Prevalence of gastrointestinal helminths in Banaraja fowls reared in semi-intensive system of management in Mayurbhanj district of Odisha

Aim: Studies on the prevalence of gastrointestinal helminths infection in Banaraja fowls of Mayurbhanj district in Odisha with respect to semi-intensive system of rearing. Materials and Methods: A total of 160 Banaraja birds (30 males and 130 females) belonging to two age groups (below 1 month age and above 1 month) were examined for the presence of different species of gastrointestinal helminth infection over a period of 1-year. The method of investigation included collection of fecal sample and gastrointestinal tracts, examination of fecal sample of birds, collection of parasites from different part of gastrointestinal tract, counting of parasites, and examination of the collected parasites by standard parasitological techniques followed by morphological identification as far as possible up to the species level. Results: Overall, 58.75% birds were found infected with various gastrointestinal helminths. Total five species of parasites were detected that included Ascaridia galli (25.63%), Heterakis gallinarum (33.75%), Raillietina tetragona (46.25%), Raillietina echinobothrida (11.87%), and Echinostoma revolutum (1.87%). Both single (19.15%) as well as mixed (80.85%) infection were observed. Highest incidence of infection was observed during rainy season (68.88%) followed by winter (66.66%) and least in summer season (41.81%). Sex-wise incidence revealed slightly higher occurrence among females (59.23%) than males (56.67%). Age-wise prevalence revealed that chicks were more susceptible (77.77%) than adults (51.30%) to gastrointestinal helminths infection. Conclusions: Present study revealed that mixed infection with gastrointestinal helminths of different species was more common than infection with single species and season-wise prevalence was higher in rainy season followed by winter and summer. Chicks were found to be more prone to this parasitic infection and a slight higher prevalence among female birds was observed

Statistical Tests for Joint Analysis of Performance Measures

\u3cp\u3eRecently there has been an increasing interest in the development of new methods using Pareto optimality to deal with multiobjective criteria (for example, accuracy and architectural complexity). Once one has learned a model based on their devised method, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Unfortunately, the standard tests used for this purpose are not able to jointly consider performance measures. The aim of this paper is to resolve this issue by developing statistical procedures that are able to account for multiple competing measures at the same time. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameter of such models, as usually the number of studied cases is very reduced in such comparisons. Real data from a comparison among general purpose classifiers is used to show a practical application of our tests.\u3c/p\u3

Training functional link neural network with ant lion optimizer

Functional Link Neural Network (FLNN) has becoming as an important tool used in machine learning due to its modest architecture. FLNN requires less tunable weights for training as compared to the standard multilayer feed forward network such as Multilayer Perceptron (MLP). Since FLNN uses Backpropagation algorithm as the standard learning algorithm, the method however prone to get trapped in local minima which affect its performance. This paper proposed the implementation of Ant Lion Algorithm as learning algorithm to train the FLNN for classification tasks. The Ant Lion Optimizer (ALO) is the metaheuristic optimization algorithm that mimics the hunting mechanism of antlions in nature. The result of the classification made by FLNN-ALO is compared with the standard FLNN model to examine whether the ALO learning algorithm is capable of training the FLNN network and improve its performance. From the result achieved, it can be seen that the implementation of the proposed learning algorithm for FLNN performs the classification task quite well and yields better accuracy on the unseen dat