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Artificial intelligence has been used in many different fields throughout its development, especially in recent years as the amount of data available has increased. Its major objective is to aid individuals in making more reliable decisions more quickly. Machine learning and artificial intelligence are being used more and more in medicine. This is particularly true in the medical field where a large number of digital files must be collected and processed to apply a variety of biomedical imaging and diagnostic techniques. Machine learning is used to analyse medical images, which helps with consistency and increases reporting accuracy. To process chest X-ray data and assist in diagnosis, this study compares transfer learning-based deep neural networks. The study focuses on comparing and evaluating deep learning methods based on convolutional neural networks for the identification of pneumonia. To establish the model that would accurately diagnose pneumonia, the researchers developed several different models. Five distinct CNN models, including VGG19, VGG16, ResNet50, InceptionNet v3, and YOLO v5, were trained using the RSNA Pneumonia Detection Challenge dataset. Validation Accuracy and Area Under Curve were used to gauge their performance. On test data, VGG16 had the highest validation accuracy (88%) and AUC-ROC (91.8%), whereas YOLO v5 was used to locate the inflammation with a 99% level of confidence.

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Oil well monitoring has metamorphosed over the years from Dump Wells completion and the era of permanent gauges through to the hydraulic control wells until this era of intelligent Well completion. These efforts are geared towards an era where Well data can be collected and interpreted with no human intervention. The aim is to improve recovery (optimization), minimized OPEX and CAPEX, and general efficiency. However, intelligent monitoring by virtue of intelligent Well completion is still an expensive venture. The paper presents an efficient IoT-based monitoring system whereby an ESP32 microcontroller and sensors are used to monitor the Well pressure, temperature, level, and flow rate on a real-time basis. The data from the oil well is available to the user at any remote location because the sensor data is sent to a cloud service on the internet. The cloud service used is the MQTT protocol and the MIT APP Inventor. The sensor data is also viewed in an android Mobile App