Intelligent Systems with Applications最新文献

{"title":"AI safety practices and public perception: Historical analysis, survey insights, and a weighted scoring framework","authors":"Maikel Leon","doi":"10.1016/j.iswa.2025.200583","DOIUrl":"10.1016/j.iswa.2025.200583","url":null,"abstract":"<div><div>Artificial Intelligence (AI) safety has evolved in tandem with advances in technology and shifts in societal attitudes. This article presents a historical and empirical analysis of AI safety concerns from the mid-twentieth century to the present, integrating archival records, media narratives, survey data, landmark research, and regulatory developments. Early anxieties (rooted in Cold War geopolitics and science fiction) focused on physical robots and autonomous weapons. In contrast, contemporary debates focus on algorithmic bias, misinformation, job displacement, and existential risks posed by advanced systems, such as Large Language Models (LLMs). This article examines the impact of key scholarly contributions, significant events, and regulatory milestones on public perception and governance approaches. Building on this context, this study proposes an improved LLM safety scoring system that prioritizes existential risk mitigation, transparency, and governance accountability. Applying the proposed framework to leading AI developers reveals significant variation in safety commitments. The results underscore how weighting choices affect rankings. Comparative analysis with existing indices highlights the importance of nuanced, multidimensional evaluation methods. The paper concludes by identifying pressing governance challenges, including the need for global cooperation, robust interpretability, and ongoing monitoring of harm in high-stakes domains. These findings demonstrate that AI safety is not static but somewhat shaped by historical context, technical capabilities, and societal values—requiring the continuous adaptation of both policy and evaluation frameworks to align AI systems with human interests.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200583"},"PeriodicalIF":4.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Federated learning for cyber attack detection to enhance security in protection schemes of cyber-physical energy systems","authors":"Lei Du,&nbsp;Qingzhi Zhu","doi":"10.1016/j.iswa.2025.200590","DOIUrl":"10.1016/j.iswa.2025.200590","url":null,"abstract":"<div><div>Cyber-attacks increasingly target the protection systems that safeguard cyber-physical energy systems (CPES), making it more difficult to deliver security and reliability requirements. The protection schemes in power grids, which depend on real-time forecasts from digital relays and Apple devices, require detection of physical faults and, simultaneously, malicious cyber attacks. This paper developed a decentralized federated learning-based framework to assist with the detection of cyber attacks in the protection schemes of cyber-physical energy systems (CPES), with the goals of privacy preservation and scalability. Attention was paid to the whole range of threats, including false data injection (FDI), man-in-the-middle, replay, and denial of service (DoS) across distributed substations without centralization of raw datasets. A lightweight neural network model was trained locally before being aggregated using federated averaging to develop a collaborative approach to learning across multiple substations. Based on the 3-machine, 9 bus case, simulations were run with synthetic attack datasets. The proposed method achieved an average detection accuracy of 96.7% while also preserving the confidentiality and non-disclosure of data. The study also highlighted some of the challenges related to implementation, conceptual drift, and the computational limits of hosting the solution, thereby providing a better understanding of planning and deploying the solution in smart grid applications.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200590"},"PeriodicalIF":4.3,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personalized privacy in OSNs: Evaluating deep learning models for context-aware image editing","authors":"Gelareh Hasel Mehri ,&nbsp;Georgi Kostov ,&nbsp;Bernardo Breve ,&nbsp;Andrei Jalba ,&nbsp;Nicola Zannone","doi":"10.1016/j.iswa.2025.200581","DOIUrl":"10.1016/j.iswa.2025.200581","url":null,"abstract":"<div><div>Online Social Networks (OSNs) have become a cornerstone of digital interaction, enabling users to easily create and share content. While these platforms offer numerous benefits, they also expose users to privacy risks such as cyberstalking and identity theft. To address these concerns, OSNs typically provide access control mechanisms that allow users to regulate content visibility. However, these mechanisms often assume that content is managed by individual users and focus primarily on preserving content integrity, which may discourage users from sharing sensitive information. In this work, we propose a privacy model that empowers users to conceal sensitive content in images according to their preferences, expressed by means of policies. Our approach employs a multi-stage pipeline that includes segmentation for object localization, scene graphs and distance metrics for determining object ownership, and inpainting techniques for editing. We investigate the use of advanced deep learning models to implement the privacy model, aiming to provide personalized privacy controls while maintaining high image fidelity. To evaluate the proposed model, we conducted a user study with 20 participants. The user study highlights that ownership is the most significant factor influencing user perceptions of policy enforcement compliance, with less impact from localization and editing. The results also reveal that participants are generally willing to adopt the fully automated privacy model for selectively editing images in OSNs based on viewer identity, although some prefer alternative use cases, such as editing or censorship tools. Participants also raised concerns about the potential misuse of the model, supporting our choice of excluding an option for object replacement.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200581"},"PeriodicalIF":4.3,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New Harris Hawks algorithms for the Close-Enough Traveling Salesman Problem","authors":"Tansel Dokeroglu,&nbsp;Deniz Canturk","doi":"10.1016/j.iswa.2025.200586","DOIUrl":"10.1016/j.iswa.2025.200586","url":null,"abstract":"<div><div>This study introduces a novel application of the Harris Hawks Optimization (HHO) algorithm to the Close-Enough Traveling Salesman Problem (CETSP), a challenging combinatorial optimization problem where circular neighborhoods rather than exact coordinates represent target points. To tackle the CETSP’s spatial complexity and high-dimensional solution space, we develop new HHO algorithms, including a parallel multi-population variant designed using the OpenMP framework. This parallel algorithm allows multiple subpopulations to evolve simultaneously, increasing diversity and computational efficiency, particularly on large-scale and real-time instances. Furthermore, new problem-specific exploration and exploitation operators are introduced, tailored to the CETSP’s geometric structure, enabling better guidance of the search process toward high-quality solutions. A comprehensive empirical evaluation is conducted on 47 benchmark instances, encompassing synthetic problem instances and a real-world robotic welding scenario in automotive manufacturing. The results show that the proposed methods outperform existing state-of-the-art techniques such as Genetic Algorithm (GA), Memetic Algorithm (MA-CETSP) and Variable Neighborhood Search (VNS)-based approaches, achieving 18 new best-known solutions. The experimental findings underline the strong convergence behavior, robustness across diverse problem sizes, and practical applicability of the algorithm. Additionally, the algorithm’s modular and extensible structure leads the way for future adaptations to multi-objective and dynamic versions of CETSP, broadening its relevance for both academic research and industrial deployment.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200586"},"PeriodicalIF":4.3,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence-driven green innovation in packaging: A systematic review of adoption and diffusion challenges","authors":"Ye Ma,&nbsp;Nor Hidayati Zakaria,&nbsp;Basheer Al-Haimi,&nbsp;Chen Wu","doi":"10.1016/j.iswa.2025.200589","DOIUrl":"10.1016/j.iswa.2025.200589","url":null,"abstract":"<div><div>Global concern about environmental protection has intensified the demand for sustainable packaging solutions. Integrating artificial intelligence (AI) into green innovation offers a transformative way to address these challenges. This study applies a systematic literature review (SLR) guided by the PRISMA 2020 framework to examine recent AI-powered packaging innovations. Forty-eight peer-reviewed articles, published between 2020 and 2025, were analyzed. The findings show that Machine Learning, Deep Learning, and general AI applications are the most frequently adopted technologies. Biodegradable packaging materials and smart packaging systems represent the main sustainable packaging types. AI applications are concentrated in process optimization, smart packaging monitoring, fraud detection, computer vision, and natural language processing. However, widespread adoption faces obstacles such as high costs, technical complexity, and regulatory uncertainty. Future trends highlight the importance of scalable technologies, advanced AI models, integration with the circular economy, and interdisciplinary collaboration. This review provides a structured framework to guide academics, industry practitioners, and policymakers in adopting AI-driven green innovation for sustainable packaging.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200589"},"PeriodicalIF":4.3,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The semantic correlation mining method of multimodal data in constructing techno-economic knowledge graph of power grid","authors":"Ling Qiu,&nbsp;Mengqi Pan,&nbsp;Nuoya Lv","doi":"10.1016/j.iswa.2025.200588","DOIUrl":"10.1016/j.iswa.2025.200588","url":null,"abstract":"<div><div>Due to the diverse formats and complex structures of multimodal data, effectively managing its complexity and correlations remains challenging. Moreover, when dealing with large-scale data, traditional methods often encounter issues such as low computational efficiency and inaccurate results. This paper proposes a semantic association mining method for multimodal data. This method utilizes ETL technology to convert text and table data from different files into nodes and relational edges in the knowledge graph. By optimizing the word vector matrix through the skip character model, it can better capture the semantic information of text data and accurately reflect semantic similarity. Through integrating nodes such as equipment, design technologies and installation addresses, a technical and economic knowledge graph of the power grid is constructed. For the calculation of multimodal object associations, the data first undergoes label preprocessing, feature processing, and semantic relationship structuring before the association is computed using the cosine similarity formula. By using the association rule algorithm to mine the correlation relationships among time-series variables, potential correlations such as the operating status of equipment and the overall performance of the power grid can be discovered, thereby improving the understanding and prediction ability of the power grid’s operating status. The experimental results demonstrate that the proposed method achieves the highest accuracy and recall rate at 98.20 %, with an F-measure of 93.89 %, a bit error rate below 0.9, and a time consumption of approximately 7.34 s.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200588"},"PeriodicalIF":4.3,"publicationDate":"2025-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A data-driven optimization approach for automated reviewer assignment using natural language processing","authors":"Meltem Aksoy ,&nbsp;Seda Yanik ,&nbsp;Mehmet Fatih Amasyali","doi":"10.1016/j.iswa.2025.200587","DOIUrl":"10.1016/j.iswa.2025.200587","url":null,"abstract":"<div><div>In many settings, such as project or publication selection, expert reviewers play a pivotal role, as their assessments serve as the primary basis for determining a project's prospective value. The effectiveness of matching and assigning qualified experts to evaluate project proposals can substantially influence the quality of the selection process and, consequently, impact the funding organization's return on investment. Despite its importance, many funding organizations continue to rely on basic manual methods for assigning reviewers. This simplistic approach can compromise the quality of project selection and lead to suboptimal financial outcomes. Moreover, it may hinder the equitable distribution of review workloads and increase conflicts of interest between reviewers and applicants. Consequently, there is a pressing need for a systematic and automated method to enhance the reviewer assignment process.</div><div>In this study, we propose an optimization-based approach using natural language processing to automate the reviewer assignment process for project proposals. The proposed approach follows a structured three-stage methodology. First, a comprehensive database is constructed by collecting multilingual data on both proposals and reviewers. Second, word embedding techniques are used to represent texts as vectors, enabling the use of cosine similarity to quantify the relevance between each proposal and reviewer. Reviewer expertise and past evaluation performance are also analyzed using predefined knowledge rules. In the final stage, a multi-objective integer linear programming model assigns reviewers by optimizing proposal-reviewer similarity and reviewer competency while preventing conflicts of interest. Additionally, a max-min strategy is employed to ensure fair treatment of less-advantaged proposals, and two supplementary models are introduced to balance reviewer workloads. Experimental results on a real-world dataset from a regional development agency demonstrate that the proposed system significantly outperforms traditional manual assignment methods. We show that automated reviewer assignment prevents subjective judgements, together with reductions in time and cost of the assignment process.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200587"},"PeriodicalIF":4.3,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced set-based particle swarm optimization for portfolio management in a walk-forward paradigm","authors":"Zander Wessels ,&nbsp;Andries Engelbrecht","doi":"10.1016/j.iswa.2025.200582","DOIUrl":"10.1016/j.iswa.2025.200582","url":null,"abstract":"<div><div>A novel approach to portfolio optimization is introduced using a variant of set-based particle swarm optimization (SBPSO), building upon the foundational work of Erwin and Engelbrecht. Although their contributions advanced the application of SBPSO to financial markets, this research addresses key practical challenges, specifically enhancing the treatment of covariance and expected returns and refining constraint implementations to align with real-world applications. Beyond algorithmic improvements, this article emphasizes the importance of robust evaluation methodologies and highlights the limitations of traditional backtesting frameworks, which often yield overly optimistic results. To overcome these biases, the study introduces a comprehensive simulation platform that mitigates issues such as survivorship and forward-looking bias. This provides a realistic assessment of the modified SBPSO’s financial performance under varying market conditions. The findings shift the focus from computational efficiency to the practical outcomes of profitability that are most relevant to investors.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200582"},"PeriodicalIF":4.3,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-predictive vaccine stability: a systems biology framework to modernize regulatory testing and cold chain equity","authors":"Sinethemba H. Yakobi,&nbsp;Uchechukwu U. Nwodo","doi":"10.1016/j.iswa.2025.200584","DOIUrl":"10.1016/j.iswa.2025.200584","url":null,"abstract":"<div><div>Vaccine instability contributes to the loss of up to 25 % of doses globally, a challenge intensified by the complexity of next-generation platforms such as mRNA–lipid nanoparticles (mRNA–LNPs), viral vectors, and protein subunits. Current regulatory frameworks (ICH Q5C, WHO TRS 1010) rely on static protocols that overlook platform-specific degradation mechanisms and real-world cold-chain variability. We introduce the Systems Biology–guided AI (SBg-AI) framework, a predictive stability platform integrating omics-derived biomarkers, real-time telemetry, and explainable machine learning. Leveraging recurrent and graph neural networks with Bayesian inference, SBg-AI forecasts degradation events with 89 % accuracy—validated in African and Southeast Asian supply chains. Federated learning ensures multi-manufacturer collaboration while preserving data privacy. In field trials, dynamic expiry predictions reduced mRNA vaccine wastage by 22 %. A phased regulatory roadmap supports transition from hybrid AI-empirical models (2024) to full AI-based stability determinations by 2030. By integrating mechanistic degradation science with real-time telemetry and regulatory-compliant AI, the SBg-AI framework transforms vaccine stability from retrospective batch testing to proactive, precision-guided assurance.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200584"},"PeriodicalIF":4.3,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Benchmarking deep neural representations for synthetic data evaluation","authors":"Nuno Bento,&nbsp;Joana Rebelo,&nbsp;Marília Barandas","doi":"10.1016/j.iswa.2025.200580","DOIUrl":"10.1016/j.iswa.2025.200580","url":null,"abstract":"<div><div>Robust and accurate evaluation metrics are crucial to test generative models and ensure their practical utility. However, the most common metrics heavily rely on the selected data representation and may not be strongly correlated with the ground truth, which itself can be difficult to obtain. This paper attempts to simplify this process by proposing a benchmark to compare data representations in an automatic manner, i.e. without relying on human evaluators. This is achieved through a simple test based on the assumption that samples with higher quality should lead to improved metric scores. Furthermore, we apply this benchmark on small, low-resolution image datasets to explore various representations, including embeddings finetuned either on the same dataset or on different datasets. An extensive evaluation shows the superiority of pretrained embeddings over randomly initialized representations, as well as evidence that embeddings trained on external, more diverse datasets outperform task-specific ones.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200580"},"PeriodicalIF":4.3,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}