Individual Comparisons by Ranking Methods

Design of Experiments

Fisher, R. A. (1936) · BMJ · doi:10.1136/bmj.1.3923.554-a · Cited by 4,980

Exploratory Power of the Harmony Search Algorithm: Analysis and Improvements for Global Numerical Optimization

Das, S., Mukhopadhyay, A., Roy, A., Abraham, A., & Panigrahi, B. K. (2010) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2010.2046035 · Cited by 250

The Use of Ranks in a Test of Significance for Comparing Two Treatments

White, C. (1952) · Biometrics · doi:10.2307/3001523 · Cited by 248

Tourists' memories, sensory impressions and loyalty: In loco and post-visit study in Southwest Portugal

Agapito, D., Pinto, P. S. L. G. D. S., & Mendes, J. D. C. (2016) · Tourism Management · doi:10.1016/j.tourman.2016.10.015 · Cited by 247

Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms

He, Q., Shahabi, H., Shirzadi, A., Li, S., Chen, W., Wang, N., Chai, H., Bian, H., Ma, J., Chen, Y., et al. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.01.329 · Cited by 246

Elevated depression symptoms, antidepressant medicine use, and risk of developing diabetes during the diabetes prevention program

Rubin, R. R., Ma, Y., Marrero, D. G., Peyrot, M., Barrett-Connor, E. L., Kahn, S. E., Haffner, S. M., Price, D. W., Knowler, W. C., & for the Diabetes Prevention Program Research Group (2008) · Diabetes Care · doi:10.2337/dc07-1827 · Cited by 242

Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets

Feinstein, W. P., & Bryliński, M. (2015) · Journal of Cheminformatics · doi:10.1186/s13321-015-0067-5 · Cited by 264

Compact Differential Evolution

Mininno, E., Neri, F., Cupertino, F., & Naso, D. (2010) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2010.2058120 · Cited by 242

Activation of Symbiosis Signaling by Arbuscular Mycorrhizal Fungi in Legumes and Rice

Sun, J., Miller, J. B., Granqvist, E., Kalil, A., Gobbato, E., Maillet, F., Cottaz, S., Samain, E., Venkateshwaran, M., Fort, S., et al. (2015) · The Plant Cell · doi:10.1105/tpc.114.131326 · Cited by 242

Chaotic dragonfly algorithm: an improved metaheuristic algorithm for feature selection

Sayed, G. I., Tharwat, A., & Hassanien, A. E. (2018) · Applied Intelligence · doi:10.1007/s10489-018-1261-8 · Cited by 245

Respiratory Syncytial Virus Infection in Adults

Hall, W. J., HALL, C. B., & SPEERS, D. M. (1978) · Annals of Internal Medicine · doi:10.7326/0003-4819-88-2-203 · Cited by 241

Estimating groundwater vulnerability to pollution using a modified DRASTIC model in the Kerman agricultural area, Iran

Neshat, A., Pradhan, B., Pirasteh, S., et al. (2013) · Environmental Earth Sciences · doi:10.1007/s12665-013-2690-7 · Cited by 241

X-inactivation–based clonality analysis and quantitative JAK2V617F assessment reveal a strong association between clonality and JAK2V617F in PV but not ET/MMM, and identifies a subset of JAK2V617F-negative ET and MMM patients with clonal hematopoiesis

Levine, R. L., Bélisle, C., Wadleigh, M., Zahrieh, D., Lee, S. J., Chagnon, P., Gilliland, D. G., & Busque, L. (2006) · Blood · doi:10.1182/blood-2005-09-3900 · Cited by 239

Preoperative chemotherapy for osteogenic sarcoma:Selection of postoperative adjuvant chemotherapy based on the response of the primary tumor to preoperative chemotherapy

Rosen, G., Caparros, B., Huvos, A. G., Kosloff, C., Nirenberg, A., Cacavio, A., Marcove, R. C., Lane, J. M., Mehta, B. M., & Urban, C. (1982) · Cancer · doi:10.1002/1097-0142(19820315)49:6 · Cited by 1,082

Implementation and Validation of Fully Relativistic GW Calculations: Spin–Orbit Coupling in Molecules, Nanocrystals, and Solids

Scherpelz, P., Govoni, M., Hamada, I., & Galli, G. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b00114 · Cited by 245

Synthetic food coloring and behavior: A dose response effect in a double-blind, placebo-controlled, repeated-measures study

Rowe, K. S., & Rowe, K. (1994) · The Journal of Pediatrics · doi:10.1016/s0022-3476(06)80164-2 · Cited by 237

What music makes us feel: At least 13 dimensions organize subjective experiences associated with music across different cultures

Cowen, A., Fang, X., Sauter, D. A., & Keltner, D. J. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1910704117 · Cited by 261

QANA: Quantum-based avian navigation optimizer algorithm

Zamani, H., Nadimi-Shahraki, M. H., & Gandomi, A. H. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104314 · Cited by 241

Fifty years of pulsar candidate selection: from simple filters to a new principled real-time classification approach

Lyon, R. J., Stappers, B. W., Cooper, S., Brooke, J. M., & Knowles, J. D. (2016) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stw656 · Cited by 253

Hit or Miss? The Effect of Assassinations on Institutions and War

Jones, B. F., & Olken, B. (2009) · American Economic Journal Macroeconomics · doi:10.1257/mac.1.2.55 · Cited by 242

Morphine and β-endorphin influence the secretion of the endocrine pancreas

Ipp, E., Dobbs, R., & Unger, R. (1978) · Nature · doi:10.1038/276190a0 · Cited by 217

Monitoring decadal lake dynamics across the Yangtze Basin downstream of Three Gorges Dam

Wang, J., Sheng, Y., & Tong, T. S. D. (2014) · Remote Sensing of Environment · doi:10.1016/j.rse.2014.06.004 · Cited by 239

Roughly balanced bagging for imbalanced data

Hido, S., Kashima, H., & Takahashi, Y. (2009) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.10061 · Cited by 244

Treatment-related Lymphopenia in Patients With Stage III Non-Small-Cell Lung Cancer

Campian, J. L., Ye, X., Brock, M. V., & Grossman, S. A. (2013) · Cancer Investigation · doi:10.3109/07357907.2013.767342 · Cited by 233

A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell–Cell Communication

Cheng, C., Chen, W., Jin, H., & Chen, X. '. (2023) · Cells · doi:10.3390/cells12151970 · Cited by 253

Rice diseases classification using feature selection and rule generation techniques

Phadikar, S., Sil, J., & Das, A. K. (2012) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2012.11.001 · Cited by 239

Identification of Early Interstitial Lung Disease in Smokers from the COPDGene Study

Washko, G. R., Lynch, D. A., Matsuoka, S., Ross, J. C., Umeoka, S., Díaz, A. A., Sciurba, F. C., Hunninghake, G. M., Estépar, R. S. J., Silverman, E. K., et al. (2009) · Academic Radiology · doi:10.1016/j.acra.2009.07.016 · Cited by 235

Fusing simulated GEDI, ICESat-2 and NISAR data for regional aboveground biomass mapping

Silva, C. A., Duncanson, L. I., Hancock, S., Neuenschwander, A., Thomas, N. M., Hofton, M. A., Fatoyinbo, T., Simard, M., Marshak, C. Z., Armston, J. D., et al. (2020) · Remote Sensing of Environment · doi:10.1016/j.rse.2020.112234 · Cited by 244

On the need for bias correction in regional climate scenarios to assess climate change impacts on river runoff

Muerth, M., St-Denis, B. G., Ricard, S., Velázquez, J. A., Schmid, J., Minville, M., Caya, D., Chaumont, D., Ludwig, R., & Turcotte, R. (2013) · Hydrology and earth system sciences · doi:10.5194/hess-17-1189-2013 · Cited by 231

Savanna fire and the origins of the ‘underground forests’ of A frica

Maurin, O., Davies, T. J., Burrows, J. E., Daru, B. H., Yessoufou, K., Muasya, A. M., van der Bank, M., & Bond, W. J. (2014) · New Phytologist · doi:10.1111/nph.12936 · Cited by 231

The Course of Visual Recovery after Optic Neuritis

Beck, R. W., Cleary, P. A., & Backlund, J. C. (1994) · Ophthalmology · doi:10.1016/s0161-6420(94)31103-1 · Cited by 228

DIALIGN-TX: greedy and progressive approaches for segment-based multiple sequence alignment

Subramanian, A. R., Kaufmann, M., & Morgenstern, B. (2008) · Algorithms for Molecular Biology · doi:10.1186/1748-7188-3-6 · Cited by 229

2-cm versus 4-cm surgical excision margins for primary cutaneous melanoma thicker than 2 mm: a randomised, multicentre trial

Gillgren, P., Drzewiecki, K. T., Niin, M., et al. (2011) · The Lancet · doi:10.1016/s0140-6736(11)61546-8 · Cited by 228

Chromosome abnormalities clustering and its implications for pathogenesis and prognosis in myeloma

Debes-Marun, C. S., Dewald, G., Bryant, S., Picken, E. B., Santana-Dávila, R., Gonzalez-Paz, N. C., Winkler, J. M., Kyle, R. A., Gertz, M. A., Witzig, T. E., et al. (2003) · Leukemia · doi:10.1038/sj.leu.2402797 · Cited by 227

Association of the Quick Sequential (Sepsis-Related) Organ Failure Assessment (qSOFA) Score With Excess Hospital Mortality in Adults With Suspected Infection in Low- and Middle-Income Countries

Rudd, K. E., Seymour, C. W., Aluisio, A. R., Augustin, M. E., Bagenda, D. S., Beane, A., Byiringiro, J. C., Chang, C.-C. H., Colas, L. N., Day, N., et al. (2018) · JAMA · doi:10.1001/jama.2018.6229 · Cited by 227

Perceived importance of information system success factors: A meta analysis of group differences

Li, E. Y. (1997) · Information & Management · doi:10.1016/s0378-7206(97)00005-0 · Cited by 231

Data mining techniques for software effort estimation: a comparative study

Dejaeger, K., Verbeke, W., Martens, D., & Baesens, B. (2012) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2011.55 · Cited by 224

Wilcoxon Signed‐Rank Test

Woolson, R. F. (2005) · Encyclopedia of Biostatistics · doi:10.1002/0470011815.b2a15177 · Cited by 225

GliZ, a Transcriptional Regulator of Gliotoxin Biosynthesis, Contributes toAspergillus fumigatusVirulence

Bok, J. W., Chung, D., Balajee, S. A., Marr, K. A., Andes, D. R., Nielsen, K. F., Frisvad, J. C., Kirby, K. A., & Keller, N. P. (2006) · Infection and Immunity · doi:10.1128/iai.00780-06 · Cited by 223

Homogenization of Climate Data: Review and New Perspectives Using Geostatistics

Costa, A. C., & Soares, A. (2008) · Mathematical Geosciences · doi:10.1007/s11004-008-9203-3 · Cited by 224

Experienced Gray Wolf Optimization Through Reinforcement Learning and Neural Networks

Emary, E., Zawbaa, H. M., & Groşan, C. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2634548 · Cited by 224

Accurate and privacy preserving cough sensing using a low-cost microphone

Larson, E. C., Lee, T.-J., Liu, S. T. H., Rosenfeld, M., & Patel, S. (2011) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2030112.2030163 · Cited by 224

A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization

Nasir, M., Das, S., Maity, D., Sengupta, S., Halder, U., & Suganthan, P. N. (2012) · Information Sciences · doi:10.1016/j.ins.2012.04.028 · Cited by 221

Detection of Alzheimer‘S and Parkinson‘S Disease From Exhaled Breath Using Nanomaterial-Based Sensors

Tisch, U., Schlesinger, I., Ionescu, R., Nassar, M., Axelrod, N., Robertman, D., Tessler, Y., Azar, F., Marmur, A., Aharon‐Peretz, J., et al. (2012) · Nanomedicine · doi:10.2217/nnm.12.105 · Cited by 223

A Novel Hybrid Deep Learning Model for Sentiment Classification

Salur, M. U., & Aydın, İ. (2020) · IEEE Access · doi:10.1109/access.2020.2982538 · Cited by 252

Version history, similar report, and structure: putting them together for improved bug localization

Wang, S., & Lo, D. (2014) · International Conference on Program Comprehension · doi:10.1145/2597008.2597148 · Cited by 222

Deep reinforcement learning for imbalanced classification

Lin, E., Chen, Q., & Qi, X. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01637-z · Cited by 223

Risk spillovers between FinTech and traditional financial institutions: Evidence from the U.S.

Li, J., Li, J., Zhu, X., Yao, Y., & Casu, B. (2020) · International Review of Financial Analysis · doi:10.1016/j.irfa.2020.101544 · Cited by 222

Temporal convolutional autoencoder for unsupervised anomaly detection in time series

Thill, M., Konen, W., Wang, H., & Bäck, T. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107751 · Cited by 234

The natural history of untreated duodenal and ampullary adenomas in patients with familial adenomatous polyposis followed in an endoscopic surveillance program

Burke, C. A., Beck, G. J., Church, J. M., et al. (1999) · Gastrointestinal Endoscopy · doi:10.1016/s0016-5107(99)70013-1 · Cited by 221

Leave-One-Out Cross-Validation Based Model Selection Criteria for Weighted LS-SVMs

Cawley, G. C. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2006.246634 · Cited by 221

A comparative study of survival models for breast cancer prognostication based on microarray data: does a single gene beat them all?

Haibe‐Kains, B., Desmedt, C., Sotiriou, C. P., & Bontempi, G. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn374 · Cited by 226

The R Language: An Engine for Bioinformatics and Data Science

Giorgi, F. M., Ceraolo, C., & Mercatelli, D. (2022) · Life · doi:10.3390/life12050648 · Cited by 227

Stream-based active learning for sentiment analysis in the financial domain

Smailović, J., Grćar, M., Lavrač, N., & Žnidaršič, M. (2014) · Information Sciences · doi:10.1016/j.ins.2014.04.034 · Cited by 228

Newsjunkie

Gabrilovich, E., Dumais, S., & Horvitz, E. (2004) · International World Wide Web Conference (WWW) · doi:10.1145/988672.988738 · Cited by 220

Autologous and allogeneic stem cell transplantations for poor-risk chronic lymphocytic leukemia

Gribben, J. G., Zahrieh, D., Stephans, K. F., Bartlett-Pandite, L., Alyea, E. P., Fisher, D. C., Freedman, A. S., Mauch, P., Schlossman, R. L., Sequist, L. V. D., et al. (2005) · Blood · doi:10.1182/blood-2005-05-1778 · Cited by 220

Mediterranean grassland soil C–N compound turnover is dependent on rainfall and depth, and is mediated by genomically divergent microorganisms

Diamond, S., Andeer, P. F., Li, Z., Crits‐Christoph, A., Burstein, D. E., Anantharaman, K., Lane, K. R., Thomas, B. C., Pan, C., Northen, T. R., et al. (2019) · Nature Microbiology · doi:10.1038/s41564-019-0449-y · Cited by 222

Asymptotically Most Powerful Rank-Order Tests

Hájek, J. (1962) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177704476 · Cited by 220

Cellular Network Traffic Scheduling With Deep Reinforcement Learning

Chinchali, S., Hu, P., Chu, T., Sharma, M., Bansal, M., Misra, R., Pavone, M., & Katti, S. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11339 · Cited by 218

Pictures and Spoken Descriptions Elicit Similar Eye Movements During Mental Imagery, Both in Light and in Complete Darkness

Johansson, R., Holšánová, J., & Holmqvist, K. (2006) · Cognitive Science · doi:10.1207/s15516709cog0000_86 · Cited by 224

E-Readers and Visual Fatigue

Benedetto, S., Drai-Zerbib, V., Pedrotti, M., Tissier, G., & Baccino, T. (2013) · PLoS ONE · doi:10.1371/journal.pone.0083676 · Cited by 219

Is Prostate Specific Antigen of Clinical Importance in Evaluating Outcome after Radical Prostatectomy

Frazier, H. A., Robertson, J. E., Humphrey, P. A., et al. (1993) · The Journal of Urology · doi:10.1016/s0022-5347(17)36132-3 · Cited by 217

The Effect of Do‐Not‐Resuscitate Orders on Physician Decision‐Making

Beach, M. C., & Morrison, R. S. (2002) · Journal of the American Geriatrics Society · doi:10.1046/j.1532-5415.2002.50620.x · Cited by 216

Improved disk-drive failure warnings

Hughes, G. F., Murray, J. F., Kreutz-Delgado, K., & Elkan, C. (2002) · IEEE Transactions on Reliability · doi:10.1109/tr.2002.802886 · Cited by 216

Telomere shortening and the clinicopathologic characteristics of human colorectal carcinomas

Takagi, S., Kinouchi, Y., Hiwatashi, N., Chida, M., Nagashima, F., Takahashi, S., Negoro, K., Shimosegawa, T., & Toyota, T. (1999) · Cancer · doi:10.1002/(sici)1097-0142(19991015)86:8 · Cited by 47

Groundwater vulnerability assessment using an improved DRASTIC method in GIS

Neshat, A., Pradhan, B., & Dadras, M. (2014) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2014.02.008 · Cited by 215

Multiobjective Vehicle Routing Problems With Simultaneous Delivery and Pickup and Time Windows: Formulation, Instances, and Algorithms

Wang, J., Zhou, Y. C., Wang, Y., Zhang, J., Chen, C. L. P., & Zheng, Z. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2015.2409837 · Cited by 221

Evaluating Information Transfer Between Auditory Cortical Neurons

Gourévitch, B., & Eggermont, J. J. (2007) · Journal of Neurophysiology · doi:10.1152/jn.01106.2006 · Cited by 210

Towards a whole‐genome sequence for rye ( Secale cereale L.)

Bauer, E., Schmutzer, T., Barilar, I., Mascher, M., Gundlach, H., Martis, M. M., Twardziok, S. O., Hackauf, B., Gordillo, A., Wilde, P., et al. (2016) · The Plant Journal · doi:10.1111/tpj.13436 · Cited by 214

A method for multi-class sentiment classification based on an improved one-vs-one (OVO) strategy and the support vector machine (SVM) algorithm

Liu, Y., Bi, J.-W., & Fan, Z. (2017) · Information Sciences · doi:10.1016/j.ins.2017.02.016 · Cited by 211

Late Effects of Treatment in Survivors of Childhood Acute Myeloid Leukemia

Leung, W. H., Hudson, M. M., Strickland, D. K., et al. (2000) · Journal of Clinical Oncology · doi:10.1200/jco.2000.18.18.3273 · Cited by 213

Fast and Accurate Time-Series Clustering

Paparrizos, J., & Gravano, L. (2017) · ACM Transactions on Database Systems · doi:10.1145/3044711 · Cited by 213

Applying genetic algorithms to set the optimal combination of forest fire related variables and model forest fire susceptibility based on data mining models. The case of Dayu County, China

Hong, H., Tsangaratos, P., Ilia, I., Liu, J., Zhu, A., & Xu, C. B. (2018) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2018.02.278 · Cited by 212

Correlation and association analyses in microbiome study integrating multiomics in health and disease

Xia, Y. (2020) · Progress in molecular biology and translational science · doi:10.1016/bs.pmbts.2020.04.003 · Cited by 239

Transcutaneous Vagus Nerve Stimulation in Humans Induces Pupil Dilation and Attenuates Alpha Oscillations

Sharon, O., Fahoum, F., & Nir, Y. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.1361-20.2020 · Cited by 214

CLINICAL TRIAL OF A TRITERPENOID LIQUORICE COMPOUND IN GASTRIC AND DUODENAL ULCER

Doll, R., Hill, I. D., Hutton, C. F., et al. (1962) · The Lancet · doi:10.1016/s0140-6736(62)92588-6 · Cited by 213

Simulating runoff under changing climatic conditions: Revisiting an apparent deficiency of conceptual rainfall‐runoff models

Fowler, K., Peel, M., Western, A. W., Zhang, L., & Peterson, T. J. (2016) · Water Resources Research · doi:10.1002/2015wr018068 · Cited by 225

Identifying significantly impacted pathways: a comprehensive review and assessment

Nguyen, T., Shafi, A., Nguyen, T., & Drăghici, S. (2019) · Genome biology · doi:10.1186/s13059-019-1790-4 · Cited by 219

Fluctuations in the Organic Carbon to Chlorophyll a Ratios for Estuarine Benthic Diatom Populations

de Jonge, V. N. (1980) · Marine Ecology Progress Series · doi:10.3354/meps002345 · Cited by 211

Shoulder disorders and postural stress in automobile assembly work

Punnett, L., Fine, L. J., Keyserling, W. M., Herrin, G. D., & Chaffin, D. B. (2000) · Scandinavian Journal of Work Environment & Health · doi:10.5271/sjweh.544 · Cited by 212

Software defect prediction using stacked denoising autoencoders and two-stage ensemble learning

Tong, H., Liu, B., & Wang, S. (2017) · Information and Software Technology · doi:10.1016/j.infsof.2017.11.008 · Cited by 211

Some effects of enhanced UV-B irradiation on the growth and composition of plants

Tevini, M., Iwanzik, W., & Thoma, U. (1981) · Planta · doi:10.1007/bf00384258 · Cited by 211

Outlier removal, sum scores, and the inflation of the Type I error rate in independent samples t tests: the power of alternatives and recommendations.

Bakker, M., & Wicherts, J. (2014) · Psychological methods · doi:10.1037/met0000014 · Cited by 173

Measuring homelessness and residential stability: The residential time‐line follow‐back inventory

Tsemberis, S. J., McHugo, G. J., Williams, V. F., Hanrahan, P. A., & Stefančić, A. (2006) · Journal of Community Psychology · doi:10.1002/jcop.20132 · Cited by 210

RDDM: Reactive drift detection method

de Barros, R. S. M., Cabral, D. R. D. L., Gonçalves, P. M., & Santos, S. G. T. D. C. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.08.023 · Cited by 210

Optic Neuritis Treatment Trial

Levi, L. (1995) · Journal of Neuro-Ophthalmology · doi:10.1097/00041327-199512000-00045 · Cited by 210

Immunotherapy in cat-induced asthma. Double-blind trial with evaluation of in vivo and in vitro responses

Ohman, J. L., Findlay, S. R., & Leitermann, K. (1984) · Journal of Allergy and Clinical Immunology · doi:10.1016/0091-6749(84)90251-3 · Cited by 210

Noise in GPS displacement measurements from Southern California and Southern Nevada

Langbein, J. O. (2008) · Journal of Geophysical Research Atmospheres · doi:10.1029/2007jb005247 · Cited by 209

Neurocognitive outcome in brain metastases patients treated with accelerated-fractionation vs. accelerated-hyperfractionated radiotherapy: an analysis from Radiation Therapy Oncology Group Study 91-04

Regine, W. F., Scott, C., Murray, K., et al. (2001) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/s0360-3016(01)01676-5 · Cited by 209

SCIP: global optimization of mixed-integer nonlinear programs in a branch-and-cut framework

Vigerske, S., & Gleixner, A. (2017) · Optimization methods & software · doi:10.1080/10556788.2017.1335312 · Cited by 212

A prescription of methodological guidelines for comparing bio-inspired optimization algorithms

LaTorre, A., Molina, D., Osaba, E., Poyatos, J., Ser, J. D., & Herrera, F. (2021) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2021.100973 · Cited by 225

Multi-labelled classification using maximum entropy method

Shenghuo Zhu, B., Ji, X., Xu, W. H., & Gong, Y. (2005) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1076034.1076082 · Cited by 208

Game-like language learning in 3-D virtual environments

Berns, A., González-Pardo, A., & Camacho, D. (2012) · Computers & Education · doi:10.1016/j.compedu.2012.07.001 · Cited by 208

Wilcoxon Rank Sum Test

Haynes, W. A. (2013) · Springer eBooks · doi:10.1007/978-1-4419-9863-7_1185 · Cited by 208

Ensembles of Multi-Objective Decision Trees

Kocev, D., Vens, C., Struyf, J., & Džeroski, S. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74958-5_61 · Cited by 207

Social signals and algorithmic trading of Bitcoin

García, D., & Schweitzer, F. (2015) · Royal Society Open Science · doi:10.1098/rsos.150288 · Cited by 209

Directional distributional similarity for lexical inference

Kotlerman, L., Dagan, I., Szpektor, I., & Zhitomirsky‐Geffet, M. (2010) · Natural Language Engineering · doi:10.1017/s1351324910000124 · Cited by 207

A psychometric study found AMSTAR 2 to be a valid and moderately reliable appraisal tool

Lorenz, R. C., Matthias, K., Pieper, D., Wegewitz, U. E., Morche, J., Nocon, M., Rissling, O., Schirm, J., & Jacobs, A. (2019) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2019.05.028 · Cited by 212

Initial immunoglobulin M ‘flare’ after rituximab therapy in patients diagnosed with Waldenstrom macroglobulinemia

Ghobrial, I. M., Fonseca, R., Greipp, P. R., Blood, E., Rué, M., Vesole, D. H., & Gertz, M. A. (2004) · Cancer · doi:10.1002/cncr.20658 · Cited by 204

Adaptive gbest-guided gravitational search algorithm

Mirjalili, S., & Lewis, A. (2014) · Neural Computing and Applications · doi:10.1007/s00521-014-1640-y · Cited by 204

Multi-class sentiment classification: The experimental comparisons of feature selection and machine learning algorithms

Liu, Y., Bi, J.-W., & Fan, Z. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.03.042 · Cited by 204

Susquehanna Chorale Spring Concert "Roots and Wings"

Hawkins, D. (2017)

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Teng, C., He, Y., Ji, D., et al. (2009) · doi:10.1109/aici.2009.311 · Cited by 4

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