FORECASTING THE TIME DELAY IN DELIVERY OF PHARMACEUTICAL PRODUCTS

Supply chain management system is a centralized system which controls and plans the activities involved from production to delivery of a product. Disruption in treatment and loss of life occurs due to delay in delivery of pharmaceutical products. The objective is to do a model using Machine learning algorithms to determine: Classification to predict which product will be delayed and Regression shows how much time it will be delayed exactly. This study will use publicly available supply chain data which helps to identify primary aspect of predicting whether HIV drugs are delivered in time or not. It will then use these factors to predict how long delays are likely to be, thus allowing HIV/Supply Chain program managers to know details of the products which are going to be delayed and quantify the exact delay. and how much it will be delayed. so that they can take mitigating action to save lives and avoid additional supply chain costs. We will use Machine learning prediction model to predict which product will be delayed and regression model shows how much time it will be delayed exactly

Fortune of smart-phones by A model recommendation

In recent market, there are several cell phones available, Smart phones differ based on their Operating system. Here we are going to do a comparison between two Operating systems i.e., “IOS and ANDROID”. The comparison starts including the basic features of smart phones. The features are varying from each other. Some of them are categorical and some are numerical. According to these data, classify the smart phones using machine learning classification model. After the classification we are going to analyse which operating system-based Smartphone will be taken for further classification. A “Recommendation system”, will be designed which recommend a better smart phone to the customer. In market a lot of smart phones are available, which are of different companies but with same cost. From classification model we will find which set is more affordable with good combination of features. Further Recommendation model will help us to find which model will be the best model according to the customer requirement and budget. On the basis of customer requirement that is what are the features and price of the phone our model is going to predict which model will be more suitable and gives the solution in a form of recommendation. It will give us the exact phone, which is having all the features and also pocket friendly

Sequence Alignment and Phylogenetic Tree Construction of Malarial Parasites

Sequence alignment is one of the basic problems in computational biology that has helped researchers analyze biological sequences. The analysis has helped biologists to detect pathogens ;to develop drugs, and to predict the secondary and tertiary structure of a protein and identity common genes. The objective of the Phylogenetic tree is to determine the branch length and to figure out how the evolutionary tree has been generated . One way to tackle MSA is to use Hidden Markov Models (HMMs), which are known to be very powerful in the related problem domain of speech recognition. The fully trained model is applied to draw a valid conclusion about the evaluation of malarial parasites

Prediction of Sub-cellular Localization of Scramblase Protein Family

In the present work, we discuss an anaysis about the localization of different members of scramblase protein family. Different scramblase sequences were picked up from organisms of all eukaryotic phyla and their localization were predicted using the P-SORT programme. Our analysis showed that the scramblase protein family shows multiple subcellular localization. Most proteins were found to be localized to the cytoplasm, where as others were found to be present in the nucleus or mitochondria. Interestingly, we found that in yeast, all putative scramblases were localized in the nucleus with a reliability of more than 95%. Our analysis shows that scramblases are a family of protein having diversed cellular localization and hence hypothesized to be performing multiple cellular functions in various organism

Functional Analysis of Artificial Neural Network for Dataset Classification

Classification is one of the most active research and application areas of artificial neural networks (ANN). One of the difficulties in using ANN is to find the most suitable combination of training, learning and transfer function for classification of data sets with increasing number of features and classified sets. In this paper we have studied the effect of different combinations of functions while using artificial neural network as a classifier and analyzed the suitability of these functions for different kinds of datasets. The appropriateness of the proposed work has been determined on the basis of mean square error, rate of convergence, and accuracy of the classified dataset. Our inferences are based on the simulation results over the datasets used.

Colistin the last resort drug in 21st century antibiotics to combat Multidrug resistance superbugs

Polymyxin' E' (Colistin) is considered the last resort therapy against Multidrug resistance (MDR) bacteria, mainly Klebsiella peumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii, and Escherichia coli and play a critical role in causing life-threatening infection, and their prevalence is increasing as a big concern globally. Apart from immunological adaptation, chromosomal mutations and plasmid-mediated genes are mostly associated with this resistance at the molecular level. Therefore, the current review extensively focused on Colistin as a drug in 21st-century antibiotics, the activities spectrum with diverse resistance mechanisms of bacteria against Colistin, and emerging approaches of Colistin from discovery to tackling MDR. In the study, we got to know about the challenges and new developments with old weapons like phage therapy as well as new approaches like Phage display and drug repurposing, in addition to the chromosomal and plasmid-mediated genes that play a role in antimicrobial resistance (AMR). The present study would provide insight into the prognostic aspect of combating MDR

Microarray Analysis Using Statistical Approach

Over the past few decades rapid developments in genomic and other molecular research technologies and developments in information technologies have combined to produce tremendous amounts of information related to molecular biology. It is not possible to research on a large number of genes using traditional methods. DNA Microarray is one such technology which enables to monitor the expression levels for tens of thousands of genes in parallel . A common task with Microarray data is to determine which genes are differentially expressed between two samples obtained under two different conditions. To solve this problem several Statistical methods have been proposed. The Support Vector Machine is one of the most efficient & widely used statistical method for Microarray classification. In this paper we have classified leukemia dataset by using support vector machine under two conditions and also showed the performance of different type of kernels