Cross Language Information Retrieval is a subfield of Information Retrieval that deals with retrieving relevant information stored in a language different from the language of user's given query. It plays a vital role in future because large volume of information is stored in the web is in English. So there is a necessity of proper mechanisms that can retrieve some required relevant information in a collection of information or in a database. Generally the collection of stored information may not be necessarily in one language. The simplest way to search for the information is to scan every item in the database and when the need to translate the languages being used arises, and then there will be a need of developing Cross Language Information Retrieval systems will take place. This paper reviews some of the recent research methods focus on topics in cross language information retrieval and there great role in On-going latest innovative research directions in wide area of information retrieval.

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Every tissue of human body needs energy and oxygen for its livelihood. In order to supply energy and oxygen, the heart pumps the blood around the body. When heart pushes the blood against the walls of arteries, it creates some pressure inside the arteries, called as blood pressure. If this pressure is more than the certain level we treat it as high blood pressure (HBP). Nowadays HBP is a silent killer of many across the globe. So here we proposed a new data-driven computational model to predict HBP. Blood Pressure (BP) may be elevated because of many changes such as physical and emotional. In the proposed model we have considered AAA++ (age, anger level, anxiety level, obesity (+), blood cholesterol (+)), for experimental analysis. Our model initially calculates the correlation coefficient (CC) between each risk factor and class label attribute. Then based on the impact of each risk factor value and CC, it assigns the corresponding weight to it. Then proposed model uses risk factor value and its weight to predict whether person becomes a victim of HBP or not. We have used real-time data set for experimental analysis. It consists of 1000 records, which are collected from Doctor C, a Medical Diagnostic center, Hyderabad, India.

High Blood Pressure (HBP) is a major health challenge of many around the world. Existing research covers extensively how to treat HBP, but predicting HBP in advance based on biological and psychological parameters of a person is not covered in the literature. The objective of this paper is to predict HBP based on Bio-Psychological factors of a person. Methods: We proposed an intelligent Rule-based classifier to predict HBP. The proposed model can be used to prevent HBP rather than using medication. In our approach, we considered AAA++ (Age, Anger level, Anxiety level, Obesity level (+), Cholesterol level (+)) of a person for experimental study. The proposed approach uses priority-based apriori rule pruning (PARP) classifier, which works in 3 stages. Stage 1: generate association rules using apriori. Stage 2: it uses the priority of an attribute to prune the association rules generated in stage 1. Step 3: Rules extracted in stage 2 are used to build a rule-based classifier to predict the class label of test instances. The Results of the proposed model are compared with JRip, PART, OneR and, ZeroR. Results: Experimentation is done on real-time data set using 10 fold cross-validations. In each fold, 90% data is used to train the model and 10% is used to test the model. The proposed approach has shown improved accuracy (86.4%) and reduced mean length of a rule (1.7) compared to existing rule-based algorithms. Although JRip is good at accuracy (86.9%), but the proposed model has outperformed at the mean length of the rule (1.7). Conclusion: The extracted rules after experimentation are understandable and informative to the technical and nontechnical community to predict HBP.

Loan delinquency prediction is one of the most critical and crucial problems faced by financial institutions and organizations. It is a remarkable effect which could result in the demolition of the profitability rate that leads to the shattering of the organization. Delinquency is a condition that arises due to the failure of payment of loans by the borrowers, which shows tremendous effect on the evolution of financial institutions. It requires more authentications to track the periodic repayments of debts and adaptation of strategies that helps in proliferating the institutions. Based on the details like date of issuance, pay-back time, amount, account details, credit score further issuance of loan is assured this helps in disseminating the delinquency problem.