International Journal of Business Intelligence and Data Mining

Ojokoh, Bolanle Adefowoke; Aboluje, Oluwatosin Olatunbosun; Igbe, Tobore

doi: 10.1504/IJBIDM.2020.109293pmid: N/A

In this paper, Pearson's correlation coefficient is employed for collaborative filtering due to its ability to manipulate numerical data as well as determine linear relationship among existing users. Its steps involve a user-user representation, similarity generation and prediction generation with a goal to produce a predicted opinion of the active user about a specific item. Concept of parental control is also incorporated for enhancement. Evaluation of the system was done using precision, recall, F-measure, discounted cumulative gain (DCG), idealised discounted cumulative gain (IDCG), normalised discounted cumulative gain (nDCG) and mean absolute error (MAE). Three hundred fortysix datasets were used, out of which 126 were gathered from local video shops and 220 were extracted from internet movie database (IMDb). These were used for the experiments and the results generated through mining of data obtained from profiles and ratings of system users prove the system's average ranking quality of the collaborative filtering algorithm is 95.9%.

Mahmoudi, Mohammad Reza; Abasalizadeh, Ali; Rahmati, Marzieh

doi: 10.1504/IJBIDM.2020.109308pmid: N/A

Conceptual metaphor is the systematic mapping of conceptual domains on each other. Love is the most important axis of mystical path. In this paper, all the lines in Moulana's are studied and different words, which are used as alternatives of love, are determined and classified in 11 areas. Then chi-square goodness of fit test is used to investigate and compare the frequency of different areas and words which are used as alternatives of love, separately. Finally, based on the clustering methods, these alternatives are clustered in three (high frequency, medium frequency, and low frequency). The results indicate the word 'fire' and the area 'human' have the highest uses as the alternatives of love.

Shanmugam, C.; Sekaran, E. Chandira

doi: 10.1504/IJBIDM.2020.109298pmid: N/A

Infrared thermography (IRT) has played an essential part in observing and examining thermal defects of electrical equipment without ending, which has vital enormity for the dependability of electrical recorded. This paper dissected the electrical parts are faulted or non-faulted with the help of segmentation and classification model. The features are calculated from the input thermal images and regions of interest (ROI) is segmented by utilising optimal region growing (ORG) technique and faults are classified using multi kernel support vector machine (MKSVM). In the tests, the classification performances from different input features are assessed. For enhancing the performance of the segmentation investigation optimisation procedure that is whale optimisation (WO) is used. Before classifying, the extracted electrical components are fused by using feature level fusion (FLF) procedure to fused vector in all images. These multi kernel classification performance indices, including sensitivity, specificity and accuracy are utilised to recognise the most appropriate input feature and the best arrangement of classifiers. The performance of SVM is contrasted with a neural network. The correlation comes about demonstrating that our technique can accomplish a superior performance with accuracy at 98.21%.

Pham, Phu; Do, Phuc

doi: 10.1504/IJBIDM.2020.109295pmid: N/A

In this paper, we propose a model of TopCPathSim in order to address the problem related to 'topic-driven' similarity searching based on 'constrained meta-path' (or also called 'restricted meta-path') between same-typed objects within the content-based heterogeneous information networks (HINs). The topic distributions over content-based objects such as: paper/article on the bibliographic network or user's comments/reviews on the social networks, etc. are obtained by using the LDA topic model. We conduct the experiments on the real DBLP, Aminer and ACM datasets which demonstrate the effectiveness of our proposed model. Throughout experiments, our proposed model gains about 73.56% in accuracy. The output results also show that the combination of probabilistic topic model with constrained meta-path is promising to leverage the output quality of topic-oriented similarity searching in content-based HINs.

Kuanr, Madhusree; Mohanty, Sachi Nandan

doi: 10.1504/IJBIDM.2020.109294pmid: N/A

This study examines the collaborative filtering in recommender system by categorising users according to their choices of place, food, local item purchase, etc. The proposed system will store the opinions of the local users about the sites, foods and products for purchase available in those sites. It uses collaborative filtering technique to find the similar users to a given querying user. The system recommends the best sites along with good foods and products available on those sites according to the recent data. Two hundred (male = 110, female = 90) married individuals from Bhubaneswar, Odisha (India) participated in this survey. Cosine similarity is used in the proposed system to find the similar users of a given input query user. The results revealed that collaborative filtering is the more reliable technique for personalised recommender systems. Experimental results show performance of the proposed system in terms of precision, recall and F-measure values.

Priya, G. Madhu; Shalinie, S. Mercy; Priya, P. Mohana

doi: 10.1504/IJBIDM.2020.109296pmid: N/A

Today's business enterprise adapts cloud-based services as its architectural design. Intelligence technique incorporated into the architecture gives massive tangible and intangible benefits in terms of performance and reliability. Such cloud-based business architecture faces many threats towards its availability. DDoS attack is the most prominent threat as its impact is more in the virtual resource-based cloud infrastructure. Therefore, there is a need for a business intelligence-based framework to detect early the attack by monitoring the virtual network traffic. The proposed intelligence framework uses a deep learning framework, continuous discriminative-deep belief network (CD-DBN). CD-DBN dynamically captures attack patterns from the network data, analyses the data and detects the intrusion to the cloud. The observed result shows that the earlier detection approach guarantees the availability of cloud services to the legitimate users and enhances the cloud resource usage.