Mangalore University

In this work, we propose hyperparameters optimization using grid search to optimize the parameters of eight existing models and apply the best parameters to predict the outcomes of HIV tests from the Ethiopian Demographic and Health Survey (EDHS), HIV/AIDS dataset. The core challenge of this work is to find the right or optimum parameter values that generate the optimal model and uncertain training computing costs and test predictive models using various values of hyperparameters. To overcome these challenges, we explore the effects of hyperparameters optimizations by applying a proposed grid search hyperparameter optimization (GSHPO) on the considered models to robust the prediction power. An extensive number of experiments are conducted to affirm the feasibility of our proposed methods. These experiments are done in two separate phases. In the first phase, we test our method with the selected models before hyperparameter optimization is applying (using the default parameters). The second phase of the experiment is done after the hyperparameter optimization is applying (using GSHPO). During the experiment, the 10-fold cross validation technique is used to solve the bias of the models. The proposed system helped to tune the hyperparameters using the grid search approach to the prediction algorithms. Several standard metrics are used to assess the method's efficiency, like accuracy, precision, recall, f1-score, AUC-ROC, MAE, RMSE, R2 and confusion matrix to compare results of each experiments. The results obtained by after applying 10-fold cross validation techniques and the proposed GSHPO are promising. Our findings suggest that the hyper-parameters of tuning models have a statistically important positive impact on the models' prediction accuracy.

Company analysis is a type of Case Study method among many types of Case study based Research Methods. While developing a Company Case study based on various issues in Management, the researcher can choose any company of any industry to study an issue or to solve a problem. Usually, a case analysis ends up with the observation of new performance pattern, interpretation of issues in the form of new information, or development of new suggestions to improve the system or to solve the problems optimally. Company analysis is considered to be a most powerful method to study new lessons required to identify, understand, and solve the problems in the process of managing and leading the organizations. Analysing business issues related to a company provides an opportunity to researchers to identify the kinds of situations, decisions, and dilemmas managers facing every day. Company analysis is a powerful tool in developing both research case study and teaching case study in business management subject. In this paper, we have discussed how ABCD Analysis as Research Methodology in company case analysis procedures in order to help the budding researchers while developing and analysing Company analysis as a Case study. In this paper, we have checked whether ABCD (Advantages, Benefits, Constraints, and Disadvantages) analysis framework can be used while analysing a company, how to consider various determinant issues of a company, selecting various affecting factors under these issues and identifying constituent critical elements for each construct using its elemental analysis technique, and the reasons to recommend the ABCD analysis framework in any kind of company analysis.

From the past few decades, it has been observed that the urbanization and industrialization are expanding in the developed nations and are confronting the overwhelming air contamination issue. The citizens and governments have experienced and expressed the increasingly concerned regarding the impact of air pollution affecting human health and proposed sustainable development for overriding air pollution issues across the worldwide. The outcome of modern industrialization contains the liquid droplets, solid particles and gas molecules and is spreading in the atmospheric air. The heavy concentration of particulate matter of size PM10 and PM2.5 is seriously caused adverse health effect. Through the determination of particulate matter concentration in atmospheric air for the betterment of human being well in primary importance. In this paper machine learning predictive models for forecasting particulate matter concentration in atmospheric air are investigated on Taiwan Air Quality Monitoring data sets, which were obtained from 2012 to 2017. These models were compared with the existing traditional models and perform better in predictive performance. The performance of these models was evaluated with statistical measures: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE), and Coefficient of Determination (R2).

Cancer is a group of diseases caused due to abnormal cell growth. Due to the innovation of microarray technology, a large variety of microarray cancer datasets are produced and hence open up avenues to carry out research work across several disciplines such as Statistics, Computational Biology, Genomic studies and other related fields. The main challenges in analyzing microarray cancer data are the curse of dimensionality, small sample size, noisy data, and imbalance class problem. In this work, we are proposing grid search-based hyperparameter tuning (GSHPT) for random forest parameters to classify Microarray Cancer Data. A grid search is designed by a set of fixed parameter values which are essential in providing optimal accuracy on the basis of n-fold cross-validation. In our work, the 10-fold cross validation is considered. The grid search algorithm provides best parameters such as the number of features to consider at each split, number of trees in the forest, the maximum depth of the tree and the minimum number of samples required to be split at the leaf node. The maximum number of trees considered are 10, 20 and 70 respectively for Ovarian, 3-class Leukemia, and 3-class Leukemia cancer data. In the case of MLL and SRBCT, 50 trees are generated to achieve the maximum classification accuracy. The Gini index is employed as criteria to split the nodes and the maximum depth of the tree is set to 2 for all datasets. Experimental results of the proposed work show an improvement over the state of the art methods. The performance of the proposed method is evaluated using standard metrics such as classification accuracy, precision, recall, f1-score, confusion matrix and misclassification rate and comparative analysis is performed and the results are provided to reveal the performance of the proposed method.

Abstract Amomum nilgiricum is one of the plant species reported from Western Ghats of India, belonging to the family Zingiberaceae , with ethno-botanical values, and is well-known for their ethno medicinal applications. In the present investigation, ethyl acetate and methanol extracts of A. nilgiricum were analyzed by Fourier transform infrared spectrometer (FTIR) and gas chromatography-mass spectrometry (GC–MS) to identify the important functional groups and phytochemical constituents. The FTIR spectra revealed the occurrence of functional characteristic peaks of aromatic amines, carboxylic acids, ketones, phenols and alkyl halides group from leaf and rhizome extracts. The GC–MS analysis of ethyl acetate and methanol extracts from leaves, and methanol extract from rhizomes of A. nilgiricum detected the presence of 25 phytochemical compounds. Further, the leaf and rhizome extracts of A. nilgiricum showed remarkable antibacterial and antifungal activities at 100 mg/mL. The results of DPPH and ferric reducing antioxidant power assay recorded maximum antioxidant activity in A. nilgiricum methanolic leaf extract. While, ethyl acetate leaf extract exhibited maximum α-amylase inhibition activity, followed by methanolic leaf extract exhibiting aldose reductase inhibition. Subsequently, these 25 identified compounds were analyzed for their bioactivity through in silico molecular docking studies. Results revealed that among the phytochemical compounds identified, serverogenin acetate might have maximum antibacterial, antifungal, antiviral, antioxidant and antidiabetic properties followed by 2,4-dimethyl-1,3-dioxane and (1,3- 13 C 2 )propanedioic acid. To our best knowledge, this is the first description on the phytochemical constituents of the leaves and rhizomes of A. nilgiricum , which show pharmacological significance, as there has been no literature available yet on GC–MS and phytochemical studies of this plant species. The in silico molecular docking of serverogenin acetate was also performed to confirm its broad spectrum activities based on the binding interactions with the antibacterial, antifungal, antiviral, antioxidant and antidiabetic target proteins. The results of the present study will create a way for the invention of herbal medicines for several ailments by using A. nilgiricum plants, which may lead to the development of novel drugs.

Foodborne pathogens have been a cause of a large number of diseases worldwide and more so in developing countries. This has a major economic impact. It is important to contain them, and to do so, early detection is very crucial. Detection and diagnostics relied on culture-based methods to begin with and have developed in the recent past parallel to the developments towards immunological methods such as enzyme-linked immunosorbent assays (ELISA) and molecular biology-based methods such as polymerase chain reaction (PCR). The aim has always been to find a rapid, sensitive, specific and cost-effective method. Ranging from culturing of microbes to the futuristic biosensor technology, the methods have had this common goal. This review summarizes the recent trends and brings together methods that have been developed over the years.

Index Fungorum, Species Fungorum and MycoBank are the key fungal nomenclature and taxonomic databases that can be sourced to find taxonomic details concerning fungi, while DNA sequence data can be sourced from the NCBI, EBI and UNITE databases. Nomenclature and ecological data on freshwater fungi can be accessed on http://fungi.life.illinois.edu/ , while http://www.marinespecies.org/provides a comprehensive list of names of marine organisms, including information on their synonymy. Previous websites however have little information on marine fungi and their ecology, beside articles that deal with marine fungi, especially those published in the nineteenth and early twentieth centuries may not be accessible to those working in third world countries. To address this problem, a new website www.marinefungi.org was set up and is introduced in this paper. This website provides a search facility to genera of marine fungi, full species descriptions, key to species and illustrations, an up to date classification of all recorded marine fungi which includes all fungal groups (Ascomycota, Basidiomycota, Blastocladiomycota, Chytridiomycota, Mucoromycota and fungus-like organisms e.g. Thraustochytriales), and listing recent publications. Currently, 1257 species are listed in the marine fungi website ( www.marinefungi.org ), in 539 genera, 74 orders, 168 families, 20 classes and five phyla, with new taxa continuing to be described. The website has curators with specialist mycological expertise who help to provide update data on the classification of marine fungi. This article also reviews knowledge of marine fungi covering a wide range of topics: their higher classification, ecology and world distribution, role in energy transfer in the oceans, origin and new chemical structures. An updated classification of marine fungi is also included. We would like to invite all mycologists to contribute to this innovative website.

In Co3O4 systems, the oxygen vacancy is reported to improve the oxygen evolution reaction (OER) activity because of higher Co2+/Co3+ surface ratio. In situ studies have revealed Co3+—site reducibility as the key factor for OER activity of cobalt oxide-based systems. In this context, we have synthesized and analyzed OER activity of two Co3O4 systems; c-Co3O4 with higher oxygen defects or Co2+/Co3+ ratio and n-Co3O4 with relatively less Co2+/Co3+ ratio but more Co3+ reducibility. The systems, n- and c-Co3O4 show overpotential of 380 and 440 mV at 10 mA/cm2 and Tafel slope of 153 and 53 mV/dec, respectively, for OER. Electrochemical characterization reveals that the lowering of OER onset potential is influenced by Co3+ reducibility rather than defects in Co3O4 systems while adsorption capacitance arising from surface irregularities, pores and their geometry, and Co3+-Oh sites cause an increase in the Tafel slope values or decrease in OER kinetics. The correlation of the key factors such as Co3+ reducibility and oxygen defects of two different Co3O4 systems toward OER activity can aid the designing of highly efficient cobalt oxide-based OER catalysts.

The systematic study of background radiation and the distribution of radionuclides in the environment of coastal Karnataka, South India, has been initiated with an objective of establishing reliable baseline data on the background radiation level of the region for future assessment of the impact of nuclear and thermal power stations that are being set up in the region. The ambient gamma radiation survey in the environment of Mangalore, a major industrial city of coastal Karnataka, revealed significantly high gamma dose in air in certain locations of the Mangalore beach area. Thermoluminescent dosimetric studies indicated conspicuously high gamma dose in air in these places. Gamma spectrometric analyses of the soil and sand samples of this high background area have been carried out. The measured gamma dose in air in high background area is in the range 44-2102 nGy h-1. The average activity of 232Th, 238U, and 40K in soil samples is 2,971 Bq kg-1, 546 Bq kg-1, and 268 Bq kg-1, respectively. In sand samples, the respective activities are 1,842 Bq kg-1, 374 Bq kg-1, and 158 Bq kg-1. Results of these systematic investigations which establish the existence of new patches of monazite in the Mangalore beach area, on the southwest coast of India, are presented in this paper.

Analysis of microarray data is a highly challenging problem due to the inherent complexity in the nature of the data associated with higher dimensionality, smaller sample size, imbalanced number of classes, noisy data‐structure, and higher variance of feature values. This has led to lesser classification accuracy and over‐fitting problem. In this work, the authors aimed to develop a deep feedforward method to classify the given microarray cancer data into a set of classes for subsequent diagnosis purposes. They have used a 7‐layer deep neural network architecture having various parameters for each dataset. The small sample size and dimensionality problems are addressed by considering a well‐known dimensionality reduction technique namely principal component analysis. The feature values are scaled using the Min–Max approach and the proposed approach is validated on eight standard microarray cancer datasets. To measure the loss, a binary cross‐entropy is used and adaptive moment estimation is considered for optimisation. The performance of the proposed approach is evaluated using classification accuracy, precision, recall, f ‐measure, log‐loss, receiver operating characteristic curve, and confusion matrix. A comparative analysis with state‐of‐the‐art methods is carried out and the performance of the proposed approach exhibit better performance than many of the existing methods.

Purpose The study aims at analyzing the perception of teachers and students about online classes. The work tries to explain the opinions of students as regards the impact of online courses, their comfortability in its usag, and the support received from teachers in online classes along with teachers' opinions on efficacy, teaching practice followed and training received for an online class. Design/methodology/approach The analysis was carried out using the data collected through two separate structured questionnaires for students and teachers in Dakshina Kannada and Udupi District in Karnataka. Data were recorded in SPSS and analyzed by using descriptive statistics. Findings The study reveals that students are comfortable with online classes and are getting enough support from teachers but they do not believe that online classes will replace traditional classroom teaching. It also finds that teachers are facing difficulties in conducting online classes due to a lack of proper training and development for doing online classes. Technical issues are the major problem for the effectiveness of the online classes. Practical implications Most of the colleges think of implementing online classes in their courses. Hence, it becomes essential to obtain the opinions of participants of online classes before applying for it. This study may help colleges to get a general view of online classes among teachers and students. Originality/value Internet and new technologies gained importance in all fields including the education sector which gave scope for online classes. In addition to this, the COVID pandemic worldwide has also added to the relevance of online classes. In this light, it is necessary to understand student–teacher perceptions regarding online classes.

Because mangrove plant species are a valuable source of useful metabolites, their endophytes have gained more importance. Randomly sampled surface-sterilized whole root segments of four mangrove plant species, Acanthus ilicifolius, Avicennia officinalis, Rhizophora mucronata, and Sonneratia caseolaris from the mangroves of Udyavara (Karnataka) on the west coast of India, were characterized for fungal communities by direct plating, damp chamber, and bubbling chamber incubation methods. The richness of endophytic fungal species from whole root segments after direct plating and damp chamber incubation was greatest for R. mucronata than for other plants (18 vs. 8-13). Incubation of whole root segments in bubbling chambers yielded conidia of two freshwater hyphomycetes: Mycocentrospora acerina (in Avicennia officinalis) and Triscelophorus acuminatus (in R. mucronata and in S. caseolaris). Surface-sterilized whole root and root bark segments of R. mucronata sampled from the mid-tide level on direct plating yielded more fungi than that of the root segments sampled from low-tide and high-tide levels. The greatest number of isolates, species richness, and diversity of fungi were shown by the whole root segments of R. mucronata from the mid-tide level. Rarefaction indices also revealed the highest expected number of species out of 150 random isolations from the mid-tide level samples of whole root and root bark segments of R. mucronata. The present study showed that fungi in mangrove roots are composed of a consortium of soil, marine, and freshwater fungi.

. The superior electrochemical performance is attributed to the synergetic effect of the composite components which not only provided enough electroactive channels for the smooth passage of electrolyte ions but also maintained the hybrid structure intact in the ongoing electrochemical process. The obtained results underpin the promising utility of this material for future electrochemical energy storage devices.

In this work we report spectroscopic characterization and reactivity study by density functional theory (DFT) and molecular dynamics (MD) simulations of two quinoline derivatives. Collected computational results for the two new derivatives have been compared with the pristine quinoline in order to investigate the consequences of modifications by introduction of chlorine atoms and methyl and OH groups. Potential energy distribution (PED) analysis has been performed in order to assign principal vibrational numbers. DFT calculations have been used to obtain global and local quantum-molecular descriptors including frontier molecular orbitals, charge distribution by molecular electrostatic potential (MEP) surface, average local ionization energy (ALIE) surface, and Fukui functions. Natural bond order (NBO) analysis has been performed in order to investigate hyper-conjugative properties. To investigate sensitivity towards autoxidation and hydrolysis we have calculated bond dissociation energies (BDE) and radial distribution functions (RDF). Molecular docking study has also been performed in order to initially assess the potential of target molecules to bind with dehydrogenase inhibitor and these quinoline derivatives can be a lead compounds for developing new anti-malarial drug.

The current technological and digital era has always directed campus recruitment process towards eruption of paradigm shifts matching new systems of industrial workforce engagements. The dilemma of job seeking graduates today in campus has ever more convoluted towards the rapid changes in industry and employment market. IEDRA Model of Student Campus Placement Realization was a more comprehensive study undertaken by Shenoy & Aithal (2017) to resolve such a dilemma of students. Therefore here, a brief study is undertaken to discover practical viabilities, understand the usefulness, resourcefulness and universal applications of IEDRA Model of campus placement determination towards concerned stakeholders. A new model of framework analysis named ABCD analysis developed by Aithal et al. (2015) is adopted here for arriving at appropriate theory, hypothesis or postulate constructs regarding the ubiquitous appeal of the IEDRA Framework.

Ketosulfone has been evaluated as a green corrosion inhibitor for mild steel in 1 M HCl medium by chemical and electrochemical methods. The effect of Ketosulfone on the corrosion rate was determined at various concentrations and temperature. Polarization measurements reveal that Ketosulfone acts as a mixed-type inhibitor. The adsorption of the inhibitor on the mild steel surface in acid solution was found to obey the Langmuir adsorption isotherm. The activation and thermodynamic parameters of dissolution and adsorption were calculated and discussed. Quantum chemical calculations were calculated and discussed, and it supports the results. SEM images of inhibited strips reveal the likely formation of a protective film.

emerged as a significant bioactive molecule among the synthesized analogues.

Abstract Background This paper presents the application of polyglycine-modified graphene paste electrode (PGMGPE) for the electrochemical detection of Hg (II) and Pb (II) ions in the water and biological samples. Method The developed electrode was characterized by field emission scanning electron microscopy. Electrochemical techniques such as cyclic voltammetry and differential pulse voltammetry were used to study the behavior of metal ions. Results The modification process improves the electrochemical behavior of heavy metal ions. The peak current varied linearly with the increase of the concentration leading to a detection limit of 6.6 μM (Hg (II)) and 0.8 μM (Pb (II)), respectively. Conclusion The developed electrode exhibits good sensitivity, selectivity, stability, and lower detection limit, and was successfully applied to the determination of heavy metal ions in water and biological samples with a good recovery range.

M. The modified electrode demonstrated many advantages such as simple preparation, high sensitivity, low detection of limit, excellent catalytic activity, short response time, and remarkable antifouling property toward IC and its oxidation product.

and detection sensitivity (2.0452 μA/μM), for Tz. The results show that the biosensor is sensitive and useful for the determination of Tz.