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AI-Powered Advertising Strategy Optimization and Click Prediction for Improved Digital Marketing 3

AI-Powered Advertising Strategy Optimization and Click Predi...

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Kumar, Ashok MetaVizen Ltd London United Kingdom

ISBN: (纸本)9798350319125

In contemporary times, traditional marketing methods have transformed, giving way to online marketing approaches that necessitate internet connectivity. Online marketing strategies offer numerous benefits, particularly in terms of cost-effectiveness and expeditious dissemination of information to the general populace. Nevertheless, one of the primary issues faced by firms in the realm of Internet marketing pertains to the identification of appropriate target consumers. This is because if organizations mistakenly target consumers not aligned with their products or services, it can result in ineffective marketing efforts and waste of resources. This research thoroughly examines ad click prediction and classification through a machine learning (ML)-based novel approach. Predictive models are developed by utilizing past ad interaction data, encompassing user demographics, ad features, and contextual information. The proposed method identifies the important factors driving ad-click behavior more easily using feature engineering approaches to extract pertinent patterns and correlations from the data by using the Pearson correlation method. Among all the implemented algorithms, the highest accuracy of 97.5% and AUC of 0.996 on test data is achieved by the logistic regression classifier. This research adds to the body of knowledge that will help advertisers pinpoint their ideal target demographic, fine-tune their ad placement techniques, and make the most of the resources they have at their disposal. © 2023 IEEE.

关键词： advertisement click CTR Machine learning Neural networks SVM

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Private data Sharing in EMU Maintenance: A Method Study Based on Federated learning 3

Private Data Sharing in EMU Maintenance: A Method Study Base...

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3rd international conference on Computer Science and Management Technology, ICCSMT 2022

作者： Yang, Jiaying Huang, Lei Wang, Ying Beijing Jiaotong University School of Economics and Management China

ISBN: (纸本)9798350320404

In the maintenance of (Electric Multiple Unit) EMU, the operation of intelligent operation and maintenance model needs the support of data sharing from multiple subjects. However, in the context of strict safety supervision in the railway industry, the data security and privacy protection requirements of each subject have formed a huge challenge to data sharing. Therefore, this paper proposes a new method of EMU maintenance data sharing based on federated learning, which solves the problem that some data subjects cannot share data due to the private data. In this paper, a case analysis of the method is carried out to realize the multi-subject joint training of a decision tree prediction model under the premise of protecting the privacy data in the real maintenance scenarios. © 2022 IEEE.

关键词： Maintenance

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A Novel Process Monitoring Method of Heat Treatment Furnace based on ReliefF-SFA 3

A Novel Process Monitoring Method of Heat Treatment Furnace ...

引用

3rd international conference on Machine learning and Computer Application, ICMLCA 2022

作者： Zhang, Yue Wang, Sen Leng, Yueyan Shan, Zhiyong Jin, Aobo Guo, Zhicheng Lin, Chenzao Zhang, Jiawei Zhang, Shiyi Shenyang Institue of Engineering Shenyang110136 China

ISBN: (纸本)9781510664814

A novel process monitoring method based on ReliefF-slow feature analysis (ReliefF-SFA) is proposed, to solve the problem of the large variable dimensions and feature selection. First, the data is mapped into a new space, and the slow characteristics of the data is obtained. The new obtained can not only extract the slowest changing components, but represent the inherent characteristics of the system. Second, the feature selection algorithm ReliefF is used to select the more weighted features, to achieve dimensionality reduction. The main subspace is established based on the obtained features, and the remaining features are put into the residual space. The corresponding statistics and control limits are calculated based on the two subspace. Finally, the process monitoring using the proposed algorithm is performed by testing the actual heat treatment furnace process data, and the results show the effectiveness of the proposed method. © 2023 SPIE.

关键词： Process monitoring

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Identification of Fake Logo Detection Using Deep learning 3

Identification of Fake Logo Detection Using Deep Learning

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Vanitha, P. Mohana Priya, T. Navasakthi, P. Rakshana Devi, V.S. Aarthi, R. Kongu Engineering College Dept. Information Technology Erode India

ISBN: (纸本)9798350372120

The development and distribution of visual content, including logos, has increased as a result of the widespread use of digital media and online platforms. Fake logos, on the other hand, have also become more common as a result of this, and they can be used for misleading activities like disinformation or brand impersonation. It is essential to identify these phony logos to protect brand integrity and guarantee reliable visual data. Convolutional Neural Networks are utilized by the suggested system to automatically identify and extract pertinent features from logo images. The suggested approach focuses on making use of Efficient Net's efficiency and scalability to accurately identify faint visual cues that indicate phony logos. To achieve accurate logo detection in an indoor environment, this work incorporates deep learning algorithms. Resource-constrained environments can benefit from a lightweight yet effective solution for logo detection thanks to MobileNet, which is wellknown for its effectiveness in real-time applications on mobile devices. The project entails building an extensive dataset with real and phony logo images that span a broad spectrum of styles, resolutions, and manipulation methodologies. The model's accuracy in differentiating between real and fake logos is measured using performance metrics like F1 score, precision, and recall. To ensure robust model generalization, the dataset is carefully curated to include variations in scale, orientation, lighting conditions, and digital manipulations. In response to the growing problem of counterfeiting logos, this work presents a ResNet50-based method for identifying phony logos. Identify the logos by using these algorithms whether it is fake or real logos. By applying these different deep learning algorithms like CNN, ResNet50, MobileNet, and EfficientNet and compare the best algorithm based on high accuracy and efficiency. © 2024 IEEE.

关键词： Digital storage

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A learning Sentiment database for Machine learning 3

A Learning Sentiment Database for Machine Learning

引用

3rd international conference on Computer, Big data and Artificial Intelligence, ICCBDAI 2022

作者： Zhang, Jing Hu, Siquan Shi, Zhiguo Han, Shumin School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shunde Innovation School University of Science and Technology Beijing Foshan China

Learners' affective states play a crucial role in learning evaluation, and the external expressions that can directly reflect affect are facial expressions. However, the sample size of the database for the learning process of learners is limited, and most of the existing expression databases are the expressions of ordinary people, which is difficult to support an in-depth study of the emotional states of learners in learning scenarios. In order to better study the influence of learners' emotion on the learning state, we built a learning expression database for machine learning, and at the same time labeled the data with expression-emotion state, and finally formed a database of 8,000 expression images containing 36 learners, which is important for studying learners' emotion state under learning scenarios. © Published under licence by IOP Publishing Ltd.

关键词： database systems

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Research on feature surface thickness prediction based on transformer deep learning networks 3

Research on feature surface thickness prediction based on tr...

引用

3rd international conference on Electronic Information engineering and data Processing, EIEDP 2024

作者： Li, Xingzuo Su, Chengzhi Jiang, Feng Zhao, Guang Mei, Yufei School of Mechanical Engineering Changchun University of Science and Technology Changchun China School of artificial intelligence Changchun University of Science and Technology Changchun China School of Telecommunications Changchun University of Science and Technology Changchun China

ISBN: (纸本)9781510680531

Aiming at the problem of predicting the amount of material surface removal, this paper is based on the development and application of deep learning and the latest research results. It takes images of paint layers of different thicknesses attached to the surface of steel plates as the research object, and predicts and classifies the paint layer thickness based on the Transformer network algorithm. First, a steel plate paint layer thickness database is established, and after image preprocessing, it meets the requirements of Transformer network training and testing. Secondly, the Cross Former network (a multi-functional visual Transformer network model based on cross-scale attention) is selected to fine-tune the Cross Former network model. After network training and hyper-parameter optimization and adjustment, it can effectively predict the thickness of the paint layer. Again, by comparing the model training accuracy and test accuracy under different training hyperparameter settings, the optimal training hyperparameter values of the model are obtained, and the Cross Former network model under the optimal training hyperparameters is obtained. Finally, the polishing of the deep learning network prediction model is verified through experiments. The surface margin prediction theory was used and the paint layer thickness distribution map on the workpiece surface was drawn. The results showed that the real values in the experimental area were within the prediction range, which verified the effectiveness of the prediction model in actual application scenarios. © 2024 SPIE.

关键词： Forecasting

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High Frequency Forecasting on Stock Market using Machine learning 3

High Frequency Forecasting on Stock Market using Machine Lea...

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3rd international conference for Emerging Technology, INCET 2022

作者： Khot, Amruta Bhat, Rashmi Urunkar, Abhijeet Mudegol, Nandinee Walchand College of Engineering Sangli India St. John College of Engineering and Management Palghar India

ISBN: (纸本)9781665494991

Intraday stock trading has become a popular trend in US, Europe, and Indian markets and forecasting these rapid market movements have become an important topic in finance. With the emergence of technology and computing power machine learning and neural network, methodologies are becoming a reality and efficient way to solve problems. The trend of intraday trading has made the researchers and developers explore neural networks approach to forecast the high frequency changing data. We focused on gathering minute to minute changing data of 80 stocks belonging to 8 different sectors from S&P 500 and developed a custom double layer neural network which efficiently captures the dependencies in the stock market and allows us to predict the returns of a specific stock at the end of next 5th minute time interval. © 2022 IEEE.

关键词： data mining

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LoRa-Based IoT Architecture Using Ant Colony Optimization for intelligent Traffic System 3rd

LoRa-Based IoT Architecture Using Ant Colony Optimization fo...

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3rd international conference on Machine learning, Image Processing, Network Security and data Sciences, MIND 2021

作者： Simaiya, Sarita Lilhore, Umesh Kumar Sandhu, Jasminder Kaur Snehi, Jyoti Garg, Atul Manhar, Advin Chitkara University Institute of Engineering & Technology Chitkara University Punjab Rajpura India Department of CSE KIET Group of Institutions Delhi-NCR Ghaziabad India Institute of Engineering Chandigarh University Mohali India JECRC University Jaipur India

ISBN: (纸本)9789811958670

Worldwide, approaches have been created to address urban transportation concerns, including the rising prevalence of traffic crashes, congestion, and poor traffic management. intelligent traffic systems (ITSs) solutions for metropolitan subways are one solution toward such problems. Long-range wide area network (LoRa) is a technological evolution utilized with LP WAN. This paper presents an IoT-based modified LoRa architecture and machine learning technique implemented toward an intelligent traffic network. The existing LoRa protocol has been modified by adding a cross-layer smart and connected communication among interconnected nodes. It improves the monitoring of the nodes and helps take accurate traffic decisions. The technology uses an ant colony optimization-based machine learning algorithm to respond in real-time based on routes’ traffic volume. This research mainly focused on examining the use of LoRa technological advances in traffic control, vehicle tracking. It also examines the platform’s best LoRa network implementation criteria. It develops a LoRa cross-layer architecture that overcomes the shortcomings of the traditional LoRa network by making it easier to do intelligent assessment and flexible network and communication service providers. The proposed modified LoRa architecture accomplishes genuinely outstanding in terms of effectiveness, accuracy, and F-measure as compared to standard LoRa. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Ant colony optimization

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Dynamic simulation of a simply supported beam subjected by multiple constant moving forces 3

Dynamic simulation of a simply supported beam subjected by m...

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2023 3rd international conference on Computational Modeling, Simulation and data Analysis, CMSDA 2023

作者： Zhang, Jun Liang, Chuan Zhu, Pengcheng School of Intelligent Construction Wuxi Taihu University Jiangsu Province Wuxi214064 China Wuxi First Engineering Research Institute Jiangsu Province Wuxi214035 China

Dynamic simulation models, which are conducted with Simulink, are introduced to study behaviors of a simply supported beam under constant moving forces based on the vibration mechanics model established by Bernoulli-Euler beam theory. By utilizing the orthogonality of vibration, a set of decoupled forced vibration equations can be obtained, which can be constructed as a subsystem of the Simulink model. The generalized loads in each decoupled vibration equation are also separately formed into submodules to facilitate the extension of the action of a single moving load to multiple moving loads. Every partial and general simulation model in Matlab/Simulink are made and the simulation results indicate that dynamic responses are related to the distance between moving loads and beam span. © Published under licence by IOP Publishing Ltd.

关键词： Simulink

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Research on the Construction of Dynamic Model of College English Blended learning Based on learning Analysis 3rd

Research on the Construction of Dynamic Model of College Eng...

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3rd EAI international conference on Application of Big data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Jia, Haie Mu, Yikun Shen, Lei Tibet Agricultural and Animal Husbandry College Tibet Nyingchi860000 China Accounting School Tong Ling University Anhui Tongling244000 China

ISBN: (纸本)9783031631290

learning is the skills through experience. In the past, learning was mainly based on passive methods, such as lectures, textbooks and memory. However, new learning methods have been developed to meet the today's learners. Active way of learning, because it encourages learners to think critically, so that learners can participate in their own learning process (Morton & Smith). Accelerate the educational reform in the promote the mixed teaching and learning model to of college English. In the early stage of this study, based on the exploratory community theory and the scale, the analysis of college English blended learning process found that there are four factors that have a significant impact on the blended learning model: teaching awareness, social role, learning atmosphere, and cognitive awareness. The K-means algorithm was used to cluster the learners. Learners can be categories: early active and late divers, and late achievers. According to the analysis results, based on the teaching scaffolding theory, this study proposes a college process, which includes the reinforcement of social existence scaffolding at the initial stage, the reinforcement of teaching existence scaffolding at the reinforcement of cognitive existence scaffolding at the end. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Reinforcement

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