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The Complexity-Entropy Causality Plane (CECP) is a representation space with two dimensions: normalized permutation entropy (Hs) and Jensen-Shannon complexity (Cjs). CECP has wide found applications in non-linear dynamic analysis to classify a given signal according to its randomness and complexity which is a motivation to investigate its application for machine fault diagnostics. In this work we...
Authorship identification is a problem of data mining and classification. There are numerous methods and algorithms have been published to understand its nature. Although, researchers still investigate best and simple solutions due to its heterogeneous and multilingual characteristics. This study introduced new authorship identification process based on artificial neural network (ANN) model using...
The classification of graphs is a key challenge within many scientific fields using graphs to represent data and is an active area of research. Graph classification can be critical in identifying and labelling unknown graphs within a dataset and has seen application across many scientific fields. Graph classification poses two distinct problems: the classification of elements within a graph and the...
While kernel methods using a single Gaussian kernel have proven to be very successful for nonlinear classification, in case of learning problems with a more complex underlying structure it is often desirable to use a linear combination of kernels with different widths. To address this issue, this paper presents a classification algorithm based on a jointly convex constrained optimization formulation...
In marketing analytics, customer segmentation (clustering) divides a customer base into groups of similar individuals, while buyer targeting (classification) identifies promising customers. Both customer segmentation and buyer targeting help the business to improve marketing performances by allocating resources to the most profitable customers. Due to the heterogeneity across the customer groups,...
The prosperity of social networks provides users with convenient communication but also attracts a large number of spammers. To solve this problem, this paper combines supervised learning and unsupervised learning algorithms, and proposes a novel hybrid model based on OPTICS and SVM. First, we collected a dataset from Sina Weibo including 10,000 users and 134,188 messages; then extracted the content...
Sentiment analysis is considered to be a category of machine learning and natural language processing. It is used to extricate, recognize, or portray opinions from different content structures, including news, audits and articles and categorizes them as positive, neutral and negative. It is difficult to predict election results from tweets in different Indian languages. We used Twitter Archiver tool...
Forecasting hourly spot prices for real-time electricity usage is a challenging task. This paper investigates a series of forecasting methods to 90 and 180 days of load data collection acquired from the Iberian Electricity Market (MIBEL). This dataset was used to train and test multiple forecast models. The Mean Absolute Percentage Error (MAPE) for the proposed Hybrid combination of Auto Regressive...
When looking for a restaurant online, user uploaded photos often give people an immediate and tangible impression about a restaurant. Due to their informativeness, such user contributed photos are leveraged by restaurant review websites to provide their users an intuitive and effective search experience. In this paper, we present a novel approach to inferring restaurant types or styles (ambiance,...
Many telecommunication companies today have actively started to transform the way they do business, going beyond communication infrastructure providers are repositioning themselves as data-driven service providers to create new revenue streams. In this paper, we present a novel industrial application where a scalable Big data approach combined with deep learning is used successfully to classify massive...
In analyzing streaming data in which the underlying data distribution may change or the concept of interest may drift over time, the ability of a classifier to adapt to drifted concepts is very important to maintaining the prediction performance. However, the true class labels of data samples are often available only after some period of time or they are obtained by experts' efforts. In this paper,...
The Support Vector Machine (SVM) is a classical classification algorithm that has a wide range of application. With kernel function, SVM can dispose the datasets that are not linearly separable in their original feature space, making it more flexible in practical use compared with linear model. However, its complexity in training is an obstacle to large-scale dataset handling. This paper proposes...
As social media has become increasingly popular in the modern world, people are using these platforms to express their opinions about products, businesses, and services. The need for categorizing these consumer reviews has been prominent. One effective solution is sentiment analysis (SA), which has been an active research topic. The goal of SA is to automatically extracting and classifying user opinions...
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