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arXiv 2024年

作者： Xia, Eric Wainwright, Martin J. Laboratory for Information and Decision Systems Statistics and Data Science Center Department of Electrical Engineering & Computer Sciences Massachusetts Institute of Technology CambridgeMA United States Laboratory for Information and Decision Systems Statistics and Data Science Center Department of Mathematics Massachusetts Institute of Technology CambridgeMA United States

We study a class of prediction problems in which relatively few observations have associated responses, but all observations include both standard covariates as well as additional "helper" covariates. While the end goal is to make high-quality predictions using only the standard covariates, helper covariates can be exploited during training to improve prediction. Helper covariates arise in many applications, including forecasting in time series;incorporation of biased or mis-calibrated predictions from foundation models;and sharing information in transfer learning. We propose "prediction aided by surrogate training" (PAST), a class of methods that exploit labeled data to construct a response estimator based on both the standard and helper covariates;and then use the full dataset with pseudo-responses to train a predictor based only on standard covariates. We establish guarantees on the prediction error of this procedure, with the response estimator allowed to be constructed in an arbitrary way, and the final predictor fit by empirical risk minimization over an arbitrary function class. These upper bounds involve the risk associated with the oracle data set (all responses available), plus an overhead that measures the accuracy of the pseudo-responses. This theory characterizes both regimes in which PAST accuracy is comparable to the oracle accuracy, as well as more challenging regimes where it behaves poorly. We demonstrate its empirical performance across a range of applications, including forecasting of societal ills over time with future covariates as helpers;prediction of cardiovascular risk after heart attacks with prescription data as helpers;and diagnosing pneumonia from chest X-rays using machine-generated predictions as helpers. © 2024, CC BY.

关键词： Prediction models

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The Prediction of Micro Plasma Impacts of Farm Fresh Vegetables Using Machine Learning

The Prediction of Micro Plasma Impacts of Farm Fresh Vegetab...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Maragatham, G. Sundaramoorthy, K. Maheswari, K. Sajana, T. Shiba, C. Mary Kumar, Ashok Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Tamil Nadu Chennai600100 India Department of Computer Science and Engineering Cmr Technical Campus Kandlakoya Telangana Hyderabad501401 India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Business Systems R. M. K Engineering College Tamil Nadu Chennai601206 India Department of Computer Science Banasthali Vidyapith Rajasthan304022 India

ISBN: (纸本)9798350334357

In general, the innovative foods produced on fruit and vegetable based farms are always high quality and healthy. Indicators of fruit and vegetable consumption include plasma vitamin C and arytenoids, which are plant pigments discovered in blood samples. The researchers decided to utilize blood samples rather than the more common food frequency questionnaire in their investigation. to assess the amount of food consumed in order to forestall measuring errors and to establish dependencies. Because vitamin C and arytenoids may be found in a wide variety of fruits and vegetables, we can use them as objective measures of our consumption of these food groups. The fact that individuals who do not consume a diet that is abundant in fruits and vegetables do not consume significant quantities of vitamin C and arytenoids is reflected in the plasma levels of these individuals. In this paper a smart machine learning algorithm was proposed to predict the micro plasma impacts. This monitors the regular shape and harvesting of different farm fresh products and predicts the impacts of it. This will helpful for farmers to enhance the harvesting. © 2023 IEEE.

关键词： Machine learning

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Optimal Deep Canonically Correlated Autoencoder-Enabled Prediction Model for Customer Churn Prediction

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Computers, Materials & Continua 2022年第11期73卷 3757-3769页

作者： Olfat M.Mirza GJose Moses R.Rajender E.Laxmi Lydia Seifedine Kadry Cheadchai Me-Ead Orawit Thinnukool Department of Computer Science College of Computers and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeNorway Program in Statistics and Information Management Faculty of ScienceMaejo UniversityChiang Mai50290Thailand Department of Modern Management and Information Technology College of Arts Media and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Presently,customer retention is essential for reducing customer churn in telecommunication *** churn prediction(CCP)is important to predict the possibility of customer retention in the quality of *** risks of customer churn also get essential,the rise of machine learning(ML)models can be employed to investigate the characteristics of customer ***,deep learning(DL)models help in prediction of the customer behavior based characteristic *** the DL models necessitate hyperparameter modelling and effort,the process is difficult for research communities and business *** this view,this study designs an optimal deep canonically correlated autoencoder based prediction(ODCCAEP)model for competitive customer dependent application *** addition,the O-DCCAEP method purposes for determining the churning nature of the *** O-DCCAEP technique encompasses preprocessing,classification,and hyperparameter ***,the DCCAE model is employed to classify the churners or ***,the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm(DHOA).The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.

关键词： Churn prediction customer retention deep learning machine learning archimedes optimization algorithm

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Molecule opacity study on low-lying states of CS

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Chinese Physics B 2022年第10期31卷 362-370页

作者： Rui Li Jiqun Sang Xiaohe Lin Jianjun Li Guiying Liang Yong Wu Department of Physics College of ScienceQiqihar UniversityQiqihar 161006China National Key Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijing 100088China Faculty of Foundation Space Engineering UniversityBeijing 101416China School of Data Science and Artificial Intelligence Jilin Engineering Normal UniversityChangchun 130052China HEDPS Center for Applied Physics and TechnologyPeking UniversityBeijing 100084China

CS molecule, which plays a key role in atmospheric and astrophysical circumstances, has drawn great attention for long time. Owing to its large state density, the detailed information of the electronic structure of CS is still lacking. In this work, the high-level MRCI+Q method is used to compute the potential energy curves, dipole moments and transition dipole moments of singlet and triplet states correlated with the lowest dissociation limit of CS, based on which high accurate vibration-rotation levels and spectroscopic constants of bound states are evaluated. The opacity of CS relevant to atmospheric circumstance is computed at a pressure of 100 atms for different temperatures. With the increase of temperature,band systems from different transitions mingle with each other, and band boundaries become blurred, which are originated from the increased population on vibrational excited states and electronic excited states at high temperature.

关键词： CS transition dipole moment opacity excited state

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Smart Contract-Based Advanced Financial Security System: A Literature Review

Smart Contract-Based Advanced Financial Security System: A L...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Prabanand, S.C. Thanabal, M.S. Mahalakshmi, P. Solairaj, A. Durai Murugan, A. Gowsika Sree, S.M. Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Tamil Nadu Sathyamanagalam India Department of Computer Science and Engineering PSNA College of Engineering and Technology Tamil Nadu Dindigul India Department of Information Technology Kamaraj College of Engineering and Technology Tamil Nadu Virudhunagar India Department of Computer Science and Engineering Nadar Saraswathi College of Engineering and Technology Tamil Nadu Theni India Department of Computer Science and Business Systems M Kumarasamy College of Engineering Tamil Nadu Karur India

ISBN: (纸本)9798350305708

The current research provides a literature evaluation of works from sources such as IEEE and science Direct that explore testing and analysis methodologies and approaches for smart contracts. Smart contracts built on the blockchain have attracted a lot of attention from academics because of their unique properties. These include the immutability of transaction records, the automatic execution of contract instructions, and the decentralized building of trust. No one can deny blockchain's revolutionary potential across a number of sectors, but there are still technological hurdles to overcome before smart contracts gain widespread use. Solutions to these problems, as well as investigations into potential new uses for smart contracts outside of their present financial and logistics-related contexts, should guide future research efforts. In general, smart contracts built on blockchain technology have the power to revolutionize a number of sectors and open the door to a future that is more decentralized. © 2023 IEEE.

关键词： Blockchain concurrency Ethereum IoT scalability security smart contracts

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Prediction-Powered Inference with Imputed Covariates and Nonuniform Sampling

arXiv

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arXiv 2025年

作者： Kluger, Dan M. Lu, Kerri Zrnic, Tijana Wang, Sherrie Bates, Stephen Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Stanford Data Science Department of Statistics Stanford University StanfordCA94305 United States

Machine learning models are increasingly used to produce predictions that serve as input data in subsequent statistical analyses. For example, computer vision predictions of economic and environmental indicators based on satellite imagery are used in downstream regressions;similarly, language models are widely used to approximate human ratings and opinions in social science research. However, failure to properly account for errors in the machine learning predictions renders standard statistical procedures invalid. Prior work uses what we call the Predict-Then-Debias estimator to give valid confidence intervals when machine learning algorithms impute missing variables, assuming a small complete sample from the population of interest. We expand the scope by introducing bootstrap confidence intervals that apply when the complete data is a nonuniform (i.e., weighted, stratified, or clustered) sample and to settings where an arbitrary subset of features is imputed. Importantly, the method can be applied to many settings without requiring additional calculations. We prove that these confidence intervals are valid under no assumptions on the quality of the machine learning model and are no wider than the intervals obtained by methods that do not use machine learning predictions. Copyright © 2025, The Authors. All rights reserved.

关键词： Economic analysis

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Supervised Machine Learning Strategy for detection of covid19 patients

Supervised Machine Learning Strategy for detection of covid1...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Punithavathi, I.S. Hephzi Deepa, K. Rao, Cheruku Poorna Venkata Srinivasa Gopal, S. Raja Rajasekar, P. Kumar, Ashok Department of Artificial Intelligence Vidya Jyothi Institute of Technology Telagana Hyderabad500075 India Department of Computer Science and Artificial Intelligence Sr University Kavaraipettai Telangana Hasanparthy India Department of Information Technology Laki Reddy Balireddy College of Engineering Andhra Pradesh Mylavaram521230 India Department of Electronics and Communication Engineering KoneruLakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science Banasthali Vidyapith Rajasthan304022 India

ISBN: (纸本)9798350334357

The current severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) public health catastrophe, both human lives have been lost and the economy has disrupted severely the current scenario. In this paper, we develop a detection module using a series of steps that involves pre-processing, feature extraction and detection of covid-19 patients based on the images collected from the computerized tomography (CT) images. The images are initially pre-processed and then the features are extracted using Gray Level Co-occurrence Matrix (GLCM) and then finally classified using back propagation neural network (BPNN). The simulation is conducted to test the efficacy of the model against various CT image datasets of numerous patients. The results of simulation shows that the proposed method achieves higher detection rate, and reduced mean average percentage error (MAPE) than other existing methodologies. © 2023 IEEE.

关键词： SARS

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An Anomaly Detection Algorithm for Estimating Interannual Variation in Crop Yields

An Anomaly Detection Algorithm for Estimating Interannual Va...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： R. Vinoth Kumar Malathy M. Manjula J. Veerandeswari M. Indumathy B S Raghul Information technology Department Rajiv Gandhi College of Engineering & Technology Kriumambakkam Puduherry Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur India

Crop production is a vital aspect of human survival. Reducing global poverty heavily relies on increasing the robustness in crop yields. The quantity of crops harvested from a given agricultural land, also known as crop yields of that land is the important parameter that helps to increase the smartness in adopting novel agricultural plans. Measuring this parameter can be done through supervised machine learning techniques which helps with accurate predictions given the historical data of crop yield. This paper proposes an ideation, that uses Support Vector Machines (SVMs) because of its strength being accuracy, robust and flexible. SVM can predict reliable crop yield estimates that help to allocate resources and smart planning for agricultural activities. Also, SVMs are good enough to handle the imperfections in historical crop yield datasets, outliers, and inconsistencies. Furthermore, SVMs can bring out the relationships and any nonlinear patterns in the crop yields which are influenced by uncertain environmental factors over various seasons. This helps to identify the anomalies in the historical data. The anomalies could be exceptionally high yield or the failures which happen to be pinpointed as abnormal years. The identified outliers are excluded by the SVM, thus bringing out a prediction model that focusses on “normal years”. By this, it is possible to represent more accurate inter-annual trends of crop yields avoiding the anomalies aiding the farmer to make better decisions for much more productivity in the crop yield. Based on the ideation, simulations are performed that showcase the success of the SVM in predicting good for the crop yields in inter-annual period.

关键词：

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Usage of Continuous Medical Monitoring in Human-Machine systems to Improve Their Reliability and Emergency Safety 16

Usage of Continuous Medical Monitoring in Human-Machine Syst...

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16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Turovskiy, Yaroslav Surovtsev, Alexander V.A. Trapeznikov Institute of Control Science of Ras Laboratory of Control Based on Incomplete Data Moscow Russia Voronezh State University Department of Digital Technologies Voronezh Russia Voronezh State University of Engineering Technologies Department of Information and Control Systems Voronezh Russia

ISBN: (纸本)9798350337907

A method of conducting continuous medical monitoring of human operators of human-machine systems is proposed. This method assumes building a decision support system that selects the control mode of the technological process. This paper has simulation modeling results of the technological process conditions changing from a potentially faulty state to a proper state. © 2023 IEEE.

关键词： Decision support systems

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Machine Learning Approaches to Identify Social Determinants of Frailty Among Middle-Aged and Older Adults in the USA, England, and China

SSRN

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SSRN 2023年

作者： Luo, Yan Guo, Mengzhuo Zhang, Qingpeng School of Data Science City University of Hong Kong Hong Kong Business School Sichuan University China Department of Pharmacology and Pharmacy LKS Faculty of Medicine The University of Hong Kong Hong Kong Musketeers Foundation Institute of Data Science The University of Hong Kong Hong Kong

Background: Frailty has become a growing global concern and is associated with social determinants of health (SDoH). However, the relative importance and cumulative contribution of multidomain SDoH to frailty, and whether these relationships differ across countries, remain ***: We included participants aged ≥45 years from the USA (N=5,792), England (N=3,773), and China (N=5,016). SDoH (n=121 for the USA, n=125 for England, and n=94 for China) were selected across seven domains. Frailty was assessed by the frailty index. We developed Extreme Gradient Boosting to predict frailty at the 4-year follow-up and used SHapley Additive exPlanations to quantify variable-wise and domain-wise contributions of SDoH, and to explore nonlinear relationships between SDoH and ***: Our models explained 0.242 (95% confidence interval [CI]: 0.203–0.281), 0.258 (95% CI: 0.191–0.325), and 0.173 (95% CI: 0.126–0.215) of the variance in FI among all participants from the USA, England, and China. Health behaviors and social connections or stressors were the most important domains in the USA and England, while material circumstances contributed largely in China. Several important SDoH predictors, such as body mass index, were consistent across countries, while country-specific risk factors, such as engagement in maintenance or gardening in the USA, were also identified. We observed nonlinear relationships between SDoH, such as sleep duration, and ***: Our findings reveal the priorities of SDoH domains for addressing aging disparities and promoting healthy aging, especially region-specific risk factors for tailored public health prevention ***: This work was supported by the National Natural science foundation of China (grant no. 71972164).Declaration of Interest: No conflicting relationship exists for any *** Approval: All studies received ethical approval from relevant local research ethics committees, and participants were

关键词： Machine learning

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