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Purpose: This paper has used the well-known machine learning (ML) computational algorithm with Internet of Things (IoT) devices to predict the COVID-19 disease and to analyze the peak rate of the disease in the world. ML is the best tool to analyze and predict the object in reasonable time with great level of accuracy. The Purpose of this paper is to develop a model to predict the coronavirus by considering majorly related symptoms, attributes and also to predict and analyze the peak rate of the disease. Design/methodology/approach: COVID-19 or coronavirus disease threatens the human lives in various ways, which leads to deaths in most of the cases. It affects the respiratory organs slowly and this penetration leads to multiple organ failure, which causes death in some cases having poor immunity system. In recent times, it has drawn the international attention because of the pandemic threat that is harder to control the spreading of infection around the world. Findings: This proposed model is implemented by support vector machine classifier and Bayesian network algorithm, which yields high accuracy. The K-means algorithm has been applied for clustering the data set models. For data collection, IoT devices and related sensors were used in the identified hotspots. The data sets were collected from the selected hotspots, which are placed on the regions selected by the government agencies. The proposed COVID-19 prediction models improve the accuracy of the prediction and peak accuracy ratio. This model is also tested with best, worst and average cases of data set to achieve the better prediction rate. Originality/value: From that hotspots, the IoT devices were fixed and accessed through wireless sensors (802.11) to transfer the data to the authors' database, which is dedicated in data collection server. The data set and the proposed model yield good results and perform well with expected accuracy rate in the analysis and monitoring of the recovery rate of COVID-19. [ABSTRACT FROM AUTHOR]

Attributable to the modernization of Artificial Intelligence (AI) procedures in healthcare services, various developments including Support Vector Machine (SVM), and profound learning. For example, Convolutional Neural systems (CNN) have prevalently engaged in a significant job of various classificational investigation in lung malignant growth, and different infections. In this paper, Parallel based SVM (P-SVM) and IoT has been utilized to examine the ideal order of lung infections caused by genomic sequence. The proposed method develops a new methodology to locate the ideal characterization of lung sicknesses and determine its growth in its early stages, to control the growth and prevent lung sickness. Further, in the investigation, the P-SVM calculation has been created for arranging high-dimensional distinctive lung ailment datasets. The data used in the assessment has been fetched from real-time data through cloud and IoT. The acquired outcome demonstrates that the developed P-SVM calculation has 83% higher accuracy and 88% precision in characterization with ideal informational collections when contrasted with other learning methods. [ABSTRACT FROM AUTHOR]

The sewage effluent is used as source of irrigation in developing as well as developed countries. The usage of the treated effluent for agricultural purpose and recreational activities are due to the demand of the water worldwide. This study aims to reveal the impact of various concentration of the treated sewage effluent on the germination and physiological parameters of green gram (Vigna radiata), red gram (Cajanus cajan) and paddy (Oryza sativa). Treated sewage effluents collected from three different sewage treatment plants of Mysore City (Kesere, Vidhyarayanapuram, Rayenakare) was used to cultivate the seedling with different concentrations (10, 20, 30, 40, 50, 60, 70, 80, 90 and 100%). It is concluded that the germination percentage of the seedlings are maximum at lower dilutions of 10-30% for all the treated water. The highest shoot length (27.53) and root length (26.91) was reported was for paddy cultivated with Kesare sewage water at an effluent concentration of 10%. The seedling cultivated in Kesare treated sewage effluent exhibited maximum shoot length and root length. The vigour index was reported to be higher for effluent concentration of 10%. This study concludes that the seed germination, root length, shoot length and vigour Index are dependable on the concentration of the effluent used. In 10% concentration of effluent is most effective and stresses the need of proper concentration of the effluent for irrigation. [ABSTRACT FROM AUTHOR]

Background: Neopterin, a metabolite of guanosine, belongs to the class of chemical compounds known as pteridines and is an early and valuable marker of cellular immunity. Recently, it was shown to be associated with the initiation and progression of periodontal disease. The aim of the present study was to assess the relationship between clinical parameters and concentrations of neopterin within gingival crevicular fluid (GCF) from inflamed gingiva and periodontitis sites before and after the treatment of periodontitis. Methods: Sixty age (35 to 65 years)-and gender-matched (30 males and 30 females) subjects were recruited and divided into the following four groups of 15 subjects each based on gingival index, Ramfjord periodontal disease index, clinical attachment loss (CAL), and radiographic parameters (bone loss): healthy (group 1), gingivitis (group 2), mild periodontitis (group 3), and moderate to severe periodontitis (group 4) A fifth group consisted of the 15 subjects from group 4, 6 to 8 weeks after treatment (scaling and root planing). GCF was collected from each patient, and the neopterin levels were determined by enzyme immunoassay. Results were analyzed statistically. Results: The mean concentration of neopterin in GCF was the highest in group 4 (51 nmol/l) and the lowest in group 1 (1.36 nmol/l). The mean neopterin concentrations in group 2 (9.69 nmol/l) and group 3 (16.58 nmol/l) fell between the highest and lowest values. This suggested a positive correlation between CAL and GCF neopterin concentrations. Neopterin levels in group 4 decreased to 1.77 nmol/l after treatment (group 5). Conclusions: Neopterin increased in parallel with the severity of inflammatory disease. Its levels in GCF may be potentially useful as an indicator of periodontal inflammation and the host response. [ABSTRACT FROM AUTHOR]

No abstract available. [ABSTRACT FROM AUTHOR]