Covid-19 pandemic has seriously affected the mankind with colossal loss of life around the world. There is a critical requirement for timely and reliable detection of Corona virus patients to give better and early treatment to prevent the spread of the infection. With that being said, current researches have revealed some critical benefits of utilizing complete blood count tests for early detection of COVID-19 positive individuals. In this research we employed different machine learning algorithms using full blood count for the prediction of COVID-19. These algorithms include: “K Nearest Neighbor, Radial Basis Function, Naive Bayes, kStar, PART, Random Forest, Decision Tree, OneR, Support Vector Machine and Multi-Layer Perceptron”. Further, “Accuracy, Recall, Precision, and F-Measure” are the performance evaluation measures that are utilized in this study.

The proper prognosis of treatment response is crucial in any medical therapy to reduce the effects of the disease and of the medication as well. The mortality rate due to hepatitis c virus (HCV) is high in Pakistan as well as all over the world. During the treatment of any disease, prediction of treatment response against any particular medicine is difficult. This paper focuses on predicting the treatment response of a drug: “L-ornithine L-Aspartate (LOLA)” in hepatitis c patients. We have used various machine learning techniques for the prediction of treatment response, including: “K Nearest Neighbor, kStar, Naive Bayes, Random Forest, Radial Basis Function, PART, Decision Tree, OneR, Support Vector Machine and Multi-Layer Perceptron”. Performance measures used to analyze the performance of used machine learning techniques include, “Accuracy, Recall, Precision, and F-Measure”.

For past couple of years agile software methods have been quite popular among the researchers. Agile models are known as light weight in contrast with conventional software development methodologies, due to their casual, versatile and adaptable style. Agile frameworks became heartily accepted by the software society in view of their concentration towards timely software conveyance, product quality and user satisfaction. For the fulfillment of requirements and needs of different software projects multiple agile frameworks are present to choose from. Out of these models Extreme Programming and Scrum are the most recognizable and generally utilized frameworks. This research contributes by investigating these two frameworks thoroughly. This paper conducts a comprehensive comparison between Scrum and Extreme programming to track down their commonalities, dissimilarities and investigate those attributes which complement each other.

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The aim of the present study was to estimate the prevalence of telogen effl uvium (TE) and to evaluate the effi cacy of vitamin D in the treatment of this problem in women belonging to various cities of south Punjab, Pakistan. In the present study, 40 adult women suffering from the problem of TE were included. Each woman was treated with oral vitamin D 3 (200,000 IU) therapy fortnightly and a total of 6 doses were given to each patient. After 15 d of the last dose, the condition of patients was assessed clinically. The mean age of female patients was 32.2Ϯ1.5 y, 42.5% of the patients between 21-30 y of age were found to be more frequently affected with TE compared to 35% females of 31-40 y of age. Results showed signifi cant improvement in hair growth in young (rϭ0.457 pϽ 0.003) women and in those, which do not use sunscreen (rϭϪ0.331 pϽ 0.037) but commonly utilize milk or milk protein (rϭϪ0.311 pϽ0.051). Vitamin D3 therapy resulted in the improvement of the condition in 82.5% (pϽ0.001) patients of TE. The use of oral vitamin D3 (200,000 IU, fortnightly) for 3 mo resulted in signifi cant improvement in hair regrowth in the patient of TE. Results showed improvement in hair growth in young women those do not use sunscreen but commonly utilize milk or milk protein.