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County level maize yield estimation using artificial neural network

MODELING EARTH SYSTEMS AND ENVIRONMENT 2021年第3期7卷 1417-1424页

作者： Mwaura, Joshua Irungu Kenduiywo, Benson Kipkemboi Jomo Kenyatta Univ Agr & Technol Dept Geomat Engn & Geospatial Informat Syst Nairobi Kenya Univ Calif Davis Environm Sci & Policy Davis CA 95616 USA

Maize yield estimates are useful for county food security preparedness. Techniques such as regression and simulation have been used by various studies to model and predict maize yield. This study used a feed-forward, back propagation artificial neural network with levenberg-marquardt algorithm for training. Artificial neural networks framework was chosen because its a data driven method that is relatively less widely used in county level yield prediction. Moreover, neural networks has key merits, such as require less formal statistical training, ability to detect nonlinear relationships by identifying likely interactions between variables and the availability of multiple training algorithms. We modelled historical maize yield between 2005-2016 as function of satellite derived precipitation, temperature, reference crop evapotranspiration, soil moisture and normalized difference vegetation index (NDVI) to predict maize yields at pixel level. The data were obtained with a spatial resolution of approximate to 4 km and subsequently, the predictions was done at approximate to 4 km pixel size. The historical reference maize yield data was divided into two sets for model training and validation. The model predicted maize yield with R-2 and root mean square error of 0.76 and 0.038 MT/ha in Trans-Nzoia county and 0.86 and 0.016 MT/ha, respectively, in Nakuru county. These findings shows a promising future for applications targeting to rapidly assess county level food preparedness in Kenya because maize is a major staple food.

关键词： boruta algorithm Artificial neural networks Machine learning Maize yield prediction Modeling Remote sensing

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Early Detection and Prevention of Cervical Cancer 5

Early Detection and Prevention of Cervical Cancer

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IEEE 5th International Conference for Convergence in Technology (I2CT)

作者： Gupta, Palak Jindal, Ishitaa Goyal, Anmolika Mukesh Patel Sch Technol Management & Engn Dept Comp Engn Mumbai Maharashtra India

ISBN: (纸本)9781538680759

Cervical cancer is the fourth most common type of cancer for women worldwide where Indian contribution is up to 25.41%. Human Papilloma Virus remains the major risk factor contributing to this cancer. Due to the decelerated growth of cervical cancer, early detection and assessment can increase the chances of remission. Detection and subsequent prevention can be done by applying Data mining technique on patient information to predict the occurrence of Cervical Cancer. In this research paper, boruta Analysis is performed to exculpate the list of determining factors which can affect a patient's chances of developing cancer. This analysis covers demographic information along with historic and medical details. The dataset of 858 patients has been collected at ''Hospital Universitario de Caracas' in Caracas' where missing values correspond to information withheld due to patient privacy. Judging with respect to the severity of the given factors, an estimate can be drawn regarding the same. In the face of desperation and general curiosity, such an algorithm can help hundreds of people worldwide who may suffer from such adversity in their lifetime.

关键词： Cervical Cancer boruta algorithm Random Forest Data Mining

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Epilepsy Detection of Generalized Tonic-Clonic Seizures based on Wrist Signals 4

Epilepsy Detection of Generalized Tonic-Clonic Seizures base...

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4th International Symposium on Artificial Intelligence for Medicine Science (ISAIMS)

作者： Yu, Jintao Lu, Wenhui Jiang, Kaiyu Liang, Tingwei Harbin Univ Commerce Sch Comp & Informat Engn Harbin Peoples R China Heilongjiang Univ Sch Comp Sci & Technol Harbin Peoples R China Harbin Inst Technol Sch Astronaut Harbin Peoples R China

ISBN: (纸本)9798400708138

Epilepsy is a common chronic brain disease, mainly caused by abnormal brain discharges. There are many types, among which generalized tonic-clonic seizures are the most dangerous. In this context, this paper proposes a method based on a combination of boruta algorithm, support vector machine (SVM), and particle swarm optimization (PSO) to detect systemic tonic-clonic seizures from wrist signals. First, a wearable device is used to collect signals from four channels of heart rate(HRT), number of wrist movements(NOWM), acceleration(ACC), and skin conductance response(SCR), and the corresponding features of the four channels are extracted respectively. The boruta algorithm is introduced for feature dimensionality reduction to eliminate noise and highly correlated features. Then the particle swarm optimization algorithm is used to assist SVM in learning. The results show that after dimensionality reduction, there are only 29 features, and SVM can detect all generalized tonic-clonic seizures, with a false recognition rate of 0.083/d. Compared with previous methods, the epilepsy detection achieved through the boruta algorithm, SVM and particle swarm optimization algorithm has lower model complexity and is easier to solve the problem of wearable device memory on the premise that the false recognition rate and accurate recognition rate are similar.

关键词： boruta algorithm wrist signal generalized tonic-clonic seizures epilepsy wearable devices

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Application of the Fuzzy Logic to Evaluation and Selection of Attribute Ranges in Machine Learning

Application of the Fuzzy Logic to Evaluation and Selection o...

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IEEE CIS International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Paja, Wieslaw Pancerz, Krzysztof Pekala, Barbara Sarzynski, Jaromir Univ Rzeszow Rzeszow Poland Szymon Szymonow State Sch Higher Educ Zamosc Poland Univ Rzeszow Univ Informat Technol & Management Rzeszow Poland

ISBN: (纸本)9781665444071

In the paper, we show how the importance of individual ranges of values of attributes describing cases can be determined using the attribute fuzzification process. The importance is determined on the basis of classification capabilities. The described approach is based mainly on fuzzy set theory and the rough set based discretization method. Moreover, an experimental study of the computer-aided classification task is presented.

关键词： Attribute selection Attribute evaluation Fuzzification boruta algorithm Discretization

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Machine Learning algorithm for Analysing Infant Mortality in Bangladesh 1

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10th International Conference on Health Information Science (HIS)

作者： Rahman, Atikur Hossain, Zakir Kabir, Enamul Rois, Rumana Jahangirnagar Univ Dept Stat Dhaka Bangladesh Univ Dhaka Dept Stat Dhaka Bangladesh Univ Southern Queensland Sch Sci Toowoomba Qld Australia

ISBN: (数字)9783030908850

ISBN: (纸本)9783030908850;9783030908843

The study aims to investigate the potential predictors associated with infant mortality in Bangladesh through machine learning (ML) algorithm. Data on infant mortality of 26145 children were extracted from the latest Bangladesh Demographic and Health Survey 2017-18. The boruta algorithm was used to extract important features of infant mortality. We adapted decision tree, random forest, support vector machine and logistic regression approaches to explore predictors of infant mortality. Performances of these techniques were evaluated via parameters of confusion matrix and receiver operating characteristics curve. The proportion of infant mortality was 9.7% (2523 out of 26145). Age at first marriage, age at first birth, birth interval, place of residence, administrative division, religion, education of parents, body mass index, gender of child, children ever born, exposure of media, wealth index, birth order, occupation of mother, toilet facility and cooking fuel were selected as significant features of predicting infant mortality. Overall, the random forest (accuracy = 0.893, precision = 0.715, sensitivity = 0.339, specificity = 0.979, F1-score = 0.460, area under the curve: AUC = 0.6613) perfectly and authentically predicted the infant mortality compared with other ML techniques, including individual and interaction effects of predictors. The significant predictors may help the policy-makers, stakeholders and mothers to take initiatives against infant mortality by improving awareness, community-based educational programs and public health interventions.

关键词： Machine learning boruta algorithm random forest auc

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Feature Selection Approach for Reducing The Power Consumption For a Greener Environment 2

Feature Selection Approach for Reducing The Power Consumptio...

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2nd International Conference on Telecommunication and Networks (TEL-NET)

作者： Nagpal, Deepshika Srivastava, Rashi Mehrotra, Deepti Anuranjana Amity Univ Uttar Pradesh Noida Uttar Pradesh India

ISBN: (纸本)9781509067107

With advancements in technologies the rate of energy utilization is also proliferating, this requires the necessity for immediate attention towards energy optimization. Researchers have created many datasets for power consumption and analysis of software codes. In this paper an initial analysis is done on one of the open source datasets, where the count of system calls and power of various versions of an application is recorded. This paper presents the system calls which have evident impact on the power consumption with the help of R tool using attribute selection approach, which happens to be a leading approach to discover the features and variables which make a significant difference in the datasets of power consumption.

关键词： Energy consumption green computing Power consumption Feature selection boruta algorithm

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Power Distribution System Equipment Failure Identification Using Machine Learning algorithms

Power Distribution System Equipment Failure Identification U...

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2017 IEEE Power & Energy Society General Meeting

作者： Doostan, Milad Chowdhury, Badrul H. Univ North Carolina Charlotte Elect & Comp Engn Dept Charlotte NC 28223 USA

ISBN: (纸本)9781538622124

In this paper, an approach for identifying equipment failure faults in distribution systems is explored. This task is considered as a binary classification problem in which outages are categorized into two classes of equipment failure and non-equipment failure types. To carry out this study, actual outage data collected by Duke Energy are utilized. First, different variables that make contributions to equipment failures are described and their relationships are examined. Afterward, the presence of imbalanced classes, as a common issue in outage data set, is addressed. Then, to assure that all features are relevant, their importance is examined by employing a novel feature selection algorithm. At the end, three classification algorithms, namely decision tree, logistic regression, and naive Bayesian classifier arc trained and tested and their performances arc evaluated.

关键词： boruta algorithm decision tree equipment failure fault cause identification logistic regression machine learning naive Bayesian classifier ROSE SMOTE

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Improving Intrusion Detection System by Estimating Parameters of Random Forest in boruta

Improving Intrusion Detection System by Estimating Parameter...

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Smart Technology and Applications (ICoSTA), 2020 International Conference on

作者： Alif Nur Iman Tohari Ahmad Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9781728130835

ISBN: (纸本)9781728130842

To overcome the security problem of computer networks, the Intrusion Detection System (IDS) is developed. It is intended to identify an attack. Various types of IDS are built according to the environment: signature-based and anomaly-based. This second type of IDS can identify attacks that have not been known. In this case, machine learning is a possible method to develop an IDS model, which comprises many processes, including feature selection. The boruta algorithm is a feature selection method that is good enough to apply to machine learning. However, in its application on the NSL-KDD dataset, this algorithm has an infinite loop problem. This paper presents the analysis and estimation of random forest parameters, precisely the depth and number of trees; additionally, the use of entropy and Gini index as z-score in the boruta algorithm is considered. The experimental result shows that the proposed method can prevent the infinite loop, which indirectly improves the performance of the existing algorithm.

关键词： boruta algorithm Intrusion detection system Machine learning Network security Random forest INTRUSION DETECTION SYSTEMS Network security intelligent decision support system Endless Loop Artificial Intelligence Machine Learning Feature extraction algorithms

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A Framework for Actual Evapotranspiration Assessment and Projection Based on Meteorological, Vegetation and Hydrological Remote Sensing Products

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REMOTE SENSING 2021年第18期13卷 3643-3643页

作者： Liu, Yuan Yue, Qimeng Wang, Qianyang Yu, Jingshan Zheng, Yuexin Yao, Xiaolei Xu, Shugao Beijing Normal Univ Coll Water Sci Beijing 100875 Peoples R China

As the most direct indicator of drought, the dynamic assessment and prediction of actual evapotranspiration (AET) is crucial to regional water resources management. This research aims to develop a framework for the regional AET evaluation and prediction based on multiple machine learning methods and multi-source remote sensing data, which combines boruta algorithm, Random Forest (RF), and Support Vector Regression (SVR) models, employing datasets from CRU, GLDAS, MODIS, GRACE (-FO), and CMIP6, covering meteorological, vegetation, and hydrological variables. To verify the framework, it is applied to grids of South America (SA) as a case. The results meticulously demonstrate the tendency of AET and identify the decisive role of T, P, and NDVI on AET in SA. Regarding the projection, RF has better performance in different input strategies in SA. According to the accuracy of RF and SVR on the pixel scale, the AET prediction dataset is generated by integrating the optimal results of the two models. By using multiple parameter inputs and two models to jointly obtain the optimal output, the results become more reasonable and accurate. The framework can systematically and comprehensively evaluate and forecast AET;although prediction products generated in SA cannot calibrate relevant parameters, it provides a quite valuable reference for regional drought warning and water allocating.

关键词： actual evapotranspiration multi-source remote sensing data boruta algorithm support vector regression random forest CMIP6

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Parameter importance assessment improves efficacy of machine learning methods for predicting snow avalanche sites in Leh-Manali Highway, India

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SCIENCE OF THE TOTAL ENVIRONMENT 2021年 794卷 148738-148738页

作者： Tiwari, Anuj Arun, G. Vishwakarma, Bramha Dutt Univ Illinois Syst Discovery Partners Inst Chicago IL 60607 USA Northwestern Univ Ctr Water Res Evanston IL USA Natl Inst Hydrol Ctr Flood Management Studies Gauhati Assam India Univ Bristol Sch Geog Sci Bristol Avon England

Due to ongoing climate change, water mass redistribution and related hazards are getting stronger and frequent. Therefore, predicting extreme hydrological events and related hazards is one of the highest priorities in geosciences. Machine Learning (ML) methods have shown promising prospects in this venture. Every ML method requires training where we know both the output (extreme event) and input (relevant physical parameters and variables). This step is critical to the efficacy of the ML method. The usual approach is to include a wide variety of hydro-meteorological observations and physical parameters, but recent advances in ML indicate that the efficacy of ML may not improve by increasing the number of input parameters. In fact, including unimportant parameters decreases the efficacy of ML algorithms. Therefore, it is imperative that the most relevant parameters are identified prior to training. In this study, we demonstrate this concept by predicting avalanche susceptibility in Leh-Manali highway (one of the most severely affected regions in India) with and without Parameter Importance Assessment (PIA). The avalanche locations were randomly divided into two groups: 70% for training and 30% for testing. Then, based on temporal and spatial sensor data, eleven avalanche influencing parameters were considered. The boruta algorithm, an extension of Random Forest (RF) ML method that utilizes the importance measure to rank predictors, was used and it found nine out of eleven parameters to be important. Support Vector Machine (SVM) based ML technique is used for avalanche prediction, and to be comprehensive, four different kernel functions were employed (linear, polynomial, sigmoid, and radial basis function (RBF)). The prediction accuracy for linear, polynomial, sigmoid, and RBF kernels, with all the eleven parameters were found to be 80.4%, 81.7%, 39.2%, and 85.7%, respectively. While, when using selected parameters, the prediction accuracy for linear,

关键词： Parameter Importance Assessment (PIA) Avalanche susceptibility modeling Machine learning (ML) boruta algorithm Support Vector Machine (SVM)

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