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共 5726 条记录，以下是411-420 订阅

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A Comparative Study of Machine Learning algorithms for Predicting Customer Churn: Analyzing Sequential, Random Forest, and Decision Tree Classifier models

A Comparative Study of Machine Learning Algorithms for Predi...

引用

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

作者： Ratnam Dodda C. Raghavendra Marpu Aashritha Hima Varshini Macherla Anusha Reddy Kuntla Department of CSE (AI&ML) CVR College of Engineering Hyderabad India Department of CSE (DS) CVR College of Engineering Hyderabad India Department of Computer Science and Information Technology CVR College of Engineering Hyderabad India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Predicting customer churn is vital for businesses aiming to retain their customer base and enhance profitability. This study investigates the effectiveness of three prominent machine learning algorithms Sequential, Random Forest, and Decision Tree Classifier in forecasting customer churn. Utilizing a dataset encompassing historical customer information such as demographics, transaction records, and interaction patterns, we preprocess the data by addressing missing values, encoding categorical variables, and scaling numerical features. Subsequently, each model is trained on the processed dataset, and their performance is assessed using accuracy as the evaluation metric. Our findings demonstrate promising predictive capabilities, with the Sequential model achieving an accuracy score of 94%, followed by Random Forest with 92%, and Decision Tree Classifier with 91%. Additionally, we conduct a comparative analysis of these models, focusing on interpretability, computational complexity, and robustness. While the Sequential model excels in predictive accuracy by capturing temporal dependencies in sequential data, Random Forest and Decision Tree Classifiers offer simplicity and interpretability, catering to scenarios where model transparency is paramount.

关键词： Analytical models Accuracy Machine learning algorithms Computational modeling Predictive models Prediction algorithms data models Numerical models Decision trees Random forests

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Research on Individual Recognition and Prediction of Cocaine Addiction Based on Convolutional Neural Networks 3

Research on Individual Recognition and Prediction of Cocaine...

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3rd international conference on Electronic InformationTechnology and Smart Agriculture, ICEITSA 2023

作者： Xiao, Hongbo Xiao, Jianhua Zhang, Lebing Tang, Bo Cao, Zhuyou Luo, Tianwang School of Computer Science and Engineering Huaihua University Hunan Huaihua China Key Lab. of Wuling-Mt. Hlth. Big Data Intelligent Proc. and Applic. in Hunan Province Universities Key Lab. of Intelligent Contr. Technol. for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9798400716775

In response to the current difficulty in identifying and predicting cocaine addicted individuals based on brain MRI images, this paper designs a deep learning recognition and prediction model based on convolutional neural networks. 29 cocaine addicted individuals and 24 healthy controls were selected from brain MRI images. After per-forming a series of data preprocessing operations such as skull dissection and data augmentation on brain MRI images, a deep learning model based on convolutional neural networks is constructed to process the processed brain MRI images of cocaine addicted individuals and healthy controls to identify and predict cocaine addicted individuals. The experimental results show that the recognition and prediction accuracy of deep learning models based on convolutional neural networks is 89%. Compared with machine learning models such as SVM and support vector machines, it greatly improves the accuracy of model prediction and can accurately, quickly, and effectively identify and predict cocaine addicted individuals. © 2023 ACM.

关键词： Forecasting

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Detection of COVID-19 in Point of Care Lung Ultrasound 44

Detection of COVID-19 in Point of Care Lung Ultrasound

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44th Annual international conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Maximino, Joana Coimbra, Miguel Pedrosa, Joao Univ Porto FCUP Fac Sci Porto Portugal Inst Syst & Comp Engn Technol & Sci INESC TEC Porto Portugal Univ Porto FEUP Fac Engn Porto Portugal

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

The coronavirus disease 2019 (COVID-19) evolved into a global pandemic, responsible for a significant number of infections and deaths. In this scenario, point-of-care ultrasound (POCUS) has emerged as a viable and safe imaging modality. Computer vision (CV) solutions have been proposed to aid clinicians in POCUS image interpretation, namely detection/segmentation of structures and image/patient classification but relevant challenges still remain. As such, the aim of this study is to develop CV algorithms, using Deep Learning techniques, to create tools that can aid doctors in the diagnosis of viral and bacterial pneumonia (VP and BP) through POCUS exams. To do so, convolutional neural networks were designed to perform in classification tasks. The architectures chosen to build these models were the VGG16, ResNet50, DenseNet169 e MobileNetV2. Patients images were divided in three classes: healthy (HE), BP and VP (which includes COVID-19). Through a comparative study, which was based on several performance metrics, the model based on the DenseNet169 architecture was designated as the best performing model, achieving 78% average accuracy value of the five iterations of 5-Fold Cross-Validation. Given that the currently available POCUS datasets for COVID-19 are still limited, the training of the models was negatively affected by such and the models were not tested in an independent dataset. Furthermore, it was also not possible to perform lesion detection tasks. Nonetheless, in order to provide explainability and understanding of the models, Gradient-weighted Class Activation Mapping (GradCAM) were used as a tool to highlight the most relevant classification regions.

关键词： COVID-19 Training Ultrasonic imaging Point of care Lung Computer architecture data models

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A Machine Learning Methodology for Optimal Big data processing in Advanced Smart City Environments 23rd

A Machine Learning Methodology for Optimal Big Data Processi...

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23rd international conference on Computational Science and its Applications (ICCSA)

作者： Cuzzocrea, Alfredo Canade, Luigi Nicolicchia, Riccardo Roldo, Luca Univ Calabria IDEA Lab Arcavacata Di Rende Italy Univ Paris City Dept Comp Sci Paris France ISIRES Turin Italy

ISBN: (纸本)9783031368042;9783031368059

This paper introduces a Machine Learning methodology for supporting optimal big data processing in advanced smart city environments. In particular, the proposed work focuses the attention on the issue of efficiently charging Electric Vehicles (EVs) in an urban area, given various specific constraints. Our preliminary evaluation and analysis proofs the quality of our heuristics.

关键词： Network algorithms Theory of Computing Heuristic algorithms

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FairEd: A Systematic Fairness Analysis Approach Applied in a Higher Educational Context 22

FairEd: A Systematic Fairness Analysis Approach Applied in a...

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12th Annual international conference on Learning Analytics and Knowledge (LAK)

作者： Vasquez Verdugo, Jonathan Gitiaux, Xavier Ortega, Cesar Rangwala, Huzefa George Mason Univ Fairfax VA 22030 USA Univ Valparaiso Valparaiso Chile Univ Chile Fac Econ & Negocios Santiago Chile

ISBN: (纸本)9781450395731

Higher education institutions increasingly rely on machine learning models. However, a growing body of evidence shows that these algorithms may not serve underprivileged communities well and at times discriminate against them. This is all the more concerning in education as negative outcomes have long-term implications. We propose a systematic process for framing, detecting, documenting, and reporting unfairness risks. The systematic approach's outcomes are merged into a framework named FairEd, which would help decision-makers to understand unfairness risks along the environmental and analytical fairness dimension. The tool allows to decide (i) whether the dataset contains risks of unfairness;(ii) how the models could perform along many fairness dimensions;(iii) whether potentially unfair outcomes can be mitigated without degrading performance. The systematic approach is applied to a Chilean University case study, where a predicting student dropout model is aimed to build. First, we capture the nuances of the Chilean context where unfairness emerges along income lines and demographic groups. Second, we highlight the benefit of reporting unfairness risks along a diverse set of metrics to shed light on potential discrimination. Third, we find that measuring the cost of fairness is an important quantity to report on when doing the model selection.

关键词： algorithmic fairness student dropout educational data mining

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Design and Implementation of Intelligent Transportation System Based on Big data

Design and Implementation of Intelligent Transportation Syst...

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

作者： Lingxue Zhang Jilin Communication Polytechnic College of Traffic Information Changchun China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

The rise of big data technology brings new development opportunities for intelligent transportation systems. This paper introduces the design and implementation of an intelligent transportation system based on big data. The system architecture consists of data layer, computing layer, service layer, and application layer. It integrates heterogeneous traffic data through data fusion module, utilizes machine learning algorithms for traffic flow analysis, road condition prediction, etc., and provides decision support services. In terms of implementation, it elaborates on development environment configuration, key algorithm implementation, and system testing. Finally, taking Beijing as an example, it demonstrates the application cases of using the system for traffic status evaluation, future road condition prediction, and traffic management decision-making. Practice proves that big data-driven intelligent transportation systems help improve urban traffic operation efficiency and are an important part of building smart cities.

关键词： Machine learning algorithms Smart cities Roads Buildings data integration Transportation Systems architecture Big data Timing Intelligent transportation systems

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CITE: Compact Interactive TransformEr for Multilingual Image Captioning 23

CITE: Compact Interactive TransformEr for Multilingual Image...

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6th international conference on Image and Graphics processing, ICIGP 2023

作者： Xu, Yueyuan Hu, Zhenzhen Zhou, Yuanen Hao, Shijie Hong, Richang Key Laboratory of Knowledge Engineering with Big Data School of Computer and Information Hefei University of Technology China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9781450398572

Current state-of-the-art image captioning models generate captions in a single language, requiring a combination of multiple language specific models to build a multilingual image captioning system. However, as the number of supported languages increases, it leads to the parameters of the multilingual image captioning system grow linearly. To tackle this issue, we propose a single Compact Interactive TransformEr (CITE) model, which can describe an image in multiple languages simultaneously, making the captioning system more compact. Specifically, based on the standard Transformer model, we share the encoder and decoder backbone parameters and replace the self-attention sub-layer in the decoder with the interactive attention sub-layer. In addition, we extend the traditional monolingual reinforcement learning mechanism to a multilingual version to promote better description generation. Due to the wide use of Chinese and English, we evaluate the performance of our CITE model by simultaneously generating English and Chinese captions. We expand the image captions of the whole MSCOCO dataset and release a COCO-EN-CN dataset. Extensive experiments on the COCO-EN-CN dataset show that our single CITE model with more parameter-efficient can maintain the competitive performance or even better than the monolingual captioning models. © 2023 ACM.

关键词： Reinforcement learning

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Keyword-based Socially Tenuous Group Queries 39

Keyword-based Socially Tenuous Group Queries

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39th IEEE international conference on data Engineering, ICDE 2023

作者： Zhu, Huaijie Liu, Wei Yin, Jian Cui, Ningning Xu, Jianliang Huang, Xin Lee, Wang-Chien Sun Yat-sen University China and Laboratory of Big Data Analysis and Processing Guangzhou China An Hui University China Hong Kong Baptist University Hong Kong The Pennsylvania State University United States

ISBN: (纸本)9798350322279

Socially tenuous groups (or simply tenuous groups) in a social network/graph refer to subgraphs with few social interactions and weak relationships among members. However, existing studies on tenuous group queries do not consider the user profiles (keywords) of the members whereas in many social network applications, e.g., finding reviewers for paper selection and recommending seed users in social advertising, keywords also need to be considered. Thus, in this paper, we investigate the problem of keywords-based socially tenous group (KTG) queries. A KTG query is to find top N tenuous groups in which the members of each group jointly cover the most number of query keywords. To address the KTG problem, we first propose two exact algorithms, namely KTG-VKC and KTG-VKC-DEG, which give priority to the valid keyword coverage and the combination of valid keyword coverage and degree, respectively, to select members to form a feasible group by adopting a branch and bound (BB) strategy. Moreover, we propose keyword pruning and k-line filtering to accelerate the algorithms. To yield diversified KTG results, we also study the problem of diversified keywords-based socially tenous group (DKTG) queries. To deal with the DKTG problem, we propose a DKTG-Greedy algorithm by exploiting a greedy heuristic in combination with KTG-VKC-DEG. Furthermore, we design two alternative indexes, namely NL and NLRNL, to efficiently check whether the social distance of any two members is greater than the social constraint k in the above algorithms. We conduct extensive experiments using real datasets to validate our ideas and evaluate the proposed algorithms. Experimental results show that the NLRNL index achieves a better performance than the NL index. © 2023 IEEE.

关键词： User profile

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Overcoming Limitations of Statistical Methods with Artificial Neural Networks 23

Overcoming Limitations of Statistical Methods with Artificia...

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23rd international Arab conference on Information Technology, ACIT 2022

作者： Grebovic, Marko Filipovic, Luka Katnic, Ivana Vukotic, Milica Popovic, Tomo University of Donja Gorica Faculty for Information Systems and Technologies Podgorica Montenegro Finances and Business University of Donja Gorica Faculty of International Economics Podgorica Montenegro

ISBN: (纸本)9798350320244

Traditional statistical models as tools for summarizing patterns and regularities in observed data can be used for making predictions. However, statistical prediction models contain small number of important predictors, which means limited informative capability. Also, predictive statistical models that provide some type of pseudo-correct regular statistical patterns, are used without previous understanding of the used data causality. Machine Learning (ML) algorithms as area in Artificial Intelligence (AI) provide the ability to interpret and understand data in more sophisticated way. Artificial Neural Networks as kind of ML methods use non-linear algorithms, considering links and associations between parameters, while statistical use one-step-Ahead linear processes to improve only short-Term prediction's accuracy by minimizing cost function. Disregarding that designing an optimal artificial neural network is very complex process, they are considered as potential solution for overcoming main flaws of statistical prediction models. However, they will not automatically improve predictions accuracy, so several artificial neural networks and traditional statistical methods are evaluated and analyzed through accuracy measures for prediction purposes in various fields of applications. Based on gained results, couple of techniques for improving artificial neural networks are proposed to get better accuracy results than statistical predictive methods. © 2022 IEEE.

关键词： Machine learning

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Artificial Intelligence for Text Generation: An Intellectual Property Perspective 1st

Artificial Intelligence for Text Generation: An Intellectual...

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1st international conference on AI-generated Content, AIGC 2023

作者： Zhang, Huidi Gong, Junming Wu, Wei Law School Wuhan University Wuhan China Law School Leiden University Leiden Netherlands China Institute of Boundary and Ocean Studies Wuhan University Wuhan China

ISBN: (纸本)9789819975860

Purpose: This paper aims at proposing relevant improvement measures for the intellectual property regulation on AI-generated text. Methods: This paper adopts qualitative research methods, collects and analyzes opinions from various sources to illustrate the technical logic of AI-generated text and explore existing intellectual property legal regulations and dilemmas. Results: AI-generated content, especially AI-generated text, follows the process of data collection and processing, models creation and content generation. Regarding intellectual property regulations on the AI-generated text, international law reflects hysteresis while major states’ domestic law varies in terms of intellectual property attribution. Conclusion: Based on the research on current situations, this paper proposes corresponding improvement measures, namely self-censorship, self-improvement, international cooperation and strengthening legislation. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： international cooperation

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