IEEE Transactions on Computational Social Systems最新文献

Enhancing Fatigue Detection Through Heterogeneous Multisource Data Integration and Cross-Domain Modality Imputation 基于异构多源数据集成和跨域模态插补增强疲劳检测

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-07-22 DOI: 10.1109/TCSS.2026.3713502

Luobin Cui;Yanlai Wu;Ying Tang;Weikai Li

{"title":"Enhancing Fatigue Detection Through Heterogeneous Multisource Data Integration and Cross-Domain Modality Imputation","authors":"Luobin Cui;Yanlai Wu;Ying Tang;Weikai Li","doi":"10.1109/TCSS.2026.3713502","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3713502","url":null,"abstract":"Fatigue detection for human operators is important in safety-related applications such as aviation, mining, and long-haul transport. Reliable estimation of operator fatigue can support timely warnings, adaptive task scheduling, takeover reminders, and other safety-management decisions in human–machine systems. However, the effectiveness of these functions depends on whether fatigue-related signals can be reliably captured in the deployment environment. While many studies have shown the value of high-fidelity sensors in controlled laboratory environments, their performance often degrades when used in real-world settings because of noise, lighting conditions, and field-of-view constraints, thereby limiting their practical use. This article formalizes a deployment-oriented setting for real-world fatigue detection, where high-quality sensors are often unavailable in practical applications. To address this issue, we use knowledge from heterogeneous source domains, including high-fidelity sensors that are difficult to deploy in the field but commonly used in controlled environments, to assist fatigue detection in the real-world target domain. Based on this idea, we design a heterogeneous and multisource fatigue-detection framework that uses the available modalities in the target domain while leveraging diverse configurations in the source domains through cross-domain modality imputation based on shared modalities. As a proof-of-concept, we intentionally employ well-established algorithms for both knowledge-transfer and fatigue-detection components to test the framework’s core idea. Experiments using a field-deployed sensor setup and two publicly available human–fatigue datasets demonstrate the feasibility of the proposed framework in sensor-constrained scenarios and show that knowledge from heterogeneous source domains can support fatigue detection when high-fidelity modalities are unavailable in the target domain.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"5982-5995"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Design Paradigm for Trustworthy Human–Robot Collaboration: Transparency-Driven Governance From Science Fiction Narrative Prototypes 可信赖人机协作的设计范例：来自科幻叙事原型的透明驱动治理

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 DOI: 10.1109/TCSS.2026.3730991

Fang Liu;Mingfen Huang;Bin Hu

引用次数: 0

AlzDetect: A Heterogeneous Framework for Early Alzheimer Detection Using Unified Multimodal Indicators AlzDetect：使用统一多模态指标进行早期阿尔茨海默病检测的异构框架

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-08-19 DOI: 10.1109/TCSS.2026.3716541

Aarju Dixit;Debasis Das

{"title":"AlzDetect: A Heterogeneous Framework for Early Alzheimer Detection Using Unified Multimodal Indicators","authors":"Aarju Dixit;Debasis Das","doi":"10.1109/TCSS.2026.3716541","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3716541","url":null,"abstract":"Alzheimer’s disease (AD) is a progressive neurodegenerative disorder for which early risk identification is essential for timely clinical assessment, care planning, and long-term disease management. However, computational modeling of AD remains challenging because clinically meaningful diagnosis cannot be inferred from numerical separability alone; it requires alignment with clinical assessment and biomarker validation. From a computational social systems perspective, a major challenge is the heterogeneity and fragmentation of patient-related information, including wearable actigraphy signals, clinical records, and demographic contexts. To address this challenge, we propose <italic>AlzDetect, an enhanced TabNet-based deep learning (DL) framework for screening-oriented AD risk modeling across clinically grounded and wearable-derived data domains. AlzDetect is not presented as a subject-level multimodal diagnostic system; rather, it is evaluated as a heterogeneous data risk stratification framework under domain shift, missingness, and cross-domain robustness settings. The wearable-derived (Google Pixel Watch 2) dataset is used only for feasibility and robustness analysis of behavioral signal modeling, not as biomarker-confirmed evidence for AD diagnosis or clinical staging. AlzDetect provides transparent decision support through TabNet’s sparse attention masks, which identify influential predictors at feature and modality levels. The extensive evaluation shows that AlzDetect achieves 96% predictive accuracy and outperforms representative baselines within the evaluated dataset specific settings, demonstrating strong predictive separability and robustness across heterogeneous AD relevant data sources.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"5551-5563"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A New Two-Way Ensemble Prediction for Infectious Diseases 一种新的传染病双向集合预测方法

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2025-12-26 DOI: 10.1109/TCSS.2025.3640950

Yingjie Zhao;Jianping Huang;Rui Wang;Han Li;Wei Yan;Xinbo Lian;Zihan Hao

{"title":"A New Two-Way Ensemble Prediction for Infectious Diseases","authors":"Yingjie Zhao;Jianping Huang;Rui Wang;Han Li;Wei Yan;Xinbo Lian;Zihan Hao","doi":"10.1109/TCSS.2025.3640950","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3640950","url":null,"abstract":"Infectious diseases pose a persistent global health threat, making accurate forecasting essential for effective response. A deep understanding of their underlying mechanisms and the role of environmental factors is crucial to improving predictive models. In this study, we first investigate the relationship between key meteorological variables, including average temperature, relative humidity, PM2.5, and PM10 and the progression of infectious diseases, using COVID-19 data from Beijing and Shanghai as a representative case. A generalized additive model (GAM) is employed to determine the driven meteorological factors that affected the development of infectious diseases in general. Based on this analysis, we incorporate the identified meteorological variables into a modified susceptible–exposed–infected–recovered (SEIR) model. Then, further integrated this model into a novel two-way ensemble prediction (TEP) framework. This integrated method captures temporal and spatial dynamics of disease transmission. The proposed method demonstrated a significant improvement in prediction accuracy compared to traditional epidemiological models. This two-way integrated modeling framework offers a practical and rapid response to epidemic forecasting, providing valuable insights for future pandemic predictions, especially in regions with limited data on human mobility.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"6359-6370"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MeDiTate: A Deep Learning Framework for Mental Disorder State Detection in the Era of Social Media 冥想：社交媒体时代精神障碍状态检测的深度学习框架

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-03-24 DOI: 10.1109/TCSS.2026.3669302

Shorouk Adel;Noureldin Elmadany;Omneya Attallah;Maha Sharkas

{"title":"MeDiTate: A Deep Learning Framework for Mental Disorder State Detection in the Era of Social Media","authors":"Shorouk Adel;Noureldin Elmadany;Omneya Attallah;Maha Sharkas","doi":"10.1109/TCSS.2026.3669302","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3669302","url":null,"abstract":"This article presents MeDiTate, a hybrid deep learning framework proposed to detect mental health disorders by analyzing social media content. The Menta dataset is collected, consisting of user-labeled mental health issues across 13 different disorders. The proposed framework employs various natural language processing techniques for data preprocessing and integrates deep learning models, specifically convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and transformer architectures. Ablation studies are conducted to find the top performing architecture. The proposed MeDiTate achieves an accuracy of 88.32%, an F1-score of 88.51%, precision of 88.61%, and recall of 88.45% on Menta dataset. The framework surpasses state-of-the-art methods on two publicly available datasets, namely Kim et al. (2020) and Low et al. (2020) datasets, by <inline-formula><tex-math>$sim$</tex-math></inline-formula>2%. Additionally, we leverage the effectiveness of transfer learning from models trained on user-labeled datasets on the performance. The results demonstrate a noticeable improvement. Furthermore, we empirically investigate cross dataset evaluation to assess the framework’s generalizability. Comprehensive quantitative and qualitative analyses further validate the framework’s discriminatory performance.<xref> 1</xref> <fn> <label> 1 </label> The code and dataset are available at <uri>https://github.com/NourElmadany/MeDiTate</uri>. </fn>","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"6716-6730"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Forecasting the Extreme Heat for Chongqing in China 中国重庆极端高温天气预报

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2025-12-29 DOI: 10.1109/TCSS.2025.3642290

Bo Zeng;Shiyi Du;Lean Yu;Yun Bai;Jianzhou Wang;Yingjie Yang;Chen Chen;Li Ji

{"title":"Forecasting the Extreme Heat for Chongqing in China","authors":"Bo Zeng;Shiyi Du;Lean Yu;Yun Bai;Jianzhou Wang;Yingjie Yang;Chen Chen;Li Ji","doi":"10.1109/TCSS.2025.3642290","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3642290","url":null,"abstract":"Under global warming, Chongqing, China has seen rising frequencies of extreme heat, severely impacting residents’ lives, production, and the urban ecosystem. Accurately predicting future extreme heat year (hereinafter referred to as extreme heat) is crucial for ecological protection, energy management, and infrastructure planning. Using historical temperature data, this study defines extreme heat via the quantile method, ensuring regional representativeness. To address the limitations of traditional forecasting methods in predicting extreme heat characterized by “small sample sizes and complex influencing factors,” we have proposed a new grey disaster prediction model. The new model introduces a three-element adjacent mean generation operator and uses the particle swarm optimization (PSO) algorithm for global optimization of its key parameters. Applying this model to Chongqing, China’s 1951–2024 extreme heat, we predict the next three occurrences will be in 2026, 2029, and 2031. This model offers a new approach to extreme heat prediction and supports urban heatwave emergency response optimization, aiding high-temperature disaster prevention and sustainable ecological development.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"6269-6287"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic Matching Under Capacity Constraints: Integrating Gravity Models and Strategic Queuing in Gig Economy Platforms 容量约束下的动态匹配：零工经济平台中重力模型与策略排队的集成

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-07-23 DOI: 10.1109/TCSS.2026.3704998

Youngju Cho;Heejun Park

引用次数: 0

A Fair Order Matching and Idle Vehicle Dispatching Algorithm for Ride-Hailing 一种网约车公平顺序匹配与空闲车辆调度算法

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-07-31 DOI: 10.1109/TCSS.2026.3712745

Bing Shi;Juncheng Zhang;Qi Yue;Shuai Xu;Xiaopan Zhang;Ge Mao

{"title":"A Fair Order Matching and Idle Vehicle Dispatching Algorithm for Ride-Hailing","authors":"Bing Shi;Juncheng Zhang;Qi Yue;Shuai Xu;Xiaopan Zhang;Ge Mao","doi":"10.1109/TCSS.2026.3712745","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3712745","url":null,"abstract":"Online ride hailing is increasingly prevalent, and effective order-driver matching is critical for its operations. Existing studies mostly focus on maximizing platform profits or minimizing passenger waiting time, often neglecting interdriver fairness. In fact, ignoring fairness may cause drivers to leave the platform, and thus enhancing fairness is vital to attract more drivers to participate. Furthermore, existing works prioritize short-term fairness while disregarding how current matching and fairness-enhancing operations affect future fairness and total income. Thus, we propose an idle vehicle dispatching and fair matching algorithm guided by the value function and dynamic clustering, aiming to optimize long-term fairness and total income simultaneously. Specifically, we design a fairness enhancement method, which improves fairness for drivers with different incomes and maximizes total income for those with similar incomes. To identify similar-income drivers, we use a dynamic clustering algorithm that generates clusters dynamically based on current fairness. However, due to spatiotemporal fluctuations in supply and demand, maximizing current total income does not guarantee long-term maximization. Therefore, we model the matching process within a Markov decision process, where a value function is constructed to estimate each driver’s expected long-term value across different zones, guiding matching and dispatching to maximize drivers’ long-term total income. Experimental results show our algorithm outperforms benchmarks in idle driver rate, order service rate, drivers’ total income, and fairness. The results also verify that it effectively maintains fairness over both short and long horizons while increasing overall income.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"5862-5874"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Trajectory Optimization for Heavy-Haul Trains Under Varying Wheel-Rail Adhesion Conditions 轮轨黏附条件下重载列车轨迹优化

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-01-23 DOI: 10.1109/TCSS.2026.3652416

Lulu Ji;Min Zhou;Hongwei Wang;Xi Wang;Hairong Dong

{"title":"Trajectory Optimization for Heavy-Haul Trains Under Varying Wheel-Rail Adhesion Conditions","authors":"Lulu Ji;Min Zhou;Hongwei Wang;Xi Wang;Hairong Dong","doi":"10.1109/TCSS.2026.3652416","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3652416","url":null,"abstract":"The operation of heavy-haul trains (HHTs) with heavy loads and long carriages faces significant challenges in complex weather conditions, such as rain and snow, as well as in environments with extended downhill sections. As operational demands increase, the optimization of trajectories for HHTs under varying wheel-rail contact conditions becomes critical. This study proposes a dynamic model for HHTs to optimize train trajectories in real railway environments. By analyzing variations in the wheel-rail adhesion coefficient (WRAC) under four scenarios—dry, wet with sand scattering, wet without sand scattering, and oil contamination—the study evaluates the impact of adhesion coefficient changes on train control and braking distance. Furthermore, using dry conditions and wet conditions without sand scattering as case studies, the study investigates the influence of adhesion coefficients on train operation and energy consumption. Compared to dry conditions, under wet conditions without sand scattering, the average train running time increases by 5%, while energy consumption decreases by approximately 10% (with a maximum reduction of 15%), and average acceleration drops by 22.5%. When the train brakes from 80 to 0 km/h, the braking distance under wet conditions is 200% of that under dry conditions. The experiments employ an adaptive large neighborhood search (ALNS) algorithm, which reduces computation time by 50% compared to traditional solvers such as CPLEX. This study investigates the optimization of trajectories under varying adhesion coefficients and provides robust support for the safe and punctual operation of trains in extreme weather conditions such as rain and snow.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"6421-6431"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Transactions on Computational Social Systems Publication Information IEEE计算社会系统汇刊信息

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 DOI: 10.1109/TCSS.2026.3730932

引用次数: 0