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Social Media User Behavior Prediction and Personalized Digital Marketing Model Based on Deep Learning

Social Media User Behavior Prediction and Personalized Digit...

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Computers, Information processing and Advanced Education (CIPAE), 2020 international conference on

作者： Tong Zhi Guangzhou City University of Technology Guangzhou China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

This study aims to explore the deep learning technology. By combining the hybrid model of Gradient Boosting Decision Tree (GBDT) and Long Short-Term Memory Network (LSTM), the GBDT model is good at dealing with the nonlinear relationship and high-dimensional features of social media data, and automatically filters out the features most relevant to user behavior, reduces the data dimension, and retains key information. The input selection of the LSTM model was guided to ensure that the model focused on the most relevant behavior patterns. Then the LSTM model was applied to process the time series data of user behavior to capture the dynamic changes and long-term dependence of user behavior. Therefore, a cascade architecture is adopted to generate a concise and informative feature set by using GBDT to preprocess the raw data. These features are then fed into the LSTM model for building a time series prediction model. This architecture generates highly personalized marketing recommendations based on a deep understanding of user behavior so as to improve the marketing effect and user satisfaction.

关键词： Deep learning Analytical models Social networking (online) Architecture Time series analysis Predictive models data models Decision trees Long short term memory Context modeling

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Disease data Modeling by Using Machine Learning Approach

Disease Data Modeling by Using Machine Learning Approach

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international conference on Information Technology, Computer and Electrical Engineering (ICITACEE)

作者： Gunawan Wang Rakha Gusti Wardhana Farida Sibuea Anderes Gui Information Systems Management Department BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Head of Data Analytics department Data and Information Technology Centre (Pusdatin) Ministry of Health Jakarta Indonesia School of Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350389302

ISBN: (纸本)9798350389319

Since Covid outbreak in 2020, Indonesian Ministry of Health (MoH) has intensively embraced digital transformation initiatives in healthcare management and decision-making process. The latest healthcare digital transformation roadmap 2024, has emphasized the use of AI technology in data analytics and support policy making. The article examines the use of common Machine Learning (AI) algorithms, such as: Logistic Regression, Random Forest, Support Vector Machine (SVM), Decision Tree, and XGBoost. The article applies these algorithms to analyse the common diseases faced by Indonesian such as Measles, Malaria, Leprosy, and Diphtheria. Those diseases are classical diseases, however, still infecting many people in large areas. The article takes a case study of national health profile within period 2017–2022, to study the data model of those diseases. The outcome of the article is expected to be used for further policy making.

关键词： Support vector machines Logistic regression Machine learning algorithms Malaria Measles Medical services Predictive models Prediction algorithms Decision trees Random forests

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Trimmed data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles 15th

Trimmed Data-Driven Evolutionary Optimization Using Selectiv...

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15th international conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2020

作者： Shan, Yawen Hou, Yongjin Wang, Mengzhen Xu, Fei Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811613531

data-driven evolutionary algorithms (DDEAs) have attracted much attention in recent years, due to their effectiveness and advantages in solving expensive and complex optimization problems. In an offline data-driven evolutionary algorithm, no additional information is available to update surrogate models, so we need to ensure the quality of the surrogate models built before optimization. Based on the offline data-driven evolutionary algorithm using selective surrogate ensembles (DDEA-SE), we propose a trimmed bagging based DDEA-SE (TDDEA-SE) to construct a more accurate model pool. To this end, we use trimmed bagging to prune models with large errors calculated by out-of-bag samples, thus improving the accuracy of the selective surrogate ensembles and promoting the optimization. The experimental results on benchmark problems show that the proposed algorithm can strike a balance between diversity and accuracy of models, and its competitiveness on solving offline data-driven optimization problems. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Evolutionary algorithms

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Mining the Graph Representation of Traffic Speed data for Graph Convolutional Neural Network

Mining the Graph Representation of Traffic Speed Data for Gr...

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IEEE Intelligent Transportation Systems conference (ITSC)

作者： Mao, Jiannan Huang, Hao Chen, Yuting Lu, Weike Chen, Guoqiang Liu, Lan Southwest Jiaotong Univ Sch Transportat & Logist Chengdu 610031 Sichuan Peoples R China Alabama Transportat Inst 248 Kirkbride Lane Tuscaloosa AL 35487 USA

ISBN: (纸本)9781728191423

Deep learning algorithms are considered as the best-fit methods to deal with spatial-temporal attributes of short-term traffic predictions in recent years. Further, the graph-based Graph Convolutional Network (GCN) models are widely used to handle the spatial dependence of roads in urban networks. This paper aims to explore the spatial graph representation of urban networks for GCN models. Specifically, a data-driven spatial graph representation scheme is established to measure the complex non-linear relationships among roads, together with the local and non-local impacts of urban traffics. This spatial graph representation is then combined with Sequence to Sequence structure to present a multi-input and multi-output network-wide traffic prediction model (SGDE-S2S model). A sensitive test is carried out to select the optimal threshold value of the most relevant roads to every target road. Then the SGDE-S2S model and some other baseline models are tested on real-world traffic speed data of Chengdu, China. The experiment results confirm that the SGDE-S2S model can well capture the intrinsic relationships of roads without the need for topological adjacent information and performs the best in all multi-step predictions.

关键词： Deep learning Analytical models Roads Urban areas Predictive models Prediction algorithms data models

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Inference of Missing Attributes in Multilevel Complex Network

Inference of Missing Attributes in Multilevel Complex Networ...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Nikhil Chowdary Nettem Aashish Gopal Nukala Siruguppa Vishnu Sai Kaushal Deepthi L. R Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amrita School of Computing Amritapuri India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Social networks exhibit a hierarchical structure where users are grouped at multiple levels based on communities, interests, or behaviors. The attributes associated with users provide additional information for various social network analysis tasks. However, the attributes may be missing for multiple reasons: incomplete data collection, or privacy concerns. Inferring missing attribute values within a hierarchical social network poses a challenge for accurate analysis and modeling. The state-of-the-art algorithm infers the attributes in a single level and less focus is given to infer the attributes at multiple levels. In this paper, the missing attributes are inferred by employing hierarchical clustering followed by CNN and Hierarchical Graph Neural Network. Our method's accuracy of attribute inference is compared with traditional machine learning models, including Logistic Regression, Random Forest, XGBoost, K-Nearest Neighbors (KNN), and Gaussian Naive Bayes. The results demonstrate that the hierarchical clustering with the CNN approach consistently outperforms traditional machine learning models in terms of attribute inference accuracy.

关键词： Analytical models Accuracy Social networking (online) Computational modeling Scalability Clustering algorithms Complex networks Nearest neighbor methods data models Inference algorithms

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Generating Versatile Training Samples for UAV Trajectory Prediction 1

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2nd international conference on Robotics, Computer Vision and Intelligent Systems (ROBOVIS)

作者： Becker, Stefan Hug, Ronny Huebner, Wolfgang Arens, Michael Morris, Brendan T. Fraunhofer IOSB Fraunhofer Ctr Machine Learning Ettlingen Germany Univ Nevada Las Vegas NV 89154 USA

ISBN: (数字)9783031196508

ISBN: (纸本)9783031196492;9783031196508

Following the success of deep learning-based models in various sequence processing tasks, these models are increasingly utilized in object tracking applications for motion prediction as a replacement of traditional approaches. On the one hand, these models can capture complex object dynamics while requiring less modeling, but on the other hand, they depend on a large amount of training data for parameter tuning. Towards this end, an approach for generating synthetic trajectory data of unmanned-aerial-vehicles (UAVs) in image space is presented in this paper. Since UAVs are dynamical systems, they are bound to strict physical constraints and inputs for controlling. Thus, they cannot move along arbitrary trajectories. To generate executable trajectories, it is possible to apply solutions from trajectory planning for our desired purpose of generating realistic UAV trajectory data. Accordingly, with the prerequisite that UAV trajectories fulfill a smoothness criterion corresponding to a minimal change of higher-order motion, planning methods enabling aggressive quadrotor flights are applied to generate optimal trajectories through a sequence of 3D waypoints. By projecting these maneuver trajectories to image space, a versatile trajectory data set is realized. To demonstrate the applicability of the synthetic trajectory data, we show that deep learning-based prediction models solely trained on the synthetically generated data can outperform traditional reference models on a real-world UAV tracking dataset. The evaluation is done on the publicly available ANTI-UAV dataset.

关键词： Unmanned-aerial-vehicle (UAV) Synthetic data generation Trajectory prediction Deep-learning Sequence models Training data Quadrotors

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Construction of Manufacturing data Acquisition System Based on Edge Computing

Construction of Manufacturing Data Acquisition System Based ...

引用

Computers, Information processing and Advanced Education (CIPAE), 2020 international conference on

作者： Limin Xiao Gaozhi Jia Xiumei Lin School of Intelligent Manufacturing and Equipment Shenzhen Institute of Information Technology Shenzhen China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

In recent years, industrial interconnection industrial has entered the era of many rules, many types of equipment, and many protocols, for the problem of real-time as well as diversity of data in the intelligent manufacturing system, this paper combines the edge computing to construct the production and manufacturing data acquisition system. By offloading part of the computational storage to the load side, the pressure on the service side is reduced, and the data transmission delay and reaction time are lowered. At the same time, through the deployment of load balancing in the edge end to achieve optimal collection, data extraction, data analysis and prediction, the data age calculation, the transmission delay and computation delay to achieve the optimal, to build the cloud edge-end collaboration. Combining the open-source framework of the Internet of Things and the edge gateway, and the edge-end optimization configuration of the terminal production and manufacturing data computation, storage, and network request, and uploading the processed data to the cloud, the cloud collaboratively senses the data update, and makes a reasonable decision on the lower level production, so as to construct the optimal system.

关键词： Cloud computing conferences Computational modeling data acquisition Production Predictive models data models Manufacturing Delays Edge computing

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Aspect Term Extraction via Contrastive Learning over Self-augmented data

Aspect Term Extraction via Contrastive Learning over Self-au...

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IEEE international conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / international Joint conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Pan, Yuchen Hong, Yu Xu, Qingting Chen, Jiaxiang Yao, Jianmin Soochow Univ Sch Comp Sci & Technol Suzhou Peoples R China

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Aspect Term Extraction (ATE) is a natural language processing task, which identifies the languages describing product attributes. Such languages (words) are referred to aspect terms in this field. The current neural ATE models suffer from sparsity of available training data. As a result, they fail to be robust in real applications due to overfitting. More seriously, the distinguishable underlying features cannot be learned sufficiently by neural networks, which causes high misjudgement rates. Deliberate data expansion by human annotation undoubtedly helps to alleviate the problem. However, it is time-consuming. In order to overcome the bottleneck, we utilize the Regularized Dropout (R-Drop) approach to implement self data augmentation, creating variant distributed representations for learning in the real-time computation process of neural networks. More importantly, we propose to conduct contrastive learning over the self-augmented data, which sufficiently leverages the variant distributed representations to explore the distinguishable features. We experiment on four widely-used benchmark datasets (R14-16 and L14) in the shared tasks of Semantic Evaluation (SemEval). Experimental results show that contrastive learning over self-augmented data yields significant performance gains, where the improvement is up to 1.9% F1-score at best. In addition, it is demonstrated in the experiments that our method outperforms the state of the art on two test sets (R14 and R16), and meanwhile achieves competitive performance on the rest test sets.

关键词： Aspect term extraction Contrastive learning Regularized dropout

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data-driven Energy Evaluation and Optimization Method for Industrial Robots 8

Data-driven Energy Evaluation and Optimization Method for In...

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8th international conference on Automation, Control and Robotics Engineering, CACRE 2023

作者： Yao, Ming Su, Yunzhou Shao, Zhufeng Huo, Ye Tsinghua University State Key Laboratory of Tribology Institute of Manufacturing Engineering Beijing China Tsinghua University Beijing Key Lab of Precision/Ultra-Precision Manufacturing Equipments and Control Beijing China

ISBN: (纸本)9798350302776

With the rapid development and wide application of industrial robots (IRs), it inevitably brings huge energy consumption (EC), which has become an important part of manufacturing EC. Therefore, the EC optimization of IRs has become the key to the green transformation and upgrading of the manufacturing industry, and it is of great significance for the realization of "carbon neutrality"and "carbon peaking". Therefore, this paper focuses on the energy evaluation and optimization of IR, and realizes the power and EC evaluation and motion parameter optimization of its trajectories based on data-driven method. First, the convolutional neural network (CNN) and Transformer models are combined to build the energy model of IR, and then accurate modeling of its power and EC is realized based on the deep learning algorithms. Based on the above energy model, the exhaustive method and genetic algorithm (GA) are used to find the optimal motion parameters and obtain the optimal trajectory with the least EC. Finally, the experimental results show that the proposed method can achieve more than 98% and 99% of the power and EC modeling of IR, respectively. The optimization of the trajectory motion parameters of IR is realized through exhaustive method and GA, and the maximum optimization potential on the test dataset can reach 52.77%, which verifies the effectiveness and accuracy of the proposed method. © 2023 IEEE.

关键词： Energy utilization

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Design and Implementation of External Storage Large-Scale Graph Computing System 23

Design and Implementation of External Storage Large-Scale Gr...

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7th international conference on High Performance Compilation, Computing and Communications, HP3C 2023

作者： Liu, Lingbin Huang, Jianqiang Huang, Dongqiang Bian, Haodong Wang, Xiaoying Department of Computer Technology and Application Qinghai University China

ISBN: (纸本)9781450399883

With the rise of big data, graph computing has become prevalent in many fields. To effectively address such problems, large-scale graph computing systems have emerged. Most existing systems adopt memory-based computing frameworks. However, the rapid growth of data scale often leads to insufficient memory storage. To tackle these issues, an external storage-based graph computing system called DCGraph has been designed and implemented. The bottleneck of large-scale graph computing systems based on single-machine external storage usually lies in external storage I/O. Hence, DCGraph has been optimized for external storage I/O. In the preprocessing stage, graph data is compressed and transformed into a two-dimensional CSC format. Each block records the offset of its data. This guarantees that data of the same target vertex is continuously stored in external storage within the same block. DCSC format is selected for compressing data blocks with many vertices of zero in-degree. Additionally, a jump-out calculation mode suitable for the CSC format is designed for specific graph calculation algorithms. During data reading, it is determined whether to skip the current record based on its subscript and offset. Selective scheduling is used to skip inactive data blocks during each layer iteration to reduce unnecessary data reading and processing. This approach reduces both the number and total amount of I/Os. Experimental results show that compared to GridGraph, DCGraph achieves a speedup ratio of 1.5 to 2.8 times while reducing the total amount of I/Os in calculation to less than half of that of GridGraph. CCS CONCEPTS © 2023 ACM.

关键词： Big data

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