Diabetes is a chronic, metabolic disease related to the rise of levels of blood glucose. According to the current data from the World Health Organization, 422 million adults have diabetes in the world and prevalence of diabetes is 13.2%. Disregarding the diagnosis and treatment of the disease leads to some major problems on kidneys, heart and blood vessels, eyes, nerves, pregnancy and wound healing. The most common type of diabetes and usually in adults, Type 2 diabetes occurs when the body becomes resistant to insulin or does not make enough insulin. The main objective of this study is to make more successful this disease by investigating the important attributes based on assessing the importance of attributes using the Stability Selection method. The proposed method might be a powerful tool for the importance of attributes, and effective diagnosis of this disease with the classification accuracy is 78.57% and ROC value is 0.75.

An image represents millions of words which is depend on people interpretation. If image application considers for the marketing arena, then it represents to masses of peoples of different categories as per their requirements. The image-based marketing is an innovative and creative phenomenon which highly depends on geographical areas as well as on the interest of different placed habitat consumers. At recent times, the digital devices are widespread and it possesses by common peoples for completion of their daily miscellaneous needs and services. That is why, a new globe is opened for digital marketing, especially image-based marketing. Marketers jolt out traditional marketing strategies and adopt new marketing strategies to enhance productivity and sale. However, it is possible by application of a digital communication medium due to its effectiveness and efficiency. At recent times, the marketing paradigm has been shift very rapidly as per consumer mindset, for that, it is essential to switch into modern marketing technique keeping in mind of future firm prospect and its prosperity. Hence, in this research article, we discuss of how product sale grows by adopting digital marketing strategies, with that, we analyzed consumer behavior.

The huge amount of data is being created every day by various organisations and users all over the world. Structured, semi-structured and unstructured data is being created at a very rapid speed from heterogeneous sources like reviews, ratings, feedbacks, shopping details, etc., it is termed as Big Data. This data generated from different users share many common patterns which can be filtered and analysed to give some recommendation regarding the product, goods or services in which a user is interested. Recommendation systems are the software tools used to give suggestions to users on the basis of their requirements. Today no system is available for suggesting a person on how to use their money for saving, where to invest and how to manage expenditures. Few consulting systems are available which provide investment and saving tips but they are not much effective and are much complex. The presented paper proposed a collaborative filtering based recommender system for financial analysis based on Saving, Expenditure and Investment using Apache Hadoop and Apache Mahout. Many savings and investment consulting systems are available but no system provides effective and efficient recommendation regarding management and beneficial utilisation of salary. The advantage of proposed recommender system is that it provides better suggestion to a person for saving, expenditure and investment of their salary which in turns maximises their wealth. Due to enormous amount of data involved, Apache Hadoop framework is used for distributed processing. Collaborative filtering and Apache Mahout is used for analysing the data and implementation of the recommender system.

In the article internet advertising-marketing information’s dynamic changes, indicator parameters of that characterizes effectiveness of internet advertising campaign have been analysed. For evaluation of advertising activities, analysis of dynamic changes to obtain and calculation values of selective parameters have been researched. In equal time intervals obtained values from indicator parameters that is obtained with their values value matrix can be created. In order to eliminate inequal time intervals it is recommended interpolated operations should be conducted. Therefore, by selecting parameters which have statistical communication, through using experimental values of these parameters model has been established for evaluation of effectiveness of Internet advertising campaign.

Using recommender systems with the help of computer systems technology to support the Tourist advising process offers many advantages over the traditional system. A knowledge based recommender reasons about the fit between a user’s need and the features of available products. Providing an effective service in Ethiopian Tourism sector is critical to attract more foreign and local tourists. However, there are major problems that need immediate solution. First, the difficulty of getting fast, reliable, and consistent expert advice in the sector that is suitable to each visitor’s characteristics and capabilities. Second, inadequacy of the number of experienced experts and consulting individuals who can give advice on tourism issues in the country. Therefore, this paper aims to design a recommender system for tourist attraction area and visiting time selection that can assist experts and tourists to make timely decisions that helps them to get fast and consistent advisory service. So that, visitors can identify tourist attraction areas that have the highest potential of success/satisfaction and that match their personal characteristics. The system provides recommendation to visitors based on previously solved cases and new query given by the tourist. For this study, about 615 cases which are collected from National Tour operation and 10 attributes which are collected from experts are used as case base. These attributes and cases are used as knowledge base to construct case based recommender. The system calculates similarity between existing cases and new queries that are provided by the visitors, and provide solution or recommendation by taking best cases to the new query. In this study, JCOLIBRI case base development tool is used to develop the prototype. JCOLIBRI contains both user interface which enables visitors to enter their query and programming codes with the help of Java script language. To decide the applicability of the prototype in the domain area, the system has been evaluated by involving domain experts and visitors through visual interaction using the criteria of easiness to use, time efficiency, applicability in the domain area and providing correct recommendation. Based on prototype user acceptance testing, the average performance of the system is 80% and 82% by domain experts and visitors respectively. The performance of the system is also measured using the standard measure of relevance (IR system) recall, precision and accuracy measures, where the system registers 83% recall, 61% precision and 85.4% accuracy.

In most of the applications, data in multiple data sources describes the same set of objects. The analysis of the data has to be carried with respect to all the data sources. To form clusters in subspaces of the data sources the data mining task has to find interesting groups of objects jointly supported by the multiple data sources. This paper addresses the problem of mining mutual subspace clusters in multiple sources. The authors propose a partitional model using k-medoids algorithm to determine k-exclusive subspace clusters and signature subspaces corresponding to multiple data sources, where k is the number of subspace clusters to be specified by the user. The proposed algorithm generates mutual subspace clusters in multiple data sources in less time without the loss of cluster quality when compared to the existing algorithm.