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Artificial Neural Networks (ANN) have proven to be an effective tool for solving complex engineering problems requiring estimation, pattern recognition, and classification of variables.
Table 1 gives the classification of variables used in this study.
In addition, classification of variables will not be the same in all centres.
Table 1 gives the selection and classification of variables used in this study in view of the conceptual framework.
More strict definitions and photo references with the categories indicated may be helpful in the classification of variables like soiling and wounds.
This large, population-based case control study of endometrial cancer in Alberta, Canada observed increased risks for endometrial cancer with increased levels of insulin and HOMA-IR and decreased levels of adiponectin, using both continuous and categorical classification of variables.
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Under Pickering this work included photometry, a scale of photographic magnitudes, a system of classification of variable stars, and a system of stellar spectroscopy that was for many years universally adopted.
Specifically, we use machine-learning algorithm to classify fixed-length patterns generated via sliding window technique to infer the classification of variable-length patterns from the aggregation of the machine learning based classification results.
In a similar way, this paper is designed to examine the sensitivity of efficiency classification of variable returns to scale (VRS) technology for enhancing the credibility of DEA results in practical applications where researchers are faced with a situation in which an additional DMU needs to be added to the set of the observed DMUs, a new production possibility set (PPS) is obtained.
This paper addresses issue of sensitivity of efficiency classification of variable returns to scale (VRS) technology for enhancing the credibility of data envelopment analysis (DEA) results in practical applications when an additional decision making unit (DMU) needs to be added to the set being considered.
Step 3: Classification of variable regions.
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