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14 results for “Spatial data mining”
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Research ArticleOpen accessIn the keywordsAn Efficient Algorithm for Mining Spatially Co-located Moving ObjectsG. Manikandan, S. SrinivasanMining co-location patterns from spatial databases may disclose the types of spatial features which are likely located as neighbors’ in space. Accordingly, we present an algorithm previously for mining spatially co-located moving objects using spatial data mining techniques and Prim’s Algorithm. In the previous technique, the scanning of database to mine the spatial co-location patterns took much computational cost. In … …American Journal of Applied Sciences, Vol. 10, Issue 3 · 2 Mar 2013 · 6,649 accessesView PDFCite
Research ArticleOpen accessIn the keywordsEffective Processing of Constraints based Spatial Join using R-TreesR. Parvathi, S. PalaniammalProblem statement: This study focuses on the spatial join effects with the constraints-based spatial data without any extra cost and Finding the minimum execution time of the spatial query and spatial selection method. Approach: Spatial joins are used to combine the spatial objects. The efficient processing depends upon the spatial queries. The execution time and I/O time of spatial queries … …American Journal of Applied Sciences, Vol. 8, Issue 12 · 29 Oct 2011 · 6,256 accessesView PDFCite
Research ArticleOpen accessIn the keywordsClassifying Single Trail Electroencephalogram Using Gaussian Smoothened Fast Hartley Transform for Brain Computer Interface during Motor ImageryV. B. Deepa, P. Thangaraj, S. ChitraProblem statement: Brain-Computer Interface (BCI) is a emerging research area which translates the brain signals for any motor related actions into computer understandable signals by capturing the signal, processing the signal and classifying the motor imagery. This area of work finds various applications in neuroprosthetics. Mental activity leads to changes of electrophysiological signals like the Electroencephalogram (EEG) or Electrocorticogram (ECoG). … …Journal of Computer Science, Vol. 7, Issue 5 · 7 May 2011 · 6,658 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Data Mining Driven Framework for Image-Based Crop Stress Analysis Using IPCA, FP-Growth, ConvLSTM, and Sequential ModelsA. Josephine, A. SubhashiniDetecting crop stress before it becomes serious has a major impact on the food supply. It helps in avoiding the loss of crops, increases productivity and helps farmers to practice precision farming. Most technologies today are designed specifically to either detect crop stress or to predict how well the crop will grow. Many of the technologies do not combine the … …Journal of Computer Science, Vol. 22, Issue 8 · 21 Aug 2026 · 922 accessesView PDFCite
Review ArticleOpen accessIn the abstractData Mining Revealing Recent Microplastics Pollution of Freshwater in ChinaZhuo-Yuan Xie, De-Sheng PeiThis review mainly discusses the characteristics and correlation of microplastics (MPs) in freshwater systems under various geographical environments in China. The distribution and source of MPs are analyzed and their spatial analyses are performed using the GIS method. Besides, the current and future situation of microplastic pollution in China is further evaluated through data mining. Results indicate that recent studies … …American Journal of Environmental Sciences, Vol. 18, Issue 3 · 25 Jun 2022 · 6,085 accessesView PDFCite
Literature ReviewOpen accessIn the abstractEfficient Morphometric Techniques in Alzheimer’s Disease Detection: Survey and ToolsVinutha N., P. Deepa Shenoy, K.R. VenugopalThe development of advance techniques in the multiple fields such as image processing, data mining and machine learning are required for the early detection of Alzheimer’s Disease (AD) and to prevent the progression of the disease to the later stages. The longitudinal and cross sectional images of elderly subjects were obtained from the standard datasets like ADNI, OASIS, MIRIAD and … …Neuroscience International, Vol. 7, Issue 2 · 4 Oct 2017 · 8,450 accessesView PDFCite
Research ArticleOpen accessIn the abstractAccessing the Appropriateness of a Spatial Regression Using Generalized (h1h2-)Slepian Random FieldWayan Somayasa, Jamhir Safani, La Ode Ngkoimani, Muhamad Zamrun F.In this study we derived asymptotic goodness-of-fit test (model check) for spatial regression where the critical region as well as the p-value of the tests are approximated based on the distribution of a type of the integral functional of the generalized (h1h2-)-Slepian field and the set-indexed Gaussian white noise. Such random fields are obtained as the limit process of the … …Journal of Mathematics and Statistics, Vol. 13, Issue 2 · 2 May 2017 · 7,362 accessesView PDFCite
Research ArticleOpen accessIn the abstractComparison of Classification Techniques on Fused Optical and SAR Images for Shoreline Extraction: A Case Study at Northeast Coast of Peninsular MalaysiaSyaifulnizam Abd Manaf, Norwati Mustapha, Md Nasir Sulaiman et al.Shoreline is a very important element to identify exact boundary at the coastal areas of a country. However, in order to identify land-water boundary for a large region using traditional ground survey technique is very time consuming. Alternatively, shoreline can be extracted by using satellite images that minimizes the mapping errors. The trend of extracting shoreline has been shifted from … …Journal of Computer Science, Vol. 12, Issue 8 · 27 Oct 2016 · 7,708 accessesView PDFCite
Research ArticleOpen accessIn the abstractClassification Model for Hotspot Occurrences Using Spatial Decision Tree AlgorithmImas Sukaesih Sitanggang, Razali Yaakob, Norwati Mustapha, A. N. AinuddinDeveloping a predictive model for forest fires occurrence is an important activity in a fire prevention program. The model describes characteristics of areas where fires occur based on past fires data. It is essential as an early warning system for preventing forest fires, thus major damages because of fires can be avoided. This study describes the application of data mining … …Journal of Computer Science, Vol. 9, Issue 2 · 11 Apr 2013 · 7,224 accessesView PDFCite
Research ArticleOpen accessIn the abstractWireless Sensor Networks Fault Identification Using Data AssociationT. Abirami Kongu, P. Thangaraj, P. Priakanth KonguProblem statement: Wireless Sensor Networks (WSN) are formed by thousands of lightweight nodes equipped with transducers for capturing information. The captured data are transmitted using multi hop routes to a base station, also called a sink. They can be extensively deployed during emergency response, medical monitoring and missiion critical applications requiring extensive data capture from sensors.Wireless sensor network applications include … …Journal of Computer Science, Vol. 8, Issue 9 · 11 Aug 2012 · 6,942 accessesView PDFCite