[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122375-en":3,"doc-seo-122375-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122375,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","An Effective Machine Learning Approach for Explosive Trace Detection - Article","Explosive proliferation and terrorist activity endanger public sites such as trains, airports, and government buildings, creating an urgent need for rapid, safe detection systems that do not expose security personnel. This work deploys an artificial intelligence model for explosive trace detection using deep learning trained on a large sensor-network dataset. Serial data is transformed into 2D images for CNN-based classification of explosive gas combinations (explosive vs. non-explosive). Validation on 10% of the dataset achieves 98.2% accuracy and AUC of 1, demonstrating deep learning effectiveness.","An Effective Machine Learning Approach for Explosive Trace  \nDetection  \nMonday F. Ohemu1, Ambrose A. Azeta2, Ibrahim A. Adeyanju2, Chukwuemeka C. Obasi3  \n1Department of Electrical and Electronics Engineering, Airforce Institute of Technology, Kaduna, 800272, Nigeria  \n2Department of Software Engineering, Namibia University of Science and Technology, 10005, Namibia  \n3Department of Computer Engineering, Edo State University, Uzairue, 300213, Nigeria  \nArticle Info ABSTRACT  \nArticle history:  \nReceived November 29, 2024 Revised January 15, 2025 Accepted January 24, 2025  \nKeywords:  \nExplosive Trace Detection Artificial Intelligence Machine Learning  \nDeep Learning  \nGlobally, the proliferation of explosives and terrorist attacks has caused significant harm to public areas and heightened security concerns. The majority of public places, such as trains, airports, and government buildings, are being targeted, endangering people's lives and property. These target sites must be shielded against terrorist attacks and explosives without putting human security workers in jeopardy. Animals have been used as one of various techniques to try and tackle the aforementioned issue. It has been demonstrated that machine learning models, however, offer superior results. Large volumes of data are necessary for machine learning models to be accurate, but certain specialized training methods have drawbacks of their own because they can be difficult to get. It is now essential to create systems that are highly adaptable to real-time data. This work focuses on the essence of deploying an Artificial intelligence model for effective explosive trace detection. The model used was adapted from deep learning technology trained with a large explosive trace data set that was collected from a sensor network. The dataset was converted to 2D data using serial data to an image generator. The model was developed to classify explosive gas based on the concentration of Carbon (C), Hydrogen (H), Oxygen (O), and Nitrogen (N) gases and was able to classify the gas combinations as either explosive or not. The adaptation of CNN was tested and validated using 10% of the explosive trace dataset with an accuracy of 98.2%, and an AUC of 1 was recorded. The result shows that the deep learning concept is a useful tool in explosive trace detection.  \nThis is an open access article under the CC BY-SA license.  \nCorresponding Author: Monday F. Ohemu ([e-mail: monfavour@gmail.com](e-mail: monfavour@gmail.com))  \n1. INTRODUCTION  \nTerrorist attacks on individuals and sensitive locations have grown to be a global threat, forcing governments, security services, and educational institutions to take all necessary precautions to protect citizens and vital infrastructure. Attacks using explosives against vital infrastructure, personnel, schools, and the government have increased recently because these weapons are simple to make and use and have the potential to cause significant harm [1] . This has made the development of various kinds of explosives for destroying innocent people and properties very common. The area of interest includes learning institutions, airports, government properties and military bases, which can be monitored through sensor networks. The sensor network, which comprises different types of sensors, is designed and deployed continuously to detect and identify explosive traces within specific threat locations in an environment. Trace elements, compounds, or chemical residues associated with explosives, such as TNT (trinitrotoluene), RDX (hexahydro-1,3,5-trinitro-1,3,5-triazine), known as the Royal Demolition Explosive or PETN (pentaerythritol tetranitrate) can be detected. This information collected in real-time by the sensor network can be processed either by the sensor note or by a remote server using advanced algorithms for data analysis. This has led to the development of Artificial intelligence (AI) based systems to accurately detect explosives before causin","cbCaipgMwqp3rZhb","https://ap.wps.com/l/cbCaipgMwqp3rZhb","pdf",641629,1,15,"English","en",105,"# Abstract\n# Keywords\n# 1. Introduction\n## Sensor networks for trace detection\n## Trace vs bulk explosive detection","[{\"question\":\"What problem does the approach target?\",\"answer\":\"The approach targets the detection of explosive traces in public and sensitive locations to reduce harm from terrorist attacks while avoiding risk to human security workers.\"},{\"question\":\"How is the dataset prepared for the model?\",\"answer\":\"The sensor data is converted into 2D representations using a serial-to-image generation method, enabling CNN-based learning.\"},{\"question\":\"What model performance is reported in validation?\",\"answer\":\"Using 10% of the dataset, the adapted CNN achieves 98.2% accuracy with an AUC of 1.\"}]","An Effective Machine Learning Approach for Explosive Trace Detection - Article | PDF",1785810304,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"an-effective-machine-learning-approach-for-explosive-trace-detection-article","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/an-effective-machine-learning-approach-for-explosive-trace-detection-article/122375/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the approach target?","Question",{"text":75,"@type":76},"The approach targets the detection of explosive traces in public and sensitive locations to reduce harm from terrorist attacks while avoiding risk to human security workers.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the dataset prepared for the model?",{"text":80,"@type":76},"The sensor data is converted into 2D representations using a serial-to-image generation method, enabling CNN-based learning.",{"name":82,"@type":73,"acceptedAnswer":83},"What model performance is reported in validation?",{"text":84,"@type":76},"Using 10% of the dataset, the adapted CNN achieves 98.2% accuracy with an AUC of 1.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]