Support Vector Machine Algorithm for SMS Spam Classification in The Telecommunication Industry

In recent years, we have withnessed a dramatic increment volume in the number of mobile users grows in telecommunication industry. However, this leads to drastic increase to the number of spam SMS messages. Short Message Service (SMS) is considered one of the widely used communication in telecommunication service. In reality, most of the users ignore the spam because of the lower rate of SMS and limited amount of spam classification tools. In this paper, we propose a Support Vector Machine (SVM) algorithm for SMS Spam Classification. Support Vector Machine is considered as the one of the most effective for data mining techniques. The propose algorithm have been evaluated using public dataset from UCI machine learning repository. The performance achieved is compared with other three data mining techniques such as Naïve Bayes, Multinominal Naïve Bayes and K-Nearest Neighbor with the different number of K= 1,3 and 5. Based on the measuring factors like higher accuracy, less processing time, highest kappa statistics, low error and the lowest false positive instance, it’s been identified that Support Vector Machines (SVM) outperforms better than other classifiers and it is the most accurate classifier to detect and label the spam messages with an average an accuracy is 98.9%. Comparing both the error parameter overall, the highest error has been found on the algorithm KNN with K=3 and K=5. Whereas the model with less error is SVM followed by Multinominal Naïve Bayes. Therefore, this propose method can be used as a best baseline for further comparison based on SMS spam classification

Beast to beauty transition: Design process for complex visual analytics

Visual analytics fundamentally influences the analytical process. Without proper guidance, the complex condition can become a beast that hides the beauty of analytical data. Since visual analytics is the only way to represent analytical outcomes, ineffective visual analytics will bury the relevancy of information to support any valuable decision. Thus, this research revisits the way to design more valuable and effective visual analytics, especially for the complex condition which involves i) the information complexities, ii) the nature of the activities involved and iii) the complex context of usage. Governed by the Design Science Research Methodology (DSRM), the research applied the pragmatic philosophical worldview and exploratory research. Furthermore, the design process embedded the human-activity centered design approaches as the new perspective to get a better understanding of the visual analytics users and the analytical activities involved. There are basically three important activities of the design process that comprises of five design phases. Activity 1 aims to identify the visual analytics context and its challenges, Activity 2 to develop the solution of visual analytics design and Activity 3 to evaluate the effectiveness of visual analytics to handle its complex context. By offering a set of design process, this research aims to improve the experience of the visual analytics community to design in a more flexible, dynamic and intuitive way. Finally, this paper also recommends appropriate methods, the unit of analysis, sampling strategy and data management & analysis suitable for complex visual analytics