[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 need to evolve a novel feature selection (FS) approach was motivated by the persistence necessary for a robust FS system, the time-consuming exhaustive search in traditional methods, and the favourable swarming manner in various optimization techniques. Most of the datasets have a high dimension in many issues since all features are not crucial to the problem, which reduces the algorithm's accuracy and efficiency. This article presents a hybrid feature selection approach to solve the low precision and tardy convergence of the butterfly optimization algorithm (BOA). The proposed method is dependent on combining the algorithm of BOA and the particle swarm optimization (PSO) as a search methodology using a wrapper framework. BOA is started with a one-dimensional cubic map in the proposed approach, and a non-linear parameter control technique is also implemented. To boost the basic BOA for global optimization, PSO algorithm is mixed with the butterfly optimization algorithm (BOAPSO). A 25 dataset evaluates the proposed BOAPSO to determine its efficiency with three metrics: classification precision, the selected features, and the computational time. A COVID-19 dataset has been used to evaluate the proposed approach. Compared to the previous approaches, the findings show the supremacy of BOAPSO for enhancing performance precision and minimizing the number of chosen features. Concerning the accuracy, the experimental outcomes demonstrate that the proposed model converges rapidly and performs better than with the PSO, BOA, and GWO with improvement percentages: 91.07%, 87.2%, 87.8%, 87.3%, respectively. Moreover, the proposed model's average selected features are 5.7 compared to the PSO, BOA, and GWO, with average features 22.5, 18.05, and 23.1, respectively.

A hybrid feature selection model based on butterfly optimization algorithm: COVID-19 as a case study.

There is evidence that students feel less involved in distance learning, which needs more work from their educators. While distance education allows learners to feel less reluctant andor timid to ask questions, there is also evidence that students feel less interested in distance education. Students may obtain information and knowledge at their own pace and convenience via the use of distance education. Also, it may assist in reaching the same overall performance and ultimate score as would be achieved via face-to-face instruction. The goal of this study analyzes the performance of video conferences in smart learning. This analysis contains many criteria, so the concept of multi-criteria decision-making is integrated with the neutrosophic sets to overcome uncertain information. We used the neutrosophic AHP to show the importance of each criterion. There are seven criteria used in this paper. The findings demonstrated that the utilisation of video conferences enabled students to gain access to information and educational resources, had a positive effect on the students' constructing knowledge and critical reflection, and resulted in the students reporting an extremely positive learning experience in the context of smart learning.

Mohamed Elhoseny

Papers

A hybrid feature selection model based on butterfly optimization algorithm: COVID-19 as a case study.

A Neutrosophic AHP Analysis for Using Video Conferences in Smart Learning: A Systematic Review