Comparative approach to different convolutional neural network (CNN) architectures applied to human behavior detection.

Medical diagnostics, product classification, surveillance and detection of inappropriate behavior are becoming increasingly sophisticated due to the development of methods based on image analysis using neural networks. Considering this, in this work, we evaluate state-of-the-art convolutional neural network architectures proposed in recent years to classify the driving behavior and distractions of drivers. Our main goal is to measure the performance of such architectures using only free resources (i.e., free graphic processing unit, open source) and to evaluate how much of this technological evolution is available to regular users.

Restoring Epistemic Credibility for Expert Communities in Sub-Saharan Africa

In this paper, we engage with Heidi Grasswick's argument on epistemic distrust as an epistemic value. We show how expert communities have in the past perpetuated epistemic harm to people of colour as recipients of knowledge. We highlight the adverse effects of these harms in our social milieu by limiting our discussion to COVID-19 vaccine hesitancy in sub-Saharan Africa. We then conclude that the scepticism of the COVID-19 vaccines by people of colour within the aforementioned locale can be understood in the context of epistemic distrust. Finally, We show how epistemic credibility toward the expert community, precisely scientific communities, by the non-expert community can be restored. [ FROM AUTHOR] Copyright of Revista de Filosofía is the property of Revista de Filosofia-Universidad del Zulia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Direct Multi-Material Reconstruction via Iterative Proximal Adaptive Descent for Spectral CT Imaging.

Spectral computed tomography (spectral CT) is a promising medical imaging technology because of its ability to provide information on material characterization and quantification. However, with an increasing number of basis materials, the nonlinearity of measurements causes difficulty in decomposition. In addition, noise amplification and beam hardening further reduce image quality. Thus, improving the accuracy of material decomposition while suppressing noise is pivotal for spectral CT imaging. This paper proposes a one-step multi-material reconstruction model as well as an iterative proximal adaptive decent method. In this approach, a proximal step and a descent step with adaptive step size are designed under the forward-backward splitting framework. The convergence analysis of the algorithm is further discussed according to the convexity of the optimization objective function. For simulation experiments with different noise levels, the peak signal-to-noise ratio (PSNR) obtained by the proposed method increases approximately 23 dB, 14 dB, and 4 dB compared to those of other algorithms. Magnified areas of thorax data further demonstrated that the proposed method has a better ability to preserve details in tissues, bones, and lungs. Numerical experiments verify that the proposed method efficiently reconstructed the material maps, and reduced noise and beam hardening artifacts compared with the state-of-the-art methods.

Arrival flight efficiency in pre- and post-Covid-19 pandemics

Covid-19 pandemic affected aviation severely, resulting in unprecedented reduction of air traffic. While aviation is slowly re-gaining traffic volumes, we use the opportunity to study the arrival performance in the Terminal Maneuvering Area (TMA) in non-congested scenarios. Applying flight efficiency and environmental performance indicators (PIs) to the historical data of arrivals to Stockholm Arlanda and Gothenburg Landvetter airports, we discover noticeable inefficiencies, despite significant reduction of traffic intensity. We analyze the impact of such factors as weather and traffic intensity on arrival efficiency in isolated scenarios when only one factor dominates: isolated scenario with low traffic and isolated scenario with good weather conditions. Our analysis uncovers that weather has a stronger influence than traffic intensity on the vertical efficiency, while traffic intensity has stronger effect on the lateral efficiency. Impact of traffic intensity on the lateral efficiency might be explained by frequent hold-on patterns and flight trajectory extensions due to vectoring in high traffic conditions. Further investigation is needed to explain weather and vertical/lateral efficiency correlations, the conclusions might be country-specific.

Physician scheduling problem in Mobile Cabin Hospitals of China during Covid-19 outbreak.

In this paper, we investigate a novel physician scheduling problem in the Mobile Cabin Hospitals (MCH) which are constructed in Wuhan, China during the outbreak of the Covid-19 pandemic. The shortage of physicians and the surge of patients brought great challenges for physicians scheduling in MCH. The purpose of the studied problem is to get an approximately optimal schedule that reaches the minimum workload for physicians on the premise of satisfying the service requirements of patients as much as possible. We propose a novel hybrid algorithm integrating particle swarm optimization (PSO) and variable neighborhood descent (VND) (named as PSO-VND) to find the approximate global optimal solution. A self-adaptive mechanism is developed to choose the updating operators dynamically during the procedures. Based on the special features of the problem, three neighborhood structures are designed and searched in VND to improve the solution. The experimental comparisons show that the proposed PSO-VND has a significant performance increase than the other competitors.

Employing Transfer Learning for Diagnosing COVID-19 Disease

Corona virus's correct and accurate diagnosis is the most important reason for contributing to the treatment of this disease. Radiography is one of the simplest methods to detect virus infection. In this research, a method has been proposed that can diagnose disease based on radiography (X-ray chest) and deep learning techniques. We conducted a comparative study by using three diagnosis models;the first one was developed by using traditional CNN, while the two others are our proposed models (second and third models). The proposed models can diagnose the COVID-19 infection, normal cases, lung opacity, and Viral Pneumonia according to the four categories in the covid19 radiography dataset. The transfer learning technology had used to increase the robustness and reliability of our model, also, data augmentation was used for reducing the overfitting and to increase the accuracy of the model by scaling rotation, zooming, and translation. The third model showed higher training accuracy of 93.18% compared to the two other models that are dependent on using traditional convolution neural networks with an accuracy of 70.28% of the first model, while the accuracy of the second model that uses data augmentation with traditional convolution neural is 90.1%, while the testing accuracy models was 68.27% for the first model, 87.55% for the second model, and 86.03% for the third model. © 2022,International journal of online and biomedical engineering. All Rights Reserved.

Modified spectral conjugate gradient iterative scheme for unconstrained optimization problems with application on COVID-19 model

In this work, a new class of spectral conjugate gradient (CG) method is proposed for solving unconstrained optimization models. The search direction of the new method uses the ZPRP and JYJLL CG coefficients. The search direction satisfies the descent condition independent of the line search. The global convergence properties of the proposed method under the strong Wolfe line search are proved with some certain assumptions. Based on some test functions, numerical experiments are presented to show the proposed method's efficiency compared with other existing methods. The application of the proposed method for solving regression models of COVID-19 is provided. Mathematics subject classification: 65K10, 90C52, 90C26. Copyright © 2022 Novkaniza, Malik, Sulaiman and Aldila.

Arrival flight efficiency in pre- and post-Covid-19 pandemics

Covid-19 pandemic affected aviation severely, resulting in unprecedented reduction of air traffic. While aviation is slowly re-gaining traffic volumes, we use the opportunity to study the arrival performance in the Terminal Maneuvering Area (TMA) in non-congested scenarios. Applying flight efficiency and environmental performance indicators (PIs) to the historical data of arrivals to Stockholm Arlanda and Gothenburg Landvetter airports, we discover noticeable inefficiencies, despite significant reduction of traffic intensity. We analyze the impact of such factors as weather and traffic intensity on arrival efficiency in isolated scenarios when only one factor dominates: isolated scenario with low traffic and isolated scenario with good weather conditions. Our analysis uncovers that weather has a stronger influence than traffic intensity on the vertical efficiency, while traffic intensity has stronger effect on the lateral efficiency. Impact of traffic intensity on the lateral efficiency might be explained by frequent hold-on patterns and flight trajectory extensions due to vectoring in high traffic conditions. Further investigation is needed to explain weather and vertical/lateral efficiency correlations, the conclusions might be country-specific.

AdaBias: An Optimization Method With Bias Correction for Differential Privacy Protection

A continuous increase in privacy attacks has caused the research and application of differential privacy (DP) to gradually increase. We can improve the efficiency of the DP model by Optimizing its parameters significantly. Inspired by the performance of various optimization methods for differential privacy, this paper proposes an improved RDP-AdaBound optimization method with bias correction, which is called "AdaBias", to increase the performance of Renyi differential privacy (RDP). The bias correction is used to realize the learning rate and speed up the convergence by upper and lower bound functions. We evaluate our method on the three datasets by training two different privacy model. We further compare three traditional optimization algorithms, namely, RDP-SGD, RDP-Adagrad, and RDP-Adam. And we use AdaBias to verify the performance of privacy protection on the COVID-19 dataset. Experimental results show that the new variant better implements learning rate adjustment to accommodate updates of noisy gradients. As a result, it can achieve higher accuracy and lower losses with a lower privacy budget, thereby better protecting data privacy.

Poisson Regression with Error Corrupted High Dimensional Features

Features extracted from aggregated data are often contaminated with errors. Errors in these features are usually difficult to handle, especially when the feature dimension is high. We construct an estimator of the feature effects in the context of a Poisson regression with a high dimensional feature and additive measurement errors. The procedure penalizes a target function that is specially designed to handle measurement errors. We perform optimization within a bounded region. Benefiting from the convexity of the constructed target function in this region, we establish the theoretical properties of the new estimator in terms of algorithmic convergence and statistical consistency. The numerical performance is demonstrated using simulation studies. We apply the method to analyze the possible effect of weather on the number of COVID-19 cases.

A Comparison of Machine Learning Methods for best Accuracy COVID-19 Diagnosis Using Chest X-Ray Images

Coronavirus (COVID-19) changed the view of people towards life in all the countries of the world in December 2019. The virus has made chaos that cannot be predicted. This problem requires using a variety of technologies to aid in the identification of COVID-19 patients and to control the disease spread. For suspected instances of COVID-19 disease, chest X-ray (CXR) imaging is a standard with fewer costs, but it does not need a COVID-19 examination approach without using technology to help for a suitable diagnosis. In response to this issue, a big dataset of CXR images was divided into four classes found on the website Kaggle. Dealing with large data of the images needs dataset reprocessing through choosing the optimal method for getting speed and best accuracy. Dataset reprocessing converts into gray level then adjust image intensity, resize and extract the best features then apply Machine Learning ML models. The use of different prediction models, ML algorithms, and their performances are calculated with evaluation on the dataset after reprocessing. Decision Tree (DT), Random Forest (RF), Stochastic Gradient Descent (SGD), Logistic Regression (LR), Gaussian Naive Bayes (GNB), and K-Nearest Neighbors (KNN) are models used to foretell the specialized who would be diagnosed with COVID-19 quickly by using CXR images classification. The KNN has revealed the best accuracy compared with the others such as GNB, DT, SGD, LR, and RF. Also, KNN has the best-weighted average for all parameters, which are precision, sensitivity, and F1-score compared with the other models. © 2022 IEEE.

Funding of the Energy Transition by Monetary Sovereign Countries

If global energy consumption returns to its pre-pandemic growth rate, it will be almost impossible to transition to a zero-emission or net-zero-emission energy system by 2050 in the absence of large-scale CO2 removal. Since relying on unproven technologies for CO2 removal is speculative and risky, this paper considers an energy descent scenario for reaching zero greenhouse gas emissions from energy by 2050. To drive the rapid transition from fossil fuels to carbon-free energy sources and ensure demand reduction, funding is needed urgently in order to implement four strategies: (i) technology change, i.e., implementing the growth of zero-carbon energy production, end-use energy efficiency and ‘green’ energy carriers, together with ongoing R&D on CO2 removal;(ii) reducing climate impacts;(iii) reducing energy consumption by social and behavioural changes;and (iv) improving human wellbeing while increasing social justice. Modern monetary theory explains how monetary sovereign governments, with their own fiat currencies, can create the necessary funding without financial constraints, although constraints do result from the productive capacities of their economies. The energy transition could be part-funded by a significant transfer of resources from monetary sovereign countries of the global North to the global South, financed by currency issuance.

Collaborative Truck-Drone Routing for Contactless Parcel Delivery During the Epidemic

The COVID-19 pandemic calls for contactless deliveries. To prevent the further spread of the disease and ensure the timely delivery of supplies, this paper investigates a collaborative truck-drone routing problem for contactless parcel delivery (CRP-T&D), which allows multiple trucks and multiple drones to deliver parcels cooperatively in epidemic areas. We formulate a mixed-integer programming model that minimizes the delivery time, with the consideration of the energy consumption model of drones. To solve CRP-T&D, we develop an improved variable neighborhood descent (IVND) that combines the Metropolis acceptance criterion of Simulated Annealing (SA) and the tabu list of Tabu Search (TS). Meanwhile, the integration of K-means clustering and Nearest neighbor strategy is applied to generate the initial solution. To evaluate the performance of IVND, experiments are conducted by comparing IVND with VND, SA, TS, variants of VND, and large neighborhood search (LNS) on instances with different scales. Several critical factors are tested to verify the robustness of IVND. Moreover, the experimental results on a practical instance further demonstrate the superior performance of IVND. IEEE

NOWHERE TO GO: ANTI-ASIAN HATE CRIMES IN 1945 AND TODAY

Higuchi asserts that Iyekichi Higuchi prepared to leave the Heart Mountain camp for Japanese Americans in May 1945 to return to San Jose, California, look for a home for his wife and two at-home children, and to find a job. He had been forced to sell his 14.25-acre home in San Jose three years earlier when the federal government had forced 120,000 Japanese Americans from the West Coast because of hysteria about the alleged security threat they posed in the days following the 7 December 1941, Japanese attack on the naval base at Pearl Harbor, Hawaii. What faced those returning Japanese Americans mirrors the hate crimes now facing Americans of Asian descent who are blamed for spreading the COVID-19 virus that originally started in China to the United States. Since the pandemic took over in March, thousands of Asian Americans have been accosted in public spaces, spit on or assaulted and told to go back where they came from, even if that was not Asia at all.

FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information.

Coronavirus (COVID-19) has created an unprecedented global crisis because of its detrimental effect on the global economy and health. COVID-19 cases have been rapidly increasing, with no sign of stopping. As a result, test kits and accurate detection models are in short supply. Early identification of COVID-19 patients will help decrease the infection rate. Thus, developing an automatic algorithm that enables the early detection of COVID-19 is essential. Moreover, patient data are sensitive, and they must be protected to prevent malicious attackers from revealing information through model updates and reconstruction. In this study, we presented a higher privacy-preserving federated learning system for COVID-19 detection without sharing data among data owners. First, we constructed a federated learning system using chest X-ray images and symptom information. The purpose is to develop a decentralized model across multiple hospitals without sharing data. We found that adding the spatial pyramid pooling to a 2D convolutional neural network improves the accuracy of chest X-ray images. Second, we explored that the accuracy of federated learning for COVID-19 identification reduces significantly for non-independent and identically distributed (Non-IID) data. We then proposed a strategy to improve the model's accuracy on Non-IID data by increasing the total number of clients, parallelism (client-fraction), and computation per client. Finally, for our federated learning model, we applied a differential privacy stochastic gradient descent (DP-SGD) to improve the privacy of patient data. We also proposed a strategy to maintain the robustness of federated learning to ensure the security and accuracy of the model.

Analyzing the Sentiments by Classifying the Tweets Based on COVID-19 Using Machine Learning Classifiers

In the current scenario, almost all the countries face one of the biggest disasters in COVID-19. This paper has to analyze the tweets related to COVID 19 and discuss the various machine learning algorithms and their performance analysis on the tweets associated with COVID-19. The implemented classification algorithms are applied to classify the sentiments to predict whether they relate to COVID-19 or non-COVID-19. Ten most popular classification algorithms implemented. The Linear Support Vector Machine (LSVM) achieved the highest test accuracy in these algorithms with 90.3%. Logistic regression has performed better in recall with 96.06%, F1 score of 90.46%, ROC_AUC with 90.48%. Random forest classifier has achieved the better specificity and precision of 99.16% and 96.3%, respectively. Out of all, stochastic gradient descent (SGD) has attained better results in all the computational parameters. © 2021 IEEE.

Sentiment analysis for covid-19 in Indonesia on Twitter with TF-IDF featured extraction and stochastic gradient descent

Twitter is an information platform that can be used by any internet user. The opinions of the Twitter Netizens are still random or unclassified. The technique for classifying sentiment analysis requires an algorithm. One of the classification algorithms is Stochastic Gradient Descent (SGD). The more training data provided to the machine, the accuracy of the classification function model formed by the machine is also higher. But in making representations into numerical vectors, the dimensions of data become large due to the many features. Feature optimization needs to be done to the training data by reducing the dimensions of the training data while maintaining high model accuracy. The optimization feature used is the TF-IDF (term frequency-inverse document frequency) feature extraction. sentiment analysis using TF-IDF feature extraction and stochastic gradient descent algorithm can classify Indonesian text appropriately according to positive and negative sentiment. Classification Performance using TF-IDF feature extraction and stochastic gradient descent algorithm obtained an accuracy is 85.141%.

The Puzzling Persistence of Racial Inequality in Canada

This article examines the failure of Canadian public policy in addressing racial economic inequality directly. Our analysis contends that Canada's key policy regimes were established in the postwar era, when approximately 96 per cent of Canadians were of European descent. As a result, the frameworks, problem definitions and policy tools inherited from that era were never intended to mitigate racial economic inequality. Moreover, this policy inheritance was deeply shaped by liberal universalism, which rejected racial distinctions in law and policy. These norms were carried forward into the more racially diverse Canada of today, where they have steered attention away from the use of racial categories in policy design. As a result, racial inequality was not a central priority during major policy reforms to core policy regimes in recent decades. In theoretical terms, our analysis contributes to Canadian Political Development through a sustained consideration of the intersecting roles of ideational frameworks, path dependency and policy inertia.

An approach to categorize chest X-ray images using sparse categorical cross entropy

The Coronavirus disease (COVID-19) pandemic is the most recent threat to global health. Reverse transcription-polymerase chain reaction (RT-PCR) testing, computed tomography (CT) scans, and chest X-ray (CXR) images are being used to identify Coronavirus, one of the most serious community viruses of the twenty-first century. Because CT scans and RT-PCR analyses are not available in most health divisions, CXR images are typically the most time-saving and cost-effective tool for physicians in making decisions. Artificial intelligence and machine learning have become increasingly popular because of recent technical advancements. The goal of this project is to combine machine learning, deep learning, and the health-care sector to create a categorization technique for detecting the Coronavirus and other respiratory disorders. The three conditions evaluated in this study were COVID-19, viral Pneumonia, and normal lungs. Using X-ray pictures, this research developed a sparse categorical cross-entropy technique for recognizing all three categories. The proposed model had a training accuracy of 91% and a training loss of 0.63, as well as a validation accuracy of 81% and a validation loss of 0.7108. © 2021 Institute of Advanced Engineering and Science. All rights reserved.

Multinomial Logistic Regression Model for Prioritization of COVID-19 Vaccination in Portoviejo—Ecuador

Currently, the world is in the initial phase of the distribution of the COVID-19 vaccine, and the vaccine is principally available to developed countries, that is mainly administered to older people, especially to health workers at high risk of contracting COVID-19 while the rest of the population are exposed to contagion. A classification method is to classify people with high or low priority for the administration of the vaccine, that is vital importance to curb the spread of infections in the world. Mathematical models can be helped to define the classification while the impact of increased contagion is minimized. A multinomial logistic regression model is proposed to classify subjects, that is based on the values of a set of predictor variables. The priority of vaccination is classified in the canton of Portoviejo—Ecuador, the variables are considered: age, sex, number of presented symptoms at the time of registration, cardiovascular, chronic liver, chronic kidney, chronic respiratory, oncological, diabetes, hypertension, tuberculosis, other preexisting disease, exposed days to virus. A stochastic descent gradient algorithm is proposed to minimize an objective function J(θ), that is obtained from the proposed model. The efficiency of the forecasts of the model is compared, that is reproducing accuracy in the estimates. Finally, one goodness-of-fit measure to validate the performance of the model is used, obtaining insignificant estimation error. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.