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Impact of Weather Factors on Migration Intention Using Machine Learning algorithms

Operations Research Forum 2024年第1期5卷 8页

作者： Aoga, John O. R. Bae, Juhee Veljanoska, Stefanija Nijssen, Siegfried Schaus, Pierre Ecole Doctorale Science Pour Ingenieur Université d’Abomey-Calavi Abomey-Calavi Benin School of Informatics University of Skövde Skövde Sweden Université de Rennes 1 CNRS/CREM-UMR621 Rennes France ICTEAM Université catholique de Louvain Louvain-la-Neuve 1348 Belgium

A growing attention in the empirical literature has been paid on the incidence of climate shocks and change on migration decisions. Previous literature leads to different results and uses a multitude of traditional empirical approaches. This paper proposes a tree-based Machine Learning (ML) approach to analyze the role of the weather shocks toward an individual’s intention to migrate in the six agriculture-dependent-economy countries such as Burkina Faso, Ivory Coast, Mali, Mauritania, Niger, and Senegal. We performed several tree-based algorithms (e.g., XGB, Random Forest) using the train-validation-test workflow to build robust and noise-resistant approaches. Then we determine the important features showing in which direction they influence the migration intention. This ML-based estimation accounts for features such as weather shocks captured by the Standardized Precipitation-Evapotranspiration Index (SPEI) for different timescales and various socioeconomic features/covariates. We find that (i) the weather features improve the prediction performance, although socioeconomic characteristics have more influence on migration intentions, (ii) a country-specific model is necessary, and (iii) the international move is influenced more by the longer timescales of SPEIs while general move (which includes internal move) by that of shorter timescales. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Machine learning Migration tree-based algorithms Weather shocks

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Cybersecurity Analytics: Toward an Efficient ML-based Network Intrusion Detection System (NIDS) 1

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6th International Conference on Machine Learning for Networking (MLN)

作者： Mouatassim, Tariq El Ghazi, Hassan Bouzaachane, Khadija El Guarmah, El Mandi Lahsen-Cherif, Iyad AGNOX Lab INPT Rabat Morocco Cadi Ayyad Univ FST L2IS Marrakech Morocco Royal Moroccan Air Force Acad Math & Informat Dept Marrakech Morocco

ISBN: (数字)9783031599330

ISBN: (纸本)9783031599323;9783031599330

ML-based NIDS are among the tools used within the framework of Cybersecurity analytics to tackle intrusions and alert for potential or ongoing cyberattacks. Their design relies heavily on precollected datasets on which ML algorithms are trained. However, NIDS datasets are often confronted with two major problems: imbalanced classes and outdated traffic flows. In fact, in one hand, designing NIDSs using obsolete datasets (like KDD99 and NSLKDD) may result in poor performances when implemented in nowadays network environment, due to their lack of modern attack styles and recent normal traffic scenarios. On the other hand, a high imbalance ratio could result in decrease of the efficiency of NIDS, especially for rarely encountered attack types. Therefore, in this study, binary and multiclass intrusion detection models are proposed, using tree-based algorithms: Decision tree(DT), Random Forest(RF), Extratrees, Gradient Boosting(GB), Adaboost, and XGBoost algorithms. The main advantage of this work is the use of a recent and well-ranked dataset, NF-UQ-NIDS-v2, which have been balanced using k-means undersampling, to train tree-based models for intrusion detection. Through the experiments, we found that our approach presented satisfactory prediction time and performances, with low rates of false negatives and false positives.

关键词： Cybersecurity analytics Network Intrusion Detection Systems NF-UQ-NIDS-v2 K-means clustering tree-based algorithms hyper-parameter Bayesian Optimization tree Parzen Estimator

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Enhancing Network Intrusion Detection Systems: A Review of Feature Selection algorithms for Streaming Data Processing 21

Enhancing Network Intrusion Detection Systems: A Review of F...

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21st International Conference on Privacy, Security and Trust

作者： Amamra, Abdelfattah Khettab, Rym Mezine, Raissa Calif State Polytech Univ Pomona Dept Comp Sci Pomona CA 91768 USA Univ Sci & Technol Houari Boumed Dept Comp Sci Algiers Algeria

ISBN: (纸本)9798350367102;9798350367096

The proliferation of Internet of Things (IoT) devices has precipitated a substantial expansion in network size and data volume, concurrently giving rise to heightened security concerns. Consequently, there has been a concerted effort in research to augment the execution and prediction performance of Network Intrusion Detection Systems (NIDS). The efficacy of machine learning (ML) models crucially hinges on judicious choices in algorithms and feature sets. While much of the previous research on feature selection algorithms concentrated on static datasets, real-world network intrusion detection systems grapple with the challenges posed by streaming data. This study comprehensively reviews feature selection algorithms, shedding light on their merits, drawbacks, and practical applications, with a specific emphasis on prerequisites for effective processing of streaming data. Noteworthy contributions include bridging gaps in prior research by delineating requirements tailored to the challenges of streaming data and conducting experimental analyses using contemporary datasets.

关键词： feature selection network security tree-based algorithms intrusion detection streaming data

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Machine Learning Models for Predicting Bond Strength of Deformed Bars in Concrete

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ACI STRUCTURAL JOURNAL 2022年第5期119卷 43-56页

作者： V. Degtyarev, Vitaliy New Millennium Bldg Syst LLC Ft Wayne IN 46804 USA

This paper proposes eight machine learning models for predicting the bond strength between straight deformed reinforcing bars and concrete under tensile load. The models included support vector regressor (SVR), k-nearest neighbors (KNN), random forest (RF), gradient boosting regressor (GBR), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), gradient boosting with categorical features support (CatBoost), and adap-tive boosting (AdaBoost). The models were trained using a large data set of test results from the Joint ACI-ASCE Committee 408 database. The performance of the models was evaluated through a robust tenfold cross-validation method. Optimal parameters for each model were established through an extensive tuning process. The bond strengths predicted by the proposed models agree well with the experimental data. Their accuracy exceeds the accuracy of the available descriptive equations. The relative feature importance for predicting the bond strength was evaluated using the permuta-tion and Shapley additive explanations (SHAP) methods for each model. Partial dependence of the bond force from the considered features was also presented and discussed.

关键词： bond deformed reinforcement ensemble learning gradient boosting k-nearest neighbor machine learning splice support vector machine tree-based algorithms

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The Predictability of tree-based Machine Learning algorithms in the Big Data Context

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INTERNATIONAL JOURNAL OF ENGINEERING 2021年第1期34卷 82-89页

作者： Qolipour, F. Ghasemzadeh, M. Mohammad-Karimi, N. Yazd Univ Dept Comp Engn Yazd Iran

This research work is concerned with the predictability of ensemble and singular tree-based machine learning algorithms during the recession and prosperity of the two companies listed in the Tehran Stock Exchange in the context of big data. In this regard, the main issue is that economic managers and the academic community require predicting models with more accuracy and reduced execution time;moreover, the prediction of the companies recession in the stock market is highly significant. Machine learning algorithms must be able to appropriately predict the stock return sign during the market downturn and boom days. Addressing the stated challenge will upgrade the quality of stock purchases and, subsequently, will increase profitability. In this article, the proposed solution relies on the utilization of tree-based machine learning algorithms in the context of big data. The proposed solution exploits the decision tree algorithm, which is a traditional and singular tree-based learning algorithm. Furthermore, two modern and ensemble tree-based learning algorithms, random forest and gradient boosted tree, has been utilized for predicting the stock return sign during recession and prosperity. The mentioned cases were implemented by applying the machine learning tools in python programming language and PYSPARK library that is used explicitly for the big data context. The utilized research data of the current study are the shares information of two companies of the Tehran Stock Exchange. The obtained results reveal that the applied ensemble learning algorithms have performed better than the singular learning algorithms. Additionally, adding 23 technical features to the initial data and subsequent applying of the PCA feature reduction method have demonstrated the best performance among other modes. In the meantime, it has been concluded that the initial data do not possess the proper resolution or generalizability, either during prosperity or recession.

关键词： Stock Market Big Data Prediction Machine Learning tree-based algorithms Ensemble algorithms

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TACOS: a novel approach for accurate prediction of cell-specific long noncoding RNAs subcellular localization

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BRIEFINGS IN BIOINFORMATICS 2022年第4期23卷 bbac243-bbac243页

作者： Jeon, Young-Jun Hasan, Md Mehedi Park, Hyun Woo Lee, Ki Wook Manavalan, Balachandran Sungkyunkwan Univ Dept Integrat Biotechnol Suwon South Korea Tulane Univ Tulane Ctr Aging New Orleans LA 70118 USA Tulane Univ Dept Med New Orleans LA 70118 USA Sungkyunkwan Univ Suwon South Korea

Long noncoding RNAs (lncRNAs) are primarily regulated by their cellular localization, which is responsible for their molecular functions, including cell cycle regulation and genome rearrangements. Accurately identifying the subcellular location of lncRNAs from sequence information is crucial for a better understanding of their biological functions and mechanisms. In contrast to traditional experimental methods, bioinformatics or computational methods can be applied for the annotation of lncRNA subcellular locations in humans more effectively. In the past, several machine learning-based methods have been developed to identify lncRNA subcellular localization, but relevant work for identifying cell-specific localization of human lncRNA remains limited. In this study, we present the first application of the tree-based stacking approach, TACOS, which allows users to identify the subcellular localization of human lncRNA in 10 different cell types. Specifically, we conducted comprehensive evaluations of six tree-based classifiers with 10 different feature descriptors, using a newly constructed balanced training dataset for each cell type. Subsequently, the strengths of the AdaBoost baseline models were integrated via a stacking approach, with an appropriate tree-based classifier for the final prediction. TACOS displayed consistent performance in both the cross-validation and independent assessments compared with the other two approaches employed in this study. The user-friendly online TACOS web server can be accessed at https://***/TACOS.

关键词： long noncoding RNAs stacking strategy tree-based algorithms feature extraction sequence analysis bioinformatics

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Mapping climate change induced forest fire susceptibility using tree-based machine learning algorithms in Bangladesh

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Ecological Indicators 2025年 176卷

作者： Mafrid Haydar Al Hossain Rafi Md.Kamran Hasan Khan Sakib Hosan Halima Sadia Department of Urban and Regional Planning Khulna University of Engineering & Technology (KUET) Khulna 9203 Bangladesh

Forest fires in forested areas are an environmental disaster resulting from either natural phenomena or human activities. Forest fires pose a substantial risk in the forested and grassland areas of the Chittagong Hill Tracts. Therefore, mitigating the danger of forest fires is crucial for lessening their potential negative effects. This study employs tree-based machine learning (ML) algorithms to assess Forest Fire Susceptibility in the Chittagong Hill Tracts (CHT) of Bangladesh under current and anticipated climate change scenarios. Nineteen environmental and climatic variables were analyzed, with temperature, precipitation, wind speed, and height identified as the most relevant factors. This research analyses 267 historical instances of forest fires. Historical fire data and environmental attributes were utilized to train and validate eight ML models: Random Forest (RF), LightGBM, XGBoost, Decision tree (DT), Gradient Boosting Machine (GBM), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), and Bagging. AdaBoost had the highest predictive accuracy (AUC: 0.82), classifying 4.31% of the study area as very susceptible, 66.74% as moderately susceptible, and 28.94% as having low Susceptibility. Future fire Susceptibility were projected under Representative Concentration Pathways (RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5) for respective years of 2040, 2060, 2080, and 2100, indicating an increase in fire-prone regions, with high-susceptible areas expected to grow by as much as 15.39% under RCP 8.5 by 2080. These findings underscore the growing vulnerability of forested regions due to climate change and highlight the necessity of implementing proactive fire management strategies. The results of this research give significant novel insights for authorities in forestry administration and legislators, facilitating the development of climate-adaptive fire mitigation policies to strengthen environmental resilience and safeguard natural resources, while also supporti

关键词： Climate change Fire risk tree-based algorithms Disaster management Sustainable forest management

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Balancing Fined-Tuned Machine Learning Models Between Continuous and Discrete Variables - A Comprehensive Analysis Using Educational Data 23rd

Balancing Fined-Tuned Machine Learning Models Between Contin...

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23rd International Conference on Artificial Intelligence in Education (AIED)

作者： Drousiotis, Efthyvoulos Pentaliotis, Panagiotis Shi, Lei Cristea, Alexandra, I Univ Liverpool Dept Elect Engn & Elect Liverpool Merseyside England Univ Durham Dept Comp Sci Durham England

ISBN: (纸本)9783031116445;9783031116438

Along with the exponential increase of students enrolling in MOOCs [26] arises the problem of a high student dropout rate. Researchers worldwide are interested in predicting whether students will drop out of MOOCs to prevent it. This study explores and improves ways of handling notoriously challenging continuous variables datasets, to predict dropout. Importantly, we propose a fair comparison methodology: unlike prior studies and, for the first time, when comparing various models, we use algorithms with the dataset they are intended for, thus `like for like.' We use a time-series dataset with algorithms suited for time-series, and a converted discrete-variables dataset, through feature engineering, with algorithms known to handle discrete variables well. Moreover, in terms of predictive ability, we examine the importance of finding the optimal hyperparameters for our algorithms, in combination with the most effective pre-processing techniques for the data. We show that these much lighter discrete models outperform the time-series models, enabling faster training and testing. This result also holds over fine-tuning of pre-processing and hyperparameter optimisation.

关键词： Neural networks tree-based algorithms Educational data mining Feature engineering MOOCs

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Use of tree-based machine learning methods to screen affinitive peptides based on docking data

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MOLECULAR INFORMATICS 2023年第12期42卷 e202300143页

作者： Feng, Hua Wang, Fangyu Li, Ning Xu, Qian Zheng, Guanming Sun, Xuefeng Hu, Man Li, Xuewu Xing, Guangxu Zhang, Gaiping Henan Agr Univ Coll Food Sci & Technol Zhengzhou Peoples R China Henan Univ Chinese Med Publ Hlth & Prevent Med Teaching & Res Ctr Zhengzhou Henan Peoples R China Longhu Modern Immunol Lab Zhengzhou Peoples R China Peking Univ Sch Adv Agr Sci Beijing Peoples R China Yangzhou Univ Jiangsu Coinnovat Ctr Prevent & Control Important Yangzhou Jiangsu Peoples R China

Screening peptides with good affinity is an important step in peptide-drug discovery. Recent advancement in computer and data science have made machine learning a useful tool in accurately affinitive-peptide screening. In current study, four different tree-based algorithms, including Classification and regression trees (CART), C5.0 decision tree (C50), Bagged CART (BAG) and Random Forest (RF), were employed to explore the relationship between experimental peptide affinities and virtual docking data, and the performance of each model was also compared in parallel. All four algorithms showed better performances on dataset pre-scaled, -centered and -PCA than other pre-processed dataset. After model re-built and hyperparameter optimization, the optimal C50 model (C50O) showed the best performances in terms of Accuracy, Kappa, Sensitivity, Specificity, F1, MCC and AUC when validated on test data and an unknown PEDV datasets evaluation (Accuracy=80.4 %). BAG and RFO (the optimal RF), as two best models during training process, did not performed as expecting during in testing and unknown dataset validations. Furthermore, the high correlation of the predictions of RFO and BAG to C50O implied the high stability and robustness of their prediction. Whereas although the good performance on unknown dataset, the poor performance in test data validation and correlation analysis indicated CARTO could not be used for future data prediction. To accurately evaluate the peptide affinity, the current study firstly gave a tree-model competition on affinitive peptide prediction by using virtual docking data, which would expand the application of machine learning algorithms in studying PepPIs and benefit the development of peptide therapeutics. image

关键词： affinity classification docking data machine learning peptides tree-based algorithms

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Workflow for predicting undersaturated oil viscosity using machine learning

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RESULTS IN ENGINEERING 2023年 20卷

作者： Fotias, Sofianos Panagiotis Gaganis, Vassilis Natl Tech Univ Athens Sch Min & Met Engn Athens 15773 Greece Fdn Res & Technol Inst Geoenergy Khania 73100 Greece

Undersaturated oil viscosity is a dominant fluid parameter to be measured in oil reservoirs due to its direct involvement in flow calculations. Since PVT experimental work is expensive and time costly, prediction methods are essential. In this work, viscosity data from in-house and literature measurements (500+ reports, 20,000+ data points) has been utilized for the first time to develop machine learning models predicting undersaturated oil viscosity using easy-to-get measurements. Several popular statistical metrics are used to judge the accuracy of each algorithm. Our goal is to introduce a complete workflow that demonstrates the integrity of the steps followed and guides in further research in predicting similar PVT properties. The workflow showcases the advantages of combining engineers expertise to the art of data driven models development, specifically on accuracy and ease of implementation, as well as their limitations.

关键词： Undersaturated oil viscosity Model-based correlations Data-based correlations Machine learning Supervised regression learning Support vector machines tree-based algorithms Neural networks Ensemble methods

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