NB

Forecasting is a procedure of estimating or predicting the future depends on past and nearby data. Forecasting provides information about the impending future measures and their consequences for the administration. Prediction of rainfall can play impotent role for WRM (Water resource management). After studying different literature, work can be carried out using data mining techniques and machine learning model. Due to very much changing nature of atmosphere rainfall prediction became the vital research area. In this paper we have given an experimental review, in which we have taken Meteorological Data collected by Department of Agricultural Meteorology Indira Gandhi Agricultural University, Raipur (C.G.), applied different machine learning classifier and validated using Matlab 2019a. Eventually we have proposed to use Gaussian Process Regression as classifier, proposed prediction model achieved 95.4% accuracy.

The present study aimed at developing a new approach in gene delivery of calcium phosphate nanoparticles through simulated body fluid (CaP-SBF). The physicochemical and biological characteristics of the CaP-SBF nanoparticles were compared with those made in pure water (CaP-water) via a similar procedure. The CaP-SBF and CaP-water solutions were then adjusted to two different pH values of 7.4 and 8.0, mixed with plasmid DNA (pDNA), and added in varying amounts to human embryonic kidney (HEK 293T) cells. The transfection efficiency and cell viability were studied in vitro by reporter gene (luciferase and Enhanced Green Fluorescent Protein) expression and the resazurin reduction assay, respectively, 24 and 48 h after the incubation with the nanoparticles. Our results indicated considerably high in vitro transfection efficiency for CaP-SBF/DNA complexes at physiological pH (7.4) with high amounts of CaP. Additionally, the SBF solution exhibited the ability to reduce the rapid growth of ...

The electricity has become a part of daily life, which plays an important role in our homes and industries. The system is now focused on the growing demand of power and the need of finding the alternative energy source. The idea of a 'smart city' is the key solution to these power related problems, giving us a futuristic scope. Better understanding of domestic and commercial energy usage brings with it a problem of managing and classifying the sheer amount of data that comes along with it. The work proposal is basically to overcome the demand of power using smart meter in electric power consumption benefiting customer to monitor and manage the electric power usage. This idea is made easier by applying Machine Learning's. Decision Support System an application of Artificial Intelligence (AI) to classify and distribute energy while managing and enhancing the other supporting features of an Electric S mart Meter (ES M) using Internet of Things (IOT). We plan on introducing smart meters as a 'live' communication tool connecting the provider with its customers, which will cause the electrical network industry to face a 360 degree turn around towards a customer-centric business. The system employs the Bayesian Network (BN) prediction model with the three machine learning model that is Naïve Bayes (NB), Decision Tree (DT) and Random Forest (RT). The ES M systems network model is based on the four cornerstones fundamental to IOT: sensing, computing, communication, and actuation.

To evaluate the role of salicylic acid (SA) in Nb-mediated hypersensitive resistance to Potato virus X (PVX) avirulent strain ROTH1 in Solanum tuberosum, we have constructed SA-deficient transgenic potato plant lines by overexpressing the bacterial enzyme salicylate hydroxylase (NahG), which degrades SA. Evaluation of these transgenic lines revealed hydrogen peroxide accumulation and spontaneous lesion formation in an age- and light-dependent manner. In concordance, NahG potato plants were more sensitive to treatment with methyl viologen, a reactive oxygen species–generating compound. In addition, when challenged with PVX ROTH1, NahG transgenic lines showed a decreased disease-resistance response to infection and were unable to induce systemic acquired resistance. However, the avirulent viral effector, the PVX 25-kDa protein, does induce expression of the pathogenesis-related gene PR-1a in NahG potato plants. Taken together, our data indicate that SA is involved in local and systemi...

Because of increasing the use of smartphones, it has become easier to identify location of any user. The most popular technique for outdoor positioning is the GPS signal which is commonly used in smartphones and transport vehicles. However, position detection can not be achieved indoor with GPS. Therefore, in this study, a location determination based on WiFi signal strengths was performed indoor where user could not correctly receive the GPS signal. The data includes the strengths of seven WiFi signals that provide information about four different rooms. Based on the WiFi signal strength values coming from seven different sources to smartphone, the position of the user at which room can be determined. In this study, classification was achieved for the determination of the indoor room. Six different Machine Learning (ML) methods were applied to the classification. These methods are Artificial Neural Networks (ANN), K-Nearest Neighbors (k-NN), Decision Trees (DT), Naive Bayes (NB) Classifier, Extreme Learning Machine (ELM) and Support Vector Machines (SVM). Successful results were obtained from all the methods and these results were compared with each other.