184 research outputs found
A framework for crime data analysis using relationship among named entities
No full textMany crime reports are available online in various blogs and Newswire. Though manual annotation of these massive reports is quite tedious for crime data analysis, it gives an overall crime scenario of all over the world. This motivates us to propose a framework for crime data analysis based on the online reports. Initially, the method extracts the crime reports and identifies named entities. The intermediate sequence of context words between every consecutive pair of named entities is termed as a crime vector that provides relationships between the entities. The feature vectors for each entity pair are generated from these crime vectors using the Word2Vec model. The paper considers three different types of named entity pairs to facilitate the major crime data analysis task, and for each type, similarity between every pair of entities is measured using respective feature vectors. For each type of named entity pair, a separate weighted graph is generated with entity pairs as vertices and similarity score between them as the weight of the corresponding edge. Then, Infomap, a graph-based clustering algorithm, is applied to obtain optimal set of clusters of entity pairs and a representative entity pair of each cluster. Each cluster is labelled by the relationship, represented by the crime vector, of its representative entity pair. In reality, all the entity pairs in a cluster may not reflect contextual similarity with their representative entity pair. So the clusters are further partitioned into subclusters based on WordNet-based path similarity measure which makes the entity pairs in each subcluster more contextually similar compared to their original cluster. These subclusters provide us various statistical crime information over the time period. The method is experimented only using the crime reports related to crime against women in India. The experimental results demonstrate the effectiveness and superiority of the method compared to others for analysing the crime data
An Effective Fuzzy Clustering of Crime Reports Embedded by a Universal Sentence Encoder Model
No full textCrime reports clustering is crucial for identifying and preventing criminal activities that frequently happened in society. In the proposed work, named entities in a report are recognized to extract the crime-related phrases and subsequently, the phrases are preprocessed by applying stopword removal and lemmatization operations. Next, the module of the universal encoder model, called the transformer, is applied to extract phrases of the report to get a sentence embedding for each associated sentence, aggregation of which finally provides the vector representation of that report. An innovative and efficient graph-based clustering algorithm consisting of splitting and merging operations has been proposed to get the cluster of crime reports. The proposed clustering algorithm generates overlapping clusters, which indicates the existence of reports of multiple crime types. The fuzzy theory has been used to provide a score to the report for expressing its membership into different clusters, and accordingly, the reports are labelled by multiple categories. The efficiency of the proposed method has been assessed by taking into account different datasets and comparing them with other state-of-the-art approaches with the help of various performance measure metrics
Rough-Fuzzy Based Synthetic Data Generation Exploring Boundary Region of Rough Sets to Handle Class Imbalance Problem
No full textClass imbalance is a prevalent problem that not only reduces the performance of the machine learning techniques but also causes the lacking of the inherent complex characteristics of data. Though the researchers have proposed various ways to deal with the problem, they have yet to consider how to select a proper treatment, especially when uncertainty levels are high. Applying rough-fuzzy theory to the imbalanced data learning problem could be a promising research direction that generates the synthetic data and removes the outliers. The proposed work identifies the positive, boundary, and negative regions of the target set using the rough set theory and removes the objects in the negative region as outliers. It also explores the positive and boundary regions of the rough set by applying the fuzzy theory to generate the samples of the minority class and remove the samples of the majority class. Thus the proposed rough-fuzzy approach performs both oversampling and undersampling to handle the imbalanced class problem. The experimental results demonstrate that the novel technique allows qualitative and quantitative data handling
Improving Facial Emotion Recognition Using Residual Autoencoder Coupled Affinity Based Overlapping Reduction
No full textEmotion recognition using facial images has been a challenging task in computer vision. Recent advancements in deep learning has helped in achieving better results. Studies have pointed out that multiple facial expressions may present in facial images of a particular type of emotion. Thus, facial images of a category of emotion may have similarity to other categories of facial images, leading towards overlapping of classes in feature space. The problem of class overlapping has been studied primarily in the context of imbalanced classes. Few studies have considered imbalanced facial emotion recognition. However, to the authors’ best knowledge, no study has been found on the effects of overlapped classes on emotion recognition. Motivated by this, in the current study, an affinity-based overlap reduction technique (AFORET) has been proposed to deal with the overlapped class problem in facial emotion recognition. Firstly, a residual variational autoencoder (RVA) model has been used to transform the facial images to a latent vector form. Next, the proposed AFORET method has been applied on these overlapped latent vectors to reduce the overlapping between classes. The proposed method has been validated by training and testing various well known classifiers and comparing their performance in terms of a well known set of performance indicators. In addition, the proposed AFORET method is compared with already existing overlap reduction techniques, such as the OSM, ν-SVM, and NBU methods. Experimental results have shown that the proposed AFORET algorithm, when used with the RVA model, boosts classifier performance to a greater extent in predicting human emotion using facial images
Incremental classifier in crime prediction using bi-objective Particle Swarm Optimization
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Group Incremental Adaptive Clustering based on Neural Network and Rough Set Theory for Crime Report Categorization
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