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Prognostics and System Health Management Conference (PHM)

作者： Peng Su Yeqi Wang Conglei Xiang Erik Wendel Madhav Mishra DeJiu Chen Department of Engineering Design Mechatronics Division KTH Royal Institute of Technology Stockholm Sweden Embedded Systems Master Program of Electrical Engineering and Computer Science (EECS) School KTH Royal Institute of Technology Stockholm Sweden Digital Systems Division RISE Research Institutes of Sweden Gothenburg Sweden

ISBN: (数字)9798350360585

ISBN: (纸本)9798350360592

Prognostics and health management (PHM) is an engineering discipline that aims to maintain system behaviour and function and ensure mission success, safety and effectiveness. Addressing the challenges in prognostics and health management for modern intelligent systems, especially automated driving systems, is complex due to the contextual nature of faults. This complexity necessitates a thorough understanding of spatial, and temporal conditions, and relationships within operational scenarios and life-cycle stages. This paper introduces a framework designed to automatically recognize driving scenarios in automated driving systems using graph neural networks (GNNs). The framework extracts relational data from image frames, constructing graph-based models and transforming unstructured sensory data into structured data with diverse node types and relationships. A specific graph neural network processes the graph model to reveal and detect operational conditions and relationships. The proposed framework is evaluated using the KITTI dataset, demonstrating superior performance compared to conventional feed-forward networks such as MLP, particularly in handling relational data.

关键词： Graph neural networks Data models Safety Complexity theory Data mining Prognostics and health management Intelligent systems

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WANN-Tagger: A weightless artificial neural network tagger for the portuguese language

WANN-Tagger: A weightless artificial neural network tagger f...

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作者： Carneiro, Hugo C. C. França, Felipe M. G. Lima, Priscila M. V. Systems Engineering and Computer Science Program/COPPE Universidade Federal do Rio de Janeiro Caixa Postal 68511 21941-972 Rio de Janeiro RJ Brazil Department of Mathematics Universidade Federal Rural do Rio de Janeiro Km 47 Campus Universitário BR-465 23890-000 Seropédica RJ Brazil

ISBN: (纸本)9789898425324

Weightless Artificial Neural Networks have proved to be a promising paradigm for classification tasks. This work introduces the WANN-Tagger, which makes use of weightless artificial neural networks for labelling Portuguese sentences, tagging each of its terms with its respective part-of-speech. A first experimental evaluation using the CETENFolha corpus indicates the usefulness of this paradigm and shows that it outperforms traditional feedforward neural networks in both accuracy and training time, and also that it is competitive in accuracy with the Hidden Markov Model in some cases. Additionally, WANN-Tagger shows itself capable of incrementally learning new tagged sentences during runtime.

关键词： Feedforward neural networks

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Proceedings of the 2008 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference

Proceedings of the 2008 MidSouth Computational Biology and B...

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作者： Wren, Jonathan D. Wilkins, Dawn Fuscoe, James C. Bridges, Susan Winters-Hilt, Stephen Gusev, Yuriy Arthritis and Immunology Research Program Oklahoma Medical Research Foundation 825 N.E. 13th Street Oklahoma City OK 73104-5005 United States Computer and Information Science Department The University of Mississippi University MS 38677 United States Center for Functional Genomics Division of Systems Toxicology National Center for Toxicological Research Jefferson AR 72079 United States Department of Computer Science and Engineering Mississippi State University Box 9637 Mississippi State MS 39762 United States Department of Computer Science University of New Orleans The Research Institute for Children 200 Henry Clay Ave. New Orleans LA 70118 United States Department of Surgery Health Sciences Center The University of Oklahoma Oklahoma City OK 73104 United States

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Analyzing the Behavioral Aspects of Implementing Green IT in an Academic Environment

Analyzing the Behavioral Aspects of Implementing Green IT in...

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International Conference on Information Management and Technology (ICIMTech)

作者： Dedy Syamsuar Fero Triando Maria Ulfa Darius Antoni Adele Mailangkay Deden Witarsyah Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Postgradute Department Universitas Bina Darma Palembang Indonesia Information System Study Program Computer Science Faculty Universitas Bina Darma Palembang Indonesia Faculty of Computer Science Universitas Indo Global Mandiri Palembang Indonesia School of Industrial Engineering Telkom University Bandung Indonesia

Environmental awareness has recently emerged as one of the most crucial topics. As a result, various groups advocate for these technologies and research ways to promote their usage in various contexts. This study examines the factors influencing the intention and use of green technology among academics. This study integrates Price Value (PV) and Consideration of future consequence (CFC) to Theory Planned Behavior (TPB) as a theoretical basis. Two hundred five valid replies were gathered and processed through statistical analysis. The results of this study partly support the developed hypotheses. Four hypotheses developed from TPB have presented significant relationships. However, PV and CFC were not. The findings indicate that individuals in this study did not consider CFC or PV of green IT products as critical factors in their decision-making process. Findings also suggest that for implementation success, competent parties must consider the campaign to increase individual awareness and provide financial support regarding environmental policy.

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Dynamic programming Algorithm using Furniture Industry 0/1 Knapsack Problem

Dynamic Programming Algorithm using Furniture Industry 0/1 K...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Mietha Anditta Natashya Amartya Laurens Spits Warnars Harco Leslie Hendric Spits Warnars Arief Ramadhan Teddy Siswanto Teddy Mantoro Nurulhuda Noordin Computer Science Departement School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Departement School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Departement BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia PJJ Informatika Telkom University Bandung Indonesia Information Systems Department Trisakti University Jakarta Indonesia Faculty of Engineering and Technology Sampoerna University Jakarta Indonesia School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Malaysia

Dynamic programming is a fundamental algorithm that can be found in our daily lives easily. One of the dynamic programming algorithm implementations consists of solving the 0/1 knapsack problem. A 0/1 knapsack problem can be seen from industrial production cost. It is prevalent that a production cost has to be as efficient as possible, but the expectation is to get the proceeds of the products higher. Thus, the dynamic programming algorithm can be implemented to solve the diverse knapsack problem, one of which is the 0/1 knapsack problem, which would be the main focus of this paper. The implementation was implemented using C language. This paper was created as an early implementation algorithm using a Dynamic program algorithm applied to an Automatic Identification System (AIS) dataset.

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Implementation of Mobile game for Religion Learning

Implementation of Mobile game for Religion Learning

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IEEE International Conference on Teaching, Assessment and Learning for engineering (TALE)

作者： Edy Sulistiyo Vindy Nizirwan Anwar Diana Teresia Spits Warnars Setya Chendra Wibawa Goenawan Brotosaputro Dedy Prasetya Kristiadi Harco Leslie Hendric Spits Warnars Kiyota Hashimoto Electrical Engineering Department Universitas Negeri Surabaya Jawa Timur Indonesia Game Application & Technology Program School of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Computer Science Esa Unggul University Jakarta Indonesia Information Systems Department Bina Nusantara University Jakarta Indonesia Informatics Department Universitas Negeri Surabaya Jawa Timur Indonesia Information System Department Universitas Budi Luhur Jakarta Indonesia Computer Science department Raharja University Tangerang Indonesia Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Technology and Environment Prince Songkla University Phuket Campus Thailand

ISBN: (数字)9781728126654

ISBN: (纸本)9781728126661

Currently, the growth of information and technology is rapid. It makes a lot of things in various fields including education becoming more effective and efficient. In education, one of its implementation which is a game is proven to be a useful tool to support conventional teaching methods and bring a more natural understanding of learning materials. Moreover, as a product of the popular culture in modern society, video game mirrors the general culture practice in real life and reflects it via its own culture inside the game. This makes a video game can give a contribution to the social construction of reality as it affects the player's view towards learning in real life and vice versa. From there, we see that there is an opportunity for learning about religion to be supported by its utilization to provide an interactive and fun learning experience. In this paper, we discuss how a video game is implemented to support religious learning. The game was developed with the scrum method where we surveyed to gather the user requirements before the design step. The game design was made by using the use case diagram and storyboard, and it was built using Unity version 2017.3.0f3.

关键词： Games Land mobile radio Education computer science Operating systems Information systems Media

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CLINICAL DETERIORATION PREDICTION IN BRAZILIAN HOSPITALS BASED ON ARTIFICIAL NEURAL NETWORKS AND TREE DECISION MODELS

arXiv

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arXiv 2022年

作者： Yazdanpanah, Hamed Silva, Augusto C.M. Guedes, Murilo Morales, Hugo M.P. Coelho, Leandro dos S. Moro, Fernando G. Department of Research Robô Laura PR Curitiba Brazil Department of Computer Science University of São Paulo SP São Paulo Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana PR Curitiba Brazil Electrical Engineering Graduate Program Federal University of Parana PR Curitiba Brazil

Early recognition of clinical deterioration (CD) has vital importance in patients' survival from exacerbation or death. Electronic health records (EHRs) data have been widely employed in Early Warning Scores (EWS) to measure CD risk in hospitalized patients. Recently, EHRs data have been utilized in Machine Learning (ML) models to predict mortality and CD. The ML models have shown superior performance in CD prediction compared to EWS. Since EHRs data are structured and tabular, conventional ML models are generally applied to them, and less effort is put into evaluating the artificial neural network's performance on EHRs data. Thus, in this article, an extremely boosted neural network (XBNet) is used to predict CD, and its performance is compared to eXtreme Gradient Boosting (XGBoost) and random forest (RF) models. For this purpose, 103,105 samples from thirteen Brazilian hospitals are used to generate the models. Moreover, the principal component analysis (PCA) is employed to verify whether it can improve the adopted models' performance. The performance of ML models and Modified Early Warning Score (MEWS), an EWS candidate, are evaluated in CD prediction regarding the accuracy, precision, recall, F1-score, and geometric mean (G-mean) metrics in a 10-fold cross-validation approach. According to the experiments, the XGBoost model obtained the best results in predicting CD among Brazilian hospitals' data. © 2022, CC BY-NC-SA.

关键词： Machine learning

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A Genetic Algorithm to Optimize Cargo Stability in Container Transportation

A Genetic Algorithm to Optimize Cargo Stability in Container...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Chi-Jung Hsieh Kuang-Sung Wu Tin-Yu Wu Fu Jie Tey Yi-Kai Chen Jye Yi Ho Department of Computer Science and Information Engineering National Ilan University Ilan County Taiwan Master Program of E-Learning for Multimedia and Network Communications National Ilan University Yilan Taiwan Department of Management Information Systems National Pingtung University of Science and Technology Pingtung County Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (纸本)9781665492331

In the era of the global village, frequent cross-border trade in goods has made container transportation a significant part in delivery of cargo. However, rollover accidents of container trucks often occur because of tall truck height, improper cargo stowage or crooked roads. Therefore, how to find the optimal center of gravity (CG) of the packed cargo remains an issue that the transportation industry should not neglect. To optimize cargo stability and reduce the incidence of rollovers, this paper proposes a genetic algorithm that scores the cargo stacking process to find the best CG of the cargo based on the CG of the vehicle. According to the deviation angle and the point closest to the best CG, we can Figure out the optimal cargo stacking method.

关键词： Rollover Transportation industry Roads Stacking Transportation Containers Safety

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IoT for Water Quality Categorization

IoT for Water Quality Categorization

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Hermantoro Sastrohartono Andreas Wahyu Krisdiarto Arief Ika Uktoro Reza Rahutomo Teddy Suparyanto Bens Pardamean Agricultural Engineering Department Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results.

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A Novel Hybrid Framework for Energy-Efficient Clustering and Routing in Heterogeneous IoT-Driven Wireless Sensor Networks

A Novel Hybrid Framework for Energy-Efficient Clustering and...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Gopal Kumar Gupta Arpit Jain Khemraj Sharma P. M. Ashok Kumar Shubhi Gupta Sheenu Agarwal Symbiosis Institute Of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Department of Computer Science Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Business Administration Doctoral Program in Management Sciences NANHUA UNIVERSITY Dalin Chiayi R.O.C Department of Computer Science and Engineering Amity University Greater Noida Uttar Pradesh India Department of Electronics and Communication Engineering Uttarakhand Technical University Dehradun U.K. India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Smart computing has been particularly notable in the development of wireless sensor networks (WSNs), which have many applications. Battery-powered, self-configuring sensor nodes are the basis of these networks. Energy and resources are limited when it comes to sensors. Unbalanced nodes in the network consume more power, which adversely affects the network’s lifespan. An improved energy-efficient cluster-based routing protocol for heterogeneous wireless sensor networks (WSNs) based on the Internet of Things is proposed in this study. Several performance metrics were used to evaluate the proposed model’s effectiveness, including energy efficiency, alive nodes, dead nodes, network lifetime, and residual energy. The results of the comparison were compared with existing methodologies, such as LEACH, PSO, and hybrid PSO. The proposed approach outperforms the existing model by a substantial margin in networks with 50 and 100 nodes, according to the simulation results.

关键词： Wireless communication Measurement Wireless sensor networks Simulation Heuristic algorithms Routing Energy efficiency Routing protocols Internet of Things Informatics

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