Network intrusion detection is the pressing need of every communication network. Network Intrusion Detection Systems (NIDSes) play an important role in security operations to detect and defend against cyberattacks. As artificial intelligence (AI)-powered NIDSes are adaptive to various kinds of attacks by exploring the knowledge presented in the data, they are in high demand to treat the cyber-attacks nowadays with increasing diversity and intensity. This research works finds that the efficient learning to overcome these issues. The NBMU model performs well and it holds an accuracy 89.44%. The NBM shows worst output which as 65.80%. The NBMU and NB models perform well and it have same precision value 0.89. The NBM model has poor performance which as 0.66. The NBMU and NB models perform well and it have same recall value 0.89. The NBM model has poor performance which as 0.66. The NBMU and NB models perform well and it have same F-Measure value 0.89. The NBM model has poor performance which as 0.64. The NBMU has ROC level 0.90 which as highest performance compare with other models. The NBM model has shown the worst outcome which as 0.59.The NBMU has highest PRC level 0.90. The NBM model has not satisfactory which as 0.59 of PRC value. The NBMU and NB has the highest MCC 0.88. The NBM model has poor performance which as 0.36 of MCC value. The NBMU and NB has same kappa 0.88. The NBM model has poor performance which as 0.32 of kappa value. The NBMU and NB has the least MAE 0.09. The NBM model has poor performance which as 0.32 of MAE value. The NBM has poor performance which as 0.89.The NB has the least RAE 10.52%. The NBU modes has poor performance as well same RAE which as 116.64%. The NBU modes has poor performance as well same RRSE which as 132.51%.The NBMU has poor performance for making its model which as 20.02 seconds.

Occupancy detection is crucial in many smart building applications, including reducing building energy consumption by managing heating, ventilation, and air conditioning systems, monitoring systems, and lighting system management, tracking patients in hospitals for medical issues, advertising to shoppers in malls, and search and rescue missions. The global positioning system is most frequently employed as a localization technique, yet it is incredibly imprecise when used indoors. The interior environment is challenging to manage because, in addition to the signal loss, privacy is a significant issue. Indoor tracking and wireless sensor network sensor localization share many similarities. Machine Learning helps to overcome the mentioned issues. This research works finds that the Attribute Selected Classifier with Naïve Bayes Updateable of second order ensemble model gives highest performance which as accuracy level 86.69%%, kappa statistic value 0.68, precision value 0.87, recall value 0.87, F-Measure value 0.86, Matthews connection coefficient value 0.68. The Attribute Selected Classifier with Naïve Bayes Updateable of second order ensemble model gives highest performance which as ROC value 0.89 and PRC value 0.89, MAE value 0.15, RMSE value 0.40, RAE value 47.32%, RRSE value 90.11% and it takes time consumption as 0.09 seconds to build a model which is produced an optimal results based on their performance compare with other models. This Attribute Selected Classifiers with Naïve Bayes Updateable model is performing well compare with other models.