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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1-10 订阅

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5th international conference on applied machine learning and data analytics, amlda 2022

5th International Conference on Applied Machine Learning and...

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5th international conference on applied machine learning and data analytics, amlda 2022

ISBN: (纸本)9783031342219

the proceedings contain 20 papers. the special focus in this conference is on applied machine learning and data analytics. the topics include: RU-Net: A Novel Approach for Gastro-Intestinal Tract Image Segmentation Using Convolutional Neural Network;a Credit Card Fraud Detection Model Using machine learning Methods with a Hybrid of Undersampling and Oversampling for Handling Imbalanced datasets for High Scores;Implementation of YOLOv7 for Pest Detection;online Grocery Shopping: - Key Factors to Understand Shopping Behavior from data analytics Perspective;a Novel Approach: Semantic Web Enabled Subject Searching for Union Catalogue;how People in South America is Facing Monkeypox Outbreak?;multilevel Classification of Satellite Images Using Pretrained AlexNet Architecture;handwriting Recognition for Predicting Gender and Handedness Using Deep learning;retrieval of Weighted Lexicons Based on Supervised learning Method;univariate Feature Fitness Measures for Classification Problems: An Empirical Assessment;performance Evaluation of Smart Flower Optimization Algorithm Over Industrial Non-convex Constrained Optimization Problems;securing Advanced Metering Infrastructure Using Blockchain for Effective Energy Trading;keratoconus Classification Using Feature Selection and machine learning Approach;Semantic Segmentation of the Lung to Examine the Effect of COVID-19 Using UNET Model;SMDKGG: A Socially Aware Metadata Driven Knowledge Graph Generation for Disaster Tweets;Brain MRI Image Classification Using Deep learning;a Real-Time Face Recognition Attendance Using machine learning;towards Abalone Differentiation through machine learning.

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Forecasting User Payment Behavior Using machine learning 1

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6th international conference on applied machine learning and data analytics, amlda 2023

作者： Vora, Deepali Choudhary, Rupali Kumar, Aman Kadam, Payal Lavale Pune India College of Engineering Pune India

ISBN: (数字)9783031554865

ISBN: (纸本)9783031554858

the payment prediction model is a software application that uses machine learning algorithms to predict future payments based on historical data. these models are crucial for organizations like credit card companies, banks, and other financial institutions that depend on recurring payments from their clients businesses may determine the possibility that consumers will miss their payments by utilizing payment prediction models, and they can then take action to either avoid or lessen the effects of any prospective non-payment. this system works on real-time data, data goes to different stages including data collection and storage, data pre-processing and cleaning, feature engineering, and model development. the system uses machine learning algorithms such as linear regression, decision trees, or XG boost to predict future payments based on historical payment data. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： User interfaces

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Predicting China's Marriage Rate: Causal Inference Using Dual machine learning (DML) with XGBoost, LightGBM, CatBoost, and GBDT 5

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

引用

5th international conference on Big data analytics and Practices (IBDAP)

作者： Zhang, Deyu Rueangsirarak, Worasak Uttama, Surapong Mae Fah Luang Univ Sch Informat Technol Chiang Rai Thailand Mae Fah Luang Univ Comp & Commun Engn Capac Bldg Res Unit Sch Informat Technol Chiang Rai Thailand Mae Fah Luang Univ Sch Informat Technol Ctr Excellence Artificial Intelligence & Emerging Chiang Rai Thailand

ISBN: (纸本)9798350391749

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： Marriage Rate DML XGBoost CatBoost LightGBM GBDT ATE CATE RMSE

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Handwriting Recognition for Predicting Gender and Handedness Using Deep learning 1

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5th international conference on applied machine learning and data analytics, amlda 2022

作者： Saraswat, Mala Agarwal, Ayushi School of Computer Science Engineering and Technology Bennett University UP Greater Noida India ABES Engineering College UP Ghaziabad India

ISBN: (数字)9783031342226

ISBN: (纸本)9783031342219

this research focuses on predicting the gender and handedness and also combined prediction through Handwriting Recognition. In this paper we have analyzed various Deep learning approaches using different CNN models. Our proposed approach uses two data sets IAM English dataset and Real-Time dataset that was collected by us for recognizing the handwriting pattern to perform the defined task. Using different Deep learning approaches, experiments were conducted to analyze the performance on the given two datasets for predicting. Gender and handedness. Experiments showed Google Net using CNN is the most efficient for the prediction and recognition with the handwriting samples. For combined prediction the accuracy rate is around 94% with Real-Time dataset and 95% with the IAM dataset. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Fault Diagnosis Method for Rotating machinery Based on Multi-Task Support Matrix machine 5

Fault Diagnosis Method for Rotating Machinery Based on Multi...

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5th international conference on Sensing, Measurement and data analytics in the Era of Artificial Intelligence, ICSMD 2024

作者： Pan, Haiyang Chen, Chunan Zheng, Jinde Tong, Jinyu Chen, Jian School of Mechanical Engineering Anhui University of Techonology Ma'anshan China

ISBN: (纸本)9798331529192

Support Matrix machine (SMM), as a novel classification method using the matrix as the input, has been widely used in the fault diagnosis by fully utilizing the structured information between rows and columns of the input matrix. However, For the diagnosis problem of multiple tasks, SMM does not consider the direct correlation of multiple tasks, thus unable to achieve accurate diagnosis. therefore, a Multi-task Support Matrix machine (MTSMM) is proposed in this paper, in which ascending terms are defined to make full use of the correlation between the rows and columns of matrices while dealing with complex matrix samples brought by multi-task learning. Meanwhile, and the multi-task learning theory is introduced to construct multitask classification hyperplanes, so as to achieve the sharing of matrix feature data for multiple tasks and realize the multitask classification performance. Finally, the proposed method is applied to mechanical fault diagnosis, and the results show that the proposed MTSMM has good multi-task classification performance. © 2024 IEEE.

关键词： Multi-task learning

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Towards Abalone Differentiation through machine learning 1

引用

5th international conference on applied machine learning and data analytics, amlda 2022

作者： Barrera-Hernandez, Ruben Barrera-Soto, Viridiana Martinez-Rodriguez, Jose L. Rios-Alvarado, Ana B. Ortiz-Rodriguez, Fernando Multidisciplinary Academic Unit Reynosa-Rodhe Autonomous University of Tamaulipas Reynosa Mexico Faculty of Engineering and Science Autonomous University of Tamaulipas Victoria Mexico

ISBN: (数字)9783031342226

ISBN: (纸本)9783031342219

Abalone is a marine species of mollusks of a great lineage with different properties for human consumption. Abalone farming is supported by productive and industrial infrastructure in several countries, including Mexico, where polyculture is being experimented with to optimize operating costs. One of the tasks of the producers is to identify the sex of each abalone to apply certain measures for its growth or to take steps for its preservation. However, this task can be challenging for those just starting the process. this paper presents a case study for classifying abalone sex using machine learning algorithms. A methodology is presented that involves the analysis and processing of information to train and configure four different classification algorithms to recognize three types of classes: male, female, and immature. the results demonstrate the impact of using linear classifiers for this task. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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Keratoconus Classification Using Feature Selection and machine learning Approach 1

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5th international conference on applied machine learning and data analytics, amlda 2022

作者： Smily Yeya Jothi, E. Anitha, J. Hemanth, Jude Department of Biomedical Instrumentation Engineering Avinashilingam Institute for Home Science and Higher Education for Women Coimbatore India Department of Electronics and Communication Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9783031342226

ISBN: (纸本)9783031342219

the development and design of new tools that can help identify the disease at an early stage may aid in preventing or delaying the progression of the disease. the preservation of young populations’ vision is crucial because Keratoconus (KC) typically affects people at puberty, with children being the most affected. the goal of this research is to identify the most pertinent variables in relation to the various keratoconus classifiers employed in the Harvard dataverse keratoconus dataset. Out of 3162 observations, a total of 446 parameters are examined by 3 feature selection techniques, Sequential Forward, and Backward Selection (SFS and SBS), Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO). the prediction is done through machine learning algorithms. the experimental results implied that Sequential forward selection and PSO provided the highest classification accuracy by the application of Negative Correlation based Deep Ensemble Network (NC-DEN), with an accuracy of 96.14%. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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Online Grocery Shopping: - Key Factors to Understand Shopping Behavior from data analytics Perspective 1

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5th international conference on applied machine learning and data analytics, amlda 2022

作者： Das, Pritha Chawla, Udit Chattopadhyay, Subrata University of Engineering and Management Kolkata India

ISBN: (数字)9783031342226

ISBN: (纸本)9783031342219

Happy customers affect happy retailers and fruitful businesses. In the last few decades’ online grocery shopping has relished robust development and after the deadly COVID-19 pandemic, people are forced to stay back at home, which made grocery shopping online. Not only traditional shopping but general online shopping differs from this because of its variability and perishability nature as compared to other products. With the leaping momentum of online grocery shopping, retailers and producers are required to understand the needs and perceptions of their customers towards it to satisfy them to the fullest and improve their business. From this study seven different factors have been found to be affecting the customers’ purchasing behavior, ‘Ease of Shopping’, ‘Pandemic Arisen Factors’, ‘Financial Risk’, ‘Technology Driven’, ‘Product Performance and Psycho-Social Risk’, ‘Location and Quantity Constraint’, and ‘Entertainment’. Among those, ‘Pandemic Arisen Factors’ and ‘Technology Driven’ are significantly impacting consumers’ online purchases. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： COVID-19

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Predictive analytics for Power Transformers and Energy Consumption with machine learning 5

Predictive Analytics for Power Transformers and Energy Consu...

引用

5th international conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Muthukumar, S. Swetha, S. Us, Sutharshanagayathri St. Joseph's Institute of Technology Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798331530013

the power-generating plants, along with their long transmission lines, transformers, and the network of distribution systems, result in quite a complex mesh required for the smooth delivery of electricity over long distances. Power transformers, which step up or lower voltages to facilitate effective transmission, result in failure due to wear and tear, causing disruptions in supply and huge losses in terms of economy. this project enhances grid reliability through the prediction of transformer failures and forecasting energy consumption. Historical maintenance data, operational parameters, sensor readings, and external factors in terms of weather conditions predict the health of a transformer as well as the forecast the energy demand. machine learning models applied were Linear Regression for failure prediction, while the decision tree and Random Forest were applied for energy forecasting. the other feature which was selected on the Random Forest and the Decision Tree models shows the highest performance ever achieved and could definitely help to better improve the resilience of the grid. Even a 1% reduction in outages can save high amounts every year, making the true worth of precise predictive analytics in the power industry eminent. © 2024 IEEE.

关键词： Distribution transformers

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Research on Rolling Bearing Fault Diagnosis Based on Distributed Fuzzy Broad learning System Under Imbalanced data 5

Research on Rolling Bearing Fault Diagnosis Based on Distrib...

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5th international conference on Sensing, Measurement and data analytics in the Era of Artificial Intelligence, ICSMD 2024

作者： Pan, Haiyang Liu, Haoran Zheng, Jinde Tong, Jinyu Cheng, Jian School of Mechanical Engineering Anhui University of Technology Ma'anshan243032 China

ISBN: (纸本)9798331529192

As a novel fast and effective intelligent algorithm, Broad learning System (BLS) is popularly used in data classification task. However, the real data are often imbalanced, which makes BLS have limited performance in dealing with imbalanced data and cannot achieve good classification results. In view of this, a method based on Distributed Fuzzy Broad learning System (DFBLS) under imbalanced data is proposed in this paper. the model assigns a fuzzy affiliation value to each training point, reducing the effect of imbalanced samples on the model, and DFBLS executes fuzzy function in parallel, thus improving computational efficiency and processing speed. Moreover, DFBLS learns the features in the input data by mapping features and enhancement features, which solves the deficiency of fuzzification that leads to the loss or confusion of data samples. Finally, it is validated by two rolling bearing experimental datasets, and the research results show that this technology has demonstrated excellent performance in various evaluation indexes and provides an efficient solution to the fault diagnosis problem in imbalanced data sets. © 2024 IEEE.

关键词： Adversarial machine learning

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