Assimilation of IoT sensors for Data Visualization in a Smart Campus Environment

Recently, various indoor based sensors that were formerly separated from the digital world, are now intertwined with it. The data visualization may aid in the comprehension of large amounts of information. Building on current server-based models, this study intends to display real environmental data acquired by IoT agents in the interior environment. Sensors attached to Arduino microcontrollers are used to collect environmental data for the smart campus environment, including air temperature, light intensity, and humidity. This proposed framework uses the system's server and stores sensor readings, which are subsequently shown in real time on the server platform and in the environment application. However, most current IoT installations do not make use of the enhanced digital representations of the server and its graphical display capabilities in order to improve interior safety and comfort conditions. The storage of such real-time data in a standard and organized way is still being examined even though sensor data integration with storing capacity server-based models has been studied in academics.

Construction of a Framework for Selecting an Effective Learning Procedure in the School-Level Sector of Online Teaching Informatics

Currently, there is no way soon to stop the coronavirus epidemic that has spread over the globe. People are alarmed by its quick and widespread expansion. COVID-19's transmission chain was then broken by everyone. There was a gradual decrease in social and physical closeness. Distancing yourself from others is a way to prevent the transmission of disease. The purpose of this research is to investigate how online learning can be implemented in Tamil Nadu, India, during the COVID-19 epidemic. This research works focuses to find efficient learning procedure in eLearning protocols. The findings indicated that Google Classroom, WhatsApp, and Zoom Clouds Meeting were consecutively the most commonly utilized programs to help in remote learning. Despite this, most instructors continue to use the learning paradigm while teaching in virtual environments. Online learning and remote education are the most common methods of learning. The instructor claims that the learning model used is beneficial to their work in creating a virtual classroom since it adheres to the model's structured grammar. The experimental test has been conducted with 125 students who anonymously filled out a questionnaire and voted for more visual based eLearning. The findings show that students in distance education believed that there were more tasks than in face-to-face education. At the same time, students indicated that they spent more time studying at home than in school.

Ethereum and IOTA based Battery Management System with Internet of Vehicles

The era of Electric Vehicles (EVs) has influenced the very make and manufacture of vehicles resulting in low pollution and advanced battery life. On the other hand, the internet of things has also expanded allowing a number of devices to stay connected using the internet. Massive drawbacks faced by EVs today are the limitation in battery swapping and charging stations and limitation in the range of batteries used. This proposed paper aims to efficiently manage the best battery system apart from building the essential infrastructure. In some cases battery swapping option is also provided through other EV drivers or at registered stations. Hence a complete database of the EV network is required so that it is possible to swap and charge batteries successfully. An EV management using two blockchains as a data layer and network of the application is implemented in this work. The first step involves the development of a blockchain framework using Ethereum and the next step entails a direct acyclic graph. When integrated, these two methodologies prove to be an efficient platform that offers a viable solution for battery management in Electric Vehicles.

Design of Data Mining Techniques for Online Blood Bank Management by CNN Model

A blood bank is the organisation responsible for storing blood to transfuse it to the patients in need. The primary goal of a blood bank is to be reliable and ensure that patients get the relevant non-toxic blood to avoid transfusion-related complications since blood is a critical medicinal resource. It is difficult for the blood banks to offer high levels of precision, dependability, and automation in the blood storage and transfusion process if blood bank administration includes many human processes. This research framework is proposing to maintain blood bank records using CNN model classification method. In the pre-processing of CNN method, the datasets are tokenized and set the donor’s eligibility. It will make it easier for regular blood donors to donate regularly to charitable people and organizations. A few machine learning techniques offer the automated website updation. Jupyter note book has been used to analyze the dataset of blood donors using decision trees, neural networks, and von Bays techniques. The proposed method operates online through a website. Moreover, the donor's eligibility status with gender, body mass index, blood pressure level, and frequency of blood donations is also maintained. Finally, the comparison of different machine learning algorithms with the suggested framework is tabulated.

Gas Leakage Detection in Pipeline by SVM classifier with Automatic Eddy Current based Defect Recognition Method

It's well-known that industrial safety is now a top concern. Nowadays, accidents caused by flammable gases occur frequently in our everyday lives. Gas cylinders, which are used for household purposes, wide range of businesses, and vehicles are often reported to be on the verge of exploding. Explosions have left a large number of individuals seriously wounded or could also be lethal in certain cases. This project's goal is to use a HOG features for SVM classifier which is used to identify pipeline gas leaks and keep tabs on them. In addition, the system utilises an image processing technique to identify pipeline fractures. Early detection and identification of pipeline flaws is a predominant aspect of this study. According to the suggested design, the robot capture the image down the pipe, looking for any signs of gas leakage by the Eddy Current method. This type of recognition has proved superior to other traditional methods. The methods with efficiency parameters and the results were compared and are tabulated in the results section. In the future, the data in the course of detection could be sent through GSM to a mobile application.

Design of WhatsApp Image Folder Categorization Using CNN Method in the Android Domain

Recently, the use of different social media platforms such as Twitter, Facebook, and WhatsApp have increased significantly. A vast number of static images and motion frame pictures posted on such platforms get stored in the device folder making it critical to identify the social network of the downloaded images in the android domain. This is a multimedia forensic job with major cyber security consequences and is said to be accomplished using unique traces contained in picture material (SNs). Therefore, this proposal has been endeavoured to construct a new framework called FusionNet to combine two well-established single shared Convolutional Neural Networks (CNN) to accelerate the search. Moreover, the FusionNet has been found to improve classification accuracy. Image searching is one of the challenging issues in the android domain besides being a time-consuming process. The goal of the proposed network's architecture and training is to enhance the forensic information included in the digital pictures shared on social media. Furthermore, several network designs for the categorization of WhatsApp pictures have been compared and this suggested method has shown better performance in the comparison. The proposed framework's overall performance was measured using the performance metrics.

Novel Routing Algorithm for Autonomous Vehicles in Smart Transportation System

In recent times Automation is emerging every day and bloomed in every sector. Intelligent Transportation System (ITS) is one of the important branches of Automation. The major constrain in the transportation system is traffic congestion. This slurps the individual’s time and consequently pollutes the environment. A centralized management is required for optimizing the transportation system. The current traffic condition is predicted by evaluating the historical data and thereby it reduces the traffic congestion. The periodic update of traffic condition in each and every street of the city is obtained and the data is transferred to the autonomous vehicle. These data are obtained from the simulation results of transportation prediction tool SUMO. It is proved that our proposed work reduces the traffic congestion and maintains ease traffic flow and preserves the fleet management.

A Review on Data Securing Techniques using Internet of Medical Things

At present, the traditional healthcare system is completely replaced by the revolutionary technique, the Internet of Medical Things (IoMT). Internet of Medical Things is the IoT hub that comprises of medical devices and applications which are interconnected through online computer networks. The basic principle of IoMT is machine-to-machine communication that takes place online. The major goal of IoMT is to reduce frequent or unwanted visits to the hospitals which makes it comfortable and is also highly preferred by the older people. Another advantage of this methodology is that the interpreted or collected data is stored in cloud modules unlike amazon and Mhealth, making it accessible remotely. Although there are countless advantages in IoMT, the critical factor lies in data security or encryption. A surplus number of threat related to devices, connectivity, and cloud might occur under unforeseen or threatening circumstances which makes the person in the situation helpless. Yet, with the help of data security techniques designed especially for Internet of Medical Things, it is possible to address these challenges. In this paper, a review on data securing techniques for the internet of medical things is made along with a discussion on related concepts.