Ensemble of Deep Capsule Neural Networks: An Application to Pneumonia Prediction

Pneumonia is the primary cause of death in children under the age of 5 years. Faster and more accurate laboratory testing aids in the prescription of appropriate treatment for children suspected of having pneumonia, lowering mortality. In this work, we implement a deep neural network model to efficiently evaluate pediatric pneumonia from chest radio graph images. Our network uses a combination of convolutional and capsule layers to capture abstract details as well as low level hidden features from the the radio graphic images, allowing the model to generate more generic predictions. Furthermore, we combine several capsule networks by stacking them together and connected them with dense layers. The joint model is trained as a single model using joint loss and the weights of the capsule layers are updated using the dynamic routing algorithm. The proposed model is evaluated using benchmark pneumonia dataset\cite{kermany2018identifying}, and the outcomes of our experimental studies indicate that the capsules employed in the network enhance the learning of disease level features that are essential in diagnosing pneumonia. According to our comparison studies, the proposed model with Convolution base from InceptionV3 attached with Capsule layers at the end surpasses several existing models by achieving an accuracy of 94.84\%. The proposed model is superior in terms of various performance measures such as accuracy and recall, and is well suited to real-time pediatric pneumonia diagnosis, substituting manual chest radiography examination.

Research on the Effect of Online Marketing Based on Multimodel Fusion and Artificial Intelligence in the Context of Big Data

Traditional online marketing methods use a single model to predict the advertising conversion rate, but the prediction results are not accurate, and users are not satisfied with the recommendation results. Therefore, this paper proposes an online marketing method based on multimodel fusion and artificial intelligence algorithms under the background of big data. First, it introduces big data technology and analyzes the characteristics of network advertising marketing model (RTB). Second, combined with multitask learning and fusion technology to improve the single model in advertising conversion rate prediction effect, prediction results to further improve the accuracy of results. Then, tF-IDF technology in artificial intelligence algorithm is used to measure the importance of advertising words in online marketing and calculate the contribution degree. Finally, according to XGBoost technology, the multitask fusion model of online marketing effect is classified. Experiments are used to analyze the effect of online marketing. Experimental results show that the proposed method can improve the accuracy of advertising conversion rate prediction and online sales of goods.

Hydrocarbon potential and prospects for exploration of Eastern Arctic oil and gas deposits

Conditions for the formation of hydrocarbon systems and prospects for searching for accumulations of oil and gas in the waters of the Eastern Arctic are considered. Significant hydrocarbon potential is predicted in the sedimentary basins of this region. All known manifestations of oil hydrocarbons are installed on land adjacent to the south, as well as on the east of the shelf. The East Arctic waters are included in a single model in order to perform an adequate comparative analysis of the evolution of hydrocarbon systems. The purpose of the research was to build space-time digital models of sedimentary basins and hydrocarbon systems, and to quantify the volume of generation, migration, and accumulation of hydrocarbons for the main horizons of source rocks. To achieve this goal, a spatiotemporal numerical basin simulation was carried out, based on which the distribution of probable hydrocarbon systems was determined and further analyzed. Following to the data obtained the most probable HC accumulation zones and types of fluids contained in potential traps were predicted. Keywords: numerical space-time basin modeling; modeling of hydrocarbon systems; evidence of oil and gas presence; Eastern Arctic; elements of hydrocarbon systems; oil and gas reservoirs; migration; accumulation; perspective objects

Detection of Cytopathic Effects Induced by Influenza, Parainfluenza, and Enterovirus Using Deep Convolution Neural Network

The isolation of a virus using cell culture to observe its cytopathic effects (CPEs) is the main method for identifying the viruses in clinical specimens. However, the observation of CPEs requires experienced inspectors and excessive time to inspect the cell morphology changes. In this study, we utilized artificial intelligence (AI) to improve the efficiency of virus identification. After some comparisons, we used ResNet-50 as a backbone with single and multi-task learning models to perform deep learning on the CPEs induced by influenza, enterovirus, and parainfluenza. The accuracies of the single and multi-task learning models were 97.78% and 98.25%, respectively. In addition, the multi-task learning model increased the accuracy of the single model from 95.79% to 97.13% when only a few data of the CPEs induced by parainfluenza were provided. We modified both models by inserting a multiplexer and de-multiplexer layer, respectively, to increase the correct rates for known cell lines. In conclusion, we provide a deep learning structure with ResNet-50 and the multi-task learning model and show an excellent performance in identifying virus-induced CPEs.

A Long-Term Study of Ultraluminous X-ray Sources in NGC 891

We perform empirical fits to the Chandraand XMM-Newton spectra of three ultraluminous X-ray sources (ULXs) in the edge-on spiral galaxy NGC 891, monitoring the region over a 17-year time window. One of these sources was visible since the early 1990s with ROSAT and was observed multiple times with Chandra and XMM-Newton. Another was visible since 2011. We build upon prior analyses of these sources by analyzing all available data at all epochs. Where possible Chandra data is used, since its superior spatial resolution allows for more effective isolation of the emission from each individual source, thus providing a better determination of their spectral properties. We also identify a new transient ULX, CXOU J022230.1+421937, which faded from view over the course of a two month period from Nov 2016 to Jan 2017. Modeling of each source at every epoch was conducted using six different models ranging from thermal bremsstrahlung to accretion disk models. Unfortunately, but as is common with many ULXs, no single model yielded a much better fit than the others. The two known sources had unabsorbed luminosities that remained fairly consistent over five or more years. Various possibilities for the new transient ULX are explored.

Credibility via Coupling

This study investigates Model Intercomparison Projects (MIPs) as one example of a coordinated approach to establishing scientific credibility. MIPs originated within climate science as a method to evaluate and compare disparate climate models, but MIPs or MIP-like projects are now spreading to many scientific fields. Within climate science, MIPs have advanced knowledge of: a) the climate phenomena being modeled, and b) the building of climate models themselves. MIPs thus build scientific confidence in the climate modeling enterprise writ large, reducing questions of the credibility or reproducibility of any single model. This paper will discuss how MIPs organize people, models, and data through institution and infrastructure coupling (IIC). IIC involves establishing mechanisms and technologies for collecting, distributing, and comparing data and models (infrastructural work), alongside corresponding governance structures, rules of participation, and collaboration mechanisms that enable partners around the world to work together effectively (institutional work). Coupling these efforts involves developing formal and informal ways to standardize data and metadata, create common vocabularies, provide uniform tools and methods for evaluating resulting data, and build community around shared research topics.

A Clinical Evaluation Study of Cardiothoracic Ratio Measurement Using Artificial Intelligence

Background Artificial Intelligence, particularly the Deep Learning (DL) model, can provide reliable results for automated cardiothoracic ratio (CTR) measurement on Chest x-ray (CXR) images. In everyday clinical use, however, this technology is usually implemented in a non-automated (AI-assisted) capacity because it still requires approval from radiologists. We investigated the performance and efficiency of our recently proposed models for the AI-assisted method intended for clinical practice. Methods We validated four proposed DL models (AlbuNet, SegNet, VGG-11, and VGG-16) to find the best model for clinical implementation using a dataset of 7,517 CXR images from manual operations. These models were investigated in single-model and combined-model modes to find the model with the highest percentage of results where the user could accept the results without further interaction (excellent grade), and with measurement variation within ± 1.8% of the human-operating range. The best model from the validation study was then tested on an evaluation dataset of 9,386 CXR images using the AI-assisted method with two radiologists to measure the yield of excellent grade results, observer variation, and operating time. A Bland-Altman plot with coefficient of variation (CV) was employed to evaluate agreement between measurements. Results The VGG-16 gave the highest excellent grade result (68.9%) of any single-model mode with a CV comparable to manual operation (2.12% vs 2.13%). No DL model produced a failure-grade result. The combined-model mode of AlbuNet+VGG-11 model yielded excellent grades in 82.7% of images and a CV of 1.36%. Using the evaluation dataset, the AlbuNet+VGG-11 model produced excellent grade results in 77.8% of images, a CV of 1.55%, and reduced operating time by almost ten-fold (1.07 ± 2.62 secs vs 10.6 ± 1.5 sec) compared to manual operation. Conclusion Due to its exceptional accuracy and speed, the AlbuNet+VGG-11 model could be clinically implemented to assist radiologists with CTR measurement.

Accounting Education, Accounting Profession and COVID-19?

The COVID-19 pandemic has had a significant impact, especially in terms of the many changes in regulatory and policy aspects, and in particular challenges in the accounting and education sectors. The 2020 academic year is considerably different from previous years and institutions, staff, and students are facing complex challenges. In terms of the impact of the COVID-19 pandemic on different countries’ education systems many differences exist. Online learning is an amalgamation of various pedagogical models instead of any one single model. The purpose of this article is to present the impact of the coronavirus on higher education in accounting, the challenges that students face in online learning and evaluating the impact of COVID-19 on the accounting profession.

Sidonio Apollinare, carme 9: un griphus per il lector?

Carmen 9 is a programmatic composition utterly sui generis. It has numerous points of contact with Ausonius’ Griphus ternarii numeri, and it can be considered as a riddle for the sodales, who are only given those elements which can be useful to understand the meaning of what the author presents as an original and complex ecdotic operation. None of the following carmina is exclusively historical, mythological, nor inspired by a single model. It is the reader who has to find out in what way myth, history and daily life are interwoven in the libellus, as are pagan gods and Christian faith, in a studied mixture of genres, stylistic registers, allusions, and learned reuse of the entire literary tradition, both ancient and more recent. This is in some ways a ‘new’ and complex literary endeavour, which is coherent with the renowned experimentalism of Late Latin poetry.

Forecasting Tourism Demand With a New Time-Varying Forecast Averaging Approach

Existing research has shown that combination can effectively improve tourism forecasting accuracy compared with single model. However, the model uncertainty and structural instability in combination for out-of-sample tourism forecasting may influence the forecasting performance. This paper proposes a novel forecast combination approach based on time-varying jackknife model averaging (TVJMA), which can more efficiently handle structural changes and nonstationary trends in tourism data. Using Hong Kong tourism demand from five major tourism source regions as an empirical study, we investigate whether our proposed nonparametric TVJMA-based approach can improve tourism forecasting accuracy further. Empirical results show that the proposed TVJMA-based approach outperforms other competitors including single model and three combination methods in most cases. Findings indicate the outstanding performance of our method is robust to various forecasting horizons and different estimation periods.