Stream Classification Algorithm Based on Decision Tree

Due to the rise of many fields such as e-commerce platforms, a large number of stream data has emerged. The incomplete labeling problem and concept drift problem of these data pose a huge challenge to the existing stream data classification methods. In this respect, a dynamic stream data classification algorithm is proposed for the stream data. For the incomplete labeling problem, this method introduces randomization and iterative strategy based on the very fast decision tree VFDT algorithm to design an iterative integration algorithm, and the algorithm uses the previous model classification result as the next model input and implements the voting mechanism for new data classification. At the same time, the window mechanism is used to store data and calculate the data distribution characteristics in the window, then, combined with the calculated result and the predicted amount of data to adjust the size of the sliding window. Experiments show the superiority of the algorithm in classification accuracy. The aim of the study is to compare different algorithms to evaluate whether classification model adapts to the current data environment.

3D Model Classification Based on Bayesian Classifier with AdaBoost

With the exponential increasement of 3D models, 3D model classification is crucial to the effective management and retrieval of model database. Feature descriptor has important influence on 3D model classification. Voxel descriptor expresses surface and internal information of 3D model. However, it does not contain topological structure information. Shape distribution descriptor expresses geometry relationship of random points on model surface and has rotation invariance. They can all be used to classify 3D models, but accuracy is low due to insufficient description of 3D model. This paper proposes a 3D model classification algorithm that fuses voxel descriptor and shape distribution descriptor. 3D convolutional neural network (CNN) is used to extract voxel features, and 1D CNN is adopted to extract shape distribution features. AdaBoost algorithm is applied to combine several Bayesian classifiers to get a strong classifier for classifying 3D models. Experiments are conducted on ModelNet10, and results show that accuracy of the proposed method is improved.

Nasopharyngeal microbiota in hospitalized children with Bordetella pertussis and Rhinovirus infection

Despite great advances in describing Bordetella pertussis infection, the role of the host microbiota in pertussis pathogenesis remains unexplored. Indeed, the microbiota plays important role in defending against bacterial and viral respiratory infections. We investigated the nasopharyngeal microbiota in infants infected by B. pertussis (Bp), Rhinovirus (Rv) and simultaneously by both infectious agents (Bp + Rv). We demonstrated a specific nasopharyngeal microbiome profiles for Bp group, compared to Rv and Bp + Rv groups, and a reduction of microbial richness during coinfection compared to the single infections. The comparison amongst the three groups showed the increase of Alcaligenaceae and Achromobacter in Bp and Moraxellaceae and Moraxella in Rv group. Furthermore, correlation analysis between patients’ features and nasopharyngeal microbiota profile highlighted a link between delivery and feeding modality, antibiotic administration and B. pertussis infection. A model classification demonstrated a microbiota fingerprinting specific of Bp and Rv infections. In conclusion, external factors since the first moments of life contribute to the alteration of nasopharyngeal microbiota, indeed increasing the susceptibility of the host to the pathogens' infections. When the infection is triggered, the presence of infectious agents modifies the microbiota favoring the overgrowth of commensal bacteria that turn in pathobionts, hence contributing to the disease severity.

Design of Data Mining Techniques for Online Blood Bank Management by CNN Model

A blood bank is the organisation responsible for storing blood to transfuse it to the patients in need. The primary goal of a blood bank is to be reliable and ensure that patients get the relevant non-toxic blood to avoid transfusion-related complications since blood is a critical medicinal resource. It is difficult for the blood banks to offer high levels of precision, dependability, and automation in the blood storage and transfusion process if blood bank administration includes many human processes. This research framework is proposing to maintain blood bank records using CNN model classification method. In the pre-processing of CNN method, the datasets are tokenized and set the donor’s eligibility. It will make it easier for regular blood donors to donate regularly to charitable people and organizations. A few machine learning techniques offer the automated website updation. Jupyter note book has been used to analyze the dataset of blood donors using decision trees, neural networks, and von Bays techniques. The proposed method operates online through a website. Moreover, the donor's eligibility status with gender, body mass index, blood pressure level, and frequency of blood donations is also maintained. Finally, the comparison of different machine learning algorithms with the suggested framework is tabulated.

A Two-Stage Deep Learning Architecture for Radiographic Assessment of Periodontal Bone Loss

Objective: To establish a comprehensive and accurate assessment model of periodontal alveolar bone loss based on panoramic images.Methods:A total of 640 panoramic images were included, and 3 experienced periodontal physicians marked the key points needed to calculate the degree of periodontal alveolar bone loss and the specific location and shape of the alveolar bone loss. A deep learning architecture based on UNet and YOLO-v4 was proposed to localize the tooth and key points, and the percentage and stageof periodontal alveolar bone loss were accurately calculated. The ability of the model to recognize these features was evaluated and compared with that of general dental practitioners.Results: The overall classification accuracy of the model was 0.77, and the performance of the model varied for different tooth positions and categories;model classification was generally more accurate than that of general practitioners.Conclusion: It is feasible to establish deep learning model forassessmentand staging radiographicperiodontal alveolar bone loss using two-stage architecture based on UNet and YOLO-v4.

Towards a Validation Methodology for Macroeconomic Agent-Based Models

Agent-based models provide a promising new tool in macroeconomic research. Questions have been raised, however, regarding the validity of such models. A methodology of macroeconomic agent-based model (MABM) validation, that provides a deeper understanding of validation practices, is required. This paper takes steps towards such a methodology by connecting three elements. First, is a foundation of model validation in general. Second is a classification of models dependent on how the model is validated. An important distinction in this classification is the difference between mechanism and target validation. Third, is a framework that revolves around the relationship between the structure of models of complex systems with emergent properties and validation in practice. Important in this framework is to consider MABMs as modelling multiple non-trivial levels. Connecting these three elements provides us with a methodology of the validation of MABMs and allows us to come to the following conclusions regarding MABM validation. First, in MABMs, mechanisms at a lower level are distinct from, but provide input to higher levels of mechanisms. Since mechanisms at different levels are validated in different ways we can come to a specific characterization of MABMs within the model classification framework. Second, because the mechanisms of MABMs are validated in a direct way at the level of the agent, MABMs can be seen as a move towards a more realist approach to modelling compared to DSGE.

Structural-attributive models and algorithms for the functioning of shop production and logistics complexes. Transportation subsystem

A formalized generalization of functional structures and attributive models of executive means of production logistics systems is considered, in particular, the transportation subsystem — one of the key components of the "Shop logistic system" entity. Keywords: production logistics, shop logistic system, transportation subsystem, attributive model, classification structure. [email protected]

Controls of outbursts of moraine-dammed lakes in the greater Himalayan region

Glacial lakes in the Hindu Kush–Karakoram–Himalayas–Nyainqentanglha (HKKHN) region have grown rapidly in number and area in past decades, and some dozens have drained in catastrophic glacial lake outburst floods (GLOFs). Estimating regional susceptibility of glacial lakes has largely relied on qualitative assessments by experts, thus motivating a more systematic and quantitative appraisal. Before the backdrop of current climate-change projections and the potential of elevation-dependent warming, an objective and regionally consistent assessment is urgently needed. We use an inventory of 3390 moraine-dammed lakes and their documented outburst history in the past four decades to test whether elevation, lake area and its rate of change, glacier-mass balance, and monsoonality are useful inputs to a probabilistic classification model. We implement these candidate predictors in four Bayesian multi-level logistic regression models to estimate the posterior susceptibility to GLOFs. We find that mostly larger lakes have been more prone to GLOFs in the past four decades regardless of the elevation band in which they occurred. We also find that including the regional average glacier-mass balance improves the model classification. In contrast, changes in lake area and monsoonality play ambiguous roles. Our study provides first quantitative evidence that GLOF susceptibility in the HKKHN scales with lake area, though less so with its dynamics. Our probabilistic prognoses offer improvement compared to a random classification based on average GLOF frequency. Yet they also reveal some major uncertainties that have remained largely unquantified previously and that challenge the applicability of single models. Ensembles of multiple models could be a viable alternative for more accurately classifying the susceptibility of moraine-dammed lakes to GLOFs.