Article
Computer Science, Information Systems
Stefano Ferilli
Summary: The increasing production of digital documents has created a need for automatic tools that can analyze and extract knowledge from these documents, helping humans deal with information overload. Natural language processing tools are essential in converting sentences into computer-friendly representations for inference and reasoning. However, these tools rely on manually produced linguistic resources, such as concept taxonomies, which are language-specific and costly to create. This paper proposes an intelligent module that extracts domain knowledge from free text using Concept Hierarchy Extraction techniques, with a focus on recognizing different types of indirect objects in English.
Article
Computer Science, Artificial Intelligence
Manuel Ojeda-Hernandez, Domingo Lopez-Rodriguez, Angel Mora
Summary: In this paper, a novel approach for sentiment analysis using Formal Concept Analysis (FCA) to create customised dictionaries is presented. It outperforms other standard dictionaries by achieving better performance on a dataset of tweets categorised into positive and negative polarity.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2023)
Article
Computer Science, Information Systems
Rage Uday Kiran, Pamalla Veena, Penugonda Ravikumar, Chennupati Saideep, Koji Zettsu, Haichuan Shang, Masashi Toyoda, Masaru Kitsuregawa, P. Krishna Reddy
Summary: Periodic pattern mining is an emerging technique for knowledge discovery. This paper proposes a novel model and algorithm to find partial periodic patterns in temporal databases, and demonstrates their effectiveness and scalability through comprehensive experiments.
Article
Computer Science, Artificial Intelligence
Nasrin Kalanat
Summary: In summary, data mining methods are developed to extract valuable knowledge from a vast amount of data to support decision-making. However, there is a noticeable gap between delivered patterns and business expectations, and Actionable Knowledge Discovery (AKD) aims to narrow this gap.
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
(2022)
Article
Business
Giuseppe Fenza, Mariacristina Gallo, Vincenzo Loia, Alessandra Petrone, Claudio Stanzione
Summary: Concept drift refers to the unpredictable changes in the underlying distribution of streaming data over time. Detecting, interpreting, and adapting to concept drift is crucial in concept drift research. It is found that machine learning in a concept drift environment produces poor results without handling drift. This study proposes a concept drift detection index based on Fuzzy Formal Concept Analysis theory to predict when the performance of a machine learning model for text-stream classifiers is low. Experimental results show a significant correlation between the proposed index and the accuracy of Random Forest, Naive Bayes, and Passive Aggressive models, suggesting that the index can prevent incorrect classifications and aid in retraining decisions.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2023)
Review
Multidisciplinary Sciences
Hang Yan, Mingxue Ma, Ying Wu, Hongqin Fan, Chao Dong
Summary: This study conducts a comprehensive review of text mining applications in the construction industry through surveying articles. It analyzes the prime application fields, key challenges, and future directions of text mining. This research helps the research community to understand the latest developments in text mining applications in the construction industry and identifies future research directions.
Article
Computer Science, Information Systems
Milena Frtunic Gligorijevic, Milos Bogdanovic, Natasa Veljkovic, Leonid Stoimenov
Summary: Government institutions have released a large number of datasets on their open data portals, categorized based on different criteria. However, missing information makes it difficult to find datasets in all ways, as the number of datasets on the portals grows. The EODClassifier framework is introduced to suggest the best category match for datasets and utilize formal concept analysis to categorize uncategorized open datasets.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING
(2021)
Article
Computer Science, Information Systems
Erhe Yang, Fei Hao, Yixuan Yang, Carmen De Maio, Aziz Nasridinov, Geyong Min, Laurence T. T. Yang
Summary: Knowledge graph is widely used in different fields to describe entities using RDF data. However, the increasing RDF descriptions of entities lead to information overload. In this article, the authors propose an incremental entity summarization method called IES-FCA, which leverages Formal Concept Analysis (FCA). Experimental results show that IES-FCA outperforms existing algorithms in terms of time consumption and effectiveness.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2022)
Article
Business
MyoungHoon Lee, Suhyeon Kim, Hangyeol Kim, Junghye Lee
Summary: To capture emerging technologies in the fast-changing technology market, this study proposes a new technology opportunity discovery framework that uses text mining and a knowledge graph to exploit the information from technology, new technology-based firms (NTBFs), and investors. Empirical results demonstrate the accuracy and validity of the framework.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2022)
Article
Computer Science, Artificial Intelligence
Lubomir Antoni, M. Eugenia Cornejo, Jesus Medina, Eloisa Ramirez-Poussa
Summary: The article discusses the issue of reducing information in databases through formal concept analysis, focusing on multi-adjoint concept lattices in a fuzzy environment. Algorithms are introduced to discover information in relational systems, allowing for classification and creation of minimal attribute subsets that preserve the original knowledge system's information.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Biochemistry & Molecular Biology
John A. Bachman, Benjamin M. Gyori, Peter K. Sorger
Summary: This study presents an approach to accurately assemble molecular mechanisms by using multiple natural language processing systems and INDRA, which improves the reliability of machine reading and assembles non-redundant mechanistic knowledge. Through this approach, the study extends protein-protein interaction databases and provides explanations for co-dependencies in the Cancer Dependency Map.
MOLECULAR SYSTEMS BIOLOGY
(2023)
Article
Green & Sustainable Science & Technology
JeeHee Lee, Youngjib Ham, June-Seong Yi
Summary: This study used text mining methods to examine a large amount of construction legal cases, exploring the types of contract conditions frequently referenced in the final decisions of disputes. The findings indicate that similar patterns of disputes occur repeatedly in construction-related legal cases, and the discovered dispute topics suggest that mutually agreed upon contract terms and conditions are important in dispute resolution.
Article
Computer Science, Artificial Intelligence
Anna Formica
Summary: Formal Concept Analysis (FCA) is a mathematical framework that can support critical activities for the development of the Semantic Web, such as Similarity Reasoning, which aims to identify semantically close concepts. This paper addresses FCA with Interordinal scaling (IFCA) to model uncertainty information, proposing a method for evaluating concept similarity in IFCA, a topic of increasing interest in the literature.
COMPUTING AND INFORMATICS
(2021)
Article
Mathematical & Computational Biology
Antonio Miranda-Escalada, Farrokh Mehryary, Jouni Luoma, Darryl Estrada-Zavala, Luis Gasco, Sampo Pyysalo, Alfonso Valencia, Martin Krallinger
Summary: Efficiently exploiting drug-related information from scientific literature is increasingly challenging, especially for drug-gene/protein interactions. To address this, the DrugProt track was organized at BioCreative VII, releasing the DrugProt Gold Standard corpus and generating a silver standard knowledge graph. Participants implemented deep learning approaches, achieving high precision and recall values for certain relation types.
DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
(2023)
Review
Biochemical Research Methods
Liang Chen, Changliang Wang, Huiyan Sun, Juexin Wang, Yanchun Liang, Yan Wang, Garry Wong
Summary: Circular RNAs (circRNAs) are a unique class of RNA molecules produced by back-splicing of linear RNA, and recent advances in sequencing technologies and bioinformatics tools have greatly expanded our understanding of their biological functions and practical applications. There are now numerous bioinformatics tools available for circRNA annotation, identification, and network analysis, which continue to evolve to support various research projects in this field.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Oncology
Alexander Karachunskiy, Gesche Tallen, Julia Roumiantseva, Svetlana Lagoiko, Almira Chervova, Arend von Stackelberg, Olga Aleinikova, Oleg Bydanov, Lyudmila Bajdun, Tatiana Nasedkina, Natalia Korepanova, Sergei Kuznetsov, Galina Novichkova, Marina Goroshkova, Dmitry Litvinov, Natalia Myakova, Natalia Ponomareva, Evgeniya Inyushkina, Konstantin Kondratchik, Julia Abugova, Larisa Fechina, Oleg Arakaev, Alexander Karelin, Vladimir Lebedev, Natalia Judina, Gusel Scharapova, Irina Spichak, Anastasia Shamardina, Olga Ryskal, Alexander Shapochnik, Alexander Rumjanzew, Joachim Boos, Guenter Henze
JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY
(2019)
Editorial Material
Mathematics, Applied
Marianne Huchard, Sergei O. Kuznetsov
DISCRETE APPLIED MATHEMATICS
(2020)
Article
Computer Science, Theory & Methods
Aimene Belfodil, Sergei O. Kuznetsov, Mehdi Kaytoue
INTERNATIONAL JOURNAL OF GENERAL SYSTEMS
(2020)
Article
Computer Science, Artificial Intelligence
Dmitry Ignatov, Andrey Ignatov
PATTERN RECOGNITION LETTERS
(2020)
Article
Computer Science, Theory & Methods
Christophe Demko, Karell Bertet, Cyril Faucher, Jean-Francois Viaud, Sergei O. Kuznetsov
THEORETICAL COMPUTER SCIENCE
(2020)
Article
Computer Science, Artificial Intelligence
Tatiana Makhalova, Sergei O. Kuznetsov, Amedeo Napoli
Summary: Research shows that there is great potential for development in the field of numerical dataset mining. The Mint algorithm proposed in this paper, based on the MDL principle, is able to discover useful, non-redundant, overlapping patterns that cover meaningful groups of objects.
DATA MINING AND KNOWLEDGE DISCOVERY
(2022)
Article
Computer Science, Artificial Intelligence
Man Tianxing, Mikhail Lushnov, Dmitry I. Ignatov, Yulia Alexandrovna Shichkina, Natalia Alexandrovna Zhukova, Alexander Ivanovich Vodyaho
Summary: This article proposes an ontology-based approach to help users choose appropriate data mining techniques for analyzing domain data. Users can query for suitable algorithms step by step based on the current data characteristics and task requirements.
PEERJ COMPUTER SCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Dmitry I. Ignatov, Leonard Kwuida
Summary: This paper proposes the usage of two power indices for ranking attributes of closed sets, particularly in the context of interpretable machine learning. The paper provides a detailed explanation of the computation methods and properties of the indices, and validates them through experiments with model and real datasets.
ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Muhammad Zeeshan Younas, Muhammad Shahid Iqbal Malik, Dmitry I. Ignatov
Summary: This paper proposes a novel approach that integrates word2vec and BERT models, along with domain-specific lexicon approaches, to detect product defects for cell phones. Compared to existing studies, this approach shows better performance in detecting product defects and has been proven to be effective in the Apple iPhone and Samsung cell phone industry.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Muhammad Shahid Iqbal Malik, Uswa Cheema, Dmitry I. Ignatov
Summary: This paper proposes a hierarchical classification model for identifying threatening content and target in Urdu tweets. By utilizing the Urdu-BERT language model and transfer learning, the fine-tuned model achieves state-of-the-art performance in threatening content identification and target identification tasks.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Proceedings Paper
Computer Science, Information Systems
Sergey Naumov, Marina Ananyeva, Oleg Lashinin, Sergey Kolesnikov, Dmitry I. Ignatov
Summary: This study explores the potential improvement of next-basket recommender systems by incorporating time information. Experimental results on three real-world datasets demonstrate that adding time information can enhance prediction quality, paving the way for further research directions in the field of next-basket recommendations.
ADVANCES IN INFORMATION RETRIEVAL, ECIR 2023, PT II
(2023)
Proceedings Paper
Nanoscience & Nanotechnology
Larisa Sorokina, Egor Lebedev, Dmitry Ignatov, Roman Ryazanov, Artem Sysa, Yuriy Shaman, Dmitry Gromov
11TH INTERNATIONAL CONFERENCE ON NANOMATERIALS - RESEARCH & APPLICATION (NANOCON 2019)
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Tatiana Makhalova, Sergei O. Kuznetsov, Amedeo Napoli
FORMAL CONCEPT ANALYSIS (ICFCA 2019)
(2019)
Proceedings Paper
Engineering, Electrical & Electronic
Dmitrii Ryabkin, Nadezhda Taricyna, Dmitry Ignatov, Evgenii Piankov, Mariya Kupriyanova
PROCEEDINGS OF THE 2019 IEEE CONFERENCE OF RUSSIAN YOUNG RESEARCHERS IN ELECTRICAL AND ELECTRONIC ENGINEERING (EICONRUS)
(2019)
Proceedings Paper
Computer Science, Theory & Methods
Tatiana Makhalov, Sergei O. Kuznetsov, Amedeo Napoli
2019 DATA COMPRESSION CONFERENCE (DCC)
(2019)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
