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Aim: To assess Health-related Quality of Life (HRQOL) in patients with chronic disease. Method: A community-based prospective, observational, multicentre study was conducted by home-to-home survey for 4 months. Subjects ageing 35 years or more and suffering from either Hypertension, Angina, Coronary Artery Disease, Myocardial Infarction, Hyperlipidaemia, or Diabetes Mellitus were enrolled. This data was collected by face-to-face interviewing participants using the SF- 36 questionnaire. Result: In our study 300 participants were enrolled. The majority of participants belonged to the age group of 55-64 years. Nearly half of the them belonged to the rural area (58.33%), and cardiovascular disease was highly prevalent among our study participants (56.67%). The Physical functioning (PF) domain had the lowest (67.08 ± 25.73) and the social functioning (SF) domain had the highest mean score (90.47 ±15.60). HRQOL scores declined with the increase in age. Conclusion: Chronic diseases impairs physical health, and mental health was good in our study participants. As the duration of the disease increased the HRQOL score decreased and comorbidities deteriorates HRQOL.

Handwritten character recognition is a challenging area of research. Lots of research activities in the area of character recognition are already done for Indian languages such as Hindi, Bangla, Kannada, Tamil and Telugu. Literature review on handwritten character recognition indicates that in comparison with other Indian scripts research activities on Gujarati handwritten character recognition are very less. This paper aims to bring Gujarati character recognition in attention. Recognition of isolated Gujarati handwritten characters is proposed using three different kinds of features and their fusion. Chain code based, zone based and projection profiles based features are utilized as individual features. One of the significant contribution of proposed work is towards the generation of large and representative dataset of 88,000 handwritten Gujarati characters. Experiments are carried out on this developed dataset. Artificial Neural Network (ANN), Support Vector Machine (SVM) and Naive Bayes (NB) classifier based methods are implemented for handwritten Gujarati character recognition. Experimental results show substantial enhancement over state-of-the-art and authenticate our proposals.

This paper proposes a novel approach for recommending social resources using learning from positive and unlabeled examples. Bookmarks submitted on social bookmarking system delicious1 and artists on online music system last.fm2 are considered as social resources. The foremost feature of this problem is that there are no labeled negative resources/examples available for learning a recommender/classifier. The memory based collaborative filtering has served as the most widely used algorithm for social resource recommendation. However, its predictions are based on some ad hoc heuristic rules and its success depends on the availability of a critical mass of users. This paper proposes model based two-step techniques to learn a classifier using positive and unlabeled examples to address personalized resource recommendations. In the first step of these techniques, naïve Bayes classifier is employed to identify reliable negative resources. In the second step, to generate effective resource recommender, classification and regression tree and least square support vector machine (LS-SVM) are exercised. A direct method based on LS-SVM is also put forward to realize the recommendation task. LS-SVM is customized for learning from positive and unlabeled data. Furthermore, the impact of feature selection on our proposed techniques is also studied. Memory based collaborative filtering as well as our proposed techniques exploit usage data to generate personalized recommendations. Experimental results show that the proposed techniques outperform existing method appreciably.