"CHARACTERIZATION OF SLAUGHTERED AND NON-SLAUGHTERED GOAT MEAT AT LOW FREQUENCIES"

The electrical stimulation of meat has a high potential for use in the quality control of meat tissues during the past two decades. Dielectric spectroscopy is the most used technique to measure the electrical properties of tissues. Open ended coaxial cable or two parallel plates integrated with network analyzer, impedance analyzer or LCZ meter have been used to measure the dielectric properties of meat for different purposes. The purpose of this research is to construct a capacitive device capable of differentiating slaughtered and non-slaughtered goat meats, by determining the dielectric properties of goat meat at various frequencies and storage times. The detector cell has two circular platinum plates assembled on the micrometer barrel encased within a perspex box material to form the capacitor. The test rig is validated to insure it is working well. Two goats were slaughtered in the same environment. One of the goats was slaughtered properly (Islamic method) and the second one was killed by garrote. The measurements were done on the hindlimb muscles. The sizes of samples were 2 em diameter and 5 mm thick. The slaughtered and non-slaughtered meat samples were separately placed between the capacitor plates. The capacitance and dissipation factor were measured across the capacitor device which was connected to a LCR meter. The experiment was repeated for various frequencies (from I 00 Hz to 2 kHz), and at different storage times (at I day after slaughtering to 10 days). Maxwell Garnett mixing rule was applied to obtain the theoretical value of the effective permittivity by using goat muscle and blood permittivity. The results show that the device is able to differentiate slaughtered and non-slaughtered goat meat. At all applied frequencies, the relative permittivity of the non-slaughtered meat were clearly more than the relative permittivity of the slaughtered meat which agrees with the simulation results. The dissipation factor of the non-slaughtered meat was less than the dissipation factor of the slaughtered meat

Sart: A New Association Rule Method For Mining Sequential Patterns In Time Series Of Climate Data

Technological advancement has enabled improvements in the technology of sensors and satellites used to gather climate data. The time series mining is an important tool to analyze the huge quantity of climate data. Here, we propose the Sequential Association Rules from Time series - SART method to mine association rules in time series that keeps the information of time between related events through an overlapped sliding-window approach. Also the proposed method mines association rules, while the previous ones produce frequent sequences, adding the semantic information of confidence, which was not previously defined by sequential patterns. Experiments were conducted with real data collected from climate sensors. The results showed that the proposed method increases the number of mined patterns when compared with the traditional sequential mining, revealing related events that occur over time. Also, the method adds the semantic information related to the confidence and time to the mined patterns. © 2012 Springer-Verlag.7335 LNCSPART 3743757Universidade Federal da Bahia (UFBA),Universidade Federal do Reconcavo da Bahia (UFRB),Universidade Estadual de Feira de Santana (UEFS),University of Perugia,University of Basilicata (UB)Agrawal, R., Faloutsos, C., Swami, A., Efficient similarity search in sequence databases (1993) 4th Int. CFDOA, Chicago, IL, pp. 69-84Agrawal, R., Srikant, R., Mining sequential patterns (1995) Proceedings of the 11th International Conference on Data Engineering (ICDE 1995), pp. 3-14. , Yu, P.S., Chen, A.S.P. (eds.) IEEE Press, TaipeiRibeiro, M.X., Traina, A.J.M., Traina, J.C., A new algorithm for data discretization and feature selection (2008) Proceedings of the 2008 ACM Symposium on Applied Computing, pp. 953-954. , ACM, FortalezaSrikant, R., Agrawal, R., Mining sequential patterns: Generalizations and performance improvements (1996) ICEDT, Avignon, France, pp. 3-17. , SpringerPei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth (2001) Proceedings of the 17th International Conference on Data Engineering, pp. 215-224. , IEEE Computer Society, Washington, DCLu, H., Feng, L., Han, J., Beyond intratransaction association analysis: Mining multidimensional intertransaction association rules (2000) ACM Trans. Inf. Syst., 18, pp. 423-454Romani, L.A.S., Clearminer: A new algorithm for mining association patterns on heterogeneous time series from climate data (2010) Proceedings of the 2010 ACM Symposium on Applied Computing, pp. 900-905. , ACM, New YorkSubramanyam, R.B.V., Goswami, A., A fuzzy data mining algorithm for incremental mining of quantitative sequential patterns (2005) Int. J. Uncertain. Fuzziness Knowl.-Based Syst., 13, pp. 633-652Zaki, M.J., Spade: An efficient algorithm for mining frequent sequences (2001) Mach. Learn., 42, pp. 31-60Park, J.S., Chen, M.-S., Yu, P.S., An effective hash-based algorithm for mining association rules (1995) Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data, pp. 175-186. , ACM, New YorkTung, A.K., Angelis, L., Vlahavas, I., Breaking the barrier of transactions: Mining intertransaction association rules (1999) Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 297-301. , ACM, New YorkFeng, L., Yu, X.J., Lu, H., Han, J., A template model for multidimensional intertransactional association rules (2002) The VLDB Journal, 11, pp. 153-175Hu, Y., Huang, T.C., Yang, H., Chen, Y., On mining multi-time-interval sequential patterns (2009) Knowledge Engineering, 68 (10), pp. 1112-1127Han, J., Pei, J., Mortazavi-Asl, B., Chen, Q., Dayal, U., Hsu, M.-C., Freespan: Frequent pattern-projected sequential pattern mining (2000) Proc. 2000 Int. Conf. Knowledge Discovery and Data Mining (KDD 2000), Boston, MA, pp. 355-359Saputra, D., Dayang, R.A.R., Foong, O.M., Mining sequential patterns using I-prefixSpan (2008) International Journal of Computer Science and Engineering, 2, pp. 14-16Berberidis, C., Angelis, L., Vlahavas, I., Inter-transaction Association Rules Mining for Rare Events Prediction Proc. (Companion Volume) 3rd Hellenic Conference on Artificial Intelligence (SETN 2004), Samos, Greece (2004)Zhao, Q., Bhowmick, S.S., (2003) Sequential Pattern Matching: A Survey, , Technical Report, CAIS, Nanyang Technological University, SingaporeLee, A.J.T., Wang, C.-S., An efficient algorithm for mining frequent inter-transaction patterns (2007) Inf. Sci., 177 (17), pp. 3453-347