Pub Date : 2025-12-29DOI: 10.13052/jicts2245-800X.1341
Wencai Zhao
The rapid development of artificial intelligence (AI) has opened up new avenues for improving the efficiency and quality of academic writing. This paper presents ChatGPT, an advanced model based on the GPT-4 (Generative Pre-trained Transformer 4) architecture. Traditional academic writing faces challenges such as time constraints, language barriers, and content creation difficulties. AI-driven natural language processing (NLP) tools can effectively alleviate these challenges. This paper employs a transformer-based machine learning framework, combining bidirectional encoder representation (BERT) with GPT-4 to improve the syntactic and semantic quality of generated text. Empirical analysis of academic writing samples shows that ChatGPT-assisted writing reduces grammatical errors in the evaluation samples by 2.00% and 1.92%, respectively. This research further explores the cognitive advantages of AI-assisted writing tools, proposing that AI can not only enhance the writing process but also has the potential to reshape traditional academic writing practices by improving innovation, efficiency, and academic productivity.
{"title":"Enhancing Academic Writing Efficiency with ChatGPT: A Natural Language Processing Framework for Innovation, Opportunities, and Challenges","authors":"Wencai Zhao","doi":"10.13052/jicts2245-800X.1341","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1341","url":null,"abstract":"The rapid development of artificial intelligence (AI) has opened up new avenues for improving the efficiency and quality of academic writing. This paper presents ChatGPT, an advanced model based on the GPT-4 (Generative Pre-trained Transformer 4) architecture. Traditional academic writing faces challenges such as time constraints, language barriers, and content creation difficulties. AI-driven natural language processing (NLP) tools can effectively alleviate these challenges. This paper employs a transformer-based machine learning framework, combining bidirectional encoder representation (BERT) with GPT-4 to improve the syntactic and semantic quality of generated text. Empirical analysis of academic writing samples shows that ChatGPT-assisted writing reduces grammatical errors in the evaluation samples by 2.00% and 1.92%, respectively. This research further explores the cognitive advantages of AI-assisted writing tools, proposing that AI can not only enhance the writing process but also has the potential to reshape traditional academic writing practices by improving innovation, efficiency, and academic productivity.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"13 4","pages":"359-382"},"PeriodicalIF":0.0,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11318147","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145847816","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}
Pub Date : 2025-12-29DOI: 10.13052/jicts2245-800X.1344
Yun Liu
Rapid growth in the number of devices connected to the Internet of Things (IoT) and the exponential surge in data usage clearly suggest that the development of big data is inextricably linked with the IoT. In an ever-expanding network, big data raises concerns regarding data access efficiency. This study critically reviews IoT data analytics, tools, techniques, and challenges in extracting meaningful information from IoT device-generated massive data sets. IoT data analysis approaches, including real-time analysis, predictive analysis, and anomalous behavior analysis, are discussed in detail. How big data platforms and cloud computing can tackle IoT data and why IoT data preprocessing, integration, and storage matter are explored in this paper. Additionally, it covers issues and future research directions in IoT data analytics, including data security, scalability, and privacy.
{"title":"Data Analytics in the Internet of Things Era: Tools, Approaches, Challenges, and Solutions","authors":"Yun Liu","doi":"10.13052/jicts2245-800X.1344","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1344","url":null,"abstract":"Rapid growth in the number of devices connected to the Internet of Things (IoT) and the exponential surge in data usage clearly suggest that the development of big data is inextricably linked with the IoT. In an ever-expanding network, big data raises concerns regarding data access efficiency. This study critically reviews IoT data analytics, tools, techniques, and challenges in extracting meaningful information from IoT device-generated massive data sets. IoT data analysis approaches, including real-time analysis, predictive analysis, and anomalous behavior analysis, are discussed in detail. How big data platforms and cloud computing can tackle IoT data and why IoT data preprocessing, integration, and storage matter are explored in this paper. Additionally, it covers issues and future research directions in IoT data analytics, including data security, scalability, and privacy.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"13 4","pages":"427-448"},"PeriodicalIF":0.0,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11318148","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145847818","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}
Pub Date : 2025-12-29DOI: 10.13052/jicts2245-800X.1342
Jing He
The rapid development of artificial intelligence, especially large language models (LLMs), is transforming English writing practices for both learners and researchers while creating new pathways for standardizing AI-assisted scholarly communication. This study examines how ChatGPT, built on GPT-4 and combined with BERT-based contextual analysis, can enhance writing efficiency, linguistic accuracy, and personalized learning. Using natural language processing, GPT-4 scoring models, and collaborative filtering, the system provides adaptive writing tasks and feedback, further optimized through reinforcement learning. Classroom results show notable improvements, with one student's writing score increasing from 5.00 to 7.25, highlighting ChatGPT's value in boosting writing ability and learning motivation. At the scholarly level, evaluation of manuscript samples demonstrates reductions in grammatical (~2.0%) and typographical (~1.9%) errors and a clearer argumentative structure. Opportunities such as improved accessibility and creativity coexist with challenges including transparency, ethical use, and reliance on AI. Overall, this work outlines the potential of transformer-based NLP to support standardized, scalable AI-assisted English writing across educational and academic communication ecosystems.
{"title":"Standardizing AI-Assisted English Writing: ChatGPT's Opportunities, Challenges, and Transformer-based Innovations for Scholarly Communication","authors":"Jing He","doi":"10.13052/jicts2245-800X.1342","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1342","url":null,"abstract":"The rapid development of artificial intelligence, especially large language models (LLMs), is transforming English writing practices for both learners and researchers while creating new pathways for standardizing AI-assisted scholarly communication. This study examines how ChatGPT, built on GPT-4 and combined with BERT-based contextual analysis, can enhance writing efficiency, linguistic accuracy, and personalized learning. Using natural language processing, GPT-4 scoring models, and collaborative filtering, the system provides adaptive writing tasks and feedback, further optimized through reinforcement learning. Classroom results show notable improvements, with one student's writing score increasing from 5.00 to 7.25, highlighting ChatGPT's value in boosting writing ability and learning motivation. At the scholarly level, evaluation of manuscript samples demonstrates reductions in grammatical (~2.0%) and typographical (~1.9%) errors and a clearer argumentative structure. Opportunities such as improved accessibility and creativity coexist with challenges including transparency, ethical use, and reliance on AI. Overall, this work outlines the potential of transformer-based NLP to support standardized, scalable AI-assisted English writing across educational and academic communication ecosystems.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"13 4","pages":"383-404"},"PeriodicalIF":0.0,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11318146","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145847830","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}
Pub Date : 2025-12-29DOI: 10.13052/jicts2245-800X.1343
Zhongxia Liu
This paper presents the CARE framework that integrates cloud computing and augmented reality (AR) models. Remote learning through immersion has dramatically impacted the learning environment. Cloud computing facilitates the efficient delivery platform required to transform the learning environment. Furthermore, AR facilitates immersive learning by integrating digital information into the real world. However, challenges related to network latency, security issues, device supportability, and teacher readiness limit the effective implementation of this strategy. The CARE framework meets the teaching community's requirements by implementing edge computing concepts to address network performance latency. Moreover, the framework enhances security by applying end-to-end encryption. This paper lays out proper definitions of the relevant topics and a platform for exploring the ultimate capabilities of immersive distance learning enabled by cloud computing and AR.
{"title":"CARE: A Cloud-Enhanced Augmented Reality Model for Immersive Education Opportunities and Challenges","authors":"Zhongxia Liu","doi":"10.13052/jicts2245-800X.1343","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1343","url":null,"abstract":"This paper presents the CARE framework that integrates cloud computing and augmented reality (AR) models. Remote learning through immersion has dramatically impacted the learning environment. Cloud computing facilitates the efficient delivery platform required to transform the learning environment. Furthermore, AR facilitates immersive learning by integrating digital information into the real world. However, challenges related to network latency, security issues, device supportability, and teacher readiness limit the effective implementation of this strategy. The CARE framework meets the teaching community's requirements by implementing edge computing concepts to address network performance latency. Moreover, the framework enhances security by applying end-to-end encryption. This paper lays out proper definitions of the relevant topics and a platform for exploring the ultimate capabilities of immersive distance learning enabled by cloud computing and AR.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"13 4","pages":"405-426"},"PeriodicalIF":0.0,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11318149","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145847817","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}
Pub Date : 2025-12-29DOI: 10.13052/jicts2245-800X.1345
Dante Göbbels;Robert M. van Wessel;Henk J. de Vries
The Web Content Accessibility Guidelines (WCAG) facilitate equal accessibility to websites for people with impairments. However, the adoption of this standard remains low, leaving much of the web inaccessible to millions of users with an impairment. This paper seeks to understand why this standard has had limited impact. As the European Accessibility Act required businesses to have accessible websites from June 2025 there is growing pressure to make improvements. Moving beyond the technical evaluations that dominated past research, this study looks through a standardisation lens at likely reasons for the private sectors' limited use of the web accessibility standard. We compare accessibility differences per industry quantitatively. We then go back to the literature and look at government practices to identify solutions for web accessibility barriers. This allows us to provide a new perspective on how web accessibility can be improved. Our findings identify two main obstacles: a lack of awareness of the WCAG standard, and difficulties in understanding and implementing it. Implementation is hindered by a shortage of developers with accessibility expertise, and by the absence of sanctions for non-compliance. To conclude, the new law first needs to tackle the barriers to web accessibility and introduce a reasonable risk on sanctions as impetus for change.
{"title":"Access Denied: Ignorance of Web Accessibility Standards by Dutch Business","authors":"Dante Göbbels;Robert M. van Wessel;Henk J. de Vries","doi":"10.13052/jicts2245-800X.1345","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1345","url":null,"abstract":"The Web Content Accessibility Guidelines (WCAG) facilitate equal accessibility to websites for people with impairments. However, the adoption of this standard remains low, leaving much of the web inaccessible to millions of users with an impairment. This paper seeks to understand why this standard has had limited impact. As the European Accessibility Act required businesses to have accessible websites from June 2025 there is growing pressure to make improvements. Moving beyond the technical evaluations that dominated past research, this study looks through a standardisation lens at likely reasons for the private sectors' limited use of the web accessibility standard. We compare accessibility differences per industry quantitatively. We then go back to the literature and look at government practices to identify solutions for web accessibility barriers. This allows us to provide a new perspective on how web accessibility can be improved. Our findings identify two main obstacles: a lack of awareness of the WCAG standard, and difficulties in understanding and implementing it. Implementation is hindered by a shortage of developers with accessibility expertise, and by the absence of sanctions for non-compliance. To conclude, the new law first needs to tackle the barriers to web accessibility and introduce a reasonable risk on sanctions as impetus for change.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"13 4","pages":"449-484"},"PeriodicalIF":0.0,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11318150","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145847829","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}
Pub Date : 2025-09-01DOI: 10.13052/jicts2245-800X.1335
Dedi Dedi;R. Andy Oetario Putro;Jarudin
In today's fast-paced and ever-evolving marketplace, the integration of digital technologies has become a critical driver of business success. This paper explores how businesses can harness digital tools and technologies to develop effective strategies that align with the demands of the modern marketplace. By examining the impact of digital transformation, the study addresses key challenges businesses face, such as technological adoption, customer engagement, and maintaining a competitive edge. Using a mixed-methods approach that combines qualitative interviews and quantitative surveys, this research investigates how businesses across sectors have successfully implemented digital strategies. The findings reveal that leveraging data-driven insights, embracing technological innovation, and fostering an agile organizational culture are essential for formulating sustainable business strategies. This study contributes to the growing body of knowledge on digital transformation by offering practical insights and strategic frameworks for business leaders and policymakers aiming to navigate the complexities of the digital age. The paper concludes with recommendations for future research, focusing on emerging technologies and their potential impact on business strategy development.
{"title":"Harnessing Digital Technologies: Developing Effective Business Strategies for the Modern Marketplace","authors":"Dedi Dedi;R. Andy Oetario Putro;Jarudin","doi":"10.13052/jicts2245-800X.1335","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1335","url":null,"abstract":"In today's fast-paced and ever-evolving marketplace, the integration of digital technologies has become a critical driver of business success. This paper explores how businesses can harness digital tools and technologies to develop effective strategies that align with the demands of the modern marketplace. By examining the impact of digital transformation, the study addresses key challenges businesses face, such as technological adoption, customer engagement, and maintaining a competitive edge. Using a mixed-methods approach that combines qualitative interviews and quantitative surveys, this research investigates how businesses across sectors have successfully implemented digital strategies. The findings reveal that leveraging data-driven insights, embracing technological innovation, and fostering an agile organizational culture are essential for formulating sustainable business strategies. This study contributes to the growing body of knowledge on digital transformation by offering practical insights and strategic frameworks for business leaders and policymakers aiming to navigate the complexities of the digital age. The paper concludes with recommendations for future research, focusing on emerging technologies and their potential impact on business strategy development.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"13 3","pages":"327-358"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11302064","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760852","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}
Pub Date : 2025-09-01DOI: 10.13052/jicts2245-800X.1334
Zhang Yanan
Conventional counselling workflows struggle with the scale and heterogeneity of labor-market data. This manuscript presents a semantic-web–enhanced hybrid learning framework for university career planning, embedding ontology-driven modelling and knowledge-graph representation into AI-based recommendation. The framework (i) constructs a domain ontology to organize skills, roles, and behavioral features, (ii) applies natural language processing to curate and semantically align heterogeneous resources, (iii) integrates a gradient-boosted decision tree for skill-to-role matching with a transformer-based sequence model for progression forecasting, and (iv) employs a closed-loop optimization that updates ontology weights and model parameters from longitudinal outcomes. An interpretable recommendation interface provides semantic rationales to support counsellor–student dialogue, while governance measures incorporate privacy-by-design and role-based access control. In deployment with 800 final-year students, the system improved first-round interview hit rate by 27% and six-month job satisfaction by 22% compared with a matched control cohort. Ablation confirms the complementary value of structured academic records and unstructured behavioral logs. Results indicate that ontology-driven hybrid learning enables scalable, explainable, and evidence-based career guidance.
{"title":"Semantic-Web–Enhanced Hybrid Learning for Career Planning: Ontology-Driven Matching, Sequence Forecasting, and Closed-Loop Optimization","authors":"Zhang Yanan","doi":"10.13052/jicts2245-800X.1334","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1334","url":null,"abstract":"Conventional counselling workflows struggle with the scale and heterogeneity of labor-market data. This manuscript presents a semantic-web–enhanced hybrid learning framework for university career planning, embedding ontology-driven modelling and knowledge-graph representation into AI-based recommendation. The framework (i) constructs a domain ontology to organize skills, roles, and behavioral features, (ii) applies natural language processing to curate and semantically align heterogeneous resources, (iii) integrates a gradient-boosted decision tree for skill-to-role matching with a transformer-based sequence model for progression forecasting, and (iv) employs a closed-loop optimization that updates ontology weights and model parameters from longitudinal outcomes. An interpretable recommendation interface provides semantic rationales to support counsellor–student dialogue, while governance measures incorporate privacy-by-design and role-based access control. In deployment with 800 final-year students, the system improved first-round interview hit rate by 27% and six-month job satisfaction by 22% compared with a matched control cohort. Ablation confirms the complementary value of structured academic records and unstructured behavioral logs. Results indicate that ontology-driven hybrid learning enables scalable, explainable, and evidence-based career guidance.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"13 3","pages":"301-326"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11302059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760891","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}
Pub Date : 2025-09-01DOI: 10.13052/jicts2245-800X.1332
Mo Pingyan;Li Kai;Lu Yanqian;Wen You;Li Tao
This paper addresses the core security issues faced by power trading networks, including threats from quantum computing, rigid static protocol configurations, and poor cross-domain heterogeneous communication compatibility. It also presents research on AI-driven standardized data security communication protocols. Unlike existing studies that mainly focus on single technological applications, this paper innovatively proposes an intelligent secure communication protocol framework that integrates deep reinforcement learning, post-quantum cryptography, knowledge graphs, and blockchain, achieving multi-technology collaborative optimization and standardized design across the protocol's lifecycle. Through a deep reinforcement learning agent, the framework senses network status in real-time and dynamically optimizes encryption algorithms and transmission parameters. It integrates MLWE-1024-based post-quantum cryptographic mechanisms and quantum key distribution technology to build forward-secure channels, uses graph neural networks to construct power entity knowledge graphs for high-precision anomaly detection, and incorporates a blockchain-driven trusted settlement mechanism to ensure transaction data integrity. In practical validation on a provincial power trading platform, this protocol outperformed traditional solutions in key metrics such as quantum security strength, protocol conversion delay, consensus convergence efficiency, and anomaly detection accuracy, demonstrating superior dynamic adaptability, attack resistance, and system compatibility. Furthermore, it proposes a phased standardization pathway covering architectural specifications, technical implementation, and evaluation certification, providing critical technical support and standardization foundations for building high-security, low-latency, and strongly interoperable power trading communication infrastructure.
{"title":"Research on the Standardization of AI-Driven Data Security Communication Protocols for Power Trading Networks","authors":"Mo Pingyan;Li Kai;Lu Yanqian;Wen You;Li Tao","doi":"10.13052/jicts2245-800X.1332","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1332","url":null,"abstract":"This paper addresses the core security issues faced by power trading networks, including threats from quantum computing, rigid static protocol configurations, and poor cross-domain heterogeneous communication compatibility. It also presents research on AI-driven standardized data security communication protocols. Unlike existing studies that mainly focus on single technological applications, this paper innovatively proposes an intelligent secure communication protocol framework that integrates deep reinforcement learning, post-quantum cryptography, knowledge graphs, and blockchain, achieving multi-technology collaborative optimization and standardized design across the protocol's lifecycle. Through a deep reinforcement learning agent, the framework senses network status in real-time and dynamically optimizes encryption algorithms and transmission parameters. It integrates MLWE-1024-based post-quantum cryptographic mechanisms and quantum key distribution technology to build forward-secure channels, uses graph neural networks to construct power entity knowledge graphs for high-precision anomaly detection, and incorporates a blockchain-driven trusted settlement mechanism to ensure transaction data integrity. In practical validation on a provincial power trading platform, this protocol outperformed traditional solutions in key metrics such as quantum security strength, protocol conversion delay, consensus convergence efficiency, and anomaly detection accuracy, demonstrating superior dynamic adaptability, attack resistance, and system compatibility. Furthermore, it proposes a phased standardization pathway covering architectural specifications, technical implementation, and evaluation certification, providing critical technical support and standardization foundations for building high-security, low-latency, and strongly interoperable power trading communication infrastructure.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"13 3","pages":"257-280"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11302063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760919","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}
Pub Date : 2025-09-01DOI: 10.13052/jicts2245-800X.1333
Congchi Zhang;Mingzeng Dai;Apostolis K. Salkintzis;Dimitrios Dimopoulos;Haiming Wang;Yin Xu
As the telecommunications industry gears up for the development of 6G mobile networks, the transition from the current 5G infrastructure requires careful and strategic management. This article examines the evolution from 4G to 5G, drawing valuable analysis on lessons learned and proposes strategies for the forthcoming 5G to 6G migration. We analyse standardized solutions from previous generational shifts, identifying their applicability and limitations in the context of emerging 6G technologies. By emphasizing cost-effective and strategic approaches, we provide insights to interested partners for navigating the complexities of this transition while leveraging emerging advancements for the next era of mobile communications.
{"title":"Migration Matters: The Shift from 5G to 6G","authors":"Congchi Zhang;Mingzeng Dai;Apostolis K. Salkintzis;Dimitrios Dimopoulos;Haiming Wang;Yin Xu","doi":"10.13052/jicts2245-800X.1333","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1333","url":null,"abstract":"As the telecommunications industry gears up for the development of 6G mobile networks, the transition from the current 5G infrastructure requires careful and strategic management. This article examines the evolution from 4G to 5G, drawing valuable analysis on lessons learned and proposes strategies for the forthcoming 5G to 6G migration. We analyse standardized solutions from previous generational shifts, identifying their applicability and limitations in the context of emerging 6G technologies. By emphasizing cost-effective and strategic approaches, we provide insights to interested partners for navigating the complexities of this transition while leveraging emerging advancements for the next era of mobile communications.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"13 3","pages":"281-300"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11302065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760916","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}
Pub Date : 2025-09-01DOI: 10.13052/jicts2245-800X.1331
Jianbao Zhang;Zhengang Li
The continuous spread of negative public opinion may have a detrimental impact on the stability of society, requiring timely guidance. This study used the spread of negative public opinion on Weibo as a case. Original Weibo posts related to the “Zhuhai Pedestrian Collision Case” published between 11 November 2024 and 31 November 2024 were crawled. A bidirectional gatedrecurrent unit (BiGRU) algorithm combined with an attention mechanism called the BiGRU-Att emotion classification algorithm was proposed to classify positive and negative public opinions. The negative public opinions were used to form time series data. A BiGRU-Att-Kalman filtering algorithm was designed to predict the spread of negative public opinions. It was found that the BiGRU-Att algorithm exhibited an F1 value of 0.9248 in sentiment classification, outperforming classification algorithms such as support vector machine. The root-mean-square error and mean absolute error (MAE) values of the BiGRU-Att-Kalman filtering algorithm in the prediction of negative public opinion dissemination were 201.25 and 115.62, respectively, with $R^{2}=0.98$, outperforming prediction algorithms such as GM (1,1). These results highlight the effectiveness of the proposed methods in sentiment classification and forecasting harmful opinion dissemination, thereby offering valuable insights for opinion management.
负面舆论的持续蔓延可能对社会稳定产生不利影响，需要及时引导。本研究以负面舆论在微博上的传播为案例。抓取了2024年11月11日至11月31日期间发布的与“珠海行人碰撞案”相关的微博原文。提出了一种双向门递单元（BiGRU）算法，结合注意机制BiGRU- att情绪分类算法对正面和负面舆论进行分类。负面民意被用来形成时间序列数据。设计了bigru - at - kalman滤波算法来预测负面舆论的传播。研究发现，BiGRU-Att算法在情感分类方面的F1值为0.9248，优于支持向量机等分类算法。bigru - at - kalman滤波算法预测负面舆论传播的均方根误差和平均绝对误差（MAE）值分别为201.25和115.62，其中$R^{2}=0.98$，优于GM（1,1）等预测算法。这些结果突出了本文提出的方法在情绪分类和预测有害意见传播方面的有效性，从而为意见管理提供了有价值的见解。
{"title":"Prediction and Guidance of Negative Public Opinion Dissemination Based on a Sentiment Classification Algorithm","authors":"Jianbao Zhang;Zhengang Li","doi":"10.13052/jicts2245-800X.1331","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1331","url":null,"abstract":"The continuous spread of negative public opinion may have a detrimental impact on the stability of society, requiring timely guidance. This study used the spread of negative public opinion on Weibo as a case. Original Weibo posts related to the “Zhuhai Pedestrian Collision Case” published between 11 November 2024 and 31 November 2024 were crawled. A bidirectional gatedrecurrent unit (BiGRU) algorithm combined with an attention mechanism called the BiGRU-Att emotion classification algorithm was proposed to classify positive and negative public opinions. The negative public opinions were used to form time series data. A BiGRU-Att-Kalman filtering algorithm was designed to predict the spread of negative public opinions. It was found that the BiGRU-Att algorithm exhibited an F1 value of 0.9248 in sentiment classification, outperforming classification algorithms such as support vector machine. The root-mean-square error and mean absolute error (MAE) values of the BiGRU-Att-Kalman filtering algorithm in the prediction of negative public opinion dissemination were 201.25 and 115.62, respectively, with <tex>$R^{2}=0.98$</tex>, outperforming prediction algorithms such as GM (1,1). These results highlight the effectiveness of the proposed methods in sentiment classification and forecasting harmful opinion dissemination, thereby offering valuable insights for opinion management.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"13 3","pages":"243-256"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11302067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760924","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}
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