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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111-120 订阅

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Development of machine learning Based Fraudulent Website Detection Scheme 5

Development of Machine Learning Based Fraudulent Website Det...

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5th IEEE international conference on Knowledge Innovation and Invention, ICKII 2022

作者： Chen, Shyh-Wei Chen, Po-Hsiang Tsai, Ching-Tsorng Liu, Chia-Hui Tunghai University Department of Computer Science Taichung City Taiwan Chinese Culture University Department of Applied Mathematics Taipei Taiwan

ISBN: (数字)9781665479295

ISBN: (纸本)9781665479295

the development of mobile computing and e-commerce has greatly changed traditional transactions and grown online shopping. People are buying and selling goods on websites or social platforms. However, there are many malicious and counterfeit products on fraudulent websites to deceive consumers and make high improper profits. Due to the obvious increase in the number of such fraudulent websites, it is difficult to identify and detect these websites by manual inspection. In order to solve this problem, we propose an intelligent detection mechanism by using a machine learning approach to classify fraudulent websites. We use data set containing 300 legitimate websites, 300 fraudulent websites, and 15 features for training. In two machine learning algorithms, Random Forest and Deep Neural Networks, we divided the training set and the test set in a ratio of 8:2. Finally, the prediction is compared with the previous research results. the experimental results show that the RF accuracy of the random forest algorithm is 99.3% which is better than other deep neural networks algorithms. © 2022 IEEE.

关键词： Deep neural networks

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Semi-Supervised Social Bot Detection with Initial Residual Relation Attention Networks

Semi-Supervised Social Bot Detection with Initial Residual R...

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5th international Workshop on learning with Imbalanced Domains - theory and Applications / European conference on machine learning and Principles and Practice of Knowledge Discovery in databases (ECML PKDD)

作者： Zhou, Ming Feng, Wenzheng Zhu, Yifan Zhang, Dan Dong, Yuxiao Tang, Jie Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9783031434266;9783031434273

Social bot detection is a challenging task and receives extensive attention in social security. Previous researches for this task often assume the labeled samples are abundant, which neglects the fact that labels of social bots are usually hard to derive from the real world. Meanwhile, graph neural networks (GNNs) have recently been applied to bot detection. Whereas most GNNs are based on the homophily assumption, where nodes of the same type are more likely to connect to each other. So methods relying on these two assumptions will degrade while encountering graphs with heterophily or lack of labeled data. To solve these challenges above, we analyze human-bot networks and propose SIRAN, which combines relation attention with initial residual connection to reduce and prevent the noise aggregated from neighbors to improve the capability of distinguishing different kinds of nodes on social graphs with heterophily. then we use a consistency loss to boost the detection performance of the model for limited annotated data. Extensive experiments on two publicly available and independent social bot detection datasets illustrate SIRAN achieves state-of-the-art performance. Finally, further studies demonstrate the effectiveness of our model as well. We have deployed SIRAN online: https://***/robotmain.

关键词： social networks social bot detection heterophily-aware attention semi-supervised learning

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Fake News Detection of South African COVID-19 Related Tweets using machine learning 5

Fake News Detection of South African COVID-19 Related Tweets...

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5th international conference on Artificial Intelligence, Big data, Computing and data Communication Systems (ICABCD)

作者： Khan, Yaseen thakur, Surendra Durban Univ Technol Dept Informat Technol Durban South Africa

ISBN: (数字)9781665484220

ISBN: (纸本)9781665484220

Social Media has grown in popularity in recent years' comprising of billions of users who in turn exchange and communicate content at a volume and rate impractical to examine manually. Fake News are now being used on these platforms to manipulate and affect societies across the world as was the case in the 2016 United States of America (USA) elections and of recent during the 2019 coronavirus disease (COVID-19) pandemic. South Africa is not immune to the spread of Fake News, particularly, through Social Media platforms such as Twitter, Facebook and TikTok. It is, therefore, important to detect the presence of Fake News computationally in order assist the mitigation of its spread and prevent perceivable negative effects. this study addresses the issue by developing a machine learning (ML) model to analyze large amounts of data associated with Social Media. Curated annotated datasets from CONSTRAINT AAAI 2021;COVID-19 Rumour, FNIR and Zenodo's COVID-19 datasets;Googk and Polifact Fact Checked websites;were utilized to develop the ML model Specifically, the model was trained on 36254 data points and applied on a South African related COVID-19 Twitter dataset collected for cursory analysis. In total, 27 ML models were experimented with and the collected South African COVID-19 related Twitter dataset comprised of 976087 tweets from 8 November 2020 until 19 July 2021. the results detected 329107 tweets as being 'Fake' based on the LightGBM Classifier which was chosen as the most feasible model in terms of speed and a balanced accuracy score of 0.82. the proposed model is unique as it is trained on a larger combined dataset and supplements existing efforts to combat misinformation, disinformation and malinformation spread on Twitter.

关键词： machine learning fake news covid-19 NLP

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Framework for Prediction of Diabetes using FireFly Swarm Intelligence Algorithm, Fuzzy C Mean and SVM Algorithm 5

Framework for Prediction of Diabetes using FireFly Swarm Int...

引用

5th international conference on Inventive Computation Technologies (ICICT)

作者： Kalpana, C. Booba, B. Vels Univ Dept CSE VISTAS Chennai Tamil Nadu India

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

Diabetes is a metabolic sickness that remains to be a major universal problem because it influences the health of the entire population. Over the years several researchers have attempted to build a model for predicting diabetes accurately. However, this field study remains a challenge because of the unavailability of dataset and prediction models which forces the researchers to utilize ML (machine learning) algorithm and Big data analytics. the primary objective of the study was to identify how ML and Big data analytics can be adopted in diabetes. the evaluation of outcomes demonstrates that the proposed ML-based technique might achieve 8.6% of accuracy. the authors performed a review of literature on the ML models and came out with the suggestion of an Automatic Intelligent method for the prediction of diabetes based on the findings. the authors explored ML approaches and proposed and developed an intelligent ML-based architecture for diabetes prediction. In this work, the authors used fuzzy mean, SVM learning models for the prediction of diabetes. A firefly algorithm optimized classifies was utilized for selecting the best features. It is proposed in the work, that an exclusive automatic intelligent optimize diabetic prediction model is built by utilizing ML approaches. the framework was created following a thorough analysis of existing prediction approaches in the literature and assessing their relevance to diabetes. the authors presented the training methods, model evaluation strategies, and the challenges allied to the prediction of diabetes also the remedies they provide by using the framework. Health professionals, students, researchers, and stakeholders working in diabetes forecast research development may benefit from the findings of the study. the proposed work achieves an accuracy of 86 percent with low error rates.

关键词： Fuzzy C-Means Clustering algorithm diabetes diagnosis medical data mining classification firefly algorithm

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A data-driven approach for neonatal mortality rate forecasting 5

A data-driven approach for neonatal mortality rate forecasti...

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5th international conference on Informatics and data-Driven Medicine (IDDM)

作者： Rodriguez, Elen Rodriguez, Elias Nascimento, Luiz da Silva, Aneirson Marins, Fernando Sao Paulo State Univ UNESP Ave Dr Ariberto Pereira Cunha 33 BR-12516410 Guaratingueta SP Brazil Univ Taubate UNITAU Estr Municipal Dr Jose Luiz Cembranelli 5-000 BR-1208101 Taubate SP Brazil

Neonatal mortality is an important public health problem that reflects the development of a country, as well as the quality of care provided to the newborn. this article presents the development and comparison of classical models and machine learning models for time series forecasting, applied to the forecast of monthly neonatal mortality rates in the metropolitan region of Paraiba River Valley and North Coast - Sao Paulo State - Brazil. the database used comprised the monthly rates from January 2000 to December 2020. the models compared were Seasonal Autoregressive Integrated Moving Average, random forest, support vector machine (SVM), light gradient boosting machine, categorical boosting (CatBoost), gradient boosting (GB), extreme gradient boosting, and multilayer perceptron. the best parameters and hyperparameters of the models tested were adjusted through an exhaustive computational search. the results showed that the CatBoost, SVM, and GB models presented the lowest values in the error metrics evaluated, and the SVM model presented better precision. the forecasts of the SVM model showed a behavior very close to the actual rates, which was confirmed by the application of the paired t-test. these results corroborate that time series forecasting models can significantly contribute as a decision support tool for public health problems.

关键词： Neonatal mortality time series analysis forecasting data-driven models machine learning

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A Two-stage H.264 based Video Compression Method for Automotive Cameras 5

A Two-stage H.264 based Video Compression Method for Automot...

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IEEE 5th international conference on Industrial Cyber-Physical Systems (ICPS)

作者： Wang, Yiting Chan, Pak Hung Donzella, Valentina Univ Warwick WMG Coventry W Midlands England

ISBN: (纸本)9781665497701

With the development of automated vehicles and advanced driver assistance systems, the compression of the large amount of data generated by the vehicle camera sensors becomes a necessary processing step to improve the automated driving system efficiency. H.264 is a widely adopted video compression scheme, and it has been designed for human vision. Rate control in H.264 uses fixed quantisation parameter, however, this process can lead to fluctuation in different regions of the image quality of each frame. In this paper, we propose a two-stage H.264 based video compression framework, named "Two Stage Compression (TSC)", to compress the automotive camera videos with different values of compression rate in different regions of each frame. In the first stage, each frame will be divided into the region-of-interest and the region-out-of-interest. In the second stage, different compression ratios will be applied based on the importance of the region. the experimental results show that under the same overall compression ratio, our proposed TSC increments the semantic-aware PSNR by 3.213 dB compared to uniform H.264 compression. Our method is also compared to uniform H.264 compression using a segmentation algorithm, with an improvement of 1.77% in mIOU, the average Intersection over Union.

关键词： Automotive camera data video compression semantic segmentation machine learning Intelligent Vehicles Automated and Assisted driving

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machine learning model for breast anticancer drug sensitivity prediction from gene expression 5

Machine learning model for breast anticancer drug sensitivit...

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5th IEEE international conference of Women in data Science (WiDS-PSU)

作者： Dawood, Safia Dawood, Aisha Saba, Tanzila Khan, Fatima Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Coll Comp & Informat Sci Riyadh Saudi Arabia

ISBN: (纸本)9781665408127

therapeutic action of drugs and their potential mechanisms provide an important basis for precision medicine. Cancer cell lines and drug sensitivities associated with different compounds create a valuable source for researchers to study the therapy response and help to convert in vitro findings of cell lines into in vivo therapeutic designs which will be reflected on patient care. In this study, we trained a predictive model for 26 anticancer drugs. these were chosen as the most used compounds in breast cancer therapy. these were aligned with gene expression data correlated with drug sensitivity measured by drugs' efficacy IC50 of cancer cell lines for different tumor tissues. the study identified drug gene associations using datasets from Cancer Cell Line Encyclopedia (CCLE). this research built a model that can predict drug resistance based on the sensitivity value IC50. through this study we managed to predict which drug compound class is suitable for which cancer tissue. Also, we found that according to genetic expression we can predict more cancer suppressing drugs. this model can be used for predicting preclinical drug trials for effectiveness across breast cancer types. Our proposed model successfully achieved an accuracy of over 70% of the 26 selected drug compounds.

关键词： Drug sensitivity prediction Precision medicine machine learning feature selection anticancer

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A Study for Precise Financial Support Model for Undergraduates with Financial Difficulties Using machine learning 22

A Study for Precise Financial Support Model for Undergraduat...

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5th international conference on Information Science and Systems, ICISS 2022

作者： Zhao, Zhihong Tian, Yuhe Bai, Tongyuan School of Applied Science and Civil Engineering Beijing Institute of Technology Zhuhai China School of Business Beijing Institute of Technology Zhuhai China Faculty of Natural Mathematical and Engineering Sciences King's College London United Kingdom

ISBN: (纸本)9781450396837

At present, the government pays more attention on student financial assistance in colleges and universities than before. In order to achieve advance prediction of the degree of difficulty of students and reduce the burden of relevant staff, we used the data of the school card consumption records and the data of subsidised students as the data set to achieve it. then, we established the model by using machine learning algorithms. Compared the difficulty level of economically disadvantaged students with the real data of subsidised students, it was concluded that the accuracy of the optimal logistic regression model reached at 100% and the model ran faster compared with other models at around 0.5s. the model can be better applied to student financial support work and improve the efficiency of working at the same time. © 2022 ACM.

关键词： Students

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Improving the quality of online learning: A study on teacher-student interaction based on network multi-modal data analysis 22

Improving the quality of online learning: A study on teacher...

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5th international conference on Big data and Education, ICBDE 2022

作者： He, Yang Gong, Yuqing Tianjin University China Nankai University China

ISBN: (纸本)9781450395793

Online learning has become a new trend in fragmented learning, with a large number of users. And, the emotional engagement of learners in their learning is a central topic in online learning research. However, many studies have found that online learners do not perform well in this area. this study used 10 instructional videos with 112,9 and 395,401 available subtitles and pop-ups. Emotional classifications of the corresponding data for teachers and learners were obtained based on the results of machine learning, deep learning and manual classification. A stimulus-organisms-response (S-O-R) model was applied to verify the applicability of teachers' emotional support and emotional engagement in learning to influence learners' retention intentions in online education. the results show that teachers' verbal emotions have a significant impact on students' learning experiences and that students' intention to continue learning increases when teachers' emotions have an interactive effect with students' emotions. © 2022 ACM.

关键词： Students

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Automating Battery Management System and Billing Using machine learning 5th

Automating Battery Management System and Billing Using Machi...

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5th international conference and Exhibition on Smart Grids and Smart Cities, ISUW 2019

作者： Salpekar, Moreshwar Boson Motors Pvt. Ltd. Pune India

ISBN: (纸本)9789811612985

Battery Management System (BMS) is one of core parts of Electric Vehicles. the charging station/point and electric vehicles must both accurately track the charging and the billing. the billing will also help the upstream electricity generators estimate the power used by a single charging as well as total power used by a station or a group of stations in a time period. machine learning can automate BMS tracking and billing and also provide alerts if supplemented by communication systems. It can also help predict power needed in a given period and also number of charging points required in a given area. the paper proposes a BMS that uses machine learning. It starts with Battery Management System (BMS) conceptual diagram and its explanation. It then gives the parameters to be measured. this is followed by proposed system design and it will work along with the ML algorithms that can be used. this is expected to aid the design of practical BMS system using machine learning. © 2022, Springer Nature Singapore Pte Ltd.

关键词： data analytics

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