In several nations, the majority of heart attacks lead to fatality prior to patients receiving any kind of medical intervention. The traditional healthcare system is mostly passive, requiring patients to initiate contact with healthcare services independently. People often do not request the treatment if they are unconscious during a heart disease episode. The use of Internet of Medical Things (IoMT) methods offers significant advantages in addressing the issue of caring for patients with cardiac problems. These techniques may transform service delivery into ubiquitous and activate healthcare services. Low-cost remote monitoring systems are essential to implementing a widespread healthcare service. In this article, we proposed a cost-effective Personal Health Care Device(PHCD) based on the Internet of Things (IoT). The PHCD transmits user somatic signals to data acquisition devices using a LoRa (Long-range and low-power) wireless communication network. The received data is uploaded to the cloud using IoT platforms like Adafruit IO. Further, various Machine learning (ML) algorithms, Naïve Bayes, ANN, CNN, and LSTM, were applied to collected data to predict heart rate and SpO2 behavior. The performance results of different forecast models were compared to identify precise modeling and reliable forecasts to prevent emergency cardiovascular conditions.

Inhaling formaldehyde a chemical that is widely used in many different industries can have serious health consequences. In order to precisely detect formaldehyde levels in industrial air quality environments, this study makes use of deep learning techniques. Using sensor data gathered from high-risk industrial areas the study focuses on variables like air quality index temperature and humidity. The data is processed by Convolutional Neural Networks (CNNs), which identify trends linked to increases in formaldehyde concentrations. To improve model accuracy preprocessing of the data is done including feature scaling and outlier elimination. The model's performance is assessed using evaluation metrics like Mean Squared Error (MSE), sensitivity, specificity, and prediction accuracy. Results show that when compared to conventional regression models the CNN-based model considerably lowers false positives while achieving a high prediction accuracy. Rapid reaction to hazardous formaldehyde levels is made possible by the deep learning frameworks' real-time monitoring capability which lowers possible health hazards. To improve long-term prediction accuracy and trend identification future research will investigate the use of recurrent neural networks (RNN) for time-series analysis.

Digital Twin is a leading-edge technology used to build a virtual prototype of products. Starting from the design, a Digital Twin helps to analyse the data available, predicting the possible effects of the data on the model, thereby improving the performance of a device or a process. It uses technologies like: Internet of Things, Data Analysis, Machine Learning, Artificial Intelligence, Computer Vision, Augmented Reality/Virtual Reality, Cloud Computing and others. This paper addresses the comparison of IoT and IIoT with Digital Twin. Our research on this paper motivated us to develop a keen interest towards this technology. This paper aims at discussing some real-time problems that can be solved by using Digital Twin. In addition to this, our paper discusses some real-time applications of Digital Twin and helps the reader to find a Twin-model solution for a problem that he wants to address. Hence, our paper provides a complete insight about this amazing technology to the reader.

Today, health monitoring relies heavily on technological advancements. This study proposes a low-power wide-area network (LPWAN) based, multinodal health monitoring system to monitor vital physiological data. The suggested system consists of two nodes, an indoor node, and an outdoor node, and the nodes communicate via long range (LoRa) transceivers. Outdoor nodes use an MPU6050 module, heart rate, oxygen pulse, temperature, and skin resistance sensors and transmit sensed values to the indoor node. We transferred the data received by the master node to the cloud using the Adafruit cloud service. The system can operate with a coverage of 4.5 km, where the optimal distance between outdoor sensor nodes and the indoor master node is 4 km. To further predict fall detection, various machine learning classification techniques have been applied. Upon comparing various classifier techniques, the decision tree method achieved an accuracy of 0.99864 with a training and testing ratio of 70:30. By developing accurate prediction models, we can identify high-risk individuals and implement preventative measures to reduce the likelihood of a fall occurring. Remote monitoring of the health and physical status of elderly people has proven to be the most beneficial application of this technology.

A new method is suggested here for detecting defects in construction by using PyTorch to implement a ViT-based semantic segmentation model. Problems such as cracks, corrosion and uneven surfaces on construction sites are challenging for people to check visually which is why new automated methods need to be used. Thanks to the self-attention mechanism, the ViT model is able to detect and locate faults on construction surfaces with great accuracy. Research shows that the new method delivers stronger results on average Intersection over Union (mIoU) and pixel accuracy compared to traditional CNNs and gives steady performance across diverse defects and conditions. Even though it requires careful training, the final model is fast at running and can be used right away at the work site. These results suggest that transformer-based networks could play a major role in developing quality control and monitoring applications in construction.