. (2017) · Management Systems in Production Engineering · doi:10.1515/mspe-2017-0021 · Cited by 11

Problems with research methods in medical device big data analytics

Strang, K. D. (2019) · International Journal of Data Science and Analytics · doi:10.1007/s41060-019-00176-2 · Cited by 11

Comparative study of microarray and experimental data on Schwann cells in peripheral nerve degeneration and regeneration: big data analysis

Shefa, U., & Jung, J. (2019) · Neural Regeneration Research · doi:10.4103/1673-5374.250632 · Cited by 11

Forecasting customers' response to incentives during peak periods: A transfer learning approach

Cai, L. Z., Wen, H., Gu, J., Ma, J., & Jin, Z. (2020) · International Transactions on Electrical Energy Systems · doi:10.1002/2050-7038.12251 · Cited by 11

A Two-Stage Mutual Information Based Bayesian Lasso Algorithm for Multi-Locus Genome-Wide Association Studies

Guo, H., Yu, Z., An, J., Han, G., Ma, Y., & Tang, R. (2020) · Entropy · doi:10.3390/e22030329 · Cited by 11

Effects of weather and season on human brain volume

Book, G. A., Meda, S. A., Janssen, R. J., Dager, A. D., Poppe, A. B., Stevens, M. C. G., Assaf, M., Glahn, D. C., & Pearlson, G. D. (2021) · PLoS ONE · doi:10.1371/journal.pone.0236303 · Cited by 11

Direct Error Driven Learning for Deep Neural Networks with Applications to Bigdata

Krishnan, R., Jagannathan, S., & Samaranayake, V. A. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.10.508 · Cited by 11

Tensor Methods for Additive Index Models under Discordance and HeterogeneityPreprint

Balasubramanian, K., B, J. F. A., & Yang, Z. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.06693 · Cited by 11

Normality and significance testing in simple linear regression model for large sample sizes: a simulation study

Bilon, X. J. (2021) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2021.1916824 · Cited by 10

Do job vacancies variations anticipate employment variations by sector? Some preliminary evidence from Italy

Lovaglio, P. G. (2021) · Labour · doi:10.1111/labr.12213 · Cited by 10

Interpretable Industrial Soft Sensor Design Based on Informer and SHAP

Cao, L., Ji, X., Cao, Y., Luo, Y., Wang, Y., Siang, L. C., Li, J., & Gopaluni, R. B. (2024) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2024.08.316 · Cited by 10

The Untapped Potential of Dimension Reduction in Neuroimaging: Artificial Intelligence-Driven Multimodal Analysis of Long COVID Fatigue

Rudroff, T., Klén, R., Rainio, O., & Tuulari, J. J. (2024) · Brain Sciences · doi:10.3390/brainsci14121209 · Cited by 12

State space model: a magical tool for state prediction in Nonlinear systems

Wang, Q., Jin, Y., Lu, Z., Gao, Q., Ge, X., Li, Z., & Hou, L. (2024) · Nonlinear Dynamics · doi:10.1007/s11071-024-10730-8 · Cited by 11

Rydberg communication antennas with self-decoupling for dynamic radar interference based on heterodyne technology

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