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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis

Procedia computer science 2025年 253卷 84-93页

作者： Anak Agung Ayu Putri Ardyanti Zhiqiang Xie Andrea Stevens Karnyoto Bens Pardamean Ni Made Estiyanti School of Computer Science and Technology Harbin University of Science and Technology Harbin 150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Doctoral School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, computer science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions.

关键词： integrated scheduling assembly job shop scheduling heuristic algorithm genetic algorithm bibliometric analysis

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Utilization of Waste Banks System in the COVID-19 Pandemic 4th

Utilization of Waste Banks System in the COVID-19 Pandemic

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4th International Conference on Inventive Computation and Information Technologies, ICICIT 2022

作者： Pradanang, Riyan Aditya, Bima Rayhan, Adhitya Winarlie, Michael Adrian Gaol, Ford Lumban Matsuo, Tokuro School of Information System Bina Nusantara University Alam Sutera Tangerang Indonesia Computer Science Binus Graduate Program Jakarta Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9789811974014

People’s perspectives and behaviors altered in a variety of ways during the pandemic period, most notably in areas related to health, environment, and most notably waste management. Many people still prefer to work from home and this in turn leads to a large accumulation of garbage without having any proper means of disposal. As a result, the garbage area will serve as a breeding ground for illness. Garbage is usually composed of a residual waste generated from a product or object that has outlived its useful life, but can still be recycled into a valuable item. On this basis, the solution is to use a waste bank;it is a collection point for sorted garbage. It is claimed that using a waste bank can help in preventing the garbage accumulation. The main purpose of this study is to determine the contribution of the waste bank to end garbage accumulation. The information gathered through a review of the literature and direct interviews with multiple informants. According to the findings of the literature review, waste banks can provide a variety of benefits to users, as well as the community and surrounding environment. The proposed research study's findings show that the changes in community activity patterns contribute to an increase in garbage, and that the community plays an important role in waste control. From the performed research study, it is evident that m any individuals are interested about garbage banks and their benefits based on this presumption. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recycling

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The Effect of E-Commerce on Increasing Consumption Behavior During a Pandemic 4th

The Effect of E-Commerce on Increasing Consumption Behavior ...

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4th International Conference on Inventive Computation and Information Technologies, ICICIT 2022

作者： Gaol, Ford Lumban Tompunu, Sherina Gloria Larissa, Jessica Adine Wijaksana, Arista Prajnawira, Ferdianto Matsuo, Tokuro Binus Graduate Program—Doctor of Computer Science Jakarta Indonesia School of Information System Bina Nusantara University Alam Sutera Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9789811974014

The rapid development of technology and ease of information today greatly affects all aspects of life. During the COVID-19 pandemic, technology played an important role in maintaining community activities continued smoothly despite being hampered by several health policies during the COVID-19 pandemic. Many phenomena develop with new technologies in several fields, one in the economic field is e-commerce. E-commerce is a site that makes it easier for buyers to get the goods desired by the community, as well as the various interesting promos that are the attractiveness of the community to constantly shop in e-commerce so that the community applies consumptive behavior. So that the problem that will be discussed in this study is the development of e-commerce technology that affects people’s behavior in shopping which tends to be consumptive and hedonic. The purpose of this study is to determine the impact or effect of the use of e-commerce on consumptive behavior during the pandemic period and provide an overview of the potential regarding e-commerce in the future. This research method is quantitative. The data collection technique used for this study is to use a questionnaire. Thus, this research is expected to minimize the negative impacts that can be caused by the increasing consumptive behavior of society during the pandemic and provide wise e-commerce use. The results of this study can be seen from calculations using a formula that respondents tend to be more extravagant during the pandemic and for future potential, respondents will certainly enjoy the convenience provided by e-commerce. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Bibliometric Analysis of Image Segmentation with Deep Learning: An Analytical Study 1

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International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023

作者： Windarto, Agus Perdana Yuhandri, Y. Bukhori, Saiful Information Technology Study Program Universitas Putra Indonesia YPTK Padang Indonesia STIKOM Tunas Bangsa Pematangsiantar Indonesia Universitas Putra Indonesia YPTK Padang Indonesia Computer Science Department University of Jember Jember Indonesia

ISBN: (数字)9789819984985

ISBN: (纸本)9789819984978

This study presents a comprehensive bibliometric analysis of research pertaining to the utilization of deep learning techniques for image segmentation using CNN algorithms. A dataset comprising 1078 publications from journals and conference proceedings between 2018 and 2022 was examined through keyword search, co-occurrence network analysis, and keyphrase analysis. The study offers valuable insights into the present research landscape and identifies prominent areas of investigation, encompassing deep learning's application in medical imaging, disease detection, and object detection. Our analysis reveals China as a leading contributor to deep learning research for image segmentation, with 279 publications and 4,380 citations in Scopus. Advances in Biomedical Optics and Imaging—Proceedings of SPIE are pinpointed as the most productive source for deep learning research in image segmentation, with a specific emphasis on biomedical optics and imaging. The co-occurrence network analysis highlights the red cluster primarily focusing on methods and algorithms associated with deep learning in image segmentation. In contrast, the blue cluster demonstrates the application of these methods and algorithms to other objects or methods, with the "human" node standing out in terms of frequency and centrality. Our keyphrase analysis reveals the growing trend of Cellular Artificial Neural Networks over the past five years, indicating a shift in research focus toward this domain. Overall, this study's findings demonstrate the potential of deep learning to deliver precise and efficient segmentation of medical images, thereby enhancing clinical outcomes and patient care. Furthermore, our study contributes to an enhanced understanding of the current research landscape and identifies avenues for future exploration in deep learning techniques for image segmentation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image segmentation

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A Super-Resolution Electromagnetic Inversion Scheme for Brain Stroke Detection

A Super-Resolution Electromagnetic Inversion Scheme for Brai...

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European Conference on Antennas and Propagation, EuCAP

作者： Lingqi Gao Hakan Bagci Electrical and Computer Engineering (ECE) Program Computer Electrical and Mathematical Science and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

A super-resolution electromagnetic inversion scheme is proposed for brain stroke detection. The scheme operates in two stages: (i) A quantitative Gauss-Newton method with Tikhonov regularization and frequency-hopping is employed to get a low-resolution inversion result. (ii) A U-Net-based super-resolution method is used to significantly enhance the spatial resolution of the inversion result from (i), The proposed scheme is demonstrated through several numerical examples, highlighting its effectiveness in accurately reconstructing the brain's permittivity and conductivity profiles, thereby contributing to advancements in stroke diagnosis.

关键词： Neuroimaging Location awareness Superresolution Europe Conductivity Classification algorithms Electromagnetics Permittivity Spatial resolution Image reconstruction

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A ShuffleNet Architecture-Based Multi-Layer Feature Scaling and Fusion Approach for Underwater Net Cage Damage Detection in Aquaculture

A ShuffleNet Architecture-Based Multi-Layer Feature Scaling ...

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IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)

作者： Xiang-Rui Huang Yi-Cheng Chen Liang-Bi Chen Wu-Chih Hu Department of Electrical Engineering Master Program in Electrical Engineering and Computer Science National Penghu University of Science and Technology Penghu Taiwan Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Taiwan

ISBN: (数字)9798331530839

ISBN: (纸本)9798331530846

In offshore aquaculture operations, personnel equipped with diving gear are often necessary to inspect the underwater net cages for damage, particularly on the sea floor. This manual inspection process is time-consuming and complex. To overcome this problem, this paper proposes a computer vision solution for identifying damage in underwater net cages to address the inefficiencies and challenges of traditional manual inspections. The proposed scheme utilizes a high-performance multi-branch computational architecture designed based on ShuffleNet architecture to detect net cage damage more efficiently. Experimental results demonstrate that this work performs well on the ImageNet ILSVRC-2010 dataset and achieves an accuracy of 88.54% in underwater net damage detection.

关键词： Accuracy Gears Computational modeling Sea floor computer architecture Manuals Inspection Feature extraction Personnel Aquaculture

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Machine Learning Algorithms for Natural Language Processing Tasks: A Case of COVID-19 Twitter data (Thailand)

Machine Learning Algorithms for Natural Language Processing ...

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2022 International Conference on Computational science and Computational Intelligence, CSCI 2022

作者： Kularbphettong, Kunyanuth Vichivanives, Rujijan Kanjanaprakarn, Pannawat Chutrtong, Jaruwan Ruksuntorn, Nareenart Suan Sunandha Rajabhat University Computer Science Program Bangkok Thailand Suan Sunandha Rajabhat University Faculty of Science and Technology Bangkok Thailand Suan Sunandha Rajabhat University Robotics Engineering Program Faculty of Industrial Technology Bangkok Thailand

ISBN: (纸本)9798350320282

This paper presents the use of natural language processing for the problem of information extraction and sentiment analysis. The dataset is from Twitter that has the information of people mentioning about COVID-19, this study has two tasks: (i) classification approach for information extraction task and (ii) deep learning approach for sentiment analysis task. In information extraction task, the data was gathered from twitter that related to COVID-19 information, and the sequence labelling method applied to classify text before giving it to classification algorithms (K-NN, Naïve Bayes, Decision Tree, Random Forest, and SVM). In sentiment analysis task, data was classified by convert the word into index and using word embedding, then to process deep learning algorithm (Bi-directional GRU). The accuracy of two tasks are 98% and 79% respectively. © 2022 IEEE.

关键词： COVID-19

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Find the Best Rule of Time Series Data Mining with Cluster Analysis 3

Find the Best Rule of Time Series Data Mining with Cluster A...

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3rd International Conference on Computational science and Information Management, ICoCSIM 2019

作者： Buaton, Relita Zarlis, Muhammad Mawengkang, Herman Efendi, Syahril Graduate Program of Computer Science Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

Data mining is an analytical process of knowledge discovery in large and complex data sets. Many studies wish to explore data, to find information so that knowledge can be obtained through the grouping process, classification, rules discovery, associations and data mining visualization which shows similarity. Periodic data often occurs in business applications and sciences that has big size, high dimension and continuously updated. The similarity in periodic data is based on several approaches. One of common approaches is to transform periodic series into other domains so that dimensions are reduced, followed by index mechanism. Many studies of time series do not give optimal result because limited to extracting data not able to represent time series and its pattern which is then change into rules. Rules can be found in time series data, but they are still constrained by over fitting and difficult to present. It causes time series data and non linier function of data mining decision can't be optimal. The basic idea in the method proposed is to do periodic discretization for sub-sequential formation. These sub-sequences are grouped through a measure of similarity. The simple rule-finding technique is applied to obtain hidden rules in the temporal pattern. The optimal time series data expected to generate the uncertainty trend, previously unknown and can be used to make decisions or forecasting in the future. © Published under licence by IOP Publishing Ltd.

关键词： Data mining

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Internet of Things for Smart Furniture: A Systematic Review 11

Internet of Things for Smart Furniture: A Systematic Review

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Maulana, Fairuz Iqbal Kurniawan, Bambang Kartono Pramono, Andi Purnomo, Agung Abdulah, Muhamad Ezran Bin Zainal Mohamed, Mustafa School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Design Bina Nusantara University Interior Design Department Jakarta11480 Indonesia Binus Business School Undergraduate Program Bina Nusantara University Entrepreneurship Department Jakarta11480 Indonesia Universiti Malaysia Kelantan Faculty of Creative Technology and Heritage Department of Design and Creative Technology Kelantan Bachok16300 Malaysia School of Technology of the Arts Republic Polytechnic Singapore

ISBN: (纸本)9798350389654

The research entitled 'Internet of Things for Smart Furniture: A Systematic Review' explores integrating IoT technology into smart furniture to improve functionality and user experience. By synthesizing existing literature, this research aims to discover how the Internet of Things (IoT) is leveraged to meet specific user needs with smart furniture and what hardware and sensor technologies are proposed for smart furniture design, considering factors such as scalability and need for users. Current research trends in securing user communications and data in the context of IoT-enabled smart furniture. This research involved a literature search which included journal articles and reviews from the Web of science (WOS) and the Scopus database (core collection) from 2014 to 2023, and the final results were 9 documents that would be reviewed. Several impactful means must be used to meet specific user needs, such as health monitoring, enhanced security, enhanced convenience and mobility, integration with smart home systems, and personalization and adaptability. Drawing insights from IoT data quality, machine learning applications in IoT, and smart furniture design for elderly care, this research provides a comprehensive picture of the current IoT-enabled smart furniture landscape. This research can explore existing IoT solutions for smart furniture, with a focus on cutting-edge technologies and design principles that can shape the future of this domain. © 2024 IEEE.

关键词： internet of things literature review scopus smart furniture web of science

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Trends in Scientific Research in Smart Furniture: A Bibliometric Analysis in Decade from 2014-2023 11

Trends in Scientific Research in Smart Furniture: A Bibliome...

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11th International Conference on ICT for Smart Society, ICISS 2024

ISBN: (纸本)9798350389654

Smart furniture has emerged as a significant area of interest in scientific research over the past decade. This bibliometric analysis aims to explore the trends in scientific research related to smart furniture from 2014 to 2023. By examining a range of scholarly articles, this study delves into the evolution of smart furniture technologies, applications, and implications. The research reveals a growing interest in integrating intelligent systems into furniture design, enabling network connectivity and user-controlled operations. Despite the growing number of studies, the research landscape remains fragmented, necessitating a comprehensive bibliometric analysis to consolidate existing research and identify trends. Data selection from the Scopus database yielded a total of 97 documents from 69 sources, with average citations per doc is 8.041. On the corresponding author's countries, China is the highest country. Moreover, terms like 'smart furniture' that expand in size and extend to the right of the chart indicate a growing emphasis in recent literature, signaling potential areas of increased research activity. Smart furniture is characterized as furniture that is connected to a network and equipped with intelligent technologies that make use of user data and energy sources. The study underscores the importance of addressing issues such as ease of use, privacy, security, and affordability in implementing smart furniture solutions. This study provides valuable insights into the current state and future prospects of smart furniture research, guiding further investigation and advancement in this rapidly evolving sector. © 2024 IEEE.

关键词： bibliometric Rstudio scopus smart furniture VOSviewer

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