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Implementing machine learning algorithms to Predict Particulate Matter (PM2.5): A Case Study in the Paso del Norte Region

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ATMOSPHERE 2022年第12期13卷 2100-2100页

作者： Mahmud, Suhail Ridi, Tasannum Binte Islam Miah, Mohammad Sujan Sarower, Farhana Elahee, Sanjida Karen Clark & Co Boston MA 02116 USA East West Univ Dept Econ Dhaka 1212 Bangladesh Univ Texas El Paso Dept Comp Sci El Paso TX 79968 USA New Mexico Sate Univ Math Dept Las Cruces NM 88003 USA Penn State Univ Dept Chem State Coll PA 16801 USA

This work focuses on the prediction of an air pollutant called particulate matter (PM2.5) across the Paso Del Norte region. Outdoor air pollution causes millions of premature deaths every year, mostly due to anthropogenic fine PM2.5. In addition, the prediction of ground-level PM2.5 is challenging, as it behaves randomly over time and does not follow the interannual variability. To maintain a healthy environment, it is essential to predict the PM2.5 value with great accuracy. We used different supervised machine learning algorithms based on regression and classification to accurately predict the daily PM2.5 values. In this study, several meteorological and atmospheric variables were retrieved from the Texas Commission of Environmental Quality's monitoring stations corresponding to 2014-2019. These variables were analyzed by six different machine learning algorithms with various evaluation metrics. The results demonstrate that ML models effectively detect the effect of other variables on PM2.5 and can predict the data accurately, identifying potentially risky territory. With an accuracy of 92%, random forest performs the best out of all machine learning models.

关键词： ground PM air quality machine learning algorithms Paso del Norte classification

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Prediction of Hotel Booking & Cancellation using machine learning algorithms

Prediction of Hotel Booking & Cancellation using Machine Lea...

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International Conference on Computer Communication and Informatics (ICCCI)

作者： N. Shirisha K. Anusha Ajmeera Kiran Yasam Teja Sai Buavani Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of CSE GRIET Bachupally Hyderabad

Booking cancellations in the hospitality sector not only result in revenue loss, but they also have an impact on billing and stock or resource allocations and budgeting. The hospitality industry’s demand management strategies are greatly influenced by booking cancellations. The ability to produce accurate projections, a crucial tool for revenue management success, is constrained by cancellations. According to 2016 data, the majority of cancellations are made through OTAs, with *** taking the top spot at 57% and Expedia at 26%, compared to official hotel websites, which have an average cancellation rate of 14%. Hotels often employ strict cancellation rules and overbooking techniques to avoid the issues brought on by cancelled reservations, but these tactics can also negatively affect income and reputation. The outcomes enable hotel managers to precisely forecast net demand, create better projections, enhance cancellation procedures, specify better overbooking techniques, and employ more forceful pricing and inventory management tactics.

关键词： machine learning algorithms Stars Pricing Predictive models Inventory management Data models Resource management

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Employee Attrition Prediction in Industries using machine learning algorithms

Employee Attrition Prediction in Industries using Machine Le...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Shreya Gupta Gargi Bhardwaj Megha Arora Ritu Rani Poonam Bansal Ravinder Kumar Computer Science Engineering with Specialization in Artificial Intelligence Indira Gandhi Delhi Technical University For Women New Delhi India Center of Excellence- Artificial Intelligence Indira Gandhi Delhi Technical University for Women Kashmere Gate Delhi India Skill department of CS/IT Shri Vishwakarma Skill University Palwal

Employee attrition is a major issue among all employee-related issues in the current setting, notwithstanding changes in the external environment. Attrition is defined as a gradual decrease in the number of employees due to seclusion, death, renunciation, and resignations. This research employs logistic regression to forecast whether or not a firm employee will leave. We evaluate job involvement, average monthly hours worked, years employed, gender, education level, and job satisfaction, among other things, as our features. K-nearest neighbors (KNN), Support Vector machine (SVM), Random Forest (RF), and decision trees are more methods for solving this issue. The dataset was divided, with 80% of it being used to train the algorithm and 20% of it being used to test it, yielding a Logistic Regression (LR) accuracy of 88.0952%.

关键词： Support vector machines Radio frequency Industries machine learning algorithms Computational modeling Education Forestry

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Development of prediction model for phosphate in reservoir water system based machine learning algorithms

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AIN SHAMS ENGINEERING JOURNAL 2022年第1期13卷

作者： Latif, Sarmad Dashti Birima, Ahmed H. Ahmed, Ali Najah Hatem, Dahan Mohammed Al-Ansari, Nadhir Fai, Chow Ming El-Shafie, Ahmed Komar Univ Sci & Technol Dept Civil Engn Coll Engn Sulaimany 46001 Kurdistan Regio Iraq Qassim Univ Coll Engn Dept Civil Engn Unaizah Saudi Arabia Univ Tenaga Nas Inst Energy Infrastruct Kajang 43000 Selangor Malaysia Lulea Univ Technol Civil Environm & Nat Resources Engn S-97187 Lulea Sweden Monash Univ Malaysia Sch Engn Discipline Civil Engn Jalan Lagoon Selatan Bandar Sunway 47500 Selangor Malaysia Univ Malaya Dept Civil Engn Fac Engn Kuala Lumpur 50603 Malaysia United Arab Emirates Univ Natl Water & Energy Ctr Al Ain U Arab Emirates

Phosphate (PO4) is a major component of most fertilizers, and when erosion and runoff occur, large amounts of it enter the water bodies, causing several problems such as eutrophication. Feitsui reservoir, the primary source of water supply to Taipei, reported half of the reservoir's pollutants from nonpointsource pollution. The value of the PO4 in the water body fluctuates in highly nonlinear and stochastic patterns. However, conventional modeling techniques are no longer sufficiently effective in predicting accurately such stochastic patterns in the concentrations of PO4 in water. Therefore, this study proposes different machine learning algorithms: the artificial neural network (ANN), support vector machine (SVM), random forest (RF), and boosted trees (BT) to predict the concentration of PO4. Monthly measured data between 1986 and 2014 were used to train and test the accuracy of these models. The performances of these models were examined using different statistical indices. Hyperparameters optimization such as cross-validation was performed to enhance the precision of the models. Five water quality parameters were used as input to the proposed models. Different input combinations were explored to optimize the precision. The findings revealed that ANN outperformed the other three models to capture the changes in the concentrations of PO4 with high precision where RMSE is equal to 1.199, MAE is equal to 0.858, and R-2 is equal to 0.979, MSE is equal to 1.439, and finally, CC is equal to 0.9909. The developed model could be used as a reliable means for managing eutrophication problems. (c) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Water quality parameters Phosphate (PO4), concentration machine learning algorithms Prediction and Feitsui reservoir

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A Comprehensive Analysis of the Effectiveness of machine learning algorithms for Predicting Water Quality

A Comprehensive Analysis of the Effectiveness of Machine Lea...

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Innovative Data Communication Technologies and Application (ICIDCA), International Conference on

作者： Priyanshu Rawat Madhvan Bajaj Vikrant Sharma Satvik Vats Computer Science and Engineering Graphic Era Hill University Dehradun India

This study provides a comprehensive analysis of the effectiveness of eight different machine learning algorithms for predicting water quality. The algorithms, which include Gaussian Naive Bayes, Extreme Gradient Boost Classifier, Support Vector machines (SVM), K-Nearest Neighbors (KNN), Logistic Regression, Random Forest, and Decision Tree, were tested using the water potability dataset. This study's main goals were to identify the best accurate machine learning algorithm for predicting water quality and to present a thorough comparison of these methods. Algorithm's effectiveness. The study's findings demonstrated that one algorithm performed better than the others, with the lowest mean squared error and maximum accuracy. The results of this study may be used as a guide for future research in this area and offer a strong foundation for selecting the best machine learning algorithm for predicting water quality, Predicting water quality is often hampered by a lack of data, especially in developing or rural areas. machine learning techniques may be used to predict water quality. This study highlights how crucial it is to use a suitable machine learning algorithm for predicting water quality since the precision and efficiency of these algorithms may have a big influence on the outcomes. Organizations that manage and monitor water quality, as well as academics and experts in the field of water quality forecasting, can benefit from the study's findings.

关键词： Support vector machines machine learning algorithms Water quality Organizations Prediction algorithms Classification algorithms Forecasting

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A Fundamental Analysis of Stock Returns using machine learning algorithms

A Fundamental Analysis of Stock Returns using Machine Learni...

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IEEE MIT Undergraduate Research Technology Conference (URTC)

作者： Andy Qin Mihai Boicu Plano West Senior High Plano Texas United States George Mason University Fairfax Virginia United States

ISBN: (数字)9798350309652

ISBN: (纸本)9798350309669

Predicting stocks has always been an attractive prospect for researchers. This study aims to create a more replicable model that combines machine learning and fundamental financial features to predict long-term stock movement better. We test a support vector machine (SVM) and random forest (RF) model via a regression and classification task of 432 stocks selected from the NYSE and NASDAQ. The results show that both models are best at predicting stock movement 1 quarter in the future, with the best root mean square error (RMSE) of 0.119. The models perform the best at predicting stock directionality when predicting 5 quarters in the future as the SVM peaks at 77.3% accuracy. We also find key features by using a permutation feature importance algorithm. Our models show significant improvement over baseline decision-making (50% accuracy classification) considerably far into the future (>1 year), helping provide better indicators for long-term stock returns.

关键词： Support vector machines Radio frequency Measurement Analytical models machine learning algorithms Predictive models Prediction algorithms

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Estimating the Forage Neutral Detergent Fiber Content of Alpine Grassland in the Tibetan Plateau Using Hyperspectral Data and machine learning algorithms

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷

作者： Liu, Jie Feng, Qisheng Liang, Tiangang Yin, Jianpeng Gao, Jinlong Ge, Jing Hou, Mengjing Wu, Caixia Li, Wenlong Lanzhou Univ State Key Lab Grassland Agroecosyst Lanzhou 730000 Peoples R China Lanzhou Univ Key Lab Grassland Livestock Ind Innovat Minist Agr & Rural Affairs Lanzhou 730000 Peoples R China Lanzhou Univ Engn Res Ctr Grassland Ind Minist Educ Lanzhou 730000 Peoples R China Lanzhou Univ Coll Pastoral Agr Sci & Technol Lanzhou 730000 Peoples R China Univ Elect Sci & Technol China Sch Resources & Environm Chengdu 611731 Sichuan Peoples R China

The neutral detergent fiber (NDF) content is a key factor in the forage palatability and livestock digestibility of alpine grassland. Traditional methods of measuring NDF are time-consuming and laborious, while multispectral remote sensing often has difficulty capturing subtle changes due to its broad spectral channels. In this study, we evaluate NDF using hyperspectral data and environmental factors (EFs) during 2016-2019. The artificial neural network (ANN), support vector machine (SVM), and random forest (RF) are used to construct models. The results show that: 1) compared with the correlation analysis, the least absolute shrinkage and selection operator (LASSO) regression can more effectively select the important variables for estimating NDF and achieve significant dimension reduction;2) estimation accuracy of the models (coefficient of determination (R-2) between 0.44 and 0.51, relative root mean square error (RMSE) between 4.31% and 4.61%) based on first derivative (FD) spectra is relatively better than the models based on original spectra (OR), log transformation (Log), and continuum removal (CR) spectra (R-2 between 0.35 and 0.46, RMSE between 4.39% and 4.92%). The RF model based on FD-Log-vegetation indices (VIs)-EFs is the best model (R-2 = 0.62, RMSE = 3.98%);and 3) compared with the EFs, hyperspectral feature plays a pivotal role in estimating NDF, with a total contribution of 81.66%, and red-edge and shortwave infrared (SWIR) regions are significant for estimating NDF. In general, the combination of hyperspectral data with the EFs significantly improves the accuracy of estimating NDF. This study provides important guidance for future efforts on estimating NDF of natural grassland using remote sensing and machine learning algorithms.

关键词： Biological system modeling Monitoring Hyperspectral imaging Radio frequency Support vector machines machine learning algorithms Indexes Alpine grassland hyperspectral remote sensing machine learning neutral detergent fiber (NDF)

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Estimating the thermal conductivity of soils using six machine learning algorithms

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2022年第0期136卷

作者： Li, Kai-Qi Liu, Yong Kang, Qing Wuhan Univ Inst Engn Risk & Disaster Prevent State Key Lab Water Resources & Hydropower Engn Sc 299 Bayi Rd Wuhan 430072 Peoples R China Natl Univ Singapore Dept Civil & Environm Engn 1 Engn Dr 2 Singapore 117576 Singapore

Many machine learning algorithms have been applied to determine soil properties in recent years. However, the prediction performances of thermal conductivity of soils via machine learning algorithms are still unclear due to the lack of sufficient databases. In this work, a large database containing 2197 data points from various literature was compiled. Six machine learning algorithms, namely multivariance linear regression (MLR), Gaussian process regression (GPR), support vector machine (SVM), decision tree (DT), random forest (RF) and adaptive boosting methods (AdaBoost) were implemented to predict the thermal conductivity of soils based on the compiled database. In addition, Spearman correlation analysis and grey relational analysis were adopted to compute the strength of influencing factors in thermal conductivity. For obtaining the best prediction model, the performances of six machine learning algorithms were assessed via eight evaluation indicators and compared with three typical empirical models. Results show that the AdaBoost can yield good predicted values of the thermal conductivity of soils with minimum errors (i.e., RMSE = 0.099). This study constructs a database of thermal conductivity and provides a reference for evaluating the thermal conductivity of soils via machine learning algorithms.

关键词： machine learning algorithms Thermal conductivity database Parameter correlation Performance indicators Ensemble learning model

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Social Media News Classification Using machine learning algorithms

Social Media News Classification Using Machine Learning Algo...

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International Conference on Computer Communication and Informatics (ICCCI)

作者： M. Jagadeesan T.M Saravanan P.A. Selvaraj S. Nithya K. Snegha P. Sowmiya Department of Computer Applications Kongu Engineering College Perundurai Erode TamilNadu India

Every day, a great amount of data is generated online in the age of technology. However, an unprecedented amount of material is being disseminated online, most of it false news. Fake news is created to draw in viewers, influence people’s opinions and actions, raise click through rates, and have a significant impact on important events like elections. Fake News incorporates material that steers readers in the wrong directions. It may have unfavorable repercussions in the actual world that seek to deceive people, sway public opinion, or attract attention or damage reputation. It is necessary to detect fake news mainly for media outlets to have the capability to attract viewers to their website to generate online advertising revenue. This project develops a SVM approach and uses this model to detect whether the news are fake or not. It uses a fake news dataset, which contains News text and corresponding labels (REAL or FAKE). The pervasive increase in fake news, whether it is produced by humans or algorithms, has a severe social and political impact on society and individuals. The rapid turnover of news in the age of social networks makes it difficult to assess its veracity in a timely manner. Tools for automatically detecting bogus news become essential. K- Nearest Neighbor and a classification based on a support vector machine are utilized in this case to overcome the aforementioned issue. In contrast to other research that merely took particular sentences or phrases into account, The purpose of this study is to investigate a false news posture detection mechanism based on the title and text of the news. Python 3.7 is used in the project’s development.

关键词： machine learning algorithms Social networking (online) Voting Support vector machine classification Classification algorithms Fake news Informatics

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Prediction of Chronic Kidney Disease Using Various machine learning algorithms

Prediction of Chronic Kidney Disease Using Various Machine L...

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Innovative Data Communication Technologies and Application (ICIDCA), International Conference on

作者： Daravath Anil Shaik Naimudden Aujugari Santosh Reddy A Lavanya Electronics and Communication Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

Chronic Renal Disease (CRD), often referred to as Chronic Kidney Disease (CKD), has become more prevalent over time. Only 18 days may be spent without a functioning kidney, which is why dialysis and kidney transplants are so common. Predicting CKD at an early stage is crucial. machine learning is quite effective at forecasting CKD. The model proposes an approach to predict CKD status based on clinical data by combining preprocessing, a mechanism for handling missing values, and collaborative filtering. It assists i $n$ figuring out whether someone has CKD. This research utilizes several algorithms to determine which one was the most accurate for getting the result. The creation of a trustworthy model for anticipating chronic renal disease is the goal of this research. The model in most of the current systems employs various ML algorithms separately. If the method is not sufficiently regularized, the data is too noisy, or there are too many features in the data, overfitting may happen. However, several algorithms are employed in this model to build a more precise, reliable, and adaptable model.

关键词： Training Adaptation models machine learning algorithms Prediction algorithms Chronic kidney disease Classification algorithms Reliability

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