Internet of Things (IoT) is a paradigm that has been explored in various domains, as it facilitates the automation of many tedious processes with more efficiency and robustness. This technology can be applied to effectively mitigate the rising cases of building collapse in Nigeria. Traditional means of monitoring civil structures and determining their health seems inadequate, expensive and out of reach to many, hence a novel means of Structural Health Monitoring (SHM) which IoT provides. IoT facilitates the monitoring and measurement of several structural parameters with a high degree of precision through sensors while enabling these 'things' to communicate effectively through the designed protocol. It has proved to be a cost-effective alternative to traditional SHM while providing the flexibility of alerting users when there is any damage to the structure. In this paper, an IoT platform for structural integrity evaluation and monitoring in civil buildings was developed using piezoelectric transducers (PZT), some microcontrollers, and a SIM800L. These PZTs were deployed at determined nodes to read data continually while some digital signal processing techniques were used to manipulate these data towards determining the health of the structure being monitored. In the end, a system that reads signals from structures, store the signals on the cloud and notify users if any structural damage arises were developed.
Several diseases are associated with humans; some are synonymous to female and some to male. Example of diseases synonymous to the male gender is Prostate Cancer (PC). Prostate cancer occurs when cells in the prostate gland starts to grow uncontrollably. Statistics shows that prostate cancer is becoming an epidemic among men. Hence, several research works have tried to solve this problem using various methods. Although numerous medical research works are ongoing in the area, the need to introduce technology to battle the epidemic is paramount. Because of this, some researchers have developed several models to help solve issues of prostate cancer in men, but the area is still open to contribution. Recently, some researchers have adopted some well-established Machine Learning (ML) techniques to predict and diagnose the occurrence of prostate cancer, but issues of low prediction accuracy, inability to implement model, low sensitivity; among others still lingers. This paper approached these challenges by developing an ensemble model that combines three (3) ML techniques; Support Vector Machine (SVM), Decision Tree (DT), and Multilayer Perceptron (MP) to predict PC in men. Our developed model was evaluated using sensitivity, specificity and accuracy as performance metrics, and our result showed a prediction accuracy of 99.06%, sensitivity of 98.09% and, specificity of 99.54%, which is a relative improvement on the existing systems.
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