Blockchain Scalability Analysis and Improvement of Bitcoin Network through Enhanced Transaction Adjournment Techniques
Data Lake System for Essay-Based Questions: A Scenario for the Computer Science Curriculum
Creating Secure Passwords through Personalized User Inputs
Optimizing B-Cell Epitope Prediction: A Novel Approach using Support Vector Machine Enhanced with Genetic Algorithm
Gesture Language Translator using Morse Code
Efficient Agent Based Priority Scheduling and LoadBalancing Using Fuzzy Logic in Grid Computing
A Survey of Various Task Scheduling Algorithms In Cloud Computing
Integrated Atlas Based Localisation Features in Lungs Images
A Computational Intelligence Technique for Effective Medical Diagnosis Using Decision Tree Algorithm
A Viable Solution to Prevent SQL Injection Attack Using SQL Injection
The authors have introduced the idea of architectural design validation based on human behavior analysis in a virtual reality environment. At present most architectural designs are not being validated for the clarity of navigation from the customer perspective, and in cases where verification is obtained; testing is conducted after finishing the construction of the facility, where design modification is relatively more expensive and time consuming. The authors have introduced the idea of design validation in early stages of the projects, and the authors have achieved good results in this approach.
This paper focuses on preventing Unknown Malware attack by using Intelligence Intrusion Multi Detection Prevention System (IIDPS). It describes the state's overall requirements regarding the acquisition of preventing unknown malware with the help of mathematical scheme (Euler Diagram, Allen Algebra) and a few models with newly designed algorithm. It is designed to provide a deeper understanding of existing intrusion detection principles with intelligence strategies, that will be responsible for acquiring unknown malware. It compares the false positive rate and false negative rate, which will be proven by conducting different experiments with WEKA simulation.
Generally, Information Extraction (IE) is concentrated on fulfilling exact, restricted, pre-specified solicitations from homogeneous corpora (e.g., extract the area and time of courses from a set of declarations). Information Extraction (IE) customarily relied with comprehensive individual engagement by means of hand crafted extraction guidelines as well as hand-tagged instruction illustrations. Furthermore, the user is required to clearly pre specify about every relative associated with attention. Data extraction in information control is usually associated with the artificial thinking ability throughout, and the progress associated with methods as well as algorithms for those aspects of words evaluation, as well as their laptop or computer enactment. Moving to another space requires the client to name the target relations and to physically make new extraction tenets or hand-label new preparing cases. This difficult work scales directly with the quantity of target relations. This paper, explains extraction strategy for site data focused around DOM to enhance the seeking proficiency, which is to safeguard the topic data, and to channel out the commotion data that the clients are not inspired by. The experiments are done by taking different data sets. The proposed semantic clustering gives the best way to extract the information from web than existing techniques. The experimental results clearly show that the proposed technique gives better results when compared to existing techniques.
Now-a-days humankind suffer from many health complications. People are affected by progressive diseases (like as Heart, Diabetes, AIDS, Hepatitis and Fibroid) and their complications. Data mining (also known as knowledge discovery) is the process of summarizing the data into useful information by analyzing data from different perspectives. Data Mining is a technology for processing large volume of data that combines traditional data analysis methods with highly developed algorithms. Data mining techniques can be used to support a wide range of security and business applications such as work flow management, customer profiling and fraud detection. It can be also used to predict the outcome of future observations. Data mining techniques can be developed by the Decision Tree Algorithm. According to a recent survey of the World Health Organization (WHO), all diseases and its complications are problematic health hazards of this century. A better and early diagnosis of disease may improve the lives of all people affected and people may lead healthy lives. In this paper, the authors present the Decision Tree Algorithm for better diagnosis of diseases using Association Rule mining. Using this computational intelligence technique the authors tested the performance of the method using disease data sets. The authors presented a better algorithm which is used to calculate sensitivity, specificity, comprehensibility and rule length. This gain and gain ratio achieved has promising accuracy.
The increased use of the internet today can be attributed to increase in population. Protecting data in many times tougher than securing valueables. Especially, in data transmission between user and centralized server it is difficult to protect data from attackers; to predict attackers, users must cross verify many times, and both user and server must be mutually authenticated. The authors studied the previous research work and they introduced methods for mutual authentication but still the previous method is vulnerable from attacks like denial of service attack, password guessing, masquerade, etc. The authors have done cryptanalysis on previous research work and come to know that still their method is vulnerable and therefore the authors have proposed a new method of ticket based mutual authentication between user and server. Finally, in this paper the authors have done security analysis and explain how this method predicts attacks which are possible in the existing method.
