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Explosive growth in geospatial and temporal data as well the emergence of new technologies emphasize need for automated discovery spatiotemporal knowledge. Spatiotemporal mining studies process discovering interesting previously unknown, but potentially useful patterns from large databases. It has broad application domains including ecology environmental management, public safety, transportation, earth science, epidemiology, climatology. The complexity intrinsic relationships limits...

Abstract The paper describes the usage of self-learning Hierarchical LSTM technique for classifying hatred and trolling contents in social media code-mixed data. LSTM-based learning is a novel architecture inspired from neural models. proposed HLSTM model trained to identify words available contents. systems equipped with predicting mechanism annotating transliteration domain. Hindi–English data are ordered into Hindi, English, labels classification. word embedding character-embedding...

Abstract Explosive growth in geospatial data and the emergence of new spatial technologies emphasize need for automated discovery knowledge. Spatial mining is process discovering interesting previously unknown, but potentially useful patterns from large databases. The complexity implicit relationships limits usefulness conventional techniques extracting patterns. In this paper, we explore emerging field mining, focusing on different methods to extract information. We conclude with a look at...

Increasingly, location-aware datasets are of a size, variety, and update rate that exceeds the capability spatial computing technologies. This paper addresses emerging challenges posed by such datasets, which we call Spatial Big Data (SBD). SBD examples include trajectories cellphones GPS devices, vehicle engine measurements, temporally detailed road maps, etc. has potential to transform society via next-generation routing services as eco-routing. However, envisaged SBD-based pose several...

Monitoring land-cover changes is of prime importance for the effective planning and management critical, natural man-made resources. The growing availability remote sensing data provides ample opportunities monitoring on a global scale using machine-learning techniques. However, sets exhibit unique domain-specific properties that limit usefulness traditional methods. This article presents brief overview these challenges from perspective machine learning discusses some recent advances in are...

Identification of outliers can lead to the discovery unexpected, interesting, and useful knowledge. Existing methods are designed for detecting spatial in multidimensional geometric data sets, where a distance metric is available. In this paper, we focus on graph structured sets. We define statistical tests, analyze foundation underlying our approach, design several fast algorithms detect outliers, provide cost model outlier detection procedures. addition, experimental results from...

A wide variety of smartphone applications today rely on third-party advertising services, which provide libraries that are linked into the hosting application. This situation is undesirable for both application author and advertiser. Advertising require additional permissions, resulting in permission requests to users. Likewise, a malicious could simulate behavior library, forging user's interaction effectively stealing money from paper describes AdSplit, where we extended Android allow an...

Spatial co-location patterns represent the subsets of features whose instances are frequently located together in geographic space. Co-location pattern discovery presents challenges since spatial embedded a continuous space and share variety relationships. A large fraction computation time is devoted to identifying patterns. We propose novel join-less approach for mining, which materializes neighbor relationships with no loss reduces computational cost instances. The mining algorithm...

Zonal co-location patterns represent subsets of featuretypes that are frequently located in a subset space (i.e., zone). Discovering zonal spatial is an important problem with many applications areas such as ecology, public health, and homeland defense. However, discovering these dynamic parameters repeated specification zone interest measure values according to user preferences) computationally complex due the repetitive mining process. Also, set candidate exponential number feature types,...

Mixed-drove spatio-temporal co-occurrence patterns (MDCOPs) represent subsets of two or more different object-types whose instances are often located in spatial and temporal proximity. Discovering MDCOPs is an important problem with many applications such as identifying tactics battlefields, games, predator-prey interactions. However, mining computationally very expensive because the interest measures complex, datasets larger due to archival history, set candidate exponential number...

Given a collection of Boolean spatiotemporal (ST) event-types, the cascading pattern (CSTP) discovery process finds partially ordered subsets these event-types whose instances are located together and occur serially. For example, analysis crime data sets may reveal frequent occurrence misdemeanors drunk driving after near bar closings on weekends, as well large gatherings such football games. Discovering CSTPs from ST is important for application domains public safety (e.g., identifying...

Building footprints are among the most predominant features in urban areas, and provide valuable information for planning, solar energy suitability analysis, etc. We aim to automatically rapidly identify building by leveraging deep learning techniques increased availability of remote sensing datasets at high spatial resolution. The task is computationally challenging due use large training number parameters. In related work, You-Only-Look-Once (YOLO) a state-of-the-art framework object...

Regional co-location patterns (RCPs) represent collections of feature types frequently located together in certain localities. For example, RCP < (Bar, Alcohol -- Crimes), Downtown >suggests that a pattern involving alcohol-related crimes and bars is often localized to downtown regions. Given set Boolean types, their geo-located instances, spatial neighbor relation, prevalence threshold, the discovery problem finds all prevalent RCPs (pairs co-locations localities). important many societal...

Recent developments in data mining and machine learning approaches have brought lots of excitement providing solutions for challenging tasks (e.g., computer vision). However, many limited interpretability, so their success failure modes are difficult to understand scientific robustness is evaluate. Thus, there an urgent need better understanding the reasoning behind approaches. This requires taking a transdisciplinary view science recognizing its foundations mathematics, statistics, science....

Machine learning (ML) architectures based on neural model have garnered considerable attention in the field of language classification. Code-mixing is a common phenomenon social networking sites for exhibiting opinion topic. The code-mixed text approach mixing two or more languages. This paper describes application index Indian media texts and compares complexity to identify at word level using Bi-directional Long Short-Term Memory model. major contribution work propose technique identifying...