Classification and optimization of product review information using soft computing models

A soft computing framework to classify and optimize text-based information extracted from customers' product reviews is proposed in this paper. The soft computing framework performs classification and optimization in two stages. Given a set of keywords extracted from unstructured text-based product reviews, a Support Vector Machine (SVM) is used to classify the reviews into two categories (positive and negative reviews) in the first stage. An ensemble of evolutionary algorithms is deployed to perform optimization in the second stage. Specifically, the Modified micro Genetic Algorithm (MmGA) optimizer is applied to maximize classification accuracy and minimize the number of keywords used in classification. Two Amazon product reviews databases are employed to evaluate the effectiveness of the SVM classifier and the ensemble of MmGA optimizers in classification and optimization of product related keywords. The results are analyzed and compared with those published in the literature. The outputs potentially serve as a list of impression words that contains useful information from the customers' viewpoints. These impression words can be further leveraged for product design and improvement activities in accordance with the Kansei engineering methodology.

The challenges of cloud computing technology adoption in e-government

 The endless transformation of technological innovation requires greater collaboration of Information Communication and Technology (ICT) in various areas especially in public sectors. Many attempts have been made in improving the quality of E-Government services; one of it is adopting the cloud computing technology. Successful implementation of cloud computing technology can benefit the public sector in many ways one of it is cost reduction. Most government organizations especially in the developing countries are committed in adopting the cloud technology based on the increased demands in cloud adoption in E Government services. Unfortunately, despite all the benefits, the cloud computing technology raises some major risks. The success of implementation of cloud computing technology is determined by how well the government tackles the challenges. Therefore, this paper specifically surveyed the associated challenges of adopting Cloud Technology for E-Government by choosing Malaysia as the case study.

Trust enhanced security in SaaS cloud computing

Trust problem in Software as a Service Cloud Computing is a broad range of a Data Owner’s concerns about the data in the Cloud. The Data Owner’s concerns about the data arise from the way the data is handled in locations and machines that are unknown to the Data Owner.

Applications of symbolic computing for symmetry analysis of differential equations

 This thesis presents a number of applications of symbolic computing to the study of differential equations. In particular, three packages have been produced for the computer algebra system MAPLE and used to find a variety of symmetries (and corresponding invariant solutions) for a range of differential systems.