{"id":200,"date":"2020-03-30T15:20:23","date_gmt":"2020-03-30T13:20:23","guid":{"rendered":"https:\/\/machinelearning.humanativaspa.it\/en\/?p=200"},"modified":"2023-03-03T15:02:39","modified_gmt":"2023-03-03T14:02:39","slug":"intelligent-passenger-profiling-and-survey-system-isip","status":"publish","type":"post","link":"https:\/\/machinelearning.humanativaspa.it\/en\/intelligent-passenger-profiling-and-survey-system-isip\/","title":{"rendered":"Intelligent passenger profiling and survey system (ISIP)"},"content":{"rendered":"<p><b><span data-contrast=\"auto\">OBJECTIVE<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">The aim of the project is to create an intelligent pre-screening system based on BIG DATA architectures for the control of air transport passengers with the aim of increasing anti-terrorism security levels.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">The system will be based on what is known as a <\/span><b><span data-contrast=\"auto\">Passenger Name Record<\/span><\/b><span data-contrast=\"auto\">, often abbreviated as <\/span><b><span data-contrast=\"auto\">PNR<\/span><\/b><span data-contrast=\"auto\">.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">PNRs are compiled by travel agencies, air carriers and tour operators, contain information such as medical conditions and disabilities, meal preferences, means of payment, but also work address, email, IP address if you book online and personal information of emergency contacts.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">This information is stored in a BIG DATA (<\/span><b><span data-contrast=\"auto\">Hadoop<\/span><\/b><span data-contrast=\"auto\">) architecture that is able to analyze it through Machine Learning algorithms using behavioral models, appropriately engineered, which analyze PNR with data archives, black-lists made available by the government agencies and Open Source Intelligence (OSINT) available on the network (websites, blogs, social networks, media, search engines, etc.).<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">These processing made reliable and efficient thanks to the distributed computing architecture characteristic of the Hadoop architecture, allow to assign a &#8220;risk score&#8221; of terrorism to the person in <\/span><b><span data-contrast=\"auto\">real time<\/span><\/b><span data-contrast=\"auto\">.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">This classification must then be communicated to the airlines to subject the suspect to extended baggage and\/or personal checks, and to contact law enforcement if necessary.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">It will also allow to have a series of investigation tools with high levels of analysis, since data analytics tools will be created that can identify relationships through the extraction of useful information made available, such as places, organizations and people.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">In a conventional passenger screening workflow, government agencies have little time to react to incoming data from the moment a passenger manifest is generated when the traveler reaches a border checkpoint.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">This platform makes it possible to make this process more efficient, since you can start as soon as the passenger buys the ticket, and also allows:<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<ul>\n<li><span data-contrast=\"auto\"> Imports of all advanced passenger data (API).<\/span><\/li>\n<li><span data-contrast=\"auto\"> Automatically controls all passengers.<\/span><\/li>\n<li><span data-contrast=\"auto\"> Manually check less than 1% of travelers.<\/span><\/li>\n<li><span data-contrast=\"auto\"> Catch terrorists, smuggling, missing persons, excess visas, etc.<\/span><\/li>\n<li><span data-contrast=\"auto\"> Promote legitimate travel.<\/span><\/li>\n<li><span data-contrast=\"auto\"> Speed up the screening process.<\/span><\/li>\n<li><span data-contrast=\"auto\"> Makes global travel safer for everyone.<\/span><\/li>\n<\/ul>\n<p><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559685&quot;:720,&quot;335559739&quot;:0,&quot;335559740&quot;:240}\"> <img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-204 size-full\" src=\"https:\/\/machinelearning.humanativaspa.it\/en\/wp-content\/uploads\/sites\/4\/2023\/03\/1-1.jpg\" alt=\"\" width=\"1024\" height=\"699\" srcset=\"https:\/\/machinelearning.humanativaspa.it\/en\/wp-content\/uploads\/sites\/4\/2023\/03\/1-1.jpg 1024w, https:\/\/machinelearning.humanativaspa.it\/en\/wp-content\/uploads\/sites\/4\/2023\/03\/1-1-300x205.jpg 300w, https:\/\/machinelearning.humanativaspa.it\/en\/wp-content\/uploads\/sites\/4\/2023\/03\/1-1-768x524.jpg 768w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/span><\/p>\n<p><b><span data-contrast=\"auto\">\u202fARCHITECTURE<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:330,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">The logical platform architecture should include the following architectural layers:<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:330,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<ul>\n<li><b><span data-contrast=\"auto\"> Distributed storage system, based on Hadoop, relational DB and Nosql<\/span><\/b><\/li>\n<li><b><span data-contrast=\"auto\"> Machine Learning algorithms<\/span><\/b><\/li>\n<li><b><span data-contrast=\"auto\"> Streaming Processing<\/span><\/b><\/li>\n<li><b><span data-contrast=\"auto\"> Use via Web platform \u2013 (Angular, java)<\/span><\/b><\/li>\n<\/ul>\n<p><span data-contrast=\"auto\">ISIP analyses data provided by airline departure control systems (APIs) and reservation systems (PNR). Respectively, these messages are compliant with WCO UN \/ EDIFACT PAXLST and PNRGOV formats.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:330,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<ul>\n<li><b><span data-contrast=\"auto\"> UN \/ EDIFACT PAXLST 02B and later versions<\/span><\/b><\/li>\n<li><b><span data-contrast=\"auto\"> PNRGOV 11.1 and later versions<\/span><\/b><\/li>\n<\/ul>\n<p><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">ISPI PLATFORM<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">the Intelligent system for passenger profile and screening investigation.<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">(ISIP) is a web application to improve border security.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">It allows government agencies to automate the identification of high-risk air travellers prior to their planned travel.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">The <\/span><b><span data-contrast=\"auto\">United Nations<\/span><\/b><span data-contrast=\"auto\"> has called on members to use Advance Passenger Information (API) and <\/span><b><span data-contrast=\"auto\">Passenger Name Record<\/span><\/b><span data-contrast=\"auto\"> (PNR) data to prevent the movement of high-risk travellers.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">The World Customs Organization (WCO) has partnered with U.S. Customs and Border Protection (US-CBP) because of the shared belief that every border security agency should have access to the latest tools. US-CBP has made this repository available to the WCO to facilitate deployment for its member states.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:330,&quot;335559740&quot;:240}\"> <img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-205 size-full\" src=\"https:\/\/machinelearning.humanativaspa.it\/en\/wp-content\/uploads\/sites\/4\/2023\/03\/2.jpeg\" alt=\"\" width=\"631\" height=\"537\" srcset=\"https:\/\/machinelearning.humanativaspa.it\/en\/wp-content\/uploads\/sites\/4\/2023\/03\/2.jpeg 631w, https:\/\/machinelearning.humanativaspa.it\/en\/wp-content\/uploads\/sites\/4\/2023\/03\/2-300x255.jpeg 300w\" sizes=\"auto, (max-width: 631px) 100vw, 631px\" \/><\/span><\/p>\n<p><span data-contrast=\"auto\">\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:330,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">BACK-END PLATFORM<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:330,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">The Back-end platform consists of the following elements:<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:330,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">1) Hadoop infrastructure (Cloudera) for machine learning algorithms:<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:330,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<ol>\n<li><span data-contrast=\"auto\">a) <\/span><b><span data-contrast=\"auto\">Detection of Unusual Patterns from PAXLIST, PNRGOV and APIS<\/span><\/b><\/li>\n<li><span data-contrast=\"auto\">b) Outlier identification through Neural Networks and LOF<\/span><\/li>\n<\/ol>\n<p><span data-contrast=\"auto\">2) Data persistence for front-end platform management<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:330,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<ol>\n<li><span data-contrast=\"auto\"> a) based on <\/span><b><span data-contrast=\"auto\">Maria DB<\/span><\/b><\/li>\n<li><span data-contrast=\"auto\"> b) <\/span><b><span data-contrast=\"auto\">Hadoop (Cloudera)<\/span><\/b><span data-contrast=\"auto\"> for machine learning training and execution<\/span><\/li>\n<li><span data-contrast=\"auto\"> c) <\/span><b><span data-contrast=\"auto\">Neo4j<\/span><\/b><span data-contrast=\"auto\"> for analysis and Hit identification through Graph rules<\/span><\/li>\n<\/ol>\n<p><span data-contrast=\"auto\">3) Processing interfaces<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:330,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">\u00a0\u00a0\u00a0 (a) <\/span><b><span data-contrast=\"auto\">PAXLIST, PNRGOV and APIS <\/span><\/b><span data-contrast=\"auto\">parser services<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:330,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<ol>\n<li><span data-contrast=\"auto\"> b) <\/span><b><span data-contrast=\"auto\">QM<\/span><\/b><span data-contrast=\"auto\"> services for real-time data analysis and loading<\/span><\/li>\n<li><span data-contrast=\"auto\"> c) <\/span><b><span data-contrast=\"auto\">ETL (Pentaho) procedures<\/span><\/b><span data-contrast=\"auto\"> for loading data into Hadoop and Neo4j<\/span><\/li>\n<li><span data-contrast=\"auto\"> d) HIT processing services (for Machine learning, for Graph, for Match Jaro-Winkler, for rules and for Watchlist<\/span><\/li>\n<\/ol>\n<p><span data-contrast=\"auto\">4) Technology: Java 8, Apache Tomcat 8, MariaDB 10.0 Stable, Apache ActiveMQ and Angular<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:330,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">BACK-END ELABORATION PROCESS<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:330,&quot;335559740&quot;:240}\"> <img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-206 size-full\" src=\"https:\/\/machinelearning.humanativaspa.it\/en\/wp-content\/uploads\/sites\/4\/2023\/03\/3.jpeg\" alt=\"\" width=\"628\" height=\"472\" srcset=\"https:\/\/machinelearning.humanativaspa.it\/en\/wp-content\/uploads\/sites\/4\/2023\/03\/3.jpeg 628w, https:\/\/machinelearning.humanativaspa.it\/en\/wp-content\/uploads\/sites\/4\/2023\/03\/3-300x225.jpeg 300w\" sizes=\"auto, (max-width: 628px) 100vw, 628px\" \/><\/span><\/p>\n<p><span data-contrast=\"auto\">The back-end processes:<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:330,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">1) <\/span><b><span data-contrast=\"auto\">Parser Service<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:330,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">\u00a0This Background process listens in a folder on the server, where it is tasked with processing PAXLIST, PNRGOV and APIS files and feeding a MQ queue.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:330,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">2) <\/span><b><span data-contrast=\"auto\">Processing scheduling service<\/span><\/b><span data-contrast=\"auto\">: from the MQ queue there are the following trending processes<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:330,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<ol>\n<li><span data-contrast=\"auto\"> a) Process of persistence towards the DB<\/span><\/li>\n<li><span data-contrast=\"auto\"> b) Watchlist HIT Process<\/span><\/li>\n<li><span data-contrast=\"auto\"> c) HIT process for Match Jaro-Winkler<\/span><\/li>\n<li><span data-contrast=\"auto\"> d) HIT Process by Rules<\/span><\/li>\n<li><span data-contrast=\"auto\"> e) HIT Process for Graph<\/span><\/li>\n<li><span data-contrast=\"auto\"> f) HIT process for Machine learning (neural networks) \u2013 Outlier identification through Neural Networks and LOF<\/span><\/li>\n<\/ol>\n<p><span data-contrast=\"auto\">3) <\/span><b><span data-contrast=\"auto\">ETL processes (pentaho)<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:330,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<ol>\n<li><span data-contrast=\"auto\"> a) Loading data to Neo4j<\/span><\/li>\n<li><span data-contrast=\"auto\"> b) Uploading data to Hadoop<\/span><\/li>\n<\/ol>\n<p><span data-contrast=\"auto\">4) <\/span><b><span data-contrast=\"auto\">REST services to support the Front-End<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:330,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">BACK-END PLATFORM<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:330,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:330,&quot;335559740&quot;:240}\"> <img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-207 size-full\" src=\"https:\/\/machinelearning.humanativaspa.it\/en\/wp-content\/uploads\/sites\/4\/2023\/03\/4-1.jpg\" alt=\"\" width=\"1024\" height=\"559\" srcset=\"https:\/\/machinelearning.humanativaspa.it\/en\/wp-content\/uploads\/sites\/4\/2023\/03\/4-1.jpg 1024w, https:\/\/machinelearning.humanativaspa.it\/en\/wp-content\/uploads\/sites\/4\/2023\/03\/4-1-300x164.jpg 300w, https:\/\/machinelearning.humanativaspa.it\/en\/wp-content\/uploads\/sites\/4\/2023\/03\/4-1-768x419.jpg 768w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/span><\/p>\n<p>&nbsp;<\/p>\n<p><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:330,&quot;335559740&quot;:240}\"> <img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-208 size-full\" src=\"https:\/\/machinelearning.humanativaspa.it\/en\/wp-content\/uploads\/sites\/4\/2023\/03\/5-1.jpg\" alt=\"\" width=\"1024\" height=\"548\" srcset=\"https:\/\/machinelearning.humanativaspa.it\/en\/wp-content\/uploads\/sites\/4\/2023\/03\/5-1.jpg 1024w, https:\/\/machinelearning.humanativaspa.it\/en\/wp-content\/uploads\/sites\/4\/2023\/03\/5-1-300x161.jpg 300w, https:\/\/machinelearning.humanativaspa.it\/en\/wp-content\/uploads\/sites\/4\/2023\/03\/5-1-768x411.jpg 768w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-209 size-full\" src=\"https:\/\/machinelearning.humanativaspa.it\/en\/wp-content\/uploads\/sites\/4\/2023\/03\/6-1.jpg\" alt=\"\" width=\"1024\" height=\"559\" srcset=\"https:\/\/machinelearning.humanativaspa.it\/en\/wp-content\/uploads\/sites\/4\/2023\/03\/6-1.jpg 1024w, https:\/\/machinelearning.humanativaspa.it\/en\/wp-content\/uploads\/sites\/4\/2023\/03\/6-1-300x164.jpg 300w, https:\/\/machinelearning.humanativaspa.it\/en\/wp-content\/uploads\/sites\/4\/2023\/03\/6-1-768x419.jpg 768w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/p>\n<p>&nbsp;<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-210 size-full\" src=\"https:\/\/machinelearning.humanativaspa.it\/en\/wp-content\/uploads\/sites\/4\/2023\/03\/7-1.jpg\" alt=\"\" width=\"1024\" height=\"556\" srcset=\"https:\/\/machinelearning.humanativaspa.it\/en\/wp-content\/uploads\/sites\/4\/2023\/03\/7-1.jpg 1024w, https:\/\/machinelearning.humanativaspa.it\/en\/wp-content\/uploads\/sites\/4\/2023\/03\/7-1-300x163.jpg 300w, https:\/\/machinelearning.humanativaspa.it\/en\/wp-content\/uploads\/sites\/4\/2023\/03\/7-1-768x417.jpg 768w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/p>\n<p>&nbsp;<\/p>\n<p><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:330,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:330,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>OBJECTIVE\u00a0 The aim of the project is to create an intelligent pre-screening system based on BIG DATA architectures [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":201,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[56],"tags":[],"class_list":["post-200","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articoli"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Intelligent passenger profiling and survey system (ISIP) - HN Machine Learning en<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/machinelearning.humanativaspa.it\/en\/intelligent-passenger-profiling-and-survey-system-isip\/\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:title\" content=\"Intelligent passenger profiling and 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