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Investigation of the eTS Evolving Fuzzy Systems Applied to Real Estate Appraisal

JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING 2011年第2-3期17卷 229-253页

作者： Lasota, Tadeusz Telec, Zbigniew Trawinski, Bogdan Trawinski, Krzysztof Wroclaw Univ Environm & Life Sci Dept Spatial Management PL-50375 Wroclaw Poland Wroclaw Univ Technol Inst Informat PL-50370 Wroclaw Poland European Ctr Soft Comp Mieres 33600 Asturias Spain

the evolving Takagi-Sugeno algorithm (eTS) was employed to built models assisting with property valuation on the basis of real-world data drawn from a cadastral system, registry of sales transactions, and cadastral map. the eTS performance was compared with evolutionary fuzzy systems implemented in KEEL. Statistical analysis of the results was performed using nonparametric tests followed by post hoc procedures designed for multiple comparisons. the results confirmed the advantages of the eTS algorithm.

关键词： Evolving fuzzy systems eTS learning algorithm property valuation feature selection GIS KEEL

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Improving energy efficiency in buildings using machine intelligence

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10th international conference on intelligent data engineering and automated learning, ideal 2009

作者： Sedano, Javier Villar, José Ramón Curiel, Leticia De La Cal, Enrique Corchado, Emilio Department of Electromechanical Engineering University of Burgos Burgos Spain Department of Computer Science University of Oviedo Spain Department of Civil Engineering University of Burgos Burgos Spain

ISBN: (纸本)3642043933

Improving the detection of thermal insulation in buildings -which includes the development of models for heating and ventilation processes and fabric gain - could significantly increase building energy efficiency and substantially contribute to reductions in energy consumption and in the carbon footprints of domestic heating systems. thermal insulation standards are now contractual obligations in new buildings, although poor energy efficiency is often a defining characteristic of buildings built before the introduction of those standards. Lighting, occupancy, set point temperature profiles, air conditioning and ventilation services all increase the complexity of measuring insulation efficiency. the identification of thermal insulation failure can help to reduce energy consumption in heating systems. Conventional methods can be greatly improved through the application of hybridized machine learning techniques to detect thermal insulation failures when a building is in operation. A three-step procedure is proposed in this paper that begins by considering the local building and heating system regulations as well as the specific features of the climate zone. Firstly, the dynamic thermal performance of different variables is specifically modelled, for each building type and climate zone. Secondly, Cooperative Maximum-Likelihood Hebbian learning is used to extract the relevant features. Finally, neural projections and identification techniques are applied, in order to detect fluctuations in room temperatures and, in consequence, thermal insulation failures. the reliability of the proposed method is validated in three winter zone C cities in Spain. Although a great deal of further research remains to be done in this field, the proposed system is expected to outperform conventional methods described in Spanish building codes that are used to calculate energetic profiles in domestic and residential buildings. © 2009 Springer Berlin Heidelberg.

关键词： Energy efficiency

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A Gene Expression Programming Algorithm for Multi-Label Classification

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JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING 2011年第2-3期17卷 183-206页

作者： Avila, J. L. Gibaja, E. L. Zafra, A. Ventura, S. Univ Cordoba Dept Comp Sci & Numer Anal E-14071 Cordoba Spain

this paper presents a Gene Expression Programming algorithm for multi-label classification which encodes a discriminant function into each individual to show whether a pattern belongs to a given class or not. the algorithm also applies a niching technique to guarantee that the population includes functions for each existing class. the algorithm has been compared to other recently published algorithms. the results found on several datasets demonstrate the feasibility of this approach in the tackling of multi-label problems.

关键词： Multi-label classification discriminant functions gene expression programming machine learning

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Future Performance Modeling in Athletism with Low Quality data-based Genetic Fuzzy Systems

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JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING 2011年第2-3期17卷 207-228页

作者： Palacios, Ana M. Sanchez, Luciano Couso, Ines Univ Oviedo Dept Informat Gijon 33071 Asturias Spain Univ Oviedo Dept Estadist & IO & DM Gijon 33071 Asturias Spain

A fuzzy rule-based decision system for assisting coaches in the configuration of an athletics team is presented. the knowledge base of this system combines the experience of the trainer with genetically mined information from training sessions and competitions. the novelty of our approach comes from the fact that these sources of data have low quality: they include subjective perceptions of mistakes of the athletes, different measurements taken by different observers, and interval-valued attributes. We will use a possibilistic representation of these categories of information, in combination with an extension principle-based reasoning method, and show that the predictive power of a genetic fuzzy system which is based in these principles improves other systems that discard the vagueness of the training data.

关键词： Genetic fuzzy systems low quality data intelligent data analysis

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Improving Security and Effectiveness in Banking Operations with IoT Integration 10

Improving Security and Effectiveness in Banking Operations w...

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10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and Computer engineering, UPCON 2023

作者： Johri, Shiva Singh, Sarita Kumari Reddy, Ch Vijendar Nijhawan, Ginni Alawadi, Ahmed Hussien Reddy, Uma Oriental College of Management Bhopal Barkatullah University Department of Mba Madhya Pradesh Bhopal India School of Social Financial & Human Sciences Kiit Deemed to Be University Odisha Bhubaneswar India Institute of Aeronautical Engineering Hyderabad India Lovely Professional University Phagwara India The Islamic University Najaf Iraq New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bangalore India

ISBN: (纸本)9798350382471

In order to improve security as well as operational efficiency, this empirical paper investigates the integration of Internet of things (IoT) technology into the banking industry. Because the banking sector is an attractive target for cyberattacks, strong security measures are required. IoT technology provides data-driven security improvements, and automated responses, alongside real-time surveillance through its network of connected devices and sensors. this covers the following: property monitoring, innovations in blockchain technology, biometric identification, intelligent monitoring infrastructure, along fraud detection. the technical analysis explores the benefits and drawbacks of IoT-based security for banks. data security, connectivity, adaptability, upkeep, and financial considerations are among the challenges. On the contrary, benefits like improved cybersecurity, cost-effectiveness, client trust, flexibility, and data analysis are provided by IoT-based security. the paper looks ahead, discussing the manner in which AI, blockchain technology, biometrics, cloud-based security, as well as quantum computing, will all be integrated into IoT-based security in the years to come. the financial sector is expected to implement a significant increase in IoT-based security measures. © 2023 IEEE.

关键词： Banking Security Cybersecurity Financial Services IoT Integration Operational Efficiency

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EventScore: An automated Real-time Early Warning Score for Clinical Events 10

EventScore: An Automated Real-time Early Warning Score for C...

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10th IEEE international conference on Healthcare Informatics (IEEE ICHI)

作者： Hammoud, Ibrahim Prasanna, Prateek Ramakrishnan, Iv Singer, Adam Henry, Mark thode, Henry Dept Comp Sci Stony Brook NY 11794 USA Dept Biomed Informat Stony Brook NY USA Dept Emergency Med Stony Brook NY USA

ISBN: (数字)9781665468459

ISBN: (纸本)9781665468459

Early prediction of patients at risk of clinical deterioration can help physicians intervene and alter their clinical course towards better outcomes. In addition to the accuracy requirement, early warning systems must make the predictions early enough to give physicians enough time to intervene. Interpretability is also one of the challenges when building such systems since being able to justify the reasoning behind model decisions is desirable in clinical practice. In this work, we built an interpretable model for the early prediction of various adverse clinical events indicative of clinical deterioration. the model is evaluated on two datasets and four clinical events. the first dataset is collected in a predominantly COVID-19 positive population at Stony Brook Hospital. the second dataset is the MIMIC III dataset. the model was trained to provide early warning scores for ventilation, ICU transfer, and mortality prediction tasks on the Stony Brook Hospital dataset and to predict mortality and the need for vasopressors on the MIMIC III dataset. Our model first separates each feature into multiple ranges and then uses logistic regression with lasso penalization to select the subset of ranges for each feature. the model training is completely automated and doesn't require expert knowledge like other early warning scores. We compare our model to the Modified Early Warning Score (MEWS) and quick SOFA (qSOFA), commonly used in hospitals. We show that our model outperforms these models in the area under the receiver operating characteristic curve (AUROC) while having a similar or better median detection time on all clinical events, even when using fewer features. Unlike MEWS and qSOFA, our model can be entirely automated without requiring any manually recorded features. We also show that discretization improves model performance by comparing our model to a baseline logistic regression model.

关键词： Machine learning Electronic Health Records Early Warning Score COVID-19 Event Prediction Healthcare Hospitals Electronic Medical Records MIMIC dataset Early Event Prediction Interpretable Machine learning

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Artificial Intelligence for Predictive Maintenance of Port Equipment: A Revolution in Progress 10th

Artificial Intelligence for Predictive Maintenance of Port E...

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10th international conference on Design and Modeling of Mechanical Systems (CMSM)

作者： Chaibi, Maissa Daghrir, Jinen Univ Sousse Higher Inst Transport & Logist Sousse Tunisia Univ Sousse ISITCom Sousse 4011 Tunisia

ISBN: (纸本)9783031671517;9783031671524

Artificial Intelligence (AI) is playing a crucial role in enhancing predictive Maintenance for port handling equipment. By continuously collecting data, sophisticated algorithms, and machine learning capabilities, AI enables ports to accurately anticipate the maintenance needs of their equipment, thereby reducing unplanned downtime and costs. the significance of AI in this con-text lies in its ability to analyze vast amounts of data from sensors and monitoring systems of port equipment. through this analysis, predictive models can identify trends, patterns, and early signs of failure, allowing port opera-tors to proactively plan maintenance. this not only improves the safety of port operations but also maximizes operational efficiency by avoiding unexpected disruptions. Furthermore, AI also extends the equipment's lifespan by optimizing its usage and identifying parts requiring maintenance or replacement. Ultimately, this results in significant cost reducing and better resource management. In this matter, we were interested in using predictive maintenance by handling the unexpected downtimes of port equipment. this is achieved by predicting potential failure of equipment. the goal behind predictive maintenance is mainly to enhance safety, decrease costs, and making port industry more automated and intelligent.

关键词： Artificial intelligence predictive maintenance handling equipment machine learning

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Extraction by example: Induction of structural rules for the analysis of molecular sequence data from heterogeneous sources

Extraction by example: Induction of structural rules for the...

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6th international conference on intelligent data engineering and automated learning - ideal 2005

作者： Miotto, Olivo Tan, Tin Wee Brusic, Vladimir Institute of Systems Science National University of Singapore 25 Heng Mui Keng Terrace Singapore 119615 Singapore Department of Biochemistry Faculty of Medicine National University of Singapore 10 Medical Drive Singapore 117597 Singapore Institute for Infocomm Research Singapore 21 Heng Mui Keng Terrace Singapore 119613 Singapore

Biological research requires information from multiple data sources that use a variety of database-specific formats. Manual gathering of information is time consuming and error-prone, making automated data aggregation a compelling option for large studies. We describe a method for extracting information from diverse sources that involves structural rules specified by example. We developed a system for aggregation of biological knowledge (ABK) and used it to conduct an epidemiological study of dengue virus (DENV) sequences. Additional information on geographical origin and isolation date is critical for understanding evolutionary relationships, but this data is inconsistently structured in database entries. Using three public databases, we found that structural rules can be used successfully even when applied on inconsistently structured data that is distributed across multiple fields. High reusability, combined with the ability to integrate analysis tools, make this method suitable for a wide variety of large-scale studies involving viral sequences. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Information analysis

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Automatic License Plate Recognition: automated Tax Verification for Registered Vehicles via SAMBARA Server 10

Automatic License Plate Recognition: Automated Tax Verificat...

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10th international conference on Wireless and Telematics, ICWT 2024

作者： Nawawi, Aif Umar Dendyana, Arya Muhammad Prastyo, Galuh Yudha Al Azhima, Silmi Ath thahirah Hakim, Nurul Fahmi Arief Ridwan, Azwar Mudzakkir Universitas Pendidikan Indonesia Dept. of Electrical Eng. Education Bandung Indonesia UIN Sunan Gunung Djati Dept. of Electrical Engineering Bandung Indonesia

ISBN: (纸本)9798350377057

the technology known as Automatic License Plate Recognition (ALPR) originated from the application of computer vision, a subfield of artificial intelligence systems. the purpose of developing this ALPR system was to verify the tax status and comprehensive vehicle information in the West Java region of Indonesia. the objective of this study was to instill in each motorist a sense of traffic order and to assist Indonesia in the implementation of an intelligent Traffic System (ITS). In order to operate efficiently, the system incorporated in this design is constructed using a variety of Python programming language modules. YOLOv8 (You Only Look Once version 8), EasyOCR, OpenCV, and Selenium are the primary Python modules utilized;additional modules include RegEx (Regular Expression), Time, Numpy, and BytesIO. In this application, number plates are searched for and detected using the dilation method so that EasyOCR can extract every character with minimal error. Additionally, machine learning serves as the foundation for each of the aforementioned procedures and processes. the fundamental computation of machine learning from bespoke data sets is performed in Google Colab. the outcome of this investigation is a system capable of furnishing information pertaining to motor vehicle systems to an individual who has failed to remit vehicle tax. Aside from that, the developed system can detect counterfeit license plates, making it a valuable tool for educating drivers about the importance of remaining vigilant. © 2024 IEEE.

关键词： Unsupervised learning

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An Effective Approach for Sonar Image Recognition with Improved Efficientdet and Ensemble learning 5

An Effective Approach for Sonar Image Recognition with Impro...

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2022 5th international conference on Advanced Algorithms and Control engineering, ICAACE 2022

作者： Du, Yunben Lin, Wei Zhong, Weibo Yuan, Yu. College of Electronics and Information Jiangsu University of Science and Technology Zhenjiang212000 China

Precisely recognition of underwater targets plays a critical role in the field of underwater unmanned exploration. According to the characteristics of sonar image, in order to improve the accuracy of underwater automatic target recognition, this paper proposes an underwater target sonar image recognition method combined with improved Efficientdet and ensemble learning. According to the regional dominance principle and threshold segmentation method, the sonar image is preprocessed, and preliminary effective features extracted from preprocessed data by Efficientnet backbone, so as to balance the efficiency and power consumption of the algorithm. the feature fusion adopts the improved weighted bi-directional feature pyramid ( BiFPN ) structure to aggregate the global information and strengthen the feature representation of the shallow feature map, which optimizes the problem that the small feature extraction is not ideal due to the low resolution of the sonar image and less detail information. By building a two-level classifier to complete the learning task in an ensemble way, a better classification effect is obtained than a single classifier. this method is applied to the underwater intelligent perception group of the 10th National Marine Aircraft Design and Production Competition in 2021, and won the first prize in the country. the experimental results show that the designed ensemble network model is more accurate than the conventional convolution neural network in identifying underwater targets on the measured sonar image data set while ensuring a certain prediction speed. © Published under licence by IOP Publishing Ltd.

关键词： Image recognition

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