Slime Mold Optimization with Relational Graph Convolutional Network for Big Data Classification on Apache Spark Environment

Lately, Big Data (BD) classification has become an active research area in different fields namely finance, healthcare, e-commerce, and so on. Feature Selection (FS) is a crucial task for text classification challenges. Text FS aims to characterize documents using the most relevant feature. This method might reduce the dataset size and maximize the efficiency of the machine learning method. Various researcher workers focus on elaborating effective FS techniques. But most of the presented techniques are assessed for smaller datasets and validated by a single machine. As textual data dimensionality becomes high, conventional FS methodologies should be parallelized and improved to manage textual big datasets. This article develops a Slime Mold Optimization based FS with Optimal Relational Graph Convolutional Network (SMOFS-ORGCN) for BD Classification in Apache Spark Environment. The presented SMOFS-ORGCN model mainly focuses on the classification of BD accurately and rapidly. To handle BD, the SMOFS-ORGCN model uses an Apache Spark environment. In the SMOFS-ORGCN model, the SMOFS technique gets executed for reducing the profanity of dimensionality and to improve classification accuracy. In this article, the RGCN technique is employed for BD classification. In addition, Grey Wolf Optimizer (GWO) technique is utilized as a hyperparameter optimizer of the RGCN technique to enhance the classification achievement. To exhibit the better achievement of the SMOFS-ORGCN technique, a far-reaching experiments were conducted. The comparison results reported enhanced outputs of the SMOFS-ORGCN technique over current models

Effect of organic manures, inorganic fertilizers and biofertilizers on the nutrient concentration in leaves at different growth stages of banana cv Poovan.

Banana (Musa spp) and plantain are known for their antiquity and are interwoven with Indian heritage and culture and it  is one of the most important fruits grown  and consumed worldwide.   A field experiment was laid out in randamised block design  with ten treatments and three replications consisting recommend dose of fertilizers (RDF)  and RDF combined  with   organic   manures( Farm yard manure, Vermicompost and  Neem cake) and bioferlizers (VAM, azospirillum, PSB,  T. harizianum) at different combinations  to know their nutrient concentration  in banana leaves and soil at different growth periods viz., vegetative stage, flowering stage and harvesting stage. Sample preparation was performed with closed vassal microwave digestion. The major and micronutrients were analysed using the ICP-OES (Optima 2000). E-Merck multi-elemental standard used as a reference standard and ultra pure 2% HNO3 was applied as an internal standard.  T8  treatment(50 per cent  RDF through inorganic fertilizers  ,organic manures with bio ferlizers) recorded significantly highest leaf  nitrogen and potassium  (3.24  amd 0.44%) during vegetative stage, flowering (3.58%) and  harvesting stages(2.68 %) than untreated plants  T1(2.02,2.12 and 1.51%). Highest  Leaf phosphorus (0.42,0.43 and 0.38 %), sodium(0.40, 0.44 and 0.32)  magnesium(1.61.1.81, and 0.81 %). Significantly lowest concentration was found in untreated plants. The highest micro nutrients were noted in T8 followed by   T10 treatment in all the stages

Magnetic properties of Hydrogenated Li and Co doped ZnO nanoparticles

The effect of hydrogenation on magnetic properties of Zn0.85Co0.05Li0.10O nanoparticles is presented. It was found that the sample hydrided at room temperature (RT) showed weak ferromagnetism (FM) while that hydrided at 400oC showed robust ferromagnetism at room temperature. In both cases reheating the sample at 400oC in air converts it back into paramagnetic state (P) completely. The characterization of samples by X-ray and electron diffraction (ED) showed that room temperature ferromagnetism observed in the samples hydrogenated at RT is intrinsic in nature whereas that observed in the samples hydrogenated at 400oC is partly due to the cobalt metal clusters.Comment: 10 pages, 3 figure

-Amylase production by Penicillium fellutanum isolated from mangrove rhizosphere soil

The effects of pH, temperature, incubation time, salinity, sources of carbon and nitrogen were tested in submerged fermentation process in production of -amylase by Penicillium fellutanum isolated from coastal mangrove soil. The production medium without addition of seawater and with provision ofmaltose as carbon source, peptone as nitrogen source, incubated for 96 h, maintained with pH of 6.5 at 30oC, was found optimal for production of -amylase by P. fellutanu

Morphological characterization and secondary metabolites profile of black pepper (Piper nigrum L.) genotypes from Sikkim

Quantification of volatile oil and analysis of four major metabolites using HPLC was done in 24 black pepper genotypes collected from south Sikkim. The amount of volatile oil ranged from 2.01% to 0.022%. Secondary metabolites like piperine ranged from 2.75-0.022%, myrcene from 2.094-0.022%, alpha- phellandrene from 1.373-0.008% and linalool from 0.834-0.012%. Genotype 23 had the highest amount of myrcene and linalool, genotype 13 had the highest quantity of piperine and genotype 8 had high amount of alpha-phellandrene. The principal component analysis (PCA) of analyzed metabolites grouped the genotypes into four categories. The study revealed that some of the genotypes were as good as pepper varieties grown in traditional areas. These genotypes will be useful in crop improvement strategies and suitable for Sikkim Himalaya

Application Of Principles Of Total Quality Management (TQM) In Teacher Education Institutions

The indomitable spirit of higher education paves the way for the growth of a nation in the political, economic, social, intellectual and spiritual dimensions. Teacher education is one of the areas in higher education which trains student-teachers in pedagogy, which in turn helps them to train the young minds of educational institutions. The “Fate of the nation is decided in the classroom,” is a remark made by the Education Commission of India. Such classrooms are created by committed and dedicated teachers. These teachers are trained in teacher education institutions. Teacher education institutions should maintain quality to ensure the academic excellence of trainees who come into the teaching profession. Quality is a comparative standard prescribed for those institutions that are on the quest for output brilliance. Quality assurance in teacher education reflects on the high profile of the institution and the competency of student-teachers. The present study on the application of principals of TQM in teacher education institutions in India has exposed the tangibility of institutions in the perception of teachers based on eleven quality indicators, such as principal as leader, teacher quality, linkage and interface, students, co-curricular activities, teaching, office management, relationships, material resources, examinations and job satisfaction. A total of nine colleges of education was selected to collect data. The exploratory technique under the survey method of research design was used for the study. A tool - ‘Teacher Institutional Profile’ (TIP) - was constructed, standardized and used for data collection. Quantitative and qualitative analyses were made for finding and interpreting results. The findings focus on the strong and weak areas of various teacher education institutions according to the quality indicators. The study recommends further strengthening of quality indicators, which are already strong, and the revamping of weaker quality indicators. It is also recommended that institutions should adhere to the quality standards set by national and international assessment and accreditation bodies. In conclusion, the global scenario expects skilled teachers to produce students with a versatile personality for which teacher education should be strengthened

Processing of Spatio-Temporal Hybrid Search Algorithms in Heterogenous Environment Using Stochastic Annealing NN Search

In spatio-temporal database the mixed regions are present in a random manner. The existing work produces the result to create new research opportunities in the area of adaptive and hybrid SLS algorithms. This algorithm develops initialization algorithms which are used only for the homogenous environment. Most current approaches assume, as we have done here, only the homogenous mixtures. Approach: To overcome the above issue, we are going to implement a new technique termed Stochastic Annealing Nearest Neighbor Search using hybrid search algorithms (SANN- HA) for spatio-temporal heterogeneous environment to retrieve the best solution. It provides enhanced fits for definite run length distributions, and would be useful in other contexts as well. Results: Performance of Stochastic Annealing Nearest Neighbor Search using hybrid search algorithms is to discover different sub explanations using different mixture of algorithms in terms of run length distribution and average time for execution based on data objects. Conclusion: It considers the problem of retrieving the high quality solution from the heterogeneous environment. An analytical and empirical result shows the better result with the efficient hybrid search algorithms of our proposed SANN scheme

Machine Learning Models to Predict Covid-19 Vaccination Intention: An Indian Study

Purpose: Covid 19 pandemic has taken the world by shock for last few years, and it has greatly impacted the livelihood of people across all walks of life and even the economies of many nations got greatly affected. Governments across the globe revived from the impact of covid-19 pandemic using many strategies and policies which were formulated under the guidance of the world health organization. One of the Prime weapons which helped the governments and public against covid -19 is vaccination. This research which was conducted August 2021 was done to understand the perception of the public towards the covid 19 vaccination and to predict the public intention to take up covid -19 vaccination using the health belief model constructs.   Theoretical framework:The Study has used the variables of the health belief model namely the perceived severity, perceived susceptibility, Perceived Benefits, Cues to action and other socio-demographic variables to predict the intent of the respondents towards taking Covid-19 vaccination.    Design/methodology/approach:   Data was collected using a self-administered online questionnaire distributed to the respondents from Tamil Nadu, India who are above 18 years of age. Machine Learning Algorithms like Logistic Regression, Artificial Neural Networks were used to predict the public intent to take up covid 19 vaccination.   Findings: From the Analysis of Logistic Regression and Artificial Neural Network, it was found that Health Belief Model Constructs Perceived Barriers, Perceived Benefits and Cues to action, were significant factors that affect the public intention to vaccinate.   Research, Practical & Social implications:Findings of the research will help the government, stake holders to understand the factors impacting the respondent’s intent to covid-19 vaccination which will guide them to plan better strategies for future vaccination drives   Originality/value:The Study has used to two different machine learning algorithms to compare and corroborate the research findings and in turn identifying the significant predictors of covid-19 vaccination inten