Spotting Difficult Weakly Correlated Binary Knapsack Problems

In this paper, we examine the possibility of quickly deciding whether or not an instance of a binary knapsack problem is difficult for branch and bound algorithms. We first observe that the distribution of the objective function values is smooth and unimodal. We define a measure of difficulty of solving knapsack problems through branch and bound algorithms, and examine the relationship between the degree of correlation between profit and cost values, the skewness of the distribution of objective function values and the difficulty in solving weakly correlated binary knapsack problems. We see that the even though it is unlikely that an exact relationship exists for individual problem instances, some aggregate relationships may be observed. Key words: Binary Knapsack Problems; Skewness; Computational Experiments.

Beyond job security and money : Driving factors of motivation for government doctors in India

Background Despite many efforts from government to address the shortage of medical officers (MOs) in rural areas, rural health centres continue to suffer from severe shortage of MOs. Lack of motivation to join and continue service in rural areas is a major reason for such shortage. In the present study, we aimed to assess and rank the driving factors of motivation important for in-service MOs in their current job. Methods The study participants included ninety two in-service government MOs from three states in India. The study participants were required to rank 14 factors of motivation important for them in their current job. The factors for the study were selected using Herzberg’s two-factor theory of motivation and the data were collected using an instrument that has an established reliability and validity. Test of Kendall’s coefficient of concordance (W) was carried out to assess the agreement in ranks assigned by participants to various motivation factors. Next, we studied the distributions of ranks of different motivating factors using standard descriptive statistics and box plots, which gave us interesting insights into the strength of agreement of the MOs in assigning ranks to various factors. And finally to assess whether MOs are more intrinsically motivated or extrinsically motivated, we used Kolmogorov-Smirnov test. Results The (W) test indicated statistically significant (P < 0.01) agreement of the participants in assigning ranks. The Kolmogorov-Smirnov test indicated that from policy perspectives, MOs place significantly more motivational importance to intrinsic factors than to extrinsic factors. The study results indicate that job security was the most important factor related to motivation, closely followed by interesting work and respect and recognition. Among the top five preferred factors, three were intrinsic factors indicating a great importance given by MOs to factors beyond money and job security. Conclusion To address the issue of motivation, the health departments need to pay close attention to devising management strategies that address not only extrinsic but also intrinsic factors of motivation. The study results may be useful to understand the complicated issue of work motivation and can give some useful insights to design comprehensive management strategies that are based on motivational needs of MOs

Classification of Pathological Stage of Prostate Cancer Patients Using Penalized Splines

We propose a penalized splines based method to predict the pathological stage of localized prostate cancer. A combination of prostate specific antigen, Gleason histological score, and clinical stage from a cohort study of 834 prostate cancer patients are used to build the penalized splines model. It turns out that the proposed methodology results in improved prediction of pathological stage compared to usual logistic regression after removing a few outliers. The improvement is shown to be statistically significant. Receiver operating characteristic curve is drawn and we show that the increase in area under the ROC curve over the commonly used logistic regression based classification method is also statistically significant.

Analysis of Pancreas Histological Images for Glucose Intolerance Identification using Wavelet Decomposition

Subtle structural differencescan be observed in the islets of Langer-hans region of microscopic image of pancreas cell of the rats having normal glucose tolerance and the rats having pre-diabetic(glucose intolerant)situa-tions. This paper proposes a way to automatically segment the islets of Langer-hans region fromthe histological image of rat's pancreas cell and on the basis of some morphological feature extracted from the segmented region the images are classified as normal and pre-diabetic.The experiment is done on a set of 134 images of which 56 are of normal type and the rests 78 are of pre-diabetictype. The work has two stages: primarily,segmentationof theregion of interest (roi)i.e. islets of Langerhansfrom the pancreatic cell and secondly, the extrac-tion of the morphological featuresfrom the region of interest for classification. Wavelet analysis and connected component analysis method have been used for automatic segmentationof the images. A few classifiers like OneRule, Naïve Bayes, MLP, J48 Tree, SVM etc.are used for evaluation among which MLP performed the best

A fast Algorithm for Automatic Segmentation of Pancreas Histological Images for Glucose Intolerance Identification

This paper describes a novel fast algorithm for automatic segmentation of islets of Langerhans and β-cell region from pancreas histological images for automatic identification of glucose intolerance. Here LUV color space and con- nected component analysis are used on 134 images among which 56 are of nor- mal and rest 78 are of pre-diabetic type. The paper also talks about a supervised learning approach for classifying the images based on their morphological fea- tures. In the present work we have introduced a modern classifier weighted ELM (Extreme Learning Machine) for Prediabetes identification. Performances of weighted ELM are comparable with all the present day’s robust classifiers such as Support Vector Machines (SVM), Multilayer Perceptron (MLP) etc. We have also compared the result with traditional ELM and observed better performance in the present skewed dataset with substantial improvement in training time

Increased Mortality Rate Associated with Chikungunya Epidemic, Ahmedabad, India

A total of 3,056 excess deaths epidemiologically linked to chikungunya occurred in 2006

Open Access

Beyond job security and money: driving factors of motivation for government doctors in Indi

Comparative Analysis of Feature Extraction Techniques in Motor Imagery EEG Signal Classification

Smart Innovation, Systems and Technologies7973-8

Dimensionality reduction of EEG signal using Fuzzy Discernibility Matrix.

2017 10th International Conference on Human System Interactions (HSI)131-13