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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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data analytics in Farming: Crop Yield Projection in Telangana State Employing machine learning Methods 5th

Data Analytics in Farming: Crop Yield Projection in Telangan...

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5th international conference on data Science, machine learning and Applications, ICDSMLA 2023

作者： Himaanshu, V. Suryanarayana, G. Prakash, L.N.C.K. Vignesh, Vamshi Srikar, B. Sai Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Information Technology Vardhaman College of Engineering Hyderabad India CVR College of Engineering Hyderabad India

ISBN: (纸本)9789819780426

Telangana state is in the southern part of India where the Godavari and Krishna rivers flow providing important water resources for agriculture. Agriculture is the primary occupation of the major population in the state, and it is also one of the essential sectors that majorly contributes to the state’s economy. Accurate estimation of crop yield production is essential for sustainable agricultural development, food security, and poverty reduction. therefore, various supervised machine learning procedures have been used to forecast the crop yield production in Telangana State. In this work, various algorithms are applied to the Telangana crop yield dataset. the results generated by these algorithms have been analysed and compared. With the help of crop yield analysis, farmers know their yield before their farming practice so that they can reveal and resolve their crop yield challenges. this article suggests a machine learning algorithm for the farmers to detect and solve their crop yield problems. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Leveraging machine learning to Optimize Real-Time data Analysis Performance 5th

Leveraging Machine Learning to Optimize Real-Time Data Analy...

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5th international conference on data Science, machine learning and Applications, ICDSMLA 2023

作者： Singh, Vaishali Kaushik, Harshita Verma, Archana Raghavendra, R. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Application Vivekananda Global University Jaipur India Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Leveraging system mastering (ML) algorithms has become an increasingly exciting technique for optimizing actual-time facts analysis performance. In the ever-growing age of massive facts, ML permits us to effectively make the experience of this information extra quickly and correctly. Traditionally, real-time statistics analysis requires complex guide evaluation or is constrained by the insufficient skills of conventional rule-based total strategies. ML can automate this method and identify patterns in the records that may not be apparent at once, even optimizing performance significantly. In addition, ML algorithms, including supervised and unsupervised methods, can be applied to label facts because it comes in, making it simpler to identify styles inside the statistics before a manual evaluation. this technique is called predictive analytics, and it dramatically reduces the time spent manually exploring the statistics. Usually, leveraging system learning to optimize the performance of actual-time records evaluation can improve the process of creating a sense of these facts. through that specialization in predictive analytics and utilizing supervised and unsupervised techniques, we will quickly and appropriately analyze the statistics and make powerful selections. It can be pretty helpful in a selection of industries and conditions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Predictive analytics

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Analysis of ML Models & data Balancing Techniques for Medicare Fraud Using PySpark 5

Analysis of ML Models & Data Balancing Techniques for Medica...

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5th IEEE international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Karthik, Konduru Praveen Gupta, Taduvai Satvik Kaushik, Doradla Ramesh, T.K. Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350356236

Medicare fraud is a persistent and costly issue, contributing to billions of dollars in annual losses in healthcare systems worldwide. With the increasing complexity of healthcare data and fraudulent schemes, traditional detection methods struggle to keep up, making Big data analytics and machine learning essential for effective fraud detection. this paper proposes a robust methodology for detecting Medicare fraud using a PySpark framework, leveraging datasets from the CMS, LEIE, FDA. the approach integrates these datasets to create a comprehensive profile for each claim and applies feature selection to identify indicators of fraudulent behavior. the methodology employs data balancing techniques, such as RUS and SMOTE to address class imbalances in fraud data. Various machine learning models, including Gradient Boosted Trees, Random Forest, and Logistic Regression, are trained and evaluated on their accuracy, precision, recall, and specificity scores. the experimental results show that the proposed models effectively identify fraudulent claims, with the model trained on data balanced at a 1:5 ratio of fraud to non-fraud cases achieving the highest specificity of 73%. this work not only improves Medicare fraud detection but also offers a scalable approach that can be applied to large-scale healthcare schemes globally, such as India's Ayushman Bharat program. the results underscore the potential of Big data solutions in safeguarding healthcare resources by mitigating fraud. © 2025 IEEE.

关键词： Logistic regression

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Prediction of Covid-19 Recovery Chances Using Supervised machine learning Classification 5th

Prediction of Covid-19 Recovery Chances Using Supervised Mac...

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5th international conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

作者： Dhanalakshmi, R. Nivashini, A. Vijayaraghavan, N. Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering MNM Jain Engineering College Chennai India Department of Mathematics KCG College of Technology Chennai India

ISBN: (纸本)9789819767311

Predictive analytics if combined with machine learning approaches have the potential to play a significant role in forecasting the spread of respiratory infections. machine learning approaches aid in the mining of data to better assess and forecast the presence or absence of covid-19 infection. In this paper, XGBoost Ensemble Classification using a fine-tuned approach is being used. Various machine learning algorithms are being analyzed and XGBoost Ensemble Classification algorithm is used to forecast and predict the covid-19 infected patient recovery chances that is the possible outcome of a covid-19 infected patient along with the prediction probability of recovery. the suggested categorization model is applied to a variety of situations. When compared to other ensemble models like random forest, hybrid ensemble and other individual classifiers such as decision tree, logistic regression, K-nearest neighbor, support vector machine and Naive Bayes, the XGBoost Ensemble Classification model gave the highest accuracy. Predicting the correct class is important. thus, the proposed system discusses the possibility of automated prediction for covid-19 infected patient recovery chances along with the probability of recovery and the effectiveness of such predictions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

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Comparison of Regressors applied to 3D Motion Marker data of a Historic Building Prototype Subjected to Shake Table Tests 5th

Comparison of Regressors Applied to 3D Motion Marker Data of...

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5th international conference on Protection of Historical Constructions-PROHITECH

作者： Palumbo, Domenico Ormando, Chiara Roselli, Ivan ENEA Casaccia Res Ctr Rome Italy

ISBN: (纸本)9783031873188;9783031873164;9783031873157

In the present study the performance of several machine learning regression techniques for the analysis of displacement data acquired in dynamic identification shaking table tests to assess a building's prototype state of damage were compared. the displacement data of a typical Italian historic masonry building prototype were recorded by a passive 3D motion capture system. these recorded displacement data were analyzed by calculating a widely accepted Damage Index (DI) based on the decay of the first modal frequency of the tested structure. To such purpose, a conventional Frequency Response Function (FRF) algorithm for modal analysis was adopted. then, several regressors, commonly used in machine learning applications, were applied to cepstral coefficients extracted from time-series segmentation of the 3D motion markers signals to predict the DI values. To assess the quality of the tested regressors, Coefficient of determination (R-2) and Relative Percent Deviation (RPD) parameters were evaluated. Different lengths of time-series segments were also explored and the results were statistically compared to highlight the differences between regressors. Excellent results were obtained in terms of R-2 (0.98) and RPD (6.87) with a weak dependence on the time partitioning performed on the data with optimal results for 4 or more seconds. Moreover, Stacked Regressor, Decision Tree Bagging Regressor and XGB Regressor were found as the best overall regressors. the results obtained demonstrated that machine learning methods can be effectively utilized to predict the level of damage of historic masonry buildings with very high accuracy.

关键词： machine learning regression historic buildings

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applied Health Informatics for Diabetes Risk Prediction from Food Consumption Behavior of Rural Communities in Northern thailand with data analytics 5th

Applied Health Informatics for Diabetes Risk Prediction from...

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5th international conference on Computing and Network Communications, CoCoNet 2023

作者： Nuankaew, Pratya Kadkasame, Apatcharaporn Sawasit, Kunthida Nasa-Ngium, Patchara Sararat, thapanapong Nuankaew, Wongpanya S. School of Information and Communication Technology University of Phayao Phayao56000 Thailand Faculty of Science and Technology Rajabhat Maha Sarakham University Maha Sarakham44000 Thailand

ISBN: (纸本)9789819747108

this research has three critical research objectives. the first objective is to study the context and risk of diabetes from the food consumption behavior of thai people in the northern region. the second objective is to develop a model for predicting diabetes risk from the food consumption behavior of thai people in the northern region. the last objective is to evaluate the efficiency of the model for predicting diabetes risk from the dietary behavior of thai people in the northern region. the population and sample in this research were 370 people in Chiang Muan District, Phayao Province, aged between 20 and 99 years. the research tools include an IOC-approved questionnaire and data mining analysis using machine learning and artificial intelligence tools known as CRISP-DM. Model performance was evaluated using data partitioning and testing known as split-validation methods and confusion matrix performance. the research results found that the context of rural people in northern thailand toward food consumption behavior was normal. Overall, the risk of people developing diabetes was at an acceptable level. the developed model has the highest level of accuracy with an accuracy value of 81.80%. It is given in Tables 11 and 12 that people have good food consumption habits. Moreover, the model for predicting the risk of diabetes in rural communities was found to have a high level of accuracy by comparing various techniques, as given in Table 4. this research, therefore, concludes that all objectives were achieved. the results and expectations from the research are to know the food consumption context of rural people in the northern region of thailand, which can be used to design and plan strategies to prevent the increase in people at risk of diabetes in thailand. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： applied informatics Diabetes risk prediction Food consumption behavior Health forecasting model Health informatics

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Integrating Artificial Intelligence in Supply Chain: Enhancements and Challenges

Integrating Artificial Intelligence in Supply Chain: Enhance...

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Innovative Research in applied Science, Engineering and Technology (IRASET),conference on

作者： Oussama Zabraoui Anas Chafi Salaheddine Kammouri Alami Department of Industrial Engineering Sidi Mohammed Ben Abdellah University Faculty of Science and Technology Fez Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

the integration of Artificial Intelligence (AI) into supply chain enhances processes like logistics, procurement, and decision-making. AI technologies such as machine learning and predictive analytics improve demand forecasting, inventory management, and supply chain optimization. Benefits include real-time decision-making, automation, cost reduction, and risk mitigation. However, challenges like data silos and workface readiness persist. Advances in cloud computing and loT are accelerating AI adoption, leading to more agile and resilient supply chains. Continuous investment in AI adoption, leading to more agile and resilient supply chains. Continuous investment in AI and skill development is essential for maintaining a competitive edge.

关键词： Procurement Costs Automation Supply chains Decision making machine learning Real-time systems Predictive analytics Investment Risk mitigation

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applied machine learning and data analytics 1

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丛书名： Communications in Computer and Information Science

1000年

作者： M. A. Jabbar Fernando Ortiz-Rodríguez Sanju Tiwari Patrick Siarry

ISBN: (数字)9783031342226

ISBN: (纸本)9783031342219

this book constitutes the refereed proceedings of the 5th international conference on applied machine learning and data analytics, amlda 2022, held in Reynosa, Tamaulipas, Mexico, during December 22–23, 2022. the 16 full papers and 4 short papers included in this book were carefully reviewed and selected from 89 submissions. they were organized in topical sections as follows: machine learning, Healthcare and medical imaging informatics; biometrics; forensics; precision agriculture; risk management; robotics and satellite imaging.

关键词： Artificial Intelligence Computer Applications Computer Imaging, Vision, Pattern Recognition and Graphics database Management

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A Survey on the Impact of data analytics and machine learning Techniques in E-commerce 5

A Survey on the Impact of Data Analytics and Machine Learnin...

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

作者： Dheva Rajan, S. Vavilapalli, Sivajee Hasan, Shahriar Kumar, Ritesh Rafa, Nafisa Muda, Iskandar University of Technology and Applied Sciences Almusannah Information Technology Oman Duvvada Department of Management Studies Visakhapatnam India University of Dhaka Department of Finance Bangladesh Maharishi School of Engineering and Technology Uttar Pradesh Lucknow India BBA in Marketing Institute of Business Administration University of Dhaka Dhaka Bangladesh Universitas Sumatera Utara Faculty Economic and Business Department of Doctoral Program Medan20222 Indonesia

ISBN: (纸本)9798350398267

Current article has been developed for understanding the impact of data analytics and machine learning technique in e-commerce. If the observation has been developed for the current market of this technology it can be detected that real-life industry increases their investment which showed the interest in getting advantages. Identification of the problem of costing and different algorithm suitability objectives has been set for understanding the real impact of e-commerce. Secondary data collection has been used for understanding the different usability of machine learning and data analytics for e-commerce. It has been detected that using machine learning brings the benefits of automation and improves continuous growth. data analytics provide the scope of accuracy build-up. Apart from this different area of application and the different techniques has been analyzed. Understanding become easier and the scope of future benefits can be identified. © 2022 IEEE.

关键词： data analytics

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