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A new impulsive noise removal filter, adaptive dynamically weighted median filter (ADWMF), is proposed. A popular method for removing impulsive noise is a median filter whereas the weighted median filter and center weighted median filter were also investigated. ADWMF is based on weighted median filter. In ADWMF, instead of fixed weights, weightages of the filter are dynamically assigned with the results of noise detection. A simple and efficient noise detection method is also used to detect noise candidates and dynamically assign zero or small weights to the noise candidates in the window. This paper proposes an adaptive method which increases the window size according to the amounts of impulsive noise. Simulation results show that the AMWMF works better for both images with low and high density of impulsive noise than existing methods work.

The increasing use of social media and information sharing has given major benefits to humanity. However, this has also given rise to a variety of challenges including the spreading and sharing of hate speech messages. Thus, to solve this emerging issue in social media sites, recent studies employed a variety of feature engineering techniques and machine learning algorithms to automatically detect the hate speech messages on different datasets. However, to the best of our knowledge, there is no study to compare the variety of feature engineering techniques and machine learning algorithms to evaluate which feature engineering technique and machine learning algorithm outperform on a standard publicly available dataset. Hence, the aim of this paper is to compare the performance of three feature engineering techniques and eight machine learning algorithms to evaluate their performance on a publicly available dataset having three distinct classes. The experimental results showed that the bigram features when used with the support vector machine algorithm best performed with 79% off overall accuracy. Our study holds practical implication and can be used as a baseline study in the area of detecting automatic hate speech messages. Moreover, the output of different comparisons will be used as state-of-art techniques to compare future researches for existing automated text classification techniques.

Introduction: Lipid and thyroid function abnormalities are common in IDDM and NIDDM. Very few studies have addressed this issue in Bangladesh though Bangladeshi population is very much susceptible to patient with diabetes. Aims: To study on lipid profile and thyroid function in IDDM and NIDDM and the effect of glycemic control on it. Patients and Methods: This was a retrospective study carried out in the Dept. of Endocrinology, BIRDEM, Dhaka, Bangladesh during the period of January, 2012 to May, 2012. In this study, population consisted of 120 subjects (Age between 40-72 years; and Sex matched) divided into two groups: patient with diabetes 60 subjects (male-30, female-30) and patient without diabetes 60 subjects (male-30, female-30). Plasma glucose, HbA1c and serum lipids were measured by enzymatic method. Thyroid hormones were measured by a Chemiluminescent Micro particle Immunoassay (CMIA). Results: The statistical significance was evaluated by Student's t-test, Correlation-Coefficient test. All Values are given as mean ± SD. The level of serum TSH in patient with diabetes (3.43 ± 2.71) was significantly (p < 0.05) increased compared to patient without diabetes subjects (1.98 ± 1.72). TSH levels were positively correlated with fasting plasma glucose (r = 0.240, p < 0.05), serum cholesterol (r = 0.290, p < 0.020) and triglyceride concentration (r = 0.246, p < 0.05). On the other hand, free T 4 levels were inversely correlated with postprandial blood glucose (r = −0.256, p < 0.046). Conclusions: It may be concluded that the lipid and thyroid function abnormalities with others socio-demographic and biophysical risk factors were more common in patient with diabetes cases rather than patient without diabetes cases. Therefore, further prospective studies with larger number of patients are required to strengthen the observations of the present study.

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