Ocimum species (O.americanum, O.basilicum, O.gratissimum, and O.tenuiflorum) belongs to family Lamiaceae. It is also known as Tulsi. It is currently used as a traditional medicinal plant in India, Africa and other countries in the world. It is used in Ayurveda and in traditional Chinese medicine for treating different diseases and disorders like digestive system disorders such as stomach ache and diarrhea, kidney complaints, and infections, etc. Many researchers have investigated the anti-inflammatory potential of various Ocimum species and reported various activities like anti-viral, anti-bacterial, anti-hemolytic and also different phytoconstituents like essential oil, saponins, phenols, phlobatannins, and anthraquinones etc. Exploration of the chemical constituents of the plants and pharmacological activities may provide us the basis for developing new life-saving drugs hence this review may help the traditional healers, practitioners, researchers and students who were involved in the field of ethno pharmacology.
Memecylon malabaricum cogn (Melastomataceae) is an indigenous medicinal plant used in ethno medicine for the treatment of bacterial infections, inflammation and skin diseases including herpes, chickenpox. It's also a root ecbolic. The methanolic extract of Memecylon malabaricum leaves is subjected to antidiabetic activity using experimental model of alloxan induced diabetes. The results showed that the methanolic extract significantly decrease the raised blood glucose level, comparable to reference standard, gliclazide. The results of this study explicate justification of the use of this plant in the treatment of diabetes.
Abstract:In Industrial manufacturing, Quality has become one of the most important consumer decision factors in the selection among competing products and services. Product inspection is an important step in the production process. Since product reliability is most important in mass production facilities. Neural networks are used to model complex relationships between inputs and outputs or to find patterns in data. Neural networks are being successfully applied across a wide range of application domains in business, medicine, geology and physics to solve problems of prediction, classification and control. In this paper, we investigate the use of different percentages of dataset allocation into training, validation and testing on the performance of ANN in pattern recognition for process improvement using two selected training algorithms (Levenberg-Marquardt and Quasi-Newton Algorithm). The result of this paper clearly indicates that L-M algorithm has fastest network convergence rate than Q-N algorithm in production process
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