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Applying genetic algorithms to the information sets search problem

Applying genetic algorithms to the information sets search p...

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IEEE Latin-American Conference on Communications, LATINCOM

作者： José R. Hoffmann Marcos M. Tenório Emilio C. G. Wille Walter Godoy Graduate School of Electrical Engineering and Computer Science (CPGEI) Federal University of Technology of Paraná (UTFPR) Curitiba Parana Brazil

Error-correction coding is essentially a signal processing technique that is used to improve the reliability of digital communication systems. In the Information Set (IS) decoding a collection of information sets are used to generate candidate codewords. The procedure then selects as the decoded codeword the one which is closest to the received sequence. This decoding approach can reduces the complexity and time processing in comparison to maximum-likelihood decoding (MLD), and may presents the same decoding level. The performance of the IS algorithm depends on the number of error patterns that the collection of information sets can cover. There are no known constructive procedures for finding optimum collection of information sets. This paper presents an approach for obtaining optimum collections of information sets using genetic algorithms. Using this approach we found, in short time, collections of information sets with high covering capacity. Results from computer simulation show that the performance of the IS algorithm with optimized collections is nearly identical to the maximum-likelihood decoding.

关键词： Maximum likelihood decoding Genetic algorithms Biological cells Block codes Search problems Genetics

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DCMSim: Didactic cache memory simulator 33

DCMSim: Didactic cache memory simulator

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33rd Annual Frontiers in Education, FIE 2003

作者： Cordeiro, Eduardo Santos Abdanur Stefani, Italo Giovani Do Amaral Pales Soares, Tays Cristina Da Silva Martins, Carlos Augusto Paiva Pontifical Catholic University of Minas Gerais Graduate Program in Computer Science 500 Av. Dom Cabral Coração Eucaristico Belo HarizonteMinas Gerais Brazil Pontifical Catholic University of Minas Gerais Graduate Program in Computer Science Graduate Program in Electrical Engineering 500 Av. Dom Cabral Coração Eucaristico Belo HarizonteMinas Gerais Brazil

ISBN: (纸本)0780379616

We present a functional and structural didactic simulator of Cache Memory Systems developed at the Pontifical Catholic University of Minas Gerais, Brazil. The development occurred during the undergraduate computer Architecture discipline, in the computer science course. Its implementation is one part of a new didactic method, in which developers (students of the computer Architecture discipline) must learn the concepts and theory of the discipline topics to correctly apply them in the simulator. In our simulator, DCMSim, there are features to allow students to construct and verify knowledge, testing and comparing several different configurations and memory access traces. © 2003 IEEE.

关键词： Cache memory

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Instant Messaging as Information Goldmines to Digital Forensic: A Systematic Review 3

Instant Messaging as Information Goldmines to Digital Forens...

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3rd International Conference on Information Management and Technology, ICIMTech 2018

作者： Sihombing, Harry Chandra Fajar, Ahmad Nurul Utama, Ditdit Nugeraha Mercu Buana University Master of Electrical Engineering Jakarta Indonesia Information Systems Management Department Bina Nusantara University BINUS Graduate Program-Master of Information Systems Management Jakarta11480 Indonesia Computer Science Department Bina Nusantara University BINUS Graduate Program-Master of Computer Science Jakarta11480 Indonesia

ISBN: (纸本)9781538658215

The use of instant messaging (IM) application is very communal among smartphone users, as it delivers ease in interaction and communication. The speed at which technologies, growth, and expansion of networks infrastructure for high-speed data networks (e.g. wifi, 4G, etc.) are changing and the high number of IM applications are available. IM application usage is significantly escalating, also for doing illegitimate purposes, particularly to organize and coordinate criminal acts. They are practically exploited by the criminality groups involved when they target and interact with the victims, or try to run away from criminal occurrences. By systematically conducting a review to understand and to investigate IM applications topic coming from a number of scientific sources (e.g. manuscripts, metadata, files, etc.), a broader picture and opportunity to regain the valuable evidence in the investigation is academically provided. © 2018 IEEE.

关键词： Smartphones

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Notification-Oriented and Rete Network Inference: A Comparative Study

Notification-Oriented and Rete Network Inference: A Comparat...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Adriano F. Ronszcka Roni F. Banaszewski Robson R. Linhares Cesar A. Tacla Paulo C. Stadzisz Jean M. Simão Graduate School in Electrical Engineering and Industrial Computer Science (CPGEI) Federal University of Technology - Parana (UTFPR) Curitiba PR Brazil

This paper presents a practical and a theoretical comparative study between the so-called Notification-Oriented Inference (NOI) and Rete Network inference. NOI is composed of a network of small computational entities, which communicate only when necessary, avoiding both temporal and structural redundancies in a program inference. The innovative aspect of the considered approach would be that computational entities carry out the inference by means of collaborations based upon direct notifications (reactive inference). This approach differs from usual inference mechanisms of rule-based systems, which are based on checks/searches over passive data. This search based execution is present even on advanced mechanisms such as Rete. In this sense, the focus of this paper is to analyze performance measurements over an experimental application implemented in both mechanisms. Coupling and redundancy problems are also considered in a theoretical analysis. The results show that NOI can perform around 6 times faster than the compiled Rete mechanism and around 20 times faster than the interpreted Rete mechanism in the cases proposed in this paper. Moreover, NOI presents better asymptotic complexity time and decoupling degree, presenting itself as a promising alternative to compose complex, distributed and scalable applications.

关键词： Redundancy Couplings Collaboration programming Correlation Engines Data structures

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Sentiment Analysis of Cooking Oil using Bidirectional Encoder Representations from Transformers 5

Sentiment Analysis of Cooking Oil using Bidirectional Encode...

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5th International Conference on Information and Communications Technology, ICOIACT 2022

作者： Widianto, Mochammad Haldi Heryadi, Yaya Lukas Suparta, Wayan Wibowo, Antoni Bina Nusantara University Computer Science Department Binus Graduate Program - Doctor of Computer Science Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Departement of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (数字)9781665451406

ISBN: (纸本)9781665451406

Recently, cooking oil has been widely discussed on social media. Many people ask how to analyze this phenomenon, one of which is using a sentiment analysis application on Twitter that performs a scientific analysis of the tweets of this media user. This study aims to test the performance of the sentiment analysis algorithm utilizing the Bidirectional Encoder Representations from Transformers (BERT) algorithm from Twitter, which is used to classify the types of tweets processed in 2000 tweets. The study shows the results of the confusion matrix, accuracy, and precision. The study obtained a fairly good accuracy and precision performance of 84%, which occurred at a learning rate of 1e-6 and epoch 5. The author also discusses the factors that affect the performance of BERT, such as;pre-processing, data quantity, and data variation. © 2022 IEEE.

关键词： Sentiment analysis

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Interpretable Machine Learning for Multi-Class Crop Yield Prediction 3

Interpretable Machine Learning for Multi-Class Crop Yield Pr...

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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Nurcahyo, Aldian Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Bina Nusantara University Compuer Science Binus Graduate Program Jakarta Indonesia Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia Doctor of Computer Science Bina Nusantara University Binus Graduate Program Compuer Science Departement Jakarta Indonesia

ISBN: (纸本)9798350394559

Crop Yield Analysis and Prediction is a fast-expanding discipline that is critical for optimizing agricultural methods. A lack of trustworthy data is one of the challenges in estimating crop yields. We develop predictive models for 22 different fruits and vegetables data. The goals of this study are to create accurate and interpretable crop recommendation models. We used multiple machine learning (ML) models for multi-class crop production prediction to fulfill our research goal. We thoroughly examined the influence of climate and nutrient factors on crop yield, considering their complex interactions. To improve the dataset, augmented data techniques were applied. Configuring the parameters and fine-tuning the hyperparameters is our technique to increase the model performance. Furthermore, we employ explainable artificial intelligence (XAI) techniques and interpretability tools like Shapley Additive exPlanations (SHAP) to improve the interpretability of our prediction model. Our findings reveal that the XGBoost model has the best performance model with 99.86% accuracy, followed by SVM Poly Kernel with 99.32% and Random Forest with 98.82%. Feature selection and analysis are emphasized, particularly in regional agricultural contexts. This study contributes to the creation of accurate and interpretable crop recommendation models while also addressing the issue of untrustworthy data, providing useful insights for optimizing agricultural practices. © 2023 IEEE.

关键词： Forecasting

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Indoor localization at 5GHz using dynamic machine learning approach (DMLA)

Indoor localization at 5GHz using dynamic machine learning a...

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2017 IEEE International Conference on Applied System Innovation, ICASI 2017

作者： Belay, Abebe Yen, Lei Renu, Sakthidasan Lin, Hsin-Piao Jeng, Shiann-Shiun Department of International Graduate Program in Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Department of Electrical Engineering National Dong Hwa University Taipei Taiwan

ISBN: (纸本)9781509048977

In recent years, Wi-Fi based indoor localization using received signal strength (RSS) gets considerable attention. However, RSS based Wi-Fi localization at 2.4GHz is highly susceptible and unstable. We proposed dynamic machine learning approach (DMLA) at 5GHz to effectively localize Wi-Fi users by means of feed-forward neural network algorithm. The simulation result shows that 90% of tested result has less than 0.4m estimation error, and no result shows greater than 0.47m location estimation errors. © 2017 IEEE.

关键词： Feedforward neural networks

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A formal approach for component based embedded software modelling and analysis

A formal approach for component based embedded software mode...

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IEEE International Symposium on Industrial Electronics 2005, ISIE 2005

作者： Almeida, Hyggo Oliveira Dias Da Silva, Leandro Oliveira, Elthon Perkusich, Angelo Federal University of Campina Grande Department of Electrical Engineering Campina Grande Paraiba Brazil Federal University of Campina Grande Graduate Program in Computer Science Campina Grande Paraiba Brazil

ISBN: (纸本)0780387384

To develop software for embedded systems the designer must take into account different kinds of problems and complexities. The main issues are related to late integration with the target hardware and the separation of the development environments from target execution platforms. Due to the fact that in most situations they are developed using low-level programming languages, approaches to develop complex software systems, such as component based software development, cannot be used at all. In this work we introduce a component based development process that integrates colored Petri nets and a component model in order to introduce better abstraction mechanisms in the modelling, analysis and verification processes for embedded software systems. ©2005 IEEE.

关键词： Embedded systems

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Curriculum-based Reinforcement Learning for an Effective Multi-Agent Path Planning Algorithm in Warehouse Scenarios 15

Curriculum-based Reinforcement Learning for an Effective Mul...

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15th IEEE International Conference on Industry Applications, INDUSCON 2023

作者： de Oliveira, Iure Rosa L. de Carvalho, Kevin B. Brandão, Alexandre S. Graduate Program in Computer Science Universidade Federal de Viçosa Viçosa Brazil Electrical Engineering Department Universidade Federal de Viçosa Viçosa Brazil

ISBN: (纸本)9798350314182

Multi-robot systems can provide substantial increase in efficiency and/or flexibility in different scenarios. Applications in various settings have been studied in the literature, such as disaster management, surveillance, object transportation as well as search-and-rescue. A particular case that can highly benefit from the employment of multiple agents is the logistics in a warehouse scenario. This work proposes an multi-agent Q-learning based algorithm with curriculum learning and transfer learning to perform the path planning process. With progressively more complex stages of training as well as knowledge transfer from one stage to another, the algorithm is capable of achieve high success rates. In order to validate the proposed method, simulations were done to compare it to other combinations of the used techniques, as well as using Q-learning only. Scalability tests were also performed. The proposed method achieved up to 94% success rate after training. © 2023 IEEE.

关键词： Curriculum Learning Mobile Robotics Multi-Agent System Reinforcement Learning Transfer Learning

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Social Media Analysis using Sentiment Analysis on COVID-19 from Twitter 6

Social Media Analysis using Sentiment Analysis on COVID-19 f...

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6th International Conference on Information Technology, Information Systems and electrical engineering, ICITISEE 2022

作者： Iqbal Maulana, Fairuz Heryadi, Yaya Lukas Suparta, Wayan Arifin, Yulyani Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350399615

The development of the internet is getting faster, participating in encouraging the emergence of new and innovative information. In filtering the various information that appears, we need a recommended system to perform well for users in today's internet era. A well-performing recommendation system in question is a reliable recommendation algorithm. This algorithm is fundamental to analyzing various information, such as responses on social media based on user behavior data related to the topic of COVID. This data is crawled from tweets on social media Twitter. The data analysis algorithm obtained uses Python, which is then visualized in the form of a diagram. The processed data is user comments on Twitter, and the text data is analyzed using Python, using more than 60000 data sets taken to form visualizations and conclusions. From sentiment analysis, polarity and subjectivity data are obtained to be analyzed, which are negative, neutral, or positive. The result is show positive tweets with 29.2%, negative tweets is 13%, and 57.8% neutral tweets. Lastly, sentiment analysis can help people effectively infer vast and complex data from social media like Twitter. © 2022 IEEE.

关键词： COVID-19 python sentiment analysis social media twitter

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