IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Frontiers in Big Data Pub Date : 2025-09-22 eCollection Date: 2025-01-01 DOI:10.3389/fdata.2025.1640539
Lucas Wafula Wekesa, Stephen Korir
{"title":"Enhancing intelligence source performance management through two-stage stochastic programming and machine learning techniques.","authors":"Lucas Wafula Wekesa, Stephen Korir","doi":"10.3389/fdata.2025.1640539","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>The effectiveness of intelligence operations depends heavily on the reliability and performance of human intelligence (HUMINT) sources. Yet, source behavior is often unpredictable, deceptive or shaped by operational context, complicating resource allocation and tasking decisions.</p><p><strong>Methods: </strong>This study developed a hybrid framework combining Machine Learning (ML) techniques and Two-Stage Stochastic Programming (TSSP) for HUMINT source performance management under uncertainty. A synthetic dataset reflecting HUMINT operational patterns was generated and used to train classification and regression models. The extreme Gradient Boosting (XGBoost) and Support Vector Machines (SVM) were applied for behavioral classification and prediction of reliability and deception scores. The predictive outputs were then transformed into scenario probabilities and integrated into the TSSP model to optimize task allocation under varying behavioral uncertainties.</p><p><strong>Results: </strong>The classifiers achieved 98% overall accuracy, with XGBoost exhibiting higher precision and SVM demonstrating superior recall for rare but operationally significant categories. The regression models achieved R-squared scores of 93% for reliability and 81% for deception. These predictive outputs were transformed into scenario probabilities for integration into the TSSP model, optimizing task allocation under varying behavioral risks. When compared to a deterministic optimization baseline, the hybrid framework delivered a 16.8% reduction in expected tasking costs and a 19.3% improvement in mission success rates.</p><p><strong>Discussion and conclusion: </strong>The findings demonstrated that scenario-based probabilistic planning offers significant advantages over static heuristics in managing uncertainty in HUMINT operations. While the simulation results are promising, validation through field data is required before operational deployment.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1640539"},"PeriodicalIF":2.4000,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12498342/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Big Data","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/fdata.2025.1640539","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

情报行动的有效性在很大程度上取决于人力情报(HUMINT)来源的可靠性和性能。然而,源行为通常是不可预测的、具有欺骗性的或受操作环境影响的,这使资源分配和任务决策变得复杂。方法:本研究开发了一个结合机器学习(ML)技术和两阶段随机规划(TSSP)的混合框架,用于不确定条件下的人力资源性能管理。生成了反映HUMINT操作模式的合成数据集,并用于训练分类和回归模型。采用极端梯度增强(XGBoost)和支持向量机(SVM)对信度和欺骗分数进行行为分类和预测。然后将预测输出转化为情景概率,并将其集成到TSSP模型中,以优化不同行为不确定性下的任务分配。结果:分类器达到了98%的总体准确率,XGBoost表现出更高的精度,支持向量机在罕见但操作重要的类别中表现出更高的召回率。回归模型在可靠性方面的r平方得分为93%,在欺骗方面的r平方得分为81%。将这些预测输出转化为情景概率,整合到TSSP模型中,优化不同行为风险下的任务分配。与确定性优化基线相比,混合框架的预期任务成本降低了16.8%,任务成功率提高了19.3%。讨论和结论:研究结果表明,在管理人工智能操作中的不确定性方面,基于场景的概率规划比静态启发式具有显著优势。虽然模拟结果很有希望,但在实际部署之前需要通过现场数据进行验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing intelligence source performance management through two-stage stochastic programming and machine learning techniques.

Introduction: The effectiveness of intelligence operations depends heavily on the reliability and performance of human intelligence (HUMINT) sources. Yet, source behavior is often unpredictable, deceptive or shaped by operational context, complicating resource allocation and tasking decisions.

Methods: This study developed a hybrid framework combining Machine Learning (ML) techniques and Two-Stage Stochastic Programming (TSSP) for HUMINT source performance management under uncertainty. A synthetic dataset reflecting HUMINT operational patterns was generated and used to train classification and regression models. The extreme Gradient Boosting (XGBoost) and Support Vector Machines (SVM) were applied for behavioral classification and prediction of reliability and deception scores. The predictive outputs were then transformed into scenario probabilities and integrated into the TSSP model to optimize task allocation under varying behavioral uncertainties.

Results: The classifiers achieved 98% overall accuracy, with XGBoost exhibiting higher precision and SVM demonstrating superior recall for rare but operationally significant categories. The regression models achieved R-squared scores of 93% for reliability and 81% for deception. These predictive outputs were transformed into scenario probabilities for integration into the TSSP model, optimizing task allocation under varying behavioral risks. When compared to a deterministic optimization baseline, the hybrid framework delivered a 16.8% reduction in expected tasking costs and a 19.3% improvement in mission success rates.

Discussion and conclusion: The findings demonstrated that scenario-based probabilistic planning offers significant advantages over static heuristics in managing uncertainty in HUMINT operations. While the simulation results are promising, validation through field data is required before operational deployment.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
5.20
自引率
3.20%
发文量
122
审稿时长
13 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信