Preventing Cyber Attacks using Artificial Intelligence
Exploring Advantages and Challenges of Chatbot Integration in Human-Robot Interaction
Counterfeit Currency Detection using Machine Learning
Artificial Intelligence use in Lung Cancer Screening to Improve Patient Recovery
An Analysis of Various Crypto Coins and their Suitability for Real-Time Applications
Design and Evaluation of Parallel Processing Techniques for 3D Liver Segmentation and Volume Rendering
Ensuring Software Quality in Engineering Environments
New 3D Face Matching Technique for an Automatic 3D Model Based Face Recognition System
Algorithmic Cost Modeling: Statistical Software Engineering Approach
Prevention of DDoS and SQL Injection Attack By Prepared Statement and IP Blocking
The security of email can be considered one of the important issues for scientific research since the nineties of the last century. This is mainly comes from the wide use of e-mail for exchanging various kinds of information especially that some of them are important or sensitive. Although there have been several solutions offered to solve this problem but we still facing the fact that most email messages sent so far have been without any security. The main reason behind this is that, the previous systems relied on the traditional public key cryptography were so complicated from usability point of view for most users. In this work, we exploit the use of Identity-Based Cryptography (IBC) for solving this usability problem. Indeed, to further increase the system strength, IBC has been combined with mediated RSA cryptography. Our proposal includes the deployment of the two promising hybrids of mediated IBC. In both of these hybrid cryptographic systems, all operations of encryption/decryption and signature/verification have been considered. The proposed system has met the design objectives either totally or partially. We beileve that our proposed hybrids for mediated IBC can be very helpful in simplifing the use of e-mail security so that to increase the number of users of such systems.
In software development, testing plays an important role. Software testing is an important phase that ensures the quality of the software. This paper proposes a heuristic technique to test the software at the initial stage itself so that it will be easy for software testers to test the software in the later stages. Here test cases are an important entity or criteria by which software is being evaluated. Though Test cases can be generated by various approaches, Unified Modeling Languages attracts the recent researches and industrialists. This paper focus on test case generation by means of UML Activity diagram using Genetic Algorithm which best test cases are optimized and the test cases validated by prioritization. The test cases generated using our approaches are capable of detecting more faults like synchronization faults, loop faults unlike the existing approaches A case study is used to illustrate the approach.
Present days, various practical queuing systems extensively used in computing and communication have finite capacities and in such systems, servers are prone to failures. Queuing networks are widely used in the modeling of transaction processing systems, and their interactions among nodes in communication networks. The performance modeling of a multi-node system, with heterogeneous nodes, each node serving external as well as routed internal arrivals of jobs is considered in this paper. Results obtained using the analytical model are analyzed.
Today, many businesses such as banks, insurance companies, and other service providers realize the importance of Customer Relationship Management (CRM) and its potential to help them acquire new customers retain existing ones and maximize their lifetime value. Data mining gives an opportunity, uses a variety of data analysis and modeling methods to specific trends and relationships in data detection. This helps to understand what a customer wants and anticipate what they will do. In this paper we examines, the application of k-means clustering and classification decision tree J48 algorithm of data mining on CRM in the case of EFT of POS service of the Dashen Bank S.C. These have been discovered within the framework of CRISP-DM model. The results demonstrate the final dataset consists of 110000 records in which different clustering models at k values of 6, 5, and 4 with different seed values have been traced and evaluated against their performances. Thus, the cluster model at k value of 6 with default seed value has shown a better performance by using Weka-3-7-2 tool.
Data mining is the process of extracting patterns from data. Basically Data mining is the analysis of observational data sets to find unsuspected associations and to sum up the data in new ways that are both clear and useful to the data owner .It is seen as an increasingly important tool by modern business to transform data into business intelligence giving an informational advantage. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. The review paper discusses few of the data mining techniques, algorithms and some of the organizations which have adapted data mining technology to improve their businesses and found excellent results. Data mining tools can answer business questions that traditionally were too time consuming to resolve. Data mining is becoming increasingly common in both the private and public sectors. Industries such as banking, insurance, medicine, and retailing commonly use data mining to reduce costs, enhance research, and increase sales. To be successful, data mining still requires skilled technical and analytical specialists who can structure the analysis and interpret the output that is created.
