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3rd International Conference on Information Computing and Applications, ICICA 2012

作者： Menezes, Andrea Carvalho Pinheiro, Placido Rogerio Pinheiro, Mirian Caliope Dantas Cavalcante, Tarcísio Pequeno Graduate Program in Applied Informatics Av. Washington Soares 1321 - Bl J Sl 30 60.811-905 Fortaleza Brazil

ISBN: (纸本)9783642340611

A hybrid model, combining Bayesian network and a multicriteria method, is presented in order to assist with the decision making process about which questions would be more attractive to the definition of the diagnosis of Diabetes type 2. We have proposed the application of an expert system structured in probability rules and structured representations of knowledge in production rules and probabilities (Artificial Intelligence - AI). The importance of the early diagnosis associated with the appropriate treatment is to decrease the chance of developing the complications of diabetes, reducing the impact on our society. Diabetes is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both. © 2012 Springer-Verlag.

关键词： Expert systems

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Improved LSTM hyperparameters alongside sentiment walk-forward validation for time series prediction

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Journal of Open Innovation: Technology, Market, and Complexity 2025年第1期11卷

作者： Wahyuddin, Eko Putra Caraka, Rezzy Eko Kurniawan, Robert Caesarendra, Wahyu Gio, Prana Ugiana Pardamean, Bens Department of Statistical Computing Politeknik Statistika STIS Jakarta 13330 Indonesia Statistics Indonesia (BPS) Jl. Dr Sutomo 6-8 Jakarta Indonesia School of Economics and Business Telkom University Bandung 40257 Indonesia Research Center for Data and Information Sciences Research Organization for Electronics and Informatics National Research and Innovation Agency (BRIN) Bandung 40135 Indonesia Faculty of Integrated Technologies Universiti Brunei Darussalam Gadong BE1410 Brunei Darussalam Department of Mathematics Universitas Sumatera Utara Medan 20155 Indonesia Bioinformatics and Data Science Research Centre Bina Nusantara University DKI Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University DKI Jakarta 11480 Indonesia

This study aims to address the common issue of biased estimation errors in time series modeling by analyzing the error in locating ideal hyperparameters and defining appropriate validation methods. Specifically, it focuses on predicting the stock price of Bank Rakyat Indonesia using a combination of historical stock prices, technical indicators, exchange rates, and news sentiment data, while determining the optimal variables for deep learning models. Employing a deep learning-based Long-Short Term Memory (LSTM) model, the study optimizes hyperparameters alongside walk-forward validation for time series prediction. It explores different combinations of variables and adapts the sliding window approach to the context of the data. The results highlight the importance of optimizing hyperparameters and utilizing walk-forward validation for accurate time series prediction. The model incorporating historical stock prices and sentiment scores outperforms others, achieving an RMSE of 96.61 and MAE of 86.97. Incorporating sentiment scores reduces RMSE by 39.55 % compared to models using only historical stock prices, while adding technical indicators does not yield improvement. This study contributes to the field by addressing the issue of biased estimation errors in time series modeling, offering insights into the optimization of hyperparameters and validation methods for accurate predictions. It also underscores the significance of incorporating sentiment analysis alongside historical stock prices for improved forecasting accuracy. © 2024 The Authors

关键词： LSTM Sentiment analysis Stock price prediction Time series modeling Validation methods

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Hybrid model for early identification post-Covid-19 sequelae

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Journal of Ambient Intelligence and Humanized Computing 2023年第11期14卷 14739-14752页

作者： de Andrade, Evandro Carvalho Pinheiro, Luana Ibiapina C. C. Pinheiro, Plácido Rogério Nunes, Luciano Comin Pinheiro, Mirian Calíope Dantas Pereira, Maria Lúcia Duarte de Abreu, Wilson Correia Filho, Raimir Holanda Simão Filho, Marum Pinheiro, Pedro Gabriel C. D. Nunes, Rafael Espíndola Comin Graduate Program in Applied Informatics University of Fortaleza Ceará Fortaleza Brazil Ceara State University Ceara Fortaleza Brazil University Center September 7 Ceara Fortaleza Brazil School of Nursing of Porto Porto Portugal

Artificial Intelligence techniques based on Machine Learning algorithms, Neural Networks and Naïve Bayes can optimise the diagnostic process of the SARS-CoV-2 or Covid-19. The most significant help of these techniques is analysing data recorded by health professionals when treating patients with this disease. Health professionals' more specific focus is due to the reduction in the number of observable signs and symptoms, ranging from an acute respiratory condition to severe pneumonia, showing an efficient form of attribute engineering. It is important to note that the clinical diagnosis can vary from asymptomatic to extremely harsh conditions. About 80% of patients with Covid-19 may be asymptomatic or have few symptoms. Approximately 20% of the detected cases require hospital care because they have difficulty breathing, of which about 5% may require ventilatory support in the Intensive Care Unit. Also, the present study proposes a hybrid approach model, structured in the composition of Artificial Intelligence techniques, using Machine Learning algorithms, associated with multicriteria methods of decision support based on the Verbal Decision Analysis methodology, aiming at the discovery of knowledge, as well as exploring the predictive power of specific data in this study, to optimise the diagnostic models of Covid-19. Thus, the model will provide greater accuracy to the diagnosis sought through clinical observation. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Decision support systems

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Information hiding based on histogram and pixel pattern

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Journal of Cyber Security and Mobility 2017年第4期6卷 397-425页

作者： Barmawi, Ari Moesriami Pradeka, Deden Informatics Graduate Program School of Computing Telkom University Indonesia

Recently, information exchange using internet is increasing, such that information security is necessary for securing confidential information because it is possible to eavesdrop the information. There are several methods for securing the exchanged information such as was proposed by Rejani et al. Rejani's method can be noiseless in low capacity but noisy in high capacity. In the case of high capacity, it will raise suspicion. This research proposed a method based on histogram and pixel pattern for keeping the stego image noiseless while still keeping the capacity high. Secret information can be embedded into the cover by evaluating the histogram and map the characters used in the secret message to the consecutive intensity in the cover image histogram. The map of the characters is sent to the recipient securely. Using the proposed method there is no pixel value changes during the embedding process. Based on the result of the experiments, it is shown that in noiseless condition, the proposed method has higher embedding capacity than Rejani's especially when using cover image with sizes larger than 128 × 128. Thus, in noiseless condition the embedding capacity using the proposed method is higher than Rejani's method in noiseless condition. © 2018 the Author(s).

关键词： Graphic methods

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A hybrid methodology for nesting irregular shapes: Case study on a textile industry ?

A hybrid methodology for nesting irregular shapes: Case stud...

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6th IFAC/ACM Conference on Management and Control of Production and Logistics, MCPL 2013

作者： Junior, Bonfim A. Pinheiro, Placido R. Saraiva, Rommel D. Graduate Program in Applied Informatics University of Fortaleza Av. Washington Soares 1321-J30 60.811-905 Fortaleza Brazil

The aim of this paper is to describe a hybrid methodology that combines a Genetic Algorithm and a Bottom-Left Greedy procedure for solving the two-dimensional Nesting Problem. The No-Fit Polygon construct is applied for obtaining local optima. Furthermore, a shrinking algorithm is incorporated to the metaheuristic engine to identify good quality solutions. Computational experiments performed on standard benchmark problems, as well as a practical case study developed in a textile industry, are also reported and discussed here in a manner as to testify the potentialities behind the novel approach. © IFAC.

关键词： Textile industry

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A charge-redistribution based controller for keeping charge balance in neural stimulation 58

A charge-redistribution based controller for keeping charge ...

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58th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2015

作者： Teixeira, Lucas Rodrigues, Cesar Prior, Cesar Graduate Program in Informatics Federal University of Santa Maria Brazil Dept.of Electronics and Computing - CT Federal University of Santa Maria Brazil

ISBN: (纸本)9781467365574

In this article, we present a circuit solution for sensing and controlling functional electrical stimulation (FES) currents. The objective is to keep both charge balance and residual electrode voltage under a safe limit throughout stimulation. In the proposed circuit, the stimulation current is set through an ultra-low-power charge-redistribution digital-to-analog converter (CR-DAC). Each stimulation pulse is copied by high-ratio current mirror into switched capacitors which integrate the residual direct current (DC) resulting from FES imbalances. As residual electrode voltage may buildup even for perfectly balanced current pulses, alternated cathodic-first and anodic-first stimulation is adopted to complement the charge-balancing mechanism. Simulation results suggest that the circuit is able to keep both residual charge and electrode voltage under safe limits, smaller than 0.05% and 64 mV respectively. © 2015 IEEE.

关键词： Low power electronics

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An extensible architecture for DTV middleware 11

An extensible architecture for DTV middleware

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Proceedings of the 9th European Conference on Interactive TV and Video

作者： Ghisi, Bruno Cavaler Siqueira, Frank Graduate Program in Computer Science - PPGCC Department of Informatics and Statistics - INE Federal University Os Santa Catarina - UFSC Florianópolis Brazil

ISBN: (纸本)9781450306027

The currently adopted specifications for Digital Television (DTV) middleware do not define the internal characteristics of their architecture. As a result, the existing middleware implementations are not as flexible, extensible and adaptable as they should be due to the adoption of a closely-coupled architecture. This paper proposes a modular architecture for DTV middleware, which allows easy extension and updating, providing a simple way to add new features to the middleware. This architecture is based on the OSGi framework and its software components, called bundles. A categorization of these bundles was proposed, based on different usage scenarios that were identified, according to industry time-to-market and innovation needs. Prototypes of the architecture and of bundles have been implemented, and tests performed with these prototypes show the feasibility of this approach. © 2011 ACM.

关键词： Digital television

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A hybrid methodology for tackling the irregular strip packing problem

A hybrid methodology for tackling the irregular strip packin...

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11th IFAC Workshop on Intelligent Manufacturing Systems, IMS 2013

作者： Junior, Bonfim A. Pinheiro, Placido R. Saraiva, Rommel D. Graduate Program in Applied Informatics University of Fortaleza Av. Washington Soares 1321 - J30 60.811-905 Fortaleza Brazil

ISBN: (纸本)9783902823335

This paper presents a hybrid approach for tackling the irregular strip packing problem, which requires a set of polygons to be placed within a rectangular object. The methodology includes a meta-heuristic engine (e.g., a genetic algorithm) that determines the order in which polygons are handled by a placement heuristic. In addition, differently from several approaches presented in the literature, we investigate the application of the no-fit polygon as a placement tool for obtaining local optima. The results are further improved by a shrinking algorithm that works within the meta-heuristic component. To assess the potentials of the proposed methodology, computational experiments performed on a set of difficult benchmark instances of the irregular strip packing problem are discussed here for evaluation purposes. © 2013 IFAC.

关键词： Genetic algorithms

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Predicting student's psychomotor domain on the vocational senior high school using linear regression 1

Predicting student's psychomotor domain on the vocational se...

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1st International Conference on Information and Communications Technology, ICOIACT 2018

作者： Harimurti, R. Yamasari, Y. Ekohariadi Munoto Asto, B.I.G.P. Study Program of Vocational Education on Post Graduate Universitas Negeri Surabaya Surabaya Indonesia Department of Informatics Universitas Negeri Surabaya Surabaya Indonesia

ISBN: (纸本)9781538609545

The educational data can be mined to produce the useful knowledge. This paper focuses on the educational data processing to predict student's psychomotor domain. Here, we apply linear regression method to do it. On process stage, we use 4 regularizations, namely: no regularization, ridge regression, lasso regression and elastic net regression. Furthermore, we exploit 2 sampling methods as the evaluation technique, for examples: cross-validation sampling and random sampling. The experimental result indicates that the best regularization on cross-validation and random sampling are an elastic net regression because this regularization achieves the lowest predicting error. On cross-validation, values of MSE, RMSE, and MAE are 40.079, 6.330 and 5.183, respectively. Additionally, for random sampling, respectively, values of MSE, RMSE, and MAE are 86.910, 8.428 and 6.511. © 2018 IEEE.

关键词： Students

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Determinant Factors Affecting the Information System Success of Community-Based Application to Fight Covid-19 Pandemic

Determinant Factors Affecting the Information System Success...

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2021 International Conference on Computer, Control, informatics and Its Applications - Learning Experience: Raising and Leveraging the Digital Technologies During the COVID-19 Pandemic, IC3INA

作者： Purwanto, Adnan Asih, Dybio Dompu Hot Nurjabar, Ilham Cahyani, Trisiwi Indra Hidayat, Deni Manik, Lindung Computer Science Graduate Program University of Nusa Mandiri Jakarta Indonesia Research Center for Informatics National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9781450385244

National governments around the world have made every effort to fight Covid-19. One of which is by building mobile applications that can be used to trace and monitor the citizens' health during this pandemic. These applications are expensive. It requires a significant amount of time and effort to develop and maintain the information systems. Therefore, there is a continuous struggle to identify the key factors influencing the information systems' adoption, utilization, and success. This study was performed to address the problem by identifying parameters that affect the information system success of "10 Rumah Aman (10 Safe Houses)", a community-based application in fighting the Covid-19 pandemic, using the DeLone and McLean (D&M) model. As a result, system quality positively affected the use and user satisfaction. Furthermore, service quality positively influenced use but not user satisfaction. Meanwhile, information quality had no significant effect either on use or satisfaction. Both use and user satisfaction had a significant effect on net benefits. © 2021 ACM.

关键词： Information systems

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