Jaypee Institute of Information Technology

About: Jaypee Institute of Information Technology is a education organization based out in Noida, Uttar Pradesh, India. It is known for research contribution in the topics: Computer science & Cluster analysis. The organization has 2136 authors who have published 3435 publications receiving 31458 citations. The organization is also known as: JIIT Noida.

Performance comparison for data storage - Db4o and MySQL databases

Abstract: Choice of right and appropriate database is always crucial for any information system. Since database is an integral and important part, we choose to write the performance analysis of different type of databases in context to health care data. Health care database consists of Electronic Health Records. Also, various Electronic Health Record standards like HL7, openEHR and CEN EN 13606 have been defined for relational, object and object-relational databases. So far, none of the standard has been defined using object-database. In order to do so, we must compare and analyze the performance of object-database over others. In this paper, firstly we have studied the current trends in Indian Health Care with respect to medical data storage and retrieval. Next, we have compared the performance of MySQL (relational) and Db4o (object) database in terms of persistence time and storage space for a sample hospital data of 100 users.

Comparison of Machine Learning Approaches in the Prediction of Terrorist Attacks

Abstract: Global terrorism means the use of intentionally indiscriminate and illegal force and violence for creating terror among masses in order to acquire some political, monetary, religious or legal goals. Identification of these ideologies and prediction of future attacks has proven to be of the greatest importance but is time-consuming. This paper focuses on analyzing the historical dataset of Global Terrorism Database and predicting the factors that might give blow to an increase of terrorism. Various Data mining techniques and machine learning algorithms like Support Vector Machine, Random Forest, and Logistic Regression etc. have been used to analyze the dataset and carry out predictions like the success of a particular attack, predict the group that carried out an attack and effect of the external factors. A detailed comparison for each algorithm is carried out to attain the most significant results.

New paradigm of digital marketing in emerging markets: from social media to social customer relationship management

Abstract: The purpose of this paper is to explore the usage of Facebook as one of the biggest tools in the promotion of Social Customer Relationship Management. In the last few years, the organisations in the emerging markets like India have witnessed an explosive growth in the tactic and process of building quality social and professional networks and further leveraging them for customer retention and achieving the goals of Social CRM, namely social engagement and social collaboration. Facebook is the most popular social media. It figures out the niche and with proper investment of time and resources via tools and technology provides its customers the best service. It has come out to be one of the biggest tools in promotion of Social CRM. This research paper would primarily focus on the Social CRM strategies adopted by Facebook for improving the organisation-customer interfaces, which also serve as a persuasive technology in business.

Auto Tuning of RNN Hyper-parameters using Cuckoo Search Algorithm

Abstract: Long Short Term Memory based Recurrent Neural Network (LSTM+RNN) with additional capability of learning long term dependencies in sequential data is an effective model for analyzing time series data. The hyper-parameters play a crucial role in obtaining an optimized learning model for dataset in hand. The comprehensive studies conducted on selecting the network hyper-parameters have tried limited number of combinations and mark this as a limitation. Further, once optimized for a dataset in hand, the model may not be effective for another dataset. This requires auto tuning methods for selecting hyper-parameters. In this paper, meta-heuristic algorithm named Cuckoo Search has been utilized to find suitable heuristics for auto tuning the hyper-parameters of a RNN+LSTM network. The average accuracy of the models optimized for the experimental datasets are 96.3%.

Applying data mining techniques to standardized electronic health records for decision support

Abstract: Data Mining is very popular in today's era because it provides access to the information that is hidden from a normal human being eye. The hidden information that is made available through data mining can benefit the person involved by providing an efficient decision support. Today data mining can be applied to various areas such as marketing, banking, aerospace and healthcare. It is identified that providing decision support in healthcare domain can help in saving human life. Although providing decision support through data mining in healthcare is very beneficial but it lacks standardization. A comparison of benefits gained from applying data mining techniques to standardized and non-standardized EHRs is provided. This paper aims to indentify the need of applying data mining techniques to standardized electronic healthcare records. It interrogates various issues that need to be resolved for providing an efficient standardized decision support system.