Procedia Computer Science最新文献_第8页

Design science research methodology in model digital front-of-pack nutrition label (FOPNLs) on MSME product at Maros district 在Maros地区的MSME产品上设计模型数字包装前营养标签（FOPNLs）的科学研究方法

Procedia Computer Science Pub Date : 2026-01-01 Epub Date: 2026-07-17 DOI: 10.1016/j.procs.2026.07.056

Eliyah Acantha Manapa Sampetoding, Irmayanti, Putri Juanti, Yulita Sirinti Pongtambing

{"title":"Design science research methodology in model digital front-of-pack nutrition label (FOPNLs) on MSME product at Maros district","authors":"Eliyah Acantha Manapa Sampetoding, Irmayanti, Putri Juanti, Yulita Sirinti Pongtambing","doi":"10.1016/j.procs.2026.07.056","DOIUrl":"10.1016/j.procs.2026.07.056","url":null,"abstract":"<div><div>The increasing prevalence of overweight and obesity is a risk factor for the rise of non-communicable diseases, which are the leading cause of death in many countries. Most of them have consumption patterns characterized by excessive sugar, salt, and fat consumption. FOPNLs are present as nutrition labels and promotive efforts for consumers to choose and control the consumption of packaged food and beverages, including products produced by micro and small-scale enterprises (MSMEs). This research uses the Design Science Research Methodology (DSRM) approach with five stages to develop a Digital-based FOPNLs Model, namely (1) Problem Identification, (2) Determining requirements, (3) designing and developing model artifacts, (4) demonstrating digital-based FOPNLs model artifacts, and (5) Evaluating artifacts. This research proposes a strategy for developing a digital-based FOPNLs model so that integrating Digital FOPNLs in MSME packaging will make it easier for people to understand nutritional information and become an added value for MSMEs to produce healthier products.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"284 ","pages":"Pages 722-730"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148531582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From digital touchpoints to word of mouth: investigating medical tourists’ experiences and perceptions 从数字接触点到口口相传：调查医疗游客的体验和看法

Procedia Computer Science Pub Date : 2026-01-01 Epub Date: 2026-07-17 DOI: 10.1016/j.procs.2026.07.057

Rachma Rizqina Mardhotillah, Bambang Widjanarko Otok, Mohamad Yusak Anshori, Rusdi Hidayat Nugroho

{"title":"From digital touchpoints to word of mouth: investigating medical tourists’ experiences and perceptions","authors":"Rachma Rizqina Mardhotillah, Bambang Widjanarko Otok, Mohamad Yusak Anshori, Rusdi Hidayat Nugroho","doi":"10.1016/j.procs.2026.07.057","DOIUrl":"10.1016/j.procs.2026.07.057","url":null,"abstract":"<div><div>In an increasingly digital healthcare environment, understanding what drives patients to share their experiences has become crucial for sustaining hospital competitiveness, particularly in medical tourism. This study investigates the determinants of word of mouth (WOM) among telemedicine users at a hospital in Surabaya by analyzing the effects of telemedicine utilization, destination image, medical tourist experience, and digital platform reviews. Employing a quantitative approach with Structural Equation Modeling (SEM), data from 150 telemedicine users were analyzed. The findings show that telemedicine utilization, medical tourist experience, and digital platform reviews significantly influence WOM, while destination image does not. These results emphasize the importance of functional and experiential quality over locational appeal in influencing patient advocacy. This study offers insights for hospital managers to enhance service personalization, build digital trust, and strengthen patient engagement strategies in the post-pandemic era.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"284 ","pages":"Pages 731-739"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148531583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

QC-KC-Crow: A Quantum Clustering Scheduler based on Crow Search Algorithm for Fog Computing Environments QC-KC-Crow：一种雾计算环境下基于Crow搜索算法的量子聚类调度程序

Procedia Computer Science Pub Date : 2026-01-01 Epub Date: 2026-07-08 DOI: 10.1016/j.procs.2026.05.053

Prathamesh Vijay Lahande

{"title":"QC-KC-Crow: A Quantum Clustering Scheduler based on Crow Search Algorithm for Fog Computing Environments","authors":"Prathamesh Vijay Lahande","doi":"10.1016/j.procs.2026.05.053","DOIUrl":"10.1016/j.procs.2026.05.053","url":null,"abstract":"<div><div>Purpose: The working mechanisms of K-Means Clustering (KC) along with Quantum Computing (QC) have been integrated with the existing Crow Search Algorithm, used as scheduler, to design and develop the QC-KC-Crow scheduler to address its limitations during task scheduling in the Fog Computing Environments. Crow Search Algorithm Limitations: The existing Crow scheduler does not provide a structural clustering of tasks with similar computational requirements, resulting in additional time spent. Since it works on randomness and inadequate scalability, it leads to additional time and convergence issues during its exploration phase, resulting in lower Load Balancing (LB) during task scheduling. Experiment: iFogSim simulator is utilized to conduct an experiment using existing crow and proposed QC-KC-Crow algorithms under ten scenarios, with lower to higher crow agents deployed to compute real-time Google tasks. Performance Metrics: Time metrics considered include makespan (MKSPN), Average Turn Around Time (ATAT), and Average Waiting Time (AWT); Efficiency metrics considered include (THRPT), CPU Utilization Percentage (CPU UTZ %), and Load Balancing (LB). Results: QC-KC-Crow provides an improvement of 9.27%, 9.96% and 11.09% is obtained concerning MKSPN, ATAT, and AWT when compared to the existing Crow algorithm. Also, it provides an improvement of 11.46%, 5.10%, and 14.91% concerning THPUT, CPU UTZ, and LB, respectively, when compared to the baseline Crow algorithm. Future Work: The proposed QC-KC-Crow algorithm will be implemented with deep neural networks to establish higher convergence and improved optimizations in Fog Computing and other environments.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"282 ","pages":"Pages 46-56"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148531597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI Driven Network Slicing in 5G: Detection of Deviations and Adaptive Slice Management Using Reinforcement Learning. 5G中人工智能驱动的网络切片：使用强化学习检测偏差和自适应切片管理。

Procedia Computer Science Pub Date : 2026-01-01 Epub Date: 2026-07-08 DOI: 10.1016/j.procs.2026.05.051

Maktumsab Hulagur, Pooja J, Prajwal C Naduvinamath, Om Muddapur, Meenaxi M Raikar

{"title":"AI Driven Network Slicing in 5G: Detection of Deviations and Adaptive Slice Management Using Reinforcement Learning.","authors":"Maktumsab Hulagur, Pooja J, Prajwal C Naduvinamath, Om Muddapur, Meenaxi M Raikar","doi":"10.1016/j.procs.2026.05.051","DOIUrl":"10.1016/j.procs.2026.05.051","url":null,"abstract":"<div><div>Fifth-generation (5G) networks are designed to support a vast array of services like enriched mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), and massive machine-type communication (mMTC), each needing special Quality of Service (QoS) assurance. To address these varied needs, network slicing supports the formation of virtualized and segregated network slices. But ensuring real-time optimization of slices is still a challenging task. This paper presents an intelligent 5G network slicing framework which integrates Open5GS and UERANSIM to simulate the core and radio access network, machine learning methods for anomaly detection, and reinforcement learning to adaptively tune performance. In particular, a Proximal Policy Optimization (PPO) agent is learned in a special environment to make slice-level decisions using real-time latency and throughput measurements. Experimental results show that the eMBB slice achieved its throughput from 550 Mbps to 900 Mbps and reduced latency from 35 ms to 15 ms. The URLLC slice had persistent low latency at 2 ms, while mMTC exhibited a remarkable reduction in packet loss from 0.6% to 0.1%. Under mixed traffic loads scenario, the framework managed to balance resources among slices without domination by a single service type and maintaining compliance with QoS restrictions. These results demonstrate the future potential of the presented AI-based framework in providing adaptive, efficient, and SLA-conformant network slicing for future wireless networks.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"282 ","pages":"Pages 25-34"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148531603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Analysis of Bloom Filter and Comparison with B-Tree Index 布隆过滤器的分析及与b树指数的比较

Procedia Computer Science Pub Date : 2026-01-01 Epub Date: 2026-07-08 DOI: 10.1016/j.procs.2026.06.100

Om Prakash, Sanjeev Kumar, Smita Srivastava, Shree Chand Khichar

{"title":"An Analysis of Bloom Filter and Comparison with B-Tree Index","authors":"Om Prakash, Sanjeev Kumar, Smita Srivastava, Shree Chand Khichar","doi":"10.1016/j.procs.2026.06.100","DOIUrl":"10.1016/j.procs.2026.06.100","url":null,"abstract":"<div><div>A Bloom filter is a space-efficient probabilistic data structure that determines whether an element belongs to a set. It takes less space and less time to respond to a query than a B-tree index supported by relational databases. The shortcoming of the standard Bloom filter is that it sometimes yields a false positive result. A false positive means that it might indicate that a given element is present in the set, while it is not. In this study, we analyzed how the count of hash functions, the length of the bit array, and the data set size impact the False Positive Rate[6] (FPR) of the standard Bloom filter[5]. In this paper, how to minimize the FPR is presented. We started by generating synthetic datasets of pairs of usernames and passwords to test the Bloom filter. The results show how to choose the optimal values of all the key parameters of the Bloom filter to minimize the FPR and increase the accuracy. We also compared the size of the index created and search query performance using a Bloom Filter and a B-tree in database applications. The Bloom Filter data structure outperforms in space efficiency and time efficiency for equal operator-based queries. Bloom filters can be a practical choice for fast and efficient searches on large data sets where the requirement is to detect whether an element is not in a set.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"283 ","pages":"Pages 113-122"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148531637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Business process improvement analysis for PT Almansour Tour and travel using business process management method 运用业务流程管理方法对阿尔曼苏尔旅行社进行业务流程改进分析

Procedia Computer Science Pub Date : 2026-01-01 Epub Date: 2026-07-17 DOI: 10.1016/j.procs.2026.07.065

Muhammad Zhahnur Arif, Raulia Riski, Alifiansyah Arrizy Hidayat

引用次数: 0

Determination of Social Media Reputation Recovery on Tourism Sustainability: An Analytical Hierarchy Process Analysis 社会媒体声誉恢复对旅游可持续性的影响：层次分析法

Procedia Computer Science Pub Date : 2026-01-01 Epub Date: 2026-07-17 DOI: 10.1016/j.procs.2026.07.018

Satria Fadil Persada, Adhi Murti Citra Amalia H, Gabriella Sagita Putri, Reny Nadlifatin, Belinda Dyan Christiwular, Yogi Tri Prasetyo, Michael Nayat Young

引用次数: 0

Emotional loops and digital risks: Extending LRAT to model cybercrime victimization among students 情感循环与数字风险：将LRAT扩展到学生网络犯罪受害模型

Procedia Computer Science Pub Date : 2026-01-01 Epub Date: 2026-07-17 DOI: 10.1016/j.procs.2026.06.623

January F. Naga, Mia Amor C. Tinam-isan, Hella Gay Avenido

引用次数: 0

Internet of Things: Vision, Architecture, and Applications in Healthcare 物联网：医疗保健中的愿景、架构和应用

Procedia Computer Science Pub Date : 2026-01-01 Epub Date: 2026-07-08 DOI: 10.1016/j.procs.2026.05.076

Yajnaseni Dash, Pooja Jajoria, Sumit Kumar, Deepankar Singh, Sudhir C. Sarangi

引用次数: 0

Zero-Covariance Curves in Carbon Offset Investments: Characteristic Analysis and Advantageous Portfolio Selection 碳补偿投资中的零协方差曲线：特征分析与有利投资组合选择

Procedia Computer Science Pub Date : 2026-01-01 Epub Date: 2026-09-18 DOI: 10.1016/j.procs.2026.08.031

Yue Qi, Yingying Li, Chengyue Zhang, Yixuan Zhu

{"title":"Zero-Covariance Curves in Carbon Offset Investments: Characteristic Analysis and Advantageous Portfolio Selection","authors":"Yue Qi, Yingying Li, Chengyue Zhang, Yixuan Zhu","doi":"10.1016/j.procs.2026.08.031","DOIUrl":"10.1016/j.procs.2026.08.031","url":null,"abstract":"<div><div>The mean-variance framework and Black’s zero-covariance CAPM jointly form the foundation of modern asset pricing theory. As carbon offset increasingly becomes a core concern for investors, the three-objective portfolio model incorporates it into the pricing framework alongside risk and return. However, this extension raises a new challenge. The zero-covariance portfolio evolves from a single deterministic point into a continuous curve. There is no research to systematically analyze its characteristics or locate an advantageous zero-covariance portfolio. In this area, this paper makes contributions to the literature. We reveal the strict linear trade-of between return and carbon offset on this curve and a strict convexity of the risk function that imposes non-linear penalties for deviating from the optimal point. Based on these properties, we propose three selection methods for identifying an advantageous zero-covariance portfolio. Numerical experiments illustrate the practical applicability of the proposed methods and identify a synergy interval, within which investors can simultaneously achieve positive returns and carbon offset targets. Our work provides analytical tools for capital asset pricing models that incorporate carbon offset investments.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"287 ","pages":"Pages 106-113"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148980843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0