eGovernment application has evolved from simply appearing as a website providing news and information, to the application that provides various services through online transactions. Provision of online transactions require the effectively and safely service, so the users can make sure that the entry data is secure and will not be used by other parties which are not authorized. Security is also necessary for the service provider side of online transactions, it is necessary to keep the system, and the data transactions sent through the communications media and the data stored in the database are secured. If the security guarantee is unavailable, the users will gradually lose their confidence to use the service through online transactions by using eGovernment application, and on the other hand the service provider will face the impact of the lost or corrupt data, and unauthorized access to the data which should be only accessed by involved parties based on the appropriate authority. This paper reveals the proposed strategy of Pretty Good Privacy implementation in protecting the security data messages of the Official Scripts Electronic Applications in a Local Government Regency. The proposed strategy is done by modifying on four process document letters’ management, such as an electronic form of incoming mail, print or hardcopy incoming letters, disposition letter, and outgoing mail.

The cloud computing features aim at providing security and confidentiality to its customers. The business officials have gained maximum profit using the cloud environment. Therefore the critical data moving to and from the cloud server must be secured. The security and confidentiality issue is the most critical challenge that we are facing in today’s world of the cloud environment. The business officials have gained maximum profit accessing the cloud environment. Before the disaster occurrence, the critical data moving to and from the cloud must be preserved. The privacy preservation and cloud security issue is the most serious issue that we are facing in today’s world in cloud era.

This article describes IReadWeb system, which is based on existing WebAnyWhere technology. The existing WebAnyWhere system uses depth-first search (DFS) to traverse the Document Object Model (DOM) during the Web surfing task. DFS uses an exhaustive search and crawls through an entire page until it identifies the target node thereby greatly increasing the response time to users. We developed a user-experienced based algorithm, which, unlike DFS, exploits pre-fetched information stored in a local cache to speed up the browsing task. The performance of IReadWeb is thoroughly evaluated and compared against WebAnyWhere by using a sizeable sample of blind native Arabic speakers. The experimental results show that IReadWeb outperformed WebAnyWhere in attaining fast response speed.

The systems related to safety are becoming more and more important and are dependent on complex data both in terms of volume and variety. This is especially of importance in applications demanding data analysis, intensive maintenance and focuses on the potential threats due to possible data errors, such as railway signaling, traffic management etc. Errors in analysis of data could result in loss of many lives and financial loss such as the cases of Annabella container ship- Baltic Sea accident (United Kingdom Merchant Shipping, Regulations 2005 – Regulation 5). Despite these potential errors in data leading to accidents or mishaps, this part of the system has been ignored; this study focuses on the integrity of data in safety critical applications. It did so by developing a method for building metadata through a data chain, mining this metadata and representing it in such a way that a consumer of the data can judge the integrity of the data and factor this into the decision-making aspect of their response. This research proposes a design, implementation and evaluation of a safety data model that helps to ensure integrity of data use for data analysis and decision making to prevent loss of lives and properties. Modern and sophisticated ETL software tools including Microsoft SQL Server 2012 Data Tools and Microsoft SQL Server Management Studio were explored. The data were extracted from Safety Related Condition Reports (SRCRs) dataset and used data mining techniques to transform and filter unsafe and hazardous data from the extracted data and stored the safe data into the Data Warehouses (DWs). The prototype was able to load data into designated DWs. The success of the developed model proved that the prototype was able to extract all datasets, transform and load data into the DWs and moved extracted files to archive folder within 7.406 seconds.

Twitter act as a most important medium of communication and information sharing. As tweets do not provide sufficient word occurrences i.e. of 140 characters limits, classification methods that use traditional approaches like “Bag-Of-Words” have limitations. The proposed system used an intuitive approach to determine the class labels with the set of features. The System can able to classify incoming tweets mainly into three generic categories: News, Movies and Sports. Since these categories are diverse and cover most of the topics that people usually tweet about .Experimental results using the proposed technique outperform the existing models in terms of accuracy.

Hybrid intelligent system is a combination of artificial intelligence (AI) techniques that can be applied in healthcare to solve complex medical problems. Case-based reasoning (CBR) and rule based reasoning (RBR) are the two more popular AI techniques which can be easily combined. Both techniques deal with medical data and domain knowledge in diagnosing patient conditions. This paper proposes a hybrid intelligent system that uses data mining technique as a tool for knowledge acquisition process. Data Mining solves the knowledge acquisition problem of rule based reasoning by supplying extracted knowledge to rule based reasoning system. We use WEKA for model construction and evaluation, Java NetBeans for integrating data mining results with rule based reasoning and Prolog for knowledge representation. To select the best model for disease diagnosis, four experiments were carried out using J48, BFTree, JRIP and PART. The PART classification algorithm is selected as best classification algorithm and the rules generated from the PART classifier are used for the development of knowledge base of hybrid intelligent system. In this study, the proposed system measured an accuracy of 87.5% and usability of 89.2%.