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2020 – today
- 2024
[j34]Md Shafikuzzaman
, Md. Rakibul Islam, Alex C. Rolli, Sharmin Akhter, Naeem Seliya:
An Empirical Evaluation of the Zero-Shot, Few-Shot, and Traditional Fine-Tuning Based Pretrained Language Models for Sentiment Analysis in Software Engineering. IEEE Access 12: 109714-109734 (2024)- [c40]Ryan Boston, Naeem Seliya, Mounika Vanamala:
Analyzing Tweets for Disaster Prediction. EIT 2024: 439-443 - [c39]Benjamin Kirkwood, Mounika Vanamala, Naeem Seliya:
Click Fraud Detection of Online Advertising Using Machine Learning Algorithms. EIT 2024: 586-590 - [c38]Thomas Scott, Naeem Seliya, Mounika Vanamala:
Heart Murmur Prediction with Machine Learning. EIT 2024: 615-620 - 2022
- [j33]Nyle Siddiqui, Rushit Dave, Mounika Vanamala, Naeem Seliya:
Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication. Mach. Learn. Knowl. Extr. 4(2): 502-518 (2022) - [c37]Jacob Mallet, Laura Pryor, Rushit Dave, Naeem Seliya, Mounika Vanamala, Evelyn R. Sowells Boone:
Hold On and Swipe: A Touch-Movement Based Continuous Authentication Schema based on Machine Learning. CACML 2022: 442-447 - [c36]Wei Zhong Tee, Rushit Dave, Naeem Seliya, Mounika Vanamala:
Human Activity Recognition models using Limited Consumer Device Sensors and Machine Learning. CACML 2022: 456-461 - [c35]Yash Prabhu, Naeem Seliya:
A CNN-Based Automated Stuttering Identification System. ICMLA 2022: 1601-1605 - [i13]Rushit Dave, Naeem Seliya, Laura Pryor, Mounika Vanamala, Evelyn R. Sowells Boone, Jacob Mallet:
Hold On and Swipe: A Touch-Movement Based Continuous Authentication Schema based on Machine Learning. CoRR abs/2201.08564 (2022) - [i12]Rushit Dave, Naeem Seliya, Mounika Vanamala, Wei Tee:
Human Activity Recognition models using Limited Consumer Device Sensors and Machine Learning. CoRR abs/2201.08565 (2022) - [i11]Wei Zhong Tee, Rushit Dave, Naeem Seliya, Mounika Vanamala:
A Close Look into Human Activity Recognition Models using Deep Learning. CoRR abs/2204.13589 (2022) - [i10]Laura Pryor, Jacob Mallet, Rushit Dave, Naeem Seliya, Mounika Vanamala, Evelyn R. Sowells Boone:
Evaluation of a User Authentication Schema Using Behavioral Biometrics and Machine Learning. CoRR abs/2205.08371 (2022) - [i9]Nyle Siddiqui, Rushit Dave, Naeem Seliya, Mounika Vanamala:
Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication. CoRR abs/2205.13646 (2022) - [i8]Jacob Mallet, Rushit Dave, Naeem Seliya, Mounika Vanamala:
Using Deep Learning to Detecting Deepfakes. CoRR abs/2207.13644 (2022) - [i7]Zachary Deridder, Nyle Siddiqui, Thomas Reither, Rushit Dave, Brendan Pelto, Naeem Seliya, Mounika Vanamala:
Continuous User Authentication Using Machine Learning and Multi-Finger Mobile Touch Dynamics with a Novel Dataset. CoRR abs/2207.13648 (2022) - 2021
- [j32]Joseph M. Ackerson, Rushit Dave, Naeem Seliya:
Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection. Inf. 12(7): 272 (2021) - [j31]Naeem Seliya, Azadeh Abdollah Zadeh, Taghi M. Khoshgoftaar:
A literature review on one-class classification and its potential applications in big data. J. Big Data 8(1): 122 (2021) - [c34]Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar, Naeem Seliya:
IoT Reconnaissance Attack Classification with Random Undersampling and Ensemble Feature Selection. CIC 2021: 41-49 - [i6]Owen Casey, Rushit Dave, Naeem Seliya, Evelyn R. Sowells Boone:
Machine Learning: Challenges, Limitations, and Compatibility for Audio Restoration Processes. CoRR abs/2109.02692 (2021) - [i5]Laura Pryor, Rushit Dave, Naeem Seliya, Evelyn R. Sowells Boone:
Machine Learning Algorithms In User Authentication Schemes. CoRR abs/2110.07826 (2021) - [i4]Sam Strecker, Rushit Dave, Nyle Siddiqui, Naeem Seliya:
A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods. CoRR abs/2110.07832 (2021) - [i3]Nyle Siddiqui, Rushit Dave, Naeem Seliya:
Continuous Authentication Using Mouse Movements, Machine Learning, and Minecraft. CoRR abs/2110.11080 (2021) - [i2]Cole Pearson, Naeem Seliya, Rushit Dave:
Named Entity Recognition in Unstructured Medical Text Documents. CoRR abs/2110.15732 (2021) - [i1]Rushit Dave, Naeem Seliya, Nyle Siddiqui:
The Benefits of Edge Computing in Healthcare, Smart Cities, and IoT. CoRR abs/2112.01250 (2021) - 2020
- [j30]Joffrey L. Leevy, Taghi M. Khoshgoftaar, Richard A. Bauder, Naeem Seliya:
Investigating the relationship between time and predictive model maintenance. J. Big Data 7(1): 36 (2020)
2010 – 2019
- 2019
- [j29]Tawfiq Hasanin, Taghi M. Khoshgoftaar, Joffrey L. Leevy, Naeem Seliya:
Examining characteristics of predictive models with imbalanced big data. J. Big Data 6: 69 (2019) - [c33]Tawfiq Hasanin, Taghi M. Khoshgoftaar, Joffrey L. Leevy, Naeem Seliya:
Investigating Random Undersampling and Feature Selection on Bioinformatics Big Data. BigDataService 2019: 346-356 - [c32]Joffrey L. Leevy, Taghi M. Khoshgoftaar, Richard A. Bauder, Naeem Seliya:
The Effect of Time on the Maintenance of a Predictive Model. ICMLA 2019: 1891-1896 - [e1]M. Arif Wani, Taghi M. Khoshgoftaar, Dingding Wang, Huanjing Wang, Naeem Seliya:
18th IEEE International Conference On Machine Learning And Applications, ICMLA 2019, Boca Raton, FL, USA, December 16-19, 2019. IEEE 2019, ISBN 978-1-7281-4550-1 [contents] - 2018
- [j28]Joffrey L. Leevy, Taghi M. Khoshgoftaar, Richard A. Bauder, Naeem Seliya:
A survey on addressing high-class imbalance in big data. J. Big Data 5: 42 (2018) - 2016
- [c31]Joseph D. Prusa, Taghi M. Khoshgoftaar, Naeem Seliya:
Enhancing Ensemble Learners with Data Sampling on High-Dimensional Imbalanced Tweet Sentiment Data. FLAIRS 2016: 322-328 - [p3]Maryam M. Najafabadi, Flavio Villanustre, Taghi M. Khoshgoftaar, Naeem Seliya, Randall Wald, Edin Muharemagic:
Deep Learning Techniques in Big Data Analytics. Big Data Technologies and Applications 2016: 133-156 - 2015
- [j27]Huanjing Wang, Taghi M. Khoshgoftaar, Naeem Seliya:
On the Stability of Feature Selection Methods in Software Quality Prediction: An Empirical Investigation. Int. J. Softw. Eng. Knowl. Eng. 25(9-10): 1467-1490 (2015) - [j26]Maryam M. Najafabadi, Flavio Villanustre, Taghi M. Khoshgoftaar, Naeem Seliya, Randall Wald, Edin Muharemagic:
Deep learning applications and challenges in big data analytics. J. Big Data 2: 1 (2015) - [c30]Richard Zuech, Taghi M. Khoshgoftaar, Naeem Seliya, Maryam M. Najafabadi, Clifford Kemp:
A New Intrusion Detection Benchmarking System. FLAIRS 2015: 252-256 - [c29]Joseph D. Prusa, Taghi M. Khoshgoftaar, Naeem Seliya:
The Effect of Dataset Size on Training Tweet Sentiment Classifiers. ICMLA 2015: 96-102 - 2014
- [j25]Zhiwei Xu, Kehan Gao, Taghi M. Khoshgoftaar, Naeem Seliya:
System regression test planning with a fuzzy expert system. Inf. Sci. 259: 532-543 (2014) - [c28]Maryam M. Najafabadi, Taghi M. Khoshgoftaar, Clifford Kemp, Naeem Seliya, Richard Zuech:
Machine Learning for Detecting Brute Force Attacks at the Network Level. BIBE 2014: 379-385 - 2012
- [j24]Kehan Gao, Taghi M. Khoshgoftaar, Naeem Seliya:
Predicting high-risk program modules by selecting the right software measurements. Softw. Qual. J. 20(1): 3-42 (2012) - 2011
- [j23]Kehan Gao, Taghi M. Khoshgoftaar, Huanjing Wang, Naeem Seliya:
Choosing software metrics for defect prediction: an investigation on feature selection techniques. Softw. Pract. Exp. 41(5): 579-606 (2011) - [j22]Naeem Seliya, Taghi M. Khoshgoftaar:
The use of decision trees for cost-sensitive classification: an empirical study in software quality prediction. WIREs Data Mining Knowl. Discov. 1(5): 448-459 (2011) - [c27]Huanjing Wang, Taghi M. Khoshgoftaar, Naeem Seliya:
How Many Software Metrics Should be Selected for Defect Prediction? FLAIRS 2011 - 2010
- [j21]Taghi M. Khoshgoftaar, Naeem Seliya, Dennis J. Drown:
Evolutionary data analysis for the class imbalance problem. Intell. Data Anal. 14(1): 69-88 (2010) - [j20]Yi Liu, Taghi M. Khoshgoftaar, Naeem Seliya:
Evolutionary Optimization of Software Quality Modeling with Multiple Repositories. IEEE Trans. Software Eng. 36(6): 852-864 (2010) - [c26]Yinghao Huang, Naeem Seliya, Yi Lu Murphey, Roy B. Friedenthal:
Classifying Independent Medical Examination Reports using SOM Networks. DMIN 2010: 58-64 - [c25]Naeem Seliya, Taghi M. Khoshgoftaar, Jason Van Hulse:
Predicting Faults in High Assurance Software. HASE 2010: 26-34 - [c24]Taghi M. Khoshgoftaar, Kehan Gao, Naeem Seliya:
Attribute Selection and Imbalanced Data: Problems in Software Defect Prediction. ICTAI (1) 2010: 137-144 - [c23]Naeem Seliya, Taghi M. Khoshgoftaar:
Active learning with neural networks for intrusion detection. IRI 2010: 49-54
2000 – 2009
- 2009
- [j19]Taghi M. Khoshgoftaar, Pierre Rebours, Naeem Seliya:
Software quality analysis by combining multiple projects and learners. Softw. Qual. J. 17(1): 25-49 (2009) - [j18]Dennis J. Drown, Taghi M. Khoshgoftaar, Naeem Seliya:
Evolutionary Sampling and Software Quality Modeling of High-Assurance Systems. IEEE Trans. Syst. Man Cybern. Part A 39(5): 1097-1107 (2009) - [c22]Yinghao Huang, Naeem Seliya, Yi Lu Murphey, Roy B. Friedenthal:
Name Entity Recognition and Classification in Medical Text Documents. DMIN 2009: 144-150 - [c21]Huanjing Wang, Taghi M. Khoshgoftaar, Kehan Gao, Naeem Seliya:
Mining Data from Multiple Software Development Projects. ICDM Workshops 2009: 551-557 - [c20]Naeem Seliya, Taghi M. Khoshgoftaar, Jason Van Hulse:
A Study on the Relationships of Classifier Performance Metrics. ICTAI 2009: 59-66 - [c19]Huanjing Wang, Taghi M. Khoshgoftaar, Kehan Gao, Naeem Seliya:
High-Dimensional Software Engineering Data and Feature Selection. ICTAI 2009: 83-90 - [c18]Naeem Seliya, Taghi M. Khoshgoftaar, Jason Van Hulse:
Aggregating Performance Metrics for Classifier Evaluation. IRI 2009: 35-40 - [c17]Naeem Seliya, Taghi M. Khoshgoftaar:
Value-Based Software Quality Modeling. SEKE 2009: 116-121 - 2008
- [j17]Taghi M. Khoshgoftaar, Naeem Seliya, Chris Seiffert:
Low-Effort Labeling of Network Events for Intrusion Detection in WLANs. Int. J. Artif. Intell. Tools 17(3): 521-537 (2008) - [c16]Naeem Seliya, Zhiwei Xu, Taghi M. Khoshgoftaar:
Addressing Class Imbalance in Non-binary Classification Problems. ICTAI (1) 2008: 460-466 - [c15]Zhiwei Xu, Naeem Seliya, Weibiao Wu:
An Adaptive Neural Network with Dynamic Structure for Software Defect Prediction. SEKE 2008: 79-84 - [c14]Taghi M. Khoshgoftaar, Naeem Seliya, Dennis J. Drown:
On the Rarity of Fault-prone Modules in Knowledge-based Software Quality Modeling. SEKE 2008: 279-284 - [p2]Taghi M. Khoshgoftaar, Naeem Seliya:
Software Module Risk Analysis. Wiley Encyclopedia of Computer Science and Engineering 2008 - [p1]Naeem Seliya, Taghi M. Khoshgoftaar:
Software Quality Modeling as a Reliability Tool. Wiley Encyclopedia of Computer Science and Engineering 2008 - 2007
- [j16]Naeem Seliya, Taghi M. Khoshgoftaar:
Software quality estimation with limited fault data: a semi-supervised learning perspective. Softw. Qual. J. 15(3): 327-344 (2007) - [j15]Naeem Seliya, Taghi M. Khoshgoftaar:
Software Quality Analysis of Unlabeled Program Modules With Semisupervised Clustering. IEEE Trans. Syst. Man Cybern. Part A 37(2): 201-211 (2007) - [c13]Taghi M. Khoshgoftaar, Chris Seiffert, Naeem Seliya:
Low-Effort Labeling of Network Events for Intrusion Detection in WLANs. FLAIRS 2007: 490-495 - 2006
- [j14]Taghi M. Khoshgoftaar, Vedang H. Joshi, Naeem Seliya:
Detecting Noisy Instances with the Ensemble Filter: a Study in Software Quality Estimation. Int. J. Softw. Eng. Knowl. Eng. 16(1): 53-76 (2006) - [j13]Taghi M. Khoshgoftaar, Naeem Seliya, Nandini Sundaresh:
An empirical study of predicting software faults with case-based reasoning. Softw. Qual. J. 14(2): 85-111 (2006) - [j12]Taghi M. Khoshgoftaar, Angela Herzberg, Naeem Seliya:
Resource oriented selection of rule-based classification models: An empirical case study. Softw. Qual. J. 14(4): 309-338 (2006) - [c12]Taghi M. Khoshgoftaar, Chris Seiffert, Naeem Seliya:
Labeling network event records for intrusion detection in a Wireless LAN. IRI 2006: 200-206 - 2005
- [j11]Taghi M. Khoshgoftaar, Naeem Seliya, Kehan Gao:
Assessment of a New Three-Group Software Quality Classification Technique: An Empirical Case Study. Empir. Softw. Eng. 10(2): 183-218 (2005) - [j10]Taghi M. Khoshgoftaar, Naeem Seliya, Kehan Gao:
Detecting noisy instances with the rule-based classification model. Intell. Data Anal. 9(4): 347-364 (2005) - [j9]Taghi M. Khoshgoftaar, Naeem Seliya, Angela Herzberg:
Resource-oriented software quality classification models. J. Syst. Softw. 76(2): 111-126 (2005) - [c11]Naeem Seliya, Taghi M. Khoshgoftaar, Shi Zhong:
Analyzing Software Quality with Limited Fault-Proneness Defect Data. HASE 2005: 89-98 - [c10]Taghi M. Khoshgoftaar, Shyam Varan Nath, Shi Zhong, Naeem Seliya:
Intrusion detection in wireless networks using clustering techniques with expert analysis. ICMLA 2005 - [c9]Yudong Xiao, Taghi M. Khoshgoftaar, Naeem Seliya:
The partitioning- and rule-based filter for noise detection. IRI 2005: 205-210 - 2004
- [j8]Taghi M. Khoshgoftaar, Naeem Seliya:
Software quality estimation with case-based reasoning. Adv. Comput. 62: 249-291 (2004) - [j7]Taghi M. Khoshgoftaar, Naeem Seliya:
Comparative Assessment of Software Quality Classification Techniques: An Empirical Case Study. Empir. Softw. Eng. 9(3): 229-257 (2004) - [j6]Shi Zhong, Taghi M. Khoshgoftaar, Naeem Seliya:
Analyzing Software Measurement Data with Clustering Techniques. IEEE Intell. Syst. 19(2): 20-27 (2004) - [j5]Taghi M. Khoshgoftaar, Yi Liu, Naeem Seliya:
A multiobjective module-order model for software quality enhancement. IEEE Trans. Evol. Comput. 8(6): 593-608 (2004) - [c8]Shi Zhong, Taghi M. Khoshgoftaar, Naeem Seliya:
Unsupervised Learning for Expert-Based Software Quality Estimation. HASE 2004: 149-155 - [c7]Naeem Seliya, Taghi M. Khoshgoftaar, Shi Zhong:
Semi-Supervised Learning for Software Quality Estimation. ICTAI 2004: 183-190 - [c6]Taghi M. Khoshgoftaar, Naeem Seliya, Kehan Gao:
Rule-Based Noise Detection for Software Measurement Data. IRI 2004: 302-307 - [c5]Taghi M. Khoshgoftaar, Naeem Seliya:
The Necessity of Assuring Quality in Software Measurement Data. IEEE METRICS 2004: 119-130 - [c4]Taghi M. Khoshgoftaar, Yi Liu, Naeem Seliya:
Module-Order Modeling using an Evolutionary Multi-Objective Optimization Approach. IEEE METRICS 2004: 159-169 - 2003
- [j4]Taghi M. Khoshgoftaar, Naeem Seliya:
Fault Prediction Modeling for Software Quality Estimation: Comparing Commonly Used Techniques. Empir. Softw. Eng. 8(3): 255-283 (2003) - [j3]Taghi M. Khoshgoftaar, Naeem Seliya:
Analogy-Based Practical Classification Rules for Software Quality Estimation. Empir. Softw. Eng. 8(4): 325-350 (2003) - [j2]Taghi M. Khoshgoftaar, Naeem Seliya:
Software Quality Classification Modeling Using the SPRINT Decision Tree Algorithm. Int. J. Artif. Intell. Tools 12(3): 207-225 (2003) - [c3]Taghi M. Khoshgoftaar, Yi Liu, Naeem Seliya:
Genetic Programming-Based Decision Trees for Software Quality Classification. ICTAI 2003: 374-383 - 2002
- [j1]Taghi M. Khoshgoftaar, Bojan Cukic, Naeem Seliya:
Predicting Fault-Prone Modules in Embedded Systems Using Analogy-Based Classification Models. Int. J. Softw. Eng. Knowl. Eng. 12(2): 201-221 (2002) - [c2]Taghi M. Khoshgoftaar, Naeem Seliya:
Software Quality Classification Modeling Using The SPRINT Decision Tree Algorithm. ICTAI 2002: 365-374 - [c1]Taghi M. Khoshgoftaar, Naeem Seliya:
Tree-Based Software Quality Estimation Models For Fault Prediction. IEEE METRICS 2002: 203-
Coauthor Index
[j31] [c34] [j30] [j29] [c33] [c32] [e1] [j28] [c31] [p3] [j27] [j26] [c30] [c29] [j25] [c28] [j24] [j23] [j22] [c27] [j21] [j20] [c25] [c24] [c23] [j19] [j18] [c21] [c20] [c19] [c18] [c17] [j17] [c16] [c14] [p2] [p1] [j16] [j15] [c13] [j14] [j13] [j12] [c12] [j11] [j10] [j9] [c11] [c10] [c9] [j8] [j7] [j6] [j5] [c8] [c7] [c6] [c5] [c4] [j4] [j3] [j2] [c3] [j1] [c2] [c1]
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