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Feature Selection Using the Domain Relationship with Genetic Algorithms

Knowledge and information Systems 1999年第3期1卷 377-390页

作者： Chaikla, Nidapan Qi, Yulu Computer Science and Information Management Division School of Advanced Technologies Asian Institute of Technology P.O. Box 4 Pathumthani Klong Luang12120 Thailand

Considering the importance of the domain relationship in eliminating noisy features in feature selection, we present an alternate approach to designing a multi-objective fitness function using multiple correlation for the genetic algorithm (GA), which is used as a search tool in the problem. Multiple correlation is a simple statistical technique that uses the multiple correlation coefficients to measure the relationship between a dependent variable and a set of independent variables within the domain space. Simulation studies were conducted on both real-world and controlled data sets to assess the performance of the proposed fitness function. The comparison between the traditional fitness function and our proposed function is also reported. The results show that the proposed fitness function can perform more satisfactorily than the traditional one in all cases considered, including different data types, multi-class and multi-dimensional data. © 1999, Springer-Verlag Singapore Pte. Ltd.

关键词： Genetic algorithms

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Fuzzy modeling with genetic algorithms

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computerS AND ARTIFICIAL INTELLIGENCE 1997年第3期16卷 275-293页

作者： Wuwongse, V Veluppillai, S Computer Science and Information Management Program School of Advanced Technologies Asian Institute of Technology Bangkok Thailand

Recent applications of fuzzy control have created an urgent demand for fuzzy modeling techniques. Several methods for identification of fuzzy models from numerical input-output samples have been proposed. Among them, Sugeno and Yasukawa's method [6], which employs fuzzy c-means clustering, holds significant promises. This paper improves the method of Sugeno and Yasukawa. Identified fuzzy models are tuned at various stages by means of genetic algorithms, i.e., the numbers of input variables and rules are reduced and membership function parameters are adjusted. The technique, when applied to a nonlinear system, demonstrates its efficiency in a comparison with the original method of Sugeno and Yasukama.

关键词： fuzzy modeling fuzzy c-means clustering genetic algorithms

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An approach for crop recommendation with uncertainty quantification based on machine learning for sustainable agricultural decision-making

Results in Engineering

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Results in Engineering 2025年

作者： Md. Sakib Bin Alam Vatcharaporn Esichaikul Aiman Lameesa Shams Forruque Ahmed Amir H. Gandomi Department of Information Technology University of Information Technology and Sciences (UITS) Dhaka 1212 Bangladesh Computer Science and Information Management Program School of Engineering and Technology Asian Institute of Technology Pathum Thani 12120 Thailand Department of Computer Science American International University-Bangladesh Dhaka 1229 Bangladesh School of Mathematical Sciences Sunway University Bandar Sunway Petaling Jaya 47500 Selangor Darul Ehsan Malaysia Department of Mathematics & Physics North South University Dhaka 1229 Bangladesh Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW 2007 Australia University Research and Innovation Center (EKIK) Óbuda University 1034 Budapest Hungary Department of Computer Science Khazar University Mahsati 41 Baku Azerbaijan

While machine learning (ML) models for crop recommendation have demonstrated high predictive accuracy, a critical gap persists in their practical reliability: the omission of uncertainty quantification. Existing studies predominantly deliver deterministic recommendations, neglecting inherent uncertainties arising from data noise. This raises concerns about the reliability of the decision support systems for crop recommendation. To address this, we propose an ensemble ML framework incorporating entropy-based uncertainty quantification. Trained on a publicly available Indian agricultural dataset with 2,200 samples across seven agro-climatic features (nitrogen, phosphorus, potassium, temperature, humidity, pH, and rainfall) and 22 crop classes, the model achieves a predictive accuracy of 99.54%. By estimating prediction confidence using entropy, the framework offers probabilistic recommendations that support environmentally informed decision-making under uncertainty. These findings suggest that integrating uncertainty measures into ML-driven crop recommendation systems can enhance reliability and promote sustainable agricultural practices.

关键词： Crop recommendation Machine learning Uncertainty Precision agriculture

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Mining email transaction logs to locate significant messages and users

Mining email transaction logs to locate significant messages...

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作者： Esichaikul, Vatcharaporn Guha, Sumanta Niyamosatha, Thanittha Computer Science and Information Management Program Asian Institute of Technology P.O. Box 4 Klong Luang Pathumthani 12120 Thailand

ISBN: (纸本)9788988678312

A simple and intuitive model to mine an email transactions log for significant messages and users is presented. No use is made of NLP or semantic analysis. The model is based only on scoring messages and users from a graphtheoretic analysis of the communication pattern represented in the transaction log. Practical experiments indicate the potential of the model.

关键词： Data mining

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New PN model of FMS with sequential flexibility of resource allocation 21

New PN model of FMS with sequential flexibility of resource ...

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21st International Conference on Production Research: Innovation in Product and Production, ICPR 2011

作者： Aziz, M.H. Bohez, Erik L.J. Pisuchpen, Roongrat Parnichkun, Manukid Dailey, Mathew N. Saha, Chanchal Industrial System Engineering Asian Institute of Technology Bangkok Thailand Department of Mechanical Engineering Kasetsart University Bangkok Thailand Computer Science and Information Management Asian Institute of Technology Bangkok Thailand

ISBN: (纸本)9783839602935

New Petri Net (PN) model of Flexible Manufacturing System (FMS) with sequential flexibility of servers' allocation was developed to meet customer orders while keeping Work In Process (WIP) at the minimum. Because, increase in flexibility and autonomy is basic requirement for an FMS to meet with ever increasing customer demand and global competition. Net developed in this research works on the principal of routing flexibility or context dependent resource allocation and is classified in literature as "Ordinary/Simple PN" with a property of "not a statically conflict free net", hence, a rule base for transitions firing was developed to resolve conflict among firing transitions, while achieving mentioned objectives. Simulation of the net was conducted and results were compared with existing PN model of FMS without sequential flexibility of servers' allocation. Results showed an increase in production rate with same WIP, while meeting identical customer orders.

关键词： Petri nets

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Modelling last-act attempted crime in criminal law

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Journal of Applied Non-Classical Logics 2019年第4期29卷 327-357页

作者： Pooksook, Jiraporn Dung, Phan Minh Satoh, Ken Sartor, Giovanni Computer Science and Information Management Asian Institute of Technology Pathum Thani Thailand National Institute of Informatics Tokyo Japan Law Department European University Institute Florence Italy

In the court of law, a person can be punished for attempting to commit a crime. An open issue in the study of Artificial Intelligence and Law is whether the law of attempts could be formally modelled. There are distinct legal rules for determining attempted crime whereas the last-act rule (also called proximity rule) represents the strictest approach. In this paper, we provide a formal model of the last-act rule using structured argumentation. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Attempted crime last-act (or proximity)-rule structured argumentation

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QUALYST: Data Quality Assessment System for Thailand Open Government Data 20

QUALYST: Data Quality Assessment System for Thailand Open Go...

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20th International Joint Conference on computer science and Software Engineering, JCSSE 2023

作者： Samakit, Tanapat Anutariya, Chutiporn Buranarach, Marut School of Engineering and Technology Asian Institute of Technology Computer Science & Information Management Ict Department Thailand National Electronics and Computer Technology Center Data Science and Analytics Research Group Thailand

ISBN: (纸本)9798350300505

Open Government Data (OGD) refers to the provision of data produced by the government to the general public, in a format that is readily readable and can be used by machines with ease. It can also promote transparency, improve decision-making, enhance accountability, create economic opportunities, and encourage civic engagement. The OGD can help citizens understand the government and its legitimacy and transparency. Thus, when the government shares its data with people, it helps to create trust by being transparent, accountable, and promoting innovative solutions that benefit everyone. However, each published dataset has no indication of its quality assessment at all;thus, making it difficult for citizens to assess the reliability of the data from the OGD. Therefore, a data quality assessment for OGD should be developed. This will help create effective datasets which in turn help users understand the data. This study proposes QUALYST, a system that assesses Thailand's OGD dataset and validates it for analytic and visualization purposes. The study focuses on designing the data storage and implementing the assessment system. Furthermore, the proposed data quality dimensions, the developed data pipeline, and the assessment process are elaborated. Finally, the prototype system is demonstrated using Thailand's OGD datasets with examples in a visualized format. © 2023 IEEE.

关键词： Transparency

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Linked OpenScholar: A researcher network using linked open data

Linked OpenScholar: A researcher network using linked open d...

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14th International Conference on Asia-Pacific Digital Libraries, ICADL 2012

作者： Nonthakarn, Chariya Wuwongse, Vilas Computer Science and Information Management Asian Institute of Technology Pathumthani Thailand Department of Electrical and Computer Engineering Faculty of Engineering Thammasat University Pathumthani Thailand

ISBN: (纸本)9783642347511

This paper presents a new approach to the development of a researcher network called Linked OpenScholar using Linked Open Data (LOD) technology. The prototype is developed by extending OpenScholar, an open source application under the Drupal CMS. Using Linked OpenScholar, researchers can manage their own research information and an institute can define application profiles for its researcher metadata based on some parts of VIVO ontology as well as researchers' publications. Metadata conforming to these application profiles will be published as LOD, enhancing the visibility of researcher information and the collaboration among researchers from different institutes. Additionally, the application profile would increase the interoperability, sharing and reuse of research information. © 2012 Springer-Verlag.

关键词： Metadata

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Deriving financial aid optimization models from admissions data

Deriving financial aid optimization models from admissions d...

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Frontiers in Education (FIE) Conference

作者： Le Van Thanh Peter Haddawy Computer Science and Information Management Program Asian Institute of Technology Thailand

This paper presents a novel approach to deriving probabilistic models that predict enrollment given applicant background and the amount of financial aid offered. Our Bayesian network models can be used to optimize various enrollment objectives. We present a novel efficient optimization algorithm that uses the models to maximize expected tuition revenue under capacity constraints including student-faculty ratio and accommodation. We demonstrate and evaluate our approach using four years of graduate admissions data from the asian institute of technology, consisting of 7,788 applicants from 84 different countries. This data set is particularly challenging since reliable family income data is not available for students from most of these countries. Evaluating the Bayesian network model with 10-fold cross validation yields an ROC Az value of 0.8451, with a predictive accuracy of 82.70% at a threshold of 0.5. Comparing the results of the tuition revenue optimization model to the institute's current financial aid allocation practice shows that if single-term tuition revenue is the sole optimization criterion, the institute can achieve its current enrollment numbers while realizing significant savings in its financial aid budget. The prediction and optimization software is currently being incorporated into the institute's online admissions processing system.

关键词： Predictive models Bayesian methods Logistics Accuracy Resource management Probability Error analysis Neural networks Regression tree analysis computer science

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Optimal Deep Canonically Correlated Autoencoder-Enabled Prediction Model for Customer Churn Prediction

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computers, Materials & Continua 2022年第11期73卷 3757-3769页

作者： Olfat M.Mirza GJose Moses R.Rajender E.Laxmi Lydia Seifedine Kadry Cheadchai Me-Ead Orawit Thinnukool Department of Computer Science College of Computers and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeNorway Program in Statistics and Information Management Faculty of ScienceMaejo UniversityChiang Mai50290Thailand Department of Modern Management and Information Technology College of Arts Media and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Presently,customer retention is essential for reducing customer churn in telecommunication *** churn prediction(CCP)is important to predict the possibility of customer retention in the quality of *** risks of customer churn also get essential,the rise of machine learning(ML)models can be employed to investigate the characteristics of customer ***,deep learning(DL)models help in prediction of the customer behavior based characteristic *** the DL models necessitate hyperparameter modelling and effort,the process is difficult for research communities and business *** this view,this study designs an optimal deep canonically correlated autoencoder based prediction(ODCCAEP)model for competitive customer dependent application *** addition,the O-DCCAEP method purposes for determining the churning nature of the *** O-DCCAEP technique encompasses preprocessing,classification,and hyperparameter ***,the DCCAE model is employed to classify the churners or ***,the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm(DHOA).The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.

关键词： Churn prediction customer retention deep learning machine learning archimedes optimization algorithm

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