Every year many women die of breast cancer and the saddest part is that most of them die due to late diagnosis. A number of studies have been carried out to explore the basic reasons of breast cancer. Unfortunately, most of them failed to detect the main causes and the disease itself at a primary stage. On the other hand, it has already been proved that early detection of cancer can give the patient a chance of longer survival. Therefore, early detection of breast cancer is very crucial to save a patients' life. To address this problem, we have introduced a hybrid model to identify breast cancer at primary stage. In this model, the first part includes data mining using decision tree algorithm. The second part includes an autonomous agent that takes decision based on predefined rules to detect breast cancer at the very beginning stage. These rules are deduced through a data mining tool (i.e. Weka).The autonomous agent has been developed using Java, which works in collaboration with human. In this research work, we mostly focused on creating an adjustable autonomous agent and setting its rules and behaviors effectively. The performance of the proposed hybrid model has been tested on breast cancer dataset collected from UCI (University of California, Irvine) machine learning repository. The study reveals that autonomous agent works better when it collaborates with human as a team member. In addition, this hybrid model can be used to assist medical practitioners to provide better treatment to the patients.
Abstract. Malnutrition is both over nutrition and under nutrition. As in Bangladesh the rapidity of under nutrition is too elevated, although slow, the rate of overweight and obesity in children and women is also rising. Under-nutrition results from micronutrient including essential vitamins and minerals deficiencies and both macro (protein energy deficiency). Malnutrition is anticipated to be causes of about 60% of maternal childhood and deaths in Bangladesh. Development in overall nutritional condition has been sluggish over the years. Rate of feasting and underweight both remain inappropriately very high in the country still.
Investigated whole plant extracts of Euphorbia hirta L were used for it possible phytochemistry as well as thrombolysis effect by using its water, ethyl acetate and ethanolic fractioned extracts. Pharmacological history of this plant promoted us to check the possible thrombolysis activities. This article demonstrated the thrombolytic activities of various extracts of Euphorbia hirta L. Whereas ethanol, ethyl acetate and water extracts demonstrated clot lysis 25.81%, 14.17% and 30.48 % respectively. Different conformity tests of the crude extract demonstrated that Euphorbia hirta L consists of various types of glycosides, alkaloids, tannins, flavonoids, triterpenoids etc. In-vitro Euphorbia hirta lyses blood clots. Once found Euphorbia hirta may be integrated as a thrombolytic moiety for the treatment for atherothrombotic diseases. However, the present in-vitro biological evaluation of this plant forms a primary platform for further phytochemical and pharmacological studies. These potential extracts may play vital roles for discovery new clinically effective bioactive compounds. Further investigation might be elucidating, identification of cellular and molecular mechanism of action and purification of active compounds and binding capacity of active molecules with receptors.
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