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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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Advancements in Unsupervised Feature learning for Image Data 4

Advancements in Unsupervised Feature Learning for Image Data

引用

4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Karki, Uttam Shakya, Subarna Institute of Engineering Tribhuvan University Department of Electronics and Computer Engineering Pulchowk Campus Kathmandu Nepal

ISBN: (纸本)9798350306118

this research addresses the challenge of unsupervised landmark learning in the presence of non-parameter data, where image pairs exhibit significant variations in appearance and posture. Traditionally, unsupervised landmark learning relies on synthetic image pairs with similar appearances but differing postures. However, these synthetic pairs, generated through predefined transformations, do not accurately represent real-world appearance and pose changes. To overcome this limitation, we propose a novel approach incorporating Cross-Picture Cycle Compatibility (C3). We iteratively apply the technique of reconstructed exchange twice to establish comprehensive supervision, with a primary focus on maintaining cycle consistency within the framework. Additionally, we introduce a cross-picture flow to ensure the consistency of image landmarks. © 2023 IEEE.

关键词： Iterative methods

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Real Time Monitoring of Water Quality for Rural Areas: A machine learning and Internet of things Approach 4

Real Time Monitoring of Water Quality for Rural Areas: A Mac...

引用

4th international conference on Intelligent Engineering and Management, ICIEM 2023

作者： Paul, Tushar Raj, Rohit Garg, Priyansh Jain, Shruti Jaypee University of Information Technology Department of Electronics and Communication Engineering Himachal Pradesh Solan India Jaypee University of Information Technology Department of Computer Science and Engineering Himachal Pradesh Solan India

ISBN: (纸本)9798350341126

A water quality monitoring system can aid in preserving the environment, ensuring the security of nearby water sources, and fostering economic growth in rural areas. this results in the development of a system, employing the Internet of things (IoT) and machine learning to monitor the quality of water. this paper discusses the characteristics of water that explains whether it is fit for human consumption or not. the pH and Turbidity sensors are dipped in water samples acquired from wells, lakes, rivers, ponds, and other places are used to inform the development of an effective model. the data will be delivered from the sensors to the IDE, where it will then be sent to the cloud server. the model effectively accounts for test tables, where 1 indicates the water is fit for drinking and 0 indicates the water is unfit. the model is validated by employing Support Vector machine, Random Forest and XG Boost methods. the maximum accuracy of 95.12% was observed using XGBoost. We have successfully implemented the real-time data for the water quality monitoring system for rural areas using machine learning and IoT which lets us know whether the water is fit for drinking or not. © 2023 IEEE.

关键词： Internet of things

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Research on multi-factor quantitative investment strategy of SVM model based on machine learning 4

Research on multi-factor quantitative investment strategy of...

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4th international conference on Artificial Intelligence and computer Engineering, ICAICE 2023

作者： Liu, Ting Li, Chunyuan School of Management Shanghai University China

ISBN: (纸本)9798400708831

In order to cope with the impact of the epidemic on the global economy and the challenges to the investment industries, this paper based on the characteristics of quantitative investment, which is not affected by emotions and can process information quickly, takes the constituent stocks of CSI 300 index as the research object, constructs fundamental factors related to financial management, searches the hyperparameter space of the support vector machine algorithm, finds the optimal decision surface, constructs a stock selection model and excess investment portfolio, and evaluates the investment portfolio through simulated trading. the results show that the total return in the backtesting period reaches 41.25%, and the return of CSI 300 index is 21.15%. the method shows some value in training data, and also attenuates personal cognitive bias in actual trading, which is more suitable for adaptive dynamic trading in unstable markets, and also provides new ideas for the innovation of quantitative investment financial factor tools and the application of machine learning methods in the field of finance. © 2023 ACM.

关键词： Investments

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Research and application of an Improved Apriori Algorithm in Market Basket Data 4

Research and Application of an Improved Apriori Algorithm in...

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4th international conference on machine learning and computer application, ICMLCA 2023

作者： Qiu, Xuexian Ning, Shiyong Zhang, Shihui Yang, Zhuorui School of Computer and Information Engineering Harbin University of Commerce Harbin China

ISBN: (纸本)9798400709449

In data mining, association rule mining has been proven to be a valuable strategy. the Apriori algorithm, proposed in 1993, is one of the most classic association rule mining algorithms and has been widely used in various domains to date. However, the algorithm itself requires multiple scans of the dataset, making the efficiency improvement of the algorithm an important topic. In this study, focusing on the association rule mining problem in shopping basket datasets, we address the efficiency and accuracy issues of the traditional Apriori algorithm in this domain and propose a novel improved Apriori algorithm. this algorithm enhances the accuracy and time efficiency of rule mining by introducing deletion and identification modules that exploit the characteristics of shopping basket datasets. through mathematical reasoning and experimental validation, it is demonstrated that the improved Apriori algorithm achieves higher accuracy and time efficiency, thus proving its superior © 2023 ACM.

关键词： Association rules

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Customer Churn Prediction Using machine learning: A Case Study of Libyan Internet Service Provider Company 4

Customer Churn Prediction Using Machine Learning: A Case Stu...

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4th IEEE international Maghreb Meeting of the conference on Sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2024

作者： Alteer, Shefaa A. Alariyibi, Alhaam University of Benghazi Faculty of Information Technology Benghazi Libya

ISBN: (纸本)9798350372632

Artificial intelligence (AI) is a field of science and technology that simulates human intelligence to develop intelligent machines. machine learning (ML), on the other hand, is a subset of AI that enables computers to learn from data without being explicitly programmed. In the area of business, data science is the practical application of ML, AI, and statistics, which focuses on extracting valuable insights from data. Nowadays, data science relies heavily on ML techniques, which are used for a variety of applications. One of its applications is Customer Churn Prediction (CCP). Customer churn (or attrition) is the tendency of customers to stop using a company's products or services. It is important to predict churn where businesses can proactively target the customers, who are at high risk of leaving and create marketing campaigns to retain them. this study aims to build a predictive model to predict customer churn for Internet Service Providers (ISPs). Several experiments have been conducted on a dataset obtained from a Libyan ISP company to train the models. the dataset distribution was imbalanced, with only 5% of customers churned. To mitigate this, various oversampling techniques were used. In the proposed system, five diverse classifiers were used. Namely, Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Gradient Boosting machine (GBM) tree algorithm, and Extreme Gradient Boosting (XGBoost) algorithms due to their relevance in this predictive context. the experimental findings showed that the XGBoost tree model yielded the most favorable outcomes, achieving an AUC value of 94%. © 2024 IEEE.

关键词： Decision trees

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Mobile application -Based Solution for Building Accessibility Assessment for Comprehensive and Personalized Assessment 47

Mobile Application -Based Solution for Building Accessibilit...

引用

47th IEEE-computer-Society Annual international conference on computers, Software, and applications (COMPSAC)

作者： Aktar, Sayeda Farzana Drake, Mason D. Alam, Kazi Shafiul O'Donnell, Laryn Michele Tian, Shiyu Smith, Roger O. Ahamed, Sheikh Iqbal Marquette Univ Dept Comp Sci Milwaukee WI 53233 USA Univ Wisconsin Milwaukee Dept Rehabilitat Sci & Technol Coll Hlth Sci Milwaukee WI USA

ISBN: (纸本)9798350326970

Rehabilitation and disability researchers are increasingly considering utlizing machine learning (ML) algorithms to enhance accessibility for people with disabilities (PwD) as they interact with their environments. PwD often experience environmental barriers in the community and private buildings due to a lack of accessible infrastructure design and prior accessibility information. Such environmental barriers may inhibit PwD from full participation and impede in one's overall independence and quality of life. the availability of healthcare services and information are essential for increased participation in occupations and optimal independence. In the current era of connected health, information has the ability to be accessed anywhere and providing accessibility content for PwD to use can enhance occupational performance factors. the purpose of this study was to 1) identify existing accessibility measurement challenges and barriers and 2) propose an intelligent accessibility evaluation and assessment for buildings, and 3) leverage mobile applications to address major challenges of accessibility measurement. this research aims to improve the accuracy and reliability of the accessibility measurement using a smarter system.

关键词： Mobile Devices Mobile applications machine learning People with disabilities Accessibility measurement Connected Health

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Fighting Grape Black Rot with Deep learning: A CNN-LSTM Hybrid Model for Disease Severity Classification 4

Fighting Grape Black Rot with Deep Learning: A CNN-LSTM Hybr...

引用

4th international conference for Emerging Technology, INCET 2023

作者： Gaur, Poonam Sharma, Rohit Kumar, Ravi Gupta, Amit Sharma, Rishabh Kukreja, Vinay CGC Landran Department of Computer Application Mohali India College of Engineering CGC Chandigarh Group of Technology Landran India Manav Rachna International Institute of Research and Studies India Computer Science and Engineering Graphic Era Hill University Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350335750

Grape black rot is a devastating disease that affects grape crops globally. Detecting and preventing the disease as early as possible is crucial for minimizing crop loss and increasing yield and quality. In this study, we propose a CNN-LSTM hybrid model for multi-classification of the severity of grape black rot based on six distinct disease degrees. the model obtained an accuracy of 93.06% after being trained on a dataset of 10,000 grape leaf images collected from an Indian vineyard, outperforming all other deep learning (DL) and traditional machine learning models. the capacity of the proposed model to capture both spatial and temporal characteristics of grape leaf images, as well as the application of data augmentation techniques and early halting, contributed to its superior performance. the model can be utilized as an efficient instrument for the early detection and prevention of grape black rot disease, thereby contributing to the enhancement of the yield and quality of grape crops. However, the model's performance varied depending on the degree of the disease, with reduced classification accuracy for grape leaves with severe black rot degree. To enhance the model's ability to precisely classify leaves with severe black rot, additional research is required. Overall, the CNN-LSTM hybrid model proposed for multi-classification of grape black rot severity is a promising approach for the early detection and prevention of grape black rot disease, with potential applications to other plant disease detection tasks. © 2023 IEEE.

关键词： Image classification

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Predicting Potential Customers in Direct Marketing Using Uplift Modelling and Advanced machine learning 5

Predicting Potential Customers in Direct Marketing Using Upl...

引用

5th international conference on computer and applications, ICCA 2023

作者： Kodikara, Nuwinda Shahtahmassebi, Golnaz Nottingham Trent University School of Science and Technology Nottingham United Kingdom Nottingham Trent University Department of Physics and Mathematics Nottingham United Kingdom

ISBN: (纸本)9798350303254

Uplift Modeling can be an effective machine learning method when identifying the potential customers who have the highest possibility of creating a positive impact on the marketing strategies. Unlike traditional approaches that are solely based on predicting outcomes, the Uplift model aims to understand the causal relationship between marketing strategies and customer responses. In this paper, we examine how machine learning can be effectively used in the product marketing stage. Using the Criteo uplift dataset, the study tries to solve the problem using the Uplift modelling concept to predict potential customers who react positively to direct marketing campaigns the study used three different approaches which combine with parallel computing: 1) Dual Uplift modelling with hypercomputing integration, 2) eXtreme Gradient Boosting (XGBoost), and 3) combining both the Dual Uplift model with XGBoost model. Once the model is developed, using the model, the study extends its scope to develop a web application using R shiny apps. these models were evaluated using statistical measures and discussed the business implications. © 2023 IEEE.

关键词： machine learning

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Assistive Device Based on machine learning Approach for Communication of Visually Challenged and Muted Community 4

Assistive Device Based on Machine Learning Approach for Comm...

引用

4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Sasi, Smitha Srividya, B.V. Aswatha, A.R. Dayananda Sagar College of Engineering Department of Electronics and Telecommunication Engineering Bangalore India Dayananda Sagar College of Engineering Department of Medical and Electronics Bangalore India

ISBN: (纸本)9798350300888

It is a great challenge to find a means of communication for people who are suffering from visual or hearing impairment and also for those who are speechless. this research study aims to develop a device from Raspberry Pi which can communicate with the visually challenged by converting the messages to audio. the proposed device also helps the people with hearing loss by converting audios to text and display the same. It also helps the speech impaired by converting the sign language into text or audio by using image to text conversions. © 2023 IEEE.

关键词： Audition

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proceedings - 2023 6th international conference on computer and Informatics Engineering: AI Trust, Risk and Security Management (AI Trism), IC2IE 2023

Proceedings - 2023 6th International Conference on Computer ...

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6th international conference on computer and Informatics Engineering, IC2IE 2023

ISBN: (纸本)9798350345162

the proceedings contain 67 papers. the topics discussed include: a systematic literature review of deep learning application in multiclass anomaly detection for chest medical imaging;machine learning algorithm and modeling in solar irradiance forecasting;facial palsy detection through changes in facial muscle functionality using CNN algorithm;performance measurement of ResNet-34 in convolutional neural network method for classification of mask type usage;fine-tuning deep learning model for detecting Indonesian traffic accident tweet;deep learning for safety: helmet detection in construction site image using YOLOv8;addressing wireless network capacity challenges: a novel mobile system with WOLA and NOMA;salt hash password using MD5 combination for dictionary attack protection;and cloud computing based server infrastructure analysis for payment machinery needs.

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