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Crowd-Powered Medical Diagnosis : The Potential of Crowdsourcing for Patients with Rare Diseases
(2023)
With the recent rise in medical crowdsourcing platforms,
patients with chronic illnesses increasingly broadcast their
medical records to obtain an explanation for their complex
health conditions. By providing access to a vast pool of
diverse medical knowledge, crowdsourcing platforms have
the potential to change the way patients receive a medical
diagnosis. We developed a conceptual model that details
a set of variables. To further the understanding of
crowdsourcing as an emerging phenomenon in health care,
we provide a contextualization of the various factors that
drive participants to exert effort. For this purpose, we used
CrowdMed.com as a platform from which we gathered and
examined a unique dataset that involves tasks of diagnosing
rare medical conditions. By promoting crowdsourcing
as a robust and non-discriminatory alternative to seeking
help from traditional physicians, we contribute to the acceptance
and adoption of crowdsourcing services in health
economics.
Derived from the Ancient Greek word τραῦμα (engl. wound,
damage), the word trauma refers to either physical or emotional wounds. Nowadays, it is mostly used in the context of psychological wounds, inflicted by an identity-shattering event – an event that causes the traumatised to not be able to reconcile their lived reality with the expectation of a human universal experience anymore. The last decade, the last two years in particular, and the last two weeks ad absurdum, have scarred the global landscape of human existence beyond recognition. From Putin’s unexpected reimposition of mutually assured destruction doctrines via the global SARS-Cov-2 pandemic to the lingering threat of climate doom, people all over the globe have been faced with persistent threats to their most basic perceptions of ontological safety. This article seeks to examine the impact of the SARS-Cov-2 pandemic and to which degree it is justified to speak of a pandemic trauma. In addition, it engages with the liminality of pandemic trauma as a shared, collective and an isolated, individual experience, and potential mitigation strategies for building community resilience.
Convolutional Neural network (CNN) has been one of most powerful and popular preprocessing techniques employed for image classification problems. Here, we use other signal processing techniques like Fourier transform and wavelet transform to preprocess the images in conjunction with different classifiers like MLP, LVQ, GLVQ and GMLVQ and compare its performance with CNN.
Purpose: The study is aimed to determine the Incentives for German SMEs to offshore their business activities in India and China.
Design: This study is based on quantitative approach. Primary and secondary data is being used in the study. The data was collected from individuals working in different SMEs in Germany, having relative offshoring experience. Theories from the articles, peer reviewed journal along with relevant books were consulted throughout the study.
Findings: The findingssuggest that the benefits and advantages of offshoring strategy in India and China are cost efficiency and technology. Moreover, the challenges that are being faced by the firms while executive offshoring strategy is cultural mix especially language/cultural barriers, security issues and loss of market performance.
Originality and Value: The study on incentives of German SMEs to offshore business activities in India and China enables me to understand why companies are interested in offshoring strategy in low cost countries for expanding their business while evaluating the challenges, merits and demerits of offshoring
Over the past few years, wind and solar power plants have increasingly contributed to energy production. However, due to fluctuating energy sources, the energy production data contain disruption. Such disrupted data lead to the wrong prediction performance, and they need to be estimated by other values. In this thesis, we provide a comparative study to estimate the online disrupted data based on the data of similar groups of power plants, We apply three estimation techniques, e.g., mean, interpolation, and k-nearest neighbor to estimate the disruption on training data. We then apply four clustering algorithms, e.g., k-means, neural gas, hierarchical agglomerative, and affinity propagation, with two similarity measures, e.g., euclidean and dynamic time warping to form groups of power plants and compare the results. Experimental results show that when KNN estimation is applied to data, and neural gas and agglomerative with dtw are used to cluster the data, the cluster quality scores and execution time give better results compared to others. Therefore, we conclude and choose KNN estimation to reconstruct the online disrupted data on each group of a similar power plants.
Studying and understanding the metabolism of plants is essential to better adapt them to future climate conditions. Computational models of plant metabolism can guide this process by providing a platform for fast and resource-saving in silico analyses. The reconstruction of these models can follow kinetic or stoichiometric approaches with Flux Balance Analysis being one of the most common one for stoichiometric models. Advances in metabolic modelling over the years include the increasing number of compartments, the automation of the reconstruction process, the modelling of plant-environment interactions and genetic variants or temporally and spatially resolved models. In addition, there is a growing focus on introducing synthetic pathways in plants to increase their agricultural potential regarding yield, growth and nutritional value. One example is the β-hydroxyaspartate cycle (BHAC) to bypass photorespiration. After the implementation in a stoichiometric C3 plant model, in silico flux analyses can help to understand the resulting metabolic changes. When comparing with in vivo experiments with BHAC plants, the metabolic model can reproduce most results with exceptions regarding growth and oxaloacetate. To evaluate whether the BHAC is suitable to establish a synthetic C4 cycle, the pathway is implemented in a two-cell type model that is capable of running a C4 cycle. The results show that the BHAC is only beneficial under light limitation in the bundle sheath cell. An additional engineering target for improved performance of plants is malate synthase. This work serves as the basis for further analyses combining the different factors boosting the advantages of the BHAC and for in vivo experiments in C3 and C4 plants.
In the past few years Generative models have become an interesting topic in the field of Machine Learning (ML). Variational Autoencoder (VAE) is one of the popular frameworks of generative models based on the work of D.P Kingma and M. Welling [6] [7]. As an alternative to VAE the authors in [12] proposed and implemented Information Theoretic Learning (ITL) based Autoencoder. VAE and ITL Autoencoder are a combination of the neural networks and probabilistic graphical models (PGM) [7]. In modern statistics it is difficult to compute the approximation ofthe probability densities. In this paper we make use of Variational Inference (VI) technique from machine learning that approximate the distributions through optimization. The closeness between the distributions are measured by the information theoretic divergence measures such as Kullbach-Liebler, Euclidean and Cauchy Schwarz divergences. In this thesis, we study theoretical and experimental results of two different frameworks of generative models which generate images of MNIST handwritten characters [8] and Yale face database B [3]. The results obtained show that the proposed VAE and ITL Autoencoder are capable of generating the underlying structure of the example datasets
Digital data is rising day by day and so is the need for intelligent, automated data processing in daily life. In addition to this, in machine learning, a secure and accurate way to classify data is important. This holds utmost importance in certain fields, e.g. in medical data analysis. Moreover, in order to avoid severe consequences, the accuracy and reliability of the classification are equally important. So if the classification is not reliable, instead of accepting the wrongly classified data point, it is better to reject such a data point. This can be done with the help of some strategies by using them on top of a trained model or including them directly in the objective function of the desired training model. We discuss such strategies and analyze the results on data sets in this thesis.
This Bachelor thesis investigates the learning rules of the Hebbian, Oja and BCM neuron models for their convergence to, and the stability of, the fixed points. Existing research is presented in a structured manner using consistent notation. Hebbian learning is neither convergent nor stable. Oja learning converges to a stable fixed point, which is the eigenvector corresponding to the largest eigenvalue of the covariance matrix of the input data. BCM learning converges to a fixed point which is stable, when assuming a discrete distribution of orthogonal inputs that occur with equal probability. Hebbian learning can therefore not be used in further applications, where convergence to a stable fixed point is required. Furthermore, this Bachelor thesis came to the conclusion that determining the fixed points of the BCM learning rule explicitly involves extensive calculation and other methods for verifying the stability of possible fixed points should be considered.
This scientific work deals with the current opportunities of business development. Purpose of the work is study and analysis of the organization's development strategy and its development. The subject of the study is the mechanism of formation of an organization's development strategy, understanding of business development and its core methodologies and branches. This thesis is based on the operations of the real engineering company and main part of the research could be applied in reality. Main goal of the thesis is to find recommendations on the implementation of strategic changes organization's development strategy.