Regular monitoring of blood glucose levels is essential for the management of diabetes and the development of appropriate treatment protocols. The conventional blood glucose (BG) testing have an intrusive technique to prick the finger and it can be uncomfortable when it is a regular practice. Intrusive procedures, such as fingerstick testing has negatively influencing patient adherence. Diabetic patients now have an exceptional improvement in their quality of life with the development of cutting-edge sensors and healthcare technologies. intensive care unit (ICU) and pregnant women also have facing challenges including hyperglycemia and hypoglycemia. The worldwide diabetic rate has incited to develop a wearable and accurate non-invasive blood glucose monitoring system. This research developed an Internet of Things (IoT) - enabled wearable blood glucose monitoring (iGM) system to transform diabetes care and enhance the quality of life. The TTGOT-ESP32 IoT platform with a red and near-infrared (R-NIR) spectral range for blood glucose measurement has integrated into this wearable device. The primary objective of this gadget is to provide optimal comfort for the patients while delivering a smooth monitoring experience. The iGM gadget is 98.82 % accuracy when used after 10 hours of fasting and 98.04 % accuracy after 2 hours of breakfast. The primary objective points of the research were continuous monitoring, decreased risk of infection, and improved quality of life. This research contributes to the evolving field of IoT-based healthcare solutions by streaming real-time glucose values on AWS IoT Core to empower individuals with diabetes to manage their conditions effectively. The iGM Framework has a promising future with the potential to transform diabetes management and healthcare delivery.

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.

In agriculture, promptly and accurately identifying leaf diseases is crucial for sustainable crop production. To address this requirement, this research introduces a hybrid deep learning model that combines the visual geometric group version 19 (VGG19) architecture features with the transformer encoder blocks. This fusion enables the accurate and précised real-time classification of leaf diseases affecting grape, bell pepper, and tomato plants. Incorporating transformer encoder blocks offers enhanced capability in capturing intricate spatial dependencies within leaf images, promising agricultural sustainability and food security. By providing farmers and farming stakeholders with a reliable tool for rapid disease detection, our model facilitates timely intervention and management practices, ultimately leading to improved crop yields and mitigated economic losses. Through extensive comparative analyses on various datasets and filed tests, the proposed depth wise separable convolutional-TransNet (DSC-TransNet) architecture has demonstrated higher performance in terms of accuracy (99.97%), precision (99.94%), recall (99.94), sensitivity (99.94%), F1-score (99.94%), AUC (0.98) for Grpae leaves across different datasets including bell pepper and tomato. Furthermore, including DSC layers enhances the computational efficiency of the model while maintaining expressive power, making it well-suited for real-time agricultural applications. The developed DSC-TransNet model is deployed in NVIDIA Jetson Nano single board computer. This research contributes to advancing the field of automated plant disease classification, addressing critical challenges in modern agriculture and promoting more efficient and sustainable farming practices.

Air pollution, climate change, and chemical exposure constitute the world's most significant environmental health concern, resulting in the early deaths of 6. 5 million people annually. Reducing child mortality from preventable causes, primarily pneumonia and other respiratory illnesses, would have contributed to the united nation's sustainable development goals (SDG). Some significant goals are sustainable cities, industry innovation, green and resilient infrastructure, good health, and well-being. Non-ventilator hospital-acquired pneumonia (NV-HAP) is a severe but preventable cause of morbidity and mortality in hospitalized patients. Despite being the most frequent and fatal hospital-acquired infection (HAI), NV-HAP is not tracked, documented, or avoided in most hospitals. The success of NV-HAP prevention and monitoring initiatives relies on reliable, up-to-date surveillance data. Surveillance offers the information needed to target, analyze, and quantify the efficacy of preventative activities by identifying patients at the highest risk for NV-HAP. However, pneumonia monitoring is complex due to the clinical criteria's subjective, imprecise, inconsistently recorded, and labor-intensive nature. Non-ventilator hospital-acquired pneumonia must be monitored and standardized, which demands cutting-edge technologies and the deployment of advanced sensors. In the framework of this research, initially, a wireless body area networks (WBANs) architecture has built by making use of wearable biosensors, and then real-time sensor data were uploaded to a cloud platform. Researchers have devised a wireless sensor network (WSN) to track volatile organic compounds (VOC) and other atmospheric characteristics in real time to curb the spread of NV-HAP. The ESP32 Internet of Things (IoT) and Raspberry Pi 4B graphical processing unit platforms host the finalized WBAN and WSN network. To reduce the mortality rate of NV-HAP, this research aims to investigate clinics' and hospitals' indoor and outdoor air quality. The developed biosensor-assisted IoT enabled framework is used in hospitals to keep tabs on the conditions of individual patient rooms, treatment areas, and critical care units in real time. The research found the suggested technique achieves better results than existing state-of-the-art methods regarding computing cost, communication overhead, storage cost, and energy utilization.

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%.

The early diagnosis of plant leaf diseases is crucial in the sustainable management of agriculture as it minimises crop damage and reduces the use of pesticides.