Mississippi State University

About: Mississippi State University is a education organization based out in Starkville, Mississippi, United States. It is known for research contribution in the topics: Population & Catfish. The organization has 14115 authors who have published 28594 publications receiving 700030 citations. The organization is also known as: The Mississippi State University of Agriculture and Applied Science & Mississippi State University of Agriculture and Applied Science.

Statistical Estimation of Daily Maximum and Minimum Air Temperatures from MODIS LST Data over the State of Mississippi

Abstract: Recent studies have shown that the Land Surface Temperature (LST) data measured by Moderate Resolution Imaging Spectro-Radiometer (MODIS) from both the Terra and Aqua platforms can be successfully ...

Mining fuzzy association rules and fuzzy frequency episodes for intrusion detection

Abstract: Lee, Stolfo, and Mok 1 previously reported the use of association rules and frequency episodes for mining audit data to gain knowledge for intrusion detection. The integration of association rules and frequency episodes with fuzzy logic can produce more abstract and flexible patterns for intrusion detection, since many quantitative features are involved in intrusion detection and security itself is fuzzy. We present a modification of a previously reported algorithm for mining fuzzy association rules, define the concept of fuzzy frequency episodes, and present an original algorithm for mining fuzzy frequency episodes. We add a normalization step to the procedure for mining fuzzy association rules in order to prevent one data instance from contributing more than others. We also modify the procedure for mining frequency episodes to learn fuzzy frequency episodes. Experimental results show the utility of fuzzy association rules and fuzzy frequency episodes for intrusion detection.

Decision Fusion for the Classification of Hyperspectral Data: Outcome of the 2008 GRS-S Data Fusion Contest

Abstract: The 2008 Data Fusion Contest organized by the IEEE Geoscience and Remote Sensing Data Fusion Technical Committee deals with the classification of high-resolution hyperspectral data from an urban area. Unlike in the previous issues of the contest, the goal was not only to identify the best algorithm but also to provide a collaborative effort: The decision fusion of the best individual algorithms was aiming at further improving the classification performances, and the best algorithms were ranked according to their relative contribution to the decision fusion. This paper presents the five awarded algorithms and the conclusions of the contest, stressing the importance of decision fusion, dimension reduction, and supervised classification methods, such as neural networks and support vector machines.