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2010 – 2019
- 2018
[c92]Richard A. Bauder, Taghi M. Khoshgoftaar, Amri Napolitano:
Fraud Detection with a Limited Number of Known Fraudulent Medicare Providers. FLAIRS 2018: 299-304- 2016
- [c91]Maryam M. Najafabadi, Taghi M. Khoshgoftaar, Amri Napolitano, Charles Wheelus:
RUDY Attack: Detection at the Network Level and Its Important Features. FLAIRS 2016: 288-293 - [c90]Joseph D. Prusa, Taghi M. Khoshgoftaar, Amri Napolitano:
Necessity of Feature Selection when Augmenting Tweet Sentiment Feature Spaces with Emoticons. FLAIRS 2016: 614-620 - [c89]Alireza Fazelpour, Taghi M. Khoshgoftaar, David J. Dittman, Amri Napolitano:
Investigating the Variation of Ensemble Size on Bagging-Based Classifier Performance in Imbalanced Bioinformatics Datasets. IRI 2016: 377-383 - [p1]David J. Dittman, Taghi M. Khoshgoftaar, Amri Napolitano:
Is Data Sampling Required When Using Random Forest for Classification on Imbalanced Bioinformatics Data? Theoretical Information Reuse and Integration 2016: 157-171 - 2015
- [j16]Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano:
An Empirical Investigation on Wrapper-Based Feature Selection for Predicting Software Quality. Int. J. Softw. Eng. Knowl. Eng. 25(1): 93-114 (2015) - [j15]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
Investigating Two Approaches for Adding Feature Ranking to Sampled Ensemble Learning for Software Quality Estimation. Int. J. Softw. Eng. Knowl. Eng. 25(1): 115-146 (2015) - [j14]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
Aggregating Data Sampling with Feature Subset Selection to Address Skewed Software Defect Data. Int. J. Softw. Eng. Knowl. Eng. 25(9-10): 1531-1550 (2015) - [c88]David J. Dittman, Taghi M. Khoshgoftaar, Amri Napolitano:
Selecting the Appropriate Ensemble Learning Approach for Balanced Bioinformatics Data. FLAIRS 2015: 329-334 - [c87]Alireza Fazelpour, Taghi M. Khoshgoftaar, David J. Dittman, Amri Napolitano:
Does the Inclusion of Data Sampling Improve the Performance of Boosting Algorithms on Imbalanced Bioinformatics Data? ICMLA 2015: 527-534 - [c86]Joseph D. Prusa, Taghi M. Khoshgoftaar, Amri Napolitano:
Utilizing Ensemble, Data Sampling and Feature Selection Techniques for Improving Classification Performance on Tweet Sentiment Data. ICMLA 2015: 535-542 - [c85]Alireza Fazelpour, Taghi M. Khoshgoftaar, David J. Dittman, Amri Napolitano:
Investigating New Bootstrapping Approaches of Bagging Classifiers to Account for Class Imbalance in Bioinformatics Datasets. ICMLA 2015: 987-994 - [c84]Joseph D. Prusa, Taghi M. Khoshgoftaar, Amri Napolitano:
Using Feature Selection in Combination with Ensemble Learning Techniques to Improve Tweet Sentiment Classification Performance. ICTAI 2015: 186-193 - [c83]Taghi M. Khoshgoftaar, Alireza Fazelpour, David J. Dittman, Amri Napolitano:
Ensemble vs. Data Sampling: Which Option Is Best Suited to Improve Classification Performance of Imbalanced Bioinformatics Data? ICTAI 2015: 705-712 - [c82]Alireza Fazelpour, Taghi M. Khoshgoftaar, David J. Dittman, Amri Napolitano:
Choosing an Appropriate Ensemble Classifier for Balanced Bioinformatics Data. IRI 2015: 17-24 - [c81]Joseph D. Prusa, Taghi M. Khoshgoftaar, David J. Dittman, Amri Napolitano:
Using Random Undersampling to Alleviate Class Imbalance on Tweet Sentiment Data. IRI 2015: 197-202 - [c80]Taghi M. Khoshgoftaar, Alireza Fazelpour, David J. Dittman, Amri Napolitano:
Alterations to the Bootstrapping Process within Random Forest: A Case Study on Imbalanced Bioinformatics Data. IRI 2015: 342-348 - [c79]David J. Dittman, Taghi M. Khoshgoftaar, Amri Napolitano:
The Effect of Data Sampling When Using Random Forest on Imbalanced Bioinformatics Data. IRI 2015: 457-463 - [c78]Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano:
Stability of Three Forms of Feature Selection Methods on Software Engineering Data. SEKE 2015: 385-390 - [c77]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
Combining Feature Subset Selection and Data Sampling for Coping with Highly Imbalanced Software Data. SEKE 2015: 439-444 - 2014
- [j13]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
The Use of Ensemble-Based Data Preprocessing Techniques for Software Defect Prediction. Int. J. Softw. Eng. Knowl. Eng. 24(9): 1229-1254 (2014) - [j12]Taghi M. Khoshgoftaar, Kehan Gao, Amri Napolitano, Randall Wald:
A comparative study of iterative and non-iterative feature selection techniques for software defect prediction. Inf. Syst. Frontiers 16(5): 801-822 (2014) - [c76]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano:
Using Correlation-Based Feature Selection for a Diverse Collection of Bioinformatics Datasets. BIBE 2014: 156-162 - [c75]David J. Dittman, Taghi M. Khoshgoftaar, Amri Napolitano:
Selecting the Appropriate Data Sampling Approach for Imbalanced and High-Dimensional Bioinformatics Datasets. BIBE 2014: 304-310 - [c74]Randall Wald, Taghi M. Khoshgoftaar, Richard Zuech, Amri Napolitano:
Network Traffic Prediction Models for Near- and Long-Term Predictions. BIBE 2014: 362-368 - [c73]David J. Dittman, Taghi M. Khoshgoftaar, Amri Napolitano, Alireza Fazelpour:
Select-Bagging: Effectively Combining Gene Selection and Bagging for Balanced Bioinformatics Data. BIBE 2014: 413-419 - [c72]Taghi M. Khoshgoftaar, Alireza Fazelpour, David J. Dittman, Amri Napolitano:
Effects of the Use of Boosting on Classification Performance of Imbalanced Bioinformatics Datasets. BIBE 2014: 420-426 - [c71]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Comparison of Data Sampling Approaches for Imbalanced Bioinformatics Data. FLAIRS 2014 - [c70]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano:
Optimizing Wrapper-Based Feature Selection for Use on Bioinformatics Data. FLAIRS 2014 - [c69]Ahmad Abu Shanab, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
How ranker and learner choice affects classification performance on noisy bioinformatics data. IRI 2014: 277-282 - [c68]Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano:
Stability of filter- and wrapper-based software metric selection techniques. IRI 2014: 309-314 - [c67]Taghi M. Khoshgoftaar, Alireza Fazelpour, David J. Dittman, Amri Napolitano:
Classification performance of three approaches for combining data sampling and gene selection on bioinformatics data. IRI 2014: 315-321 - [c66]Taghi M. Khoshgoftaar, Kehan Gao, Amri Napolitano:
Improving software quality estimation by combining feature selection strategies with sampled ensemble learning. IRI 2014: 428-433 - [c65]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
Comparing Two Approaches for Adding Feature Ranking to Sampled Ensemble Learning for Software Quality Estimation. SEKE 2014: 280-285 - [c64]Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano:
Choosing the Best Classification Performance Metric for Wrapper-based Software Metric Selection for Defect Prediction. SEKE 2014: 540-545 - 2013
- [c63]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Classification Performance of Rank Aggregation Techniques for Ensemble Gene Selection. FLAIRS 2013 - [c62]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
Improving Software Quality Estimation by Combining Boosting and Feature Selection. ICMLA (1) 2013: 27-33 - [c61]Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano:
An Empirical Study on Wrapper-Based Feature Selection for Software Engineering Data. ICMLA (2) 2013: 84-89 - [c60]Randall Wald, Taghi M. Khoshgoftaar, David J. Dittman, Amri Napolitano:
Random Forest with 200 Selected Features: An Optimal Model for Bioinformatics Research. ICMLA (1) 2013: 154-160 - [c59]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Simplifying the Utilization of Machine Learning Techniques for Bioinformatics. ICMLA (2) 2013: 396-403 - [c58]Taghi M. Khoshgoftaar, David J. Dittman, Randall Wald, Amri Napolitano:
Contrasting Undersampled Boosting with Internal and External Feature Selection for Patient Response Datasets. ICMLA (2) 2013: 404-410 - [c57]Randall Wald, Taghi M. Khoshgoftaar, Ahmad Abu Shanab, Amri Napolitano:
Comparative Analysis on the Stability of Feature Selection Techniques Using Three Frameworks on Biological Datasets. ICMLA (1) 2013: 418-423 - [c56]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano:
Comparison of Stability for Different Families of Filter-Based and Wrapper-Based Feature Selection. ICMLA (2) 2013: 457-464 - [c55]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano, Chris Sumner:
Which Users Reply to and Interact with Twitter Social Bots? ICTAI 2013: 135-144 - [c54]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano:
Stability of Filter- and Wrapper-Based Feature Subset Selection. ICTAI 2013: 374-380 - [c53]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano:
How the Choice of Wrapper Learner and Performance Metric Affects Subset Evaluation. ICTAI 2013: 426-432 - [c52]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano:
Should the Same Learners Be Used Both within Wrapper Feature Selection and for Building Classification Models? ICTAI 2013: 439-445 - [c51]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Maximizing Classification Performance for Patient Response Datasets. ICTAI 2013: 454-462 - [c50]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano, Chris Sumner:
Predicting susceptibility to social bots on Twitter. IRI 2013: 6-13 - [c49]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano:
The importance of performance metrics within wrapper feature selection. IRI 2013: 105-111 - [c48]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Comparison of rank-based vs. score-based aggregation for ensemble gene selection. IRI 2013: 225-231 - [c47]Taghi M. Khoshgoftaar, Randall Wald, David J. Dittman, Amri Napolitano:
Feature list aggregation approaches for ensemble gene selection on patient response datasets. IRI 2013: 317-324 - [c46]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Gene selection stability's dependence on dataset difficulty. IRI 2013: 341-348 - [c45]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano:
Filter- and wrapper-based feature selection for predicting user interaction with Twitter bots. IRI 2013: 416-423 - [c44]Huanjing Wang, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
A Study on First Order Statistics-Based Feature Selection Techniques on Software Metric Data. SEKE 2013: 467-472 - [c43]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
Exploring Ensemble-Based Data Preprocessing Techniques for Software Quality Estimation. SEKE 2013: 612-617 - 2012
- [j11]Wilker Altidor, Taghi M. Khoshgoftaar, Amri Napolitano:
Measuring stability of feature ranking techniques: a noise-based approach. Int. J. Bus. Intell. Data Min. 7(1/2): 80-115 (2012) - [j10]Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano:
Software measurement data reduction using ensemble techniques. Neurocomputing 92: 124-132 (2012) - [j9]Taghi M. Khoshgoftaar, Kehan Gao, Amri Napolitano:
An Empirical Study of Feature Ranking Techniques for Software Quality Prediction. Int. J. Softw. Eng. Knowl. Eng. 22(2): 161-183 (2012) - [j8]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano, Randall Wald:
Threshold-based feature selection techniques for high-dimensional bioinformatics data. Netw. Model. Anal. Health Informatics Bioinform. 1(1-2): 47-61 (2012) - [c42]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Similarity analysis of feature ranking techniques on imbalanced DNA microarray datasets. BIBM 2012: 1-5 - [c41]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Determining the Number of Iterations Appropriate for Ensemble Gene Selection on Microarray Data. ICMLA (1) 2012: 82-89 - [c40]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Comparing Two New Gene Selection Ensemble Approaches with the Commonly-Used Approach. ICMLA (2) 2012: 184-191 - [c39]Janell Duhaney, Taghi M. Khoshgoftaar, Amri Napolitano:
Studying the Effect of Class Imbalance in Ocean Turbine Fault Data on Reliable State Detection. ICMLA (1) 2012: 268-275 - [c38]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
A Hybrid Approach to Coping with High Dimensionality and Class Imbalance for Software Defect Prediction. ICMLA (2) 2012: 281-288 - [c37]Huanjing Wang, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
A Comparative Study on the Stability of Software Metric Selection Techniques. ICMLA (2) 2012: 301-307 - [c36]Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano:
An Empirical Study on the Stability of Feature Selection for Imbalanced Software Engineering Data. ICMLA (1) 2012: 317-323 - [c35]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano, Chris Sumner:
Using Twitter Content to Predict Psychopathy. ICMLA (2) 2012: 394-401 - [c34]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
A Novel Noise-Resistant Boosting Algorithm for Class-Skewed Data. ICMLA (2) 2012: 551-557 - [c33]Huanjing Wang, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
A novel dataset-similarity-aware approach for evaluating stability of software metric selection techniques. IRI 2012: 1-8 - [c32]Taghi M. Khoshgoftaar, Kehan Gao, Amri Napolitano:
Exploring an iterative feature selection technique for highly imbalanced data sets. IRI 2012: 101-108 - [c31]Wael Awada, Taghi M. Khoshgoftaar, David J. Dittman, Randall Wald, Amri Napolitano:
A review of the stability of feature selection techniques for bioinformatics data. IRI 2012: 356-363 - [c30]Randall Wald, Taghi M. Khoshgoftaar, David J. Dittman, Wael Awada, Amri Napolitano:
An extensive comparison of feature ranking aggregation techniques in bioinformatics. IRI 2012: 377-384 - [c29]Ahmad Abu Shanab, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Impact of noise and data sampling on stability of feature ranking techniques for biological datasets. IRI 2012: 415-422 - [c28]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
Stability of Filter-Based Feature Selection Methods for Imbalanced Software Measurement Data. SEKE 2012: 74-79 - [c27]Huanjing Wang, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
An Empirical Study of Software Metric Selection Techniques for Defect Prediction. SEKE 2012: 94-99 - 2011
- [j7]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
An exploration of learning when data is noisy and imbalanced. Intell. Data Anal. 15(2): 215-236 (2011) - [j6]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
Evaluating the Impact of Data Quality on Sampling. J. Inf. Knowl. Manag. 10(3): 225-245 (2011) - [j5]Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
Comparing Boosting and Bagging Techniques With Noisy and Imbalanced Data. IEEE Trans. Syst. Man Cybern. Part A 41(3): 552-568 (2011) - [c26]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Random forest: A reliable tool for patient response prediction. BIBM Workshops 2011: 289-296 - [c25]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
Impact of Data Sampling on Stability of Feature Selection for Software Measurement Data. ICTAI 2011: 1004-1011 - [c24]Wilker Altidor, Taghi M. Khoshgoftaar, Amri Napolitano:
A noise-based stability evaluation of threshold-based feature selection techniques. IRI 2011: 240-245 - [c23]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
A comparative evaluation of feature ranking methods for high dimensional bioinformatics data. IRI 2011: 315-320 - [c22]Xiaoyuan Su, Russell Greiner, Taghi M. Khoshgoftaar, Amri Napolitano:
Using Classifier-Based Nominal Imputation to Improve Machine Learning. PAKDD (1) 2011: 124-135 - [c21]Taghi M. Khoshgoftaar, Kehan Gao, Amri Napolitano:
A Comparative Study of Different Strategies for Predicting Software Quality. SEKE 2011: 65-70 - [c20]Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano:
An Empirical Study of Software Metrics Selection Using Support Vector Machine. SEKE 2011: 83-88 - 2010
- [j4]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
An Empirical Evaluation of Repetitive Undersampling Techniques. Int. J. Softw. Eng. Knowl. Eng. 20(2): 173-195 (2010) - [j3]Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
Supervised neural network modeling: an empirical investigation into learning from imbalanced data with labeling errors. IEEE Trans. Neural Networks 21(5): 813-830 (2010) - [j2]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance. IEEE Trans. Syst. Man Cybern. Part A 40(1): 185-197 (2010) - [c19]Taghi M. Khoshgoftaar, Kehan Gao, Amri Napolitano:
An Empirical Study of Predictive Modeling Techniques of Software Quality. BIONETICS 2010: 288-302 - [c18]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
A Novel Noise Filtering Algorithm for Imbalanced Data. ICMLA 2010: 9-14 - [c17]Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano:
A Comparative Study of Ensemble Feature Selection Techniques for Software Defect Prediction. ICMLA 2010: 135-140 - [c16]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
Evaluating the impact of data quality on sampling. IRI 2010: 31-36
2000 – 2009
- 2009
- [b1]Amri Napolitano:
Classification techniques for noisy and imbalanced data. Florida Atlantic University, Boca Raton, USA, 2009 - [j1]Andres Folleco, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
Identifying Learners Robust to Low Quality Data. Informatica (Slovenia) 33(3): 245-259 (2009) - [c15]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano, Randall Wald:
Feature Selection with High-Dimensional Imbalanced Data. ICDM Workshops 2009: 507-514 - [c14]Wilker Altidor, Taghi M. Khoshgoftaar, Amri Napolitano:
Wrapper-Based Feature Ranking for Software Engineering Metrics. ICMLA 2009: 241-246 - [c13]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
Exploring Software Quality Classification with a Wrapper-Based Feature Ranking Technique. ICTAI 2009: 67-74 - [c12]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
An Empirical Comparison of Repetitive Undersampling Techniques. IRI 2009: 29-34 - 2008
- [c11]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
Building Useful Models from Imbalanced Data with Sampling and Boosting. FLAIRS 2008: 306-311 - [c10]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
A Comparative Study of Data Sampling and Cost Sensitive Learning. ICDM Workshops 2008: 46-52 - [c9]Andres Folleco, Taghi M. Khoshgoftaar, Amri Napolitano:
Comparison of Four Performance Metrics for Evaluating Sampling Techniques for Low Quality Class-Imbalanced Data. ICMLA 2008: 153-158 - [c8]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
RUSBoost: Improving classification performance when training data is skewed. ICPR 2008: 1-4 - [c7]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
Resampling or Reweighting: A Comparison of Boosting Implementations. ICTAI (1) 2008: 445-451 - [c6]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
Improving Learner Performance with Data Sampling and Boosting. ICTAI (1) 2008: 452-459 - 2007
- [c5]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
Skewed Class Distributions and Mislabeled Examples. ICDM Workshops 2007: 477-482 - [c4]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
Experimental perspectives on learning from imbalanced data. ICML 2007: 935-942 - [c3]Taghi M. Khoshgoftaar, Chris Seiffert, Jason Van Hulse, Amri Napolitano, Andres Folleco:
Learning with limited minority class data. ICMLA 2007: 348-353 - [c2]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
Mining Data with Rare Events: A Case Study. ICTAI (2) 2007: 132-139
1980 – 1989
- 1982
- [c1]G. Cosmai, Umberto Cugini, Piero Mussio, Amri Napolitano:
An interactive drafting system based on two dimensional primitives. DAC 1982: 521-529
Coauthor Index
[c92] [c91] [c90] [c89] [p1] [j16] [j15] [j14] [c88] [c87] [c86] [c85] [c84] [c83] [c82] [c81] [c80] [c79] [c78] [c77] [j13] [j12] [c76] [c75] [c74] [c73] [c72] [c71] [c70] [c69] [c68] [c67] [c66] [c65] [c64] [c63] [c62] [c61] [c60] [c59] [c58] [c57] [c56] [c55] [c54] [c53] [c52] [c51] [c50] [c49] [c48] [c47] [c46] [c45] [c44] [c43] [j11] [j10] [j9] [j8] [c42] [c41] [c40] [c39] [c38] [c37] [c36] [c35] [c34] [c33] [c32] [c31] [c30] [c29] [c28] [c27] [j7] [j6] [j5] [c26] [c25] [c24] [c23] [c22] [c21] [c20] [j4] [j3] [j2] [c19] [c18] [c17] [c16] [j1] [c15] [c14] [c13] [c12] [c11] [c10] [c9] [c8] [c7] [c6] [c5] [c4] [c3] [c2]
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