scholarly journals Enhanced Classification of Service Usages with Human Trajectory Data for Location Recommendation Systems

2019 ◽  
Vol 8 (4) ◽  
pp. 11788-11795
Keyword(s):  
Traffic Flows ◽  
Trajectory Data ◽  
Huge Amount ◽  
Class Label ◽  
Service Usage ◽  
The Past ◽  
Proposed Model ◽  
Different Types ◽  
Packet Length

The rapid growth of mobile messaging apps has led to an important process to manage social networks based on the localization of internet traffic in different types of use of in-app services. In the past researches, Improved Multi-Context Trajectory Embedding Model with Service Usage Classification Method (IMC-TEM-SUCM) has been proposed to recommend the locations based on the trajectory data of individuals and their service usage types. In this model, the traffic features were classified by using Random Forest (RF) classifier whereas the outlier was detected by clustering Hidden Markov Model (HMM). However, the RF was supervised classifier which requires knowledge about the class label of data. Also, a huge amount of data was needed to train a clustering HMM. Therefore, in this article, an IMC-TEM with Enhanced SUCM (IMC-TEM-ESUCM) is proposed in which an unsupervised classifier, namely K-means clustering is proposed to classify the service usage types. Initially, traffic flows are split into different sessions and dialogs using a combined hierarchical clustering and thresholding heuristics technique. Then, the traffic features are extracted based on the packet length and time delay. After that, K-means classification is proposed to classify the service usage types and also DBSCAN is proposed to detect the outliers. Finally, the experimental results on two different datasets show that the proposed model achieves higher performance than the existing model in terms of precision, recall, f-measure and accuracy.

Topology of Flow Separation on Three-Dimensional Bodies

1991 ◽  
Vol 44 (7) ◽  
pp. 329-345 ◽  
Author(s):  
Gary T. Chapman ◽  
Leslie A. Yates
Keyword(s):  
Skin Friction ◽  
Flow Separation ◽  
Three Dimensional ◽  
Phenomenological Approach ◽  
Three Dimensional Flow ◽  
The Past ◽  
Different Types ◽  
Primary Separation ◽  
Actual Flow

In recent years there has been extensive research on three-dimensional flow separation. There are two different approaches: the phenomenological approach and a mathematical approach using topology. These two approaches are reviewed briefly and the shortcomings of some of the past works are discussed. A comprehensive approach applicable to incompressible and compressible steady-state flows as well as incompressible unsteady flow is then presented. The approach is similar to earlier topological approaches to separation but is more complete and in some cases adds more emphasis to certain points than in the past. To assist in the classification of various types of flow, nomenclature is introduced to describe the skin-friction portraits on the surface. This method of classification is then demonstrated on several categories of flow to illustrate particular points as well as the diversity of flow separation. The categories include attached, two-dimensional separation and three different types of simple, three-dimensional primary separation, secondary separation, and compound separation. Hypothetical experiments are utilized to illustrate the topological terminology and its role in characterizing these flows. These hypothetical experiments use colored oil injected onto the surface at singular points in the skin-friction portrait. Actual flow-visualization information, if available, is used to corroborate the hypothetical examples.

scholarly journals WATER DISCHARGE MEASURING INSTRUMENTS: AN UP TO DATE OVERVIEW

2021 ◽  
Vol 3 ◽  
pp. 116-123
Author(s):  
Artem Iukhno ◽  
Sergei Buzmakov ◽  
Alisa Zorina
Keyword(s):  
Technological Progress ◽  
Water Discharge ◽  
Channel Width ◽  
Measuring Instruments ◽  
Management Decisions ◽  
The Past ◽  
Different Types ◽  
Analytical Review ◽  
New Instruments

Technological progress could not but affect the sphere of hydrometric measurements. New instruments have been implemented to add to such traditional measuring instruments as mechanical current meters or to replace them. Over the past 20 years, the number of different types measuring instruments has increased dramatically. That is why the analytical review and classification of these devices are needed to help with making appropriate management decisions in the field of streamflow monitoring and surveys. The article presents the multivariable classification of measuring instruments, based on such factors as: morphology scaling (channel width and depth), measuring conditions (open, weed or ice-covered channel), logistical factor (mobile or stationary) and required accuracy. Characteristics of each type of measuring instruments were also considered and the limitations of their applicability were described. The results presented in the paper are expected to expand the horizons of approaches used for estimation of water discharge.

Analytical Model of Passing Events for One-Way Heterogeneous Bicycle Traffic Flows

2018 ◽  
Vol 2672 (36) ◽  
pp. 125-135 ◽  
Author(s):  
Liang Xu ◽  
Meiqi Liu ◽  
Xiang Song ◽  
Sheng Jin
Keyword(s):  
Analytical Model ◽  
Probability Density Functions ◽  
Level Of Service ◽  
Traffic Flows ◽  
Proposed Model ◽  
Different Types ◽  
Bicycle Level Of Service ◽  
Positive Correlations ◽  
Shared Bicycle ◽  
The Relationship

Heterogeneous bicycle traffic flows, consisting of electric bicycles (e-bicycles) and regular bicycles (r-bicycles), have become the main traffic form on shared bicycle routes in China due to the increasing number of e-bicycles. As a result, overtaking occurs frequently among bicycles, which affects cyclists’ safety and perception. This paper presents an analytical model to estimate the number of passing events in heterogeneous bicycle traffic flows. The relationships between passing events and the parameters of the heterogeneous bicycle traffic flow is established in the proposed model. The probability density functions of the speed of r-bicycles and e-bicycles are taken into consideration. The results of the model analysis show that the number of passing events increases with an increase in the flow rate and density. Both a difference in speed between different types of bicycle and the standard deviation of speed of each type of bicycle have positive correlations with the number of passing events. In addition, when the proportion of e-bicycles increases, the number of passing events first increases, and then decreases. The proposed model is calibrated against field data collected in Hangzhou. The results show that the model prediction is consistent with field observations. The model proposed in this paper provides an analytical approach to study the relationship between the characteristics of heterogeneous bicycle traffic flows and the number of passing events. This work can be considered a prerequisite for the development of the bicycle level of service criteria for heterogeneous bicycle flows.

scholarly journals 3D-semantic segmentation and classification of stomach infections using uncertainty aware deep neural networks

2021 ◽  
Author(s):  
Javaria Amin ◽  
Muhammad Sharif ◽  
Eman Gul ◽  
Ramesh Sunder Nayak
Keyword(s):  
Research Work ◽  
Semantic Segmentation ◽  
Gastrointestinal Infections ◽  
Method Performance ◽  
Low Contrast ◽  
Proposed Model ◽  
Different Types ◽  
Automated Technique ◽  
Wireless Capsule

AbstractWireless capsule endoscopy (WCE) might move through human body and captures the small bowel and captures the video and require the analysis of all frames of video due to which the diagnosis of gastrointestinal infections by the physician is a tedious task. This tiresome assignment has fuelled the researcher’s efforts to present an automated technique for gastrointestinal infections detection. The segmentation of stomach infections is a challenging task because the lesion region having low contrast and irregular shape and size. To handle this challenging task, in this research work a new deep semantic segmentation model is suggested for 3D-segmentation of the different types of stomach infections. In the segmentation model, deep labv3 is employed as a backbone of the ResNet-50 model. The model is trained with ground-masks and accurately performs pixel-wise classification in the testing phase. Similarity among the different types of stomach lesions accurate classification is a difficult task, which is addressed in this reported research by extracting deep features from global input images using a pre-trained ResNet-50 model. Furthermore, the latest advances in the estimation of uncertainty and model interpretability in the classification of different types of stomach infections is presented. The classification results estimate uncertainty related to the vital features in input and show how uncertainty and interpretability might be modeled in ResNet-50 for the classification of the different types of stomach infections. The proposed model achieved up to 90% prediction scores to authenticate the method performance.

scholarly journals LogSum + L2 penalized logistic regression model for biomarker selection and cancer classification

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Xiao-Ying Liu ◽  
Sheng-Bing Wu ◽  
Wen-Quan Zeng ◽  
Zhan-Jiang Yuan ◽  
Hong-Bo Xu
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Cancer Classification ◽  
Classification Problems ◽  
Proposed Model ◽  
Different Types ◽  
Penalized Logistic Regression ◽  
Biomarker Selection

AbstractBiomarker selection and cancer classification play an important role in knowledge discovery using genomic data. Successful identification of gene biomarkers and biological pathways can significantly improve the accuracy of diagnosis and help machine learning models have better performance on classification of different types of cancer. In this paper, we proposed a LogSum + L2 penalized logistic regression model, and furthermore used a coordinate decent algorithm to solve it. The results of simulations and real experiments indicate that the proposed method is highly competitive among several state-of-the-art methods. Our proposed model achieves the excellent performance in group feature selection and classification problems.

scholarly journals Institutional Changes in Financial Systems of Poland and the Czech Republic

2013 ◽  
Vol 16 (1) ◽  
pp. 123-142
Author(s):  
Joanna Romaniuk ◽  
Anna Sznajderska
Keyword(s):  
Czech Republic ◽  
Initial Conditions ◽  
Transformation Process ◽  
Primary Objective ◽  
Financial Systems ◽  
The Czech Republic ◽  
The Past ◽  
Development Level ◽  
Different Types

Over the past 23 years the financial sectors in both Poland and the Czech Republic have changed beyond recognition. The process of transformation was a tough and challenging task in both countries. There were significant differences in the initial conditions, as well as approaches to the transformation process, in Poland and the Czech Republic. It seems that according to the classification of Knell and Srholec (2005), the two countries represent different types of capitalism. In this article we try to demonstrate that the organization and development level of the financial systems in these seemingly similar countries are different as well. The primary objective of the study is to compare the path of development and today’s performance of the financial systems in Poland and in the Czech Republic.

Omniscience and ignorance: Variation in Nuaulu knowledge, identification and classification of animals

1979 ◽  
Vol 8 (2-3) ◽  
pp. 337-359 ◽  
Author(s):  
Roy F. Ellen
Keyword(s):  
Social Context ◽  
Social Conditions ◽  
The Past ◽  
Eastern Indonesia ◽  
Single Body ◽  
Different Types ◽  
Knowledge Identification ◽  
Ethnographic Analysis ◽  
Made In

ABSTRACTThe ethnographic analysis of categories is still largely based on assumptions of cultural uniformity, although, during the past decade, the significance of variation has become increasingly evident as attempts have been made to measure it. Delineation and measurement are themselves complex tasks, however. In a single body of data there may be variation according to many criteria which are often cross-cutting and reinforce each other irregularly. These issues are discussed in this paper in relation to different types and contexts of variation evident in animal classifications of the Nuaulu of eastern Indonesia. Yet, the kinds of assumptions made in formal studies of individual variation are as problematic as those concerning cultural uniformity. It is important to appreciate that the techniques and representations employed to describe classifications and their variation are often inadequate, concealing those things that are operationally of most significance and reifying ‘classifications’ which do not always exist in practice. The products of classifying behaviour inevitably reflect the immediate social conditions of the situations in which they are used. (Analysis of categories, cultural variability, ethnozoology, social context; Nuaulu of eastern Indonesia.)

scholarly journals Gravitation Theory Based Model for Multi-Label Classification

2017 ◽  
Vol 12 (5) ◽  
pp. 689
Author(s):  
Liwen Peng ◽  
Yongguo Liu
Keyword(s):  
High Performance ◽  
Text Categorization ◽  
Classification Algorithms ◽  
Universal Gravitation ◽  
The Past ◽  
Proposed Model ◽  
Series Of Experiments ◽  
Music Information ◽  
Better Than

The past decade has witnessed the growing popularity in multi-label classification algorithms in the fields like text categorization, music information retrieval, and the classification of videos and medical proteins. In the meantime, the methods based on the principle of universal gravitation have been extensively used in the classification of machine learning owing to simplicity and high performance. In light of the above, this paper proposes a novel multi-label classification algorithm called the interaction and data gravitation-based model for multi-label classification (ITDGM). The algorithm replaces the interaction between two objects with the attraction between two particles. The author carries out a series of experiments on five multi-label datasets. The experimental results show that the ITDGM performs better than some well-known multi-label classification algorithms. The effect of the proposed model is assessed by the example-based F1-Measure and Label-based micro F1-measure.

scholarly journals I.—Mediaeval Fortresses

1906 ◽  
Vol 12 ◽  
pp. 259-276 ◽  
Author(s):  
Ramsay Traquair
Keyword(s):  
Middle Ages ◽  
Original Structure ◽  
Military Architecture ◽  
The Past ◽  
Different Types ◽  
The Cost ◽  
The Middle Ages

The troubled state of the Peloponnesus during the Middle Ages left its mark on no buildings more evidently than on its castles. Each successive owner obtained his title at the cost of some part of the building, and his first thought on gaining possession was either to strengthen the fortress he had just captured, or to dismantle it utterly and leave behind him a useless pile of ruins. Military architecture too, is little influenced by respect for the past and the more important castles must have been frequently modernised, so but little is left of their original structure. The lack of those ornamental details which are the main clue to the age of more elaborate buildings renders a classification of the different types of plan and of masonry of some importance; where mouldings or other details are found their evidence is usually conclusive, but in their absence we must be guided by the form of the plan and by the masonry.

scholarly journals NGS read classification using AI

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261548
Author(s):  
Benjamin Voigt ◽  
Oliver Fischer ◽  
Christian Krumnow ◽  
Christian Herta ◽  
Piotr Wojciech Dabrowski
Keyword(s):  
Neural Network ◽  
Reference Database ◽  
Metagenomic Sequencing ◽  
Huge Amount ◽  
Coding Sequences ◽  
The Past ◽  
Novel Approach ◽  
Reference Databases ◽  
Powerful Diagnostic Tool

Clinical metagenomics is a powerful diagnostic tool, as it offers an open view into all DNA in a patient’s sample. This allows the detection of pathogens that would slip through the cracks of classical specific assays. However, due to this unspecific nature of metagenomic sequencing, a huge amount of unspecific data is generated during the sequencing itself and the diagnosis only takes place at the data analysis stage where relevant sequences are filtered out. Typically, this is done by comparison to reference databases. While this approach has been optimized over the past years and works well to detect pathogens that are represented in the used databases, a common challenge in analysing a metagenomic patient sample arises when no pathogen sequences are found: How to determine whether truly no evidence of a pathogen is present in the data or whether the pathogen’s genome is simply absent from the database and the sequences in the dataset could thus not be classified? Here, we present a novel approach to this problem of detecting novel pathogens in metagenomic datasets by classifying the (segments of) proteins encoded by the sequences in the datasets. We train a neural network on the sequences of coding sequences, labeled by taxonomic domain, and use this neural network to predict the taxonomic classification of sequences that can not be classified by comparison to a reference database, thus facilitating the detection of potential novel pathogens.

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