Big data analytics and processing requires complex architectures and sophisticated techniques for extracting useful information from the accumulated information. Visualizing the extracted data for real-time solutions is demanding in accordance to the semantics and the classification employed by the processing models. This article introduces fuzzy-optimized data management (FDM) technique for classifying and improving coalition of accumulated information based semantics and constraints. The dependency of the information is classified on the basis of the relationships modeled between the data based on the attributes. Different from the conventional fuzzy-decision systems, this technique segregates the considered attributes based on similarity index boundaries. This helps to process complex data in a controlled time and also supports large data processing. The performance of the proposed FDM is analyzed using a real-time weather forecast dataset consisting of sensor data (observed) and image data (captured). With this dataset, the functions of FDM such as input semantics analytics and classification based on similarity are performed. The metrics classification and processing time and similarity index are analyzed for the varying data sizes, classification instances, and dataset records.

A chest x-ray radiograph is still the global standard for diagnosing pneumonia and helps distinguish between bacterial and viral pneumonia. Despite several studies, radiologists and physicians still have trouble correctly diagnosing and classifying pneumonia without false negatives. Modern mathematical modeling and artificial intelligence could help to reduce false-negative rates and improve diagnostic accuracy. This research aims to create a novel and efficient multiclass machine learning framework for analyzing and classifying chest x-ray images on a graphics processing unit (GPU). Researchers initially applied a geometric augmentation using a positional transformation function to the original dataset to enhance the sample size and aid future transfer learning. Models with the best accuracy, area under the receiver operating characteristics (AUROC), F1 score, precision, recall, and specificity are chosen from a pool of nine state-of-the-art neural network models. The best-performing models are then retrained using an ensemble technique using depth-wise convolutions, demonstrating significant improvements over the baseline models employed in this research. With a remarkable 97.69% accuracy, 100% recall, and 0.9977 AUROC scores, the proposed Bek-Bas network (B2-Net) model can differentiate between normal, bacterial, and viral pneumonia in chest x-ray images. A superior model is retrained using the chosen dense convolutional network-160, residual network-121, and visual geometry group network-16 ensemble models. The diagnostic accuracy of the x-ray classification unit is enhanced by the newly designed multiclass network, the B2-Net model. The developed GPU-based framework has been examined and tested to the highest clinical standards. After extensive clinical testing, the final B2-Net model is implemented on an NVIDIA Jetson Nano GPU computer. Healthcare facilities have confirmed the B2-Net is the most effective framework for identifying bacterial and viral pneumonia in chest x-rays.

Future Internet of Things (FIoT) is a service concentric distributed architecture that is used by the smart city users for information sharing and access. The design of FIoT focuses in achieving reliable service and response to the growing user demands through different interoperability features. In this manuscript, concurrent service access and management framework is introduced to improve the swiftness in user concentric request processing. Based on the availability of the services and the density of the users, the concurrency in information access is provided to the users in a reliable manner. The framework incorporates convolution neural learning process in linear and differential manner for improving the access and service usage rates of the requesting users. The access sessions are differentiated for the accessible and offloaded requests to the available service providers based on the learning instances. The proposed framework is assessed using the metrics access rate, service usage rate, access delay, time lag, and failure ratio.

In healthcare settings, hand hygiene is a crucial step in preventing the spread of disease. Healthcare-associated infections, such as community-acquired pneumonia (CAP) and hospital-acquired pneumonia (HAP), can be mitigated with the help of cutting-edge solutions and intelligent alcohol-based hand sanitizers. Specifically, this research presents a long-range (LoRa) network powered by the Internet of Things (IoT) that can detect, monitor and track hand hygiene habits to reduce the spread of hospital-acquired infections (HAIs). The wearable Bluetooth low energy (BLE) device used by hospital personnel and patients will monitor and track the hygiene activities using LoRa and alcohol-based sanitizers deployed in intensive care units (ICUs), elevators, inpatient rooms, and other hospital facilities. The alcohol dispenser is equipped with a Jetson Nano graphics processing unit (GPU) from NVIDIA, a hand sanitizer, and a door that opens once the user has washed their hands. Using a LoRa wide area network (LoRaWAN) and short-range BLE technology, hand hygiene information, with a user ID and GPS location, has it sent to a LoRa gateway and a cloud server. Various field tests in hospital settings and simulated environments are performed to effectively guarantee the clinical performance of the proposed IoT-enabled LoRa network. In conclusion, the total success percentage in real-world LoRa network conditions is 92.78%, whereas the success rate (SR) in laboratory testing is 98%.