Browse
Subjects
Records with Subject: Intelligent Systems
Showing records 26 to 50 of 261. [First] Page: 1 2 3 4 5 6 Last
Qualitative Analysis of the Perception of Company Managers in Knowledge Management in the Maintenance Activity in the Era of Industry 4.0
Javier Cárcel-Carrasco, Consuelo Gómez-Gómez
March 28, 2023 (v1)
Keywords: industrial maintenance, Industry 4.0, knowledge management, large building maintenance, tacit knowledge
In industrial maintenance activity, very sophisticated technical and human factors are needed to achieve the great process or service that the company provides, with a large dose of knowledge based on the personal experience of maintenance technicians. However, the management and application of knowledge in this activity is often relegated to a third level (or simply forgotten). The aim of this study is to identify, classify and prioritize the different barriers and facilitators that can be found in maintenance organizations of the company in reference to knowledge management in strategic maintenance activities, and what competitive advantages could be used for their appropriate introduction in the company. For this, qualitative techniques have been used through a field study and observation, as well as semi-structured interviews between company directors and maintenance managers of first-level companies in various sectors (industrial or services), to draw conclusions on the applicatio... [more]
Assessment of the Operation Process of Wind Power Plant’s Equipment with the Use of an Artificial Neural Network
Stanisław Duer
March 27, 2023 (v1)
Keywords: artificial neural networks, diagnostics information, expert system, intelligent system, knowledge base, servicing process, system modeling, wind power plant
In this article, a description is presented of simulation investigations concerning the quality of regeneration effects of a technical object in an intelligent system with an artificial neural network. All repairable technical objects used are subject to a cyclic (random) process of damages and repairs in the time of their operation. A reduction of the parameters connected with the use of objects is the fundamental feature of this process. This results in the need of a regeneration (technical maintenance) of this object. Regeneration of an object in an intelligent system with an artificial neural network constitutes an effective approach to this problem. The problem of qualitative assessments of a maintenance process organized in this manner is the focus of this article. For this purpose, a program of simulation investigations is presented. The research program consists of a description of the models of the operation processes of technical objects, determination of the input data to th... [more]
Assessment of Barriers to Knowledge and Experience Transfer in Major Maintenance Activities
Lilian. O. Iheukwumere-Esotu, Akilu Yunusa Kaltungo
March 24, 2023 (v1)
Keywords: failure analysis, industrial maintenance management, knowledge management, major overhauls-outages-shutdowns-turnarounds, multicriteria decision analysis
Systematic failure analysis generally enhances the ability of engineering decision-makers to obtain a holistic view of the causal relationships that often exist within the systems they manage. Such analyses are made more difficult by uncertainties and organisational complexities associated with critical and inevitable industrial maintenance activities such as major overhauls, outages, shutdowns, and turnarounds (MoOSTs). This is perhaps due to the ratio of tasks-to-duration typically permitted. While core themes of MoOSTs including planning, contracts, costing, execution, etc., have been the focus of most research activities, it is worth noting that the ability to successfully transfer and retain MoOSTs knowledge is still under-investigated. Effectively implementing a case study-based approach for data collection, the current study explores the harmonisation of various risk assessments (i.e., fault tree analysis and reliability block diagrams) and multicriteria decision analysis (MCDA)... [more]
Predictive Maintenance Neural Control Algorithm for Defect Detection of the Power Plants Rotating Machines Using Augmented Reality Goggles
Krzysztof Lalik, Filip Wątorek
March 6, 2023 (v1)
Keywords: augmented reality, intelligent systems, smart sensors, vibrodiagnostics
The concept of predictive and preventive maintenance and constant monitoring of the technical condition of industrial machinery is currently being greatly improved by the development of artificial intelligence and deep learning algorithms in particular. The advancement of such methods can vastly improve the overall effectiveness and efficiency of systems designed for wear analysis and detection of vibrations that can indicate changes in the physical structure of the industrial components such as bearings, motor shafts, and housing, as well as other parts involved in rotary movement. Recently this concept was also adapted to the field of renewable energy and the automotive industry. The core of the presented prototype is an innovative interface interconnected with augmented reality (AR). The proposed integration of AR goggles allowed for constructing a platform that could acquire data used in rotary components technical evaluation and that could enable direct interaction with the user.... [more]
Application of Artificial Intelligence Technologies to Assess the Quality of Structures
Anton Zhilenkov, Sergei Chernyi, Vitalii Emelianov
March 6, 2023 (v1)
Keywords: intelligent system, metallographic analysis, neural networks, precedents method, software
The timeliness of the complex automated diagnostics of the metal condition for all characteristics has been substantiated. An algorithm for the automation of metallographic quality control of metals is proposed and described. It is based on the use of neural networks for recognizing images of metal microstructures and a precedent method for determining the metal grade. An approach to preliminarily process the images of metal microstructures is described. The structure of a neural network has been developed to determine the quantitative characteristics of metals. The results of the functioning of neural networks for determining the quantitative characteristics of metals are presented. The high accuracy of determining the characteristics of metals using neural networks is shown. Software has been developed for the automated recognition of images of metal microstructures, and for the determination of the metal grade. Comparative results of carrying out metallographic analysis with the dev... [more]
Adaptation of Fire-Fighting Systems to Localization of Fires in the Premises: Review
Geniy Kuznetsov, Nikolay Kopylov, Elena Sushkina, Alena Zhdanova
March 3, 2023 (v1)
Keywords: different purpose premises, extinguishing compositions, fire, fire confinement, fire detection
Fire protection is a basic safety issue for all categories of buildings. The criteria for effective fire suppression and the characteristics of extinguishing systems in insulated areas depend on a combination of factors. The main influences include the type of combustible material, ambient temperature, type of spray extinguisher, air inflow and outflow conditions, and space geometry. This article analyzes the most widely used fire-extinguishing technologies in different locations. The main aspects of using the pulsed delivery technology of extinguishing liquid are considered. Based on the analysis of publications from the last decade, it is possible to develop intelligent systems for recording fires and extinguishing fires in the premises.
Reliability Testing of Wind Power Plant Devices with the Use of an Intelligent Diagnostic System
Stanislaw Duer, Jacek Paś, Marek Stawowy, Aneta Hapka, Radosław Duer, Arkadiusz Ostrowski, Marek Woźniak
February 28, 2023 (v1)
Keywords: expert system, intelligent systems, Markov processes, reliability, servicing process, simulation testing
This paper introduces the issue of reliability simulation studies of wind farm equipment in the process of an operation. By the improvement, retrofitting and insertion of new (optimal) solutions to change the quality and terms of the use of wind farm equipment, an evaluation of their impact on reliability under real conditions can be carried out over a long period of time. Over a brief period, testing the reliability of a technical facility is only possible in a simulation. The aspect of evaluating the reliability of wind farm equipment after the application of intelligent systems, including the Wind Power Plant Expert System (WPPES), can be tested in the manner of a simulation. It was accepted in this article that the operation of the wind farm equipment is detailed based on Markov processes. The results of such research activities are the development of reliable and appropriate strategies and an exploitation policy of PE facilities. The above-mentioned issues in such a comprehensive... [more]
Responsible Knowledge Management in Energy Data Ecosystems
Valentina Janev, Maria-Esther Vidal, Dea Pujić, Dušan Popadić, Enrique Iglesias, Ahmad Sakor, Andrej Čampa
February 28, 2023 (v1)
Keywords: big data analytic, data exchange, data integration systems, energy big data, knowledge graphs, semantic interoperability
This paper analyzes the challenges and requirements of establishing energy data ecosystems (EDEs) as data-driven infrastructures that overcome the limitations of currently fragmented energy applications. It proposes a new data- and knowledge-driven approach for management and processing. This approach aims to extend the analytics services portfolio of various energy stakeholders and achieve two-way flows of electricity and information for optimized generation, distribution, and electricity consumption. The approach is based on semantic technologies to create knowledge-based systems that will aid machines in integrating and processing resources contextually and intelligently. Thus, a paradigm shift in the energy data value chain is proposed towards transparency and the responsible management of data and knowledge exchanged by the various stakeholders of an energy data space. The approach can contribute to innovative energy management and the adoption of new business models in future ene... [more]
Assessment of the Reliability of Wind Farm Devices in the Operation Process
Stanisław Duer, Jacek Paś, Aneta Hapka, Radosław Duer, Arkadiusz Ostrowski, Marek Woźniak
February 28, 2023 (v1)
Keywords: diagnostic information, diagnostic process, expert system, intelligent systems, knowledge base, neural networks, reliability, servicing process, wind farm device
The article deals with simulation tests on the reliability of the equipment of the wind farm WF in the operation process. The improvement, modernization, and introduction of new solutions that change the reliability, as well as the quality and conditions of use and operation of wind farm equipment, require testing. Based on these tests, it is possible to continuously evaluate the reliability of the equipment of WF. The issue of reliability assessment of wind farm equipment, for which intelligent systems, diagnostic systems DIAG, and Wind Power Plant Expert System (WPPES) are used to modernize the operation process, can only be tested in a simulative way. The topic of testing the reliability of complex technical objects is constantly developing in the literature. In this paper, it is assumed that the operation of wind farm equipment is described and modeled based on Markov processes. The adoption of this assumption justified the use of the Kolmogorov−Chapman equations to describe the de... [more]
Reliability Testing of Wind Farm Devices Based on the Mean Time between Failures (MTBF)
Stanisław Duer, Marek Woźniak, Jacek Paś, Konrad Zajkowski, Dariusz Bernatowicz, Arkadiusz Ostrowski, Zbigniew Budniak
February 27, 2023 (v1)
Keywords: diagnostic data, diagnostic process, expert system, intelligent systems, knowledge base, mean time between failures (MTBF), neural networks, serviceability, wind farm device
Among the most valuable types of renewable energy available today is wind energy. The reliability of WF systems must be regularly evaluated at every stage of their “life,” from design to operation, if a wind farm energy system is to be effective and function damage-free. Three key goals are presented in the article. The theory of fundamental quantities in reliability and maintenance analysis should be derived and explained first. Second, as a consequence of maintainability, theoretical correlations between reliability and mean time between failures (MTBF) are provided. The three-state theory of the WF procedure for operation presented in the research serves as the foundation for the analytical analysis of WF reliability. The time between failures is investigated as a function of maintainability, and the dependability of the WF under examination is assessed as a function of service life. The WF owner can make the best decisions to renew the WF and increase its reliability, energy, finan... [more]
Intelligent Systems Supporting the Use of Energy Devices and Other Complex Technical Objects: Modeling, Testing, and Analysis of Their Reliability in the Operating Process
Stanisław Duer, Krzysztof Rokosz, Konrad Zajkowski, Dariusz Bernatowicz, Arkadiusz Ostrowski, Marek Woźniak, Atif Iqbal
February 27, 2023 (v1)
Among the technological developments for complex technical objects such as civil aircraft, energy systems, medical devices, etc [...]
Organization and Reliability Testing of a Wind Farm Device in Its Operational Process
Stanisław Duer, Krzysztof Rokosz, Dariusz Bernatowicz, Arkadiusz Ostrowski, Marek Woźniak, Konrad Zajkowski, Atif Iqbal
February 27, 2023 (v1)
Keywords: diagnostic process, expert system, intelligent systems, reliability, servicing process, wind farm device
This article deals with the importance of simulation studies for the reliability of wind farm (WF) equipment during the operation process. Improvements, upgrades, and the introduction of new solutions that change the reliability, quality, and conditions of use and operation of wind farm equipment present a research problem during study. Based on this research, it is possible to continuously evaluate the reliability of WF equipment. The topic of reliability testing of complex technical facilities is constantly being developed in the literature. The article assumes that the operation of wind farm equipment is described and modeled based on Markov processes. This assumption justified the use of Kolmogorov−Chapman equations to describe the developed research model. Based on these equations, an analytical model of the wind farm operation process was created and described. As a result of the simulation analysis, the reliability of the wind farm was determined in the form of a probability fun... [more]
OpΕnergy: An Intelligent System for Monitoring EU Energy Strategy Using EU Open Data
Kleanthis Koupidis, Charalampos Bratsas, Christos Vlachokostas
February 24, 2023 (v1)
Keywords: data analysis, energy indicators, energy system, informed governance, intelligent systems, open data
In this paper, the basic structure of an ICT platform of energy indicators, Openergy, is analytically presented, leveraging energy open data to help address the energy crisis more democratically. More specifically, its applicability as a dynamic tool for the management of climate, environmental, and socioeconomic information is described, and its efficiency in helping uncover insights for optimal data-driven decisions is depicted. Openergy uses data from the official portal for European data and the Eurostat site. Its database consists of data related to six energy categories, EU 2020 energy targets, energy balance, electricity production, transport fuels, heat production, and gas emissions, and each one includes its own indicators for EU countries. The platform includes visualizations of these data as well as time series modeling and forecasting, and the results are depicted at Openergy platform. The time series modeling provides forecasts with confidence intervals of each indicator u... [more]
Application of Intelligent and Digital Technologies to the Tasks of Wind Energy
Vladislav N. Kovalnogov, Ruslan V. Fedorov, Andrei V. Chukalin, Mariya I. Kornilova, Tamara V. Karpukhina, Anton V. Petrov
February 24, 2023 (v1)
Keywords: atmospheric boundary layer, Computational Fluid Dynamics, intelligent system, mathematical modeling, wind farm
The article considers the relevance and issues of wind turbine modeling, the principles of wind energy conversion (WEC) system operation, working areas and regulation. The influence of soft computing technologies on the different aspects of wind power systems, particularly in the fields of operation and maintenance, is considered. This article discusses the recent research, development and trends in soft computing techniques for wind-energy-conversion systems. For reliable analysis, the interaction of the wind-generator operation with the atmospheric boundary layer is considered. The authors give a detailed description of the approaches for the study and numerical modeling of the atmospheric boundary layer (ABL) in the vicinity of a wind farm. The study of the atmospheric boundary layer in the vicinity of the Ulyanovsk wind farm on the basis of cluster analysis of meteorological data is performed. Ten localizations of ABL homogeneous properties are identified. The subject of the study... [more]
Assessment of the Reliability of Wind Farm Device on the Basis of Modeling Its Operation Process
Stanisław Duer, Marek Woźniak, Arkadiusz Ostrowski, Jacek Paś, Radosław Duer, Konrad Zajkowski, Dariusz Bernatowicz
February 23, 2023 (v1)
Keywords: expert system, intelligent systems, Markov processes, reliability, servicing process, simulation testing, wind power plant
The evaluation and analysis of the procedures for determining the dependability of WF wind farm equipment employed in a few publications are this article’s main problems. The publications chosen for review specifically mention investigations into the dependability of WF wind farm machinery. The following topics were the authors’ main areas of analysis: description and review of the techniques used to represent how technical items operate and the selection of the weight of the theoretical ideas of reliability that were used to gauge the dependability of the wind farm equipment under study. The authors of the studied works set out to address a number of significant problems pertaining to the modernization of the management of the WF equipment renewal process. The subjects of the studied works suggest that the established models of the technical object’s operational process are particularly significant in both the theory and practice of the reliability of technical objects. Using Kolmogor... [more]
Understanding the Evolution and Applications of Intelligent Systems via a Tri-X Intelligence (TI) Model
Min Zhao, Zhenbo Ning, Baicun Wang, Chen Peng, Xingyu Li, Sihan Huang
February 23, 2023 (v1)
Keywords: cyber-physical systems, human-cyber systems, intelligent manufacturing, intelligent systems, Tri-X Intelligence
The evolution and application of intelligence have been discussed from perspectives of life, control theory and artificial intelligence. However, there has been no consensus on understanding the evolution of intelligence. In this study, we propose a Tri-X Intelligence (TI) model, aimed at providing a comprehensive perspective to understand complex intelligence and the implementation of intelligent systems. In this work, the essence and evolution of intelligent systems (or system intelligentization) are analyzed and discussed from multiple perspectives and at different stages (Type I, Type II and Type III), based on a Tri-X Intelligence model. Elemental intelligence based on scientific effects (e.g., conscious humans, cyber entities and physical objects) is at the primitive level of intelligence (Type I). Integrated intelligence formed by two-element integration (e.g., human-cyber systems and cyber-physical systems) is at the normal level of intelligence (Type II). Complex intelligence... [more]
A Sensorless Intelligent System to Detect Dust on PV Panels for Optimized Cleaning Units
Faris E. Alfaris
February 22, 2023 (v1)
Keywords: artificial intelligence (AI), cost minimization, dust cleaning, Optimization, photovoltaic (PV) systems, Renewable and Sustainable Energy
Deployment of photovoltaic (PV) systems has recently been encouraged for large-scale and small-scale businesses in order to meet the global green energy targets. However, one of the most significant hurdles that limits the spread of PV applications is the dust accumulated on the PV panels’ surfaces, especially in desert regions. Numerous studies sought the use of cameras, sensors, power datasets, and other detection elements to detect the dust on PV panels; however, these methods pose more maintenance, accuracy, and economic challenges. Therefore, this paper proposes an intelligent system to detect the dust level on the PV panels to optimally operate the attached dust cleaning units (DCUs). Unlike previous strategies, this study utilizes the expanded knowledge and collected data for solar irradiation and PV-generated power, along with the forecasted ambient temperature. An expert artificial intelligence (AI) computational system, adopted with the MATLAB platform, is utilized for a high... [more]
Supporting Management Disciplines for Research and Development in Public Organizations
Zeeshan Asim, Shahryar Sorooshian
February 21, 2023 (v1)
Keywords: innovation management (IM), knowledge management (KM), technology management (TM)
In practice, R&D in public organizations in developing countries is confronted with a variety of failures related to supporting management disciplines. The primary goal of this study is to address this issue through multiple-criteria decision making, which includes the DANP (DEMATEL-based ANP) approach. The DANP approach helps to resolve the classification issue that arises as a result of interdependence and feedback characteristics among the capabilities related to supporting management disciplines, allowing weak capabilities to be prioritized based on their interdependence. In the case of criteria weighting, the empirical result in terms of the degree of the net causal relationship had a greater influence on other criteria; however, in terms of dimensions, the technology management process capability had a greater significance on other dimensions, while the innovation management process capability had the least significance on other dimensions. The studies were based on relevant capa... [more]
Perspectives on the Integration between First-Principles and Data-Driven Modeling
William Bradley, Jinhyeun Kim, Zachary Kilwein, Logan Blakely, Michael Eydenberg, Jordan Jalvin, Carl Laird, Fani Boukouvala
November 7, 2021 (v1)
Keywords: gaussian process regression, hybrid modeling, Machine Learning, model calibration, neural networks, physics-informed machine learning
Efficiently embedding and/or integrating mechanistic information within data-driven models is essentially the only approach to simultaneously take advantage of both engineering principles and data-science. The opportunity for hybridization occurs in many scenarios, such as the development of a faster model of an accurate high-fidelity computer model; the correction of a mechanistic model that does not fully-capture the physical phenomena of the system; or the integration of a data-driven component approximating an unknown correlation within a mechanistic model. At the same time, different techniques have been proposed and applied in different literatures to achieve this hybridization, such as hybrid modeling, physics-informed Machine Learning (ML) and model calibration. In this paper we review the methods, challenges, applications and algorithms of these three research areas and discuss them in the context of the different hybridization scenarios. Moreover, we provide a comprehensive c... [more]
Integration of Artificial Intelligence into Biogas Plant Operation
Samet Cinar, Senem Onen Cinar, Nils Wieczorek, Ihsanullah Sohoo, Kerstin Kuchta
October 14, 2021 (v1)
Keywords: anaerobic digestion, Artificial Intelligence, automation, biogas plant, predictive monitoring, process monitoring, process optimization
In the biogas plants, organic material is converted to biogas under anaerobic conditions through physical and biochemical processes. From supply of the raw material to the arrival of the products to customers, there are serial processes which should be sufficiently monitored for optimizing the efficiency of the whole process. In particular, the anaerobic digestion process, which consists of sequential complex biological reactions, requires improved monitoring to prevent inhibition. Conventional implemented methods at the biogas plants are not adequate for monitoring the operational parameters and finding the correlation between them. As Artificial Intelligence has been integrated in different areas of life, the integration of it into the biogas production process will be inevitable for the future of the biogas plant operation. This review paper first examines the need for monitoring at the biogas plants with giving details about the process and process monitoring as well. In the follow... [more]
Using Neural Networks to Obtain Indirect Information about the State Variables in an Alcoholic Fermentation Process
Anca Sipos, Adrian Florea, Maria Arsin, Ugo Fiore
October 14, 2021 (v1)
Keywords: fermentation process, neural network, prediction application
This work provides a manual design space exploration regarding the structure, type, and inputs of a multilayer neural network (NN) to obtain indirect information about the state variables in the alcoholic fermentation process. The main benefit of our application is to help experts reduce the time needed for making the relevant measurements and to increase the lifecycles of sensors in bioreactors. The novelty of this research is the flexibility of the developed application, the use of a great number of variables, and the comparative presentation of the results obtained with different NNs (feedback vs. feed-forward) and different learning algorithms (Back-Propagation vs. Levenberg−Marquardt). The simulation results show that the feedback neural network outperformed the feed-forward neural network. The NN configuration is relatively flexible (with hidden layers and a number of nodes on each of them), but the number of input and output nodes depends on the fermentation process parameters.... [more]
Machine Learning for Ionic Liquid Toxicity Prediction
Zihao Wang, Zhen Song, Teng Zhou
October 14, 2021 (v1)
Keywords: ionic liquid, Machine Learning, neural network, support vector machine, toxicity
In addition to proper physicochemical properties, low toxicity is also desirable when seeking suitable ionic liquids (ILs) for specific applications. In this context, machine learning (ML) models were developed to predict the IL toxicity in leukemia rat cell line (IPC-81) based on an extended experimental dataset. Following a systematic procedure including framework construction, hyper-parameter optimization, model training, and evaluation, the feedforward neural network (FNN) and support vector machine (SVM) algorithms were adopted to predict the toxicity of ILs directly from their molecular structures. Based on the ML structures optimized by the five-fold cross validation, two ML models were established and evaluated using IL structural descriptors as inputs. It was observed that both models exhibited high predictive accuracy, with the SVM model observed to be slightly better than the FNN model. For the SVM model, the determination coefficients were 0.9289 and 0.9202 for the training... [more]
Establishment of the Predicting Models of the Dyeing Effect in Supercritical Carbon Dioxide Based on the Generalized Regression Neural Network and Back Propagation Neural Network
Zhuo Zhang, Fayu Sun, Qingling Li, Weiqiang Wang, Dedong Hu, Shuangchun Li
July 26, 2021 (v1)
Keywords: back propagation neural network, generalized regression neural network, prediction model, supercritical carbon dioxide, the dyeing effect
With the growing demand of supercritical carbon dioxide (SC-CO2) dyeing, it is important to precisely predict the dyeing effect of supercritical carbon dioxide. In this work, Generalized Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) models have been employed to predict the dyeing effect of SC-CO2. These two models have been constructed based on published experimental data and calculated values. A total of 386 experimental data sets were used in the present work. In GRNN and BPNN models, two input parameters, such as temperature, pressure, dye stuff types, carrier types and dyeing time, were selected for the input layer and one variable, K/S value or dye-uptake, was used in the output layer. It was found that the values of mean-relative-error (MRE) for BPNN model and for GRNN model are 3.27−6.54% and 1.68−3.32%, respectively. The results demonstrate that both BPNN and GPNN models can accurately predict the effect of supercritical dyeing but the former is be... [more]
Extreme Learning Machine Based on Firefly Adaptive Flower Pollination Algorithm Optimization
Ting Liu, Qinwei Fan, Qian Kang, Lei Niu
June 29, 2021 (v1)
Keywords: extreme learning machine, firefly algorithm, flower pollination algorithm, Optimization
Extreme learning machine (ELM) has aroused a lot of concern and discussion for its fast training speed and good generalization performance, and it has been used diffusely in both regression and classification problems. However, on account of the randomness of input parameters, it requires more hidden nodes to obtain the desired accuracy. In this paper, we come up with a firefly-based adaptive flower pollination algorithm (FA-FPA) to optimize the input weights and thresholds of the ELM algorithm. Nonlinear function fitting, iris classification and personal credit rating experiments show that the ELM with FA-FPA (FA-FPA-ELM) can obtain significantly better generalization performance (such as root mean square error, classification accuracy) than traditional ELM, ELM with firefly algorithm (FA-ELM), ELM with flower pollination algorithm (FPA-ELM), ELM with genetic algorithm (GA-ELM) and ELM with particle swarm optimization (PSO-ELM) algorithms.
A Genetic Programming Strategy to Induce Logical Rules for Clinical Data Analysis
José A. Castellanos-Garzón, Yeray Mezquita Martín, José Luis Jaimes Sánchez, Santiago Manuel López García, Ernesto Costa
June 21, 2021 (v1)
Keywords: clinical data, data mining, evolutionary computation, feature selection, genetic programming, Machine Learning
This paper proposes a machine learning approach dealing with genetic programming to build classifiers through logical rule induction. In this context, we define and test a set of mutation operators across from different clinical datasets to improve the performance of the proposal for each dataset. The use of genetic programming for rule induction has generated interesting results in machine learning problems. Hence, genetic programming represents a flexible and powerful evolutionary technique for automatic generation of classifiers. Since logical rules disclose knowledge from the analyzed data, we use such knowledge to interpret the results and filter the most important features from clinical data as a process of knowledge discovery. The ultimate goal of this proposal is to provide the experts in the data domain with prior knowledge (as a guide) about the structure of the data and the rules found for each class, especially to track dichotomies and inequality. The results reached by our... [more]
Showing records 26 to 50 of 261. [First] Page: 1 2 3 4 5 6 Last
[Show All Subjects]