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

共 33935 条记录，以下是1071-1080 订阅

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A Comprehensive Analysis of Twitter data: A Case Study of Tourism in Indonesia 1

A Comprehensive Analysis of Twitter Data: A Case Study of To...

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1st international conference on Smart Technology, Applied Informatics, and engineering, APICS 2022

作者： A'La, Fiddin Yusfida Hartatik Firdaus, Nurul Safi'ie, Muhammad Asri Riasti, Berliana Kusuma Vocational School Universitas Sebelas Maret Department of Informatics Engineering Surakarta Indonesia

This paper extensively analyzes Twitter data based on a study of tourism in Indonesia. Some keywords 'candi borobudur','danau toba', 'gunung bromo','pantai kuta', and 'raja ampat... 详细信息

ISBN: (数字)9781665489850

ISBN: (纸本)9781665489850

This paper extensively analyzes Twitter data based on a study of tourism in Indonesia. Some keywords 'candi borobudur','danau toba', 'gunung bromo','pantai kuta', and 'raja ampat' are applied. Some techniques are applied in order to analyze obtained data. Based on word cloud technique, those tourism destination keywords contain top 10 popular word such as 'bali','Indonesia', 'banget','jazz', 'gue','papua barat', 'liburan','sih', 'daerah','orang'. Besides, machine learning approaches are also applied to these Twitter data, and the result shows that some noisy or ambiguous words affect the accuracy of the machine learning model. © 2022 IEEE.

关键词： machine learning

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ResNet-50 vs. EfficientNet-B0: Multi-Centric Classification of Various Lung Abnormalities Using Deep learning "session id: ICMLDsE.004" 2

ResNet-50 vs. EfficientNet-B0: Multi-Centric Classification ...

引用

2nd international conference on machine learning and data engineering, icmlde 2023

作者： Kansal, Kajal Chandra, Tej Bahadur Singh, Akansha Bennett University UP Greater Noida India

Lung abnormalities are among the significant contributors to morbidity and mortality worldwide. It induces symptoms like coughing, sneezing, fever, breathlessness, etc., which, if left untreated, may lead to death. In current clinical practice, chest X-ray (CXR) images are widely preferred to diagnose different lung abnormalities. However, the pathological tests are time-consuming, expensive and require domain experts. On the other hand, diagnosis through CXR images is manual and subject to inter-observer and intra-observer variability. The recent advancement in deep learning (DL) algorithms may be employed to address these challenges. However, the selection of correct algorithms along with finetuned parameters is challenging. In this study, we comprehensively compared the performance of two state-of-the-art DL algorithms, Resnet-50 and Efficient-B0. These two models are pervasively used in literature and have shown promising classification performance. The performance of the used algorithms is validated using the multi-centric dataset from Kaggle (having Covid-19, Normal, Pneumonia classes) and Mendeley chest X-ray dataset (having Normal, Pneumonia-Bacterial, Pneumonia-Viral, Covid-19 classes). Upon training the algorithms with a mini-batch size 32 and a maximum epoch of 40 using training data, we achieved 0.9807 and 0.9874 accuracy for Kaggle dataset and Mendeley dataset, respectively. Similarly, we achieved 0.9962 and 9.9978 for Kaggle dataset and Mendeley dataset for test data, respectively. From the result, it is evident that the EfficientNet-B0 model outperformed the multi-centric datasets. © 2024 Elsevier B.V.. All rights reserved.

关键词： Diagnosis

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Gradient Saliency-aware CutMix for Semi-Supervised Medical Image Segmentation

Gradient Saliency-aware CutMix for Semi-Supervised Medical I...

引用

IEEE international conference on Multimedia and Expo (ICME)

作者： Jiang, Yuxuan Zhu, Guobin Ding, Yi Qin, Zhen Pang, Minghui Univ Elect Sci & Technol China Sch Informat & Software Engn Network & Data Secur Key Lab Sichuan Prov Chengdu Peoples R China Yibin GREAT Technol Co Ltd Yibin 644000 Peoples R China Sichuan Prov Peoples Hosp Dept Geriatr Gen Surg Chengdu 610054 Peoples R China

ISBN: (纸本)9798350390155;9798350390162

In semi-supervised medical image segmentation, the use of CutMix in the Mean Teacher architecture is considered an effective strong data augmentation strategy. However, we believe that randomly selecting patches from the source image might mislead the model into learning unexpected feature representations. Therefore, we propose Gradient Saliency-aware CutMix for semi-supervised medical image segmentation (GSC-Seg). Utilizing the gradient from pre-trained models to detect salient regions and then copies and pastes the large gradient areas from labeled data into corresponding areas of unlabeled data based on the gradient, and vice versa, guiding the model to learn more appropriate feature representations. Furthermore, we propose a gradient augmentation strategy, which generates disruptions in the gradient through the network itself and enhances the gradient representation abilities of the network. Experiment results show that our approach achieves the state-of-the-art performance on three medical image segmentation datasets. Code is available at https://***/UESTC-Med424-JYX/GSC-Seg.

关键词： Semi-Supervised learning Medical Image Segmentation CutMix Saliency-aware Gradient Augmentation

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Performance Evaluation of machine learning and Deep learning Models for Temperature Prediction in Poultry Farming 10

Performance Evaluation of Machine Learning and Deep Learning...

引用

10th international conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET-SIP-22 2022

作者： Goyal, Vikas Yadav, Ajay Mukherjee, Rahul Bennett University Department of Electronics & Communication Engineering Greater Noida India

ISBN: (纸本)9781665467414

Temperature is a commonly used environmental factor that directly impacts both health of chicks and production in poultry farming. The cold weather makes the environment more conductive for certain infection diseases like Newcastle and Avian influenza, whereas heat stress or high temperature cause poor food efficiency and decreased production. Hence, the prediction of temperature with the support of machine learning (ML) and deep learning (DL) models in advance is advantageous for poultry farming. Real-time temperature data is captured with the support of Internet of Things (IoT) nodes and sent to clouds by wireless communication;this data is analyzed using various machine learning models on clouds, and decisions are made based on the knowledge extracted from received data. In this article, different machine learning (Random Forest, Linear Regression) and deep learning models (LSTM, BiLSTM) are used to process the temperature and provide temperature predictions after every 10 mins. All the models are compared on the various performance evaluation factors like mean absolute error (MAE), root mean square error (RMSE), determination coefficient (R2), and Spearman's correlation coefficient (SPCC). The comparison results show that the R2 value for random forest i.e., 0.992 is highest compared to other models. Such models with high prediction rates significantly impact the environment management decisions and production on the farm. © 2022 IEEE.

关键词： Deep learning Performance evaluation Wireless communication Cloud computing Temperature distribution Production Predictive models

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Gestational Diabetes and Poor Healthcare Affordability among Pregnant Women: A Double Whammy! 2

Gestational Diabetes and Poor Healthcare Affordability among...

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2nd international conference on machine learning and data engineering, icmlde 2023

作者： Parikh, Riya Bhargava, Yesoda Shukla, Anupam Indian Institute of Information Technology Pune India

The prevalence of Gestational Diabetes Mellitus (GDM) is increasing at a rapid pace globally. This is concerning because GDM can lead to serious health problems like Type 2 Diabetes, Cardiovascular Diseases, and Depression in both the mother and her baby. GDM also increases healthcare expenditure during and after pregnancy and this is particularly worrisome for women belonging to households with poor healthcare affordability. Given the rising prevalence of GDM and its adverse impact, it is crucial to identify its risk factors and explore the factors associated with healthcare affordability among pregnant women. Our work attempts to identify the association between lifestyle (exercise, fruit, and vegetable consumption), sociodemographic factors, and Gestational Diabetes. Second, attempts to investigate the determinants of Health care affordability among pregnant women. Multivariate stepwise logistic regression is used for investigation purposes. We found Depression, higher BMI, and lower fruit intake to be associated with higher odds of GDM among pregnant women. Whereas the presence of GDM, lower socioeconomic position, absence of health insurance, and single motherhood were found to be associated with poor healthcare affordability. Lifestyle and mental health are associated with the GDM risk in pregnant women. Marital status, health insurance, and socioeconomic position are associated with healthcare affordability among pregnant women. The study can aid in better health outcomes for pregnant women and the creation of inclusive health policies. © 2024 Elsevier B.V.. All rights reserved.

关键词： machine learning

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Stock Prediction Using machine learning and Sentiment Analysis 2nd

Stock Prediction Using Machine Learning and Sentiment Analys...

引用

2nd international conference on Computational Intelligence in machine learning, ICCIML 2022

作者： Bailke, Preeti Kunte, Onkar Bitke, Sayali Karwa, Pulkit Deparment of Information Technology Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9789819979530

Predicting stock market behavior has been a sector of interest to many researchers, especially in the field of statistics and data analysis. The ability to analyze the stock market that appears to lack consistency, but also appears to be impacted by historical events is still a challenge. Long Short-Term Memory (LSTM) network model, Linear Regression model, Autoregressive Integrated Moving Average (ARIMA), and the stock market prediction using Sentiment Analysis are included in the proposed model. The dataset used is based on real-time stock market data from Yahoo finance website. The calculated root mean square value predicts how much the data is concentrated near the best equity line. Emotional/Sentiment Analysis is done using twitter API and natural language processing. The result obtained proves that the proposed model can be used to predict future stock market behavior. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Long short-term memory

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An Empirical Study of Parameter Efficient Fine-tuning on Vision-Language Pre-train Model

An Empirical Study of Parameter Efficient Fine-tuning on Vis...

引用

IEEE international conference on Multimedia and Expo (ICME)

作者： Tian, Yuxin Yang, Mouxing Li, Yunfan Liu, Dayiheng Ren, Xingzhang Peng, Xi Lv, Jiancheng Sichuan Univ Coll Comp Sci Chengdu Peoples R China Peking Univ Sch Software & Microelect Beijing Peoples R China Minist Educ Engn Res Ctr Machine Learning & Ind Intelligence Chengdu Peoples R China

ISBN: (纸本)9798350390155;9798350390162

Recent studies applied Parameter Efficient Fine-Tuning techniques (PEFTs) to efficiently narrow the performance gap between pre-training and downstream. There are two important factors for various PEFTs, namely, the accessible data size and fine-tunable parameter size. A natural expectation for PEFTs is that the performance of various PEFTs is positively related to the data size and fine-tunable parameter size. However, according to the evaluation of five PEFTs on two downstream vision-language (VL) tasks, we find that such an intuition holds only if the downstream data and task are not consistent with pre-training. For downstream fine-tuning consistent with pre-training, data size no longer affects the performance, while the influence of fine-tunable parameter size is not monotonous. We believe such an observation could guide the choice of training strategy for various PEFTs.

关键词： Parameter Efficient Fine-tuning Vision-Language Pre-training

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Case machine learning Experience into Modelling and Simulation to Improve the Efficiency of Construction engineering Process Management 2

Case Machine Learning Experience into Modelling and Simulati...

引用

2nd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Alanya-Beltran, Joel Kanwer, Budesh Buddhi, Dharam Jeelani, Syed Hamim Valderrama-Zapata, Carlos Jaiswal, Sushma Universidad Tecnológica del Perú Electronic Department Peru Science and Engineering Poornima Institute of Engineering and Technology Department of Computer Rajasthan Jaipur India Uit Uttaranchal University Uttarakhand Dehradun India Engineering Koneru Lakshmaiah Education Foundation. Deemed to Be University Department of Civil Andhra Pradesh Vaddeswaram522502 India Universidad San Ignacio de Loyola C.G. Bilaspur India

ISBN: (数字)9781665437899

ISBN: (纸本)9781665437899

The research work has highlighted that the main purpose of this research work is to identify the role of ML (machine learning) in simulation modelling and its effect on construction project engineering. In this research, it has been clearly identified that ML achieves the advantages such as risk identification, quality enhancement, and reduction of financial burden. These critical aspects are integrated into the engineering of the construction projects enabled by ML. The present research also shed light on the machine learning technologies for the completion of a construction project with much ease. Lastly, the research consists of some of the data collection methods by which the researcher could appropriately gather the research data and information for the smooth completion of the construction project. © 2022 IEEE.

关键词： machine learning

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Design of Electromagnetic Transmission/Absorption Structures Based on machine learning 7

Design of Electromagnetic Transmission/Absorption Structures...

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7th IEEE international conference on Electronic Information and Communication Technology, ICEICT 2024

作者： He, Fan Zhang, Zhiming Ye, Zheng He, Yun China Ship Development and Design Center National Key Laboratory of National Key Laboratory of Electromagnetic Effect and Security on Marine Equipment Wuhan China Wuhan University of Technology School of Information Engineering Wuhan China

ISBN: (纸本)9798350384437

In this paper, a machine learning method is used to design an electromagnetic Rasorber. The XGBoost and Inception V3 model is used to design a transmission coded metasurface structure, and the full-wave simulation method is used to design a FSS absorbing structure. By combining the two methods, we design and optimize an electromagnetic Rasorber, which can realize microwave transmission around 6.5 GHz while microwave absorption in 8-10 GHz band. © 2024 IEEE.

关键词： Transfer learning

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Towards Hardware-Based Application Fingerprinting with Microarchitectural Signals for Zero Trust Environments 56

Towards Hardware-Based Application Fingerprinting with Micro...

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56th Annual Hawaii international conference on System Sciences (HICSS)

作者： Langehaug, Tor J. Graham, Scott R. Air Force Inst Technol Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9780998133164

The interactions between software and hardware are increasingly important to computer system security. This research collects sequences of microprocessor control signals to develop machine learning models that identify software tasks. The proposed approach considers software task identification in hardware as a general problem with attacks treated as a subset of software tasks. Two lines of effort are presented. First, a data collection approach is described to extract sequences of control signals labeled by task identity during real (i.e., non-simulated) system operation. Second, experimental design is used to select hardware and software configuration to train and evaluate machine learning models. The machine learning models significantly outperform a naive classifier based on Euclidean distances from class means. Various configurations produce balanced accuracy scores between 26.08% and 96.89%.

关键词： machine learning microarchitectural data constant monitoring cache attack zero trust

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