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An Advertising Recommendation Algorithm Based on Deep Learning Fusion Model

Chunhui Li, Wei Zhang
2022 Journal of Sensors  
Aiming to realize personalized advertising recommendation, an advertising recommendation algorithm based on deep learning fusion model is proposed.  ...  The bipartite graph model is applied to network representation learning method to decompose user and advertising content into two networks.  ...  Literature [12] reassessed recommendation system based on deep learning, etc.  ... 
doi:10.1155/2022/1632735 fatcat:5otybu5hkbadpfdnzqxgkbt4fi

Review of Deep Learning Algorithms and Architectures

Ajay Shrestha, Ausif Mahmood
2019 IEEE Access  
INDEX TERMS Machine learning algorithm, optimization, artificial intelligence, deep neural network architectures, convolution neural network, backpropagation, supervised and unsupervised learning. 53040  ...  Deep neural network (DNN) uses multiple (deep) layers of units with highly optimized algorithms and architectures.  ...  TABLE 3 . 3 Deep learning algorithm comparison table. Table 4 4 provides a summary of deep learning algorithm shortcomings and resolutions techniques.  ... 
doi:10.1109/access.2019.2912200 fatcat:lgmzqlk2efckzjgmtqiw3o6woa

Augmenting Organizational Decision-Making with Deep Learning Algorithms: Principles, Promises, and Challenges [article]

Yash Raj Shrestha, Vaibhav Krishna, Georg von Krogh
2020 arXiv   pre-print
In particular, recent advances in deep learning (DL) algorithms promise benefits for decision-making within organizations, such as assisting employees with information processing, thereby augment their  ...  Economic challenges Even though the cost for training in traditional ML is rapidly decreasing, the cost of applying cutting-edge DL is rising sharply due to complexity of models used, size of required  ...  outcomes as deep learning-augmented decision-making (DLADM) .  ... 
arXiv:2011.02834v1 fatcat:m3erlvb22bfblehze2edsbshza

Deep Learning Algorithms and Their Applications in the Perception Problem

Redouane Lhiadi
2020 Proceedings of the 3rd International Conference on Networking, Information Systems & Security  
Classification plays a vital role in deep learning algorithms and we found that, though the error backpropagation learning algorithm as provided by supervised learning model is very efficient for a number  ...  The objective of this paper is to summarize a comparative account of unsupervised and supervised deep learning models and their applications.  ...  edge.  ... 
doi:10.1145/3386723.3387866 dblp:conf/niss/Lhiadi20 fatcat:xrfer36oprdlvhtfwtqtrjvehy

News Video Title Extraction Algorithm Based on Deep Learning

Shuyin Li, Yang Liu
2021 IEEE Access  
This article proposes a video target extraction method based on deep learning.  ...  This method deeply combines deep learning models and traditional target extraction methods and frameworks.  ...  Deep learning is one of many machine learning algorithms based on representation learning.  ... 
doi:10.1109/access.2021.3051613 fatcat:t5yytvvkdvd2bmabo6fsf2pr4y

Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression and Challenge [article]

Zhiyong Du, Yansha Deng, Weisi Guo, Arumugam Nallanathan, Qihui Wu
2019 arXiv   pre-print
On the one hand, deep reinforcement learning (DRL) provides a powerful tool for scalable optimization for high dimensional RRM problems in a dynamic environment.  ...  On the algorithm level, compression approaches are introduced for both deep neural networks and the underlying Markov Decision Processes, enabling accurate low-dimensional representations of challenges  ...  Thus, reducing the DRL algorithm size is crucial for cutting down computation complexity and energy consumption.  ... 
arXiv:1910.05054v1 fatcat:6xgjbxuexvfptjtmocsmf5haty

Skin cancer detection by deep learning and sound analysis algorithms: A prospective clinical study of an elementary dermoscope

A. Dascalu, E.O. David
2019 EBioMedicine  
Experimental studies have indicated a potential diagnostic role for deep learning (DL) algorithms in identifying SC at varying sensitivities.  ...  Methods: Dermoscopy images acquired by SMP were processed by a first deep learning algorithm and sonified. Audio output was further analyzed by a different secondary DL.  ...  Data processed by a deep learning algorithm was sonified and output compared between a low-cost and advanced dermoscope.  ... 
doi:10.1016/j.ebiom.2019.04.055 fatcat:yfjz65twyzfzhjsemghzk4eaii

Evaluation of Interpretability for Deep Learning algorithms in EEG Emotion Recognition: A case study in Autism [article]

Juan Manuel Mayor-Torres, Sara Medina-DeVilliers, Tessa Clarkson, Matthew D. Lerner, Giuseppe Riccardi
2022 arXiv   pre-print
There has been an increase in the application of Deep Learning in clinical trials to predict early diagnosis of neuro-developmental disorders, such as Autism Spectrum Disorder (ASD).  ...  Moreover, in ASD research the inclusion of deep classifiers that use neural measures to predict viewed facial emotions is relatively unexplored.  ...  Code for the CNN can be found in  ... 
arXiv:2111.13208v3 fatcat:22dsxkde25fhhggoczbbh6wude

Usability of Computer-Aided Translation Software Based on Deep Learning Algorithms

Junjun Liu, Qiangyi Li
2022 Advances in Multimedia  
This article references the practicality of computer translation software as the benchmark and uses computer-aided translation software based on deep learning as the core.  ...  The experiments show that the effect of this study on computer-aided software with the help of deep learning knowledge can produce good results, and the robustness and scalability of the software have  ...  With deep learning, all features can be learned at once.  ... 
doi:10.1155/2022/9047053 fatcat:7xb3b7ckfzgxzmbqsw5rzps5oi

Deep Reinforcement Learning [article]

Yuxi Li
2018 arXiv   pre-print
We start with background of artificial intelligence, machine learning, deep learning, and reinforcement learning (RL), with resources.  ...  We discuss deep reinforcement learning in an overview style. We draw a big picture, filled with details.  ...  PSRO/DCH generalizes previous algorithms, like independent RL, iterative best response, double oracle, and fictitious play.  ... 
arXiv:1810.06339v1 fatcat:kp7atz5pdbeqta352e6b3nmuhy

Privacy in Deep Learning: A Survey [article]

Fatemehsadat Mireshghallah, Mohammadkazem Taram, Praneeth Vepakomma, Abhishek Singh, Ramesh Raskar, Hadi Esmaeilzadeh
2020 arXiv   pre-print
The ever-growing advances of deep learning in many areas including vision, recommendation systems, natural language processing, etc., have led to the adoption of Deep Neural Networks (DNNs) in production  ...  In this survey, we review the privacy concerns brought by deep learning, and the mitigating techniques introduced to tackle these issues.  ...  Differential Privacy This section briefly discusses methods for modifying deep learning algorithms to satisfy differential privacy. Figure 4 shows an overview of a deep learning framework.  ... 
arXiv:2004.12254v5 fatcat:4w63htwzafhxxel2oq3z3pwwya

Deep learning algorithm predicts diabetic retinopathy progression in individual patients

Filippo Arcadu, Fethallah Benmansour, Andreas Maunz, Jeff Willis, Zdenka Haskova, Marco Prunotto
2019 npj Digital Medicine  
Here, we describe an algorithm to predict DR progression by means of deep learning (DL), using as input color fundus photographs (CFPs) acquired at a single visit from a patient with DR.  ...  Upon further development on larger and more diverse datasets, such an algorithm could enable early diagnosis and referral to a retina specialist for more frequent monitoring and even consideration of early  ...  edge research to help patients with high unmet need.  ... 
doi:10.1038/s41746-019-0172-3 pmid:31552296 pmcid:PMC6754451 fatcat:ptaki76lufcsdjnlfwqtwkde5a

Service Recommendations with Deep Learning: A Study on Neural Collaborative Engines

Pasquale De Rosa, Michel Deriaz, Marco De Marco, Luigi Laura
2022 Pacific Asia Journal of the Association for Information Systems  
The present paper aims to investigate the adoption of Neural Networks for recommendation systems and to propose Deep Learning architectures as advanced frameworks for designing Collaborative Filtering  ...  the suitability of those models for designing cutting-edge recommenders capable of overcoming stable traditional benchmarks like the Singular Value Decomposition and the k-Nearest Neighbors algorithms  ...  Neural Networks and Deep Learning In the previous section, we affirmed that model-based techniques constitute a significant advance in the development of cutting-edge Collaborative engines; more specifically  ... 
dblp:journals/pajais/RosaDML22 fatcat:dchbq6srfrh37ctqfa7vfp7pk4

Developing Cybersecurity Systems Based on Machine Learning and Deep Learning Algorithms for Protecting Food Security Systems: Industrial Control Systems

Hasan Alkahtani, Theyazn H. H. Aldhyani
2022 Electronics  
On the one hand, machine learning includes logistic regression, k-nearest neighbors (KNN), linear discriminant analysis (LDA), and decision tree (DT) algorithms, deep learning long short-term memory (LSTM  ...  The sensitivity analysis results showed that the KNN and DT algorithms achieved R2 = 100% in both stages.  ...  The majority of current research has focused on developing new approaches that have taken advantage of cutting-edge technologies, such as big data and machine learning/deep learning.  ... 
doi:10.3390/electronics11111717 fatcat:sy7s5jncgjdszdwrqsqfxhzobu

Deep Learning Meets SAR [article]

Xiao Xiang Zhu, Sina Montazeri, Mohsin Ali, Yuansheng Hua, Yuanyuan Wang, Lichao Mou, Yilei Shi, Feng Xu, Richard Bamler
2021 arXiv   pre-print
of deep learning applied to SAR in depth, summarize available benchmarks, and recommend some important future research directions.  ...  Deep learning in remote sensing has become an international hype, but it is mostly limited to the evaluation of optical data.  ...  The dataset therefore can be considered a large-scale data fusion and classification benchmark dataset for cutting-edge machine learning methodological developments, such as automatic topology learning  ... 
arXiv:2006.10027v2 fatcat:s3tiroz4qve6nbhavtz77fbis4
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