The remote real-time patient monitoring system is a healthcare solution that uses ESP32 microcontroller and Blynk IoT cloud platform to monitor the vital signs of patients, including temperature, oxygen saturation, and heartbeat. The system also monitors the environmental factors surrounding the patient, such as temperature and humidity, and determines the GPS location of the patient. Additionally, the system includes an alarm device that alerts healthcare providers in case of emergency. In this paper we design system aims to provide continuous care and monitoring for patients, whether they are in hospitals, at home, or outside. By using Blynk IoT cloud platform, the system aims to reduce the percentage of medical errors and deaths by providing real-time monitoring of the patient's vital signs and environmental conditions, allowing healthcare providers to respond to emergencies quickly and efficiently. The IoT-based patient monitoring system consists of sensors that collect data on the patient's vital signs and environmental factors. The collected data is transmitted wirelessly to the Blynk IoT cloud platform, where it is processed and analyzed. Healthcare providers can access the data through the Blynk mobile app and receive alerts in case of any abnormalities or emergencies.

A post, review, or news article's emotional tone can be automatically ascertained using sentiment analysis, a natural language processing approach. Sorting the text into positive, negative, or neutral categories is the aim of sentiment analysis. Many methods, including rule-based systems and machine learning algorithms, can be used to analyse sentiment, or deep learning models. These techniques typically involve analyzing various features of the text, such as word choice, sentence structure, and context, to identify the overall sentiment. Here long short-term memory-based deep learning is applied in this research for the model development purpose. Deeply interconnected neural networks are used in this method. Sentiment analysis can be used in many different applications, such as market research, brand reputation management, customer feedback analysis, and social media monitoring. It shows the use of sentiment analysis in a variety of fields and increases the need of technology to perform it on the existing machines.

The idea behind the paper is to transform the conventional school bag into a smart bag connected to the Internet of Things and aimed at elementary school pupils. Its concept uses GPS to follow the student's location; whenever it detects dangers like gas and smoke around the student, it sends a signal to the user. By lessening the weight on the student with the use of the load sensor, it can also determine the true weight of a bag. It can also be utilized on school buses in case a student is overlooked by notifying the driver of their presence via an LCD on the vehicle that is connected to the gas sensor. The results obtained have shown that the proposed research work successfully developed a prototype that is able to provide security and safety by delivering messages to the user, determining the actual weight of the bag, and tracking the student's location.

Radio Modulation Classification is implemented by using the Deep Learning Techniques. The raw radio signals where as inputs and can automatically learn radio features and classification accuracy. The LSTM (Long short-term memory) based classifiers and CNN (Convolutional Neural Network) based classifiers were proposed in this paper. In the proposed work, two CNN based classifiers are implemented such as the LeNet classifier and the ResNet classifier. For visualizing the radio modulation, a class activation vector (w) is used. Finally in the proposed work, it is performed the classification by using the Deep learning models like CNN and LSTM based modulation classifiers. These deep learning models extract the important radio features that are used for classification. Here, the bench mark dataset RadioML2016.10a is used. This is an open dataset which contains the modulated signal I and Q values fewer than ten modulation categories. After evolution of proposed model with bench mark dataset, it is applied with real time data collected through the SDR Dongle receiver. From the obtained real time signal, the modulation categories have been classified and visualized the radio features extracted from the radio modulation classifiers.

The failure behavior of beam-to-column connections can be minimized or avoided to some extent by using PET waste fibers. With the change of composition, different seismic performances of concrete joints can be adjusted. FEM analysis was performed in ABAQUS software to compare the performance of concrete beam-to-column connections reinforced with conventional concrete fibers and waste PET under cyclic loading. The concrete mix is designed to achieve a concrete grade of M25. Seven figures of the external beam-to-column connections were modeled as a quarter of the architectural prototype. The first joint is conventional concrete and designed according to IS 1893 (Part 1):2022 and the reinforcement in the joint part are specified according to the ductility requirements of IS 13920:2016. Six other samples were designed to contain different PET waste fibers (0.25% to 1.50%) in the seam area. Beam-to-column connections have 0.75% to 1.25% PET fiber inclusions that have better performance in terms of strength, load-carrying capacity, energy dissipation capacity, joint shear strength, and ductility in the joint area. Incorporating PET waste fibers into concrete can provide the best solution for waste management, and also has the potential to reduce the cost of reinforced concrete by 15%-20% holds economic significance, and concrete with PET waste fibers indeed demonstrates better seismic performance, and could lead to increased safety and longevity of structures in seismic-prone areas. This suggests that experimental work or studies might have explored how these fibers affect the concrete's properties, strength, durability, and other characteristics.