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检索条件"机构=Machine Learning and Data Engineering"

共 597 条记录，以下是101-110 订阅

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Mediguide:A machine learning based Smart Prescription System for Personalized Healthcare

Mediguide:A Machine Learning based Smart Prescription System...

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2025 International Conference on Artificial Intelligence and data engineering, AIDE 2025

作者： Kumar K, Jeyaganesh Mithilesh, G. Monisha, K.M. Nandhini, S. Nithish Kumar, M. M.Kumarasamy College of Engineering Department of Artificial Intelligence and Data Science Karur India M.Kumarasamy College of Engineering Department of Artificial Intelligence and Machine Learning Karur India

ISBN: (纸本)9798331527518

This project uses a Decision Tree Classifier model which gives accurate health predictions based on the symptoms provided by users. The model has been trained at thousands of symptoms against diseases and thus predicts very accurately for the inputted data provided by the user. The system has an easy-to-use interface that helps users input their symptoms with ease and get instant predictions of potential diseases. In addition to predicting ailments, the application provides extensive information on the detected disease. Users are given a comprehensive breakdown of the disease, recommended medications, precautions, and personalized advice on diet and exercise regimes. It provides health insights that are meant to help users better manage their health and make better lifestyle decisions. With feedback loops, prediction accuracy and user experience can always be improved directly without worrying much about each setup. It seeks to use advanced technology to make practical health advice possible. Combining disease prediction in real time with meaningful health advice creates added value for users and encourages informed health choices. It enables users to own their health by providing them with a complete picture of their possible health problems and possible solutions to address them. This Project provides a powerful tool for more proactive users, with its combo of predictive analytics and health education resources.(And it helps us recognise the sport itself the user-friendly and detailed output that makes it easy and helpful for everyone to understand their health and what they can do about it.) It upskill traditionally trained AI learners to be perpendicular over ruinous health data towards an accessibly governed future, furnished with health facts and outcomes to improvement on the go. © 2025 IEEE.

关键词： Personalized medicine

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data Assimilation for Microstructure Evolution in Kinetic Monte Carlo 152nd

Data Assimilation for Microstructure Evolution in Kinetic Mo...

引用

152nd Annual Meeting and Exhibition of The Minerals, Metals and Materials Society, TMS 2023

作者： Tran, Anh Wang, Yan Rodgers, Theron Scientific Machine Learning Sandia National Laboratories AlbuquerqueNM United States Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30332 United States Computational Materials and Data Science Sandia National Laboratories AlbuquerqueNM United States

ISBN: (纸本)9783031225239

Modeling grain growth has been a subject of interest in computational material science, as it occurs in thermal-based processing methods such as annealing and sintering. Kinetic Monte Carlo with Potts model is often used as an integrated computational materials engineering (ICME) grain growth model and can generate high-fidelity synthetic microstructures. In this paper, we offer a data-driven stochastic calculus perspective on the kinetics of grain growth and model the microstructure evolution through the lens of stochastic differential equations, based on Langevin dynamics and Fokker-Planck equation to forecast the grain size distribution. We demonstrate that our proposed approach agrees reasonably well with the hybrid Potts-phase field model using SPPARKS in forecasting the long-term evolution of grain size distribution. © 2023, The Minerals, Metals & Materials Society.

关键词： Grain growth

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Improving Hotel Search Autocomplete in Online Travel Agent (OTA) Mobile Apps Using Elasticsearch and learning to Rank

Improving Hotel Search Autocomplete in Online Travel Agent (...

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International Conference on ICT For Smart Society (ICISS)

作者： Yoga Aliarham Pratama Elisafina Siswanto Setia Budi Jonathan Liono Arif Qodari Irvan B. Arief-Ang Machine Learning Engineering PT. Global Tiket Network Indonesia Data Science PT. Global Tiket Network Indonesia Data Science PT. Global Tiket Network Singapore

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

The use of smart search autocomplete for accommodation discovery within Online Travel Agent (OTA) platforms is essential, as it significantly influences the user experience when utilizing these services. This paper presents a method to improve search autocomplete for accommodation discovery in OTA mobile apps using a learning to Rank (LTR) model. The LTR model is implemented as the core module in Elasticsearch's Query Auto Completion (QAC) service. The proposed method uses a two-step process for generating autocomplete results, involving candidate filtering and final result generation based on the LTR prediction score. This study demonstrates the effectiveness of the proposed method in enhancing the average click-through position by 7.74% for queries with two to four characters, resulting in a 6.46 % increase in the number of items clicked in the first position and a reduction of the average time required for users to click on autocomplete results by 33 %. The findings that we conclude in this extensive study could be used to inform and provoke the work of researchers and engineers in the field of search autocomplete by providing insights on how to improve user experiences through the implementation of LTR techniques in Elasticsearch.

关键词： Filtering User experience Mobile applications

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Recommending View Bundles in data Marketplaces

Recommending View Bundles in Data Marketplaces

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： De Carvalho, Thiego Buenos Aires Martins, Denis M. Lima De Lima Neto, Fernando B. Vossen, Gottfried University of Pernambuco Computing Engineering Program Recife Brazil University of Münster Ercis Machine Learning and Data Engineering Münster Germany University of Münster Ercis Databases and Information Systems Münster Germany

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

For companies to have a competitive advantage, they need to extract relevant information from data and for that, they need to complement their own data with other data sources. data marketplaces are platforms on which data providers and data consumers do business. However, every data interaction incurs a monetary cost. Therefore, data consumers are interested in buying a set of interesting views that fit their (goal and) budget. Views allow data to be represented visually, enabling users to make sense of patterns and insights. Besides allowing for easier cost control, buying a set of views bundled together increases the chance of finding what consumers want over buying them view-by-view. Selecting the suitable views to compose an interesting bundle is non-trivial, due to the vast number of view combinations that potentially meet the data consumer's needs. In this paper, we address the problem of view bundle recommendation in data marketplaces, in which the utility of a bundle depends on the interplay among candidate views. We propose the use of Self-Organizing Maps as a means to compute this interplay and use a Genetic Algorithm to design near-optimal bundles. Our empirical results demonstrate that our approach can effectively aid data consumers to find relevant view bundles under budget constraints. © 2022 IEEE.

关键词： Genetic algorithms

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Anisotropic Diffusion-Based Enhancement of Scene Segmentation with Instance Labels 19th

Anisotropic Diffusion-Based Enhancement of Scene Segmentatio...

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19th International Conference on Computer Analysis of Images and Patterns, CAIP 2021

作者： Kleitsiotis, Ioannis Mariolis, Ioannis Giakoumis, Dimitrios Likothanassis, Spiridon Tzovaras, Dimitrios Thessaloniki Greece Large Scale Machine Learning and Cloud Data Engineering Lab Computer Engineering and Informatics University of Patras Patras Greece

ISBN: (纸本)9783030891305

Many visual scene understanding applications, especially in visual servoing settings, may require high quality object mask predictions for the accurate undertaking of various robotic tasks. In this work we investigate a setting where separate instance labels for all objects under view are required, but the available instance segmentation methods produce object masks inferior to a semantic segmentation algorithm. Motivated by the need to add instance label information to the higher fidelity semantic segmentation output, we propose an anisotropic label diffusion algorithm that propagates instance labels predicted by an instance segmentation algorithm inside the semantic segmentation masks. Our method leverages local topological and color information to propagate the instance labels, and is guaranteed to preserve the semantic segmentation mask. We evaluate our method on a challenging grape bunch detection dataset, and report experimental results that showcase the applicability of our method. © 2021, Springer Nature Switzerland AG.

关键词： Diffusion

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NC-ALG: Graph-Based Active learning under Noisy Crowd 40

NC-ALG: Graph-Based Active Learning under Noisy Crowd

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Zhang, Wentao Wang, Yexin You, Zhenbang Li, Yang Cao, Gang Yang, Zhi Cui, Bin Center for Machine Learning Research Peking University China Key Lab of High Confidence Software Technologies Peking University China Institute of Advanced Algorithms Research Shanghai China Institute of Computational Social Science Peking University Qingdao China National Engineering Labratory for Big Data Analytics and Applications China TEG Tencent Inc. Department of Data Platform China Beijing Academy of Artificial Intelligence China

ISBN: (纸本)9798350317152

Graph Neural Networks (GNNs) have achieved great success in various data mining tasks but they heavily rely on a large number of annotated nodes, requiring considerable human efforts. Despite the effectiveness of existing GNN-based Active learning (AL) methods, they assume that the annotated labels are always correct, which is contradictory to the error-prone labeling process in a practical crowdsourcing environment. Besides, due to this impractical assumption, existing works only focus on optimizing the node selection in AL but neglect optimizing the labeling process. Therefore, we present NC-ALG, the first GNN-based AL framework that optimizes both the node selection and node labeling process under a noisy crowd. For node selection, NC-ALG introduces a new measurement to model influence reliability and an effective influence maximization objective to select nodes. For node labeling, NC-ALG significantly reduces the labeling cost by considering the model-predicted labels and the labels of mirror nodes. To the best of our knowledge, this is the first attempt to consider GNN-based AL under the practical noisy crowd. Empirical studies on public datasets demonstrate that NC-ALG significantly outperforms existing methods in terms labeling efficiency. Notably, it only takes NC-ALG one-third of the labeling budget that the competitive baseline GRAIN needs to achieve an accuracy of 70.7 % on PubMed. © 2024 IEEE.

关键词： Crowdsourcing

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An Enhanced Product Quality Evaluation using Hybrid LSTM-FCNN Model and GWO Algorithm for Social Media Sentiment Analysis 8

An Enhanced Product Quality Evaluation using Hybrid LSTM-FCN...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Raj Kumar, V.S. Kumaresan, T. Kanna, P. Rajesh Jagadeesan, S. Showmiya, P. Nithin, P. Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India Nandha Engineering College Department of Computer Science and Engineering Erode India Nehru Institute of Technology Department of Computer Science - Cyber Security Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Erode India

ISBN: (纸本)9798350367904

Sentiment analysis is still in its developing era, and sometimes, struggles are faced in evaluating user sentiment due to the use of traditional models to analyze the relationship structures in social media interactions. To tackle the issue, we proposed an innovative approach Hybrid LSTM neural network and a fully connected neural network (FCNN) integrated with the Grey Wolf Optimization (GWO) algorithm. The objective of this proposed method is to improve sentiment analysis by employing the LSTM to capture the sequence of data and improve the ability of FCNNs to improve feature extraction and the analysis process. The proposed system process initiated with the data preprocessing of input Twitter sentiment dataset transformed into a suitable input to the Hybrid LSTM-FCNN neural network. Then, temporal dependency of the data captured by LSTM and FCNN is used to extract the features. The GWO method mimics the grey wolf hunting behaviour, for optimizing the proposed Hybrid LSTM-FCNN model's parameters to enhance the classification accuracy. The system models start with the initialization of the model, parameters, and training the models using the dataset to reduce the cross-entropy loss, estimating the testing dataset from unseen data and fine-tuning using GWO as necessary. Results demonstrate significant improvements in sentiment categorization compared to traditional methods. The Hybrid LSTM-FCNN model, enhanced with GWO, effectively captures nuanced sentiment patterns, leading to more accurate classification. Additionally, GWO's computational efficiency contributes to scalability. In conclusion, our proposed approach offers a promising solution for enhancing product quality evaluation through advanced neural network architectures and optimization algorithms applied to social media sentiment analysis. © 2024 IEEE.

关键词： Tweets

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A Comprehensive Study on Parkinson's Disease Detection Through machine learning Techniques

A Comprehensive Study on Parkinson's Disease Detection Throu...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Burle, Rushikesh Gaurkhede, Sanskruti Fulkar, Bhushan Gundewar, Swapnil Pacharaney, Utkarsha Verma, Prateek Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Computer Science and Design Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Data Science Faculty of Engineering and Technology Mahartashtra Wardha442001 India

ISBN: (纸本)9798350375237

One of the biggest issues with worldwide public health is Parkinson's disease (PD). It is usually known that about are around one million Americans who have Parkinson's disease, whereas there are approximately five million cases of the condition globally. As a result, it's critical to identify Parkinson's disease early on and make the appropriate treatment plans. These days, machine learning is essential for analysing problems in real time and offering answers thanks to its widely used algorithms. These days, machine learning algorithms are used in the healthcare industry to identify patients' health problems. This research went into great detail about the methods used using algorithms that use machine learning to classify Parkinson's disease. A fault in the brain cells that generate dopamine, a necessary chemical for synapses to connect with one another, is the cause of Parkinson's disease. The brain's dopamine-producing neurons are in charge of control, flexibility, and event familiarity. When 60-80% of these cells disappear, inadequate dopamine is produced, which causes Parkinson's disease symptoms to manifest. In this case, the Random Forest, Decision Tree, K-Nearest Neighbours, and Support Vector machine techniques were used to identify the illness. Compared to Decision Tree and Support Vector machine, Random Forest and K-Nearest Neighbours offer the best performance. © 2024 IEEE.

关键词： Support vector machines

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Prediction of Heart Disease Using machine learning Algorithms 2

Prediction of Heart Disease Using Machine Learning Algorithm...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Burle, Rushikesh Gaurkhede, Sanskruti Dewangan, Leelkanth Pacharaney, Utkarsha Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Computer Science and Design Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798350360523

In India, heart disease claims the lives of about one person every day. The human body's most vital organ is the heart. The heart has two primary purposes: first, it gathers blood from body tissues and pumps it to the lungs;second, it gathers blood from the lungs and pumps it to all body *** disease has claimed the lives of many people. Predicting that disease at the appropriate moment is crucial, for this reason. Making use of machine learning Additionally, illnesses are easily identified and anticipated. It also covers the use of many algorithms on the Heart Disease dataset. The dataset used in this research was obtained from Kaggle. Analyzing fundamental machine learning algorithms for the prediction of heart disease, such as Random Forest, K-Nearest Neighbours, Decision Tree Classifier, and Support Vector machine, is the primary goal of this study. Our Acquired Model for The Decision Tree Classifier has a score of 79.0%. And the Random Forest Classifier has an 84.0% score. With a linear kernel, the Support Vector Classifier scores 83.0%. Next, K Neighbours Classifier~ outperformed all previously employed methods, achieving the maximum score of {87%} with ~8 nearest result. RF (Random Forest), K-Nearest Neighbours Algorithm, and Decision Tree, together with Python programming and Visual Studio, which excels at both data and code. © 2024 IEEE.

关键词： Heart

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Stroke Home Rehabilitation Approach Using Mobile Application Based on PostNet machine learning Model 23

Stroke Home Rehabilitation Approach Using Mobile Application...

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7th International Conference on Medical and Health Informatics, ICMHI 2023

作者： Das, Utpal Chandra Le, Ngoc Thien Benjapolakul, Watit Vitoonpong, Timporn Pluempitiwiriyawej, Charnchai Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Department of Electrical Engineering Chulalongkorn University Bangkok10330 Thailand Department of Rehabilitation Medicine Faculty of Medicine Chulalongkorn University Bangkok10330 Thailand Multimedia Data Analytics and Processing Research Unit Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand

ISBN: (纸本)9798400700712

Stroke is a significant cause of mortality and disability globally, with its occurrence in the human brain and motor function being linked to various parts of the human body. Stroke victims often experience disabilities or mobility problems in affected body parts, either on one or both sides of the body. Physiotherapy exercises are the primary treatment and medication for stroke patients, necessitating daily monitoring by a physiotherapist. However, this approach is expensive, and rehabilitation centers and physicians are scarce. Numerous research studies have been conducted to address this issue from various perspectives. This study proposes a Convolutional Neural Network (CNN) based pose net machine learning (ML) model for stroke home rehabilitation using pose detection and classification with a skeleton base model and human pose estimation drawing. We trained our ML model using different human pose images for this model. The tested accuracy of our CNN model is 100% for our exercise pose image test case. Later we build and mobile application for remote rehabilitation. Tested the application with ten different subjects, achieving a 98% for elbow extension, and in the case of elbow flexion and Normal position of balancing both sides, all the persons achieved a 100 % accuracy rate under a laboratory environment. © 2023 ACM.

关键词： Patient treatment

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