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

Authors: Sierra-Enriquez, Edgar E. | Valdez-Rodríguez, José E. | Felipe-Riveró, Edgardo M. | Calvo, Hiram

Article Type: Research Article

Abstract: In the medical area, the detection of invasive ductal carcinoma is the most common sub-type of all breast cancers; about 80% of all breast cancers are invasive ductal carcinomas. Detection of this type of cancer shows a great challenge for specialist doctors since the digital images of the sample must be analyzed by sections because the spatial dimensions of this kind of image are above 50k × 50k pixels; doing this operation manually takes long time to determine if the patient suffers this type of cancer. Time is essential for the patient because this cancer can invade quickly other parts …of the body. Its name reaffirms this characteristic, with the term "invasive" forming part of its name. With the purpose of solving this task, we propose an automatic methodology consisting in improving the performance of a convolutional neural network that classifies images containing invasive ductal carcinoma cells by highlighting cancer cells using several preprocessing methods such as histogram stretching and contrast enhancement. In this way, characteristics of the sub-images are extracted from the panoramic sample and it is possible to learn to classify them in a better way. Show more

Keywords: Invasive ductal carcinoma, histopathological images, convolutional neural networks, cancer classification

DOI: 10.3233/JIFS-219250

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4623-4631, 2022

Authors: Damian, Sergio | Calvo, Hiram | Gelbukh, Alexander

Article Type: Research Article

Abstract: The paper presents a classifier for fake news spreaders detection in social media. Detecting fake news spreaders is an important task because this kind of disinformation aims to change the reader’s opinion about a relevant topic for the society. This work presents a classifier that can compete with the ones that are found in the state-of-the-art. In addition, this work applies Explainable Artificial Intelligence (XIA) methods in order to understand the corpora used and how the model estimates results. The work focuses on the corpora developed by members of the PAN@CLEF 2020 competition. The score obtained surpasses the state-of-the-art with …a mean accuracy score of 0.7825. The solution uses XIA methods for the feature selection process, since they present more stability to the selection than most of traditional feature selection methods. Also, this work concludes that the detection done by the solution approach is generally based on the topic of the text. Show more

Keywords: Fake news spreaders detection, fake news detection, feature selection, user profiling, classification

DOI: 10.3233/JIFS-219251

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4633-4640, 2022

Authors: Agarwal, Raksha | Chatterjee, Niladri

Article Type: Research Article

Abstract: The present paper proposes a fuzzy inference system for query-focused multi-document text summarization (MTS). The overall scheme is based on Mamdani Inferencing scheme which helps in designing Fuzzy Rule base for inferencing about the decision variable from a set of antecedent variables. The antecedent variables chosen for the task are from linguistic and positional heuristics, and similarity of the documents with the user-defined query. The decision variable is the rank of the sentences as decided by the rules. The final summary is generated by solving an Integer Linear Programming problem. For abstraction coreference resolution is applied on the input sentences …in the pre-processing step. Although designed on the basis of a small set of antecedent variables the results are very promising. Show more

Keywords: Query-focused text summarization, mamdani fuzzy inference, text similarity, fuzzy ranking, integer linear programming

DOI: 10.3233/JIFS-219252

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4641-4652, 2022

Authors: Guzmán-Cabrera, Rafael | Hernández-Robles, Iván A. | González-Ramírez, Xiomara | Guzmán-Sepúlveda, José Rafael

Article Type: Research Article

Abstract: Probabilistic approaches are frequently used to describe irregular activity data to assist the design and development of devices. Unfortunately, useful estimations are not always feasible due to the large noise in the data modeled, as it occurs when estimating the sea waves potential for electricity generation. In this work we propose a simple methodology based on the use of joint probability models that allow discriminating extreme values, collected from measurements as pairs of independent points, while allowing the preservation of the essential statistics of the measurements. The outcome of the proposed methodology is an equivalent data series where large-amplitude fluctuations …are suppressed and, therefore, can be used for design purposes. For the evaluation of the proposed method, we used year-long databases of hourly-collected measurements of the wave’s height and period, performed at maritime buoys located in the Gulf of Mexico. These measurements are used to obtain a fluctuations-reduced representation of the energy potential of the waves that can be useful, for instance, for the design of electric generators. Show more

Keywords: Bivariate distributions, probability models, irregular activity, wave potential estimation

DOI: 10.3233/JIFS-219253

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4653-4658, 2022

Authors: Romero-Coripuna, Rosario Lissiet | Hernández-Farías, Delia Irazú | Murillo-Ortiz, Blanca | Córdova-Fraga, Teodoro

Article Type: Research Article

Abstract: Breast cancer is a very important health concern around the world. Early detection of such a disease increases the chances of survival. Among the available screening tools, there is the Electro-Impedance Mammography (EIM), which is a novel and less invasive method that captures the potential difference stored in breast tissues under the assumption that electrical properties among normal and pathologically altered tissues are different. In this paper, we address breast cancer detection as a multi-class problem aiming to determine the corresponding label in terms of the Breast Imaging Electrical Impedance classification system, the standard used by physicians for interpreting an …EIM mammogram. For experimental purposes, for the first time in the literature, we took advantage of a dataset comprising EIM of Mexican patients. Aiming to establish a baseline for this task, traditional supervised learning methods were used together with two different feature extraction techniques: raw pixel data and transfer learning. Besides, data augmentation was exploited for compensating data imbalance. Different experimental settings were evaluated reaching classification rates over 0.85 in F-score. KNN emerges as a very promising classifier for addressing this task. The obtained results allow us to validate the usefulness of traditional methods for classifying electro-impedance mammograms. Show more

Keywords: Breast cancer screening, electro-impedance mammography, medical image classification, BI-EIM, machine learning, transfer learning

DOI: 10.3233/JIFS-219254

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4659-4671, 2022

Authors: Reyes-Cocoletzi, Lauro | Olmos-Pineda, Ivan | Olvera-López, J. Arturo

Article Type: Research Article

Abstract: The cornerstone to achieve the development of autonomous ground driving with the lowest possible risk of collision in real traffic environments is the movement estimation obstacle. Predicting trajectories of multiple obstacles in dynamic traffic scenarios is a major challenge, especially when different types of obstacles such as vehicles and pedestrians are involved. According to the issues mentioned, in this work a novel method based on Bayesian dynamic networks is proposed to infer the paths of interest objects (IO). Environmental information is obtained through stereo video, the direction vectors of multiple obstacles are computed and the trajectories with the highest probability …of occurrence and the possibility of collision are highlighted. The proposed approach was evaluated using test environments considering different road layouts and multiple obstacles in real-world traffic scenarios. A comparison of the results obtained against the ground truth of the paths taken by each detected IO is performed. According to experimental results, the proposed method obtains a prediction rate of 75% for the change of direction taking into consideration the risk of collision. The importance of the proposal is that it does not obviate the risk of collision in contrast with related work. Show more

Keywords: Autonomous ground driving, estimation, Bayesian dynamic networks, trajectories, probability of occurrence

DOI: 10.3233/JIFS-219255

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4673-4684, 2022

Authors: Ballinas-Hernández, Ana Luisa | Olmos-Pineda, Ivan | Olvera-López, José Arturo

Article Type: Research Article

Abstract:  A current challenge for autonomous vehicles is the detection of irregularities on road surfaces in order to prevent accidents; in particular, speed bump detection is an important task for safe and comfortable autonomous navigation. There are some techniques that have achieved acceptable speed bump detection under optimal road surface conditions, especially when signs are well-marked. However, in developing countries it is very common to find unmarked speed bumps and existing techniques fail. In this paper a methodology to detect both marked and unmarked speed bumps is proposed, for clearly painted speed bumps we apply local binary patterns technique to extract …features from an image dataset. For unmarked speed bump detection, we apply stereo vision where point clouds obtained by the 3D reconstruction are converted to triangular meshes by applying Delaunay triangulation. A selection and extraction of the most relevant features is made to speed bump elevation on surfaces meshes. Results obtained have an important contribution and improve some of the existing techniques since the reconstruction of three-dimensional meshes provides relevant information for the detection of speed bumps by elevations on surfaces even though they are not marked. Show more

Keywords: Speed bump detection, road segmentation, stereo vision, triangular surface meshes, machine learning

DOI: 10.3233/JIFS-219256

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4685-4697, 2022

Authors: Rodriguez-Medina, Alma Eloisa | Dominguez-Isidro, Saul | Ramirez-Martinell, Alberto

Article Type: Research Article

Abstract: This paper presents the technical proposal of a novel approach based on Ant Colony Optimization (ACO) to recommend personalized microlearning paths considering the learning needs of the learner. In this study, the information of the learner was considered from a disciplinary ICT perspective, since the characteristics of our learner correspond to those of a professor with variable characteristics, such as the level of knowledge and their learning status. The recommendation problem is approached as an instance of the Traveling Salesman Problem (TSP), the educational pills represent the cities, the paths are the relationships between educational pills, the cost of going …from one pill to another can be estimated by their degree of difficulty as well as the performance of the learner during the individual test. The results prove the approach proposal capacity to suggest microlearning path personalized recommendation according to the different levels of knowledge of the learners. The higher the number of learners, the behavior of the algorithm benefits in terms of stability. Show more

Keywords: Ant colony optimization, personalized recommendations, microlearning, learning path, higher education

DOI: 10.3233/JIFS-219257

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4699-4708, 2022

Authors: Sánchez, Belém Priego | Cabrera, Rafael Guzman | Carrillo, Michel Velazquez | Castro, Wendy Morales

Article Type: Research Article

Abstract: The rise of digital communication systems provides an almost infinite source of information that can be useful to feed classification algorithms, so it makes use of an already categorized collection of opinions of the social network Twitter for the formation and generation of a model of classification of short texts; which aims to categorize the emotional tone found in an author’s Spanish-language digital text. In addition, linguistic, lexicographic and opinion mining computational tools are used to implement a series of methods that allow to automatically finding coincidences or orientations that allow determining the polarity of sentences and categorize them as …positive, negative or neutral considering their lemmas. The results obtained from the analysis of emotions and polarity of this project, on the test phrases allow to observe a direct relationship between the categorized emotional tone and it is positive, negative or neutral classification, which allows to provide additional information to know the intention that the author had when he created the sentence. Determining these characteristics can be useful as a consistent information objective that can be leveraged by sectors where the prevalence of a product or service depends on user opinion, product rating or turns with satisfaction metrics. Show more

Keywords: Sentiment analysis, polarity classification, Spanish emotion

DOI: 10.3233/JIFS-219258

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4709-4717, 2022

Authors: López-Medina, Marco A. | Marcial-Romero, J. Raymundo | De Ita Luna, Guillermo | Hernández, José A.

Article Type: Research Article

Abstract: We present a novel algorithm based on combinatorial operations on lists for computing the number of models on two conjunctive normal form Boolean formulas whose restricted graph is represented by a grid graph G m ,n . We show that our algorithm is correct and its time complexity is O ( t · 1 . 618 t + 2 + t · 1 . 618 2 t + 4 ) , where t  = n  · m is the total number of vertices in the graph. For this …class of formulas, we show that our proposal improves the asymptotic behavior of the time-complexity with respect of the current leader algorithm for counting models on two conjunctive form formulas of this kind. Show more

Keywords: #SAT, #2SAT, models of boolean formulas, combinatorial algorithms, complexity theory

DOI: 10.3233/JIFS-219259

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4719-4726, 2022