Dr. Minghe Sun
The University of Texas at San Antonio
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Academic Career Length
34Years (1993-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications16 December 2019 in Computational Intelligence and Neuroscience
Journal Article
This study develops tomato disease detection methods based on deep convolutional neural networks and object detection models. Two different models, Faster R-CNN and Mask R-CNN, are used in these methods, where Faster R-CNN is used to identify the types of tomato diseases and Mask R-CNN is used to detect and segment the locations and shapes of the infected areas. To select the model that best fits the tomato disease detection task, four different deep convolutional neural networks are combined with the two object detection models. Data are collected from the Internet and the dataset is divided into a training set, a validation set, and a test set used in the experiments. The experimental results show that the proposed models can accurately and quickly identify the eleven tomato disease types and segment the locations and shapes of the infected areas.
1 April 1998 in European Journal of Operational Research
Journal Article
Minghe SunJAJay E. Aronson
Jay E. Aronson
PMPatrick G. McKeown
Patrick G. McKeown
DDDennis Drinka
Dennis Drinka
1 December 2012 in European Journal of Operational Research
Journal Article
1 October 2014 in European Journal of Operational Research
Journal Article
24 July 2013 in IEEE Transactions on Information Forensics and Security
Journal Article
NBNicole Beebe
Nicole BeebeLMLaurence A. Maddox
Laurence A. Maddox
LLLishu Liu
Lishu Liu
Minghe SunOver 20 studies have been published in the past decade involving file and data type classification for digital forensics and information security applications. Methods using n-grams as inputs have proven the most successful across a wide variety of types; however, there are mixed results regarding the utility of unigrams and bigrams as inputs independently. In this study, we use support vector machines (SVMs) consisting of unigrams and bigrams, as well as complexity and other byte frequency-based measures, as inputs. Using concatenated unigrams and bigrams as input and a linear kernel SVM, we achieve significantly improved results over those previously reported (73.4% classification rate across 38 file and data types). We are the first to use concatenated n-grams as the sole input, and we show their superiority over inputs used previously. We also found that too many different types of features as inputs result in overfitting and poor generalization properties. We include several types seldom or not studied in the past (Microsoft Office 2010 files, file system data, base64, base85, URL encoding, flash video, M4A, MP4, WMV, and JSON records). The “winning” approach is instantiated in an open source software tool called Sceadan - Systematic Classification Engine for Advanced Data ANalysis.
About Me
MINGHE SUN received the Bachelor of Science degree in metallurgy from Northeastern University, China, in 1982, the M.B.A. degree from The Chinese University of Hong Kong, in 1987, and the Ph.D. degree in business administration with a major in management science and information technology and a minor in operations management from the University of Georgia, in 1992.
He has been working with The University of Texas at San Antonio since he received his doctorate. His research interests include data mining, machine learning, data analytics, mathematical programming and related areas. His work has appeared in almost all major journals in the management science/operations research/decision sciences area including Management Science, Operations Research, Decision Sciences, Transportation Science, INFORMS Journal on Computing, European Journal of Operational Research, Decision Support Systems, Journal of Heuristics, Omega, Annals of Operations Research, Networks, Journal of Operational Society, Computers and Operations Research, among others. He received the prestigious Decision Sciences Institute Elwood S. Buffa Doctoral Dissertation Competition Award, in 1993, and the Decision Sciences Institute Best Theoretical/Empirical Research Paper Award, in 2003 and 2006. He teaches courses in management science/operations management and business statistics at all levels. He received the University of Texas System Chancellor's Council Outstanding Teaching Award, in 1999.