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Recent Innovation in Smart and Sustainable Technology (ICRISST), International Conference on

作者： D. Rosy Salomi Victoria Gowtham G K. Arulan Sabari Ashwin Roberts R Gokul S Department of CSE St. Joseph’s College of Engineering Chennai India Department of CS St. Joseph’s College of Engineering Chennai India

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309157

The advent of web-based media has provided a platform for individuals to express their opinions freely, facilitating easier communication compared to the past. This convenience comes with the risk of deliberate dissemination of misinformation. The ready access to a plethora of online information sources exacerbates the problem, as people may be exposed to and potentially believes false information. It is crucial for us to be aware of and flag such content on social media platforms. Given the rapid generation of information through online media, distinguishing between authentic news and fabrications is challenging without knowing the news source. The spread of fake news can have serious consequences, emphasizing the need for measures to reduce and differentiate between genuine and false messages. Term Frequency measures how often a word appears in a document, indicating its relevance. Inverse Document Frequency assesses the significance of a term in the entire corpus, helping filter out irrelevant words. An examination of spam detection is conducted by employing diverse classifiers, including BERT and RoBERTa to evaluate accuracy and furnish comparative findings.

关键词： Fabrication Technological innovation Social networking (online) Named entity recognition Predictive models Filtering algorithms prediction algorithms Frequency measurement Classification algorithms Fake news

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Data Mining and prediction algorithms for Takeout Purchase Behavior

Data Mining and Prediction Algorithms for Takeout Purchase B...

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Industrial IoT, Big Data and Supply Chain (IIoTBDSC), International Conference on

作者： Ying Li Sino-Anglian Joint Postdoctoral Research Station Beijing China

ISBN: (数字)9798331539818

ISBN: (纸本)9798331539825

In recent years, the food delivery industry has witnessed significant growth, with a rising number of individuals turning to online platforms for their meal orders. Understanding and predicting consumer purchasing behavior in this context is essential for businesses to enhance their services and optimize user satisfaction. This paper presents a study on data mining and prediction algorithms for food delivery purchasing behavior, utilizing deep learning methods. By leveraging deep learning techniques, we analyze user data to uncover patterns and trends in purchasing behavior. The primary aim is to develop accurate prediction models that can forecast future food delivery purchases with high precision. This work explores intelligent prediction of takeaway purchasing behavior through deep learning. Firstly, this paper introduces multi-head self-attention into the scenario of predicting takeaway purchasing behavior and models the dependency relationships between users' historical purchases using positional encoding. The paper utilizes the AUGRU structure to filter the evolutionary path of user interests, obtaining a more accurate representation of user interest vectors. Secondly, this work combines the aforementioned technologies to propose a user takeaway purchasing prediction network for recommendation of takeaway products. Thirdly, comprehensive experiments on the proposed method validate its correctness.

关键词： Deep learning Industries Accuracy Predictive models prediction algorithms Market research Encoding Data models Behavioral sciences Data mining

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An Adaptive Extended Kalman Filter Based on Variational Bayesian and Sage-Husa prediction algorithms

An Adaptive Extended Kalman Filter Based on Variational Baye...

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IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Lian Ma Ran Cao Chun Long Huang Harbin Engineering University Harbin China Key Laboratory of Ocean Observation Technology MNR Tianjin China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

An adaptive extended Kalman filter based on variational Bayesian and Sage-Husa prediction algorithms is proposed to address the problems of motion model mismatch and high-level environmental noise interference in target tracking. It combines variational Bayesian inference and Sage-Husa Kalman filtering. This algorithm can adaptively modify the present motion model and uncertain measurement variance caused by stochastic ocean environmental noise. Through simulation results, it has been verified that the tracking accuracy and robustness of this method are superior to traditional Kalman filter tracking estimation methods.

关键词： Adaptation models Target tracking Working environment noise Simulation Signal processing algorithms Stochastic processes prediction algorithms Inference algorithms Bayes methods Kalman filters

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Using prediction algorithms to Adjust the Frequency in Case of an Intermittent Synchronization Signal or Switching to Holdover Mode

Using Prediction Algorithms to Adjust the Frequency in Case ...

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Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO)

作者： A. S. Fen D. K. Mazurenko M. L. Schwartz Research Institute of Radio Moscow Russia Moscow Technical University of Communications and Informatics Moscow Russia

ISBN: (数字)9798350373448

ISBN: (纸本)9798350373455

Precision Time Protocol (PTP) is a key candidate for delivering synchronization signals in packet switched networks. The use of clock signal rejection filters when transmitting a clock signal using the Precision Time Protocol (PTP) through traditional Ethernet packet communication networks leads to the need to use an intermittent clock signal in the frequency adjustment interval, a controlled oscillator (CO). In addition, it should be noted that the periodic transition of the CO into the holdover mode, associated with the loss of the clock signal, can also be regarded as a result of the occurrence of an intermittent clock signal. This article discusses issues related to the possibility of using prediction algorithms to ensure synchronous operation of the CO in the case of an intermittent synchronizing signal, as well as correction of the oscillator frequency drift in the holding mode

关键词： Time-frequency analysis Protocols Switches prediction algorithms Frequency measurement Telecommunications Synchronization

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Utilizing Genomic prediction algorithms for Enhancing Resistance to Fungal Root Rot in Vertical Soilless Farming

Utilizing Genomic Prediction Algorithms for Enhancing Resist...

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International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： T. Prabahar Godwin James K. Bala Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai Tamil Nadu India Department of Computer Science and Engineering School of Computing Bharath Institute of Higher Education and Research Chennai Tamil Nadu India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

The goal of this project is to improve the resistance of crops to fungal root rot diseases, including Fusarium, Rhizoctonia, and Pythium within vertical soilless farming systems using genomic prediction approaches. Beside merging of the phenotypic and genetic data the study aims at discovering relevant gene markers that are responsible for disease resistance. Molecular biology and bioinformatics approaches shall be used to design decision support systems that will predict the vulnerability of different cultivars to root rot infection given environmental and genetic factors. Vertical soilless farming imposes specific risks and opportunities for disease control and the management of crop disease requires accurate data *** investigation will contribute to better understanding the genetic variability of resistance-related genes in the target pathogens, as well as help to identify proper, genetically adapted for the high-stress conditions. Lastly, the purpose of the findings is to enhance the stability and productivity of crop yield and food security under conditions of limited soil availability and other aspects of soilless systems. This work therefore provides a call for adopting the best breeding practices combined with modern genomic tools for developing stronger agricultural systems that will be able to withstand biotic stresses.

关键词： Accuracy Genomics Food security Machine learning Soil prediction algorithms Bioinformatics Immune system Diseases Farming

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Comparative Study on Different Wind Power prediction algorithms Based on Comprehensive Performance Evaluation

Comparative Study on Different Wind Power Prediction Algorit...

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IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Ke Han Limengqian Zheng Yuanyi Bao Yangwu Shen Chang Liu Lipeng Zhu Yuchen Huang Yongjin Yu CTG Wuhan Science and Technology Innovation Park China Three Gorges Corporation Wuhan China College of Electrical and Information Engineering Hunan University Changsha China POWERCHINA Chengdu Engineering Corporation Limited Chengdu China

ISBN: (数字)9798350386127

ISBN: (纸本)9798350386134

Considering that existing studies on wind power prediction performance evaluation struggle to fully capture the prediction performance, this paper constructs a comprehensive evaluation scheme that incorporates fundamental indices, long time-scale indices, and key time-period indices. Subsequently, weights are automatically assigned to these indices through the entropy weighting method, and the weighted sum is calculated to derive the final comprehensive score for each prediction result, enabling a scientific and comprehensive evaluation of wind power prediction performance across various time scales. Comparative studies are conducted using measured data from a wind farm in China and predicted power data from various vendors. The results demonstrate that the comprehensive evaluation scheme enables a scientific and holistic assessment of wind farm power prediction results. Furthermore, an analysis of the error characteristics is conducted, revealing potential limitations of the wind power prediction algorithms deployed by actual vendors.

关键词： Performance evaluation Power measurement Error analysis Wind power generation Wind farms prediction algorithms Entropy Power systems

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Missing Data Imputation in Bridge Monitoring System Based on the prediction algorithms

Missing Data Imputation in Bridge Monitoring System Based on...

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Electrical Engineering, Big Data and algorithms (EEBDA), IEEE International Conference on

作者： Jiaqi Xu School of Civil Engineering Tongji University of China Shanghai China

In this paper, combined with the popular prediction model in the field of artificial intelligence, experimental simulation and prediction of missing data of bridge health monitoring are carried out. The prediction models involved include support vector regression model SVM, neural network prediction model, Gaussian process regression model and time series prediction model. Firstly, by introducing the background and research status of BSHMS, the existing problems in the literature and the core work of this paper are proposed. Secondly, this paper introduces the basic principles of the related algorithms, which lays a foundation for the later model building. Third, the three groups of data were tested for normal distribution by the KPSS test method, and it was decided to use 3σ rule in order to identify abnormal data,while box chart rule is used for S2 and S3 measuring points. Then, the super parameters of the four models are optimized through several experiments, and the time series prediction model is simplified to AR model by using the characteristics of ACF and PACF images. Finally, the four models are simulated and the results are compared. The results show that SVR, BP and GPR all have good prediction effects, especially BP neural network, but the relative deviation of the prediction results of AR model is large, which may be due to the fact that only the impact of time series is considered in this case, and the limitation of reference data is not considered.

关键词： Support vector machines Bridges Time series analysis Neural networks Predictive models prediction algorithms Data models

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Heuristic and Statistical prediction algorithms Survey or Smart Environments

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JOURNAL OF INFORMATION PROCESSING SYSTEMS 2020年第5期16卷 1196-1213页

作者： Malik, Sehrish Ullah, Israr Kim, DoHyeun Lee, KyuTae Jeju Natl Univ Dept Comp Engn Jeju South Korea Kongju Natl Univ Div Informat & Commun Engn Gongju South Korea Virtual Univ Pakistan Dept Comp Sci Lahore Pakistan

There is a growing interest in the development of smart environments through predicting the behaviors of inhabitants of smart spaces in the recent past. Various smart services are deployed in modern smart cities to facilitate residents and city administration. prediction algorithms are broadly used in the smart fields in order to well equip the smart services for the future demands. Hence, an accurate prediction technology plays a vital role in the smart services. In this paper, we take out an extensive survey of smart spaces such as smart homes, smart farms and smart cars and smart applications such as smart health and smart energy. Our extensive survey is based on more than 400 articles and the final list of research studies included in this survey consist of 134 research papers selected using Google Scholar database for period of 2008 to 2018. In this survey, we highlight the role of prediction algorithms in each sub-domain of smart Internet of Things (IoT) environments. We also discuss the main algorithms which play pivotal role in a particular IoT subfield and effectiveness of these algorithms. The conducted survey provides an efficient way to analyze and have a quick understanding of state of the art work in the targeted domain. To the best of our knowledge, this is the very first survey paper on main categories of prediction algorithms covering statistical, heuristic and hybrid approaches for smart environments.

关键词： prediction algorithms Smart Cities Smart Service

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prediction algorithms using specialized software tools for steel industry equipment

Prediction algorithms using specialized software tools for s...

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International Symposium for Design and Technology of Electronics Packages (SIITME)

作者： E. Raducan V. Nicolau M. Andrei G. Petrea G.M. Vlej “Dunarea de Jos” University of Galati Romania Liberty Steel Group Galati Romania

ISBN: (数字)9781728175065

ISBN: (纸本)9781728175072

The paper aims to present a model for predictive maintenance applicable in steel industry for critic equipment. This paper investigates the application of multi-step time prediction to sustain the turbo blowers (TB) equipment prognostics using software analytics algorithms which describe forecasting models, statistical approach or the formulas specifies for industrial equipment developed based by DAX formulas. This application represent a new method for realize a predictive maintenance describe as an industrial revolution characterized by smart systems and Internet-based solutions.

关键词： Maintenance engineering Predictive models Atmospheric modeling Predictive maintenance Forecasting Steel industry prediction algorithms

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Improving Traditional Stock Market prediction algorithms using Covid-19 Analysis

Improving Traditional Stock Market Prediction Algorithms usi...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Rajni Jindal Nikhil Bansal Nitin Chawla Sanskriti Singhal Delhi Technological University New Delhi India

ISBN: (数字)9781728185194

ISBN: (纸本)9781728185200

The stock market is an organized body where public companies offer their stocks through initial public offerings and traders buy/sell these stocks so as to obtain profits. It is dynamic and volatile in nature which makes the task of stock market trend prediction a complex problem. In recent times, the COVID-19 pandemic has made this task even harder. With the rising number of COVID-19 cases across the globe, the market has never been more volatile. This has resulted in the poor performance of various traditional trend prediction algorithms because these algorithms do not account for the impact of the pandemic on the stock market trends. The proposed work aims to enhance the stock market prediction ability of various common prediction models by taking into account the factors related to COVID-19. The forecasting techniques analysed are Decision Tree Regressor, Random Forest Regressor and Support Vector Regressor (SVR). Currently the most affected countries by COVID-19 are the United States of America, India and Russia. Therefore we have analysed the prediction performance of various approaches discussed in this paper on S&P 500 Index, Nifty50 Index and RTS Index using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Results obtained showcase that all the techniques used perform better when the COVID-19 features were included.

关键词： COVID-19 Vegetation Market research prediction algorithms Indexes Stock markets Regression tree analysis

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