In the era of globalization and the Internet, due to the unprecedented business pressure from competition, in order to make long-term benefits and achieve sustainable development, enterprises should select competitive...
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In the era of globalization and the Internet, due to the unprecedented business pressure from competition, in order to make long-term benefits and achieve sustainable development, enterprises should select competitive markets for their products to maximize the benefits of limited resources. The key to corporate survival and development is to find the most profitable customer bases all over the world and develop products to meet their demands. Traditionally, market targeting relies on the decisions of a small number of senior managers in enterprises;however, due to novel and changing customer demands, the business environment has become increasingly complex. If previous traditional methods are adopted, enterprises may select the wrong markets, which can lead to complete destruction. Therefore, this study proposes a new market targeting method and replaces human decisions with artificial intelligence (AI) algorithms, in order to render market targeting more scientific and systematic, improve the quality of marketing decisions, maximize corporate profits, occupy the optimal market with limited resources, and achieve the goal of sustainable business. This study applied three AI algorithms, the naive Bayes algorithm, J48 algorithm, and oner algorithm, for model training and analytical prediction of the testing datasets. According to the results, the model accuracies of the naive Bayes algorithm, J48 algorithm, and oner algorithm are 100, 91.7, and 83.3%, respectively;the F-measures of the naive Bayes algorithm, J48 algorithm, and oner algorithm are 1, 0.909, and 0.8, respectively, which indicates that the three algorithms have reliable predictions. The results show that AI algorithms can help enterprises in market targeting.
This paper describes about an intelligent agent based intrusion detection and prevention system for mobile ad hoc network. This system collects data from application layer and network layer and classifies them using t...
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ISBN:
(纸本)9781424409969
This paper describes about an intelligent agent based intrusion detection and prevention system for mobile ad hoc network. This system collects data from application layer and network layer and classifies them using the log Me data collected from these layers and local anomalies are computed using local agents finally. it is sent to a global agent for integration. The local agents monitor the two layers of the network to determine the correlation among the observed anomalous patterns, reporting such abnormal behavior to the administrator for taking possible action. Our simulation results obtained after integration shows that it is possible to obtain high intrusion-detection rates (99.2%) and low false-alarm rates.
This paper describes about an intelligent agent based intrusion detection and prevention system for mobile ad hoc network. This system collects data from application layer and network layer and classifies them using t...
详细信息
ISBN:
(纸本)9789889867140
This paper describes about an intelligent agent based intrusion detection and prevention system for mobile ad hoc network. This system collects data from application layer and network layer and classifies them using the log file data collected from these layers and local anomalies are computed using local agents finally it is sent to a global agent for integration. The local agents monitor the two layers of the network to determine the correlation among the observed anomalous patterns, reporting such abnormal behavior to the administrator for taking possible action. Our simulation results obtained after integration shows that it is possible to obtain high intrusion-detection rates (99.2%) and low false-alarm rates.
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