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A data-informed analytical approach to human-scale greenway planning: Integrating multi-sourced urban data with machine learning algorithms

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URBAN FORESTRY & URBAN GREENING 2020年 56卷 126871-126871页

作者： Tang, Ziyi Ye, Yu Jiang, Zhidian Fu, Chaowei Huang, Rong Yao, Dong Tongji Univ Coll Architecture & Urban Planning Shanghai Peoples R China Tongji Univ Joint Int Res Lab Ecourban Design Shanghai Peoples R China Shanghai Tongji Urban Planning & Design Inst Shanghai Peoples R China

Urban greenways have been recognized as an important strategy to improve human-scale quality in high-density built environments. Nevertheless, current greenway suitability analysis mainly focuses on geographical and natural issues, failing to account for human-scale urban design factors. Accordingly, this study proposes a data-informed approach to planning urban greenway networks using a combination of classical urban design theories, multi-sourced urban data, and machine learning algorithms. Maoming City in China was used as a case study. Per classical urban design theories, specifically, Cervero and Ewing's 5D variables, density, diversity, design, dimensions of destination accessibility, and distance-to-transit, were selected as key factors. A series of new urban data, including points of interest (PoIs), location-based service (LBS) positioning data, and street view images, were applied in conjunction with machine learning algorithms and geographical information system (GIS) tools to measure these key factors at a human-scale resolution and generate an optimized greenway suitability analysis. This analytical approach is an attempt to take human-scale concerns into account on a city-wide scale regarding greenway network generation. It also pushes the methodological boundaries of greenway planning by combining classical urban design thinking with new urban data and new techniques.

关键词： Greenway planning Multi-sourced urban data Data-informed urban design machine learning algorithms

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Medical Internet of things using machine learning algorithms for lung cancer detection

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Journal of Management Analytics 2020年第4期7卷 591-623页

作者： Kanchan Pradhan Priyanka Chawla Lovely Professional University CSEPhagwaraIndia

This paper empirically evaluates the several machine learning algorithms adaptable for lung cancer detection linked with IoT *** this work,a review of nearly 65 papers for predicting different diseases,using machine learning algorithms,has been *** analysis mainly focuses on various machine learning algorithms used for detecting several diseases in order to search for a gap toward the future improvement for detecting lung cancer in medical *** technique was analyzed on each step,and the overall drawbacks are pointed *** addition,it also analyzes the type of data used for predicting the concerned disease,whether it is the benchmark or manually collected ***,research directions have been identified and depicted based on the various existing *** will be helpful for the upcoming researchers to detect the cancerous patients accurately in early stages without any flaws.

关键词： disease prediction lung cancer machine learning algorithms internet of things

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Performance evaluation of machine learning algorithms in predicting dew point pressure of gas condensate reservoirs

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SN APPLIED SCIENCES 2020年第12期2卷 2124页

作者： Ikpeka, Princewill Ugwu, Johnson Russell, Paul Pillai, Gobind Teesside Univ Sch Comp Engn & Digital Technol Middlesbrough Cleveland England

Accurate knowledge of the dew point pressure for a gas condensate reservoir is necessary for optimizing mitigation operations during field development plan. This study explores the use of machine learning models in predicting the dew point pressure of gas condensate reservoirs. 535 experimental dew point pressure data-points with maximum temperature and pressure of 304 degrees F and 10,500 psi were used for this analysis. First, a standard multiple linear regression (MLR) was used as a benchmark for comparing the performance of the machine learning models. Multilayer perceptron Neural Networks (MLP) [optimized for the number of neurons and hidden layers], Support Vector machine (SVM) [using radial basis function kernel] and Decision Tree [Gradient boost Method (GBM) and XG Boost (XGB)] algorithms were used to predict the dew point pressure. The performance of these algorithms was then compared with results obtained from published machine learning models. The input parameters for the model include;gas composition, specific gravity, the molecular weight of the heavier component and compressibility factor. The performances of these algorithms were analysed using root mean square error (RMSE), absolute average relative deviation percentage (AARD %) and coefficient of determination (R-2).

关键词： Dew point pressure prediction machine learning algorithms Gas condensate reservoir

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Land use/cover classification in an arid desert-oasis mosaic landscape of China using remote sensed imagery: Performance assessment of four machine learning algorithms

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GLOBAL ECOLOGY AND CONSERVATION 2020年 22卷

作者： Ge, Genbatu Shi, Zhongjie Zhu, Yuanjun Yang, Xiaohui Hao, Yuguang Chinese Acad Forestry Inst Desertificat Studies Beijing 100091 Peoples R China Chinese Acad Forestry Expt Ctr Desert Forestry Dengkou 015200 Inner Mongolia Peoples R China State Forestry Adm Dengkou Desert Ecosyst Res Stn Dengkou 015200 Peoples R China

The importance of land use and cover change (LUCC) has gradually attracted more attention due to its influence on the climate and ecosystem. Consequently, the necessity of accurate LUCC mapping has become increasingly apparent. Over the past decades, although a large number of machine learning algorithms have been developed to improve the accuracy and reliability of remote sensing image classification, especially for LUCC classification, there is a lack of studies that assess the performance of machine learning algorithms in arid desert-oasis mosaic landscapes. In this study, the main objective is to provide a reference for the extraction of LUCC information in dryland regions with oasis-desert mosaic landscapes by comparing the performances of the k-nearest neighbor (KNN), random forest (RF), support vector machine (SVM) and artificial neural network (ANN) for the LUCC classification of the Dengkou Oasis, China. Landsat-8 Operational Land Imager (OLI) image data were used with spectral indices and auxiliary variables that were derived from a digital terrain model to classify 7 different land cover categories. The highest overall accuracy was produced by the ANN (97.16%), which was closely followed by the RF (96.92%), SVM (96.20%), and finally KNN (93.98%);statistically similar accuracies were obtained for the ANN, SVM and RF. The RF algorithm performed well across several aspects, such as stability, ease of use and processing time during the parameter tuning. Overall, the random forest algorithm is a good first choice method for land-cover classification in this study area, and the elevation and some spectral indices, such as the NDVI, MSAVI2 and MNDWI, should be used as variables to improve the overall accuracy. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： Land use/cover change machine learning algorithms Arid area Desert-oasis mosaic landscape

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Employee Promotion Prediction by using machine learning algorithms for Imbalanced Dataset

Employee Promotion Prediction by using Machine Learning Algo...

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Computing and machine Intelligence (ICMI), International Conference on

作者： Kevser Şahinbaş Department of Management Information System Istanbul Medipol University Istanbul Turkey

ISBN: (数字)9781665474832

ISBN: (纸本)9781665474849

Promotion processes are one of the most important processes in terms of human resources. A promotion process organized fairly within the organization is a managerial tool that motivates employees and contributes to business continuity. Promotion is an important extrinsic motivation for many employees. It ensures the employee’s engagement and commitment to the organization and contributes to the continuity of his current performance. It is also an important rewarding and performance control mechanism for the organization. Many factors such as seniority, performance level, competencies, age, awards, training score, organizational commitment of the personnel who will be promoted are taken into consideration. In this study, a prediction methodology will be studied based on the criteria evaluated for the employees in the promotion processes by machine learning algorithms such as Support Vector machine, Artificial Neural Network, and Random Forest. Random Forest achieved the highest performance with 98% accuracy, 96% precision, 1.0% recall and 98% f1-score values with ROS approach. This study could be used by HR and manager to predict the probability of promotion so that managers can find the right parameters for someone to get promoted.

关键词： Support vector machines Radio frequency Training machine learning algorithms Key performance indicator Organizations Artificial neural networks

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Investigating the Reliability of machine learning algorithms for Inverse Design: Polarization Rotator Unit-Cell Model

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IEEE JOURNAL ON MULTISCALE AND MULTIPHYSICS COMPUTATIONAL TECHNIQUES 2020年 5卷 176-187页

作者： Samadi, Fereshteh Sebak, Abdel-Razik Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G2W1 Canada

In this article, we implement three types of data-driven algorithms for the inverse design of the polarization rotator (PR) unit cell. Toward this end, a novel configuration pattern of the PR unit cell is proposed, which is reshapable to different geometries with a large number of design variables and recasts as a regression problem. The state-of-the-art algorithms including the neural network (NN), the deep neural networks (DNNs) with multiple hidden layers, and the support vector regression (SVR) are experimented with cross validations for ensuring the prediction generalizability. Averaged over all the experiments with competitive performances, the highest prediction accuracy about 95.23% was achieved for the SVR algorithm. This demonstrates the enormous capability of the data-driven algorithms in the geometrical dimension prediction of the unit cells for any given frequency band designated in the radar range (X, Ku, K, and Ka). The proposed inverse design procedure can expedite, facilitate, and to some extent replace the conventional and time-consuming manual design approaches with electromagnetic (EM) simulation software. Although these models can be very efficient in practice, they might be vulnerable against adversarial attacks that craft fake inputs to purposely fool the victim regressors toward adversary's wishes. This poses security concerns for the learning-based algorithms and might negatively affect their prediction reliabilities in runtime. In this article, we only characterize the existence of the adversarial attacks for the regression models using the fast gradient sign method. Our conducted experiments uncover that the fooling rate of all the aforementioned cutting-edge NN and DNN-based regressors is above 98% and this rate for the SVR model is about 11% better than other models.

关键词： Prediction algorithms Reliability Interference Predictive models machine learning algorithms Computational modeling Neural networks Adversarial attacks deep neural networks (DNNs) inverse unit-cell modeling polarization conversion regression models support vector regression (SVR)

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Predicting Terror Attacks Using Neo4j Sandbox and machine learning algorithms

Predicting Terror Attacks Using Neo4j Sandbox and Machine Le...

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International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Ankit Raj Sunil B. Somani School Of ECE Dr. Vishwanath Karad MIT – World Peace University Pune India

ISBN: (纸本)9781665484527

Terrorism, and radicalization are major economic, political, and social issues faced by the world in today's era. The challenges that governments and citizens face in combating terrorism are growing by the day. Artificial intelligence, including machine learning and deep learning, has shown promising results in predicting terrorist attacks. In this paper, we attempted to build a machine learning model to predict terror activities using a global terrorism database in both relational and graphical forms. Using the Neo4j Sandbox, you can create a graph database from a relational database. We used the node2vec algorithm from Neo4j Sandbox's graph data science library to convert the high-dimensional graph to a low-dimensional vector form. In order to predict terror activities, seven machine learning models were used, and the performance parameters that were calculated were accuracy, precision, recall, and F1 score. According to our findings, the Logistic Regression model was the best performing model which was able to classify the dataset with an accuracy of 0.90, recall of 0.94 precision of 0.93, and an F1 score of 0.93.

关键词： Deep learning Support vector machines machine learning algorithms Terrorism Computational modeling Relational databases Predictive models

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A Comparison of Various machine learning algorithms for Automated Fever Detection

A Comparison of Various Machine Learning Algorithms for Auto...

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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Kumari Monika Himanshu Jindal CSE/IT Jaypee University of Information Technology Solan Himachal Pradesh India

ISBN: (纸本)9781665454025

Various diseases causing fever-like germinal infection, viral infection, mononucleosis, epidemic catarrh, acute inflammatory disease is a neurodegenerative illness that manifests itself in a range of motor and non-motor symptoms. Patients with various diseases frequently experience difficulties in the initial stages of the condition. Patients with other diseases have often symptoms like cough, cold, headache, and fever. Research provides machine learning algorithms for classifying various diseases utilizing data that are built on machine learning algorithm. This paper describes the diverse technique of supervised machine learning classification that determines the best magnificence algorithm based on statistics set, quantity of cases, and variability (features). To implement the algorithms, Fever data set was used for the classification with no of instances with attributes as independent variable and one as dependent variable for the analysis. Therefore, ML algorithms requires precision, accuracy and minimum error to have supervised predictive machine learning.

关键词： Support vector machines Training Measurement machine learning algorithms Neural networks Prediction algorithms Feature extraction

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A Comparative Study of Various machine learning algorithms to Detect Power Plant Stability

A Comparative Study of Various Machine Learning Algorithms t...

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International Conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Bhavya Dhingra Sunil Yadav Anuradha Tomar Department of Electrical Engineering Netaji Subhas University of Technology Delhi India Department of Inst. and Control Engg. Netaji Subhas University of Technology Delhi India

ISBN: (纸本)9781665474177

Smart grid’s concept has emerged in past decade as a key transition for the power system. The continuous integration of renewable energy sources and increasing demand affects the stability of the grid. A power grid that is not stable can lead to blackouts, brownouts, and other types of disruption. This can cause a great deal of inconvenience and financial losses, so it is important to ensure that the power grid stays stable. This study focuses on the identification of the smart grid stability by applying various machine learning algorithms i.e., Light Gradient Boosting machine, Extra Trees Classifier, Extreme Gradient Boosting, CatBoost Classifier, Random Forest Classifier, Gradient Boosting Classifier, K Neighbors Classifier, Decision Tree Classifier, Naive Bayes, Logistic Regression all of which are evaluated using Stratified K-fold cross validation to assess their performance. Finally, this work compares the various machine learning models on the basis of their characteristics for better perspective. Of all the models tested CatBoost classifier had the best scores as it handles categorical variables effectively and works efficiently even on non-preprocessed data and provided an accuracy of 0.9506, Area under the curve (AUC) of 0.9913, recall value of 0.9725, precision value of 0.9512 and F1 score of 0.9617.

关键词： Renewable energy sources machine learning algorithms Power system stability Boosting Stability analysis Data models Smart grids

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Importance of machine learning algorithms to Detect Botnet DDoS Attacks

Importance of Machine Learning Algorithms to Detect Botnet D...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： Swapna Thota D. Menaka Noorul Islam Centre for Higher Education Kanyakumari TamilNadu

ISBN: (纸本)9781665489638

Botnet are one of the threats in a network. These Botnets can be controlled remotely by BotMaster. These Bots are keys to Importance of machine learning algorithms to Detect Botnet DDoS attacks, Malwares and phishing attacks. The DDoS attacks are most dangerous malware events that disrupt whole network. To solve DDoS attacks various methods and algorithms are proposed. In that K-means algorithm is Unsupervised learning (USML) proposed in this paper. In this paper we conduct a practically analyzing using ML algorithms i.e. K-means algorithms for the detecting Botnet DDoS attacks. For experimental analysis use UNBS-NB real-time datasets. For experimental analysis we compare K- means algorithms with Support Vector machine (SVM), Artificial Neural network (ANN), Naive Bayes (NB) and Decision Tree (dt) for performance based comparison. In results we find K-means (USML) is showing better performance than other machine learning algorithms.

关键词： Support vector machines machine learning algorithms Sensitivity Botnet Artificial neural networks Denial-of-service attack Real-time systems

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