[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)

Building a secure routing protocol in WSN is not trivial process. It looks like an optimization process through which we try to find the optimum solution that maximize the network performance in an environment with a set of complicated constraints. The main purpose is not only to design new routing protocol that guarantee the network efficiency, but also balancing between this efficiency and the security requirements. For that purpose, we designed and followed a general framework to simplify the process of building such that protocol. In this chapter, an overview of the working steps towards building the proposed protocol is described. Then the protocol objectives and methodology are discussed.

An Encryption Model for Data Processing in WSN

Predicting bankruptcies and assessing credit risk are two of the most pressing issues in finance. Therefore, financial distress prediction and credit scoring remain hot research topics in the finance sector. Earlier studies have focused on the design of statistical approaches and machine learning models to predict a company's financial distress. In this study, an adaptive whale optimization algorithm with deep learning (AWOA-DL) technique is used to create a new financial distress prediction model. The goal of the AWOA-DL approach is to determine whether a company is experiencing financial distress or not. A deep neural network (DNN) model called multilayer perceptron based predictive and AWOA-based hyperparameter tuning processes are used in the AWOA-DL method. Primarily, the DNN model receives the financial data as input and predicts financial distress. In addition, the AWOA is applied to tune the DNN model's hyperparameters, thereby raising the predictive outcome. The proposed model is applied in three stages: preprocessing, hyperparameter tuning using AWOA, and the prediction phase. A comprehensive simulation took place on four datasets, and the results pointed out the supremacy of the AWOA-DL method over other compared techniques by achieving an average accuracy of 95.8%, where the average accuracy equals 93.8%, 89.6%, 84.5%, and 78.2% for compared models. 

/pdf/deep-learning-based-model-for-financial-distress-prediction-1z52pbfu.pdf

Deep Learning-Based Model for Financial Distress Prediction

In the current pandemic, smart technologies such as cognitive computing, artificial intelligence, pattern recognition, chatbot, wearables, and blockchain can sufficiently support the collection, analysis, and processing of medical data for decision making. Particularly, to aid medical professionals in the disease diagnosis process, cognitive computing is helpful by processing massive quantities of data rapidly and generating customized smart recommendations. On the other hand, the present world is facing a pandemic of COVID-19 and an earlier detection process is essential to reduce the mortality rate. Deep learning (DL) models are useful in assisting radiologists to investigate the large quantity of chest X-ray images. However, they require a large amount of training data and it needs to be centralized for processing. Therefore, federated learning (FL) concept can be used to generate a shared model with no use of local data for DL-based COVID-19 detection. In this view, this paper presents a federated deep learning-based COVID-19 (FDL-COVID) detection model on an IoT-enabled edge computing environment. Primarily, the IoT devices capture the patient data, and then the DL model is designed using the SqueezeNet model. The IoT devices upload the encrypted variables into the cloud server which then performs FL on major variables using the SqueezeNet model to produce a global cloud model. Moreover, the glowworm swarm optimization algorithm is utilized to optimally tune the hyperparameters involved in the SqueezeNet architecture. A wide range of experiments were conducted on benchmark CXR dataset, and the outcomes are assessed with respect to different measures . The experimental outcomes pointed out the enhanced performance of the FDL-COVID technique over the other methods.

/pdf/cognitive-computing-based-covid-19-detection-on-internet-of-3gvejqsp0z.pdf

Cognitive computing-based COVID-19 detection on Internet of things-enabled edge computing environment.

This paper provides a quick review about business intelligence approaches and techniques in risk management. The important research articles from 2007 to 2021 are involved in this review. We found a little contribution from researchers in this research direction, however the vital role of business intelligence in risk management. Moreover, we provide a novel business approach for risk management. This approach includes deploying the trendiest techniques in this era which are social media and big data analysis. Social media represents the source of identifying risks through the discussions of social media users as well as big data analysis techniques which represent the control tool for potential risks. The new approach will help firms and organizations in many sectors to manage risks efficiently and make the best decisions. Further, we provide the challenges of the new framework and the further research points.

Business Intelligence for Risk Management: A Review

Although the extensive work towards building Optical Character Recognition systems(OCR) for Arabic handwritten characters, the unlimited variation and different writing styles of each character make building such these systems a big research challenge. In Arabic alphabetic system, each character has different forms (three or four) depending on its position in a word. In this paper, a handwritten character recognition system was proposed. The proposed system is implemented using a set of well-known optimizers, Bat Algorithm (BAT), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Grey Wolf Optimization (GWO) algorithm. The proposed system was tested by well-known classifiers to test the efficiency; linear discriminant analysis, support vector machines and random forest. Among all of them, GWO greatly improves the classification accuracy and time efficiency. Compared to the state-of-the-art methods, the optimized feature sets were efficient than the whole feature set in terms of accuracy as well as time consumption.

Mohamed Elhoseny

Papers

An Encryption Model for Data Processing in WSN

Deep Learning-Based Model for Financial Distress Prediction

Cognitive computing-based COVID-19 detection on Internet of things-enabled edge computing environment.

Business Intelligence for Risk Management: A Review

Arabic handwritten characters recognition system, towards improving its accuracy