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International Conference on ICT For Smart Society (ICISS)

作者： Dyna Marisa Khairina Purwanto Purwanto Dinar Mutiara Kusumo Nugraheni Doctoral Program in Information Systems School of Postgraduate Studies Universitas Diponegoro Semarang Indonesia Department of Chemical Engineering Faculty of Engineering Universitas Diponegoro Semarang Indonesia Department of Informatics Faculty of Science and Mathematics Universitas Diponegoro Semarang Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

This study provides an initial exploration and further understanding of the factors that are parameters in evaluating the success of Enterprise Architecture (EA) implementation. An evaluation process is needed to ensure that the EA project implementation provides effectiveness, efficiency, and feasibility of the EA information system as well as assessing previous project experience to avoid future EA project risks. The research aims to present a Systematic Literature Review based on the stages of identification, screening, eligibility, and inclusion so that 14 main articles are selected for review with strong actual relevance. The study results provide insights and references that can be used to further explore key factors in evaluating the success of EA implementation in the future. It was found that there were ten factors that most influenced the evaluation of the success of EA implementation and it was concluded that two potential factors were the most influential and required further exploration in evaluating the success of EA implementation which could be applied in various sectors/dimensions. This study contributes as an initial reference for overcoming problems in investigating and exploring the factors that influence the success of EA implementation as an effort to assess the success of achieving future organizational goals.

关键词： Bibliographies Testing information systems

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Heterogeneous remote computing system for computational astrophysics with OmniRPC

Heterogeneous remote computing system for computational astr...

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Workshops on Applications and the Internet (SAINT)

作者： K. Onuma T. Boku M. Sato D. Takahashi H. Susa M. Umemura Doctoral Program of Systems and Information Engineering University of Tsukuba Japan Institute of Information Sciences and Electronics Centre for Computational Physics University of Tsukuba Japan Department of Physics Rikkyo University Japan Institute of Physics Centre for Computational Physics University of Tsukuba Japan

We have developed a hybrid computing system named HMCS-G (Grid-enabled Heterogeneous Multi-Computer System) which combines multiple cluster systems and a special purpose machine for gravity calculations. The purpose of HMCS-G is to provide ideal computation environment for computational astrophysics, which requires multiple physics phenomena for precise and detailed simulation. There are two categories of machines required for this system, that is, multiple PC-clusters for general purpose computation such as hydro-dynamics and a special purpose machine GRAPE-6 which calculates gravity for all pairs of particles in the system with very high-speed. These systems are connected with a grid-RPC named OmniRPC which supports Globus and ssh for authentication. Our HMCS-G enables the worldwide access to GRAPE-6 cluster which provides several TFLOPS performance for gravity calculation. All participating PC-clusters share this resource in time sharing manner with grid technology. For efficient usage of GRAPE-6 resource, we modified the original server cluster of it for multiple clients running in various network environment. Currently, precise simulations on galaxy formation are being performed on clusters in several institutes with smoothed particle hydrodynamics and radiative transfer under complete gravity calculation, as the first real application on HMCS-G.

关键词： Astrophysics Gravity Pipelines Physics computing Grid computing Computational modeling Authentication Time sharing computer systems File servers Network servers

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Glucose Sensing Utilizing Complex-Valued Neural Networks 23

Glucose Sensing Utilizing Complex-Valued Neural Networks

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4th International Conference on Computing, Networks and Internet of Things, CNIOT 2023

作者： Lv, Yiqi Meng, Xiang Luo, Yong Pei, Yan The Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai University China and Jt. International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University Shanghai China Doctoral Program Grad. Sch. of Comp. Science and Engineering Graduate Department of Computer and Information Systems University of Aizu Aizuwakamatsu Japan Computer Science Division University of Aizu Aizuwakamatsu Japan

ISBN: (纸本)9798400700705

This paper proposes a Complex-Valued Neural Network (CVNN) for glucose sensing in milli-meter wave (mmWave). Based on the propagation characteristics of millimeter wave in glucose medium, we obtain the S21 parameter of glucose with the concentration range of 0-300mg/dL in the 60-80 GHz frequency band by High Frequency Structure Simulator (HFSS) simulations. Then we combine the sensing model with a neural network to detect and predict the glucose concentration relying on the learning ability of the neural network. In the prediction of the concentration of unknown samples, the absolute error between the predicted value and the true value is within 5mg/dL, which confirms the ability of the proposed CVNN model. © 2023 ACM.

关键词： Millimeter waves

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Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

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International Conference on Computer Applications and Industrial Electronics (ICCAIE)

作者： Wandy Wandy Kusworo Adi Media Anugerah Ayu Doctoral Program of Information Systems School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Science Faculty of Engineering and Technology Sampoerna University South Jakarta Indonesia

Sports Science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport Science recognizes and prevents injuries. Sensors and statistics formalize Sports Science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM 2.5 and PM 10 ). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM 2.5 and PM 10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results.

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International Conference of Computer and Informatics engineering (IC2IE)

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

Full-marathon and Half-marathon distances are categorized as road running. Full-marathon running is becoming increasingly popular, and Half-marathon is increasing worldwide in both sexes and all age groups. Some aspects might relate to Full-marathon and Half-marathon running performance during training and races. Technology also plays an essential role in supporting runners and running races. Technology like artificial intelligence (AI) now supports the running athlete, not only predicting performance and results. It can also be used later to help the coach generate training programs for the athlete. This research aimed to find many aspects of marathons and performance and analyze them to see if artificial intelligence could later support them. It used secondary data and a systematic literature review proposed by Kitchenham. Out of the 58 articles, 21 of them (36.21%) received a score of 1 from Q1. Additionally, 19 articles (32.76%) received a score of 1 from both Q2 and Q3. Among the 58 articles, 9 (15.52%) received a total score of 3, with all three Q1, Q2, and Q3 scores being 1. This indicates that artificial intelligence will likely support the content of these nine articles. Several factors were also discovered to be connected to marathons and athletic performance. These findings suggested that additional investigation into marathons and performance, later backed by artificial intelligence, remained pertinent and essential.

关键词： Training Roads Artificial intelligence Informatics

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Rice Variety Identification Using Edge Feature Extraction and Texture-Based Segmentation

Rice Variety Identification Using Edge Feature Extraction an...

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International Seminar on Application for Technology of information and Communication (ISemantic)

作者： Aji Setiawan Kusworo Adi Catur Edi Widodo Doctoral Program of Information Systems School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Information Technology Faculty of Engineering Darma Persada University Jakarta Indonesia School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia

This research proposes a methodology for identifying the optimal feature combination using Support Vector Machine (SVM) based on edge and texture features. Canny, Sobel, and Prewitt for edge detection and GLCM, Gabor, and LBP for texture analysis are popular feature extraction techniques widely employed in various cases. The approach combines edge and texture features with SVM to classify rice types into five categories: Ipsala, Jasmine, Karacadag, Arborio, and Basmati. Experimental results demonstrate that SVM using Canny, Sobel, and Prewitt combined Gabor texture features outperforms the comparative methods. A comparative analysis based on accuracy scores using confusion matrices is conducted. Combining all feature edge methods, Canny, Sobel, and Prewitt with Gabor on SVM classification is the best model for rice object classification, achieving an accuracy of 96%.

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Predicting online item-choice behavior: A shape-restricted regression approach

arXiv

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arXiv 2020年

作者： Nishimura, Naoki Sukegawa, Noriyoshi Takano, Yuichi Iwanaga, Jiro Graduate School of Systems and Information Engineering University of Tsukuba Ibaraki305-8577 Japan Recruit Lifestyle Co. Ltd. Tokyo100-6640 Japan Department of Information and Computer Technology Tokyo University of Science Tokyo125-8585 Faculty of Engineering Information and Systems University of Tsukuba Ibaraki305-8577 Doctoral Program in Policy and Planning Sciences University of Tsukuba Ibaraki305-8577 Erdos Inc. Kanagawa222-0033

This paper examines the relationship between user pageview (PV) histories and their item-choice behavior on an e-commerce website. We focus on PV sequences, which represent time series of the number of PVs for each user-item pair. We propose a shape-restricted optimization model that accurately estimates item-choice probabilities for all possible PV sequences. This model imposes monotonicity constraints on item-choice probabilities by exploiting partial orders for PV sequences, according to the recency and frequency of a user's previous PVs. To improve the computational efficiency of our optimization model, we devise efficient algorithms for eliminating all redundant constraints according to the transitivity of the partial orders. Experimental results using real-world clickstream data demonstrate that our method achieves higher prediction performance than that of a state-of-the-art optimization model and common machine learning methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph theory

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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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Smart Computing, IoT and Machine Learning (SIML), International Conference on

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

ISBN: (数字)9798350364101

ISBN: (纸本)9798350364118

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.

关键词： Wireless communication Wireless sensor networks Energy consumption Q-learning Machine learning algorithms Energy resources Classification algorithms

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Data-Driven Approaches to Detecting Misdeliveries in Truck Logistics using GPS Data

Data-Driven Approaches to Detecting Misdeliveries in Truck L...

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IEEE International Conference on Big Data

作者： Ayumu Hidaka Ryota Shin Atsushi Tsuchiya Norihiko Nakabayashi Yukihiko Okada Yohei Shida Master’s Program in Service Engineering University of Tsukuba Tsukuba Japan Doctoral Programs in Policy and Planning Sciences University of Tsukuba Tsukuba Japan Yamato Transport Co. Ltd. Minato Japan Yamato Transport Co. Ltd. University of Tsukuba Tsukuba Japan Institute of Systems and Information Engineering Center for Artificial Intelligence Research University of Tsukuba Tsukuba Japan Institute of Systems and Information Engineering University of Tsukuba Tsukuba Japan

Misdelivery in logistic services leads to increased costs and degradation of packages. For deliveries using trucks, erroneous deliveries are prevented by checking identification numbers on packages as the packages pass through delivery points. In recent years, it has become possible to monitor real-time location by attaching GPS receivers to packages, but few concrete efforts have been made to detect anomalies using truck transport data. This study introduces a basic framework for the detection of misdeliveries and a summary of the problems in actual truck misdelivery using special medical supply delivery data provided by a major Japanese logistics company. It is shown that the system can detect erroneous deliveries with high accuracy, even for actual delivery data with coarse resolution owing to the cost of installing the equipment. This study has the potential not only to improve logistics and reduce costs, but also to solve various social problems such as driver shortages.

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Two variables compactly supported fluency sampling functions-based scalable conversion for fundus photograph

Two variables compactly supported fluency sampling functions...

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IEEE Pacific Rim Conference on Communications, Computers and Signal Processing

作者： S. Kai K. Katagishi K. Toraichi Y. Mitamura H. Murakami A. Koike T. Asami Doctoral Program Graduate School of Systems and Information Engineering University of Tsukuba Tsukuba Ibaraki Japan Institute of Informalion Sciences and Electronics University of Tsukuba Tsukuba Ibaraki Japan Graduate School of Engineering Hokkaido University Sapporo Japan KDDI Corporation Tokyo Japan KDD Research and Development Laboratories Inc. Kamifukuoka Saitama Japan

To match the different resolution capabilities of devices used in the telemedicine network, we must perform resolution conversion for fundus photograph to the required resolution with the information important to ophthalmologists' diagnosing well preserved. In this paper, in order to keep important information, such as color and shapes of blood vessels, optic disk and lesion areas on the fundus photograph after resolution conversion, we adopt three kinds of fluency functions to approximate shapes and propose a shape-directed resolution conversion method by using two variables compactly supported fluency sampling functions. The method leads the fundus photograph resolution conversion with features of scalability and good information preservation.

关键词： Sampling methods Interpolation Shape control Blood vessels Lesions Medical diagnostic imaging Biomedical optical imaging Photography Diseases Telemedicine

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