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According to the survey on various health centres, smart log-based multi access physical monitoring system determines the health conditions of humans and their associated problems present in their lifestyle. At present, deficiency in significant nutrients leads to deterioration of organs, which creates various health problems, particularly for infants, children, and adults. Due to the importance of a multi access physical monitoring system, children and adolescents’ physical activities should be continuously monitored for eliminating difficulties in their life using a smart environment system. Nowadays, in real-time necessity on multi access physical monitoring systems, information requirements and the effective diagnosis of health condition is the challenging task in practice. In this research, wearable smart-log patch with Internet of Things (IoT) sensors has been designed and developed with multimedia technology. Further, the data computation in that smart-log patch has been analysed using edge computing on Bayesian deep learning network (EC-BDLN), which helps to infer and identify various physical data collected from the humans in an accurate manner to monitor their physical activities. Then, the efficiency of this wearable IoT system with multimedia technology is evaluated using experimental results and discussed in terms of accuracy, efficiency, mean residual error, delay, and less energy consumption. This state-of-the-art smart-log patch is considered as one of evolutionary research in health checking of multi access physical monitoring systems with multimedia technology.

Rubber blend of ethylene-propylene-diene monomer and acrylonitrile-butadiene rubber (NBR) (50/50) has been incorporated with increasing contents, up to 10 parts per hundred rubber (phr), of reinforcing nano-filler, namely, halloysite nanotubes (HNTs). Mechanical properties, namely, tensile strength, hardness, stress at 100% elongation (100% modulus), tear strength, abrasion resistance, elongation at break, and rebound resilience have been carried out as a function of the degree of incorporation with nano-filler. Similarly, swelling resistance in terms of mole percent uptake as a physical property of rubber compounds, as a function of the degree of loading with nano-filler, penetrants size, and temperature, however, in aromatic, aliphatic, and chlorinated solvents has been undertaken. The FESEM of the tensile fractured composites was also investigated. The tensile properties and abrasion resistance improved until optimum content of HNTs (6 phr) and hardness, tear strength, and swelling resistance increased with increasing HNTs loading.

In this study, the influence of the organoclay (OC) nano-fillers (Cloisite 15A (CE15A), Cloisite 20A (CE20A), and Cloisite 30B (CE30B)) on the cure and swelling behaviors, compression set, abrasion resistance, and mechanical properties of a blend of 50/50 ethylene-propylene-diene monomer and acrylonitrile-butadiene rubber (EPDM/NBR) has been examined. It has been noted that the maximum torque values increase as filler loading increases. Comparing filled nanocomposites to unfilled samples, it has been observed that filled systems have a lower tendency to absorb solvent. Due to improved filler reinforcement, nanocomposites reinforced with CE30B showed the least solvent uptake across OC filled systems. The morphology of the CE30B-filled samples was more homogeneous when compared to the other (CE15A and CE20A) filler-reinforced nanocomposites. The mechanical properties of the CE30B-filled samples, followed by those of the CE20A and CE15A-filled systems, improved the most. This has been explained by CE30B OC's increased interaction between nanofiller and rubber matrix. Mechanical testing experimental results have been contrasted with various theoretical models.

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.

Farming and agricultural production account for a substantial part of the global economic system, and most people rely on them for their living. In this perspective, real‐time agricultural field monitoring and smart irrigation using modern technologies are now important for effective farming in green homes, smart cities, and rural areas. Water is an essential resource to be conserved using the newest technology. The Internet of Things (IoT) and Industry 4.0 enable smart farming, including using Quadrotor unmanned aerial vehicles (Q‐UAV) with computer vision. The IoT‐based smart irrigation management systems with real‐time sensors and Q‐UAVs have contributed to the optimum use of water resources in precision farming. The research presented an intelligent irrigation and field surveillance system using atmospheric and soil data such as temperature, humidity, salinity, wind speed, as well as photographs of the field using UAVs. The parameters mentioned above are available on the smartphone of the farmers using IoT and are hosted without any delay in the Firebase console. In addition to this, a user can control the water pump on various fields via Firebase Cloud Message platform. The intelligence and smartness of the proposed system are implemented with a powerful and low‐cost platform Raspberry Pi 4B system on chip computer with Industry 4.0 standard dedicated for IoT, real‐time embedded protocol interfacing, and computer vision applications. Core Ideas Internet of Things and Industry 4.0 enable smart farming, which includes the use of Quadrotor unmanned aerial vehicles. A user can control the water pump on various field via Firebase Cloud Message platform. The intelligence and smartness of the proposed system is implemented with a powerful and low cost Raspberry Pi 4B.

Data Dissemination is an essential transmitting method for a sensor network to the end-users across any set of interconnected frameworks. WSN is often used within an IoT system, in other words. As in a mesh network, a wide collection of sensors can collect data individually and send data to the web via an IoT system through a router. The conventional defined solution for data dissemination in Wireless Sensor Networks (WSN) does not include the wide range of new applications built on the Internet of Things (IoT)systems. Hence, it is observed that searching for an appropriate transmission link while distributing data with optimized utilization of energy is a significant challenge in the IoT communication infrastructure. Therefore, in this paper, an Optimized Energy Management Model for Data Dissemination (OEM-DD) framework has been proposed to optimize energy during data transmission efficiently across all sensor network nodes in the IoT system. The efficiency of the data dissemination across an interconnected network has been achieved by introducing a Non-adaptive routing approach in which data is distributed effectively from a single source to various points. Besides, Non-adaptive routing involves the dispersed collaboration system and the priority task planning principle combined with an integer framework for the efficient energy processing and grouping of data in the sensor’s network. Optimization of the energy management model through Non-adaptive routing allows low power consumption and minimal energy usage for each sensor node in the IoT system to improve the transfer and handling of data in severe interruption. The experimental results show that the suggested model enhances the data transmission rate of 96.33% with less energy consumption of 20.11% in WSN, which is the subset of IoT systems.

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.

The development of miniature electronic devices that are implanted directly in heart is made possible by advancements in enrichment of high-density power electronic technologies. It include pacing devices to maintain normal heart rates, long-term rhythm analysis tools for detecting arrhythmias in cases of unexplained syncope, and heart failure tools that allow for real-time monitoring of cardiac pressures to identify and warn against early fluid overload. This paper proposes, Improved Notch Filter to mitigate noise contained by the recorded electrocardiogram (ECG) signal. Fast Fourier Transform (FFT) spectrum analysis approach, which is critical in embedded biomedical applications is engaged to detect patterns. To achieve low power operation, FFT-based R-peak signal processing is typically computed by Application Specific Integrated Circuits in deeply integrated systems such as cardiac pacemakers. The entire operating life cycle of pacemaker adopts FFT based feature extraction and classification which in turn consumes a considerable part of energy. This insists on the need for power optimization in FFT algorithm and hence a meta-heuristic multi-objective Genetic Algorithm is incorporated with FFT for improving arithmetic computation efficiency. The proposed approach considers the inherent spatial properties of ECG signals for efficient generation of frequency spectrum by FFT and also the number of execution cycles gets reduced. The framework is examined using MATLAB and the generated results obtained reveal that, the suggested work enables improved battery life for the cardiac pacemaker with reduced dynamic power consumption.

Cyber-physical systems have improved modern transportation by allowing vehicles and road systems to communicate through Vehicular Ad Hoc Networks (VANETs). Existing anomaly detection approaches often struggle with high false-positive rates, poor adaptability, and significant computational demands, compromising their real-time efficacy and scalability. To address these problems, this research presents an Anomaly Detection using Machine Learning Algorithms (AD-MLA) framework that employs a Random Forest model to accurately detect abnormal activities. The framework encompasses feature selection, data clustering, and an energy-efficient routing strategy that incorporates node energy, signal strength, hop count, and link stability. Evaluations demonstrate that AD-MLA reduces false alarms, improves detection accuracy, and operates with lower energy and computational requirements. It offers a smart, rapid, and efficient security system for real-time VANET environments, rendering it appropriate for transportation systems characterised by high reliability and safety. By integrating a Random-Forest-based anomaly detector with intelligent feature selection and an energy-efficient routing method that accounts for residual energy, signal strength, and link stability, the suggested framework systematically addresses these challenges. This approach delivers 95.33% accuracy, 96.09% recall, 94.25% computational efficiency, and 91.45% resource-use efficiency. This effectively addresses the scalability, latency, and energy challenges that previous systems have faced in incorporating blockchain technology and deep learning architectures.

This study investigates the enhancement of the mechanical and physical properties of a 50/50 chloroprene rubber (CR) and natural rubber (NR) blend by incorporating halloysite nanotubes (HNTs) as a nanofiller. HNTs, a member of the nanoclay family, were added in varying concentrations up to 10 parts per hundred rubber (phr). Cure properties, including torques and cure times, were measured and analysed to understand the effects of HNT incorporation. The innovative aspect of this work lies in the exploration of HNTs’ potential to enhance mechanical properties such as tensile strength, hardness, tear strength and elongation at break, as well as swelling resistance across different solvent environments. The swelling resistance was quantified through mole percent solvent uptake in aromatic, aliphatic and chlorinated solvents, revealing the influence of HNT loading and penetrant size. The study also employed field-emission scanning electron microscopy to examine the morphology of fractured composites. The findings demonstrated that the mechanical properties and swelling resistance of the CR/NR blend improved with increasing HNTs loading, with optimal enhancements observed at 6 phr of HNTs. This work offers valuable insights into the use of HNTs as a reinforcing agent for rubber blends, providing a pathway for developing materials with improved performance across multiple properties.