The aim of the Project is to recommend music using hashtags. We recommend music using the emotions of the song. We analyze users emotion and their music choices. We extract information from hashtags by applying sentimental approach. The rawtext is preprocessed for mining and machine learning algorithm is applied. The data set is taken from Pandas and using NLTK we tokenize the words. We then combine sentimental computation and ranking of individual music track to recommend the music.

Where the placing and compaction of concrete are difficult (such as structural element jacking, filling near retaining structure) self-compacting concrete play a vital role in these condition. Self-compacting concrete (SCC) is widely used where the ability of flow and self-compaction is required. Main benefit of these concrete (SCC) is to save labour cost and minimize the construction time. In the view of materials, there is slightly different from commonly used material for construction. The coarse aggregate used in SCC is taken less i.e. up to 50 per cent. Bagasse ash is used as a partial replacement for cement. The research work is done, by considering the variation of water cement ration from 0.25 to 0.35 and the percentage of bagasse ash varies from 10 to 20 per cent. For defining the flow-ability of SCC slump test, V –funnel, U-box test and L- box test had conducted. The value of horizontal slump flow varies from 560 to 760 mm. The flow time in V- funnel test varies from 8 to 12 second is taken satisfactory.When the bagasse ash is used up to limited range from 10 to 20 per cent slump flow gradually decreases but when super plasticizer is used there will be improvement in slump flow. By the use of bagasse ash the compressive strength will also increase at lower w/c ration 0.275 and with 15% bagasse ash. When we further increase in the bagasse ash per cent there is very less increase in compressive strength.

The purpose of this project is to analyze drugs known to treat a particular disease and find other drugs that can potentially treat the same disease, with some modifications, if required. Drug discovery is an expensive process. It takes a lot of time and resources to find new drugs to treat a disease. For this process, our project can be used to predict some potential drugs to treat a particular disease and those drugs can be further analyzed so as to study their properties with respect to the disease to be treated. For this, we have considered similarity between drugs chemically as well as the shared genes that are affected by these drugs.

The rapid revolution and adoption of big data by organizations has changed the approaches for using sophisticated information technologies as well as to gain insight knowledge for proactive decisions making. This data-oriented concept is remarkable as data is generated and available easily via various living (normal users) as well as non living media (sensors, web media etc) also and is increasing exponentially at rapid pace. Due to advancement in technologies, data storage is not trouble but how to analyze data is a major issue. Taking into account analysis of data, considerable data mining techniques are association, classification, clustering and regression analysis. These techniques have position in the design phase of Decision making process. Clustering have the property to acquire knowledge from data and can be considered the best technique to improve decision making process. Existing clustering algorithms are appropriate for small data sets but for big data or real life data it is challenging task, no unique algorithm for clustering can be applied directly. Scaling, correct parameterization, parallelization, cluster validity are some problems in using clustering techniques. In consideration of all aforementioned problems, continuous efforts are being made by data mining researchers. Big data Clustering techniques are discussed in this paper with main focus on unsupervised K-means clustering algorithms and their limitations. In addition with unsupervised clustering, semisupervised clustering methods are also reviewed and

T Studies have systematically shown that twenty percent to thirty p.c of medication prescriptions square measure neer stuffed, which close to fifty p.c of medicines for chronic illness dont seem to be taken as prescribed in keeping with a review in Annals of medicine. People who do take prescription medications — whether or not its for an easy infection or a serious condition — generally take solely regarding 0.5 the prescribed doses. Add your third bullet point here. This lack of adherence is calculable to cause just about one hundred twenty five,000 deaths and at least 10 percent of hospitalizations. One of the major number of medications which are not taken time to time is diabetes. The number of individuals with polygenic disorder keeps increasing over consequent decade. Diabetes requires lifetime treatment in patients daily life. Although several patients already apprehend that tight aldohexose management may be a essential issue of success for the standard of life, they still fail to observe their glucose level in actual clinical practice because of the frequent and painful finger stick tests.

In the history of Structural Engineering, the methods available for numerical simulation include Finite Element Method (FEM), Finite Difference method (FDM) and Finite Volume method (FVM). In these numerical methods the process of grid formation for irregular geometry requires tedious mathematical transformation; and the treatment of deformation in meshing requires rezoning which is tedious & time consuming. Hence the grid based methods cannot be used to solve problems with large discontinuities and deformations. So meshfree particle method is chosen to overcome this limitation. Mesh free particle method is a particle based mesh free approach where the computational frames in MPM are moving particles in space. There is no necessity of predefined connectivity between the particles. This particle method based on the concepts of Lagrangian theory, whereas the approximation of any desired function in MPM method is carried out in two stages namely kernel and particle approximation. The resulting function from the particle approximation is numerically simulated to obtain the required functional output. Hence MPM serves as a versatile method to handle problems with highly irregular and even dynamic geometry with large deformations. Owing to these advantages, this method has been largely applied to solve different problems in Structural engineering. Typical examples include issues with free surface, deformable frame, moving interface and large deformation. This paper presents an overview on the concepts and benefits of MPM method, its recent developments and the future scope for meshfree particle methods in Structural Engineering applications.