[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Data Mining Practical Machine Learning Tools and Techniques

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

비만아 부모의 돌봄 경험: q 방법론

Executive Summary Introduction Methodology Used to Prepare the Guideline Epidemiology Incidence Etiology Major Epidemiologic Points Pathogenesis Major Points for Pathogenesis Modifiable Risk Factors Intubation and Mechanical Ventilation Aspiration, Body Position, and Enteral Feeding Modulation of Colonization: Oral Antiseptics and Antibiotics Stress Bleeding Prophylaxis, Transfusion, and Glucose Control Major Points and Recommendations for Modifiable Risk Factors Diagnostic Testing Major Points and Recommendations for Diagnosis Diagnostic Strategies and Approaches Clinical Strategy Bacteriologic Strategy Recommended Diagnostic Strategy Major Points and Recommendations for Comparing Diagnostic Strategies Antibiotic Treatment of Hospital-acquired Pneumonia General Approach Initial Empiric Antibiotic Therapy Appropriate Antibiotic Selection and Adequate Dosing Local Instillation and Aerosolized Antibiotics Combination versus Monotherapy Duration of Therapy Major Points and Recommendations for Optimal Antibiotic Therapy Specific Antibiotic Regimens Antibiotic Heterogeneity and Antibiotic Cycling Response to Therapy Modification of Empiric Antibiotic Regimens Defining the Normal Pattern of Resolution Reasons for Deterioration or Nonresolution Evaluation of the Nonresponding Patient Major Points and Recommendations for Assessing Response to Therapy Suggested Performance Indicators

Guidelines for the management of adults with hospital-acquired, ventilator-associated, and healthcare-associated pneumonia the official statement of the American Thoracic Society and the Infectious Disease Society of America (特集 救急診療ガイドライン) -- (海外のガイドライン)

The Internet of Things (IoT) has penetrating all things and objects around us giving them the ability to interact with the Internet, i.e., things become Smart Things (SThs). As a result, SThs produce massive real-time data (i.e., big IoT data). Smartness of IoT applications bases mainly on services such as automatic control, events handling, and decision making. Consumers of the IoT services are not only human users, but also SThs. Consequently, the potential of IoT applications relies on supporting services such as searching, retrieving, mining, analyzing, and sharing real-time data. For enhancing search service in the IoT, our previous work presents a promising solution, called Cluster Representative (ClRe), for indexing similar SThs in IoT applications. ClRe algorithms could reduce similar indexing by O(K - 1), where K is number of Time Series (TS) in a cluster. Multiple extensions for ClRe algorithms were presented in another work for enhancing accuracy of indexed data. In this theme, this paper studies performance analysis of ClRe algorithms, proposes two novel execution methods: (a) Linear execution (LE) and (b) Pair-merge execution (PME), and studies sorting impact on TS execution for enhancing similarity rate for some ClRe extensions. The proposed execution methods are evaluated with real examples and proved using Szeged-weather dataset on ClRe 3.0 and its extensions; where they produce representatives with higher similarities compared to the other extensions. Evaluation results indicate that PME could improve performance of ClRe 3.0 by = 20.5%, ClRe 3.1 by = 17.7%, and ClRe 3.2 by = 6.4% in average.

Performance analysis for similarity data fusion model for enabling time series indexing in internet of things applications.

Despite the great efforts to find an effective way for COVID-19 prediction, the virus nature and mutation represent a critical challenge to diagnose the covered cases. However, developing a model to predict COVID-19 via Chest X-Ray (CXR) images with accurate performance is necessary to help in early diagnosis. In this paper, a hybrid quantum-classical convolutional Neural Networks (HQCNN) model used the random quantum circuits (RQCs) as a base to detect COVID-19 patients with CXR images. A collection of 6952 CXR images, including 1161 COVID-19, 1575 normal, and 5216 pneumonia images, were used as a dataset in this work. The proposed HQCNN model achieved higher performance with an accuracy of 98.4\% and a sensitivity of 99.3\% on the first dataset cases. Besides, it obtained an accuracy of 99\% and a sensitivity of 99.7\% on the second dataset cases. Also, it achieved accuracy, and sensitivity of 88.6\%, and 88.7\%, respectively, on the third multi-class dataset cases. Furthermore, the HQCNN model outperforms various models in balanced accuracy, precision, F1-measure, and AUC-ROC score. The experimental results are achieved by the proposed model prove its ability in predicting positive COVID-19 cases.

Hybrid quantum convolutional neural networks model for COVID-19 prediction using chest X-Ray images

Most existing realizations of quantum key distribution (QKD) are point-to-point systems with one source transferring to only one destination. Growth of these single-receiver systems has now achieved a reasonably sophisticated point. However, many communication systems operate in a point-to-multi-point (Multicast) configuration rather than in point-to-point mode, so it is crucial to demonstrate compatibility with this type of network in order to maximize the application range for QKD.

Quantum Key Distribution Over Multi-point Communication System: An Overview

Automated toxicity test model based on a bio-inspired technique and AdaBoost classifier

The Internet of Things (IoT) and sensor technologies are combined with various communication networks in smart appliances and perform a significant role. Connected devices sense, analyze, and send environmental data, as well as support applications’ connections. Mobile agents can be explored to provide sensing intelligence with IoT-based systems. Many strategies have been proposed to address the issue of energy efficiency while maintaining the sensor load at a low cost. However, advancements are still desired. Furthermore, without fully trustworthy relationships, sensitive data are at risk, and the solution must provide privacy protection against unexpected events. With the development of two algorithms, this study proposes a mobile agent-based efficient energy resource management solution and also protects IoT appliances. Firstly, the software agents perform a decision using past and present precepts, and by exploring rule-based conditions, it offers an energy-efficient recommended system. Second, data from IoT appliances are securely evaluated on edge interfaces before being transferred to end-centers for verification. Simulations-based tests are conducted and verified the significance of the proposed protocol against other studies in terms of network metrics.

/pdf/energy-efficient-mobile-agent-protocol-for-secure-iot-3h04httn.pdf

Mohamed Elhoseny

Papers

Performance analysis for similarity data fusion model for enabling time series indexing in internet of things applications.

Hybrid quantum convolutional neural networks model for COVID-19 prediction using chest X-Ray images

Quantum Key Distribution Over Multi-point Communication System: An Overview

Automated toxicity test model based on a bio-inspired technique and AdaBoost classifier

Energy-Efficient Mobile Agent Protocol for Secure IoT Sustainable Applications