关键词： Supervised machine learning   Textual data analysis   Sustainable development goals   Social media  

摘要： In 2015, the United Nation General Assembly adopted the 2030 Agenda for Sustainable Development and its 17 Sustainable Development Goals aiming at ending all forms of poverty, fighting inequalities, and tackling climate change. We collected Twitter data about the 2030 Agenda from May 9th to November 9th, 2018. The aim of this work is to obtain a classification of each tweet in the corpus according to the "Information"-"Action" categories, in order to detect whether a tweet refers to an event or it has only an informative-disclosure purpose. It seems particularly interesting to understand how and to what extent people and organizations are playing a more active role in shaping the process of responding locally and internationally to climate change. Explicit intention to act or inform had been captured by hand coding of a randomly selected sample of tweets and then the classification had been extended to the whole corpus through a supervised machine learning method. Overall, our classification supervised model has produced satisfactory results.

关键词： Liquid fuel   Dehydration   Adsorption by zeolite   Smart modeling   Generalized regression neural networks  

摘要： Impurities have severe negative influences on both the application and energy content of the dimethyl aminoethyl azide (DMAZ) fuel. Therefore, it is necessary to dehydrate the DMAZ liquid fuel using an appropriate separation scenario. This separation is often performed through an adsorption process employing the LTA zeolite (Linde type A). Hence, this study aims to employ different artificial intelligence techniques to simulate the performance of LTA zeolite for dehydration of DMAZ liquid fuel. Initial water concentration, contact time, temperature, and zeolite per solution (DMAZ + water) mass ratio are those features utilized to estimate zeolite capacity for water adsorption. Systematic ranking analyses applied on several statistical criteria approved that the generalized regression neural network is the best smart model for the considered matter. The proposed generalized regression approach predicted a big reliable experimental database with the AARD = 5.68%, RMSE = 11.26, MAE = 6.97%, and R-2 = 0.9675. The influence of different operating conditions on the performance of zeolite adsorption capacity (ZAC) is investigated both experimentally and by using the generalized regression approach. Modeling results justified that the maximum water removal per unit mass of the LTA zeolite is about 0.202 in the optimum conditions.

关键词： Superpopulation modeling   Machine learning   Online surveys and simulation  

摘要： Online surveys, despite their cost and effort advantages, are particularly prone to selection bias due to the differences between target population and potentially covered population (online population). This leads to the unreliability of estimates coming from online samples unless further adjustments are applied. Some techniques have arisen in the last years regarding this issue, among which superpopulation modeling can be useful in Big Data context where censuses are accessible. This technique uses the sample to train a model capturing the behavior of a target variable which is to be estimated, and applies it to the nonsampled individuals to obtain population-level estimates. The modeling step has been usually done with linear regression or LASSO models, but machine learning (ML) algorithms have been pointed out as promising alternatives. In this study we examine the use of these algorithms in the online survey context, in order to evaluate and compare their performance and adequacy to the problem. A simulation study shows that ML algorithms can effectively volunteering bias to a greater extent than traditional methods in several scenarios. (c) 2020 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： Positron emission tomography   computed tomography   Radiomics   Lung cancer   Lung metastasis   Solitary pulmonary lesion   Machine learning  

摘要： Purpose This study was designed and performed to assess the ability of F-18-fluorodeoxyglucose (FDG) positron emission tomography (PET) and computed tomography (CT) radiomics features combined with machine learning methods to differentiate between primary and metastatic lung lesions and to classify histological subtypes. Moreover, we identified the optimal machine learning method. Methods A total of 769 patients pathologically diagnosed with primary or metastatic lung cancers were enrolled. We used the LIFEx package to extract radiological features from semiautomatically segmented PET and CT images within the same volume of interest. Patients were randomly distributed in training and validation sets. Through the evaluation of five feature selection methods and nine classification methods, discriminant models were established. The robustness of the procedure was controlled by tenfold cross-validation. The model's performance was evaluated using the area under the receiver operating characteristic curve (AUC). Results Based on the radiomics features extracted from PET and CT images, forty-five discriminative models were established. Combined with appropriate feature selection methods, most classifiers showed excellent discriminative ability with AUCs greater than 0.75. In the differentiation between primary and metastatic lung lesions, the feature selection method gradient boosting decision tree (GBDT) combined with the classifier GBDT achieved the highest classification AUC of 0.983 in the PET dataset. In contrast, the feature selection method eXtreme gradient boosting combined with the classifier random forest (RF) achieved the highest AUC of 0.828 in the CT dataset. In the discrimination between squamous cell carcinoma and adenocarcinoma, the combination of GBDT feature selection method with GBDT classification had the highest AUC of 0.897 in the PET dataset. In contrast, the combination of the GBDT feature selection method with the RF classification had the highes

关键词： Anticoagulants   Continuous renal replacement therapy   Machine learning   Intensive care units  

摘要： Background Regional citrate anticoagulation (RCA) is an important local anticoagulation method during bedside continuous renal replacement therapy. To improve patient safety and achieve computer assisted dose monitoring and control, we took intensive care units patients into cohort and aiming at developing a data-driven machine learning model to give early warning of citric acid overdose and provide adjustment suggestions on citrate pumping rate and 10% calcium gluconate input rate for RCA treatment. Methods Patient age, gender, pumped citric acid dose value, 5% NaHCO3 solvent, replacement fluid solvent, body temperature value, and replacement fluid PH value as clinical features, models attempted to classify patients who received regional citrate anticoagulation into correct outcome category. Four models, Adaboost, XGBoost, support vector machine (SVM) and shallow neural network, were compared on the performance of predicting outcomes. Prediction results were evaluated using accuracy, precision, recall and F1-score. Results For classifying patients at the early stages of citric acid treatment, the accuracy of neutral networks model is higher than Adaboost, XGBoost and SVM, the F1-score of shallow neutral networks (90.77%) is overall outperformed than other models (88.40%, 82.17% and 88.96% for Adaboost, XGBoost and SVM). Extended experiment and validation were further conducted using the MIMIC-III database, the F1-scores for shallow neutral networks, Adaboost, XGBoost and SVM are 80.00%, 80.46%, 80.37% and 78.90%, the AUCs are 0.8638, 0.8086, 0.8466 and 0.7919 respectively. Conclusion The results of this study demonstrated the feasibility and performance of machine learning methods for monitoring and adjusting local regional citrate anticoagulation, and further provide decision-making recommendations to clinicians point-of-care.

关键词： Blockchains (Databases)   Internet of things.   Machine learning.  

ISBN: (数字)9781119761884 ISBN: (纸本)9781119761747

摘要： The digital revolution is characterized by the convergence of technologies, rapidly advancing the 4 th industrial revolution thereby blurring the lines between physical, digital and biological objects. The speed of the fourth revolution which evolves at an exponential rate cannot by any means be compared with any previous technologies. AI and IoT employ the interactions and operations in various fields such as home appliances, autonomous vehicles, nanotechnology, robotics, cognitive systems, self-driving cars and wearable devices. The potential of blockchain technology is realized in many sectors as security plays a crucial role everywhere. This book deeply discusses two of the most critical emerging fields of machine learning: blockchain technology and the Internet of Things.

关键词： Abusive comment   Hate speech   Classification   Social media   Instagram   Dataset  

摘要： Instagram is a free photo-sharing platform where each user has a profile and can upload photos for followers to view, like, and comment. Abusive comments on images can be humiliating and harmful to those who share photos. Developing a comment filter in languages other than English is difficult and time-consuming. This paper proposes a dataset called Abusive Turkish Comments (ATC) to detect abusive Instagram comments in Turkish. It is composed of a large number of Instagram comments posted to tabloid and sports accounts (i.e., 10,528 abusive and 19,826 not-abusive). It is the first public dataset dedicated to detecting abusive Turkish messages, as far as we know. The sentiment annotation has been done in sentence-level by assigning polarity to each comment. The performance of the abusive message detection models was evaluated using several performance metrics: Convolutional Neural Network (CNN), five well-known classifiers (i.e., Naive Bayes, Support Vector Machine, Decision Tree, Random Forest, and Logistic Regression), and two reweighted classifiers (i.e., Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost)) were compared in terms of F1-score, precision, and recall. The results showed that the best performance (i.e., Micro-averaged F1-score: 0.974, Macro-averaged F1-score: 0.973, Kappa-value: 0.946) was yielded by the CNN model on the oversampled ATC dataset. The abusive message detection model proposed in this study can contribute to the development of Turkish comment filters on Instagram. Different model combinations are considered to select the best model that gives better recognition accuracy.

关键词： Adhesive bonded joint   Composite material   Finite element model   Genetic programming   Deep neuron network  

摘要： The aerospace, automotive and marine industries have witnessed a rapid increase of using adhesive bonded joints due to their advantages in joining dissimilar and/or new engineering materials. Joint strength is the key property in evaluating the capability of the adhesive joint. In this paper, developments of black-box and grey-box machine learning (ML) models are presented to allow accurate predictions of the failure load of single lap joints by considering a mix of continuous and discrete design (geometry and material) variables. Firstly, the failure loads of 300 single lap joint samples with different geometry/material parameters are calculated by FE models to generate a data set of which accuracy is validated by experimental results. Then, a deep neuron network (blackbox) and a genetic programming (grey-box) model are developed for accurately predicting the failure load of the joint. Based on both ML models, a case study is conducted to explore the relationships between specific design variables and overall mechanical performances of the single lap adhesive joint, and optimal designs of structure and material can be obtained.

关键词： Cloud computing..Machine *** of things.  

ISBN: (数字)9781119785873 ISBN: (纸本)9781119785804

摘要： In this era of IoT, edge devices generate gigantic data during every fraction of a second. The main aim of these networks is to infer some meaningful information from the collected data. For the same, the huge data is transmitted to the cloud which is highly expensive and time-consuming. Hence, it needs to devise some efficient mechanism to handle this huge data, thus necessitating efficient data handling techniques.  Sustainable computing paradigms like cloud and fog are expedient to capably handle the issues of performance, capabilities allied to storage and processing, maintenance, security, efficiency, integration, cost, energy and latency. However, it requires sophisticated analytics tools so as to address the queries in an optimized time. Hence, rigorous research is taking place in the direction of devising effective and efficient framework to garner utmost advantage. Machine learning has gained unmatched popularity for handling massive amounts of data and has applications in a wide variety of disciplines, including social media. Machine Learning Approach for Cloud Data Analytics in IoT details and integrates all aspects of IoT, cloud computing and data analytics from diversified perspectives. It reports on the state-of-the-art research and advanced topics, thereby bringing readers up to date and giving them a means to understand and explore the spectrum of applications of IoT, cloud computing and data analytics.

关键词： 动脉损伤   矫形外科   下肢创伤   损伤严重程度评分   住院期间   截肢手术   出院后   机器学习  

摘要： 肢体损伤严重程度评分被用于评估下肢创伤相关动脉损伤患者截肢必要性已经30余年。30年来,随着血管、创伤和矫形外科的发展,传统的肢体损伤严重程度评分在敏感性上已经过时。同时,相当一部分患者首诊住院未截肢而在出院后30 d再住院期间接受了截肢手术,这部分患者数据在以往的分析中未获得重视。该研究开发了一种基于机器学习的创伤后截肢预测模型。