A comparison of fuzzy approaches to e-commerce review rating prediction

This paper presents a comparative analysis of the performance of fuzzy approaches on the task of predicting customer review ratings using a computational intelligence framework based on a genetic algorithm for data dimensionality reduction. The performance of the Fuzzy C-Means (FCM), a neurofuzzy approach combining FCM and the Adaptive Neuro Fuzzy Inference System (ANFIS), and the Simplified Fuzzy ARTMAP (SFAM) was compared on six datasets containing customer reviews. The results revealed that all computational intelligence predictors were suitable for the rating prediction problem, and that the genetic algorithm is effective in reducing the number of dimensions without affecting the prediction performance of each computational intelligence predictor

A computational intelligence approach to efficiently predicting review ratings in e-commerce

Sentiment analysis, also called opinion mining, is currently one of the most studied research fields which aims to analyse people's opinions. E-commerce websites allow users to share opinions about a product/service by providing textual reviews along with numerical ratings. These opinions greatly influence future consumer purchasing decisions. This paper introduces an innovative computational intelligence framework for efficiently predicting customer review ratings. The framework has been designed to deal with the dimensionality and noise which is typically apparent in large datasets containing customer reviews. The proposed framework integrates the techniques of Singular Value Decomposition (SVD) and dimensionality reduction, Fuzzy C-Means (FCM) and the Adaptive Neuro-Fuzzy Inference System (ANFIS). The performance of the proposed approach returned high accuracy and the results revealed that when large datasets are concerned, only a fraction of the data is needed for creating a system to predict the review ratings of textual reviews. Results from the experiments suggest that the proposed approach yields better prediction performance than other state-of-the-art rating predictors which are based on the conventional Artificial Neural Network, Fuzzy C-Means, and Support Vector Machine approaches. In addition, the proposed framework can be utilised for other classification and prediction tasks, and its neuro-fuzzy predictor module can be replaced by other classifiers

Deep-FS: a feature selection algorithm for deep Boltzmann machines

A Deep Boltzmann Machine is a model of a Deep Neural Network formed from multiple layers of neurons with nonlinear activation functions. The structure of a Deep Boltzmann Machine enables it to learn very complex relationships between features and facilitates advanced performance in learning of high-level representation of features, compared to conventional Artificial Neural Networks. Feature selection at the input level of Deep Neural Networks has not been well studied, despite its importance in reducing the input features processed by the deep learning model, which facilitates understanding of the data. This paper proposes a novel algorithm, Deep Feature Selection (Deep-FS), which is capable of removing irrelevant features from large datasets in order to reduce the number of inputs which are modelled during the learning process. The proposed Deep-FS algorithm utilizes a Deep Boltzmann Machine, and uses knowledge which is acquired during training to remove features at the beginning of the learning process. Reducing inputs is important because it prevents the network from learning the associations between the irrelevant features which negatively impact on the acquired knowledge of the network about the overall distribution of the data. The Deep-FS method embeds feature selection in a Restricted Boltzmann Machine which is used for training a Deep Boltzmann Machine. The generative property of the Restricted Boltzmann Machine is used to reconstruct eliminated features and calculate reconstructed errors, in order to evaluate the impact of eliminating features. The performance of the proposed approach was evaluated with experiments conducted using the MNIST, MIR-Flickr, GISETTE, MADELON and PANCAN datasets. The results revealed that the proposed Deep-FS method enables improved feature selection without loss of accuracy on the MIR-Flickr dataset, where Deep-FS reduced the number of input features by removing 775 features without reduction in performance. With regards to the MNIST dataset, Deep-FS reduced the number of input features by more than 45%; it reduced the network error from 0.97% to 0.90%, and also reduced processing and classification time by more than 5.5%. Additionally, when compared to classical feature selection methods, Deep-FS returned higher accuracy. The experimental results on GISETTE, MADELON and PANCAN showed that Deep-FS reduced 81%, 57% and 77% of the number of input features, respectively. Moreover, the proposed feature selection method reduced the classifier training time by 82%, 70% and 85% on GISETTE, MADELON and PANCAN datasets, respectively. Experiments with various datasets, comprising a large number of features and samples, revealed that the proposed Deep-FS algorithm overcomes the main limitations of classical feature selection algorithms. More specifically, most classical methods require, as a prerequisite, a pre-specified number of features to retain, however in Deep-FS this number is identified automatically. Deep-FS performs the feature selection task faster than classical feature selection algorithms which makes it suitable for deep learning tasks. In addition, Deep-FS is suitable for finding features in large and big datasets which are normally stored in data batches for faster and more efficient processing

A new approach to ontology-based semantic modelling for opinion mining

With the fast growth of World Wide Web 2.0, a great number of opinions about a variety of products have been published in blogs, forums, and social networks. Opinion mining tools are needed to enable users to efficiently process a large number of reviews found online, in order to determine the underlying opinions. This paper presents a new methodology for semantic modelling of the domain knowledge for opinion mining. In particular, the new methodology focuses on modelling the domain knowledge in such a way that it can be translated to a formal ontology, which can then be automatically enriched with ground facts obtained from public Linked Open Data resources. The methodology also considers procedures to link between the formal ontology and Natural Language Processing. Our approach successfully enriches the ontology with the relevant ground facts. This ontology can then be used to perform a variety of data mining tasks including sentiment analysis and information retrieval

Bio-inspired ganglion cell models for detecting horizontal and vertical movements

The retina performs the earlier stages of image processing in living beings and is composed of six different groups of cells, namely, the rods, cones, horizontal, bipolar, amacrine and ganglion cells. Each of those group of cells can be sub-divided into other types of cells that vary in shape, size, connectivity and functionality. Each cell is responsible for performing specific tasks in these early stages of biological image processing. Some of those cells are sensitive to horizontal and vertical movements. This paper proposes a multi-hierarchical spiking neural network architecture for detecting horizontal and vertical movements using a custom dataset which was generated in laboratory settings. The proposed architecture was designed to reflect the connectivity, behaviour and the number of layers found in the majority of vertebrates retinas, including humans. The architecture was trained using 2303 images and tested using 816 images. Simulation results revealed that each cell model is sensitive to vertical and horizontal movements with a detection error of 6.75 percent

Breast cancer diagnosis using a hybrid genetic algorithm for feature selection based on mutual information

Feature Selection is the process of selecting a subset of relevant features (i.e. predictors) for use in the construction of predictive models. This paper proposes a hybrid feature selection approach to breast cancer diagnosis which combines a Genetic Algorithm (GA) with Mutual Information (MI) for selecting the best combination of cancer predictors, with maximal discriminative capability. The selected features are then input into a classifier to predict whether a patient has breast cancer. Using a publicly available breast cancer dataset, experiments were performed to evaluate the performance of the Genetic Algorithm based on the Mutual Information approach with two different machine learning classifiers, namely the k-Nearest Neighbor (KNN), and Support vector machine (SVM), each tuned using different distance measures and kernel functions, respectively. The results revealed that the proposed hybrid approach is highly accurate for predicting breast cancer, and it is very promising for predicting other cancers using clinical data

111In-Octreoscan SPECT/CT hybrid imaging and 68Ga-DOTANOC PET/CT in neuroendocrine adenoma of the middle ear (NAME)

Neuroendocrine adenoma of the middle ear (NAME) represents a rare tumour consisting of an adenoma with mixed neuroendocrine differentiation. A 40-year-old woman was referred to our attention to further investigate the occurrence of a pathological tissue located in the mastoid process of the left temporal bone depicted by head CT and MRI scans. Histopathological examination revealed an epithelial neoplasm with neuroendocrine differentiation features, consistent with the diagnosis of NAME. In order to obtain an accurate differential diagnosis and confirmation of this rare disease, 111In-Octreoscan single photon emission computed tomography (SPECT)/CT and 68Ga-DOTANOC positron emission tomography (PET)/CT were performed, both showing overexpression of somatostatin receptors and thus corroborating the histopathological findings

Early 18F-FDG PET/CT in COVID-19

We report the case of who has been hospitalized complaining about fever in the last 4 days. Laboratory data revealed lymphocytopenia and leucopenia with an elevation of C-reactive protein and positive real-time reverse transcription-polymerase chain reaction (RT-PCR). Six days before this hospitalization, when the patient was asymptomatic, 18F-FDG PET/CT images were performed for the therapeutic control of liver metastases of colorectal cancer. Lung abnormality findings at 18F-FDG PET/CT images, unrelated to cancer metastases, but suspicious for viral infection, may suggest the presence of COVID-19 disease in its early phase before symptoms onset. Since PET/CT is more sensitive than CT scan in detecting host’s reaction, the added value of this technique could be monitoring disease progression and could be used as a biomarker of lung disease activity and therefore as an important tool for a better understanding of the factors that contribute to the progression of lung disease