Constituent vs Dependency Parsing-Based RDF Model Generation from Dengue Patients’ Case Sheets

Electronic Health Record (EHR) systems in healthcare organisations are primarily maintained in isolation from each other that makes interoperability of unstructured(text) data stored in these EHR systems challenging in the healthcare domain. Similar information may be described using different terminologies by different applications that can be evaded by transforming the content into the Resource Description Framework (RDF) model that is interoperable amongst organisations. RDF requires a document’s contents to be translated into a repository of triplets (subject, predicate, object) known as RDF statements. Natural Language Processing (NLP) techniques can help get actionable insights from these text data and create triplets for RDF model generation. This paper discusses two NLP-based approaches to generate the RDF models from unstructured patients’ documents, namely dependency structure-based and constituent(phrase) structure-based parser. Models generated by both approaches are evaluated in two aspects: exhaustiveness of the represented knowledge and the model generation time. The precision measure is used to compute the models’ exhaustiveness in terms of the number of facts that are transformed into RDF representations.

Breast Cancer Histopathological Image Classification using Stochastic Dilated Residual Ghost Model

A new deep learning-based classification model called the Stochastic Dilated Residual Ghost (SDRG) was proposed in this work for categorizing histopathology images of breast cancer. The SDRG model used the proposed Multiscale Stochastic Dilated Convolution (MSDC) model, a ghost unit, stochastic upsampling, and downsampling units to categorize breast cancer accurately. This study addresses four primary issues: first, strain normalization was used to manage color divergence, data augmentation with several factors was used to handle the overfitting. The second challenge is extracting and enhancing tiny and low-level information such as edge, contour, and color accuracy; it is done by the proposed multiscale stochastic and dilation unit. The third contribution is to remove redundant or similar information from the convolution neural network using a ghost unit. According to the assessment findings, the SDRG model scored overall 95.65 percent accuracy rates in categorizing images with a precision of 99.17 percent, superior to state-of-the-art approaches.

Ornaments are equally informative in males and females: a bivariate meta-analysis in birds

Female ornaments are often reduced, male-like traits. Although these were long perceived as nonadaptive, it is now broadly accepted that female ornaments can be functional. However, it is unclear whether this is as common in females as it is in males, and whether ornaments fulfil similar signalling roles. To test this, we conduct a systematic review and apply a phylogenetically controlled bivariate meta-analysis to a large dataset of ornaments in mutually ornamented birds. As expected, female ornament expression tends to be reduced compared to males. However, ornaments are equally strongly associated with indicators of body condition and aspects of reproductive success in both sexes, regardless of the degree of sexual dimorphism. Thus, ornaments in birds provide similar information in both sexes: more ornamented individuals are in better condition and achieve higher reproductive success. Although limited by their correlational nature, these outcomes imply that female ornaments could widely function in a similar manner as male ornaments.

DEVELOPMENT OF A MATHEMATICAL MODEL FOR A NEURAL NETWORK FOR RECOGNIZING TEXT INFORMATION

The article considers a mathematical model of a system that provides recognition of images that represent text or use similar information in the generation process.

DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine

Recent global developments underscore the prominent role big data have in modern medical science. But privacy issues constitute a prevalent problem for collecting and sharing data between researchers. However, synthetic data generated to represent real data carrying similar information and distribution may alleviate the privacy issue. In this study, we present generative adversarial networks (GANs) capable of generating realistic synthetic DeepFake 10-s 12-lead electrocardiograms (ECGs). We have developed and compared two methods, named WaveGAN* and Pulse2Pulse. We trained the GANs with 7,233 real normal ECGs to produce 121,977 DeepFake normal ECGs. By verifying the ECGs using a commercial ECG interpretation program (MUSE 12SL, GE Healthcare), we demonstrate that the Pulse2Pulse GAN was superior to the WaveGAN* to produce realistic ECGs. ECG intervals and amplitudes were similar between the DeepFake and real ECGs. Although these synthetic ECGs mimic the dataset used for creation, the ECGs are not linked to any individuals and may thus be used freely. The synthetic dataset will be available as open access for researchers at OSF.io and the DeepFake generator available at the Python Package Index (PyPI) for generating synthetic ECGs. In conclusion, we were able to generate realistic synthetic ECGs using generative adversarial neural networks on normal ECGs from two population studies, thereby addressing the relevant privacy issues in medical datasets.

Forecasting Stock Price using LSTM-CNN Method

Foreseeing assumes an indispensable part in setting an exchanging methodology or deciding the ideal opportunity to purchase or sell stock. We propose an element combination long transient memory-convolutional neural organization (LSTM-CNN) model, which joins highlights gained from various presentations of similar information, i.e., stock timetable and stock outline pictures, to anticipate stock costs. The proposed model is created by LSTM and CNN, which extricate impermanent and picture components. We assessed the proposed single model (CNN and LSTM) utilizing SPDR S&P 500 ETF information. Our LSTM-CNN combination highlight model surpasses single models in foreseeing evaluating. Also, we track down that the candle graph is the most precise image of a stock diagram that you can use to anticipate costs. Subsequently, this examination shows that prescient mistake can be viably decreased by utilizing a blend of transitory and picture components from similar information as opposed to utilizing these provisions independently.

Despite Appearances: Comparing Emotion Recognition in Abstract and Humanoid Avatars Using Nonverbal Behavior in Social Virtual Reality

The ability to perceive emotional states is a critical part of social interactions, shaping how people understand and respond to each other. In face-to-face communication, people perceive others’ emotions through observing their appearance and behavior. In virtual reality, how appearance and behavior are rendered must be designed. In this study, we asked whether people conversing in immersive virtual reality (VR) would perceive emotion more accurately depending on whether they and their partner were represented by realistic or abstract avatars. In both cases, participants got similar information about the tracked movement of their partners’ heads and hands, though how this information was expressed varied. We collected participants’ self-reported emotional state ratings of themselves and their ratings of their conversational partners’ emotional states after a conversation in VR. Participants’ ratings of their partners’ emotional states correlated to their partners’ self-reported ratings regardless of which of the avatar conditions they experienced. We then explored how these states were reflected in their nonverbal behavior, using a dyadic measure of nonverbal behavior (proximity between conversational partners) and an individual measure (expansiveness of gesture). We discuss how this relates to measures of social presence and social closeness.

Different neural codes serve long and short-term memory functions in primate Hippocampus and Lateral Prefrontal Cortex during virtual navigation

The primate hippocampus (HPC) and lateral prefrontal cortex (LPFC) are two brain structures deemed essential to long- and short-term memory functions respectively. Here we hypothesize that although both structures may encode similar information about the environment, the neural codes mediating neuronal communication in HPC and LPFC have differentially evolved to serve their corresponding memory functions. We used a virtual reality task in which animals navigated through a maze using a joystick and selected one of two targets in the arms of the maze according to a learned context-color rule. We found that neurons and neuronal populations in both regions encode similar information about the different task periods. Moreover, using statistical analyses and linear classifiers, we demonstrated that many HPC neurons concentrate spikes temporally into bursts, whereas most LPFC neurons sparsely distribute spikes over time. When integrating spike rates over short intervals, HPC neuronal ensembles reached maximum decoded information with fewer neurons than LPFC ensembles. We propose that HPC principal cells have evolved intrinsic properties that enable burst firing and temporal summation of synaptic potentials that ultimately facilitates synaptic plasticity and long-term memory formation. On the other hand, LPFC pyramidal cells have intrinsic properties that allow sparsely distributing spikes over time enabling encoding of short-term memories via persistent firing without necessarily triggering rapid changes in the synapses.

Credibility of Scientific Information on Social Media: Variation by Platform, Genre and Presence of Formal Credibility Cues

Responding to calls to take a more active role in communicating their research findings, scientists are increasingly using open online platforms, such as Twitter, to engage in science communication or to publicize their work. Given the ease at which misinformation spreads on these platforms it is important for scientists to present their findings in a manner that appears credible. To examine the extent to which the online presentation of science information relates to its perceived credibility, we designed and conducted two surveys on Amazon’s Mechanical Turk. In the first survey, participants rated the credibility of science information on Twitter compared with the same information other media, and in the second, participants rated the credibility of tweets with modified characteristics: presence of an image, text sentiment, and the number of likes/retweets. We find that similar information about scientific findings is perceived as less credible when presented on Twitter compared to other platforms, and that perceived credibility increases when presented with recognizable features of a scientific article. On a platform as widely distrusted as Twitter, use of these features may allow researchers who regularly use Twitter for research-related networking and communication to present their findings in the most credible formats. Peer Review https://publons.com/publon/10.1162/qss_a_00151

A Different Way for Detecting Anaemia Disease using Bioelectronics Circuit

All in all, clinical science characterizes anaemia disease as less tally of a red platelet. This illness is exceptionally successive in India and a few pieces of the entire world. This isn't straightforwardly influencing illness however the maker and initiator of numerous other blood-related issues. Biomedical has effectively built up a testing technique for the recognizable proof of such infection and sorted out the strategy for computing the quantity of check or scope of red platelets in human bodies. In view of a correlation of such information with ordinary human blood attributes specialists can distinguish the level of the pallor and its connected stage. This requires time like conventional blood revealing just as the precision of testing philosophy. With the help of this article, another method of recognizing similar information inside a brief timeframe is introduced. By processing the ordinary human platelet check with anaemia influenced blood through the bioelectronics circuit, it will be useful to sort out the presence of the illness. Rather than utilizing genuine blood, a substance blend comparable to human blood serum has been thought of and for sickness, synthetic creation has been modified. This may change results for genuine human blood however then circuit changes may assist us with improving the outcomes. The sugar meter and heart meter are as of now utilized in everyday life by normal individuals without the assistance of specialists. These commonsense outcomes may lead such instrument makers for building up the gadget for sickliness identification.