Analysis on the Bus Arrival Time Prediction Model for Human-Centric Services Using Data Mining Techniques.

The human-computer interaction has become inevitable in digital world. HCI helps humans to incorporate technology to resolve even their day-to-day problems. The main objective of the paper is to utilize HCI in Intelligent Transportation Systems. In India, the most common and convenient mode of transportation is the buses. Every state government provides the bus transportation facility to all routes at an affordable cost. The main difficulty faced by the passengers (humans) is lack of information about bus numbers available for the particular route and Estimated Time of Arrival (ETA) of the buses. There may be different reasons for the bus delay. These include heavy traffic, breakdowns, and bad weather conditions. The passengers waiting in the bus stops are neither aware of the delay nor the bus arrival time. These issues can be resolved by providing an HCI-based web/mobile application for the passengers to track their bus locations in real time. They can also check the Estimated Time of Arrival (ETA) of a particular bus, calculated using machine learning techniques by considering the impacts of environmental dynamics, and other factors like traffic density and weather conditions and track their bus locations in real time. This can be achieved by developing a real-time bus management system for the benefit of passengers, bus drivers, and bus managers. This system can effectively address the problems related to bus timing transparency and arrival time forecasting. The buses are equipped with real-time vehicle tracking module containing Raspberry Pi, GPS, and GSM. The traffic density in the current location of the bus and weather data are some of the factors used for the ETA prediction using the Support Vector Regression algorithm. The model showed RMSE of 27 seconds when tested. The model is performing well when compared with other models.

The role of contemporary digital tools and technologies in COVID-19 crisis: An exploratory analysis.

Following the COVID-19 pandemic, there has been an increase in interest in using digital resources to contain pandemics. To avoid, detect, monitor, regulate, track, and manage diseases, predict outbreaks and conduct data analysis and decision-making processes, a variety of digital technologies are used, ranging from artificial intelligence (AI)-powered machine learning (ML) or deep learning (DL) focused applications to blockchain technology and big data analytics enabled by cloud computing and the internet of things (IoT). In this paper, we look at how emerging technologies such as the IoT and sensors, AI, ML, DL, blockchain, augmented reality, virtual reality, cloud computing, big data, robots and drones, intelligent mobile apps, and 5G are advancing health care and paving the way to combat the COVID-19 pandemic. The aim of this research is to look at possible technologies, processes, and tools for addressing COVID-19 issues such as pre-screening, early detection, monitoring infected/quarantined individuals, forecasting future infection rates, and more. We also look at the research possibilities that have arisen as a result of the use of emerging technology to handle the COVID-19 crisis.

An efficient and magnetically recoverable g-C3N4/ZnS/CoFe2O4 nanocomposite for sustainable photodegradation of organic dye under UV-visible light illumination.

Effective improvement of an easily recoverable photocatalyst is equally vital to its photocatalytic performance from a practical application view. The magnetically recoverable process is one of the easiest ways, provided the photocatalyst is magnetically strong enough to respond to an external magnetic field. Herein, we prepared graphitic carbon nitride nanosheet (g-C3N4), and ZnS quantum dots (QDs) supported ferromagnetic CoFe2O4 nanoparticles (NPs) as the gC3N4/ZnS/CoFe2O4 nanohybrid photocatalyst by a wet-impregnation method. The loading of CoFe2O4 NPs in the g-C3N4/ZnS nanohybrid resulted in extended visible light absorption. The ferromagnetic g-C3N4/ZnS/CoFe2O4 nanohybrid exhibited better visible-light-active photocatalytic performance (97.11%) against methylene blue (MB) dye, and it was easily separable from the aqueous solution by an external bar magnet. The g-C3N4/ZnS/CoFe2O4 nanohybrid displayed excellent photostability and reusability after five consecutive cycles. The favourable band alignment and availability of a large number of active sites affected the better charge separation and enhanced photocatalytic response. The role of active species involved in the degradation of MB dye during photocatalyst by g-C3N4/ZnS/CoFe2O4 nanohybrid was also investigated. Overall, this study provides a facile method for design eco-friendly and promising g-C3N4/ZnS/CoFe2O4 nanohybrid photocatalyst as applicable in the eco-friendly dye degradation process.

Rice leaf diseases prediction using deep neural networks with transfer learning.

Rice (Oryza sativa) is a principal cereal crop in the world. It is consumed by greater than half of the world's population as a staple food for energy source. The yield production quantity and quality of the rice grain is affecting by abiotic and biotic factors such as precipitation, soil fertility, temperature, pests, bacteria, virus, etc. For disease management, farmers spending lot of time and resources and they detect the diseases through their penniless naked eye approach which leads to unhealthy farming. The advancement of technical support in agriculture greatly assists for automatic identification of infectious organisms in the rice plants leaves. The convolutional neural network algorithm (CNN) is one of the algorithms in deep learning has been triumphantly invoked for solving computer vision problems like image classification, object segmentation, image analysis, etc. In our work, InceptionResNetV2 is a type of CNN model utilized with transfer learning approach for recognizing diseases in rice leaf images. The parameters of the proposed model is optimized for the classification task and obtained a good accuracy of 95.67%.

Prediction of batch sorption of barium and strontium from saline water.

Celestite and barite formation results in contamination of barium and strontium ions hinder oilfield water purification. Conversion of bio-waste sorbent products deals with a viable, sustainable and clean remediation approach for removing contaminants. Biochar sorbent produced from rice straw was used to remove barium and strontium ions of saline water from petroleum industries. The removal efficiency depends on biochar amount, pH, contact time, temperature, and Ba/Sr concentration ratio. The interactions and effects of these parameters with removal efficiency are multifaceted and nonlinear. We used an artificial neural network (ANN) model to explore the correlation between process variables and sorption responses. The ANN model is more accurate than that of existing kinetic and isotherm equations in assessing barium and strontium removal with adj. R2 values of 0.994 and 0.991, respectively. We developed a standalone user interface to estimate the barium and strontium removal as a function of sorption process parameters. Sensitivity analysis and quantitative estimation were carried out to study individual process variables' impact on removal efficiency.