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Impacts of Dirty Data on Classification and Clustering Models
Since dirty data have negative influence on the accuracy of machine learning models, the relation between data quality and model results could be...
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Cost-Sensitive Decision Tree Induction on Dirty Data
As the rapid growth of data in our society, dirty data are increasingly common. In the process of cost-sensitive decision tree induction, dirty data...
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Density-Based Clustering for Incomplete Data
In real world, missing values exist in a lot of data sets and cause data incompleteness. However, traditional missing value imputation methods are...
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Application of Swin Transformer Model to Retrieve and Classify Endoscopic Images
The machine learning community is very interested in image classification and retrieval, especially in the area of computer vision and with an...
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Image Recommendation Based on Pre-trained Deep Learning and Similarity Matching
Recommender systems are widely used in many domains, especially in E-commerce. It can be used for attracting users by recommending appropriate...
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Topic Classification Based on Scientific Article Structure: A Case Study at Can Tho University Journal of Science
With a massive amount of stored articles, text-based topic classification plays a vital role in enhancing the document management efficiency of...
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Fall Detection Using Intelligent Walking-Aids and Machine Learning Methods
Walking aids are commonly given to older adults to prevent falls, but paradoxically, their use has been identified as a risk factor for falling,...
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Feature Selection on Inconsistent Data
With the explosive growth of data size, inconsistent data appear more frequently. Due to inconsistent data detection and repairing in data...
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FDPS: A YOLO-Based Framework for Fire Detection and Prevention
Accidental fires or explosions pose a significantly threat to human life and social safety, making them a major concern for humanity. In this study,...
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Transliterating Nom Script into Vietnamese National Script Using Multilingual Neural Machine Translation
Traditional methods for Nom transliteration have been relying on statistical machine translation. While there have been initial attempts to apply...
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Multi-pair Contrastive Learning Based on Same-Timestamp Data Augmentation for Sequential Recommendation
The core of sequential recommendations is to model users’ dynamic preferences from their sequential historical behaviors. Bidirectional...
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DYGL: A Unified Benchmark and Library for Dynamic Graph
Difficulty in reproducing the code and inconsistent experimental methods hinder the development of the dynamic network field. We present DYGL, a...
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Identifying Backdoor Attacks in Federated Learning via Anomaly Detection
Federated learning has seen increased adoption in recent years in response to the growing regulatory demand for data privacy. However, the opaque...
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PV-PATE: An Improved PATE for Deep Learning with Differential Privacy in Trusted Industrial Data Matrix
Differential privacy (DP) has been widely used in many domains of statistics and deep learning (DL), such as protecting the parameters of DL models....
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FBCA: FPGA-Based Balanced Convolutional Attention Module
Large-scale computation and data processing are common tasks in machine learning. While traditional central processors are capable of performing...
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An Investigation of the Effectiveness of Template Protection Methods on Protecting Privacy During Iris Spoof Detection
With the development of iris biometrics, more and more industries and fields begin to apply iris recognition methods. However, as technology...
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OR-SPESC: Design of an Advanced Smart Contract Language for Data Ownership
Owing to the open and sharing characteristics, blockchain can be applied for data ownership management in data circulation. The smart contract, as a...
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Diversified Group Recommendation Model for Social Network
Group recommendation can recommend satisfactory activities to group members in the recommendation system. In the research of group recommendation,...
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Role R-Calculus for Binary-Valued DL
There are four kinds of validity.
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Introduction
Traditional binary-valued logic [16,20,21,32] has two values, corresponding to true and false. For many-valued logics, it is not obvious to give the...