Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
February 17, 2026
Neural Networks and Rules-based Systems used to Find Rational and
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Data mining based information processing in Wireless Sensor Network (WSN) is at its preliminary stage, as compared to traditional machine learning and WSN. Currently researches mainly focus on applying machine learning techniques to solve a particular problem in WSN. Different researchers will have different assumptions, application scenarios and preferences in applying machine learning algorithms. These differences represent a major challenge in allowing researchers to build upon each other’s work so that research results will accumulate in the community. Thus, a common architecture across the WSN machine learning community would be necessary. One of the major objectives of many WSN research works is to improve or optimize the performance of the entire network in terms of energy conservation and network lifetime. This paper will survey Data Mining in WSN application from two perspectives, namely the Network associated issue and Application associated issue. In the Network associated issue, different machine learning algorithms applied in WSNs to enhance network performance will be discussed. In Application associated issue, machine learning methods that have been used for information processing in WSNs will be summarized.
E. Jagadeeswararao. 2012. \u201cPredilection Perspective of Peremptory Evaluation of Wireless Sensor Networks with Machine Learning Approach\u201d. Global Journal of Computer Science and Technology - E: Network, Web & Security GJCST-E Volume 12 (GJCST Volume 12 Issue E10): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
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Neural Networks and Rules-based Systems used to Find Rational and
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Data mining based information processing in Wireless Sensor Network (WSN) is at its preliminary stage, as compared to traditional machine learning and WSN. Currently researches mainly focus on applying machine learning techniques to solve a particular problem in WSN. Different researchers will have different assumptions, application scenarios and preferences in applying machine learning algorithms. These differences represent a major challenge in allowing researchers to build upon each other’s work so that research results will accumulate in the community. Thus, a common architecture across the WSN machine learning community would be necessary. One of the major objectives of many WSN research works is to improve or optimize the performance of the entire network in terms of energy conservation and network lifetime. This paper will survey Data Mining in WSN application from two perspectives, namely the Network associated issue and Application associated issue. In the Network associated issue, different machine learning algorithms applied in WSNs to enhance network performance will be discussed. In Application associated issue, machine learning methods that have been used for information processing in WSNs will be summarized.
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