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

Authors: Laskar, Sahinur Rahman | Khilji, Abdullah Faiz Ur Rahman | Pakray, Partha | Bandyopadhyay, Sivaji

Article Type: Research Article

Abstract: Language translation is essential to bring the world closer and plays a significant part in building a community among people of different linguistic backgrounds. Machine translation dramatically helps in removing the language barrier and allows easier communication among linguistically diverse communities. Due to the unavailability of resources, major languages of the world are accounted as low-resource languages. This leads to a challenging task of automating translation among various such languages to benefit indigenous speakers. This article investigates neural machine translation for the English–Assamese resource-poor language pair by tackling insufficient data and out-of-vocabulary problems. We have also proposed an approach of …data augmentation-based NMT, which exploits synthetic parallel data and shows significantly improved translation accuracy for English-to-Assamese and Assamese-to-English translation and obtained state-of-the-art results. Show more

Keywords: English–Assamese, NMT, low-resource, transformer, RNN

DOI: 10.3233/JIFS-219260

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4727-4738, 2022

Authors: Rangel, Nahum | Godoy-Calderon, Salvador | Calvo, Hiram

Article Type: Research Article

Abstract: Artificial music tutors are needed for assisting a performer during his/her practice time whenever a human tutor is not available. But for these artificial tutors to be intelligent and fulfill the role of a music tutor, they have to be able to identify errors made by the performer while playing a musical sequence. This task is not a trivial one, since all musical activities are considered as open-ended domains. Therefore, not only there is no unique correct way of performing a musical sequence, but also the analysis made by the tutor has to consider the development level of the performer, …the difficulty level of the performed musical sequence, and many other variables. This paper describes an ongoing research that uses cascading connected layers of symbolic processing as the core of a human-performed error identification and characterization module able to overcome the complexity of the studied open-ended domain. Show more

Keywords: Artificial intelligence, intelligent music tutors, musical sequence, symbolic processing

DOI: 10.3233/JIFS-219261

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4739-4750, 2022

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

Article Type: Research Article

Abstract: Frequent pattern mining (FIM) identifies the most important patterns in data sets. However, due to the huge and high-dimensional nature of transactional data, classical pattern mining techniques suffer from the limitations of dimensions and data annotations. Recently, data mining while preserving privacy is considered as an important research area. Information privacy is a tradeoff that must be considered when using data. Through many years, privacy-preserving data mining (PPDM) made use of methods that are mostly based on heuristics. The operation of deletion was used to hide the sensitive information in PPDM. In this study, we used deep active learning to …protect private and sensitive information. This paper combines entropy-based active learning with an attention-based approach to effectively hide sensitive patterns. The constructed models are then validated using high-dimensional transactional data with attention-based and active learning methods in a reinforcement environment. The results show that the proposed model can support and improve the effectiveness of decision-making by increasing the number of training instances through the use of a pooling technique and an entropy uncertainty measure. The proposed paradigm can achieve data sanitization by the hiding sensitive items and avoiding to hide the non-sensitive items. The model outperforms greedy, genetic, and particle swarm optimization approaches. Show more

Keywords: deep learning, attention network, data mining, reinforcement learning, classification

DOI: 10.3233/JIFS-219262

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4751-4758, 2022

Authors: Fuentes-Ramos, Mirta | Sánchez-DelaCruz, Eddy | Meza-Ruiz, Iván-Vladimir | Loeza-Mejía, Cecilia-Irene

Article Type: Research Article

Abstract: Neurodegenerative diseases affect a large part of the population in the world and also in Mexico, deteriorating gradually the quality of patients’ life. Therefore, it is important to diagnose them with a high degree of reliability. In order to solve it, various computational methods have been applied in the analysis of biomarkers of human gait. In this study, we propose employing the automatic model selection and hyperparameter optimization method that has not been addressed before for this problem. Our results showed highly competitive percentages of correctly classified instances when discriminating binary and multiclass sets of neurodegenerative diseases: Parkinson’s disease, …Huntington’s disease, and Spinocerebellar ataxias. Show more

Keywords: Random forest, categorization, gait recognition, biomarkers

DOI: 10.3233/JIFS-219263

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4759-4767, 2022

Authors: Ashraf, Noman | Rafiq, Abid | Butt, Sabur | Shehzad, Hafiz Muhammad Faisal | Sidorov, Grigori | Gelbukh, Alexander

Article Type: Research Article

Abstract: On YouTube, billions of videos are watched online and millions of short messages are posted each day. YouTube along with other social networking sites are used by individuals and extremist groups for spreading hatred among users. In this paper, we consider religion as the most targeted domain for spreading hate speech among people of different religions. We present a methodology for the detection of religion-based hate videos on YouTube. Messages posted on YouTube videos generally express the opinions of users’ related to that video. We provide a novel dataset for religious hate speech detection on Youtube comments. The proposed methodology …applies data mining techniques on extracted comments from religious videos in order to filter religion-oriented messages and detect those videos which are used for spreading hate. The supervised learning algorithms: Support Vector Machine (SVM), Logistic Regression (LR), and k-Nearest Neighbor (k-NN) are used for baseline results. Show more

Keywords: Hate speech detection, religious extremism detection, YouTube comment analysis, hate speech dataset

DOI: 10.3233/JIFS-219264

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4769-4777, 2022

Authors: Vázquez-González, Stephanie | Somodevilla-García, María | López, Rosalva Loreto | Gómez-Adorno, Helena

Article Type: Research Article

Abstract: The aim of this article is to contextualize and describe the gathering and annotation of a conventual Hispanic and Novo Hispanic texts corpus for emotions identification. Such corpus will be the dataset for an emotions identification model based on machine learning ∖ deep learning techniques. Furthermore, this document describes several exploratory experiments carried out on the corpus. Within these experiments, it is described how the corpus is also used to obtain a lexicon mapped to polarities and emotions, and how some of the documents are hand-labeled by experts for the evaluation of the Machine Learning ∖ Deep learning -based emotion …classification model. Finally, the future uses and experiments with said corpus are described. Show more

Keywords: Corpus building, sentiment analysis, historical documents, emotions identification

DOI: 10.3233/JIFS-219265

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4779-4787, 2022

Authors: Tahir, Bilal | Mehmood, Muhammad Amir

Article Type: Research Article

Abstract:  The confluence of high performance computing algorithms and large scale high-quality data has led to the availability of cutting edge tools in computational linguistics. However, these state-of-the-art tools are available only for the major languages of the world. The preparation of large scale high-quality corpora for low-resource language such as Urdu is a challenging task as it requires huge computational and human resources. In this paper, we build and analyze a large scale Urdu language Twitter corpus Anbar . For this purpose, we collect 106.9 million Urdu tweets posted by 1.69 million users during one year (September 2018-August 2019). Our …corpus consists of tweets with a rich vocabulary of 3.8 million unique tokens along with 58K hashtags and 62K URLs. Moreover, it contains 75.9 million (71.0%) retweets and 847K geotagged tweets. Furthermore, we examine Anbar using a variety of metrics like temporal frequency of tweets, vocabulary size, geo-location, user characteristics, and entities distribution. To the best of our knowledge, this is the largest repository of Urdu language tweets for the NLP research community which can be used for Natural Language Understanding (NLU), social analytics, and fake news detection. Show more

Keywords: Social media analytic, Urdu Language corpus, large scale repository, text corpus, regional languages corpora

DOI: 10.3233/JIFS-219266

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4789-4800, 2022

Article Type: Retraction

DOI: 10.3233/JIFS-219320

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4801-4801, 2022

Article Type: Retraction

DOI: 10.3233/JIFS-219321

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4803-4803, 2022