Journal of Intelligent & Fuzzy Systems - Volume 42, issue 5

Authors: Pinto, David | Beltrán, Beatriz | Singh, Vivek

Article Type: Editorial

Abstract: Language & Knowledge Engineering is essential for the successfully development of artificial intelligence. The technologies proposed in international forums are meant to improve all areas of our daily life whether it is related to production industries, social communities, government, education, or something else. We consider very important to reveal the recent advances Intelligent and Fuzzy Systems applied to Language & Knowledge Engineering because they are the base for the society of tomorrow. Thus, the aim of this special issue of Journal of Intelligent and Fuzzy Systems is to present a collection of papers that cover recent research results on the …two wide topics: language and knowledge engineering. Even if the special issue is structured into these two general topics, we have covered specific themes such as the following ones: Natural Language Processing, Knowledge engineering, Pattern recognition, Artificial Intelligence and Language, Information Processing, Machine Learning Applied to Text Processing, Image and Text Classification, Multimodal data analysis, sentiment analysis, etc. Show more

Keywords: Language engineering, knowledge engineering

DOI: 10.3233/JIFS-219220

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4299-4305, 2022

Authors: Ahmed, Usman | Lin, Jerry Chun-Wei | Srivastava, Gautam

Article Type: Research Article

Abstract: Deep learning methods have led to the state-of-the-art medical applications, such as image classification and segmentation. The data-driven deep learning application can help stakeholders for further collaboration. However, limited labeled data set limits the deep learning algorithms to be generalized for one domain into another. To handle the problem, meta-learning helps to solve this issue especially it can learn from a small set of data. We proposed a meta-learning-based image segmentation model that combines the learning of the state-of-the-art models and then used it to achieve domain adoption and high accuracy. Also, we proposed a prepossessing algorithm to increase the …usability of the segment part and remove noise from the new test images. The proposed model can achieve 0.94 precision and 0.92 recall. The ability is to increase 3.3% among the state-of-the-art algorithms. Show more

Keywords: Meta-learning, transfer learning, feature extraction, classification, segmentation

DOI: 10.3233/JIFS-219221

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4307-4313, 2022

Authors: Utsuki-Alexander, Taku | Rios-Martinez, Jorge | Madera, Francisco A. | Pérez-Espinosa, Humberto

Article Type: Research Article

Abstract: This work has been focused on the part of the population with hearing impairment who owns a dog and that worries about not listening the dog barks, specially when a risky situation is taking place at home. A survey was carried out on people with deafness problems to find out hazard situations which they are exposed at home. A system prototype was developed to be integrated as a component of ambient intelligence (AmI) for ambient assisted living (AAL) that serves to Hearing Impaired People (HIP). The prototype detects dog barks and notifies users through both a smart mobile app and …a visual feedback. It consists of a connection between a Raspberry Pi 3 card and a ReSpeaker Mic Array v2.0 microphone array; a communication module with a smartphone was implemented, which displays written messages or vibrations when receiving notifications. The cylinder-shaped device was designed by the authors and sent it to 3D print with a resin material. The prototype recognized the barking efficiently by using a machine learning model based on Support Vector Machine technique. The prototype was tested with deaf people which were satisfied with precision, signal intensity, and activation of lights. Show more

Keywords: Ambient intelligence, ambient assisted living, dog bark recognition, smart assistant device

DOI: 10.3233/JIFS-219222

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4315-4326, 2022

Authors: Trevino-Sanchez, Daniel | Alarcon-Aquino, Vicente

Article Type: Research Article

Abstract: The need to detect and classify objects correctly is a constant challenge, being able to recognize them at different scales and scenarios, sometimes cropped or badly lit is not an easy task. Convolutional neural networks (CNN) have become a widely applied technique since they are completely trainable and suitable to extract features. However, the growing number of convolutional neural networks applications constantly pushes their accuracy improvement. Initially, those improvements involved the use of large datasets, augmentation techniques, and complex algorithms. These methods may have a high computational cost. Nevertheless, feature extraction is known to be the heart of the problem. …As a result, other approaches combine different technologies to extract better features to improve the accuracy without the need of more powerful hardware resources. In this paper, we propose a hybrid pooling method that incorporates multiresolution analysis within the CNN layers to reduce the feature map size without losing details. To prevent relevant information from losing during the downsampling process an existing pooling method is combined with wavelet transform technique, keeping those details "alive" and enriching other stages of the CNN. Achieving better quality characteristics improves CNN accuracy. To validate this study, ten pooling methods, including the proposed model, are tested using four benchmark datasets. The results are compared with four of the evaluated methods, which are also considered as the state-of-the-art. Show more

Keywords: Convolutional neural network, feature extraction, lifting scheme, pooling layer, wavelet transform

DOI: 10.3233/JIFS-219223

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4327-4336, 2022

Authors: Ruiz Alonso, Dorian | Zepeda Cortés, Claudia | Castillo Zacatelco, Hilda | Carballido Carranza, José Luis | García Cué, José Luis

Article Type: Research Article

Abstract: This work deals with educational text mining, a field of natural language processing applied to education. The objective is to classify the feedback generated by teachers in online courses to the activities sent by students according to the model of Hattie and Timperley (2007), considering that feedback may be at the levels task, process, regulation, praise and other. Four multi-label classification methods of the data transformation approach - binary relevance, classification chains, power labelset and rakel-d - are compared with the base algorithms SVM, Random Forest, Logistic Regression and Naive Bayes. The methodology was applied to a case study in …which 11013 feedbacks written in Spanish language from 121 online courses of the Law degree from a public university in Mexico were collected from the Blackboard learning manager system. The results show that the random forests algorithms and vector support machines will have the best performance when using the binary relevance transformation and classifier chains methods. Show more

Keywords: Text mining, multi-label classification, educational data mining, online education

DOI: 10.3233/JIFS-219224

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4337-4343, 2022

Authors: Herrera, Oscar | Priego, Belém

Article Type: Research Article

Abstract: Traditionally, a few activation functions have been considered in neural networks, including bounded functions such as threshold, sigmoidal and hyperbolic-tangent, as well as unbounded ReLU, GELU, and Soft-plus, among other functions for deep learning, but the search for new activation functions still being an open research area. In this paper, wavelets are reconsidered as activation functions in neural networks and the performance of Gaussian family wavelets (first, second and third derivatives) are studied together with other functions available in Keras-Tensorflow. Experimental results show how the combination of these activation functions can improve the performance and supports the idea of extending …the list of activation functions to wavelets which can be available in high performance platforms. Show more

Keywords: deep learning, neural network, activation functions, wavelets, Keras-Tensorflow

DOI: 10.3233/JIFS-219225

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4345-4355, 2022

Authors: Martín-del-Campo-Rodríguez, Carolina | Sidorov, Grigori | Batyrshin, Ildar

Article Type: Research Article

Abstract: This paper presents a computational model for the unsupervised authorship attribution task based on a traditional machine learning scheme. An improvement over the state of the art is achieved by comparing different feature selection methods on the PAN17 author clustering dataset. To achieve this improvement, specific pre-processing and features extraction methods were proposed, such as a method to separate tokens by type to assign them to only one category. Similarly, special characters are used as part of the punctuation marks to improve the result obtained when applying typed character n -grams. The Weighted cosine similarity measure is applied to …improve the B 3 F-score by reducing the vector values where attributes are exclusive. This measure is used to define distances between documents, which later are occupied by the clustering algorithm to perform authorship attribution. Show more

Keywords: Authorship attribution, features selection, similarity measure, clustering, features extraction

DOI: 10.3233/JIFS-219226

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4357-4367, 2022

Authors: Loranca, Maria Beatriz Bernábe | Rosales, José Espinosa | Orea, Mirna Huerta | Cardiff, John

Article Type: Research Article

Abstract: The objective of this paper is to compare and evaluate statistically the behavior of two vaccines against cysticercus in a sample of female rabbits. The two vaccines under discussion are 1) S3Pvac-Papaya12 mg and 2) Wild Type (WT) or S3P Wild and also 3) Saline Solution. The challenge is to show that the developed vaccine, S3Pvac-Papaya, produces more antibodies and with better stability than the other vaccine and saline solution. With the aim of proving this conjecture, an analysis of variance (ANOVA) and multiple Fisher comparisons at 95% confidence were performed. The vaccine of interest, S3Pvac-Papaya, revealed in the box …diagram at T2 that the development of antibodies was high and showed little dispersion, which implies that the vaccine S3Pvac Papaya is statistically efficient in the production of antibodies. Finally, the mathematical contribution centers on highlighting the low use of inferential statistical techniques, comparing means of generated antibodies by a set of vaccines in order to determine which one is more efficient and reliable. Tacitly, a methodology both statistical and procedural has been proposed along this work, to apply when contrasting other kinds of vaccines in both animals and humans for diverse conditions. Show more

Keywords: Cysticercosis Pisiformis, S3Pvac-Papaya vaccine, cellular and humoral immune response, statistical analysis

DOI: 10.3233/JIFS-219227

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4369-4378, 2022

Authors: Chatterjee, Niladri | Roy, Aayush Singha | Yadav, Nidhika

Article Type: Research Article

Abstract: The present work proposes an application of Soft Rough Set and its span for unsupervised keyword extraction. In recent times Soft Rough Sets are being applied in various domains, though none of its applications are in the area of keyword extraction. On the other hand, the concept of Rough Set based span has been developed for improved efficiency in the domain of extractive text summarization. In this work we amalgamate these two techniques, called Soft Rough Set based Span (SRS), to provide an effective solution for keyword extraction from texts. The universe for Soft Rough Set is taken to be …a collection of words from the input texts. SRS provides an ideal platform for identifying the set of keywords from the input text which cannot always be defined clearly and unambiguously. The proposed technique uses greedy algorithm for computing spanning sets. The experimental results suggest that extraction of keywords using the proposed scheme gives consistent results across different domains. Also, it has been found to be more efficient in comparison with several existing unsupervised techniques. Show more

Keywords: Keyword extraction, Rough Set, Soft Rough Set, Rough Set based Span, natural language processing

DOI: 10.3233/JIFS-219228

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4379-4386, 2022

Authors: Morveli-Espinoza, Mariela | Nieves, Juan Carlos | Tacla, Cesar Augusto

Article Type: Research Article

Abstract: Human-aware Artificial Intelligent systems are goal directed autonomous systems that are capable of interacting, collaborating, and teaming with humans. Activity reasoning is a formal reasoning approach that aims to provide common sense reasoning capabilities to these interactive and intelligent systems. This reasoning can be done by considering evidences –which may be conflicting–related to activities a human performs. In this context, it is important to consider the temporality of such evidence in order to distinguish activities and to analyse the relations between activities. Our approach is based on formal argumentation reasoning, specifically, Timed Argumentation Frameworks (TAF), which is an appropriate technique …for dealing with inconsistencies in knowledge bases. Our approach involves two steps: local selection and global selection. In the local selection, a model of the world and of the human’s mind is constructed in form of hypothetical fragments of activities (pieces of evidences) by considering a set of observations. These hypothetical fragments have two kinds of relations: a conflict relation and a temporal relation. Based on these relations, the argumentation attack notion is defined. We define two forms of attacks namely the strong and the weak attack. The former has the same characteristics of attacks in TAF whereas for the latter the TAF approach has to be extended. For determining consistent sets of hypothetical fragments, that are part of an activity or are part of a set of non-conflicting activities, extension-based argumentation semantics are applied. In the global selection, the degrees of fulfillment of activities is determined. We study some properties of our approach and apply it to a scenario where a human performs activities with different temporal relations. Show more

Keywords: Formal argumentation, intention recognition, activity recognition, activity reasoning, timed argumentation frameworks

DOI: 10.3233/JIFS-219229

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4387-4398, 2022