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检索条件"任意字段=5th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2022"

共 261 条记录，以下是141-150 订阅

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data-adaptive Transfer learning for Translation: A Case Study in Haitian and Jamaican 5

Data-adaptive Transfer Learning for Translation: A Case Stud...

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5th Workshop on Technologies for machine Translation of Low-Resource Languages, LoResMT 2022 at 29th international conference on Computational Linguistics. COLING 2022

作者： Robinson, Nathaniel R. Hogan, Cameron J. Fulda, Nancy Mortensen, David R. Language Technologies Institute Carnegie Mellon University PittsburghPA United States Department of Computer Science Brigham Young University ProvoUT United States

Multilingual transfer techniques often improve low-resource machine translation (MT). Many of these techniques are applied without considering data characteristics. We show in the context of Haitian-to-English translation that transfer effectiveness is correlated with amount of training data and relationships between knowledge-sharing languages. Our experiments suggest that for some languages beyond a threshold of authentic data, back-translation augmentation methods are counterproductive, while cross-lingual transfer from a sufficiently related language is preferred. We complement this finding by contributing a rule-based French-Haitian orthographic and syntactic engine and a novel method for phonological embedding. When used with multilingual techniques, orthographic transformation makes statistically significant improvements over conventional methods. And in very low-resource Jamaican MT, code-switching with a transfer language for orthographic resemblance yields a 6.63 BLEU point advantage. © 2022 COLING. All Rights Reserved.

关键词： Computational linguistics

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Aspect Analysis Based on Statistical Description and Visualization of data 5

Aspect Analysis Based on Statistical Description and Visuali...

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5th international conference on Contemporary Computing and Informatics, IC3I 2022

作者： Tomar, Priyanka Sharma, Nonita Kamble, Shailesh D. Indira Gandhi Delhi Technical University for Women Information Technology Department Delhi India Indira Gandhi Delhi University for Women Artificial Intelligence and Data Science Delhi India

ISBN: (纸本)9798350398267

the trending machine learning algorithms have been applied in almost all spheres of life and able to give accurate results. However, none of the algorithms perform aspect analysis beforehand to see the nature and inferences of data. this research work presents the aspect analysis of heart failure data. As per the day today life scenario where each one is living a busy life, it's very important to know the health of our heart. this work will show case different types of models for cardiovascular diseases and its prediction through various heart patients' data and detect various heart disease. this work is developed based on the data visualization approach using python by plotting graphs between different types of attributes in dataset and analyzing the graphs which are plotted between various attributes of our data like age, cholesterol, trestbps, sex and fbs. Research work would visualize the pattern of heart diseases according to the age and gender of a heart patient and would like to present a profound and relevant result which will be helpful for the doctors to analyze and provide proper treatment to heart patients. © 2022 IEEE.

关键词： data visualization

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Analysis and Prediction of Employee Promotions Using machine learning 5

Analysis and Prediction of Employee Promotions Using Machine...

引用

5th international conference on data Science and Information Technology, DSIT 2022

作者： Alqahtani, Fatma Ayed Almaleh, Abdulaziz College of Computer Science King Khalid University Abha Saudi Arabia

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

Predicting employee performance is essential for organizations. the success or failure of a company often depends upon the competence of its employees, so CEOs and managers who want their organizations to succeed face the difficult task of determining which employees should be promoted. the current promotion process used in most organizations should be considered misleading because it depends on supervisors' judgments. the major aim of this paper is to use classification algorithms to develop predictive models for predicting whether an employee is qualified for promotion or not and identifying the most important attributes affecting employee promotion. the dataset used in this paper is from Kaggle 2020. It contains information on multinational companies arranged in 54,808 rows and 13 columns. this dataset covers nine broad verticals across organizations. Several predictive modeling techniques, including K-Nearest Neighbors, Logistic Regression, Decision Tree, Random Forest, Support Vector machine, and Ensemble models (Adaboosting and Gradient Boosting models) were used to predict employee promotion. Based on measurements of accuracy, Fl-score, and AUC, Gradient Boosting outperforms the other classification algorithms. the results also show that the most significant factor contributing to predicting employee promotion is the previous year's rating. Department had no effect on employee promotions. © 2022 IEEE.

关键词： Decision trees

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5th international conference on applied Informatics, ICAI 2022

5th International Conference on Applied Informatics, ICAI 20...

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5th international conference on applied Informatics, ICAI 2022

ISBN: (纸本)9783031196461

the proceedings contain 32 papers. the special focus in this conference is on applied Informatics. the topics include: Statistical Characterization of Image Intensities in Regions Inside Arteries to Facilitate the Extraction of Center Lines in Atherosclerosis Frameworks;Text Encryption with Advanced Encryption Standard (AES) for Near Field Communication (NFC) Using Huffman Compression;a Systematic Review on Phishing Detection: A Perspective Beyond a High Accuracy in Phishing Detection;application of Duality Properties of Renyi Entropy for Parameter Tuning in an Unsupervised machine learning Task;preliminary Study for Impact of Social Media Networks on Traffic Prediction;website Phishing Detection Using machine learning Classification Algorithms;AESRSA: A New Cryptography Key for Electronic Health Record Security;Detection of COVID-19 Using Denoising Autoencoders and Gabor Filters;development of a Mobile Application to Save Lives on Catastrophic Situations;COVID-19 Article Classification Using Word-Embedding and Different Variants of Deep-learning Approach;internet of things with Wearable Devices and Artificial Intelligence for Elderly Uninterrupted Healthcare Monitoring Systems;a Wayuunaiki Translator to Promote Ethno-Tourism Using Deep learning Approaches;rating the Acquisition of Pre-writing Skills in Children: An Analysis Based on Computer Vision and data Mining Techniques in the Ecuadorian Context;study Techniques: Procedure to Promote the Comprehension of Texts in University Students;TPACK and Toondoo Digital Storytelling Tool Transform Teaching and learning;Fully Automated Lumen Segmentation Method and BVS Stent Struts Detection in OCT Images;predictive Modeling Toward the Design of a Forensic Decision Support System Using Cheiloscopy for Identification from Lip Prints;Automotive Industry Applications Based on Industrial Internet of things (IIoT). A Review;implementation of an IoT Architecture for Automated Garbage Segregation;architecture on Knowledge Managem

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Multimodal 12-lead ECG data classification using multi-label DenseNet for heart disease detection 5

Multimodal 12-lead ECG data classification using multi-label...

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5th international conference on data Science and Information Technology, DSIT 2022

作者： Han, Xiao Tu, Enmei Yang, Jie School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

Electrocardiogram(ECG) signal is one of the most effective diagnostic method for arrhythmia. Since cardiac dis-ease is a main cause of death and the allocation of medical resources is unbalanced, automatic recognition of arrhythmia by machine learning algorithm is of great significance. In this work, we develop a multimodal DenseNet to classify 12-lead ECG. Our method combines hand-crafted features with features from DenseNet. In addition, we propose to use softmax function and dynamic threshold optimization for our multi-level ECG classification task. We tested our model on the 2020 PhysioNet/CinC Challenge dataset. Using the official Challenge Score for evaluation, we achieve a Challenge Score of 0.602 on the validation set, which outperforms the champion model by an apparent margin. © 2022 IEEE.

关键词： Electrocardiograms

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Group RandAugment: Video Augmentation for Action Recognition 5

Group RandAugment: Video Augmentation for Action Recognition

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5th international conference on data Science and Information Technology, DSIT 2022

作者： An, Fengmin Zhang, Bingbing Wang, Zhenwei Dong, Wei Zhang, Jianxin School of Computer Science and Engineering Dalian Minzu University Dalian China Institute of Machine Intelligence and Bio-computing Dalian Minzu University Dalian China SEAC Key Lab of Big Data Applied Technology Dalian Minzu University Dalian China School of Information and Communication Engineering Dalian University of Technology Dalian China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

data augmentation, as a critical strategy in deep learning, well improves the sample diversity for network training, leading to the obvious improvement of model generalization ability. Besides, automatic data augmentation, while sparking in image tasks, has attracted too little attention in video recognition task. therefore, this work explores a novel group random augmentation (GRA) that automatically augments video data for recognition. GRA first collects 21 augmentation transformations to enrich the sample diversity, and then divides these transformations into four groups (i.e., pixel-transform, rigidtransform, erasing-transform, environment-transform) to reduce the excessive regularization caused by similar augmentation. To reduce the computational cost of finding the optimal setting, GRA adopts the combination form to select the optimal situation. For frames in the same video clip, GRA uses synchronous GRA. Additionally, the proposed GRA can be integrated into any existing video frameworks. To prove the effectiveness of GRA, we conduct experiments on two commonly used video action recognition benchmarks (Something-Something V1 & V2) and three typical frameworks (TSM, GSM, and TEA), whose results demonstrate GRA can improve performance in all cases without adding additional computational cost. © 2022 IEEE.

关键词： Deep learning

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Cybersecurity Scientometric Analysis: Mapping of Scientific Articles using Scopus API for data Mining and Webscrapping 5

Cybersecurity Scientometric Analysis: Mapping of Scientific ...

引用

5th international conference on data Science and Information Technology, DSIT 2022

作者： Godoy, Carlo H. Diego, Nicole Jehru R. Tagumasi, Russel E. Lerit, Jordan C. Costales, Jefferson A. Bayan Academy Quezon City Philippines Eulogio Amang Rodriguez Institute of Science and Technology Manila Philippines

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

In terms of computers, cybersecurity has experienced massive technological and operational transformations in recent years, with data science at the forefront. Cybersecurity in the Philippines is still believed to be in its infancy. though it is commonplace in other areas of the world, just a few scholars in the Philippines have dared to concentrate in this field. As a result, a need for assistance in promoting cybersecurity in the Philippines must be identified. Because it is difficult to persuade scholars in the Philippines to work on a certain field of computing without figures, Scientometric Analysis is required to persuade scholars in the Philippines to handle cybersecurity. the findings of this study revealed that there is no link between the frequency with which authors publish cybersecurity and their chances of receiving a high number of citations, either by year or overall. the study also found that having a lot of publications does not ensure a higher Google Scholar ranking. According to the study, the most interesting themes in the field of cybersecurity are to connect it with another discipline, thus an author should choose an attractive topic in this field to acquire a higher rank in Googlescholar and especially in Scopus. machine learning and IoT are the two fields that most people want to see integrated into cybersecurity. Hence, this topics can be used to encourage cybersecurity experts in the Philippines to used this topic on their research or speaking engagements. © 2022 IEEE.

关键词： Mapping

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An Automatic Water Metering System using Computer Vision and ARIMA-based machine learning 5

An Automatic Water Metering System using Computer Vision and...

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5th international conference on machine learning and Natural Language Processing, MLNLP 2022

作者： Lin, Felix Fl Sun, Yu Ys Arnold O. Beckman High School United States Computer Science Department California State Polytechnic University United States

ISBN: (纸本)9781450399067

Having access to sufficient water usage data real-time is crucial not only to finding water leaks and preventing water bills (which can cost up to thousands of dollars) but also to tracking water usage and savings. However, to record a California household's water usage, a worker from the respective water district must personally check the water meter in order to update the data at a monthly frequency. this is because the current water infrastructure in California is outdated while also being expensive to replace. Additionally, while certain cities such as San Jose has Advanced Metering Systems for water installed, due to a lack of budget, it can be difficult for government agencies to develop a solution that can be implemented statewide, then nationwide, in an estimated 2-3 years from now. this paper proposes an IoT-based smart water monitor that utilizes computer vision to record water data, then compares the data's deviance from the predicted usage to check for water leaks. We applied our application to monitoring the water usage of my own household and conducted a qualitative evaluation of the approach. the results show that the water monitor is an effective and affordable way to water management. © 2022 ACM.

关键词： Budget control

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Predicting China's Marriage Rate: Causal Inference Using Dual machine learning (DML) with XGBoost, LightGBM, CatBoost, and GBDT

Predicting China's Marriage Rate: Causal Inference Using Dua...

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Big data analytics and Practices (IBDAP), international conference on

作者： Deyu Zhang Worasak Rueangsirarak Surapong Uttama School of Information Technology Mae Fah Luang University Chiang Rai Thailand Computer and Communication Engineering for Capacity Building Research Unit School of Information Technology Mae Fah Luang University Chiang Rai Thailand Center of Excellence in Artificial Intelligence and Emerging Technologies School of Information Technology Mae Fah Luang University Chiang Rai Thailand

ISBN: (数字)9798350391749

ISBN: (纸本)9798350391756

After China's accession to the WTO and 20 years of rapid development, the marriage rate has shown a downward trend. this study aims to analyze the impact of socio-economic factors on the crude marriage rate (CMR) panel data in China from 2003 to 2022 using Dual machine learning (DML) for Causal Inference and machine learning models. DML was used to estimate ATE, CATE, and HTE for various factors. Four models—XGBoost, LightGBM, CatBoost, and GBDT—were employed for predictions, using 10-fold cross-validation for model evaluation. the results indicate that education (ATE 5.666) and birth rate (ATE 5.492) had the most significant positive impacts on CMR, while GDP (ATE 3.196) showed positive but varying effects, and the female proportion (ATE - 2.353) had a notable negative impact. CatBoost performed best in MSE (0.942) and RMSE (0.958), while LightGBM excelled in MAE (0.777). Education, GDP, and birth rate are key factors influencing CMR. CatBoost and LightGBM proved to be effective prediction models, though improvements are needed for regions with significant variability.

关键词： Analytical models Accuracy Economic indicators Education machine learning Predictive models Big data

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A Hierarchical Intrusion Detection System Based on machine learning 5

A Hierarchical Intrusion Detection System Based on Machine L...

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2022 5th international Symposium on Big data and applied Statistics, ISBDAS 2022

作者： Kong, Dehua Peng, Sicheng Zhai, Yihong Liu, Zhangyuan Zhang, Luming Wan, Zixuan Wuhan Donghu University School of Computer Science Wuhan430212 China China Three Gorges University College of Electrical Engineering and New Energy Hubei Yichang443002 China

the Intrusion detection system (IDS) is one of the most important tools for defending against abnormal flow and attack messages. Most of the existing IDSs use detection technology based on security policies, and there is a risk that it cannot be accurately analyzed and evaluated. therefore, machine learning techniques provide a new direction for solving this problem. this paper uses and analyzes the CIC-IDS series datasets, but there is a data imbalance in this dataset. In order to solve the problem of data imbalance and reduce the accuracy of the model, this paper proposes a hierarchical detection model. Experiments have shown that the stratified detection module has good classification accuracy for attack types with a small sample size. © Published under licence by IOP Publishing Ltd.

关键词： machine learning

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