The location s of a company has been attracting the attention of many researchers in various industrial, services, economic and social field, which consequently inspired the scientists to published number of articles and studies concerning the criteria, influencing factors and quantitative methodologies for the location. For example goal programming, fuzzy logic and genetic algorithm method logic. However, the failure of these methodologies to integrate the qualitative and quantitative factors for the location decisions, justified the use of AHP, which developed by saaty in 1970, to overcome the complicated issues 0f several measures, saaty has shown explicitly the significance of this technique in resolving the problems of application. The fundamental of the technique is based on the relative importance of each factor according to decision maker’s estimation and the translation of these estimated elements into digital value, which assign the priorities of the decision maker. This research aims to work out a theoretical frame about location decision and the role of AHP as a multi- criteria tool in choosing suitable locations.
Abstract:
This research aims to compare Bayesian Method and Full Maximum Likelihood to estimate hierarchical Poisson regression model.
The comparison was done by simulation using different sample sizes (n = 30, 60, 120) and different Frequencies (r = 1000, 5000) for the experiments as was the adoption of the Mean Square Error to compare the preference estimation methods and then choose the best way to appreciate model and concluded that hierarchical Poisson regression model that has been appreciated Full Maximum Likelihood Full Maximum Likelihood with sample size (n = 30) is the best to represent the maternal mortality data after it has been reliance value param
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Most companies use social media data for business. Sentiment analysis automatically gathers analyses and summarizes this type of data. Managing unstructured social media data is difficult. Noisy data is a challenge to sentiment analysis. Since over 50% of the sentiment analysis process is data pre-processing, processing big social media data is challenging too. If pre-processing is carried out correctly, data accuracy may improve. Also, sentiment analysis workflow is highly dependent. Because no pre-processing technique works well in all situations or with all data sources, choosing the most important ones is crucial. Prioritization is an excellent technique for choosing the most important ones. As one of many Multi-Criteria Decision Mak
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