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A review and guide on selecting and optimizing machine learning algorithms for daylight prediction

BUILDING AND ENVIRONMENT 2023年 244卷

作者： Liu, Qiuping Chen, Yaodong Liu, Yang Lei, Yuanfang Wang, Yibo Hu, Pantin Southwest Jiaotong Univ Sch Architecture Chengdu Peoples R China SWJTU Southwest Jiaotong Univ Chengdu Sichuan Peoples R China

Daylight confers extensive benefits for building occupants and improves energy efficiency;thus, its prediction and performance are significant for design decision-making on building and lighting. However, real-time prediction with high precision is difficult since daylight is spatiotemporal correlated and highly dynamic (e.g., periodic, irregularity). In recent years, numerous studies have been focused on machine learning algorithms due to their high generalization ability, robustness, and accuracy in dealing with complex problems. In contrast, selecting the optimal algorithms for a specific research problem is time-consuming and challenging, especially for new learners. This review attempts to develop a systematic guide for algorithms selection and optimization by: (i) discussing the main principles, model structure, and characteristics of 36 of the most representative algorithms;(ii) reviewing and conducting a whole process statistical analysis on the machine learning-based daylight-prediction studies, while abstracting the statistical issues (regression and classification) and prediction tasks (time series, spatial, and spatiotemporal predictions) of different research topics and purposes;and (iii) integrating the findings, developing the selection and optimization methodologies for algorithms. In conclusion, the order of specific machine learning algorithms is highly recommended for different problem types: (i) for regression, Back Propagation Neural Network (recommendation level = 0.82) > Convolutional Neural Network (0.78) > Kernel Support Vector Regression (0.72);(ii) for classification, Kernel Support Vector machine (0.8) > Radial Basis Function Neural Network (0.56) > Random Forest (0.53);(iii) for time series prediction, Back Propagation Neural Network (0.66) > Random Forest (0.61) > Gated Recurrent Unit Network (0.52) = EXtreme Gradient Boosting (0.52);(iv) for spatial prediction, Back Propagation Neural Network (0.82) > Convolutional Neural Network (0.78

关键词： Daylight prediction machine learning algorithms Time-series prediction Spatial prediction Artificial neural network

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Distinguishing lacto-fermented and fresh carrot slice images using the Multilayer Perceptron neural network and other machine learning algorithms from the groups of Functions, Meta, Trees, Lazy, Bayes and Rules

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EUROPEAN FOOD RESEARCH AND TECHNOLOGY 2022年第9期248卷 2421-2429页

作者： Ropelewska, Ewa Natl Inst Hort Res Fruit & Vegetable Storage & Proc Dept Konstytucji 3 Maja 1-3 PL-96100 Skierniewice Poland

This study was aimed at evaluating the effect of spontaneous lacto-fermentation of carrot slices on flesh structure using different machine learning approaches. The textures computed from digital images of lacto-fermented and fresh carrot slices were compared using neural networks and other algorithms from different groups. In the case of Multilayer Perceptron, accuracies for training, testing and validation were considered. For some of the networks, lacto-fermented and fresh samples were completely distinguished. The accuracies for training, testing and validation were equal to 100%. For models built using other algorithms (LDA (Linear Discriminant Analysis), Multi Class Classifier, LMT (Logistic Model Tree), KStar, Naive Bayes, PART), the following metrics were used for the evaluation of model effectiveness: accuracies, time taken to build model, Kappa statistic, mean absolute error, root mean squared error, PRC (Precision-Recall) Area, ROC (Receiver Operating Characteristic) Area, MCC (Matthews Correlation Coefficient), F-Measure, Recall, Precision, FP (False Positive) Rate and TP (True Positive) Rate. The most satisfactory results were obtained for the LDA. The lacto-fermented and fresh carrot slices were distinguished with an average accuracy of 99%, low values of errors (mean absolute error: 0.0117, root mean squared error: 0.1014) and FP Rate (0.010). The weighted averages of other metrics were greater than or equal to 0.98 (Kappa statistic: 0.98, PRC Area: 0.987, ROC Area: 0.991, MCC: 0.980, F-Measure: 0.990, Recall: 0.990, Precision: 0.990, TP Rate: 0.990). The obtained results demonstrated the usefulness of different machine learning approaches to the evaluation of the effect of fermentation on changes in the carrot flesh structure.

关键词： Spontaneous lacto-fermentation Image textures Discrimination machine learning algorithms

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Crop-type mapping and acreage estimation in smallholding plots using Sentinel-2 images and machine learning algorithms: Some comparisons

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EGYPTIAN JOURNAL OF REMOTE SENSING AND SPACE SCIENCES 2022年第1期25卷 147-156页

作者： Hudait, Manas Patel, Priyank Pravin Presidency Univ Dept Geog Kolkata India

Crop acreage analysis and yield estimation are of prime importance in field-level agricultural monitoring and management. This enables prudent decision making during any crop failure event and for ensuing crop insurance. The free availability of the high resolution Sentinel-2 satellite datasets has created new possibilities for mapping and monitoring agricultural lands in this regard. In the present study conducted on the Tamluk Subdivision of the Purba Medinipur District of West Bengal, the heterogeneous crop area was mapped according to the respective crop type, using Sentinel-2 multi-spectral images and two machine learning algorithms-K Nearest Neighbour (KNN) and Random Forest (RF). Plot-level field information was collected from different cropland types to frame the training and validation datasets (comprising 70% and 30% of the total dataset, respectively) for cropland classification and accuracy assessment. Through this, the major summer crop acreage was identified (Boro rice, vegetables and betel vine-the three main crops in the study area). The extracted maps had an overall accuracy of 97.16% and 97.22%, respectively, in the KNN and RF classifications, with respective Kappa index values of 95.99% and 96.08%, and the RF method proved to be more accurate. This study was particularly useful in mapping the betel leaf acreage herein since scant information exists for this crop and it is cultivated by many smallholder farmers in the region. The methods used in this paper can be readily applied elsewhere for accurately enumerating the respective crop acreages.(c) 2022 National Authority of Remote Sensing & Space Science. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： machine learning algorithms Smallholder agriculture Crop acreage Betel vine cultivation

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Magnitude Type Conversion Models for Earthquakes in Turkey and Its Vicinity with machine learning algorithms

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JOURNAL OF EARTHQUAKE ENGINEERING 2023年第9期27卷 2533-2554页

作者： Coban, Kaan Hakan Sayil, Nilgun Karadeniz Tech Univ Engn Fac Dept Geophys Engn Trabzon Turkey

In this study, three new regression models are created for magnitude-type conversion with different machine learning algorithms (linear regression, regression trees, support vector machines, Gaussian process regression models, ensembles of trees) by using the earthquakes (M >= 4.0) that occurred in Turkey (1900-2020). Additionally, eight new equations are formed with linear and orthogonal regression methods. Developed equations and models are compared to equations selected from the literature by test data. As a result of the study, it is observed that machine learning algorithms create better models and provide results closer to the real values than created and selected equations.

关键词： machine learning algorithms regression trees support vector machines magnitude Regression

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Hospital Length-of-Stay Prediction Using machine learning algorithms-A Literature Review

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APPLIED SCIENCES-BASEL 2024年第22期14卷 10523页

作者： Almeida, Guilherme Correia, Fernanda Brito Borges, Ana Rosa Bernardino, Jorge Polytech Univ Coimbra Coimbra Inst Engn ISEC Rua Misericordia P-3045093 Coimbra Portugal Univ Coimbra Ctr Informat & Syst Polo 2 P-3030290 Coimbra Portugal

Predicting hospital length of stay is critical for efficient hospital management, enabling proactive resource allocation, the optimization of bed availability, and optimal patient care. This paper explores the potential of machine learning algorithms to revolutionize hospital length-of-stay predictions, contributing to healthcare efficiency and patient care. The main objective is to identify the most effective machine learning algorithm for building a predictive model capable of predicting hospital length of stay. The bibliographic search of the existing literature on machine learning algorithms applied to hospital length of stay predictions highlighted the most relevant papers within this area of research. The papers were analyzed in terms of model types and metrics that contributed to the considerable impact on healthcare decision making. We also discuss the challenges and limitations of machine learning algorithms for predicting length of stay, and the importance of data quality and ethical considerations.

关键词： hospital length of stay prediction models machine learning algorithms

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Comparative Analysis of machine learning algorithms for 5G Coverage Prediction: Identification of Dominant Feature Parameters and Prediction Accuracy

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IEEE ACCESS 2024年 12卷 18939-18956页

作者： Yuliana, Hajiar Iskandar Hendrawan Inst Teknol Bandung Sch Elect Engn & Informat Bandung 40132 Indonesia

5G technology is a key factor in delivering faster and more reliable wireless connectivity. One crucial aspect in 5G network planning is coverage prediction, which enables network providers to optimize infrastructure deployment and deliver high-quality services to customers. This study conducts a comprehensive analysis of machine learning algorithms for 5G coverage prediction, focusing on dominant feature parameters and accuracy. Notably, the Random Forest algorithm demonstrates superior performance with an RMSE of 1.14 dB, MAE of 0.12, and R2 of 0.97. The CNN model, the standout among deep learning algorithms, achieves an RMSE of 0.289, MAE of 0.289, and R2 of 0.78, showcasing high accuracy in 5G coverage prediction. Random Forest models exhibit near-perfect metrics with 98.4% accuracy, precision, recall, and F1-score. Although CNN outperforms other deep learning models, it slightly trails Random Forest in performance. The research highlights that the final Random Forest and CNN models outperform other models and surpass those developed in previous studies. Notably, 2D Distance Tx Rx emerges as the most dominant feature parameter across all algorithms, significantly influencing 5G coverage prediction. The inclusion of horizontal and vertical distances further improves prediction results, surpassing previous studies. The study underscores the relevance of machine learning and deep learning algorithms in predicting 5G coverage and recommends their use in network development and optimization. In conclusion, while the Random Forest algorithm stands out as the optimal choice for 5G coverage prediction, deep learning algorithms, particularly CNN, offer viable alternatives, especially for spatial data derived from satellite images. These accurate predictions facilitate efficient resource allocation by network providers, ensuring high-quality services in the rapidly evolving landscape of 5G technology. A profound understanding of coverage prediction remains pivotal for suc

关键词： machine learning algorithms Prediction algorithms 5G mobile communication Deep learning machine learning Predictive models Classification algorithms Performance evaluation 5G coverage prediction classifier algorithm deep learning performance evaluation feature importance

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A Multiprocessing-Based Sensitivity Analysis of machine learning algorithms for Load Forecasting of Electric Power Distribution System

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IEEE ACCESS 2021年 9卷 31684-31694页

作者： Zainab, Ameema Syed, Dabeeruddin Ghrayeb, Ali Abu-Rub, Haitham Refaat, Shady S. Houchati, Mahdi Bouhali, Othmane Banales Lopez, Santiago Texas A&M Univ College Stn TX 77843 USA Texas A&M Univ Qatar Doha 77845 Qatar Qatar Sci & Technol Pk Iberdrola Innovat Middle East Doha 210000 Qatar

For the utility to plan the resources accurately and balance the electricity supply and demand, accurate and timely forecasting is required. The proliferation of smart meters in the grids has resulted in an explosion of energy datasets. Processing such data is challenging and usually takes a longer time than the requirement of a short-term load forecast. The paper addresses this concern by utilizing parallel computing capabilities to minimize the execution time while maintaining highly accurate load forecasting models. In this paper, a thousand smart meter energy datasets are analyzed to perform day ahead, hourly short-term load forecast (STLF). The paper utilizes multi-processing to enhance the overall execution time of the forecasting models by submitting simultaneous jobs to all the processors available. The paper demonstrates the efficacy of the proposed approach through the choice of machine learning (ML) models, execution time, and scalability. The proposed approach is validated on real energy consumption data collected at distribution transformers' level in Spanish Electrical Grid. Decision trees have outperformed the other models accomplishing a tradeoff between model accuracy and execution time. The methodology takes only 4 minutes to train 1,000 transformers for an hourly day-ahead forecast of (similar to 24 million records) utilizing 32 processors.

关键词： Big data applications machine learning algorithms parallel processing load forecast smart grids

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Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin,Asir Region,Saudi Arabia

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Geoscience Frontiers 2021年第2期12卷 639-655页

作者： Ahmed Mohamed Youssef Hamid Reza Pourghasemi Geology Department Faculty of ScienceSohag UniversityEgypt Geological Hazards Department Applied Geology SectorSaudi Geological SurveyP.O.Box 54141Jeddah21514Saudi Arabia Department of Natural Resources and Environmental Engineering College of AgricultureShiraz UniversityShirazIran

The current study aimed at evaluating the capabilities of seven advanced machine learning techniques(MLTs),including,Support Vector machine(SVM),Random Forest(RF),Multivariate Adaptive Regression Spline(MARS),Artificial Neural Network(ANN),Quadratic Discriminant Analysis(QDA),Linear Discriminant Analysis(LDA),and Naive Bayes(NB),for landslide susceptibility modeling and comparison of their *** machine learning algorithms with spatial data types for landslide susceptibility mapping is a vitally important *** study was carried out using GIS and R open source software at Abha Basin,Asir Region,Saudi ***,a total of 243 landslide locations were identified at Abha Basin to prepare the landslide inventory map using different data *** the landslide areas were randomly separated into two groups with a ratio of 70%for training and 30%for validating *** landslide-variables were generated for landslide susceptibility modeling,which include altitude,lithology,distance to faults,normalized difference vegetation index(NDVI),landuse/landcover(LULC),distance to roads,slope angle,distance to streams,profile curvature,plan curvature,slope length(LS),and *** area under curve(AUC-ROC)approach has been applied to evaluate,validate,and compare the MLTs *** results indicated that AUC values for seven MLTs range from 89.0%for QDA to 95.1%for *** findings showed that the RF(AUC=95.1%)and LDA(AUC=941.7%)have produced the best performances in comparison to other *** outcome of this study and the landslide susceptibility maps would be useful for environmental protection.

关键词： Landslide susceptibility machine learning algorithms Variables importance Saudi Arabia

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IoT Network Traffic Classification Using machine learning algorithms: An Experimental Analysis

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IEEE INTERNET OF THINGS JOURNAL 2022年第2期9卷 989-1008页

作者： Kumar, Rakesh Swarnkar, Mayank Singal, Gaurav Kumar, Neeraj Natl Inst Technol Hamirpur Dept Comp Sci & Engn Hamirpur 177005 India Indian Inst Technol BHU Varanasi Dept Comp Sci & Engn Varanasi 221005 Uttar Pradesh India Netaji Subhas Univ Technol Dept Comp Sci & Engn New Delhi 110078 India Thapar Inst Engn & Technol Dept Comp Sci & Engn Patiala 147004 Punjab India Univ Petr & Energy Studies Sch Comp Sci Dehra Dun 248007 Uttarakhand India Asia Univ Dept Comp Sci & Informat Engn Taichung 413 Taiwan King Abdulaziz Univ Jeddah Saudi Arabia

Internet of Things (IoT) refers to a wide variety of embedded devices connected to the Internet, enabling them to transmit and share information in smart environments with each other. The regular monitoring of IoT network traffic generated from IoT devices is important for their proper functioning and detection of malicious activities. One such crucial activity is the classification of IoT devices in the network traffic. It enables the administrator to monitor the activities of IoT devices which can be useful for proper implementation of Quality of Service, detect malicious IoT devices, etc. In the literature, various methods are proposed for IoT traffic classification using various machine learning algorithms. However, the accuracy of these machine learning algorithms depends on the data generated from various IoT devices, features extracted from network traffic, site at which IoT is deployed, etc. Moreover, the selection of features and machine learning algorithms are manual operations that are prone to error. Therefore, it is important to study the network traffic characteristics as well as suitable machine learning algorithms for accurate and optimized IoT traffic classification. In this article, we perform an in-depth comparative analysis of various popular machine learning algorithms using different effective features extracted from IoT network traffic. We utilize a public data set having 20 days of network traces generated from 20 popular IoT devices. Network traces are first processed to extract the significant features. We then selected state-of-the-art machine learning algorithms based on the recent survey papers for the IoT traffic classification. We then comparatively evaluated the performance of those machine learning algorithms on the basis of classification accuracy, speed, training time, etc. Finally, we provided a few suggestions for selecting the machine learning algorithm for different use cases based on the obtained results.

关键词： Internet of Things (IoT) devices machine learning algorithms network flow network packet network traffic classification

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Computational predictions for protein sequences of COVID-19 virus via machine learning algorithms

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2021年第9期59卷 1723-1734页

作者： Afify, Heba M. Zanaty, Muhammad S. Higher Inst Engn El Shorouk City Syst & Biomed Engn Dept Cairo Egypt Fac Comp & Informat Sci Cairo Egypt

The rapid spread of coronavirus disease (COVID-19) has become a worldwide pandemic and affected more than 15 million patients reported in 27 countries. Therefore, the computational biology carrying this virus that correlates with the human population urgently needs to be understood. In this paper, the classification of the human protein sequences of COVID-19, according to the country, is presented based on machine learning algorithms. The proposed model is based on distinguishing 9238 sequences using three stages, including data preprocessing, data labeling, and classification. In the first stage, data preprocessing's function converts the amino acids of COVID-19 protein sequences into eight groups of numbers based on the amino acids' volume and dipole. It is based on the conjoint triad (CT) method. In the second stage, there are two methods for labeling data from 27 countries from 0 to 26. The first method is based on selecting one number for each country according to the code numbers of countries, while the second method is based on binary elements for each country. According to their countries, machine learning algorithms are used to discover different COVID-19 protein sequences in the last stage. The obtained results demonstrate 100% accuracy, 100% sensitivity, and 90% specificity via the country-based binary labeling method with a linear support vector machine (SVM) classifier. Furthermore, with significant infection data, the USA is more prone to correct classification compared to other countries with fewer data. The unbalanced data for COVID-19 protein sequences is considered a major issue, especially as the US's available data represents 76% of a total of 9238 sequences. The proposed model will act as a prediction tool for the COVID-19 protein sequences in different countries.

关键词： COVID-19 protein sequences Conjoint triad (CT) machine learning algorithms Support vector machine (SVM)

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