International Journal of Advanced Computer Science and Applications
About: International Journal of Advanced Computer Science and Applications is an academic journal. The journal publishes majorly in the area(s): Cloud computing & Wireless sensor network. It has an ISSN identifier of 2156-5570. It is also open access. Over the lifetime, 7138 publication(s) have been published receiving 37312 citation(s).
TL;DR: In this article, a data mining model for higher education system in the university is presented, where the classification task is used to evaluate student's performance and as there are many approaches that are used for data classification, the decision tree method is used here.
Abstract: The main objective of higher education institutions is to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in a particular course, alienation of traditional classroom teaching model, detection of unfair means used in online examination, detection of abnormal values in the result sheets of the students, prediction about students' performance and so on. The knowledge is hidden among the educational data set and it is extractable through data mining techniques. Present paper is designed to justify the capabilities of data mining techniques in context of higher education by offering a data mining model for higher education system in the university. In this research, the classification task is used to evaluate student's performance and as there are many approaches that are used for data classification, the decision tree method is used here. By this task we extract knowledge that describes students' performance in end semester examination. It helps earlier in identifying the dropouts and students who need special attention and allow the teacher to provide appropriate advising/counseling. Keywords-Educational Data Mining (EDM); Classification; Knowledge Discovery in Database (KDD); ID3 Algorithm.
TL;DR: This paper presents and modifies the technology acceptance model (TAM) in an attempt to assist public universities, particularly in Saudi Arabia, in predicting the behavioural intention to use learning management systems (LMS).
Abstract: Although e-learning is in its infancy in Saudi Arabia, most of the public universities in the country show a great interest in the adoption of learning and teaching tools. Determining the significance of a particular tool and predicting the success of implantation is essential prior to its adoption. This paper presents and modifies the technology acceptance model (TAM) in an attempt to assist public universities, particularly in Saudi Arabia, in predicting the behavioural intention to use learning management systems (LMS). This study proposed a theoretical framework that includes the core constructs in TAM: namely, perceived ease of use, perceived usefulness, and attitude toward usage. Additional external variables were also adopted— namely, the lack of LMS availability, prior experience (LMS usage experience), and job relevance. The overall research model suggests that all mentioned variables either directly or indirectly affect the overall behavioural intention to use an LMS. Initial findings suggest the applicability of using TAM to measure the behavioural intention to use an LMS. Further, the results confirm the original TAM's findings.
TL;DR: Two important clustering algorithms namely centroid based K-means and representative object based FCM (Fuzzy C-Means) clustering algorithm are compared and performance is evaluated on the basis of the efficiency of clustering output.
Abstract: In the arena of software, data mining technology has been considered as useful means for identifying patterns and trends of large volume of data. This approach is basically used to extract the unknown pattern from the large set of data for business as well as real time applications. It is a computational intelligence discipline which has emerged as a valuable tool for data analysis, new knowledge discovery and autonomous decision making. The raw, unlabeled data from the large volume of dataset can be classified initially in an unsupervised fashion by using cluster analysis i.e. clustering the assignment of a set of observations into clusters so that observations in the same cluster may be in some sense be treated as similar. The outcome of the clustering process and efficiency of its domain application are generally determined through algorithms. There are various algorithms which are used to solve this problem. In this research work two important clustering algorithms namely centroid based K-Means and representative object based FCM (Fuzzy C-Means) clustering algorithms are compared. These algorithms are applied and performance is evaluated on the basis of the efficiency of clustering output. The numbers of data points as well as the number of clusters are the factors upon which the behaviour patterns of both the algorithms are analyzed. FCM produces close results to K-Means clustering but it still requires more computation time than K-Means clustering. Keywords—clustering; k-means; fuzzy c-means; time complexity
TL;DR: This paper presents a survey on the techniques used to design Chatbots and a comparison is made between different design techniques from nine carefully selected papers according to the main methods adopted.
Abstract: Human-Computer Speech is gaining momentum as a technique of computer interaction. There has been a recent upsurge in speech based search engines and assistants such as Siri, Google Chrome and Cortana. Natural Language Processing (NLP) techniques such as NLTK for Python can be applied to analyse speech, and intelligent responses can be found by designing an engine to provide appropriate human like responses. This type of programme is called a Chatbot, which is the focus of this study. This paper presents a survey on the techniques used to design Chatbots and a comparison is made between different design techniques from nine carefully selected papers according to the main methods adopted. These papers are representative of the significant improvements in Chatbots in the last decade. The paper discusses the similarities and differences in the techniques and examines in particular the Loebner prize-winning Chatbots.
Abstract: In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. Text summarization is the task of shortening a text document into a condensed version keeping all the important information and content of the original document. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.
Related Journals (5)
Journal of Computer Science
2.6K papers, 20.7K citations
International Journal of Computer Applications
26K papers, 135.9K citations
The International Arab Journal of Information Technology
1.3K papers, 11.2K citations
International Journal of Electrical and Computer Engineering
4.6K papers, 25K citations