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rule-based algorithms for Automatic Pronunciation of Portuguese Verbal Inflections 11

11th International Conference on Computational Processing of Portuguese (PROPOR)

作者： Marquiafavel, Vanessa Shulby, Christopher Veiga, Arlindo Proenca, Jorge Candeias, Sara Perdigao, Fernando Univ Estadual Paulista IBILCE Sj Do Rio Preto Brazil Univ Sao Paulo ICMC Sao Carlos Brazil Inst Telecomunicacoes Pole Coimbra Coimbra Portugal Univ Coimbra DEEC Coimbra Portugal

ISBN: (纸本)9783319097619;9783319097602

The correct automatic pronunciation of words is a nontrivial problem, even for inflexions of Portuguese verbs, and has not been systematically solved yet, if verbal irregularity is taken into account. The purpose of this work is to enhance a grapheme-to-phoneme system with a verb pronunciation system for both varieties of Portuguese, Brazilian (BP) and European (EP), given only its infinitive form. The most common verbs for BP and EP (1000 and 2600 respectively) constituted our database to test the pronunciation system. A detailed and systematic analysis of regular and non-regular pronunciation forms of the inflected verbs was performed, and an index of irregularity for verb pronunciation is proposed. A rule-based algorithm to pronounce all inflexions according to verb paradigms is also described. The defined paradigms are, with a high level of certainty, representative of all the verbs for Portuguese.

关键词： Brazilian Portuguese European Portuguese Verbs Pronunciation rule-based algorithms

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Methods for identifying health status from routinely collected health data: An overview

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INTEGRATIVE MEDICINE RESEARCH 2025年第1期14卷 101100页

作者： Liu, Mei Deng, Ke Wang, Mingqi He, Qiao Xu, Jiayue Li, Guowei Zou, Kang Sun, Xin Wang, Wen Sichuan Univ West China Hosp Inst Integrated Tradit Chinese & Western Med Chinese Evidence Based Med & Cochrane China Ctr Chengdu Peoples R China Hosp Chengdu Univ Tradit Chinese Med Chengdu Peoples R China NMPA Key Lab Real World Data Res & Evaluat Hainan Chengdu Peoples R China Sichuan Ctr Technol Innovat Real World Data Chengdu Peoples R China McMaster Univ Dept Hlth Res Methods Evidence & Impact Hamilton ON Canada Guangdong Second Prov Gen Hosp Ctr Clin Epidemiol & Methodol Guangzhou Guangdong Peoples R China St Josephs Healthcare Hamilton Res Inst Biostat Unit Hamilton ON Canada Sichuan Univ West China Sch Publ Hlth Chengdu Peoples R China Sichuan Univ West China Hosp 4 Chengdu Peoples R China

Routinely collected health data (RCD) are currently accelerating publications that evaluate the effectiveness and safety of medicines and medical devices. One of the fundamental steps in using these data is developing algorithms to identify health status that can be used for observational studies. However, the process and methodologies for identifying health status from RCD remain insufficiently understood. While most current methods rely on International Classification of Diseases (ICD) codes, they may not be universally applicable. Although machine learning methods hold promise for more accurately identifying the health status, they remain underutilized in RCD studies. To address these significant methodological gaps, we outline key steps and methodological considerations for identifying health statuses in observational studies using RCD. This review has the potential to boost the credibility of findings from observational studies that use RCD.

关键词： Routinely collected health data Health status Machine learning algorithms rule-based algorithms

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Using genetic algorithm based knowledge refinement model for dividend policy forecasting

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第18期39卷 13472-13479页

作者： Won, Chaehwan Kim, Jinhwa Bae, Jae Kwon Keimyung Univ Dept Management Informat Syst Taegu 704701 South Korea Sogang Univ Sch Business Seoul 121742 South Korea

Dividend policy is one of most important managerial decisions affecting the firm value. Although there are many studies regarding decision-making problems, such as credit policy decisions through bankruptcy prediction and credit scoring, there is no research, to our knowledge, about dividend prediction or dividend policy forecasting using machine learning approaches in spite of the significance of dividends. For dealing with the problems involved in literature, we suggest a knowledge refinement model that can refine the multiple rules extracted through rule-based algorithms from dividend data sets by utilizing genetic algorithm (GA). The new technique, called "GAKR (genetic algorithm knowledge refinement)", aims to combine the advantages of both knowledge consolidation and GA. The main result of the cross-validation procedure is the average accuracy rate of prediction in the five sets over the five iterations. The experiments show that GAKR model always outperforms other models in the performance of dividend policy prediction;we can predict future dividend policy more correctly than any other models. The major advantages of GAKR model can be summarized as follows: (1) Classification process of GAKR can be very fast with a compact set of rules. In other words, fast training mechanism of GAKR is possible regardless of data set sizes. (2) Multiple rules extracted by GAKR development process are much simpler and easier to understand. Moreover, GAKR model can discriminate redundant rules and inconsistent rules. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Dividend policy rule-based algorithms Genetic algorithm Knowledge refinement GAKR model

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SM-ruleMiner: Spider monkey based rule miner using novel fitness function for diabetes classification

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COMPUTERS IN BIOLOGY AND MEDICINE 2017年 81卷 79-92页

作者： Cheruku, Ramalingaswamy Edla, Damodar Reddy Kuppili, Venkatanareshbabu Natl Inst Technol Goa Dept Comp Sci & Engn Ponda 403401 Goa India

Diabetes is a major health challenge around the world. Existing rule-based classification systems have been widely used for diabetes diagnosis, even though they must overcome the challenge of producing a comprehensive optimal ruleset while balancing accuracy, sensitivity and specificity values. To resolve this drawback, in this paper, a Spider Monkey Optimization-based rule miner (SM-ruleMiner) has been proposed for diabetes classification. A novel fitness function has also been incorporated into SM-ruleMiner to generate a comprehensive optimal ruleset while balancing accuracy, sensitivity and specificity. The proposed rule-miner is compared against three rule-based algorithms, namely ID3, C4.5 and CART, along with several meta-heuristic based rule mining algorithms, on the Pima Indians Diabetes dataset using 10-fold cross validation. It has been observed that the proposed rule miner outperforms several well-known algorithms in terms of average classification accuracy and average sensitivity. Moreover, the proposed rule miner outperformed the other algorithms in terms of mean rule length and mean ruleset size.

关键词： Spider monkey optimization Classification rules rule-based algorithms Diabetes diagnosis Optimal ruleset

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Multiscale fully convolutional network-based approach for multilingual character segmentation

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IET COMPUTER VISION 2021年第6期15卷 449-461页

作者： Yu, Chao Liu, Jin Li, Yunhui Shanghai Maritime Univ Shanghai Peoples R China

Character segmentation is a challenging task for optical character recognition systems. Traditional methods usually utilize rule-based algorithms but most of them are not applicable in modern intelligent recognition applications that require high accuracy. It is especially the case for text containing Eastern Asian language characters with complex pictograph structures, such as Chinese. To alleviate this problem, this study proposes an encoder-decoder structure-based multiscale fully convolutional network (MSFCN) model for optical character segmentation. Comparing with other methods, MSFCN can not only effectively extract semantic details from images but also exploit boundary information of intervals between characters, thereby distinguishing characters from a background in pixel level. Extensive experiments have been conducted on two benchmark data sets of ICDAR2013 and MLCS. Obtained results prove that MSFCN achieves state-of-the-art segmentation performance and indicated its practical application value.

关键词： MSFCN image segmentation Eastern Asian language characters optical character segmentation convolutional network model image classification optical character recognition systems complex pictograph structures practical application value state-of-the-art segmentation performance multilingual character segmentation optical character recognition rule-based algorithms modern intelligent recognition applications encoder-decoder structure-based fully convolutional network-based approach

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ACRIPPER: A New Associative Classification based on RIPPER Algorithm

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JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2021年第1期20卷

作者： Abu-Arqoub, Mohammed Hadi, Wael Ishtaiwi, Abdelraouf Univ Petra Dept Comp Sci Amman Jordan Univ Petra Dept Informat Secur Amman Jordan

Associative Classification (AC) classifiers are of substantial interest due to their ability to be utilised for mining vast sets of rules. However, researchers over the decades have shown that a large number of these mined rules are trivial, irrelevant, redundant, and sometimes harmful, as they can cause decision-making bias. Accordingly, in our paper, we address these challenges and propose a new novel AC approach based on the RIPPER algorithm, which we refer to as ACRIPPER. Our new approach combines the strength of the RIPPER algorithm with the classical AC method, in order to achieve: (1) a reduction in the number of rules being mined, especially those rules that are largely insignificant;(2) a high level of integration among the confidence and support of the rules on one hand and the class imbalance level in the prediction phase on the other hand. Our experimental results, using 20 different well-known datasets, reveal that the proposed ACRIPPER significantly outperforms the well-known rule-based algorithms RIPPER and J48. Moreover, ACRIPPER significantly outperforms the current AC-based algorithms CBA, CMAR, ECBA, FACA, and ACPRISM. Finally, ACRIPPER is found to achieve the best average and ranking on the accuracy measure.

关键词： Associative classification classification rule-based algorithms data mining

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Artificial Intelligence-based System for Retinal Disease Diagnosis

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algorithms 2024年第7期17卷 315页

作者： Orlova, Ekaterina V. Ufa Univ Sci & Technol Dept Econ & Management Ufa 450076 Russia

The growth in the number of people suffering from eye diseases determines the relevance of research in the field of diagnosing retinal pathologies. Artificial intelligence models and algorithms based on measurements obtained via electrophysiological methods can significantly improve and speed up the analysis of results and diagnostics. We propose an approach to designing an artificial intelligent diagnosis system (AI diagnosis system) which includes an electrophysiological complex to collect objective information and an intelligent decision support system to justify the diagnosis. The task of diagnosing retinal diseases based on a set of heterogeneous data is considered as a multi-class classification on unbalanced data. The decision support system includes two classifiers-one classifier is based on a fuzzy model and a fuzzy rule base (RB-classifier) and one uses the stochastic gradient boosting algorithm (SGB-classifier). The efficiency of algorithms in a multi-class classification on unbalanced data is assessed based on two indicators-MAUC (multi-class area under curve) and MMCC (multi-class Matthews correlation coefficient). Combining two algorithms in a decision support system provides more accurate and reliable pathology identification. The accuracy of diagnostics using the proposed AI diagnosis system is 5-8% higher than the accuracy of a system using only diagnostics based on electrophysical indicators. The AI diagnosis system differs from other systems of this class in that it is based on the processing of objective electrophysiological data and socio-demographic data about patients, as well as subjective information from the anamnesis, which ensures increased efficiency of medical decision-making. The system is tested using actual data about retinal diseases from the Russian Institute of Eye Diseases and its high efficiency is proven. Simulation experiments conducted in various scenario conditions with different combinations of factors ensured the identific

关键词： artificial intelligence decision support system fuzzy model rule-based algorithms stochastic gradient boosting algorithm retinal disease diagnosis healthcare

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NooJ Grammars and Ethical algorithms: Tackling On-Line Hate Speech 12th

NooJ Grammars and Ethical Algorithms: Tackling On-Line Hate ...

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12th International Conference on Automatic Processing of Natural-Language Electronic Texts with NooJ (NooJ)

作者： Monteleone, Mario Univ Salerno Dipartimento Sci Polit Sociali & Comunicaz Fisciano Italy

ISBN: (纸本)9783030108670;9783030108687

The definition of "on-line hate speech" covers all forms of expression that propagate, incite, promote or justify hatred based on intolerance, including that expressed in the form of discrimination and hostility against minorities. Moreover, the concept of hatred includes other sub-concepts such as Homophobia, Racism, Chauvinism, Terrorism, Nationalism, Tolerance/Intolerance, and so on. Specifically, on-line hate speech is used in cases of cyber-harassment, to harm others deliberately, repeatedly and aggressively, in a way so to weaken victims psychologically. To contrast this phenomenon, EC has allocated a relevant amount of H2020 funds for the completion of specific research projects, the goal of which is the construction of computer tools to locate, evaluate and eventually block on-line hate speech. Today, the automatic tackling of online hatred is a daily-performed operation on Social Forums like Facebook, Twitter and Instagram. However, the algorithms these Social Forums use are stochastic/statistical, therefore not suitable to contextualize syntactically and semantically the words used inside posts. Therefore, with on-line hate speech tackling, statistical algorithms may produce inaccurate or even false results, with rather serious consequences.

关键词： NooJ NooJ finite-state automata/transducers On-line hate speech Ethical algorithms rule-based algorithms Statistical algorithms

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Applying Classification Techniques To Predict Students' Academic Results

Applying Classification Techniques To Predict Students' Acad...

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IEEE International Conference on Current Trends in Advanced Computing (ICCTAC)

作者： Senthil, S. Lin, Wong Mu Reva Univ Sch Comp Sci & Applicat Bengaluru India

ISBN: (纸本)9781509049974

The ability to predict students' academic results is due to the application of various models developed using Data Mining techniques. It is one of the many applications available in Educational Data Mining (EDM). Although there are many classification algorithms available and there have been many researches done to compare certain algorithms, this study aims to compare Bayes-based, ANN-based, Regression-based, SVM-based, Instance-based, Tree-based and rule-based classification algorithms using a dataset downloaded online from UCI Machine Learning Repository. The data were preprocessed and categorized to different datasets before applying Data Mining techniques. Dimension reduction and data re-balancing were applied and proved to increase prediction accuracy. The algorithms were compared in terms of accuracy rate, precision rate, AUC and model building time. Tree-based algorithms performed better than other categories of algorithms. Within Tree-based algorithms, RandomForest algorithm proved to be the best classification algorithm. Tree-based and rule-based algorithms reveal the key attributes contributing to accurate prediction of students' results.

关键词： Educational Data Mining (EDM) Classification Knowledge Discovery in Databases (KDD) Tree-based algorithms rule-based algorithms

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Automated generation of region based geometric questions 26

Automated generation of region based geometric questions

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26th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Singhal, Rahul Henz, Martin Natl Univ Singapore Sch Comp Singapore 117548 Singapore

ISBN: (纸本)9781479965731

We extend our previously proposed framework that combines a combinatorial approach, pattern matching and automated deduction to generate geometry questions which, directly or indirectly, require finding the congruent regions formed by the intersection of geometric objects. The extension involves proposing a knowledge representation for regions and a rule-based algorithm for generation of region-based knowledge representation. In addition, several algorithms such as circle/arc projection to straight line(s) are proposed to avoid numerical reasoning for proving congruent regions, making the solution eligible for high school geometry domain. Furthermore, we propose the integration of this framework with our previously proposed framework to generate questions involving both implicit construction and congruent regions. The system is able to generate the solution(s) of the questions for their validation. Such a system would help teachers to quickly generate large numbers of questions based on several properties of geometric objects such as length, angle, area and perimeter. Students can explore, revise and master specific topics covered in classes and textbooks based on generated questions. This system may also help standardize tests such as Primary School Leaving Exam (PSLE), GMAT and SAT. Our methodology uses (i) a combinatorial approach for generating geometric figures (ii) Pattern matching and rule-based approach for region generation (iii) automated deduction for checking equality of properties of geometric objects (iv) linear equation solver to generate new questions and solutions. By combining these methods, we are able to generate questions involving finding or proving congruence relationships between the regions generated by the geometric objects based on a various specifications such as objects and concepts. Experimental results show that a large number of questions can be generated in a short time. A survey shows that the generated questions and the solutions a

关键词： deductive reasoning rule-based algorithms pattern matching high school geometry

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