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Time-Aware lstm Neural networks for Dynamic Personalized Recommendation on Business Intelligence

Tsinghua Science and Technology 2024年第1期29卷 185-196页

作者： Xuan Yang James A.Esquivel Graduate School Angeles University FoundationAngeles City 2009Philippines Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 262700China

Personalized recommendation plays a critical role in providing decision-making support for product and service analysis in the field of business ***,deep neural network-based sequential recommendation models gained considerable ***,existing approaches pay litle attention to users'dynamically evolving interests,which are influenced by product attributes,especially product *** overcome these challenges,we propose a dynamic personalized recommendation model:***,we first embed product information and attribute information into a unified data ***,we exploit long short-term memory(lstm)networks to characterize sequential behavior over multiple time periods and seize evolving interests by hierarchical lstm ***,similarity values between users are measured through pairwise interest features,and personalized recommendation lists are generated.A series of experiments reveal the superiority of the proposed method compared withotheradvanced methods.

关键词： personalized recommendations evolving interests embedding lstm networks

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Nuclear atypia grading in breast cancer histopathological images based on CNN feature extraction and lstm classification

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CAAI Transactions on Intelligence Technology 2021年第4期6卷 426-439页

作者： Sanaz Karimi Jafarbigloo Habibollah Danyali Departtnent of Electrical and Electronics Engineering Shifa2 Univetsity of TechnologyShirazIran Department of Electrical Engtneering-Comtnunication System Shitaz Univetsity of TechnologyShirazIran

Early diagnosis of breast cancer,the most common disease among women around the world,increases the chance of treatment and is highly *** atypia grading in histopathological images plays an important role in the final diagnosis and grading ofbreast *** images by pathologists is a time consuming and subjective ***,the existence of a computer-aided system for nuclear atypia grading is very useful and necessary;In this stud%two automatic systems for grading nuclear atypia in breast cancer histopathological images based on deep learning methods are proposed.A patch-based approach is introduced due to the large size of the histopathological images and restriction of the training *** the proposed system I,the most important patches in the image are detected first and then a three-hidden-layer convolutional neural network(CNN)is designed and trained for feature extraction and to classify the patches *** proposed system II is based on a combination of the CNN for feature extraction and a two-layer Long short-term memoty(lstm)network for *** lstm network is utilised to consider all patches of an image simultaneously for image *** simulation results show the efficiency of the proposed systems for automatic nuclear atypia grading and outperform the current related studies in the literature.

关键词： breast cancer CNN histopathological image lstm networks nuclear atypia

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