International Journal of Knowledge Engineering and Data Mining
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International Journal of Knowledge Engineering and Data Mining (7 papers in press)
Study on the Electric Vehicle Sales Forecast with TEI@I Methodology by Jiang Ping Wan, Le Qi Xie, Xue Fang Hu Abstract: The research was decomposition and integration based on TEI@I methodology: the prediction model applied principal component regression analysis (PCR) to deal the linear relationship, and then applied BP neural network and support vector machine (SVM) to deal the nonlinear relationship, and finally, they are all integrated together. Granger causality test and grey correlation degree are used to quantitatively analyze the factors affecting the sales of electric vehicles through mining consumer network data, The research results of electric vehicle models show that the Baidu search index lags behind for three months is time-sensitive to the sales of electric vehicles. Finally, taking the data of two car models as examples, it is found that the PCR-BP model and the PCR-SVM model have better prediction performance than the single model. Keywords: electric vehicle sales forecast; CiteSpace; TEI@I methodology; principal component regression analysis; BP neural network; support vector machine; Baidu search index. DOI: 10.1504/IJKEDM.2020.10030715
Improving E-Health Governance through Syndromic Surveillance Systems and Data Mining in KSA by Ghada Al Omran Abstract: Recently, the KSA has witnessed significant technical advances in health sector, where local hospitals are using high-quality systems and technologies to serve patients. However, even with this high progress in healthcare systems, the communication is still limited with other decision makers in different sectors whose need to access some health-related information to take the best decisions for serving patients. Therefore, this project aims to utilise from the concept of electronic health governance (e-health governance) to build an automated system, which will help the health sector to know common coming diseases and facilitate the decision-making process through providing them with the necessary health information to help them provide the best service for patients in various fields. To do that this research will apply classification data mining techniques through using naive Bayes classification algorithm; where this project aims to build a common diseases prediction system (CDPS) to working as syndromic surveillance system. Keywords: data mining; electronic governance; syndromic surveillance system; SSS; naive Bayesian; common disease prediction system. DOI: 10.1504/IJKEDM.2020.10035583
The impacts of Knowledge Management on Organizational Entrepreneurship with the Moderating Role of Social Capital in the Melli Bank (Mashhad Branches) by Bahare Khayyami, Amirali Motamedi, Elham Shadkam Abstract: The main goal of this research is the relationship between knowledge management and entrepreneurship with the role of social capital adjustment in Melli banks employees. The sample was analysed using Cochran table and two-stage cluster sampling that 117 employees of the Mashhads Mellis bank. In this research, four standard questionnaires are used to measure the variables under study. The results of this study showed: there is a relationship between knowledge management and organisational entrepreneurship and the social capital has a moderating effect on knowledge management and entrepreneurship. Keywords: knowledge management; social capital; enterprise entrepreneurship; Melli Bank; laser. DOI: 10.1504/IJKEDM.2020.10037411
Sentiment Analysis of Book Reviews using CNN with n-grams Method by ADDANKI MOUNIKA, S. SARASWATHI Abstract: The fundamental job of sentiment analysis (SA) is to decide the sentiment polarity (positive or negative) of the text. It is a problematic task to take the sentiments in the document level sentences accurately. In the proposed system we developed sentiment analysis of book reviews using CNN with n-grams method by utilising two levels. In first level, grouping of similar tagged words by semantic network is completed taking pre-processed data utilising parts of speech (POS) Tagger from the datasets of books and reviewers by WuPalmer word similarity technique. In second level, SA is completed in two stages which are training phase and testing phase by utilising deep learning approaches likely convolutional neural networks (CNN) with n-gram method using document to vector (Doc2Vec) and distributed bag of words (DBOW) embedding. The proposed system CNN+Doc2Vec+DBOW+n-gram divides the book reviews into positive or negative reviews with better accuracy results compared to existing methods. Keywords: sentiment classification; semantic network; feature extraction; polarity of review; WuPalmer; WordNet; convolutional neural networks; CNN; n-gram. DOI: 10.1504/IJKEDM.2021.10041686
Application of Automated System for University Course Timetable Scheduling: an Algerian case study by Talib Hicham Betaouaf, Rabab Boukli-Hacene, Mohamed Amine CHERIER Abstract: Scheduling is an NP-hard problem which most universities are grappling with. For each academic semester, the timetabling process must be carried out regularly, it is an overwhelming and time-consuming activity. The aim contribution of this study is developing an automated system based on a multi-agent (MA) approach and genetic algorithms (GA) to generate an university timetable. Three agents named capture agent (CA), processing agent (PA) and distributing agent (DA) have been worked collaboratively and cooperatively to develop the university timetable. The study has been applied in real case study in to perform the course schedules in electrical and electronic engineering department of our university. The system implemented has considerably reduced the time and effort in the timetables realisation of our department from about ten days to only a few minutes. It has also significantly improved the quality of timetable by guaranteeing a satisfaction rate of over 95% of the constraints. Keywords: course timetable scheduling; multi-agent; intelligent agents; genetic algorithms. DOI: 10.1504/IJKEDM.2021.10042011
A Data Mining Approach to Predicting the Inventory Day of Used Cars by Dedy Suryadi, Alfian Tan, Donny Boy Abstract: This paper studies the decision making process in purchasing used cars at a company. The companys main objective is to purchase cars that may be sold within 30 days. Currently, the decision is solely made based on subjective judgment of a supervisor. Alternatively, utilizing the data that has been collected by the company, a data mining approach is proposed to improve the decision making process. Out of the 45 aspects of a car, 12 features are selected as being important using the Contingency Table method. Six data mining methods are applied. Support Vector Machine (SVM) prediction model performs the best. The SVM model provides an accuracy of 69.44% in predicting whether or not a used car would be successfully sold within the acceptable inventory days, i.e., 30 days. In contrast, the predictive accuracy of the current decision making process is just around 50%. Keywords: data mining; decision making; prediction; feature selection; used car; inventory day. DOI: 10.1504/IJKEDM.2021.10042632
An Evaluation Dataset for Depression Detection in Arabic Social Media by Somaia Elimam, Mohamed Bouguessa Abstract: Studying depression in Arabic social media has been neglected compared to other languages and the traditional way of dealing with depression (face-to-face medical diagnose) is not enough as the number of people that suffer from depression in Arabic communities increased dramatically. This paper proposes the first dataset to detect depressed users in Arabic social media. We pondered tweets from Twitter, pre-processed and converted it to a structured format. A notable advantage of the elaborated dataset is that it allows effective evaluation of machine learning algorithms for depression detection. We employ several classification algorithms such as deep neural network, logistic regression, multinomial Naive Bayes, Bernoulli Naive Bayes, AdaBoost, passive aggressive, nearest centroid, and linear SVC. The F-score, AUC, precision, and accuracy scores were selected as performance measures to compare algorithms, and the result showed that it is very challenging to classify Arabic tweets especially with the sparse nature of Twitter data. Keywords: Arabic dataset; depression detection; Arabic social media; machine learning. DOI: 10.1504/IJKEDM.2021.10042777