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The world we live in today is becoming increasingly less tethered, with many applications depending on wireless signals to ensure safety and security. Proactive security measures can help prevent the loss of property due to actions such as larceny/theft and burglary. An IoT-based smart Surveillance System for High-Security Areas (SS-HSA) has been developed to address this issue effectively. This system utilizes a Gravity Microwave Sensor (GMS), which is highly effective due to its ability to penetrate nonmetallic obstructions. Combining GMS with Arduino UNO is a highly effective technique for detecting suspected objects behind walls. The GMS can also be integrated with the global system for mobile (GSM) communications, making it an IoT-based solution. The SS-HSA system utilizes machine learning AI algorithms operating at a GMS frequency to analyze and calculate accuracy, precision, F1-Scores, and Recall. After a thorough evaluation, it was determined that the Random Forest Classifier achieved an accuracy rate of 95%, while the Gradient Boost Classifier achieved an accuracy rate of 94%. The Naïve Bayes Classifier followed closely behind with a rate of 93%, while the K Nearest Neighbor and Support Vector Machine both achieved an accuracy rate of 96%. Finally, the Decision Tree algorithm outperformed the others in terms of accuracy, presenting a value of 97%. Furthermore, in the studied machine learning AI algorithms, it was observed that the Decision Tree was optimal for SS-HSA.

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Measuring ontology matching is a critical issue in knowledge engineering and supports knowledge sharing and knowledge evolution. Recently, linguistic scientists have defined semantic relatedness as being more significant than semantic similarities in measuring ontology matching. Semantic relatedness is measured using synonyms and hypernym–hyponym relationships. In this paper, a systematic approach for measuring ontology semantic relatedness is proposed. The proposed approach is developed with a clear and fully described methodology, with illustrative examples used to demonstrate the proposed approach. The relatedness between ontologies has been measured based on class level by using lexical features, defining semantic similarity of concepts based on hypernym–hyponym relationships. For evaluating our proposed approach against similar works, benchmarks are generated using five properties: related meaning features, lexical features, providing technical descriptions, proving applicability, and accuracy. Technical implementation is carried out in order to demonstrate the applicability of our approach. The results demonstrate an achieved accuracy of 99%. The contributions are further highlighted by benchmarking against recent related works.