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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 33672 条记录，以下是501-510 订阅

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Topic Modelling and Opinion Analysis on Climate Change Twitter data Using LDA and BERT Model.

Topic Modelling and Opinion Analysis on Climate Change Twitt...

引用

2022 international conference on machine learning and data engineering, icmlde 2022

作者： Uthirapathy, Samson Ebenezar Sandanam, Domnic School of Computing Science Galgotias University Greater Noida201306 India Department of Computer Applications National Institute of Technology Tiruchirappalli620015 India

Nowadays, Climate change is an important environmental factor that affects every living thing on the earth. It is very essential to study the public perceptions regarding the disaster events frequently happening due to climate change. In today's digital era individuals are using social network platforms namely Twitter, Facebook, and Weibo now and then to express their views about any events. In this paper, the climate change Twitter data set was considered for analyzing the topics and the opinions discussed by the public regarding climate change. The Latent Dirichlet Allocation(LDA) method was used to list out the various topics present in the data set and the Bidirectional Encoder Representation from Transformers(BERT uncased) is an efficient deep learning technique used to classify the sentiments present in the data set. Here the sentiments were labelled as pro news, support, neutral and anti. The performance of the proposed topic modelling and sentiment classification model was compared using the precision, recall, and accuracy measures. The BERT uncased model with has shown the best results such as precision of 91.35%, recall of 89.65%, and accuracy of 93.50% compared to other methods. © 2022 Elsevier B.V.. All rights reserved.

关键词： Sentiment analysis

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A Scheme for Effective Skin Disease Detection using Optimized Region Growing Segmentation and Autoencoder based Classification

A Scheme for Effective Skin Disease Detection using Optimize...

引用

2022 international conference on machine learning and data engineering, icmlde 2022

作者： Reddy, Dasari Anantha Roy, Swarup Kumar, Sanjay Tripathi, Rakesh Department of Information Technology NIT Raipur India Department of Computer Applications Sikkim University Gangtok India

Detecting skin disorders just through visual inspection is difficult due to the complicated and overlapping nature of sick lesions, background skin textures, skin hair, low illumination, etc. Computer Vision and machine learning are playing a great role in identifying the correct type of diseased lesion. However, in the presence of the aforementioned artefacts, current computational approaches are equally limited in their ability to detect complicated lesion structures. We provide a novel detection framework to enhance skin disease diagnosis. Beginning with segmentation and feature extraction from diseased lesions, this framework uses autoencoder based classification model. Diseased lesions are segmented using Optimized Region Growing using Grey Wolf Optimization (GWO). Texture features are extracted from the segmented lesion using Gray Level Co-occurrence Matrix (GLCM), and Weber Local Descriptor (WLD). Finally, a reduced feature set is generated through latent representation using the autoencoder. An integrated convolutional neural network is used to classify the diseased lesions from latent representation. The proposed framework significantly outperformed the traditional deep classification strategies, according to experimental findings. © 2023 The Authors. Published by Elsevier B.V.

关键词： learning systems

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A study of a federated learning framework based on the interstellar file system and blockchain: Private Blockchain Federated learning 3

A study of a federated learning framework based on the inter...

引用

3rd international conference on Computer Vision, Image and Deep learning and international conference on Computer engineering and Applications, CVIDL and ICCEA 2022

作者： Zhang, Peng Liu, Gang Chen, Zhiyu Guo, Jianwei Liu, Peng University of Technology School of Computer Science and Engineering Changchun ChangChun China Jilin Heshun Hengtong Technology Co. Product Department ChangChun China

ISBN: (纸本)9781665459112

Federated learning is a scalable machine learning paradigm in which a large group of individuals collaborates to build a high-quality machine learning model. This paper proposes Private Blockchain Federated learning (PriChainFL). It is a federated learning method that combines blockchain and IPFS, in response to the fact that federated learning is still vulnerable to attacks such as data poisoning and member inference attacks. It also reduces the storage cost of blockchain. © 2022 IEEE.

关键词： Blockchain

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data-Driven Techniques in Logistics & Supply Chain Management: A Literature Review

Data-Driven Techniques in Logistics & Supply Chain Managemen...

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2022 OPJU international Technology conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Detwal, Pankaj Kumar Soni, Gunjan Kumar, Devesh Ramtiyal, Bharti Indian Institute of Technology Roorkee Department of Management Studies Roorkee India Malaviya National Institute of Technology Jaipur Department of Mechanical Engineering Jaipur India Department of Management Studies Dehradun India

ISBN: (纸本)9781665492942

The importance of supply chain management to business operations and social growth cannot be overstated. Today's supply chains are very different from those of a few years ago and continually change in a highly competitive climate. Investing in technology that can handle the sheer complexity of dynamic supply chain operations is necessary. Current supply chain solutions cannot completely mitigate the risks of inefficiencies at various supply chain stages. Several functional supply chain applications based on machine learning (ML) have appeared in recent years;however, few studies have analyzed data-driven logistics and supply chain management applications. Robotics, machine learning, and natural language processing are potential supply chain transformation enablers. This paper offers a thorough and up-to-date literature review that examines what researchers have done regarding data-driven techniques in the supply chain context and identifies what needs further exploration. We reviewed 135 research articles published between 2008 and 2022 on the Scopus database and created a classification of the research material on data-driven logistics and supply chain management. This comprehensive literature evaluation will enable researchers and business administrators to undertake innovation initiatives better and redirect money and human resource efforts. © 2023 IEEE.

关键词： machine learning

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Long-Term Load Forecasting for Luzon, Philippines using Optimized Hybrid machine learning Algorithm 16

Long-Term Load Forecasting for Luzon, Philippines using Opti...

引用

16th international conference on Computer and Automation engineering (ICCAE)

作者： Maceda, Deiscart D'Mitrio C. Codaye, Jamael Dela Cruz, Jennifer C. Apolinario, Gerard Francesco D. G. Serrano, Mell Marc M. Reyes, Abegail D. Ariega, Bill Philip A. Calano, Roel Bitancor, Andrew G. Sawal, Leonardo C. Technol Inst Philippines Manila Philippines Mapua Univ Sch Elect & Elect Engn Manila Philippines Pamantasan Lungsod Maynila Coll Engn Manila Philippines Colegio Muntinlupa Muntinlupa Philippines Muralla St Manila 1002 Philippines 1338 Arlegui St Manila Philippines

ISBN: (纸本)9798350370058;9798350370164

This research paper discusses the growing demand for electricity in the Philippines due to population growth, economic development, and the need for accurate long-term electrical load forecasting to sustain the power system since most load forecasting studies focus on short-term duration. The study aims to optimize, evaluate, and compare accurate load forecasting models for the load demand of Luzon. This study used machine learning techniques for forecasting, specifically Linear Support Vector machine (Linear SVM), Multi-layer Perceptron (MLP), and Linear Regression with XGBoost (LR-XGBoost). The objectives include identifying and constructing relevant features, optimizing hyperparameters, and comparing performances using appropriate evaluation metrics. The study is significant to the power system industry. Electrical load forecasting equipped them to create operational decisions in energy management, generation scheduling, and assessment that maintain supply and demand. The comparison will solely focus on the three models and identify how the models performed using the Luzon hourly load demand from 2013 to 2022. Based on the results, the optimized LR-XGboost is the best-performing Long-Term Load Forecasting Model for the Load Demand of Luzon with a MAPE of 4.61%, outperforming the optimized Linear SVR and MLP models. The researchers recommend adding external regressors such as historical weather and economic data, performing Scenario forecasting, and adding holiday indicators to improve the performance of each model.

关键词： Long Term Load Forecasting Linear Support Vector Regression Multilayer Perceptron Multiple Linear Regression XGBoost

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Adaptive Federated learning Aggregation Strategies Based on Mobile Edge Computing 3

Adaptive Federated Learning Aggregation Strategies Based on ...

引用

3rd international conference on machine learning and Computer Application, ICMLCA 2022

作者： Liu, Shucheng Xu, Fangqin Donghua University Renmin North road 2999 Shanghai201620 China Shanghai Publishing and Printing College Shuifeng road 100 Shanghai200093 China

ISBN: (纸本)9781510664814

Federated learning is a machine learning paradigm that can protect data privacy, but the high communication cost and the arithmetic limitation of clients become one of the bottlenecks of federated learning. To address this problem, an adaptive federated learning aggregation strategy based on mobile edge computing called A-FedAvg is proposed, which first evaluates the accuracy gain metrics and cost metrics from each round of global iteration in federated learning, and then combines them into an adaptive metric by weighting parameters to indicate the number of local iterations needed for the next round of global iteration. The A-FedAvg algorithm is designed and then experimented on the MNIST and EMNIST datasets for IID and non-IID data distributions, respectively. The results show that the A-FedAvg algorithm can reduce the number of global communications compared to the FedAvg algorithm with guaranteed accuracy and without affecting the normal use of the mobile device client. © 2023 SPIE.

关键词： Mobile edge computing

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machine learning-Based Prognostic Prediction of Hemorrhagic Stroke Patients and Exploration of Key Factors 4

Machine Learning-Based Prognostic Prediction of Hemorrhagic ...

引用

4th international conference on Electronic Information engineering and Computer Science, EIECS 2024

作者： Wang, Zongyu Zhang, Jin Yeerjiang, Ahealisi Chen, Qi He, Jia School of Health Sciences and Engineering University of Shanghai for Science and Technology Shanghai200093 China Naval Medical University Department of Health Statistics Shanghai200433 China

ISBN: (纸本)9798331531409

Hemorrhagic stroke, a critical medical emergency marked by spontaneous cerebral bleeding, presents substantial challenges in diagnosis, treatment, and prognosis. Representing approximately 23% of all strokes in China, it is associated with a high mortality rate and severe long-term disabilities. This study aims to improve prognosis predictions by integrating patient demographics, clinical data, and advanced imaging features into a comprehensive analysis utilizing machine learning models. A total of 160 patients were enrolled, and their data were processed through decision tree, random forest, XGBoost, and LightGBM regression models. The LightGBM model demonstrated superior performance in predicting the 90-day modified Rankin Scale score, a measure of disability post-stroke, with a high degree of accuracy. Furthermore, principal component analysis and structural equation modeling were applied to identify and validate the relationships between patient outcomes and various clinical and imaging features. The study underscores the significance of early diagnosis, prompt and appropriate treatment, and the potential of machine learning in enhancing patient management and outcomes in hemorrhagic stroke. These findings contribute to the growing body of knowledge on stroke management and suggest pathways for future research in improving stroke care protocols. © 2024 IEEE.

关键词： Patient treatment

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BINADAPTER: Leveraging Continual learning for Inferring Function Symbol Names in a Binary 19

BINADAPTER: Leveraging Continual Learning for Inferring Func...

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19th ACM Asia conference on Computer and Communications Security (ACM AsiaCCS)

作者： Murodova, Nozima Koo, Hyungjoon Sungkyunkwan Univ Suwon South Korea

ISBN: (纸本)9798400704826

Binary reverse engineering is crucial to gaining insights into the inner workings of a stripped binary. Yet, it is challenging to read the original semantics from a binary code snippet because of the unavailability of high-level information in the source, such as function names, variable names, and types. Recent advancements in deep learning show the possibility of recovering such vanished information with a well-trained model from a pre-defined dataset. Albeit a static model's notable performance, it can hardly cope with an ever-increasing data stream (e.g., compiled binaries) by nature. The two viable approaches for ceaseless learning are retraining the whole dataset from scratch and fine-tuning a pre-trained model;however, retraining suffers from large computational overheads and fine-tuning from performance degradation (i.e., catastrophic forgetting). Lately, continual learning (CL) tackles the problem of handling incremental data in security domains (e.g., network intrusion detection, malware detection) using reasonable resources while maintaining performance in practice. In this paper, we focus on how CL assists in the improvement of a generative model that predicts a function symbol name from a series of machine instructions. To this end, we introduce BINADAPTER, a system that can infer function names from an incremental dataset without performance degradation from an original dataset by leveraging CL techniques. Our major finding shows that incremental tokens in the source (i.e., machine instructions) or the target (i.e., function names) largely affect the overall performance of a CL-enabled model. Accordingly, BINADAPTER adopts three built-in approaches: (1) inserting adapters in case of no incremental tokens in both the source and target, (2) harnessing multilingual neural machine translation (M-NMT) and fine-tuning the source embeddings with (1) in case of incremental tokens in the source, and (3) fine-tuning target embeddings with (2) in case of incremen

关键词： Binary analysis Software security Reverse engineering Continual learning

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Combination of data-Driven and Physics-Based Models for Thick Sintered Electrode Lithium-Ion Batteries 31st

Combination of Data-Driven and Physics-Based Models for Thic...

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31st international conference on Flexible Automation and Intelligent Manufacturing

作者： Cai, Chen Liu, Shengyu Nie, Ziyang Kim, Kyoung-Yun Koenig, Gary M., Jr. Univ Virginia Charlottesville VA 22904 USA Wayne State Univ Detroit MI 48202 USA

ISBN: (纸本)9783031176289;9783031176296

This work explored the use of physics-based and machine learning models in the context of a promising battery material system. The battery materials system was electrodes comprised of only electroactive material, which provides increased energy density at the cell level. The overall target of the modeling platform is to develop tools to aid in accelerating the experimental material discovery process. machine learning models provide a route to more accurately predict the experimental electrochemical capacity of the materials in battery cells, although appropriate data training sets are needed. The combined application of the physics-based model and machine learning model resulted in the most accurate prediction of electrochemical cell outcomes.

关键词： machine learning Pseudo-two-dimensional model Combined model

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Research on Feedback Service for Teaching Based on Educational data Mining

Research on Feedback Service for Teaching Based on Education...

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2022 international conference on machine learning and Knowledge engineering, MLKE 2022

作者： Yang, Ting Chen, Bo Wang, Wenyi Li, Shanshan Shaanxi University of Chinese School of Foreign Language Studies Xianyang China Xianyang Normal University School of Physics Electronic Engineering Xianyang China

ISBN: (纸本)9781665495677

Under the serious influence of COVID-19, online teaching has become a mainstream teaching mode. During the online teaching, it is difficult for teachers to evaluate and intervene in students' learning in real time. Therefore, for students who lack self-control, it is possible to be stuck in low learning efficiency and even failure of course assessment. How to obtain valid information of students' learning status in time during the online teaching process is a hot research topic at present. This paper proposes a feedback service for teaching based on educational data mining. It can, through a reasonable analysis of the data submitted in form of students' homework, accurately screen out students who have difficulties in learning a certain course and give directions to achieve the purpose of optimizing the teaching. © 2022 IEEE.

关键词： Feedback

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