Handbook of Parametric and Nonparametric Statistical Procedures: Third Edition

A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms

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Understanding bland altman analysis

Giavarina, D. (2015) · Biochemia Medica · doi:10.11613/bm.2015.015 · Cited by 4,022

Slime mould algorithm: A new method for stochastic optimization

Li, S., Chen, H., Wang, M., Heidari, A. A., & Mirjalili, S. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.03.055 · Cited by 3,123

A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization

García, S., Molina, D., Lozano, M., & Herrera, F. (2008) · Journal of Heuristics · doi:10.1007/s10732-008-9080-4 · Cited by 1,721

METODOLOGI PENELITIAN KESEHATANPreprint

Eravianti, E. (2021) · doi:10.31219/osf.io/cmv89 · Cited by 1,384

Why Psychologists Should by Default Use Welch’s t-test Instead of Student’s t-test

Delacre, M., Lakens, D., & Leys, C. (2017) · International Review of Social Psychology · doi:10.5334/irsp.82 · Cited by 1,037

How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?

Schurch, N. J., Schofield, P., Gierliński, M., Cole, C., Sherstnev, A., Singh, V., Wrobel, N., Gharbi, K., Simpson, G. G., Owen-Hughes, T., et al. (2016) · RNA · doi:10.1261/rna.053959.115 · Cited by 966

Interrater agreement and interrater reliability: Key concepts, approaches, and applications

Gisev, N., Bell, J. S., & Chen, T. F. (2013) · Research in Social and Administrative Pharmacy · doi:10.1016/j.sapharm.2012.04.004 · Cited by 924

Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review

Bonyadi, M. R., & Michalewicz, Z. (2016) · Evolutionary Computation · doi:10.1162/evco_r_00180 · Cited by 886

Local Shannon entropy measure with statistical tests for image randomness

Wu, Y., Zhou, Y., Saveriades, G., Agaian, S., Noonan, J. P., & Natarajan, P. (2013) · Information Sciences · doi:10.1016/j.ins.2012.07.049 · Cited by 722

Blind Image Quality Assessment Using Joint Statistics of Gradient Magnitude and Laplacian Features

Xue, W., Mou, X., Zhang, L., Bovik, A. C., & Feng, X. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2355716 · Cited by 641

Observations on the use of statistical methods in Food Science and Technology

Granato, D., Calado, V. M. D. A., & Jarvis, B. (2013) · Food Research International · doi:10.1016/j.foodres.2013.10.024 · Cited by 534

SMOTE-RSB *: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory

Ramentol, E., Mota, Y. C., Bello, R., & Herrera, F. (2011) · Knowledge and Information Systems · doi:10.1007/s10115-011-0465-6 · Cited by 496

A Systematic Review of the Application and Empirical Investigation of Search-Based Test Case Generation

Ali, S., Briand, L., Hemmati, H., & Panesar-Walawege, R. K. (2009) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2009.52 · Cited by 452

Classification of Rockburst in Underground Projects: Comparison of Ten Supervised Learning Methods

Zhou, J., Li, X., & Mitri, H. S. (2016) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000553 · Cited by 449

Detecting Differential and Correlated Protein Expression in Label-Free Shotgun Proteomics

Zhang, B., VerBerkmoes, N. C., Langston, M. A., Uberbacher, E. C., Hettich, R. L., & Samatova, N. F. (2006) · Journal of Proteome Research · doi:10.1021/pr0600273 · Cited by 395

A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems With Genetic Rule Selection and Lateral Tuning

Alcalá‐Fdez, J., Alcalá, R., & Herrera, F. (2011) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2011.2147794 · Cited by 371

Unified Blind Quality Assessment of Compressed Natural, Graphic, and Screen Content Images

Min, X.-K., Ma, K.-D., Gu, K., Zhai, G.-T., Wang, Z., & Lin, W.-S. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2735192 · Cited by 271

Modeling spatial patterns of saturated areas: A comparison of the topographic wetness index and a dynamic distributed model

Grabs, T., Seibert, J., Bishop, K., & Laudon, H. (2009) · Journal of Hydrology · doi:10.1016/j.jhydrol.2009.03.031 · Cited by 337

Mini nutritional assessment test long and short form are valid screening tools in Turkish older adults

Sarikaya, D., Halil, M. G., Kuyumcu, M. E., Kılıç, M. K., Yeşil, Y., Kara, Ö., Öztürk, Ş., Güngör, A. E., Karabulut, E., Doğu, B. B., et al. (2015) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2015.04.006 · Cited by 273

Objective Quality Evaluation of Dehazed Images

Min, X., Zhai, G., Gu, K., Yang, X., & Guan, X. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2018.2868771 · Cited by 275

AbYSS: Adapting Scatter Search to Multiobjective Optimization

Nebro, A. J., Luna, F., Alba, E., Dorronsoro, B., Durillo, J. J., & Beham, A. (2008) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2007.913109 · Cited by 268

Automatic Stress Detection in Working Environments From Smartphones’ Accelerometer Data: A First Step

Garcia-Ceja, E., Osmani, V., & Mayora, O. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2446195 · Cited by 267

MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation

Charte, F., Rivera, A. J., del Jesús, M. J., & Herrera, F. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.07.019 · Cited by 258

A Competitive Divide-and-Conquer Algorithm for Unconstrained Large-Scale Black-Box Optimization

Mei, Y., Omidvar, M. N., Li, X., & Yao, X. (2016) · ACM Transactions on Mathematical Software · doi:10.1145/2791291 · Cited by 252

The influence of involvement on purchase intention for new world wine

Hollebeek, L. D., Jaeger, S. R., Brodie, R. J., & Balemi, A. (2007) · Food Quality and Preference · doi:10.1016/j.foodqual.2007.04.007 · Cited by 245

Unraveling tumor–immune heterogeneity in advanced ovarian cancer uncovers immunogenic effect of chemotherapy

Jiménez-Sánchez, A., Cybulska, P., Mager, K. L., Koplev, S., Cast, O. M., Couturier, D., Memon, D., Selenica, P., Nikolovski, I., Mazaheri, Y., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-0630-5 · Cited by 248

Using the ISO/IEC 9126 product quality model to classify defects : a controlled experiment

Vetrò, A., Zazworka, N., Seaman, C. B., & Shull, F. J. (2012) · International Conference on Evaluation and Assessment in Software Engineering (EASE) · doi:10.1049/ic.2012.0025 · Cited by 243

Ranking and Clustering Software Cost Estimation Models through a Multiple Comparisons Algorithm

Mittas, N., & Angelis, L. (2012) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2012.45 · Cited by 227

STAC: A web platform for the comparison of algorithms using statistical tests

Rodriguez-Fdez, I., Canosa, A., Mucientes, M., & Bugarín, A. (2015) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz-ieee.2015.7337889 · Cited by 230

Probing quantum gravity using photons from a flare of the active galactic nucleus Markarian 501 observed by the MAGIC telescope

Albert, J., Aliu, E., Anderhub, H., Antonelli, L. A., Antoranz, P., Backes, M., Baixeras-Aiguabella, C., Barrio, J. A., Bartko, H., Bastieri, D., et al. (2008) · Physics Letters B · doi:10.1016/j.physletb.2008.08.053 · Cited by 222

A Recursive Decomposition Method for Large Scale Continuous Optimization

Sun, Y., Kirley, M., & Halgamuge, S. K. (2017) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2017.2778089 · Cited by 208

Distribution, sources and ecological risk assessment of PAHs in historically contaminated surface sediments at Bhavnagar coast, Gujarat, India

Dudhagara, D. R., Rajpara, R. K., Bhatt, J. K., Gosai, H. B., Sachaniya, B. K., & Dave, B. P. (2016) · Environmental Pollution · doi:10.1016/j.envpol.2016.02.030 · Cited by 205

Optimizing Multistage Discriminative Dictionaries for Blind Image Quality Assessment

Jiang, Q., Shao, F., Lin, W., Gu, K., Jiang, G., & Sun, H. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2763321 · Cited by 204

Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data

Gupta, V., Choudhary, K. K., Tavazza, F., Campbell, C. E., Liao, W., Choudhary, A., & Agrawal, A. (2021) · Nature Communications · doi:10.1038/s41467-021-26921-5 · Cited by 209

The Impact of Mandatory IFRS Adoption on Accounting Quality: Evidence from Australia

Chua, Y. L., Cheong, C. S., & Gould, G. (2012) · Journal of International Accounting Research · doi:10.2308/jiar-10212 · Cited by 199

An evaluation of feature selection methods for environmental data

Effrosynidis, D., & Arampatzis, A. (2021) · Ecological Informatics · doi:10.1016/j.ecoinf.2021.101224 · Cited by 209

Preaggregation Functions: Construction and an Application

Lucca, G., Sanz, J., Dimuro, G. P., Bedregal, B., Mesiar, R., Kolesárová, A., & Bustince, H. (2015) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2015.2453020 · Cited by 195

Brief parent training in pivotal response treatment for preschoolers with autism

Coolican, J., Smith, I. M., & Bryson, S. E. (2010) · Journal of Child Psychology and Psychiatry · doi:10.1111/j.1469-7610.2010.02326.x · Cited by 188

Environmental risk, Oxytocin Receptor Gene (OXTR) methylation and youth callous-unemotional traits: a 13-year longitudinal study

Cecil, C. A. M., Lysenko, L., Jaffee, S. R., Pingault, J., Smith, R. G., Relton, C. L., Woodward, G., McArdle, W. L., Mill, J. S., & Barker, E. D. (2014) · Molecular Psychiatry · doi:10.1038/mp.2014.95 · Cited by 189

Lung Sound Classification Using Co-Tuning and Stochastic Normalization

Nguyen, T. T. K., & Pernkopf, F. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3156293 · Cited by 190

Students’ Sense of Belonging at School in 41 Countries: Cross-Cultural Variability

Chiu, M. M., Chow, B. W.-Y., McBride, C., & Mol, S. T. (2016) · Journal of Cross-Cultural Psychology · doi:10.1177/0022022115617031 · Cited by 180

Automatic grading of prostate cancer in digitized histopathology images: Learning from multiple experts

Nir, G., Hor, S., Karimi, D., Fazli, L., Skinnider, B., Tavassoli, P., Turbin, D. A., Villamil, C. F., Wang, G., Wilson, R. S., et al. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.09.005 · Cited by 186

A New Local Search-Based Multiobjective Optimization Algorithm

Chen, B., Zeng, W., Lin, Y., & Zhang, D. (2014) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2014.2301794 · Cited by 171

Quantifying the Relationships among Drug Classes

Hert, J., Keiser, M. J., Irwin, J. J., Oprea, T. I., & Shoichet, B. K. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci8000259 · Cited by 169

Gene Expression Profiling of Childhood Adrenocortical Tumors

West, A. N., Neale, G. A. M., Pounds, S. B., de Figueiredo, B. C., Galindo, C. R., Pianovski, M. A. D., Filho, A. G. O., Malkin, D. M., Lalli, E., Ribeiro, R. C., et al. (2007) · Cancer Research · doi:10.1158/0008-5472.can-06-3767 · Cited by 165

Improving Classical and Decentralized Differential Evolution With New Mutation Operator and Population Topologies

Dorronsoro, B., & Bouvry, P. (2011) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2010.2081369 · Cited by 163

CF -integrals: A new family of pre-aggregation functions with application to fuzzy rule-based classification systems

Lucca, G., Sanz, J., Dimuro, G. P., Bedregal, B., Bustince, H., & Mesiar, R. (2017) · Information Sciences · doi:10.1016/j.ins.2017.12.029 · Cited by 165

Blind Image Quality Assessment Based on Multichannel Feature Fusion and Label Transfer

Wu, Q., Li, H., Meng, F., Ngan, K. N., Luo, B., Huang, C., & Zeng, B. (2015) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2015.2412773 · Cited by 163

A Multiobjective Evolutionary Approach to Concurrently Learn Rule and Data Bases of Linguistic Fuzzy-Rule-Based Systems

Alcalá, R., Ducange, P., Herrera, F., Lazzerini, B., & Marcelloni, F. (2009) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2009.2023113 · Cited by 164

The frontal aslant tract underlies speech fluency in persistent developmental stuttering

Kronfeld-Duenias, V., Amir, O., Ezrati-Vinacour, R., Civier, O., & Ben‐Shachar, M. (2014) · Brain Structure and Function · doi:10.1007/s00429-014-0912-8 · Cited by 174

Good Practice in Large-Scale Learning for Image Classification

Akata, Z., Perronnin, F., Harchaoui, Z., & Schmid, C. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.146 · Cited by 158

A Fast and Scalable Multiobjective Genetic Fuzzy System for Linguistic Fuzzy Modeling in High-Dimensional Regression Problems

Alcalá, R., Gacto, M. J., & Herrera, F. (2011) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2011.2131657 · Cited by 155

Integration of an Index to Preserve the Semantic Interpretability in the Multiobjective Evolutionary Rule Selection and Tuning of Linguistic Fuzzy Systems

Gacto, M. J., Alcalá, R., & Herrera, F. (2010) · IEEE transactions on fuzzy systems · doi:10.1109/tfuzz.2010.2041008 · Cited by 156

Plasmodium falciparum var gene expression is modified by host immunity

Warimwe, G. M., Keane, T., Fegan, G., Musyoki, J., Newton, C. R. J. C., Pain, A., Berriman, M., Marsh, K., & Bull, P. C. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0907590106 · Cited by 155

An image watermarking scheme in wavelet domain with optimized compensation of singular value decomposition via artificial bee colony

Ali, M., Ahn, C. W., Pant, M., & Siarry, P. (2015) · Information Sciences · doi:10.1016/j.ins.2014.12.042 · Cited by 146

Motor movement matters: The flexible abstractness of inner speech

Oppenheim, G. M., & Dell, G. S. (2010) · Memory & Cognition · doi:10.3758/mc.38.8.1147 · Cited by 145

MOOC performance prediction via clickstream data and social learning networks

Brinton, C. G., & Chiang, M. (2015) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2015.7218617 · Cited by 144

Designing benchmark problems for large-scale continuous optimization

Omidvar, M. N., Li, X., & Tang, K. (2015) · Information Sciences · doi:10.1016/j.ins.2014.12.062 · Cited by 143

Optogenetic Editing Reveals the Hierarchical Organization of Learned Action Sequences

Geddes, C. E., Li, H., & Jin, X. (2018) · Cell · doi:10.1016/j.cell.2018.06.012 · Cited by 148

IVTURS: A Linguistic Fuzzy Rule-Based Classification System Based On a New Interval-Valued Fuzzy Reasoning Method With Tuning and Rule Selection

Sanz, J., Fernández, A., Bustince, H., & Herrera, F. (2013) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2013.2243153 · Cited by 142

Cognitive Processes Associated with Sequential Tool Use in New Caledonian Crows

Wimpenny, J. H., Weir, A. A. S., Clayton, L., Rutz, C., & Kacelnik, A. (2009) · PLoS ONE · doi:10.1371/journal.pone.0006471 · Cited by 140

Transfer Learning for Molecular Cancer Classification Using Deep Neural Networks

Sevakula, R. K., Singh, V., Verma, N. K., Kumar, C., & Cui, Y. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2822803 · Cited by 147

Assessing Conceptual Knowledge Using Three Concept Map Scoring Methods

Watson, M. K., Pelkey, J., Noyes, C. R., & Rodgers, M. O. (2015) · Journal of Engineering Education · doi:10.1002/jee.20111 · Cited by 141

Cost premium prediction of certified green buildings: A neural network approach

Tatari, O., & Küçükvar, M. (2010) · Building and Environment · doi:10.1016/j.buildenv.2010.11.009 · Cited by 137

Growth Rate Analysis and Efficient Experimental Design for Tumor Xenograft Studies

Hather, G. J., Liu, R., Bandi, S., Mettetal, J. T., Manfredi, M. G., Shyu, W.-C., Donelan, J., & Chakravarty, A. (2014) · Cancer Informatics · doi:10.4137/cin.s13974 · Cited by 137

A Hybrid grey wolf optimizer and genetic algorithm for minimizing potential energy function

Tawhid, M. A., & Ali, A. F. (2017) · Memetic Computing · doi:10.1007/s12293-017-0234-5 · Cited by 136

Construction of detrital mineral populations: insights from mixing of U–Pb zircon ages in Himalayan rivers

Amidon, W. H., Burbank, D. W., & Gehrels, G. E. (2005) · Basin Research · doi:10.1111/j.1365-2117.2005.00279.x · Cited by 134

An Empirical Analysis of Build Failures in the Continuous Integration Workflows of Java-Based Open-Source Software

Rausch, T., Hummer, W., Leitner, P., & Schulte, S. (2017) · International Conference on Mining Software Repositories (MSR) · doi:10.1109/msr.2017.54 · Cited by 138

No-reference color image quality assessment: from entropy to perceptual quality

Chen, X., Zhang, Q., Lin, M., Yang, G., & He, C. (2019) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-019-0479-7 · Cited by 127

Clickstream Data and Inventory Management: Model and Empirical Analysis

Huang, T., & Mieghem, J. A. V. (2011) · Production and Operations Management · doi:10.1111/poms.12046 · Cited by 125

Factors Associated with Structure Loss in the 2013–2018 California Wildfires

Syphard, A., & Keeley, J. (2019) · Fire · doi:10.3390/fire2030049 · Cited by 122

Intra-Spike Crosslinking Overcomes Antibody Evasion by HIV-1

Galimidi, R. P., Klein, J. S., Politzer, M. S., Bai, S., Seaman, M. S., Nussenzweig, M. C., West, A. P., & Björkman, P. J. (2015) · Cell · doi:10.1016/j.cell.2015.01.016 · Cited by 121

Generating Test Data from OCL Constraints with Search Techniques

Ali, S., Iqbal, M. Z., Arcuri, A., & Briand, L. (2013) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2013.17 · Cited by 121

Active Emergence from Propofol General Anesthesia Is Induced by Methylphenidate

Chemali, J. J., Dort, C. J. V., Brown, E. N., & Solt, K. (2012) · Anesthesiology · doi:10.1097/aln.0b013e3182518bfc · Cited by 121

Mixing of Source Populations Recorded in Detrital Zircon U-Pb Age Spectra of Modern River Sands

Saylor, J. E., Knowles, J. N., Horton, B. K., Nie, J., & Mora, A. R. (2012) · The Journal of Geology · doi:10.1086/668683 · Cited by 117

Benchmarking Ensemble Classifiers with Novel Co-Trained Kernel Ridge Regression and Random Vector Functional Link Ensembles [Research Frontier]

Zhang, L., & Suganthan, P. N. (2017) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2017.2742867 · Cited by 117

A practical guide to select quality indicators for assessing pareto-based search algorithms in search-based software engineering

Wang, S., Ali, S., Yue, T., Li, Y., & Liaaen, M. C. (2016) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/2884781.2884880 · Cited by 115

Educational Data Mining Techniques for Student Performance Prediction: Method Review and Comparison Analysis

Zhang, Y., Yun, Y., An, R., Cui, J., Dai, H., & Shang, X. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.698490 · Cited by 152

Assessment of the effects and limitations of the 1998 to 2008 Abbreviated Injury Scale map using a large population-based dataset

Palmer, C. S., & Franklyn, M. (2011) · Scandinavian Journal of Trauma Resuscitation and Emergency Medicine · doi:10.1186/1757-7241-19-1 · Cited by 114

A Large-Scale Empirical Study on Code-Comment Inconsistencies

Wen, F., Nagy, C., Bavota, G., & Lanza, M. (2019) · 2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC) · doi:10.1109/icpc.2019.00019 · Cited by 105

Comparing saliency maps and eye-tracking focus maps: The potential use in visual impact assessment based on landscape photographs

Dupont, L., Ooms, K., Antrop, M., & Eetvelde, V. V. (2016) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2015.12.007 · Cited by 112

Diagnosis of breast cancer using diffuse reflectance spectroscopy: Comparison of a Monte Carlo versus partial least squares analysis based feature extraction technique

Zhu, C., Palmer, G. M., Breslin, T. M., Harter, J. M., & Ramanujam, N. (2006) · Lasers in Surgery and Medicine · doi:10.1002/lsm.20356 · Cited by 111

Ensemble Transfer Learning Algorithm

Liu, X., Liu, Z., Wang, G., Cai, Z., & Zhang, H. (2017) · IEEE Access · doi:10.1109/access.2017.2782884 · Cited by 111

AXL/AKT axis mediated-resistance to BRAF inhibitor depends on PTEN status in melanoma

Zuo, Q., Liu, J., Huang, L., Qin, Y., Hawley, T. S., Seo, C., Merlino, G. T., & Yu, Y. (2018) · Oncogene · doi:10.1038/s41388-018-0205-4 · Cited by 115

Predicting loan default in peer‐to‐peer lending using narrative data

Xia, Y., He, L., Li, Y., Liu, N., & Ding, Y. (2019) · Journal of Forecasting · doi:10.1002/for.2625 · Cited by 110

Real-time prediction of shield moving trajectory during tunnelling

Shen, S., Elbaz, K., Shaban, W. M., & Zhou, A. (2022) · Acta Geotechnica · doi:10.1007/s11440-022-01461-4 · Cited by 109

Mining MOOC Clickstreams: Video-Watching Behavior vs. In-Video Quiz Performance

Brinton, C. G., Buccapatnam, S., Chiang, M., & Poor, H. V. (2016) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2016.2546228 · Cited by 104

Robust statistical approaches for local planar surface fitting in 3D laser scanning data

Nurunnabi, A., Belton, D., & WEST, G. A. W. (2014) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2014.07.004 · Cited by 103

Machine Learning-Based Student’s Native Place Identification for Real-Time

Verma, C., Stoffová, V., Illés, Z., Tanwar, S., & Kumar, N. (2020) · IEEE Access · doi:10.1109/access.2020.3008830 · Cited by 103

How have the Eastern European countries of the former Warsaw Pact developed since 1990? A bibliometric study

Kozak, M., Bornmann, L., & Loet Leydesdorff, A. (2014) · Scientometrics · doi:10.1007/s11192-014-1439-8 · Cited by 112

Abundance of small cetaceans in waters of the central Spanish Mediterranean

de Segura, A. G., Crespo, E. A., Pedraza, S. N., Hammond, P. S., & Raga, J. A. (2006) · Marine Biology · doi:10.1007/s00227-006-0334-0 · Cited by 103

A survey study on major technical barriers affecting the decision to adopt cloud services

Phaphoom, N., Wang, X., Samuel, S., Helmer, S., & Abrahamsson, P. (2015) · Journal of Systems and Software · doi:10.1016/j.jss.2015.02.002 · Cited by 102

Describing the physiological responses of different rice genotypes to salt stress using sigmoid and piecewise linear functions

Radanielson, A. M., Angeles, O. R., Li, T., Ismail, A. M., & Gaydon, D. S. (2017) · Field Crops Research · doi:10.1016/j.fcr.2017.05.001 · Cited by 100

Women infected with parasite Toxoplasma have more sons

Kaňková, Š., Šulc, J., Nouzová, K., Fajfrlík, K., Frynta, D., & Jaroslav Flegr, A. (2006) · Die Naturwissenschaften · doi:10.1007/s00114-006-0166-2 · Cited by 100

Multi-objective genetic fuzzy classifiers for imbalanced and cost-sensitive datasets

Ducange, P., Lazzerini, B., & Marcelloni, F. (2009) · Soft Computing · doi:10.1007/s00500-009-0460-y · Cited by 97

Enhancing the effectiveness and interpretability of decision tree and rule induction classifiers with evolutionary training set selection over imbalanced problems

García, S., Fernández, A., & Herrera, F. (2009) · Applied Soft Computing · doi:10.1016/j.asoc.2009.04.004 · Cited by 97

Grip Strength and Its Relationship to Police Recruit Task Performance and Injury Risk: A Retrospective Cohort Study

Orr, R. M., Pope, R., Stierli, M., & Hinton, B. (2017) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph14080941 · Cited by 96

Understanding Statistical Hypothesis Testing: The Logic of Statistical Inference

Emmert‐Streib, F., & Dehmer, M. (2019) · Machine Learning and Knowledge Extraction · doi:10.3390/make1030054 · Cited by 129

Does Credit Securitization Reduce Bank Risk? Evidence from the European CDO MarketPreprint

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Handbook of parametric and nonparametric statistical procedures, 2nd ed.

Sheskin, D. J. (2000) · Cited by 353

Handbook of Parametric and Nonparametric Statistical Procedures, 5th edition

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