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Study on Wind Forecasting for Beijing 2022 Winter Olympic Games Based on Unsupervised machine learning

Study on Wind Forecasting for Beijing 2022 Winter Olympic Ga...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Haoye Liu Yuhui Duan Jiangbo Li Tongtong Li Qinhuangdao Meteorological Bureau Qinhuangdao Hebei China Hebei Meteorological Observatory Shijiazhuang Hebei China

the accurate prediction of wind speed is an important part of the weather forecasting service for the Winter Olympic Games. the Zhangjiakou zone of the Beijing 2022 Winter Olympic Games is located in the complex terrain of the Dama Mountains, and the current numerical forecasts have large deviations in forecasting wind speed in small-scale complex terrain. Based on the unsupervised machine learning method, this paper performs historical modeling on the basis of five element forecasts of two numerical forecasts for nine stations in the Zhangjiakou Zone of the Beijing 2022 Winter Olympic Games, thus achieving objective correction to the numerical forecasts of average winds and extreme winds. the results show that the unsupervised machine learning-based winds forecasts during the Beijing 2022 Winter Olympics Test Games are better, with significant improvements over the original numerical forecasts in terms of mean absolute error and mean squared error.

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Research on Optimization of Sliding Pressure Curve of 300MW Unit Based on data mining 4

Research on Optimization of Sliding Pressure Curve of 300MW ...

引用

4th international conference on machine learning, Big data and Business Intelligence, MLBDBI 2022

作者： Kong, Dean Liu, Jiangshan Shen, Zhongxin Zhen, Xin Kang, Pengpeng Zhao, Wenqing Zhai, Wenhui Xinjiang Xinneng Group Urumqi Electric Power Construction and Commissioning Institute Urumqi China State Grid Xin Jiang Company Limited Electric Power Research Institute Urumqi China State Grid Xinjiang Electric Power Co. Ltd Urumqi China

ISBN: (纸本)9798350333947

Under the background of new power system, the flexibility transformation of coal-fired power plants and the lowcarbon transformation of coal-fired power plants have brought about problems such as the decline of unit economy, the drastic change of load, and the difficulty in applying for unit characteristic test conditions. this paper proposes a method of unit sliding pressure curve optimization based on data mining. the specific steps are as follows: first, collect historical data and delete historical data processing. then, the fuzzy clustering algorithm is used to preliminarily divide the data with the ambient temperature as the clustering center. Afterward, for each temperature interval data, the maximum boiler efficiency and the minimum coal consumption of the unit are taken as the optimization objectives to optimize and simplify. Finally, the sliding pressure curve model corresponding to each temperature interval is obtained. After calculation, the method has the characteristics of simple operation, good real-time and stable operation, and has certain reference significance. © 2022 IEEE.

关键词： data mining

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the Detection and Analysis of Fake News Using machine learning 4

The Detection and Analysis of Fake News Using Machine Learni...

引用

international conference on 4th Industrial Revolution Based Technology and Practices, ICFIRTP 2022

作者： Singh, Shalini Sharma, Chandani Agarwal, Shashwat Garg, Umang Gupta, Neha ABES Engineering College Computer Science UP Ghaziabad India Quantum University Uttarakhand Roorkee India Quantum University CSE Uttarakhand Roorkee India Krishna Engineering College UP Ghaziabad India Graphic Era Hill University CSE Dehradun India Graphic Era Deemed to Be University ECE Dehradun India

ISBN: (纸本)9798350345919

Social media has become one of the hotbeds for the dissemination of fake news in the market. Although, the role of social media is very important for the campaign, broadcaster of any news, and trend formulation. However, it may be the reason for the dissemination of fake news and bring negative impactual results on society and individuals. Even some significant impacts on election campaigns, politics, trend settings, and marketing agencies can be executed using social media. So the detection of fake news is one of the most perpetual ways to set the right trend in the market. Traditional methods with manual filtering are not feasible for the detection of fake news effectively. Although, there are several techniques used to detect fake news such as data mining, natural language processing, social network analysis, and machine learning algorithms. In this paper, the focus is to detect false news using distinctive machine learning algorithms. the model is trained and tested of the data-set available freely as an open-source. the positive prediction rate is very high in the ROC curve indicates the prediction of fake news effectively. Our experiment indicates the high accuracy with the support vector machine classifier algorithm. © 2022 IEEE.

关键词： Classification techniques data prepossessing Digital media Fake-News machine learning

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Zeroth Order GreedyLR: An Adaptive learning Rate Scheduler for Deep Neural Network Training

Zeroth Order GreedyLR: An Adaptive Learning Rate Scheduler f...

引用

pattern recognition and machine learning (PRML), IEEE international conference on

作者： Shreyas Subramanian Vignesh Ganapathiraman Amazon Web Services Seattle USA

Deep neural networks are a powerful tool for a wide range of applications, including natural language processing (NLP) and computer vision (CV). However, training these networks can be a challenging task, as it requires careful selection of hyperparameters such as learning rates and scheduling strategies. Despite significant advances in designing dynamic (and adaptive) learning rate schedulers, choosing the right learning rate / schedule for a machine learning task is still more art than science. In this paper, we introduce Zeroth order GreedyLR, a novel scheduler that adaptively adjusts the learning rate during training based on the current loss and gradient information. To validate the effectiveness of our proposed method, we conduct experiments on several NLP and CV tasks. the results show that our approach outperforms several state-of-the-art schedulers in terms of accuracy, speed, and convergence. Furthermore, our method is easy to implement, computationally efficient, and requires minimal hyperparameter tuning. Overall, our study provides a useful tool for researchers and practitioners in the field of deep learning.

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Combating Advanced Persistent threats for Imminent Low Earth Orbit Cognitive Communications Systems 16

Combating Advanced Persistent Threats for Imminent Low Earth...

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16th Annual IEEE international Systems conference (SysCon)

作者： LaMar, Suzanna Gosselin, Jordan J. Happel, Lisa Jayasumana, Anura Northrop Grumman Corp 15120 Innovat Dr San Diego CA 92128 USA Colorado State Univ Dept Elect & Comp Engn Ft Collins CO USA

ISBN: (纸本)9781665439923

With the proliferation of Low Earth Orbit (LEO) spacecraft constellations, comes the rise of space-based wireless cognitive communications systems (CCS) and the need to safeguard and protect data against potential hostiles to maintain widespread communications for enabling science, military and commercial services. For example, known adversaries are using advanced persistent threats (APT) or highly progressive intrusion mechanisms to target high priority wireless space communication systems. Specialized threats continue to evolve with the advent of machine learning and artificial intelligence, where computer systems inherently can identify system vulnerabilities expeditiously over naive human threat actors due to increased processing resources and unbiased pattern recognition. this paper presents a disruptive abuse case for an APT-attack on such a CCS and describes a trade-off analysis that was performed to evaluate a variety of machine learning techniques that could aid in the rapid detection and mitigation of an APT-attack. the trade results indicate that with the employment of neural networks, the CCS's resiliency would increase its operational functionality, and therefore, on-demand communication services reliability would increase. Further, modelling, simulation, and analysis (MS&A) was achieved using the Knowledge Discovery and data mining (KDD) Cup 1999 data set as a means to validate a subset of the trade study results against Training Time and Number of Parameters selection criteria. Training and cross-validation learning curves were computed to model the learning performance over time to yield a reasonable conclusion about the application of neural networks.

关键词： cognitive communications system wireless communications cybersecurity advanced persistent threat intrusion detection resiliency and machine learning

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Comparative Analysis of data mining Based Performance Evaluation Using Hybrid Deep learning Approach 4th

Comparative Analysis of Data Mining Based Performance Evalua...

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4th international conference on Mobile Radio Communications and 5G Networks, MRCN 2023

作者： Chhabra, Gurpreet Singh William, P. Lanke, Govinda Rajulu Jain, Kirti Lakshmi, T. R. Vijaya Varshney, Neeraj Department of CSE GITAM School of Technology GITAM University Visakhapatam India Department of Information Technology Sanjivani College of Engineering Kopargaon India ResMed Digital Health Technology San DiegoCA United States University Bhopal India Mahatma Gandhi Institute of Technology Hyderabad Gandipet500075 India Department of Computer Engineering and Applications GLA University Mathura India

ISBN: (纸本)9789819706990

Educational failure is prevalent. the surge in the number of students who quit school has multiple root factors. the inability to succeed academically is a primary factor in why students drop out of school. Since many students struggle to adjust to their new school, this affects performance. Our study aims to identify all elements affecting undergraduate academic achievement. thus, this initiative aims to help students identify the factors that lead to their successes so they can take steps to change their results. Students, course instructors, and others can improve the environment after identifying and assessing its main components. We used a Recurrent Neural Network and Long Short-Term Memory classification technique to forecast student academic success early. this method is compared to numerous machine learning classifiers and a deep learning classification model. Using study findings from numerous trials, we examined the classification performance of several standard machine learning techniques, such as support vector machine, random forest, J48, artificial neural network, and naive bayes, as well as deep learning models, such as RNN. RNN-LSTM sigmoid, Tan–h, and ReLU function are used to predict student performance and enhance teaching. the results are compared to deep learning and machine learning methods. RNN-LSTM (ReLU) has the highest accuracy rate of 97%, as per experiments. Our technique has great classification accuracy on different datasets or real-time complex huge datasets of students with multivalued variables. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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4th EAI international conference on Big data Innovation for Sustainable Cognitive Computing, BDCC 2021

4th EAI International Conference on Big Data Innovation for ...

引用

4th EAI international conference on Big data Innovation for Sustainable Cognitive Computing, BDCC 2021

ISBN: (纸本)9783031076534

the proceedings contain 18 papers. the special focus in this conference is on Big data Innovation for Sustainable Cognitive Computing. the topics include: Solar Radiation Prediction Using the Random Forest Regression Algorithm;online Covid-19 Risk Analysis System for Early Detection of Possible Infection;finger Knuckle Print recognition Using Complex Conjugate Feature Vector;a Systematic Literature Review on data Freshness for Reinforcing Mutual Authentication in Wireless Body Area Networks;vehicular Support System for User and Vehicle Accident Prevention;application of Multi-Focused and Multimodal Image Fusion Using Guided Filter on Biomedical Images;Modelling Cognitive Scores for Alzheimer’s Disease Progression Prediction Using Longitudinal MRI data;an Automated Cervical Cancer Detection Mechanism Using Pap Smear Images;Experimental Comparative Analysis on Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) on Aspect-Level Sentiment Analysis;AI-Enabled Pregnancy Risk Monitoring and Prediction: A Review;detection of Non-Technical Losses in Power Utilities Using machine learning;data Hiding in Binary Images for Secret and Secure Communication Using Decision Tree;private and Secure Blockchain-Based Mechanism for an Online Voting System;A Novel Real-Time 3D Object Detection Network in Autonomous Driving Using Reformed RS-ResNet network;various Physiological Methods to Identify Sleep Onset;Internet of things (IoT) Adoption: Challenges Among Small and Medium Scale Enterprises (SMEs).

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machine learning based Sentiment Analysis of Hindi data with TF-IDF and Count Vectorization 7

Machine Learning based Sentiment Analysis of Hindi Data with...

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7th international conference on Computing, Communication and Security, ICCCS 2022 and 2022 4th international conference on Big data and Computational Intelligence, ICBDCI 2022

作者： Gupta, Ashwani Sharma, Utpal Tezpur University Department of Computer Science and Engineering Tezpur India

ISBN: (纸本)9798350309904

Sentiment refers to emotions. Sentiment analysis, often known as opinion mining, is the technique of identifying and extracting subjective data from pre-web and post-web reviews using text analytics, computational linguistics, and natural language processing. Hindi is an Indian language which is used by many of Indians. Due to phenomenal growth of online product reviews in Hindi post-web Hindi reviews are also increasing rapidly. A machine learning based method in this paper to analysis postweb text data. the present method is divided into four steps. First of all, an annotated Hindi review data set is developed from post-web sources. In second step, feature extraction is performed on annotated Hindi review dataset using the Term-Frequency/ Inverse-Document Frequency (TF-IDF) and count vectorization techniques. In the third step, the retrieved features are given to the classifier so it can make predictions. Moreover, annotated dataset translated into English. Second step and third step are performed on annotated English dataset in last step. A range of evaluation criteria, including precision, recall, and F1- score, are presented in the results. In both instances, the support vector machine produced the most pertinent results. © 2022 IEEE.

关键词： Sentiment analysis

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Comparison of Multi-Modal Federated learning Framework and SPSS in the Evaluation of Lymph Node Metastasis Probability in Gynecological Malignancies

Comparison of Multi-Modal Federated Learning Framework and S...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Zhijun. Hu Ling. Ma Ding Yue Guangyu Wu Xiaohua Shi Shalayiding Sirejiding Kaijiang. Liu Department of Gynecologic Oncology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai China Library Shanghai Jiao Tong University Shanghai China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai China

this study aimed to evaluate the diagnostic efficacy of CT, MRI, and PET/CT in detecting lymph node metastasis (LNM) in patients with gynecologic malignancies. A total of 567 patients diagnosed with cervical carcinoma (CC) and endometrial carcinoma (EC) were included in this study. the results of this study indicated that PET/CT demonstrated higher levels of sensitivity but lower levels of specificity compared to CT and MRI. Additionally, a deep learning-based multimodal federated learning model was implemented, which integrated non-image and MRI data, to improve diagnostic accuracy in the detection of LNM. the model exhibited heightened sensitivity and specificity in comparison with individual imaging modalities. Overall, the findings of this study underscore the potential of employing multimodal federated learning models to accurately detect LNM in patients with gynecological malignancies, which in turn emphasizes the necessity for further research and validation.

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Performance optimization algorithm for CRF layer of Chinese sequence labeling model based on deep learning 4

Performance optimization algorithm for CRF layer of Chinese ...

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4th international conference on machine learning, Big data and Business Intelligence, MLBDBI 2022

作者： Zhu, Li Zhang, Yue Dong, Yu Cui, Chengyu Engineering University of Pap Xi'an China Shijiazhuang Division of Plaa Infantry College Shijiazhuang China National Defense University Joint Operations College Shijiazhuang China China Mobile Chengdu Institute of Research and Development Chengdu China

ISBN: (纸本)9798350333947

the 'deep learning model + CRF' model architecture such as Bi-LSTM-CRF has outstanding performance| in the tasks of part-of-speech tagging, named entity recognition, and semantic role tagging in the field of natural language processing, and has become the most popular algorithm for sequence tagging tasks. However, in practical applications, due to the strong fitting ability of the feature extractor in the deep learning model, the use efficiency of the CRF layer is relatively low;at the same time, the calculation cost of the CRF layer is relatively large. this paper proposes a performance optimization algorithm for the CRF layer in the sequence annotation task based on the deep learning model. the algorithm introduces five rules. First of all, the tag result of the feature extractor is checked by the five rules, if the adjacent tag is found to be logically incorrect, then use the CRF layer for logic correction. the experimental results show that the algorithm shows great advantages in the process of word segmentation, part-of-speech tagging and named entity recognition, a lot of computing time has been saved. © 2022 IEEE.

关键词： Semantics

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