[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-606483-105":59,"doc-detail-606483-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","optimal-feature-selection-and-hybrid-deep-learning-for-direct-marketing-campaigns-in-banking-applications","Optimal feature selection and hybrid deep learning for direct marketing campaigns in banking applications","","Market-based companies aim to maximize returns from targeted direct marketing by accurately predicting response rates and characterizing prospects. This paper proposes a new direct marketing campaign model for banking applications using a hybrid deep learning architecture. The workflow includes data acquisition, optimal feature selection via the SA-SLnO meta-heuristic to reduce feature dimensionality, and prediction using an integrated Deep Belief Network and Recurrent Neural Network.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/optimal-feature-selection-and-hybrid-deep-learning-for-direct-marketing-campaigns-in-banking-applications/606483/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/optimal-feature-selection-and-hybrid-deep-learning-for-direct-marketing-campaigns-in-banking-applications/606483.png","ImageObject",300,407,{"name":92,"@type":93},"Ivy","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-28","2026-10-16",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main goal of the proposed direct marketing approach in banking applications?","Question",{"text":112,"@type":113},"To maximize the return of targeted direct marketing campaigns by predicting response rates and supporting decisions about customer actions such as banking deposits.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the paper perform feature selection?",{"text":117,"@type":113},"It reduces the dimensionality of the feature vector using an optimal feature selection method based on a variant of a meta-heuristic algorithm called Self Adaptive-Sea Lion Optimization (SA-SLnO).",{"name":119,"@type":110,"acceptedAnswer":120},"What hybrid deep learning components are used for prediction?",{"text":121,"@type":113},"The model integrates a Deep Belief Network (DBN) and a Recurrent Neural Network (RNN), with hidden neurons optimized using the same SA-SLnO approach.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},606483,1793005902,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":81,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},549758252649,"https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819","Evolutionary Intelligence  \n[https://doi.org/10.1007/s12065-021-00604-y](https://doi.org/10.1007/s12065-021-00604-y)  \nOptimal feature selection and hybrid deep learning for direct marketing campaigns in banking applications  \nN Srikanth Reddy1  \nReceived: 23 June 2020 / Revised: 17 March 2021 / Accepted: 7 April 2021  \n© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021  \nAbstract  \nAs stated by a mass marketing technique, the objective of market-based companies is to maximize the product of targeted direct marketing campaigns, other than reaching prospects and customers differently. The maximum return of the direct marketing campaigns is based on an appropriate prediction of the response rate and an elaborated description of the prospects. This paper plans to develop a new direct marketing campaign model in banking applications using a hybrid deep learning architecture. The proposed model consists of several steps like (a) Data acquisition,(b) Optimal feature selection, and (c) prediction. Initially, the direct marketing campaign data corresponding to banking applications are collected from other Google sites like Portuguese banking institutions. With the collected information, optimal feature selection is performed in order to reduce the dimension of the attribute or feature vector, thus can reduce the complexity of the prediction algorithm. Here, the optimal feature selection is performed by a new variant of a meta-heuristic algorithm termed as Self Adaptive-Sea Lion Optimization (SA-SLnO) Algorithm. Further, the optimally collected features are subjected to deep hybrid learning with the integration of Deep Belief Network (DBN) and Recurrent Neural Network (RNN). As an improvement to the deep learning model, the hidden neurons of both DBN and RNN are optimized by the same SA-SLnO. The outcome of the hybrid classifier is the decision of the customer on banking deposits. Finally, the performance of the proposed model is certified by comparing over the conventional methods through the evaluation of relevant performance measures.  \nKeywords Direct marketing campaign · Hybrid deep learning · Portuguese banking institution · Optimal feature selection · Prediction · Self adaptive sea lion optimization algorithm · Deep belief network · Recurrent neural network · Banking application  \nNomenclature  \nSA-SLnO  \nDBN  \nRNN  \nCRM  \nPSO  \nANNs  \nMLP  \nSOM  \nAUROC  \nGVNS  \nGWO  \nSelf adaptive-sea lion optimization algorithm Deep belief network  \nRecurrent neural network Customer relationship management Particle swarm optimization Artificial neural networks  \nMulti-layer perceptron Self-organizing map  \nArea under the receiver operating characteristic curve  \nGeneral variable neighbourhood search Grey wolf optimization  \nGGVNS  \nTODMC  \nGVNS  \nSVM  \nGA  \nRBM  \nLSTM  \nNN  \nGRU  \nWOA  \nKNN  \naMTL  \nGreedy generalized variable neighbourhood search  \nTargeted offers problem in direct marketing campaigns  \nGeneral variable neighbourhood search Support vector machine  \nGenetic algorithm Restricted boltzmann machine Long short-term memory Neural network  \nGate recurrent unit  \nWhale optimization algorithm K-nearest neighbour Adaptive multi task-learning  \n* N Srikanth Reddy [srikanthreddyn@presidencyuniversity.in](srikanthreddyn@presidencyuniversity.in)  \n1 School of Management, Presidency University, Itigalapura, Bangalore 560064, India  \n1 Introduction  \nMarket-based companies seize a direct marketing technique inan increasing manner by concentrating on chances, which are persuaded by specific offers, as against an existing huge marketing technique, in which the changes occur indifferently, and the promotional activities are labeled to customers [1] . Direct marketing, which is described by the Direct Marketing Association as the ‘collective system of marketing that employs single or more advertising media to result in a quantifiable response and transaction at any position’, has achieved power","cbCainFP4ASohiZh","https://ap.wps.com/l/cbCainFP4ASohiZh","pdf",8062405,22,"English","# Abstract\n# Keywords\n# Nomenclature\n# Introduction","[{\"question\":\"What is the main goal of the proposed direct marketing approach in banking applications?\",\"answer\":\"To maximize the return of targeted direct marketing campaigns by predicting response rates and supporting decisions about customer actions such as banking deposits.\"},{\"question\":\"How does the paper perform feature selection?\",\"answer\":\"It reduces the dimensionality of the feature vector using an optimal feature selection method based on a variant of a meta-heuristic algorithm called Self Adaptive-Sea Lion Optimization (SA-SLnO).\"},{\"question\":\"What hybrid deep learning components are used for prediction?\",\"answer\":\"The model integrates a Deep Belief Network (DBN) and a Recurrent Neural Network (RNN), with hidden neurons optimized using the same SA-SLnO approach.\"}]","Optimal feature selection and hybrid deep learning for direct marketing campaigns in banking applications | PDF",1792184529,55]