AES-Based Encoding and Decoding Images using MATLAB
A Novel Technique of Sign Language Recognition System using Machine Learning for Differently Abled Person
Implementation of Machine Learning Techniques for Depression in Text Messages: A Survey
A Study of Ransomware Attacks on Windows Platform
Techniques of Migration in Live Virtual Machine and its Challenges
Efficient Agent Based Priority Scheduling and LoadBalancing Using Fuzzy Logic in Grid Computing
A Survey of Various Task Scheduling Algorithms In Cloud Computing
A Viable Solution to Prevent SQL Injection Attack Using SQL Injection
A Computational Intelligence Technique for Effective Medical Diagnosis Using Decision Tree Algorithm
Integrated Atlas Based Localisation Features in Lungs Images
Data mining is used to extract meaningful information and develop significant relationship among variables stored in large data. Data mining techniques can be applied in various fields like microbiology, bio-informatics, medical imaging, finance, healthcare, education, etc. Education is the one of the fields where we can apply data mining algorithms to find unidentified patterns. An educational organization is one of the most important part of a society that plays a vital role in growth and development of any nation. The main objective of higher education institution is to provide quality education to their students. This paper predicts the relationship between post-graduation and research enrollment of students. In this paper, the authors present a model in context of higher education admission. In this paper, student enrollment for post-graduation and research from various higher education providers of UK for 5 consequent academic years (2009-2014) has been taken as the data set. Based on candidate enrollment, association rule mining has been applied and classified to show the variations in admission. Based on findings, decision makers from Higher Education System (HES) can frame norms to improve students enrollment.
Data mining is a technology which is used to find interesting pattern between huge datasets. Commodity market is said to be a huge collection of various commodities, Gold, Oil, etc, which are referred to as hard commodities. In ancient days, gold coins were a medium of exchange. Another important commodity is oil. The price of oil changes daily, which has an impact on every goods and services provided. A country can make a payment via paper currency. This mode can be changed to exchange of gold at a fixed rate. The exchange rate between currencies was based on the amount of currencies needed to purchase one ounce of gold. US dollar is widely accepted as an instrument of global currency exchange. The gold price is directly related to USD. This paper examines the relationship between the rate of gold and oil with respect to USD. This also explores the commodity Market based on USD. The price of Gold, Oil and the US dollar share different relationships, in different circumstances. This paper explores the interesting pattern that exists in the commodity market.
The increasing use of multicast applications on the internet, has an urgent need for the high speed router/switches, which handles the multicast traffic efficiently. The core component of the router is the switch fabric. The scheduling algorithm which configures the switch fabric to arbitrate and transfer the cells between the input and output ports. Most of the existing multicast scheduling algorithms performed well under uniform traffic, but failed to achieve maximum throughput under non-uniform traffic. In this paper, the authors have proposed a multicast scheduling algorithm called Enhanced Multicast Due-Date Round Robin Scheduling Algorithm (E-MDDR), which got the improved throughput compared with Multicast Due-Date Round Robin Scheduling algorithm (MDDR). Since E-MDDR computes the residue of the cells waiting until M cell times and those cells are declared as the emergency cells, which are transferred between every M time slot for an NxM switch. So that it achieves the improved throughput and minimum delay under non-uniform Bernoulli and bursty traffic patterns.
These researchers trace a few of the modern development in the field of learning imbalanced data. Review approaches were adopted for this problem and it identifies challenges and points out potential directions in this comparatively new field. In medical province, data features frequently contain missing values. This can make grave bias in the logical modeling. Characteristic standard data mining methods often produce poor performance measures. In this paper, the authors proposed a new method to concurrently classify large datasets and decrease the belongings of missing values. The proposed method is based on a multilevel structure of the cost-sensitive ASVM (Adaptive Support Vector Machine) and the probable maximization charge method for missing principles, which learn the breakdown analysis of excessive dataset. Thus the authors developed the PBI2D- (Priority Based Intelligent Imbalanced Data Classification) of HealthCare data with missing values to produce contrast classification results of multilevel ASVM-based algorithms on public benchmark datasets with imbalanced classes and missing values as well as real data in health applications. This method produces fast, more accurate and robust classification results.