[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4224":3,"related-4224":72},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":21,"classification":22,"search_phrases":50,"slug":51,"view_count":15,"doi":52,"paper":53,"created_at":71},4224,"Artificial Intelligence Applications for Enhancing Security and Threat Detection in Northeast Nigeria","https:\u002F\u002Fdoi.org\u002F10.64388\u002Firev10i4-1723634","The security landscape of Northeast Nigeria has, for over a decade and a half, been shaped by the protracted Boko Haram and Islamic State West Africa Province (ISWAP) insurgency, whose mass-casualty attacks, large-scale abductions and cross-border operations across the Lake Chad basin have produced one of the world's largest internally displaced populations. Conventional security architectures, reliant on manual intelligence gathering, static checkpoints and after-the-fact investigation, have struggled to keep pace with the speed, mobility and adaptive tactics of these threat actors. This paper presents a comprehensive review and conceptual framework for the application of Artificial Intelligence (AI) — spanning machine learning, deep learning, natural language processing, computer vision, and predictive geospatial analytics — to security and threat detection in Northeast Nigeria. The paper synthesizes evidence from the global and Nigeria-specific literature on AI-enabled surveillance, biometric identification, unmanned aerial vehicle (UAV) reconnaissance, social-media-based extremism monitoring, financial-crime detection, and predictive crime mapping, and proposes a layered architecture that integrates these techniques into a coherent threat-detection pipeline suited to a low-connectivity, resource-constrained operating environment. The discussion further examines the practical, infrastructural, and ethical constraints that condition AI adoption in the region — including unreliable electricity supply, data scarcity, algorithmic bias, and privacy risk — and closes with policy-oriented recommendations for security agencies, technology developers, and government institutions. The paper argues that AI should be understood not as a stand-alone solution but as a force multiplier that augments human intelligence and community policing, provided that deployment is grounded in transparency, local data governance, and human rights safeguards.","过去十五年来，尼日利亚东北部的安全格局一直受到博科圣地（Boko Haram）和“伊斯兰国西非省”（ISWAP）旷日持久的叛乱活动所塑造，其大规模伤亡袭击、大规模绑架以及跨越乍得湖流域的跨境行动，已造成全球规模最大的境内流离失所人口之一。依赖人工情报搜集、固定检查站和事后调查的传统安全架构，难以跟上这些威胁行为体的速度、机动性和适应性战术。本文提出了一项综合性综述与概念框架，探讨人工智能（AI）——涵盖机器学习、深度学习、自然语言处理、计算机视觉和预测性地理空间分析——在尼日利亚东北部安全与威胁检测中的应用。本文综合了全球及尼日利亚本土文献中关于AI赋能监控、生物特征识别、无人机（UAV）侦察、基于社交媒体的极端主义监测、金融犯罪检测和预测性犯罪制图等方面的证据，并提出了一种分层架构，将这些技术整合为一条适用于低连通性、资源受限操作环境的连贯威胁检测流水线。讨论进一步审视了制约该地区AI应用的实际、基础设施和伦理限制——包括电力供应不可靠、数据稀缺、算法偏见和隐私风险——最后为安全机构、技术开发者和政府机构提出了政策导向的建议。本文认为，AI不应被理解为一种独立解决方案，而应被视为一种力量倍增器，用以增强人类情报和社区警务，前提是部署必须以透明度、本地数据治理和人权保障为基础。",null,"Iconic Research and Engineering Journals","2026-10-02T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文聚焦尼日利亚东北部反恐与威胁检测中的人工智能应用，与三农、农业信息化、智慧农业、数字乡村等主题无关，不予入选。",[19],{"name":10,"url":6},[12],[],{"status":23,"locked":14,"version":24,"method":25,"evidence":26},"needs_review","2026-10-22.2","rules",{"candidates":27,"agricultural_evidence":14,"reason":49},[28,42],{"code":29,"score":30,"supported":31,"title":32,"abstract":34,"tags":41},"I2",17,true,[33],"artificial intelligence",[35,36,37,38,39,33,40],"深度学习","机器学习","计算机视觉","machine learning","deep learning","computer vision",[],{"code":43,"score":44,"supported":31,"title":45,"abstract":46,"tags":48},"I4",3,[],[47],"空间分析",[],"农业相关性需要复核",[],"ArtificialIntelligenceApplicationsforEnh-4224","10.64388\u002Firev10i4-1723634",{"doi":52,"openalex_id":54,"authors":55,"venue":10,"cited_by_count":15,"oa_url":62,"card":63,"direction":69,"ingested_from":70},"W7215713828",[56,58,60],{"name":57,"orcid":9},"Ibrahim Musa Ibrahim",{"name":59,"orcid":9},"Ahmad Abdulkareem",{"name":61,"orcid":9},"Mohammed Wakilbe","https:\u002F\u002Fwww.irejournals.com\u002Fformatedpaper\u002F1723634.pdf",{"tldr":64,"method":65,"finding":66,"direction":67,"opportunity":68},"综述并构建AI用于尼日利亚东北部安全威胁检测的分层框架。","文献综述与概念框架，涵盖机器学习、深度学习、NLP、CV及预测性地理空间分析。","AI应作为增强人类情报与社区警务的力量倍增器，而非独立方案。","其他","可探索低连接、资源受限环境下AI安全监测的轻量化与本地数据治理方案。","智慧农业 \u002F 农业物联网","openalex","2026-10-05T23:30:14.372581Z",{"total":15,"page":73,"page_size":73,"items":74},1,[]]