Fuzzy models to recognize consumer preferences were developed as part of an automated inspection system for biscuits. Digital images were used to estimate physical features of chocolate chip cookies including size, shape, baked dough color, and fraction of top surface area that was chocolate chips. Polls were conducted to determine consumer ratings of cookies. Four fuzzy models were developed to predict consumer ratings based on three of the features. There was substantial variation in consumer ratings in terms of individual opinions (30 panelists in each poll) as well as poll-to-poll differences (three calibration polls). Parameters for the inference system, including fuzzy values for cookie features and consumer ratings, were defined based on judgment and statistical analysis of data from the calibration polls. Two of the fuzzy models gave satisfactory estimates of average consumer ratings for two validation sets (44 cookies). One was a Mamdani inference system that was based on eight fuzzy values for consumer ratings. These were defined using rating distributions from calibration polls. The second model was a Sugeno inference system developed using the adaptive neurofuzzy inference system (ANFIS) algorithm (MatLab ® Version 5.2, The MathWorks Inc., Natick, MA) with the calibration poll data.

Malware is any software aiming to disrupt computer operation. Malware is also used to gather sensitive information or gain access to private computer systems. This is widely seen as one of the major threats to computer systems nowadays. Traditionally, anti-malware software is based on a signature detection system which keeps updating from the Internet malware database and thus keeping track of known malwares. While this method may be very accurate to detect previously known malwares, it is unable to detect unknown malicious codes. Recently, several machine learning methods have been used for malware detection, achieving remarkable success. In this paper, we propose a method in this strand by using Genetic Programming for detecting malwares. The experiments were conducted with the malwares collected from an updated malware database on the Internet and the results show that Genetic Programming, compared to some other well-known machine learning methods, can produce the best results on both balanced and imbalanced datasets.

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