scholarly journals A New Deep Learning-Based Methodology for Video Deepfake Detection Using XGBoost

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5413
Author(s):  
Aya Ismail ◽  
Marwa Elpeltagy ◽  
Mervat S. Zaki ◽  
Kamal Eldahshan
Keyword(s):  
Operating Characteristic ◽  
Detection Method ◽  
State Of The Art ◽  
Characteristic Curve ◽  
Gradient Boosting ◽  
Face Area ◽  
Video Frames ◽  
Extreme Gradient Boosting ◽  
The Face ◽  
Face Detector

Currently, face-swapping deepfake techniques are widely spread, generating a significant number of highly realistic fake videos that threaten the privacy of people and countries. Due to their devastating impacts on the world, distinguishing between real and deepfake videos has become a fundamental issue. This paper presents a new deepfake detection method: you only look once–convolutional neural network–extreme gradient boosting (YOLO-CNN-XGBoost). The YOLO face detector is employed to extract the face area from video frames, while the InceptionResNetV2 CNN is utilized to extract features from these faces. These features are fed into the XGBoost that works as a recognizer on the top level of the CNN network. The proposed method achieves 90.62% of an area under the receiver operating characteristic curve (AUC), 90.73% accuracy, 93.53% specificity, 85.39% sensitivity, 85.39% recall, 87.36% precision, and 86.36% F1-measure on the CelebDF-FaceForencics++ (c23) merged dataset. The experimental study confirms the superiority of the presented method as compared to the state-of-the-art methods.

Can Computers Outperform Humans in Detecting User Zone-Outs? Implications for Intelligent Interfaces

2022 ◽  
Vol 29 (2) ◽  
pp. 1-33
Author(s):  
Nigel Bosch ◽  
Sidney K. D'Mello
Keyword(s):  
Operating Characteristic ◽  
State Of The Art ◽  
Characteristic Curve ◽  
Ground Truth ◽  
Mind Wandering ◽  
High Stakes ◽  
Machine Learning Model ◽  
Improve Accuracy ◽  
Comparable Accuracy ◽  
Operating Characteristic Curve

The ability to identify whether a user is “zoning out” (mind wandering) from video has many HCI (e.g., distance learning, high-stakes vigilance tasks). However, it remains unknown how well humans can perform this task, how they compare to automatic computerized approaches, and how a fusion of the two might improve accuracy. We analyzed videos of users’ faces and upper bodies recorded 10s prior to self-reported mind wandering (i.e., ground truth) while they engaged in a computerized reading task. We found that a state-of-the-art machine learning model had comparable accuracy to aggregated judgments of nine untrained human observers (area under receiver operating characteristic curve [AUC] = .598 versus .589). A fusion of the two (AUC = .644) outperformed each, presumably because each focused on complementary cues. Furthermore, adding more humans beyond 3–4 observers yielded diminishing returns. We discuss implications of human–computer fusion as a means to improve accuracy in complex tasks.

scholarly journals HyP-ABC: A Novel Automated Hyper-Parameter Tuning Algorithm Using Evolutionary Optimization

2021 ◽  
Author(s):  
Leila Zahedi ◽  
Farid Ghareh Mohammadi ◽  
M. Hadi Amini
Keyword(s):  
Parameter Optimization ◽  
Real World ◽  
Optimization Problems ◽  
State Of The Art ◽  
Parameter Tuning ◽  
Gradient Boosting ◽  
Support Vector ◽  
Wide Range ◽  
Extreme Gradient Boosting ◽  
Art Techniques

Machine learning techniques lend themselves as promising decision-making and analytic tools in a wide range of applications. Different ML algorithms have various hyper-parameters. In order to tailor an ML model towards a specific application, a large number of hyper-parameters should be tuned. Tuning the hyper-parameters directly affects the performance (accuracy and run-time). However, for large-scale search spaces, efficiently exploring the ample number of combinations of hyper-parameters is computationally challenging. Existing automated hyper-parameter tuning techniques suffer from high time complexity. In this paper, we propose HyP-ABC, an automatic innovative hybrid hyper-parameter optimization algorithm using the modified artificial bee colony approach, to measure the classification accuracy of three ML algorithms, namely random forest, extreme gradient boosting, and support vector machine. Compared to the state-of-the-art techniques, HyP-ABC is more efficient and has a limited number of parameters to be tuned, making it worthwhile for real-world hyper-parameter optimization problems. We further compare our proposed HyP-ABC algorithm with state-of-the-art techniques. In order to ensure the robustness of the proposed method, the algorithm takes a wide range of feasible hyper-parameter values, and is tested using a real-world educational dataset.

scholarly journals An Interpretable Early Dynamic Sequential Predictor for Sepsis-Induced Coagulopathy Progression in the Real-World Using Machine Learning

2021 ◽  
Vol 8 ◽  
Author(s):  
Ruixia Cui ◽  
Wenbo Hua ◽  
Kai Qu ◽  
Heran Yang ◽  
Yingmu Tong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Time Series Data ◽  
Time Window ◽  
Medical Center ◽  
Characteristic Curve ◽  
Series Data ◽  
Gradient Boosting ◽  
Early Management ◽  
Extreme Gradient Boosting

Sepsis-associated coagulation dysfunction greatly increases the mortality of sepsis. Irregular clinical time-series data remains a major challenge for AI medical applications. To early detect and manage sepsis-induced coagulopathy (SIC) and sepsis-associated disseminated intravascular coagulation (DIC), we developed an interpretable real-time sequential warning model toward real-world irregular data. Eight machine learning models including novel algorithms were devised to detect SIC and sepsis-associated DIC 8n (1 ≤ n ≤ 6) hours prior to its onset. Models were developed on Xi'an Jiaotong University Medical College (XJTUMC) and verified on Beth Israel Deaconess Medical Center (BIDMC). A total of 12,154 SIC and 7,878 International Society on Thrombosis and Haemostasis (ISTH) overt-DIC labels were annotated according to the SIC and ISTH overt-DIC scoring systems in train set. The area under the receiver operating characteristic curve (AUROC) were used as model evaluation metrics. The eXtreme Gradient Boosting (XGBoost) model can predict SIC and sepsis-associated DIC events up to 48 h earlier with an AUROC of 0.929 and 0.910, respectively, and even reached 0.973 and 0.955 at 8 h earlier, achieving the highest performance to date. The novel ODE-RNN model achieved continuous prediction at arbitrary time points, and with an AUROC of 0.962 and 0.936 for SIC and DIC predicted 8 h earlier, respectively. In conclusion, our model can predict the sepsis-associated SIC and DIC onset up to 48 h in advance, which helps maximize the time window for early management by physicians.

scholarly journals Comparative Analysis of Deepfake Image Detection Method Using Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Hasin Shahed Shad ◽  
Md. Mashfiq Rizvee ◽  
Nishat Tasnim Roza ◽  
S. M. Ahsanul Hoq ◽  
Mohammad Monirujjaman Khan ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Operating Characteristic ◽  
Lower Cost ◽  
Detection Method ◽  
Characteristic Curve ◽  
Data Driven ◽  
Main Element ◽  
Generation Z ◽  
Technological Advances ◽  
Artificial Intelligence Technology

Generation Z is a data-driven generation. Everyone has the entirety of humanity’s knowledge in their hands. The technological possibilities are endless. However, we use and misuse this blessing to face swap using deepfake. Deepfake is an emerging subdomain of artificial intelligence technology in which one person’s face is overlaid over another person’s face, which is very prominent across social media. Machine learning is the main element of deepfakes, and it has allowed deepfake images and videos to be generated considerably faster and at a lower cost. Despite the negative connotations associated with the phrase “deepfakes,” the technology is being more widely employed commercially and individually. Although it is relatively new, the latest technological advances make it more and more challenging to detect deepfakes and synthesized images from real ones. An increasing sense of unease has developed around the emergence of deepfake technologies. Our main objective is to detect deepfake images from real ones accurately. In this research, we implemented several methods to detect deepfake images and make a comparative analysis. Our model was trained by datasets from Kaggle, which had 70,000 images from the Flickr dataset and 70,000 images produced by styleGAN. For this comparative study of the use of convolutional neural networks (CNN) to identify genuine and deepfake pictures, we trained eight different CNN models. Three of these models were trained using the DenseNet architecture (DenseNet121, DenseNet169, and DenseNet201); two were trained using the VGGNet architecture (VGG16, VGG19); one was with the ResNet50 architecture, one with the VGGFace, and one with a bespoke CNN architecture. We have also implemented a custom model that incorporates methods like dropout and padding that aid in determining whether or not the other models reflect their objectives. The results were categorized by five evaluation metrics: accuracy, precision, recall, F1-score, and area under the ROC (receiver operating characteristic) curve. Amongst all the models, VGGFace performed the best, with 99% accuracy. Besides, we obtained 97% from the ResNet50, 96% from the DenseNet201, 95% from the DenseNet169, 94% from the VGG19, 92% from the VGG16, 97% from the DenseNet121 model, and 90% from the custom model.

scholarly journals Supervised Machine-learning Predictive Analytics for Prediction of Postinduction Hypotension

2018 ◽  
Vol 129 (4) ◽  
pp. 675-688 ◽  
Author(s):  
Samir Kendale ◽  
Prathamesh Kulkarni ◽  
Andrew D. Rosenberg ◽  
Jing Wang
Keyword(s):  
Machine Learning ◽  
Receiver Operating Characteristic Curve ◽  
Operating Characteristic ◽  
Predictive Analytics ◽  
Characteristic Curve ◽  
Supervised Machine Learning ◽  
Gradient Boosting ◽  
Machine Learning Methods ◽  
Gradient Boosting Machine ◽  
Operating Characteristic Curve

AbstractEditor’s PerspectiveWhat We Already Know about This TopicWhat This Article Tells Us That Is NewBackgroundHypotension is a risk factor for adverse perioperative outcomes. Machine-learning methods allow large amounts of data for development of robust predictive analytics. The authors hypothesized that machine-learning methods can provide prediction for the risk of postinduction hypotension.MethodsData was extracted from the electronic health record of a single quaternary care center from November 2015 to May 2016 for patients over age 12 that underwent general anesthesia, without procedure exclusions. Multiple supervised machine-learning classification techniques were attempted, with postinduction hypotension (mean arterial pressure less than 55 mmHg within 10 min of induction by any measurement) as primary outcome, and preoperative medications, medical comorbidities, induction medications, and intraoperative vital signs as features. Discrimination was assessed using cross-validated area under the receiver operating characteristic curve. The best performing model was tuned and final performance assessed using split-set validation.ResultsOut of 13,323 cases, 1,185 (8.9%) experienced postinduction hypotension. Area under the receiver operating characteristic curve using logistic regression was 0.71 (95% CI, 0.70 to 0.72), support vector machines was 0.63 (95% CI, 0.58 to 0.60), naive Bayes was 0.69 (95% CI, 0.67 to 0.69), k-nearest neighbor was 0.64 (95% CI, 0.63 to 0.65), linear discriminant analysis was 0.72 (95% CI, 0.71 to 0.73), random forest was 0.74 (95% CI, 0.73 to 0.75), neural nets 0.71 (95% CI, 0.69 to 0.71), and gradient boosting machine 0.76 (95% CI, 0.75 to 0.77). Test set area for the gradient boosting machine was 0.74 (95% CI, 0.72 to 0.77).ConclusionsThe success of this technique in predicting postinduction hypotension demonstrates feasibility of machine-learning models for predictive analytics in the field of anesthesiology, with performance dependent on model selection and appropriate tuning.

scholarly journals The prediction of asymptomatic carotid atherosclerosis with electronic health records: a comparative study of six machine learning models

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jiaxin Fan ◽  
Mengying Chen ◽  
Jian Luo ◽  
Shusen Yang ◽  
Jinming Shi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Health Records ◽  
Carotid Atherosclerosis ◽  
Characteristic Curve ◽  
Gradient Boosting ◽  
Learning Models ◽  
Health Records ◽  
Extreme Gradient Boosting ◽  
Electronic Health ◽  
Machine Learning Models

Abstract Background Screening carotid B-mode ultrasonography is a frequently used method to detect subjects with carotid atherosclerosis (CAS). Due to the asymptomatic progression of most CAS patients, early identification is challenging for clinicians, and it may trigger ischemic stroke. Recently, machine learning has shown a strong ability to classify data and a potential for prediction in the medical field. The combined use of machine learning and the electronic health records of patients could provide clinicians with a more convenient and precise method to identify asymptomatic CAS. Methods Retrospective cohort study using routine clinical data of medical check-up subjects from April 19, 2010 to November 15, 2019. Six machine learning models (logistic regression [LR], random forest [RF], decision tree [DT], eXtreme Gradient Boosting [XGB], Gaussian Naïve Bayes [GNB], and K-Nearest Neighbour [KNN]) were used to predict asymptomatic CAS and compared their predictability in terms of the area under the receiver operating characteristic curve (AUCROC), accuracy (ACC), and F1 score (F1). Results Of the 18,441 subjects, 6553 were diagnosed with asymptomatic CAS. Compared to DT (AUCROC 0.628, ACC 65.4%, and F1 52.5%), the other five models improved prediction: KNN + 7.6% (0.704, 68.8%, and 50.9%, respectively), GNB + 12.5% (0.753, 67.0%, and 46.8%, respectively), XGB + 16.0% (0.788, 73.4%, and 55.7%, respectively), RF + 16.6% (0.794, 74.5%, and 56.8%, respectively) and LR + 18.1% (0.809, 74.7%, and 59.9%, respectively). The highest achieving model, LR predicted 1045/1966 cases (sensitivity 53.2%) and 3088/3566 non-cases (specificity 86.6%). A tenfold cross-validation scheme further verified the predictive ability of the LR. Conclusions Among machine learning models, LR showed optimal performance in predicting asymptomatic CAS. Our findings set the stage for an early automatic alarming system, allowing a more precise allocation of CAS prevention measures to individuals probably to benefit most.

scholarly journals Non-invasive detection of silicosis based on array sensing and pattern recognition

2021 ◽  
Vol 271 ◽  
pp. 03030
Author(s):  
Wufan Xuan ◽  
Zhen Han ◽  
Lina Zheng
Keyword(s):  
Pattern Recognition ◽  
Sensor Array ◽  
Characteristic Curve ◽  
Cross Sectional Study ◽  
Gradient Boosting ◽  
Exhaled Breath ◽  
Cross Sectional ◽  
Linear Discriminant ◽  
Extreme Gradient Boosting ◽  
Fibrotic Lung Disease

Silicosis is a fibrotic lung disease caused by inhalation of silica dusts, early and accurate diagnosis of which remains a challenge. We aimed to assess the performance of a nanofiber sensor array and pattern recognition to promptly and noninvasively detect silicosis. A total of 210 silicosis cases and 430 non-silicosis controls were enrolled in a cross-sectional study. Exhaled breath was analysed by a portable analytical system incorporating an array of 16x organic nanofiber sensors. Models were established by Deep Neural Network and eXtreme Gradient Boosting. Linear Discriminant Analysis was used for dimensionality reduction and visualized data analysis. Receiver Operating Characteristic Curve, accuracy, sensitivity and specificity were used to evaluate models. Results: 99.3% AUC, 96.0% accuracy, 94.1% sensitivity, and 96.3% specificity were achieved in test set. Silicosis cases present different breath patterns from healthy controls, classification results using which were highly consistent with the experts’ diagnosis. Breath analysis performed with the sensor array and pattern recognition is expected to provide a quick, stable recognition for silicosis. In this paper, different forms of features, different algorithms and data sets over long time periods were used, which provides a reference for silicosis expiratory diagnosis scheme.

scholarly journals A Comparative Analysis of Machine Learning Models for Prediction of Insurance Uptake in Kenya

2020 ◽  
Author(s):  
Nelson Yego ◽  
Juma Kasozi ◽  
Joseph Nkrunziza
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Characteristic Curve ◽  
Confusion Matrix ◽  
Gradient Boosting ◽  
Support Vector ◽  
Sampled Data ◽  
Learning Models ◽  
Extreme Gradient Boosting ◽  
Machine Learning Models

The role of insurance in financial inclusion as well as in economic growth is immense. However, low uptake seems to impede the growth of the sector hence the need for a model that robustly predicts uptake of insurance among potential clients. In this research, we compared the performances of eight (8) machine learning models in predicting the uptake of insurance. The classifiers considered were Logistic Regression, Gaussian Naive Bayes, Support Vector Machines, K Nearest Neighbors, Decision Tree, Random Forest, Gradient Boosting Machines and Extreme Gradient boosting. The data used in the classification was from the 2016 Kenya FinAccess Household Survey. Comparison of performance was done for both upsampled and downsampled data due to data imbalance. For upsampled data, Random Forest classifier showed highest accuracy and precision compared to other classifiers but for down sampled data, gradient boosting was optimal. It is noteworthy that for both upsampled and downsampled data, tree-based classifiers were more robust than others in insurance uptake prediction. However, in spite of hyper-parameter optimization, the area under receiver operating characteristic curve remained highest for Random Forest as compared to other tree-based models. Also, the confusion matrix for Random Forest showed least false positives, and highest true positives hence could be construed as the most robust model for predicting the insurance uptake. Finally, the most important feature in predicting uptake was having a bank product hence bancassurance could be said to be a plausible channel of distribution of insurance products.

scholarly journals Prediction of Sepsis in COVID-19 Using Laboratory Indicators

2021 ◽  
Vol 10 ◽  
Author(s):  
Guoxing Tang ◽  
Ying Luo ◽  
Feng Lu ◽  
Wei Li ◽  
Xiongcheng Liu ◽  
...  
Keyword(s):  
Laboratory Test ◽  
Test Data ◽  
Early Warning ◽  
Clinical Symptoms ◽  
Characteristic Curve ◽  
Health Concern ◽  
Inflammatory Factors ◽  
Gradient Boosting ◽  
Coagulation Function ◽  
Extreme Gradient Boosting

BackgroundThe outbreak of coronavirus disease 2019 (COVID-19) has become a global public health concern. Many inpatients with COVID-19 have shown clinical symptoms related to sepsis, which will aggravate the deterioration of patients’ condition. We aim to diagnose Viral Sepsis Caused by SARS-CoV-2 by analyzing laboratory test data of patients with COVID-19 and establish an early predictive model for sepsis risk among patients with COVID-19.MethodsThis study retrospectively investigated laboratory test data of 2,453 patients with COVID-19 from electronic health records. Extreme gradient boosting (XGBoost) was employed to build four models with different feature subsets of a total of 69 collected indicators. Meanwhile, the explainable Shapley Additive ePlanation (SHAP) method was adopted to interpret predictive results and to analyze the feature importance of risk factors.FindingsThe model for classifying COVID-19 viral sepsis with seven coagulation function indicators achieved the area under the receiver operating characteristic curve (AUC) 0.9213 (95% CI, 89.94–94.31%), sensitivity 97.17% (95% CI, 94.97–98.46%), and specificity 82.05% (95% CI, 77.24–86.06%). The model for identifying COVID-19 coagulation disorders with eight features provided an average of 3.68 (±) 4.60 days in advance for early warning prediction with 0.9298 AUC (95% CI, 86.91–99.04%), 82.22% sensitivity (95% CI, 67.41–91.49%), and 84.00% specificity (95% CI, 63.08–94.75%).InterpretationWe found that an abnormality of the coagulation function was related to the occurrence of sepsis and the other routine laboratory test represented by inflammatory factors had a moderate predictive value on coagulopathy, which indicated that early warning of sepsis in COVID-19 patients could be achieved by our established model to improve the patient’s prognosis and to reduce mortality.

scholarly journals Drug-Target Interaction Prediction Based on Multisource Information Weighted Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shuaiqi Liu ◽  
Jingjie An ◽  
Jie Zhao ◽  
Shuhuan Zhao ◽  
Hui Lv ◽  
...  
Keyword(s):  
Drug Target ◽  
Operating Characteristic ◽  
State Of The Art ◽  
Characteristic Curve ◽  
Relationship Matrix ◽  
Target Interaction ◽  
Interaction Prediction ◽  
Weighted Fusion ◽  
Precision Recall Curve ◽  
The Relationship

Recently, in most existing studies, it is assumed that there are no interaction relationships between drugs and targets with unknown interactions. However, unknown interactions mean the relationships between drugs and targets have just not been confirmed. In this paper, samples for which the relationship between drugs and targets has not been determined are considered unlabeled. A weighted fusion method of multisource information is proposed to screen drug-target interactions. Firstly, some drug-target pairs which may have interactions are selected. Secondly, the selected drug-target pairs are added to the positive samples, which are regarded as known to have interaction relationships, and the original interaction relationship matrix is revised. Finally, the revised datasets are used to predict the interaction derived from the bipartite local model with neighbor-based interaction profile inferring (BLM-NII). Experiments demonstrate that the proposed method has greatly improved specificity, sensitivity, precision, and accuracy compared with the BLM-NII method. In addition, compared with several state-of-the-art methods, the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPR) of the proposed method are excellent.

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