关键词： F-18-FDG PET   CT   Textural analysis   Esophageal cancer  

摘要： Objective This study evaluates the ability of several machine learning (ML) algorithms, developed using volumetric and texture data extracted from baseline F-18-FDG PET/CT studies performed initial staging of patient with esophageal cancer (EC), to predict survival and histopathology. Methods The initial staging F-18-FDG PET/CT images obtained on newly diagnosed EC patients between January 2008 and June 2019 were evaluated using LIFEx software. A region of interest (ROI) of the primary tumor was created and volumetric and textural features were obtained. A significant relationship between these features and pathological subtypes, 1-year, and 5-year survival was investigated. Due to the nonhomogeneity of the data, nonparametric test (The Mann-Whitney U test) was used for each feature, in pairwise comparisons of independent variables. A p value of < 0.05 was considered significant. Receiver operating curve (ROC) analysis was performed for features with p < 0.05. Correlation between the significant features was evaluated with Spearman correlation test;features with correlation coefficient < 0.8 were evaluated with several ML algorithms. Results In predicting survival in a 1-year follow-up J48 was obtained as the most successful algorithm (AUC: 0.581, PRC: 0.565, MCC: 0.258, acc: 64.29%). 5-year survival results were more promising than 1-year survival results with (AUC: 0.820, PRC: 0.860, MCC: 271, acc: 81.36%) by logistic regression. It is revealed that the most successful algorithm was naive bayes (AUC: 0.680 PRC: 0.776, MCC: 0.298, acc: 82.66%) in the histopathological discrimination. Conclusion Texture analysis with ML algorithms could be predictive of overall survival and discriminating histopathological subtypes of EC.

关键词： RSFC   Individual difference   Individual-specific connectivity   Predict behavioral scores   Fingerprint analysis  

摘要： Resting-state functional connectivity (RSFC) can be used for mapping large-scale human brain networks during rest. There is considerable interest in distinguishing the individual-shared and individual-specific components in RSFC for the better identification of individuals and prediction of behavior. Therefore, we propose a multitask learning based sparse convex alternating structure optimization (MTL-sCASO) method to decompose RSFC into individual-specific connectivity and individual-shared connectivity. We used synthetic data to validate the efficacy of the MTL-sCASO method. In addition, we verified that individual-specific connectivity achieves higher identification rates than the Pearson correlation (PC) method, and the individual-specific components observed in 886 individuals from the Human Connectome Project (HCP) examined in two sessions over two consecutive days might serve as individual fingerprints. Individual-specific connectivity has low inter-subject similarity (0.005 +/- 0.023), while individual-shared connectivity has high inter-subject similarity (0.822 +/- 0.061). We also determined the anatomical locations (region or subsystem) related to individual attributes and common features. We find that individual-specific connectivity exhibits low degree centrality in the sensorimotor processing system but high degree centrality in the control system. Importantly, the individual-specific connectivity estimated by the MTL-sCASO method accurately predicts behavioral scores (improved by 9.4% compared to the PC method) in the cognitive dimension. The decomposition of individual-specific and individual-shared components from RSFC provides a new approach for tracing individual traits and group analysis using functional brain networks.

关键词： Classification   Credit scoring   Feature selection  

摘要： Credit scoring concerns with emerging empirical model to assist the financial institutions for financial decision-making process. Credit risk analysis plays a vital role for decision-making process;statistical and machine learning approaches are utilized to estimate the risk associated with a credit applicant. Enhancing the performance of credit scoring model, particularly toward non-trustworthy "or non-creditworthy" group, may result incredible effect for financial institution. However, credit scoring data may have excess and unimportant data and features which degrades the performance of model. So, selection of important features (or reduction in irrelevant and redundant features) may play the key role for improving the effectiveness and reducing the complexity of the model. This study presents a experimental results analysis of various combinations of feature selection approaches with various classification approaches and impact of feature selection approaches. For experimental results analysis, nine feature selection and sixteen classification state-of-the-art approaches have been applied on seven benched marked credit scoring datasets.

关键词： artificial intelligence   cardiac resynchronization therapy   heart failure   machine learning   prediction  

摘要： Introduction The efficacy of cardiac resynchronization therapy (CRT) has been widely studied in the medical literature;however, about 30% of candidates fail to respond to this treatment strategy. Smart computational approaches based on clinical data can help expose hidden patterns useful for identifying CRT responders. Methods We retrospectively analyzed the electronic health records of 1664 patients who underwent CRT procedures from January 1, 2002 to December 31, 2017. An ensemble of ensemble (EoE) machine learning (ML) system composed of a supervised and an unsupervised ML layers was developed to generate a prediction model for CRT response. Results We compared the performance of EoE against traditional ML methods and the state-of-the-art convolutional neural network (CNN) model trained on raw electrocardiographic (ECG) waveforms. We observed that the models exhibited improvement in performance as more features were incrementally used for training. Using the most comprehensive set of predictors, the performance of the EoE model in terms of the area under the receiver operating characteristic curve and F1-score were 0.76 and 0.73, respectively. Direct application of the CNN model on the raw ECG waveforms did not generate promising results. Conclusion The proposed CRT risk calculator effectively discriminates which heart failure (HF) patient is likely to respond to CRT significantly better than using clinical guidelines and traditional ML methods, thus suggesting that the tool can enhanced care management of HF patients by helping to identify high-risk patients.

关键词： Social media   Disaster management   Machine learning   Semantic topic analysis   Geospatial analysis  

摘要： Up-to-date information about an emergency is crucial for effective disaster management. However, severe restrictions impede the creation of spatiotemporal information by current remote sensing-based monitoring systems, especially at the beginning of a disaster. Multiple publications have shown promising results in complementing monitoring systems through spatiotemporal information extracted from social media data. However, various monitoring system criteria, such as near-real-time capabilities or applicability for different disaster types and use cases, have not yet been addressed. This paper presents an improved version of a recently proposed methodology to identify disaster-impacted areas (hot spots and cold spots) by combining semantic and geospatial machine learning methods. The process of identifying impacted areas is automated using semi-supervised topic models for various kinds of natural disasters. We validated the portability of our approach through experiments with multiple natural disasters and disaster types with differing characteristics, whereby one use case served to prove the near-real-time capability of our approach. We demonstrated the validity of the produced information by comparing the results with official authority datasets provided by the United States Geological Survey and the National Hurricane Centre. The validation shows that our approach produces reliable results that match the official authority datasets. Furthermore, the analysis result values are shown and compared to the outputs of the remote sensing-based Copernicus Emergency Management Service. The information derived from different sources can thus be considered to reliably detect disaster-impacted areas that were not detected by the Copernicus Emergency Management Service, particularly in densely populated cities.

关键词： potato   VOCs   olfactory machine   ANN   LDA   PCA  

摘要： In response to one of the most important challenges of the century, i.e., the estimation of the food demands of a growing population, advanced technologies have been employed in agriculture. The potato has the main contribution to people's diet worldwide. Therefore, its different aspects are worth studying. The large number of potato varieties, lack of awareness about its new cultivars among farmers to cultivate, time-consuming and inaccurate process of identifying different potato cultivars, and the significance of identifying potato cultivars and other agricultural products (in every food industry process) all necessitate new, fast, and accurate methods. The aim of this study was to use an electronic nose, along with chemometrics methods, including PCA, LDA, and ANN as fast, inexpensive, and non-destructive methods for detecting different potato cultivars. In the present study, nine sensors with the best response to VOCs were adopted. VOCs sensors were used at various VOCs concentrations (1 to 10,000 ppm) to detect different gases. The results showed that a PCA with two main components, PC1 and PC2, described 92% of the total samples' dataset variance. In addition, the accuracy of the LDA and ANN methods were 100 and 96%, respectively.

关键词： Biometrics   Iris   Identity recognition   Discrete fast Fourier transform   Principal component analysis   Support vector machines   Artificial neural networks  

摘要： One of the most important modules of computer systems is the one that is responsible for user safety. It was proven that simple passwords and logins cannot guarantee high efficiency and are easy to obtain by the hackers. The well-known alternative is identity recognition based on biometrics. In recent years, more interest was observed in iris as a biometrics trait. It was caused due to high efficiency and accuracy guaranteed by this measurable feature. The consequences of such interest are observable in the literature. There are multiple, diversified approaches proposed by different authors. However, neither of them uses discrete fast Fourier transform (DFFT) components to describe iris sample. In this work, the authors present their own approach to iris-based human identity recognition with DFFT components selected with principal component analysis algorithm. For classification, three algorithms were used-k-nearest neighbors, support vector machines and artificial neural networks. Performed tests have shown that satisfactory results can be obtained with the proposed method.

关键词： Hydrocarbon   reservoirs   oil   gas   water   injection   production   time series analysis   forecasting   RNN-LSTM   numerical simulation   data processing  

摘要： The focus of this paper is on application of advance data analytics and deep machine learning methods for time series forecasting of injection/production data from subsurface hydrocarbon recovery processes. Injection and production data, from subsurface reservoir developed though implementation of water flooding recovery mechanism, is used with aim of developing a machine learning based forecasting methodology to eventually replace numerical simulation-based forecasting methods. Different machine learning algorithms exists for single- and multi-step time series forecasting, e.g., nonlinear regression, artificial neural network (ANN) and long-short-term-memory (LSTM) based recurrent neural networks (RNN). In this paper, RNN-LSTM algorithm is tested on real field data, comprising of a number of injection and production well patterns, and results of RNN-LSTM forecasting model are presented. Complexities related to data acquisition, analysis, and processing are also discussed in detail. The results presented in this paper are unique as it is for the first time that ML model is applied to forecast production from a tight carbonate hydrocarbon reservoir, which is developed through water flooding with help of long horizontal wells. Such an application for time series forecasting using RNN-LSTM model has never been presented, before in literature, to the best of author's knowledge.

关键词： Cloud   Virtual machine   Volatile memory   Malware   Linux   Detection   Machine learning   Feature extraction   Volatility  

摘要： Most organizations today use cloud-computing environments and virtualization technology. Linux-based clouds are the most popular cloud environments among organizations, and thus have become the target of cyber-attacks launched by sophisticated malware. Existing malware detection solutions for Linux-based VMs are installed and operated on the VM itself and are considered untrusted since malware can detect, interfere with, and even evade them. Thus, Linux cloud-based environments remain exposed to various malware-based attacks. This paper presents the first trusted framework for detecting unknown malware in Linux VM cloud-environments. Our framework acquires volatile memory dumps from the inspected VM by querying the hypervisor in a trusted manner and overcoming malware's ability to detect the security mechanism and evade detection. Then, using machine-learning algorithms we leverage informative traces (our 171 proposed features) from different parts of the VM's volatile memory. The framework was evaluated in seven rigorous experiments, on a total of 21,800 volatile memory dumps taken from two widely used virtual servers (10,900 from each server) during the execution of a diverse yet representative collection of benign and malicious Linux applications. Notably, the results show that our proposed framework can accurately (with high TPRs and low FPRs): (a) detect unknown malware (b) detect new unknown malware from unseen malware categories, which is a critical ability for coping with new malware trends and phenomena;(c) categorize an unknown malware by its attack category;(d) detect unknown malware on an unknown virtual-server;and lastly (e) detect fileless malware, a critical capability demonstrating the ability to detect substantially different attack modus operandi. (C) 2021 Elsevier B.V. All rights reserved.