160 research outputs found
Hybrid feature selection based ScC and forward selection methods
Operational data is always huge. A preprocessing step is needed to prepare such data for the analytical process so the process will be fast. One way is by choosing the most effective features and removing the others. Feature selection algorithms (FSAs) can do that with a variety of accuracy depending on both the nature of the data and the algorithm itself. This inspires researchers to keep on developing new FSAs to give higher accuracies than the existing ones. Moreover, FSAs are essential for reducing the cost and effort of developing information system applications. Merging multiple methodologies may improve the dimensionality reduction rate retaining sensible accuracy. This research proposed a hybrid feature selection algorithm based on ScC and forward selection methods (ScCFS). ScC is based on stability and correlation while forward selection is based on Random Forest (RF) and Information Gain (IG). A lowered subset generated by ScC is fed to the forward selection method which uses the IG as a decision criterion for selecting the attribute to split the node of the RF to generate the optimal reduct. ScCFS was compared to other known FSAs in terms of accuracy, AUC, and F-score using several classification algorithms and several datasets. Results showed that the ScCFS excels other FSAs employed for all classifiers in terms of accuracy except FLM where it comes in second place. This proves that ScCFS is the pioneer in generating the reduced dataset with remaining high accuracies for the classifiers used
Big Data - Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques
This article intends to systematically identify and comparatively analyze
state-of-the-art supply chain (SC) forecasting strategies and technologies. A
novel framework has been proposed incorporating Big Data Analytics in SC
Management (problem identification, data sources, exploratory data analysis,
machine-learning model training, hyperparameter tuning, performance evaluation,
and optimization), forecasting effects on human-workforce, inventory, and
overall SC. Initially, the need to collect data according to SC strategy and
how to collect them has been discussed. The article discusses the need for
different types of forecasting according to the period or SC objective. The SC
KPIs and the error-measurement systems have been recommended to optimize the
top-performing model. The adverse effects of phantom inventory on forecasting
and the dependence of managerial decisions on the SC KPIs for determining model
performance parameters and improving operations management, transparency, and
planning efficiency have been illustrated. The cyclic connection within the
framework introduces preprocessing optimization based on the post-process KPIs,
optimizing the overall control process (inventory management, workforce
determination, cost, production and capacity planning). The contribution of
this research lies in the standard SC process framework proposal, recommended
forecasting data analysis, forecasting effects on SC performance, machine
learning algorithms optimization followed, and in shedding light on future
research
CLASSIFICATION OF TODDLER’S NUTRITIONAL STATUS USING THE ROUGH SET ALGORITHM
The health and nutrition of children at the age of five are very important aspects in the children’s growth and development. An assessment of the nutritional status of toddlers that is commonly used is anthropometry. This study aims to obtain the decision rules used to classify toddlers into nutritional status groups using the rough set algorithm and determine the level of classification accuracy of the resulting decision rules. The index used in this study is the weight-for-age index. Attributes used in this study were the mother’s education level, mother’s level of knowledge, the status of exclusive breastfeeding, history of illness in the last month, and nutritional status of toddlers. The results of the analysis show that there are 21 decision rules. In this study, the resulting decision rules experience inconsistencies. The selection of decision rules that experience inconsistencies is based on each decision rule’s highest strength value. The rough set algorithm can be used for the classification process with an accuracy rate of 86.36%
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
A Comprehensive study on (α,β)-multi-granulation bipolar fuzzy rough sets under bipolar fuzzy preference relation
The rough set (RS) and multi-granulation RS (MGRS) theories have been successfully extended to accommodate preference analysis by substituting the equivalence relation (ER) with the dominance relation (DR). On the other hand, the bipolar fuzzy sets (BFSs) are effective tools for handling bipolarity and fuzziness of the data. In this study, with the description of the background of risk decision-making problems in reality, we present -optimistic multi-granulation bipolar fuzzified preference rough sets (-MG-BFPRSs) and -pessimistic multi-granulation bipolar fuzzified preference rough sets (-MG-BFPRSs) using bipolar fuzzy preference relation (BFPR). Subsequently, the relevant properties and results of both -MG-BFPRSs and -MG-BFPRSs are investigated in detail. At the same time, a relationship among the -BFPRSs, -MG-BFPRSs and -MG-BFPRSs is given
LIPIcs, Volume 274, ESA 2023, Complete Volume
LIPIcs, Volume 274, ESA 2023, Complete Volum
Mathematical Fuzzy Logic in the Emerging Fields of Engineering, Finance, and Computer Sciences
Mathematical fuzzy logic (MFL) specifically targets many-valued logic and has significantly contributed to the logical foundations of fuzzy set theory (FST). It explores the computational and philosophical rationale behind the uncertainty due to imprecision in the backdrop of traditional mathematical logic. Since uncertainty is present in almost every real-world application, it is essential to develop novel approaches and tools for efficient processing. This book is the collection of the publications in the Special Issue “Mathematical Fuzzy Logic in the Emerging Fields of Engineering, Finance, and Computer Sciences”, which aims to cover theoretical and practical aspects of MFL and FST. Specifically, this book addresses several problems, such as:- Industrial optimization problems- Multi-criteria decision-making- Financial forecasting problems- Image processing- Educational data mining- Explainable artificial intelligence, etc
Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico
Conference proceedings info:
ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologies
Raleigh, HI, United States, March 24-26, 2023
Pages 529-542We provide a model for systematic implementation of telemedicine within a large evaluation center for COVID-19 in the area of Baja California, Mexico. Our model is based on human-centric design factors and cross disciplinary collaborations for scalable data-driven enablement of smartphone, cellular, and video Teleconsul-tation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID testing, and for subsequent treatment and quar-antine decisions. A multidisciplinary team was rapidly created, in cooperation with different institutions, including: the Autonomous University of Baja California, the Ministry of Health, the Command, Communication and Computer Control Center
of the Ministry of the State of Baja California (C4), Colleges of Medicine, and the College of Psychologists. Our objective is to provide information to the public and to evaluate COVID-19 in real time and to track, regional, municipal, and state-wide data in real time that informs supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. RESUMEN Proporcionamos un modelo para la implementaciĂłn sistemática de la telemedicina dentro de un gran centro de evaluaciĂłn de COVID-19 en el área de Baja California, MĂ©xico. Nuestro modelo se basa en factores de diseño centrados en el ser humano y colaboraciones interdisciplinarias para la habilitaciĂłn escalable basada en datos de tecnologĂas de teleconsulta de telĂ©fonos inteligentes, celulares y video para vincular hospitales, clĂnicas y servicios mĂ©dicos de emergencia para evaluaciones de COVID en el punto de atenciĂłn. pruebas, y para el tratamiento posterior y decisiones de cuarentena. Rápidamente se creĂł un equipo multidisciplinario, en cooperaciĂłn con diferentes instituciones, entre ellas: la Universidad AutĂłnoma de Baja California, la SecretarĂa de Salud, el Centro de Comando, Comunicaciones y Control Informático.
de la SecretarĂa del Estado de Baja California (C4), Facultades de Medicina y Colegio de PsicĂłlogos. Nuestro objetivo es proporcionar informaciĂłn al pĂşblico y evaluar COVID-19 en tiempo real y rastrear datos regionales, municipales y estatales en tiempo real que informan las cadenas de suministro y la asignaciĂłn de recursos con la anticipaciĂłn de un aumento de COVID-19. 19 casos.ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologieshttps://doi.org/10.1007/978-981-99-3236-
Uncertain Multi-Criteria Optimization Problems
Most real-world search and optimization problems naturally involve multiple criteria as objectives. Generally, symmetry, asymmetry, and anti-symmetry are basic characteristics of binary relationships used when modeling optimization problems. Moreover, the notion of symmetry has appeared in many articles about uncertainty theories that are employed in multi-criteria problems. Different solutions may produce trade-offs (conflicting scenarios) among different objectives. A better solution with respect to one objective may compromise other objectives. There are various factors that need to be considered to address the problems in multidisciplinary research, which is critical for the overall sustainability of human development and activity. In this regard, in recent decades, decision-making theory has been the subject of intense research activities due to its wide applications in different areas. The decision-making theory approach has become an important means to provide real-time solutions to uncertainty problems. Theories such as probability theory, fuzzy set theory, type-2 fuzzy set theory, rough set, and uncertainty theory, available in the existing literature, deal with such uncertainties. Nevertheless, the uncertain multi-criteria characteristics in such problems have not yet been explored in depth, and there is much left to be achieved in this direction. Hence, different mathematical models of real-life multi-criteria optimization problems can be developed in various uncertain frameworks with special emphasis on optimization problems
Multiple-Criteria Decision Making
Decision-making on real-world problems, including individual process decisions, requires an appropriate and reliable decision support system. Fuzzy set theory, rough set theory, and neutrosophic set theory, which are MCDM techniques, are useful for modeling complex decision-making problems with imprecise, ambiguous, or vague data.This Special Issue, “Multiple Criteria Decision Making”, aims to incorporate recent developments in the area of the multi-criteria decision-making field. Topics include, but are not limited to:- MCDM optimization in engineering;- Environmental sustainability in engineering processes;- Multi-criteria production and logistics process planning;- New trends in multi-criteria evaluation of sustainable processes;- Multi-criteria decision making in strategic management based on sustainable criteria
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