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International Conference of Computer and Informatics Engineering (IC2IE)

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

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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Working ratio estimation method for a long distance fixed wireless access (22 GHz band, 6.3 Mbit/s) link

Working ratio estimation method for a long distance fixed wi...

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IEEE Pacific Rim Conference on Communications, Computers and Signal Processing

作者： Y. Ohmiya K. Katagishi A. Vasilache T. Asami H. Murakami K. Toraichi Doctoral Program Graduate School of Systems and Information Engineering University of Tsukuba Tsukuba-shi Ibaraki Japan Institute of Information Sciences and Electronics University of Tsukuba Tsukuba-shi Ibaraki Japan Japan Science and Technology Corporation University of Tsukuba TARA Center Ibaraki Japan KDDI R&D Laboratories Inc. Kamifukuoka-shi Saitama Japan KDDI Corporation KDDI bldg. Shinjuku-ku Tokyo Japan

This paper discusses a method to determine the optimal node location before a long distance FWA network (22 GHz band, 6.3 Mbit/s) is set. During our 3 years experiment, we found out that the long distance FWA net with 2 links (about 60 km) was down for approximately 42 hours per year due to heavy rain fall conditions. This means that the long distance FWA net is practicable to some extent. We discuss a method to estimate the working ratio between two unknown locations using AMeDAS weather data. By using this method, an appropriate node location can be decided before FWA net is deployed. This approach can improve the working ratio in the sense of link-up continuation.

关键词： Rain Space technology Costs systems engineering and theory Research and development Laboratories Telegraphy Delay Interference Space stations

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A Moment-Based Method for Large-Scale Generalized Eigenvalue Problems

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Applied Numerical Analysis & Computational Mathematics 2004年第2期1卷

作者： Tetsuya Sakurai Hiroto Tadano Yuichi Inadomi Umpei Nagashima Department of Computer Science University of Tsukuba Tsukuba 305-8573 Japan Core Research for Evolutional Science and Technology Japan (CREST) Doctoral Program of Systems and Information Engineering University of Tsukuba Tsukuba 305-8573 Japan Grid Technology Research Center National Institute of Advanced Industrial Science and Technology Tsukuba 305-8568 Japan

In this paper we consider a method for finding several eigenvalues and corresponding eigenvectors of large-scale generalized eigenvalue problems. In this method, a small matrix pencil that has only the desired eigenvalues is derived using complex moments obtained via numerical integration. Since the process to derive the moments can be performed in parallel and we do not need to exchange data between processes, the presented method is suitable for master-worker programming models. We have implemented and tested the proposed method in a grid RPC (remote procedure call) system. Numerical examples illustrate the properties of our approach. (© 2004 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

关键词： Eigenvalue problems moment-based method parallel algorithm

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The next cryptocurrency price movement prediction application uses patterned datasets

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AIP Conference Proceedings 2024年第1期3116卷

作者： Rizky Parlika Mustafid Basuki Rahmat Diponegoro University Information Systems Doctoral Study Program Graduate School 50241 Jl. Imam Bardjo SH Pleburan Semarang Indonesia University of Pembangunan Nasional “Veteran” Jawa Timur Department of Informaton Technology Faculty of Computer Science 60294 Rungkut Madya Gunung Anyar Surabaya Indonesia

Researchers and experts have developed various techniques, models, and methods to predict the price movements of cryptocurrencies, especially Bitcoin. However, among the many techniques studied in the literature, there is still a lack of focus on mining, creating, and developing datasets with specific patterns for predicting the next cryptocurrency price movement. This is an exciting reason to conduct further research. A web-based Patterned Dataset Application and a Telegram bot were constructed to address this issue. These tools read the price position of each cryptocurrency and predict the next price direction based on the last position indicated by the Patterned Dataset Application. The experiment’s results show that when the Patterned Dataset Application shows a diamond crash position, it is time to make a purchase; conversely, when it shows a diamond moon position, it is time to make a sale. It is hoped that by utilizing the Patterned Dataset Application, potential losses can be minimized, and there is more potential for profit in cryptocurrency trading. Even though the initial data source comes from Indonesia’s most prominent digital cryptocurrency trading market, according to coinmarketcap, namely Indodax, the results of this patterned dataset application can often describe the same cryptocurrency conditions globally. The novelty of this research is to produce a new way of predicting the next cryptocurrency price movement using patterned datasets. At the end of this paper, it will be proven that hypothesis 1 and hypothesis 2 on the results of the patterned dataset are true.

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Modelling and reducing fuel flow pulsation of a fuel-metering system during pump mode switching in a turbofan engine

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IOP Conference Series: Earth and Environmental Science 2019年第5期240卷

作者： S Masuda F Shimizu M Fuchiwaki K Tanaka Control Systems Engineering Department Research & Engineering Division Aero Engines Space & Defense Business Area IHI Corporation 229 Tonogaya Mizuho-machi Nishitama-gun Tokyo 190-1297 Japan Department of Computer Science and Systems Engineering Doctoral Program Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 kawazu Iizuka-shi Fukuoka 820-8502 Japan Department of Mechanical Information Science and Technology School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 kawazu Iizuka-shi Fukuoka 820-8502 Japan

In this research, the transient characteristics of a new type fuel metering system for aircraft turbofan engines using two types of pumps, a gear pump and a centrifugal pump, were studied by modelling and simulation. This new fuel metering system is capable of hydraulically unloading the operating state of each pump by the pump mode switching mechanism within the permissible range of the pump rotation speed and the fuel flow rate and it is possible to reduce the fuel temperature rise due to the pressure loss in the circuit in the operation region of the entire fuel system. In addition to this, by using a small centrifugal pump that can realize high discharge flow rate, it is possible to reduce weight compared to the conventional fuel system. However, when the pump mode is switched, unexpected fluctuation in the fuel flow rate can occur when a large change in the inlet pressure of the system occurs, the pressure control unit cannot follow the change, and the transient phenomenon cannot be suppressed. At this time, the pressure waveform generated at the time of changing the pump mode includes a high frequency component such that the pressure control unit, which plays a role of keeping the metering valve differential pressure constant, cannot follow up. Therefore, in this paper, we focused on the frequency of the pressure change waveform when the pump mode was changed and studied the model of the fuel system inlet pressure in the gear pump mode. Based on that model, we propose a method to reduce the frequency characteristics of the pressure change occurring at the time of pump mode change.

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"So what if ChatGPT wrote it?" Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy

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International Journal of information Management 2023年 71卷

作者： Dwivedi, Yogesh K. Kshetri, Nir Hughes, Laurie Slade, Emma Louise Jeyaraj, Anand Kar, Arpan Kumar Baabdullah, Abdullah M. Koohang, Alex Raghavan, Vishnupriya Ahuja, Manju Albanna, Hanaa Albashrawi, Mousa Ahmad Al-Busaidi, Adil S. Balakrishnan, Janarthanan Barlette, Yves Basu, Sriparna Bose, Indranil Brooks, Laurence Buhalis, Dimitrios Carter, Lemuria Chowdhury, Soumyadeb Crick, Tom Cunningham, Scott W. Davies, Gareth H. Davison, Robert M. Dé, Rahul Dennehy, Denis Duan, Yanqing Dubey, Rameshwar Dwivedi, Rohita Edwards, John S. Flavián, Carlos Gauld, Robin Grover, Varun Hu, Mei-Chih Janssen, Marijn Jones, Paul Junglas, Iris Khorana, Sangeeta Kraus, Sascha Larsen, Kai R. Latreille, Paul Laumer, Sven Malik, F. Tegwen Mardani, Abbas Mariani, Marcello Mithas, Sunil Mogaji, Emmanuel Nord, Jeretta Horn O'Connor, Siobhan Okumus, Fevzi Pagani, Margherita Pandey, Neeraj Papagiannidis, Savvas Pappas, Ilias O. Pathak, Nishith Pries-Heje, Jan Raman, Ramakrishnan Rana, Nripendra P. Rehm, Sven-Volker Ribeiro-Navarrete, Samuel Richter, Alexander Rowe, Frantz Sarker, Suprateek Stahl, Bernd Carsten Tiwari, Manoj Kumar van der Aalst, Wil Venkatesh, Viswanath Viglia, Giampaolo Wade, Michael Walton, Paul Wirtz, Jochen Wright, Ryan Digital Futures for Sustainable Business & Society Research Group School of Management Swansea University Bay Campus Fabian Bay Wales Swansea United Kingdom Maharashtra Pune India Bryan School of Business and Economics University of North Carolina at Greensboro United States University of Bristol Business School University of Bristol BS8 1SD United Kingdom Professor of Information Systems Raj Soin College of Business Wright State University 3640 Colonel Glenn Highway DaytonOH45435 United States School of Artificial Intelligence Indian Institute of Technology Delhi Hauz Khas New Delhi India Department of Management Studies Indian Institute of Technology Delhi Hauz Khas New Delhi India Department of Management Information Systems Faculty of Economics and Administration King Abdulaziz University Jeddah Saudi Arabia School of Computing Middle Georgia State University MaconGA United States Client Advisory and Transformation Stackroute NIIT Limited India Department of Information Systems Analytics and Operations College of Business University of Louisville United States Northumbria University London United Kingdom IRC-FDE KFUPM Saudi Arabia ISOM KFUPM Business School KFUPM Saudi Arabia Innovation and Technology Transfer Center Sultan Qaboos University Oman Department of Business Communication Sultan Qaboos University Oman Department of Management Studies National Institute of Technology Tiruchirappalli India Montpellier France FORE School of Management New Delhi India Indian Institute of Management Ahmedabad Vastrapur Ahmedabad380015 India Information School University of Sheffield United Kingdom Bournemouth University Business School Poole United Kingdom School of Information Systems and Technology Management University of New South Wales Sydney Australia Information Operations and Management Sciences Department TBS Business School 1 Place Alphonse Jourdain Toulouse31068 France Department of Education & Childhood Studies Swansea Univers

Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT's capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT's use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics;digital transformation of organisations and societies;and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI;examining biases of generative AI attributable to training datasets and processes;exploring business and societal contexts best suited for generative AI implementation;determining optimal combinations of human and generative AI for various tasks;identifying ways to assess accuracy of text produced by generative AI;and uncovering the ethical and legal issues in using generative AI across different contexts. © 2023 The Authors

关键词： Marketing

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Prediction of traffic congestion based on time series dataset number of vehicles using neural network algorithm

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AIP Conference Proceedings 2024年第1期2982卷

作者： Prasetyo Wibowo Yunanto Rahmat Gernowo Oky Dwi Nurhayati Doctoral Program of Information System Diponegoro University Jl. Imam Bardjo SH No.5 Semarang Indonesia Information Systems and Technology Universitas Negeri Jakarta Jl. Rawamangun Muka Jakarta Indonesia Department of Physics Diponegoro University Jl. Prof. H. Soedarto Semarang Indonesia Computer Engineering Department Diponegoro University Jl. Prof. H. Soedarto Semarang Indonesia

The increasing number of vehicles that was not accompanied by an adequate road infrastructure readiness causes traffic jams. Traffic jams often occur repeatedly every day, especially at certain times, such as when people are going to and from work. The same traffic jams can also occur at the beginning or the end of the week; this usually repeats every week. As a result, if the congestion dataset is known, the repeating traffic congestion for daily and weekly congestion can be anticipated. In this study, traffic congestion predictions are modeled using the Neural Network Algorithm based on traffic congestion data collected within 24 hours for one to two weeks. The parameters used in optimizing Neural Network performance are learning rate, momentum, and epochs (training cycles). Based on these experiments, the Neural Network Algorithm successfully modeled traffic density patterns which are recurring congestion patterns, with quite good results.

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Correction: Using Digital Media to Improve Adolescent Resilience and Prevent Mental Health Problems: Protocol for a Scoping Review

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JMIR research protocols 2024年 13卷 e67759页

作者： Riris D Rachmayanti Fatwa Sari Tetra Dewi Diana Setiyawati Hario Magatsari Rian Diana Retno Vinarti Department of Epidemiology Biostatistic Population Study and Health Promotion Public Health Faculty Universitas Airlangga Surabaya Indonesia. Doctoral Program in Public Health Faculty of Medicine Public Health and Nursing Universitas Gadjah Mada Yogyakarta Indonesia. Department of Health Behavior Environment and Social Medicine Faculty of Medicine Public Health and Nursing Universitas Gadjah Mada Yogyakarta Indonesia. Center for Public Mental Health Faculty of Psychology Universitas Gadjah Mada Yogyakarta Indonesia. Innovation in Health Communication Information and Education Research Group Universitas Airlangga Surabaya Indonesia. Department of Information Systems Faculty of Intelligent Electrical and Information Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia.

[This corrects the article DOI: 10.2196/58681.].

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