Performance Evaluation of Exponential Discriminant Analysis with Feature Selection for Steganalysis

The performance of supervised learning-based seganalysis depends on the choice of both classifier and features which represent the image. Features extracted from images may contain irrelevant and redundant features which makes them inefficient for machine learning. Relevant features not only decrease the processing time to train a classifier but also provide better generalisation. Linear discriminant classifier which is commonly used for classification may not be able to classify in better way non-linearly separable data. Recently, exponential discriminant analysis, a variant of linear discriminant analysis (LDA), is proposed which transforms the scatter matrices to a new space by distance diffusion mapping. This provides exponential discriminant analysis (EDA) much more discriminant power to classify non-linearly separable data and helps in improving classification accuracy in comparison to LDA. In this paper, the performance of EDA in conjunction with feature selection methods has been investigated. For feature selection, Kullback divergence, Chernoff distance measures and linear regression measures are used to determine relevant features from higher-order statistics of images. The performance is evaluated in terms classification error and computation time. Experimental results show that exponential discriminate analysis in conjunction with linear regression significantly performs better in terms of both classification error and compilation time of training classifier.Defence Science Journal, 2012, 62(1), pp.19-24, DOI:http://dx.doi.org/10.14429/dsj.62.143

Risk profiles and one-year outcomes of patients with newly diagnosed atrial fibrillation in India: Insights from the GARFIELD-AF Registry.

BACKGROUND: The Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF) is an ongoing prospective noninterventional registry, which is providing important information on the baseline characteristics, treatment patterns, and 1-year outcomes in patients with newly diagnosed non-valvular atrial fibrillation (NVAF). This report describes data from Indian patients recruited in this registry. METHODS AND RESULTS: A total of 52,014 patients with newly diagnosed AF were enrolled globally; of these, 1388 patients were recruited from 26 sites within India (2012-2016). In India, the mean age was 65.8 years at diagnosis of NVAF. Hypertension was the most prevalent risk factor for AF, present in 68.5% of patients from India and in 76.3% of patients globally (P < 0.001). Diabetes and coronary artery disease (CAD) were prevalent in 36.2% and 28.1% of patients as compared with global prevalence of 22.2% and 21.6%, respectively (P < 0.001 for both). Antiplatelet therapy was the most common antithrombotic treatment in India. With increasing stroke risk, however, patients were more likely to receive oral anticoagulant therapy [mainly vitamin K antagonist (VKA)], but average international normalized ratio (INR) was lower among Indian patients [median INR value 1.6 (interquartile range {IQR}: 1.3-2.3) versus 2.3 (IQR 1.8-2.8) (P < 0.001)]. Compared with other countries, patients from India had markedly higher rates of all-cause mortality [7.68 per 100 person-years (95% confidence interval 6.32-9.35) vs 4.34 (4.16-4.53), P < 0.0001], while rates of stroke/systemic embolism and major bleeding were lower after 1 year of follow-up. CONCLUSION: Compared to previously published registries from India, the GARFIELD-AF registry describes clinical profiles and outcomes in Indian patients with AF of a different etiology. The registry data show that compared to the rest of the world, Indian AF patients are younger in age and have more diabetes and CAD. Patients with a higher stroke risk are more likely to receive anticoagulation therapy with VKA but are underdosed compared with the global average in the GARFIELD-AF. CLINICAL TRIAL REGISTRATION-URL: http://www.clinicaltrials.gov. Unique identifier: NCT01090362

Engineering thermodynamics, 3rd ed. [cd]/ Rajput

xx, 955 hal.: ill, tab.; 23 cm

Engineering thermodynamics, 3rd ed./ Rajput

xx, 955 hal.: ill, tab.; 23 cm

Engineering thermodynamics, 3rd ed./ Rajput

xx, 955 hal.: ill, tab.; 23 cm

Solarization of nursery soil induces production of fruit bodies of mushrooms and enhances growth of tropical forest tree seedlings

The aim of this work was to find out the effect of soil solarization on microbial population and its effect on growth of two species of tropical forest trees. For this purpose, solar heating of nursery seedbeds (1 x 5m) was done during April- May 2009 for one month, by application of a thin clear sheet of polyethylene. The top soil (5 inches) consists of a mix of loam soil, sand and farm yard manure in 2:1:0.5 ratios (v/v). Temperature variations were recorded daily for a period of one month, at 2 depths, (5 cm and 10 cm). Maximum differences in temperature between solar treatment and control was recorded as high as 12.1° C at 5 cm and 9.1° C at 10 cm depth. After one month, population of Aspergillus, Fusarium, Penicillium, Rhizopus and nematodes were completely eliminated from upper 5 cm depth, although population of AM fungi, bacteria and Trichoderma were reduced, but not completely eliminated. Seedlings of Gmelina arborea Roxb. and Tectona grandis Linn.f. were raised through seeds on treated and control beds. After three months, the production of fruit bodies of mushrooms, namely Amanita populiphila Tullos & E. Moses, Lepiota longicauda Henn. and Scleroderma sp. were observed. It was noticed that these mushrooms only appeared on treated soil with white mycelial growth in rhizosphere under fruit bodies. Lepiota longicauda produced the maximum number of fruit bodies on teak seedbeds followed by Scleroderma sp. on G. arborea seedbeds. Due to solar heating there was 23.9% increase in plant height and 22.1% increase in collar diameter of G. arborea seedlings, where as 17.4% increase in plant height and 9.8% increase in collar diameter in case of T. grandis, as compared to control seedlings

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Predicting hydraulic properties of seasonally impounded soils

Not AvailableAgricultural crop management decisions often require data on hydraulic properties of soils. Little information is available on hydraulic properties of clay soils that are impounded by rainwater (known as ‘Haveli’ lands) every year during the monsoon season in large tracts of Madhya Pradesh in India. Estimating hydraulic properties using global pedotransfer functions (PTFs) is one possible way to collect such information. Rules in the widely used global PTF Rosetta were executed to obtain estimates of two important hydraulic properties, namely soil water retention characteristics (SWRC) and saturated hydraulic conductivity (Ks). SWRC estimates obtained with maximum input (particle size distribution, bulk density, field capacity and permanent wilting point) in Rosetta were relatively closer to the laboratory-measured data as compared with the estimates obtained with lower levels of input. Root mean square error (RMSE) of estimates ranged from 0.01 to 0.05 m3/m3. Hierarchical PTFs to predict Ks from basic soil properties were derived using statistical regression and artificial neural networks. Evaluation of these indicated that neural PTFs were acceptable and hence could be used without loss of accuracy.Not Availabl

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