Fabrication of Enhanced UV Protective Cotton Fabric Using Activated Nano-Biocarbon Derived from Teff Hay Grafted by Polyaniline: RSM-Based Optimization and Characterization.

In the present study, a hybrid cotton fabric with an enhanced ultraviolet (UV) shielding property was developed by coating with functionally activated nanocarbon (FACN) which was grafted by polyaniline (PANI) using in situ polymerization. In light of this, Teff hay biomass was used to establish the activated nanocarbon (ANC), that was subsequently given a surface functionalization using a silane coupling agent. Using the response surface (RSM) statistical analysis, the study was optimized for the weight percent of ANC and PANI with respect to the cotton fabric that was found to offer remarkable UV protection, with an ultraviolet protection factor (UPF) of 64.563, roughly 17 times more than that of primitive cotton (UPF = 3.7). The different characterization techniques, such as UV absorption, Fourier transform infrared (FTIR), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and thermal behavior studies were investigated. In addition, the basic textile properties on optimized hybrid material were found to be appreciably increased. The results suggested that activated FACN made from Teff hay could be an effective alternative organic source material for developing UV protective hybrid cotton fabrics.

Effective Evaluation of Medical Images Using Artificial Intelligence Techniques.

This work is implemented for the management of patients with epilepsy, and methods based on electroencephalography (EEG) analysis have been proposed for the timely prediction of its occurrence. The proposed system is used for crisis detection and prediction system; it is useful for both patients and medical staff to know their status easily and more accurately. In the treatment of Parkinson's disease, the affected patients with Parkinson's disease can assess the prognostic risk factors, and the symptoms are evaluated to predict rapid progression in the early stages after diagnosis. The presented seizure prediction system introduces deep learning algorithms into EEG score analysis. This proposed work long short-term memory (LSTM) network model is mainly implemented for the identification and classification of qualitative patterns in the EEG of patients. While compared with other techniques like deep learning models such as convolutional neural networks (CNNs) and traditional machine learning algorithms, the proposed LSTM model plays a significant role in predicting impending crises over 4 different qualifying intervals from 10 minutes to 1.5 hours with very few wrong predictions.

Applications of Intelligent Model to Analyze the Green Finance for Environmental Development in the Context of Artificial Intelligence.

Green finance can be referred to as financial investments made on sustainable projects and policies that focus on a sustainable economy. The procedures include promoting renewable energy sources, energy efficiency, water sanitation, industrial pollution control, transportation pollution control, reduction of deforestation, and carbon emissions, etc. Mainly, these green finance initiatives are carried out by private and public agents like business organizations, banks, international organizations, government organizations, etc. Green finance provides a financial solution to create a positive impact on society and leads to environmental development. In the age of artificial intelligence, all industries adopt AI technologies. In this research, we see the applications of the intelligent model to examine the green finance for ecological advancement with regard to artificial intelligence. Feasible transportation and energy proficiency and power transmission are two significant fields to be advanced and focused on minimizing the carbon impression in these industries. Renewable sources like solar energies for power generation and electric vehicles are to be researched and developed. This R&D requires a considerable fund supply, thus comes the green finance. Globally, green finance plays a vital role in creating a sustainable environment. In this research, for performing the green finance analysis, financial maximally filtered graph (FMFG) algorithm is implemented in different domains. The proposed algorithm is compared with the neural model and observed that the proposed model has obtained 98.85% of accuracy which is higher than the neural model.

Implementation of Artificial Neural Network to Predict Diabetes with High-Quality Health System.

Patients with diabetes who are closely monitored have a higher overall quality of life than those who are not. Costs associated with healthcare can be decreased by utilising the Internet of Things (IoT), thanks to technological advancements. To satisfy the expectations of e-health applications, it is required for the development of the intelligent systems as well as increases the number of applications that are connected to the network. As a result, in order to achieve these goals, the cellular network should be capable of supporting intelligent healthcare applications that require high energy efficiency. In this paper, we model a neural network-based ensemble voting classifier to predict accurately the diabetes in the patients via online monitoring. The study consists of Internet of Things (IoT) devices to monitor the instances of the patients. While monitoring, the data are transferred from IoT devices to smartphones and then to the cloud, where the process of classification takes place. The simulation is conducted on the collected samples using the python tool. The results of the simulation show that the proposed method achieves a higher accuracy rate, higher precision, recall, and f-measure than existing state-of-art ensemble models.

Empowerment of Primary Healthcare Providers on the Prevention and Management of Dental or Oral Health Issues among Postchemotherapy Patients in Pandemic.

Objectives: The study aim was to evaluate the empowerment of primary healthcare providers on the prevention and management of dental or oral health issues among postchemotherapy (PC) patients, in selected rural regions, India, during a pandemic. Methods: Initially, 240 PHPs were recruited by convenient and snow ball sampling with 90.3% response rate. A descriptive, cross-sectional study was adopted using a self-administered questionnaire with 5 sections: demographics, identification of dental/oral health issues, knowledge, attitude, and practice on prevention and management of dental/oral health problems in PC patients. Statistical Packages for Social Sciences (SPSS) version 23.0 was used for statistical analysis. Results: The overall knowledge was better among nurses (64.56%), followed by pharmacists (54.5%). 81.65% of PHPs were willing to learn more and expressed the need for collaboration with dentists. In the past 3 months, 18.81% of them had PC patients with dental/oral health issues, but only 3.5% of nurses and 0.8% of pharmacists treated them. The logistic regression model revealed higher scores in mucositis/mucosal pain (OR = 1.41), altered taste sensation (OR = 1.34), sensitive gums (OR = 1.71), and dental caries (OR = 1.32) domains (p < 0.05). Those who had readiness to learn (OR = 5.37), nurses and pharmacists, and having less years of experience (OR = 1.31) and higher degree (OR = 1.4) had a positive attitude (p < 0.05). Conclusion: PHPs had limited empowerment in terms of knowledge and practice but showed a positive attitude toward the prevention and management of dental/oral health issues of PC patients. For better practice, continuing education and collaboration with dental professionals is essential.