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6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022

作者： Gautama, Adri Rochim, Adian Fatchur Bayuaji, Luhur Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Diponegoro University Department of Computer Engineering Semarang Indonesia Computer Science Budi Luhur University Jakarta Indonesia

ISBN: (纸本)9798350399615

Every patient has health record, it was written as statement of patient's conditions, treatments, and medications, and nowadays it is become digitalized, it can be copied and shared easily, but the nature of EHR is private, not for public, private between patients and healthcare provider. Privacy preserving can be done by implementing Blockchain network for storing EHR, only authorized users or nodes can access and write data to the network. New block in blockchain created as per consensus among nodes, every node can join a blockchain network if it is public, to meet a consensus required resources intensive cryptographic challenge, in private blockchain, only selected can join and involved in the network or specific transaction. In healthcare, data privacy is the main concerned, need a secure, scalable, and efficient blockchain hence implementing consortium blockchain is perfect match for EHR where multiple healthcare provider can operate in a single platform to conduct transaction or sharing EHR among members. This study analyzes 15 articles derived from IEEE Explore, science Direct and Scopus by implementing screening with keyword Consortium Blockchain and EHR and proposing new blockchain model accommodating solution for challenges on access, storage, sharing, privacy, and performance. © 2022 IEEE.

关键词： Consortium Blockchain e-health EHR Privacy

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A Vision-Based Neural Networks Model for Turbine Trench-Filler Diagnosis 1

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7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023

作者： Isaza, Cesar Guerrero-Garcia, Fernando Anaya, Karina Jenab, Kouroush Ortega-Moody, Jorge Networking and Telecommunication Academic Program Polytechnic University of Queretaro Queretaro Mexico Productive System Master Academic Program Polytechnic University of Queretaro Queretaro Mexico School of Engineering and Computer Science Morehead State University MoreheadKY40351 United States

ISBN: (数字)9783031396199

ISBN: (纸本)9783031396182

Vision-based neural networks as artificial intelligence models have been critical in many manufacturing industries, including automotive, food, and aerospace. Machine vision and deep learning have provided practical, promising, and accessible innovations to address many problems during manufacturing and diagnostics. Recent studies offer beneficial results in implementing industrial artificial intelligence systems that require determining, comparing, and evaluating optimal technological solutions. The aerospace industry has to deal with the problem of diagnosing critical components that continually need the expertise of a trained human being. On the other hand, automatic diagnostics are becoming a critical technology to deal with this problem. However, these systems require a particular configuration of computer vision algorithms which, in the case of turbine trench-filling components, have yet to be added to the literature. Considering the above, we report in this paper a new methodology that uses a pre-trained deep neural network framework available online to automatically diagnose geometric nonconformities in aeronautical components that protect the turbines from vibrations and reduce noise from the engines to the aircraft deck. The method is based on the following stages: a computer vision system with a monochrome camera to acquire images, training of deep neural networks with transfer learning, and a stage to analyze nonconformities automatically. In addition, a benchmark of several pre-trained deep neural networks is presented to address the problem. According to the experimental results, the YOLO deep neural network topology significantly contributes to automatic diagnosis with high precision and exact rate. Finally, we show that integrating deep neural networks with control graphs is a promising strategy to optimize online diagnostics in the production lines of the aeronautical industry. © The Author(s), under exclusive license to Springer Nature Switzerland

关键词： Deep neural networks

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Enduring interaction: An approach to analysis and design of animated gestural interfaces in creative computing systems 11

Enduring interaction: An approach to analysis and design of ...

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8th ACM Conference on Creativity and Cognition, C and C 2011

作者： Chow, Kenny K. N. Fox Harrell, D. School of Design Hong Kong Polytechnic University Hong Kong Comparative Media Studies Program Computer Science and AI Laboratory Massachusetts Institute of Technology United States

ISBN: (纸本)9781450308205

This paper provides an interdisciplinary reflection on the nature meaning-making involving users and animated gestural interfaces. In particular, we propose a new model for analysis of creative computing systems incorporating gestural input into dynamically animated interfaces. Our contributions are based on a theoretical framework synthesizing embodied cognition approaches in cognitive science, phenomenology in philosophy, and user interface design in computing. We introduce the term enduring interaction to refer to the phenomenon of bodily and conceptually engaging interaction within constantly changing computational environments. Our construct centralizes the issue of how users' motor-sensory experiences inform their construction of meaning in the design of interactive systems. We argue that creative computing systems, a class of artifacts including types of hobbyist websites, video games, and computer-based artworks, require a new design perspective quite distinct from user-centric interface design approaches focused on productivity-oriented applications. Using examples including outcomes of the Gestural Narrative and Interactive Expression (GeNIE) project (Harrell, PI;Chow and Erik Loyer collaborators) along with existing prevalent, exceptional, or historically significant artifacts, we articulate a continuum of various kinds of engagement, showing design implications of our perspective, enabling users to use gestural interaction (through multi-Touch and gyroscope/accelerometer-based input devices) to result in narratively salient, evocative, and even intimate interaction mechanisms in interactive narrative environments. Copyright 2011 ACM.

关键词： Digital storage

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Effect of dual modification on structural, functional and dielectric properties of cassava starch

Food Physics

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Food Physics 2025年 2卷

作者： Cremasco, Paula Figueiredo M. Siqueira, Karine Mayuri Pistoni Sobral, Paulo José do Amaral Velloso, Walter Ferreira de Lima, César Gonçalves Gut, Jorge Andrey Wilhelms Moraes, Izabel Cristina Freitas Postgraduate Program in Materials Science and Engineering Faculty of Animal Science and Food Engineering University of São Paulo Avenue Duque de Caxias Norte São Paulo Pirassununga 13635900 Brazil Department of Biossistems Engineering Faculty of Animal Science and Food Engineering University of São Paulo Avenue Duque de Caxias Norte São Paulo Pirassununga 13635900 Brazil Department of Food Engineering Faculty of Animal Science and Food Engineering University of São Paulo Avenue Duque de Caxias Norte São Paulo Pirassununga 13635900 Brazil Food Research Center (FoRC) University of São Paulo Lake Street São Paulo São Paulo 05508-080 Brazil Departament of Basic Sciences Faculty of Animal Science and Food Engineering University of São Paulo Avenue Duque de Caxias Norte São Paulo Pirassununga 13635900 Brazil Department of Chemical Engineering Polytechnic School University of São Paulo Lake Street São Paulo São Paulo 05508-080 Brazil

The combined effect of hydrocolloid addition and microwave heating on the structural, physical and functional properties of cassava starch remains underexplored, despite individual studies on these modification methods. The dielectric properties, important for the microwave treatment of starch, have received little attention in recent studies. Cassava starch with and without addition of hydrocolloids (guar and xanthan gums) was modified by microwave heating for two different exposure times, and its structural, functional, and dielectric properties were studied. The dual modification enhanced the swelling power and oil holding capacity of the starch, reduced its gelatinization temperature, and did not alter its crystallinity. Notably, xanthan-modified starch microwaved for 6 min exhibited higher color variation (ΔE*) and increased granules agglomeration, while the microwave treatment alone roughened the surface of the starch granules. The mean diameters D[4,3] of starch increased with xanthan gum addition, microwave treatment (6 min), and their combination. The relative permittivity (ε’), loss factor (ε”) and penetration depth (D_p) were lower for samples modified by the combined method. Considering the type of hydrocolloid and the microwave exposure time, the combined modification significantly influenced the properties of starch, offering a promising approach to producing starch with enhanced functional properties. © 2024 KeAi Communications Co., Ltd

关键词： Biopolymers Cassava starch Guar Microwave Xanthan

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Integration of Cleaner Production and Waste Water Treatment on Tofu Small Industry for Biogas Production using AnSBR Reactor 2

Integration of Cleaner Production and Waste Water Treatment ...

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2nd International Conference on Energy, Environmental and Information System, ICENIS 2017

作者： Rahayu, Suparni Setyowati Budiyono, Budiyono Purwanto, Purwanto Mechanical Engineering Department State Polytechnic of Semarang Indonesia Doctoral Program of Environmental Science School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Chemical Engineering Faculty of Engineering Diponegoro University Semarang Indonesia

A research on developing a system that integrates clean production and waste water treatment for biogas production in tofu small industry has been conducted. In this research, tofu waste water was turned into biogas using an AnSBR reactor. Mud from the sewage system serves as the inoculums. This research involved: (1) workshop;(2) supervising;(3) technical meeting;(4) network meeting, and (5) technical application. Implementation of clean production integrated with waste water treatment reduced the amount of waste water to be treated in a treatment plant. This means less cost for construction and operation of waste water treatment plants, as inherent limitations associated with such plants like lack of fund, limited area, and technological issues are inevitable. Implementation of clean production prior to waste water treatment reduces pollution figures down to certain levels that limitations in waste water treatment plants can be covered. Results show that biogas in 16 days HRT in an AnSBR reactor contains CH4(78.26 %) and CO2 (20.16 %). Meanwhile, treatments using a conventional bio-digester result in biogas with 72.16 % CH4 and 18.12 % CO2. Hence, biogas efficiency for the AnSBR system is 2.14 times greater than that of a conventional bio-digester. © 2018 The Authors, published by EDP sciences.

关键词： Biogas

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Selection Relay-Based RF-VLC Underwater Communication System

Selection Relay-Based RF-VLC Underwater Communication System

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International Conference on Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication, MDCWC 2020

作者： Furqan Ali, Mohammad Ponnimbaduge Perera, Tharindu D. Sergeevich, Vladislav S. Arbid Irfan, Sheikh Ekaterina Viktorovna, Unzhakova Zhang, Weijia Camponogara, Ândrei Jayakody, Dushantha Nalin K. School of Computer Science and Robotics National Research Tomsk Polytechnic University Tomsk Russia Federal University of Juiz de Fora Juiz de Fora Brazil School of Postgraduate Studies Sri Lanka Technological Campus Padukka Sri Lanka

ISBN: (纸本)9789811602887

Visible light communication (VLC) has become recently attracted a renewed communication trade in underwater environment. However, the deployment of underwater applications and oceanographic data collection is more challenging than a terrestrial basis communication. In this regard, a more sophisticated communication system needs to deploy in harsh aqueous medium. Afterward, collected data transmits with the inland base station for further analysis. The necessity of real-time data streaming for military and scientific purposes a dual-hop hybrid cooperative communication is needed. Throughout this research, a dual-hop hybrid RF and underwater visible light (UVLC) relayed communication system developed under consideration of strong turbulence channel conditions along with miss-alignment of transceivers. Moreover, RF link is modeled by nakagami-m fading distribution, while the UVLC link is modeled by Gamma-Gamma distribution for strong turbulence channel conditions. Furthermore, an amplify-and-forward (AF) and decode-and-forward (DF) protocols were considered to assist information transmission with underwater-based destination. The simulation results are used to analyze RF-UVLC link combination and bit error rate (BER) performance through both AF and DF signal protocols in different waters along with the large- and small-scale factors in highly turbid water mediums and pointing errors of the propagated light beam. We used to Monte Carlo approach for the best fitting curves to yield simulation results. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Cooperative communication

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Q-Learning Energy Management System (Q-EMS) in Wireless Sensor Network

Q-Learning Energy Management System (Q-EMS) in Wireless Sens...

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2024 International Conference on Smart Computing, IoT and Machine Learning, SIML 2024

作者： Ardiansyah, Deden Mustafid Mantoro, Teddy Universitas Diponegoro Doctoral Program of Information Systems School of Postgraduate Central Java Semarang Indonesia Universitas Pakuan Vocational School Computer Engineering Department West Java Bogor Indonesia Sampoerna University Media-Tech-Lab Computer Science Department DKI Jakarta Indonesia

ISBN: (纸本)9798350364101

The energy control of a Wireless Sensor Network (WSN) often leads to an unbalanced state between the battery storage system, energy extraction through photovoltaic systems energy, and energy utilization in the WSN. These disparities result in suboptimal wireless sensor network performance. The reliance on batteries is a major factor contributing to this problem. The Q-Learning Energy Management System (Q-EMS) is designed to address these challenges and improve energy management strategies. The Q-EMS algorithm used a learning process resulting in optimal actions for sensor nodes in different situations. The rewards or punishments nodes receive determine their decisions, which are determined by the Q-EMS algorithm. The Q-learning model approach is aimed at reducing energy consumption and supply in WSNs. Energy supply is categorized into battery-based, transfer-based, and harvesting, while energy consumption can be classified into task-cycle, mobility-based, and data-driven. The development of the Q Learning algorithm model has three scenarios: determining energy needs for WSN, effective energy harvesting strategy, and effective energy transfer. As a result, effective energy demand, harvesting and transfer using the Q-learning algorithm are balanced. However, it needs to be studied in more depth using real data in the field. In future research, I will use real data and optimize the use of the Q-Learning algorithm. © 2024 IEEE.

关键词： Energy management systems

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Semi-analytical finite element method applied for characterizing micropolar fibrous composites

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Applied Mathematics and Mechanics(English Edition) 2024年第12期45卷 2147-2164页

作者： J.A.OTERO Y.ESPINOSA-ALMEYDA R.RODRIGUEZ-RAMOS J.MERODIO School of Engineering and Sciences Monterrey Institute of TechnologyEstado de Mexico 52926Mexico Institute of Engineering and Technology Autonomous University of Ciudad JuarezCiudad JuarezChihuahua 32310Mexico Postgraduate Program in Computational Modeling in Science and Technology Federal Fluminense UniversityRıo de Janeiro 27255-125Brazil Faculty of Mathematics and Computer Science University of HavanaLa Habana 10400Cuba Department of Mathematics Applied to ICT School of Informatics Systems EngineeringTechnical University of MadridMadrid 28031Spain

A semi-analytical finite element method(SAFEM),based on the two-scale asymptotic homogenization method(AHM)and the finite element method(FEM),is implemented to obtain the effective properties of two-phase fiber-reinforced composites(FRCs).The fibers are periodically distributed and unidirectionally aligned in a homogeneous *** framework addresses the static linear elastic micropolar problem through partial differential equations,subject to boundary conditions and perfect interface contact *** mathematical formulation of the local problems and the effective coefficients are presented by the *** local problems obtained from the AHM are solved by the FEM,which is denoted as the *** numerical results are provided,and the accuracy of the solutions is analyzed,indicating that the formulas and results obtained with the SAFEM may serve as the reference points for validating the outcomes of experimental and numerical computations.

关键词： semi-analytical approach fiber-reinforced composite(FRC) effective property finite element method(FEM) asymptotic homogenization method(AHM) micropolar elasticity

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Expert System Application of Forward Chaining and Certainty Factors Method for the Decision of Contraception Tools 2

Expert System Application of Forward Chaining and Certainty ...

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2nd International Conference on Energy, Environmental and Information System, ICENIS 2017

作者： Prambudi, Dwi Arief Widodo, Catur Edi Widodo, Aris Puji Magister Program of Information System School of Postgraduate Studies Diponegoro University Semarang50242 Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang50275 Indonesia Department of Computer Science Faculty of Science and Mathematics Diponegoro University Semarang50275 Indonesia

The choice of contraceptive tools is not an easy thing because the risks or effects will give impact on the body that never using it previously. in the other side, there is no contraception always suit for everybody because the circumstances of each body is different, so the extensive knowledge must be needed to know the advantages and disadvantages of each contraceptive tools then adjusted to the user's *** expert system for contraceptive tools uses Forward Chaining search method combined with Certainty Factors Method. These method value the patient's indication. The Expert system gives the output data which define the kind of tool uses of the patient. the results obtained will be able to help people to find indications that lead to appropriate contraceptive tools and advice or suggestions about these tools. The success rate of the contraceptive tools decision experienced by experienced by the user by using forward chaining combined with the CF computation method is also influenced by the number of indication criteria selected by the user. Based on testing that has been done, expert system contraception tools has accuracy level equal to 75%. © 2018 The Authors, published by EDP sciences.

关键词： Expert systems

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UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled, and Small Expert-Labeled Tweets for Foodborne Illness Detection

UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled,...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Hu, Ruofan Zhang, Dongyu Tao, Dandan Zhang, Huayi Feng, Hao Rundensteiner, Elke Worcester Polytechnic Institute Data Science Program Worcester United States Tsinghua University Vanke School of Public Health Beijing China ByteDance San Jose United States North Carolina A&T State University College of Ag&Environ Sciences Greensboro United States Worcester Polytechnic Institute Computer Science/Data Science Program Worcester United States

ISBN: (纸本)9798350324457

Foodborne illnesses significantly impact public health. Deep learning surveillance applications using social media data aim to detect early warning signals. However, labeling foodborne illness-related tweets for model training requires extensive human resources, making it challenging to collect a sufficient number of high-quality labels for tweets within a limited budget. The severe class imbalance resulting from the scarcity of foodborne illness-related tweets among the vast volume of social media further exacerbates the problem. Classifiers trained on a classimbalanced dataset are biased towards the majority class, making accurate detection difficult. To overcome these challenges, we propose EGAL, a deep learning framework for foodborne illness detection that uses small expert-labeled tweets augmented by crowdsourced-labeled and massive unlabeled data. Specifically, by leveraging tweets labeled by experts as a reward set, EGAL learns to assign a weight of zero to incorrectly labeled tweets to mitigate their negative influence. Other tweets receive proportionate weights to counter-balance the unbalanced class distribution. Extensive experiments on real-world TWEET-FID data show that EGAL outperforms strong baseline models across different settings, including varying expert-labeled set sizes and class imbalance ratios. A case study on a multistate outbreak of Salmonella Typhimurium infection linked to packaged salad greens demonstrates how the trained model captures relevant tweets offering valuable outbreak insights. EGAL, funded by the U.S. Department of Agriculture (USDA), has the potential to be deployed for real-time analysis of tweet streaming, contributing to foodborne illness outbreak surveillance efforts. © 2023 IEEE.

关键词： foodborne illness learning with noisy labels semi-supervised learning social media text classification

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