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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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Mental Status Identification for Infertile Patients Using Artificial Intelligence Models 14th

Mental Status Identification for Infertile Patients Using Ar...

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14th International Conference on Frontier Computing, FC 2024

作者： Hsu, Hao-Siang Wang, Jong-Yi Chiou, Hai-Chi Wu, Fu-Hsing Lin, Chuen-Horng Chan, Yung‑Kuan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung404336 Taiwan Department of Health Services Administration China Medical University Taichung406040 Taiwan Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung404336 Taiwan Department of Management Information Systems National Chung-Hsing University Taichung402202 Taiwan

ISBN: (纸本)9789819623945

Infertile patients may be a high-risk group of mental disorder. The precise identification of the mental status of infertile patients can provide decision support to healthcare professionals and may be helpful in providing preventive measures accordingly. Rare studies focused on the mental status identification using artificial intelligence (AI) models for infertile people. This study aimed to precisely identify the mental status (anxiety and depression) of infertile patients using three AI models, the XGBoost, decision tree, and stacking models. The dataset, retrieved from a structured questionnaire survey in 2011, which included 400 infertility outpatients seeking reproductive treatments at three teaching hospitals in Taiwan was adopted for training and testing AI models. Aforementioned AI models were designed and reached identification performances of accuracy, precision, recall and area under ROC curve (AUC) equaling 0.75–0.82, 0.70–0.80, 0.64–0.72, and 0.64–0.72, respectively. The XGBoost model (accuracy = 0.82, precision = 0.80, recall = 0.72, and AUC = 0.72) outperformed or equaled the decision tree and stacking models in terms of accuracy, precision, recall, and AUC values. Designed AI models for mental status identification can be integrated into the clinical decision support system (CDSS) to provide the anxiety and depression group automatically. Accordingly, the worsening of infertility-related anxiety and depression can be prevented. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： artificial intelligence infertile patients Mental status identification stacking model XGBoost

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Investigating Neural Network-Based Deep Learning Strategies for Real-Time Data Analysis in Machine Learning

Investigating Neural Network-Based Deep Learning Strategies ...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Walke, Suchita Nalluri, Mounika Lavanya, R. Rongali, Aruna Sri Vanjari, Hrishikesh Bhanudas Ritu Pillai Collage of Engg and Technology Mumbai University Computer Engineering India Murray State University Masters in Computer Information Systems United States Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Ece India Graduate Doctoral Information Technology Information Technology Emphasis/Information Technology University of the Cumberlands United States BVCOE Lavale Sppu Pune E &tc Engineering Dept Maharashtra India Ambala Haryana Mullana India

ISBN: (纸本)9798350357769

there are numerous distinct strategies and techniques that fall under the huge class of neural network-primarily based deep gaining knowledge of in device getting to know. those strategies revolve around developing and schooling synthetic neural networks, that are computational models that mimic the capabilities and structure of the human brain, to analyze huge datasets and make predictions or decisions based totally on the *** of the key challenges in actual-time information analysis is being capable of method incoming information fast and efficiently, as well as adapt to converting data styles or sudden occasions. traditional device learning algorithms are frequently restricted in their ability to deal with these demanding situations, that is in which deep gaining knowledge of techniques are available *** getting to know techniques contain education neural networks with a couple of layers, every of which strategies one-of-a-kind aspects of the data. This lets in for greater complex and accurate evaluation of the facts, in addition to the potential to handle larger and more numerous datasets. One famous method in deep studying is known as convolutional neural networks (CNNs). Those networks are specifically properly-suitable for studying visual information, including snap shots, by means of breaking down the data into smaller parts and studying them at one of a kind ranges of abstraction. This permits for the identification of patterns and features in the data which could then be used for category or prediction responsibilities. © 2023 IEEE.

关键词： actual-time convolutional neural networks numerous prediction synthetic neural networks

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FA-FENet: A Feature Attention Front-End Network Based on a Lightweight CNN Architecture for Recognizing Abnormal Underwater Illegal Fishing Behavior

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IEEE Access 2025年 13卷 87753-87776页

作者： Huang, Xiang-Rui Chen, Liang-Bi National Penghu University of Science and Technology Master Program in Electrical Engineering and Computer Science Department of Electrical Engineering Penghu880011 Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu880011 Taiwan

In the past decade, studies on illegal fishing have neglected to consider illegal underwater fishing. Traditionally, supervisor-based methods have been used to manually interpret underwater behavior;however, existing artificial intelligence (AI) and Internet of Things (IoT) methods are not applicable to illegal underwater fishing. Therefore, in this study, we used a computer vision AI method to perform the challenging task of illegal underwater fishing image recognition. Illegal underwater fishing images have unclear features and high feature similarity. In this study, we define normal underwater travel as wearing diving clothing. Abnormal underwater behavior includes underwater personnel with illegal fishing equipment. Herein, we propose a feature attention front-end network (FA-FENet), a novel end-to-end convolutional neural network (CNN) architecture that differs from previous methods in that it allows flexible integration with various backbone networks. The front end of the backbone network can be directly replaced to improve the learning efficiency and performance of the proposed network. The convolutional layers in our proposed FA-FENet include dilated convolutions, group convolutions, padded convolutions, pooling layers, and activation functions. We also comprehensively review the performance of the proposed FA-FENet. A series of ablation studies demonstrate that the proposed FA-FENet can be applied in shallow backbone networks. In addition, FA-FENet has potential in emerging networks with good performance in illegal underwater fishing identification tasks. Finally, we conducted experiments with an underwater fishing behavior dataset. The experimental results demonstrate that our proposed method outperforms recently reported methods with attention mechanisms. Specifically, FA-FENet-RegNet achieves an accuracy of 88.81%, FA-FENet-ResNet18 reaches 78.32%, and FA-FENet-ShuffleNetV2 attains 80.41%. © 2013 IEEE.

关键词： Convolutional neural networks

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Predictive Value Chain Analysis to Improve Generalization Accuracy In Quality Assurance

Predictive Value Chain Analysis to Improve Generalization Ac...

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Control & Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence (CERIA), IEEE International Conference on

作者： Harmayani Poltak Sihombing Mahyudin Marischa Elveny Postgraduate Program in Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Faculty of Computer Science and Information Technology Postgraduate Doctorate in Computer Science Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798331511166

ISBN: (纸本)9798331511173

Adaptive predictive analysis model utilizing Spiking Neural Networks (SNNs) to enhance quality assurance processes in Smart Campus environments. Traditional predictive models often struggle with the dynamic and complex nature of educational data, leading to inaccurate predictions and inefficient decision-making. SNNs, inspired by biological neurons, are particularly suited for processing temporal data and adapting to evolving patterns. The proposed model integrates spike-timing-dependent plasticity (STDP) and dynamic weight adjustment to continuously learn and refine predictions in response to real-time data, ensuring improved accuracy and robustness. The study uses historical data from the Internal Quality Assurance System (SPMI) over a three-year period, focusing on key performance indicators such as student enrollment trends, faculty productivity, and resource utilization. Experimental results demonstrate that the SNN-based model outperforms traditional neural networks, achieving a 20% reduction in Mean Squared Error (MSE) and a higher R-squared value. These findings indicate that the model not only enhances predictive accuracy but also offers significant improvements in operational efficiency and resource management. By providing timely and precise insights, this model supports proactive decision-making in educational institutions, enabling administrators to optimize resource allocation, monitor academic quality, and anticipate future challenges. This research contributes to the growing field of artificial intelligence in education, offering a scalable, adaptive approach to managing complex data and improving institutional performance in real-time.

关键词： Adaptation models Quality assurance Accuracy Biological system modeling Decision making Spiking neural networks Predictive models Real-time systems Resource management Predictive analytics

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Exploring Regression and Classification in Big Data with Deep Learning

Exploring Regression and Classification in Big Data with Dee...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Pandey, Ravindra Rongali, Aruna Sri Gopinath, S. Anute, Nilesh Nama, Forda Pund, Sachin Hult International Business School United States University of the Cumberlands Doctoral Information Technology Information Technology Emphasis Information Technology United States Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India ASM's Institute of Business Management and Research Pune India University of Lampung Doctoral Program of Environtmental Science Department of Informatics Bandar Lampung Indonesia Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India

ISBN: (纸本)9798350357769

Deep gaining knowledge is a synthetic intelligence approach used to recognize complex facts. It uses many facts to recognize the complicated relationships between various factors. With deep learning, models can be built which might be capable of understanding the complicated patterns within big datasets. In this paper, we can explore using deep mastering in regression and type tasks on large datasets. In particular, we can use deep learning strategies to build a regression model that predicts a target value primarily based on the functions in a massive dataset and a classification version that can classify an object into one of the many classes of an extensive dataset. We can then discuss the overall performance of the fashions in comparison to standard models and talk about capability areas for development. Moreover, we will analyze the scalability of deep mastering strategies used on large datasets and discuss exceptional practices for building deep mastering fashions on massive datasets. Finally, we will summarize our findings and offer our conclusions approximately the use of deep studying in regression and type obligations on large datasets. © 2023 IEEE.

关键词： approximately gaining knowledge mastering performance regression

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Single-cell transcriptomics uncovers EGFR signaling-mediated gastric progenitor cell differentiation in stomach homeostasis (vol 14, 3750, 2023)

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NATURE COMMUNICATIONS 2023年第1期14卷 1-16页

作者： Takada, Hitomi Sasagawa, Yohei Yoshimura, Mika Tanaka, Kaori Iwayama, Yoshimi Hayashi, Tetsutaro Isomura-Matoba, Ayako Nikaido, Itoshi Kurisaki, Akira Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan Master’s/Doctoral Program in Life Science Innovation (Bioinformatics) Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan

Defects in gastric progenitor cell differentiation are associated with various gastric disorders, including atrophic gastritis, intestinal metaplasia, and gastric cancer. However, the mechanisms underlying the multilineage differentiation of gastric progenitor cells during healthy homeostasis remain poorly understood. Here, using a single-cell RNA sequencing method, Quartz-Seq2, we analyzed the gene expression dynamics of progenitor cell differentiation toward pit cell, neck cell, and parietal cell lineages in healthy adult mouse corpus tissues. Enrichment analysis of pseudotime-dependent genes and a gastric organoid assay revealed that EGFR-ERK signaling promotes pit cell differentiation, whereas NF-κB signaling maintains gastric progenitor cells in an undifferentiated state. In addition, pharmacological inhibition of EGFR in vivo resulted in a decreased number of pit cells. Although activation of EGFR signaling in gastric progenitor cells has been suggested as one of the major inducers of gastric cancers, our findings unexpectedly identified that EGFR signaling exerts a differentiation-promoting function, not a mitogenic function, in normal gastric homeostasis.

关键词： Adult stem cells Stem-cell differentiation Stem-cell niche

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Offsets Reconstruction Method for Clock Skew Measurement Over High-Jitter Communication

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SN computer science 2025年第5期6卷 1-14页

作者： Sastra, Nyoman Putra Saputra, Komang Oka Teng, Wei-Chung Study Program of Electrical Engineering Faculty of Engineering Udayana University Jalan Raya Kampus Bukit Jimbaran Bali Badung 80361 Indonesia Department of Computer Science and Information Engineering National Taiwan University of Science and Technology No. 43 Section 4 Keelung Rd Taipei 106 China

Measuring clock skew of devices over a network fully relies on the offsets, the differences between sending and receiving times. Offsets that shape a thick line are the most ideal one as their slope is directly the clock skew sought. When some outliers separate from the thick line due to higher delay jitter, current advanced approaches use the lower bound or the majority of offsets to derive accurate clock skew. However, outliers in offsets that shape parallel dotted lines, reduce the availability of offsets in the lower region, and scatter widely the offsets from their majority zone. These conditions drive the current skew estimation approaches to lose their accuracy. This paper then developed a new method, which reconstructs back the outliers into their origin positions, in order to provide ideal offsets for clock skew measurement. The experimental evaluations have demonstrated that the clock skew accuracy of dotted-lines offsets tampered by outliers was improved from 2.1 to 0.9 ppm by the proposed reconstruction method. For a broader purpose, the reconstruction method has been adjusted to be implemented also on non-dotted-lines offsets. The robustness of the proposed method for this case has also been exhibited by the very ideal reconstructed offsets that shape like a thick line. © The Author(s) 2025.

关键词： Clock skew Delay jitter Dotted lines Hough transform

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Long Short Term Memory (LSTM) Improvement Accuracy Using FastText and Glove for Language Translation Batak - Indonesian 2

Long Short Term Memory (LSTM) Improvement Accuracy Using Fas...

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2nd International Conference on technology Innovation and Its Applications, ICTIIA 2024

作者： Mandasari, Atikah Rahayu Nababan, Erna Budhiarti Mawengkang, Herman Informatics Program Universitas Sumatera Utara Medan Indonesia Universitas Sumatera Utara Faculty Of Computer Science and Information Technology Medan Indonesia Universitas Sumatera Utara Faculty of Mathematics and Natural Sciences Medan Indonesia

ISBN: (纸本)9798350351613

This research discusses the improvement of Long Short-Term Memory (LSTM) model accuracy in translating Batak to Indonesian by utilizing FastText and GloVe embeddings. Traditional LSTM models often struggle with the complexity and richness of underrepresented languages like Batak. By integrating FastText and GloVe, which provide word vectors with deeper semantic meanings, the translation quality can be significantly enhanced. Experimental results show that this approach offers a substantial performance improvement compared to conventional LSTM models, producing more accurate and contextual translations. These findings have the potential to be applied in broader multilingual translation systems and support the preservation and digital accessibility of minority languages. © 2024 IEEE.

关键词： Semantics

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Physician Scheduling using Constraint Satisfaction programming 11

Physician Scheduling using Constraint Satisfaction Programmi...

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11th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Bakr, Hend A. Salama, Ahmed M. Fares, Ahmed Zaky, Ahmed B. Cairo University Biomedical Engineering Program Faculty of Engineering Giza Egypt Benha University Computer Systems Engineering Program Faculty of Engineering at Shoubra Banha Egypt Computer science and information technology Programs Alexandria Egypt Benha University On leave from Computer Systems Engineering Program Faculty of Engineering at Shoubra Egypt

ISBN: (纸本)9798350369328

Physician scheduling is a critical task that impacts the quality of patient care, staff satisfaction, and operational efficiency in healthcare institutions. The traditional approach to physician scheduling is manual and time-consuming, which can result in errors, staff burnout, and suboptimal schedules. To address these challenges, researchers have turned to optimization techniques like CSP, which has shown promise in solving physician scheduling problems. This paper reviews the existing literature on CSP for physician scheduling and highlights the benefits and limitations of this approach. CSP's benefits include generating schedules quickly and efficiently, incorporating complex constraints and preferences, and handling changes and disruptions in real time. However, CSP also has some limitations, such as the need for a formalized model and the fact that it may not always generate the most intuitive schedules. Overall, the findings suggest that CSP is a promising approach to physician scheduling that can produce high-quality schedules while minimizing staff burnout and improving operational efficiency. © 2023 IEEE.

关键词： Constraint satisfaction problems

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