Machine learning for water bodies identification from satellite imagesKontos, Konstantinos; Maragoudakis, Manolis
doi: 10.1504/IJDMMM.2018.093881pmid: N/A
Examining satellite images on residential areas and more particularly bodies of water such as swimming pools are of great interest in the field of image mining. Initially, the unobstructed water consumption for pool operation can lead to the reduction of water supplies especially during summer months, a fact that can influence water sources for firefighting. Moreover, they may serve as potential mosquito habitat, especially if they are surrounded by dense vegetation. Towards this direction, this paper presents an efficient classification system for identifying swimming pools from satellite images. A new method of trainable segmentation is presented for feature extraction. In this study, a support vector machine algorithm is used for reducing the feature set to the more appropriate one. The proposed method was tested on different areas of Greece with an overall accuracy of 99.82% that was achieved by using an ensemble algorithm.
To identify the usage of clustering techniques for improving search result of a websiteMehrotra, Shashi; Kohli, Shruti; Sharan, Aditi
doi: 10.1504/IJDMMM.2018.093879pmid: N/A
Clustering has drawn much attention to research community due to its advantages and wide applications. However, clustering is a challenging problem, as many factors play a significant role. The same algorithm may generate different output if there is a change in parameters, presentation order or similarity measure. The search option is used excessively on almost every website. Grouping the search results in various folders will improve web browsing and that can be achieved by applying clustering over results. Clustering web elements facilitate data analysis in various ways. In this paper, we present well-known clustering algorithms and identify their different usages for the web elements. The paper discusses some significant work conducted in this field.
ABCD: agent based model for document classificationNasr, Abdurrahman A.
doi: 10.1504/IJDMMM.2018.093878pmid: N/A
Document classification is the task of analysing, identifying and categorising collection of documents into their annotated classes based on their contents. This paper presents ABCD as an agent-based classifier for documents. ABCD is autonomous by depending on software agents in collecting and distributing documents, and smart by exploiting machine learning techniques to train the underlying classifier. As such, the system consists of two essential components, namely, the agent component and the classification component. To be comprehensive and to facilitate comparative results, five statistical classifiers are exploited. These classifiers are based on Naïve Bayes (NB), Hidden Markov Model (HMM), Repeated Incremental Pruning to Produce Error Reduction (RIPPER), Support Vector Machine (SVM) and Random Forest (RF) algorithms. The proposed model is experimentally tested on both BBC news articles dataset and News Aggregator dataset from artificial intelligence lab. The obtained results indicate the superiority of the Random Forest algorithm for classifying unimodal documents.
Improving the efficacy of clustering by using far enhanced clustering algorithmMishra, Bikram Keshari; Rath, Amiya Kumar
doi: 10.1504/IJDMMM.2018.093886pmid: N/A
There are several aspects on which research works are carried out on clustering. The prime focus is on finding the near optimal cluster centres and determining the best possible clusters. Hence, we have emphasised our work on finding a technique which contemplates on these facets in a way which is far more efficient than several novel approaches. In this paper, we have examined four varieties of clustering algorithms namely; K-Means, FEKM, ECM and proposed FECA implemented on varying data sets. We used few internal cluster validity indices like Dunn's index, Davies-Bouldin's index, Silhouette Coefficient, C index and Calinski index for quantitative evaluation of the clustering results obtained. The results obtained from simulation were compared, and as per our expectation it was found that, the quality of clustering produced by FECA is far more satisfactory than the others. Almost every value of validity indices used give encouraging results for FECA, implying good cluster formation. Further experiments support that the proposed algorithm also produces minimum quantisation error almost for all the data sets used.