[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)

A sustainable yield prediction in agricultural fields is a very critical task that aims to help farmers have an idea about agricultural operations. Generally, we can find a variety of applications proposed for this purpose that include fruit counting. These applications are based on Artificial Intelligence, especially Deep Learning (DL) and Machine Learning (ML) approaches. These approaches give reliable counting accuracy, but the problem is the use of a large database to achieve the desired accuracy. That makes these approaches limited. For this reason, in this work, we propose a low-complexity algorithm that aims to count green and red apples based on our real dataset collected in the Moroccan region, Fes-Meknes. This algorithm allowed us to further increase sustainability in agricultural fields based on yield prediction. The proposed approach was based on HSV conversion and the Hough transform for fruit counting. The algorithm was divided into three blocks based on image acquisition and filtering for the first block. The second block is the conversion to HSV and the detection of fruits. Finally, the counting operation for the third block. Subsequently, we proposed an implementation based on the low-cost Raspberry system and a desktop. The results show that we can reach 15 fps in the case of the Raspberry architecture and 40 fps based on the desktop. Our proposed system can inform agricultural policy by providing accurate and timely information on crop production, which can be used to guide decisions on food supply and distribution.

https://www.mdpi.com/2071-1050/15/3/2707/pdf?version=1675330669

Sustainable Yield Prediction in Agricultural Areas Based on Fruit Counting Approach

Detecting distributed DoS attacks in autonomous vehicles external environment using machine learning techniques

/pdf/retraction-note-energy-consumption-analysis-of-virtual-m0yv9bdj.pdf

Retraction Note: Energy consumption analysis of Virtual Machine migration in cloud using hybrid swarm optimization (ABC–BA)

Due to the advances in the computerized world, security has turned into an indivisible issue while transmitting the image. Secret Image Sharing (SIS) scheme is an updated cryptographic strategy which can be utilized to transmit a unique image from the sender to the receiver with incomparable privacy and secrecy in Wireless Sensor Network (WSN). Most traditional security techniques for popular systems, similar to the Internet, are not appropriate for WSN, requesting appropriate examination around there. It manages the procedure utilized to remodel the data among justifiable and deep structures through encryption and decryption strategies under the intensity of the keys. In this part, a high-security model for DI is proposed using a secret image share creation scheme with a novel Light Weight Symmetric Algorithm (LWSA). This cryptographic strategy partitioned the secret image into shares for the security analysis by optimal key selection in LWSA technique; here Adaptive Particle Swarm Optimization (APSO) is used. Moreover, this methodology is applying full encryption to image transmission over WSNs by offloading the computational outstanding task at hand of sensors to a server. The proposed LWSA plot offered better security for shares and furthermore reduced the deceitful shares of the secret image. The trial results and the investigations inferred that the proposed model can viably encrypt the image with quick execution speed and expanded PSNR value.

Optimal Lightweight Encryption Based Secret Share Creation Scheme for Digital Images in Wireless Sensor Networks

Vehicular adhoc network (VANET) plays a vital role in smart transportation. VANET includes a set of vehicles that communicate with one another via wireless links. The vehicular communication in VANET necessitates an intelligent clustering protocol to maximize energy efficiency. Since energy acts as an essential factor in the design of VANET, energy-aware clustering protocols depending upon metaheuristic optimization algorithms are required to be developed. This study introduces an intelligent energy-aware oppositional chaos game optimization-based clustering (IEAOCGO-C) protocol for VANET. The presented IEAOCGO-C technique aims to select cluster heads (CHs) in the network proficiently. The proposed IEAOCGO-C model constructs clusters based on oppositional-based learning (OBL) with the chaos game optimization (CGO) algorithm to improve efficiency. Besides, it computes a fitness function involving five parameters, namely throughput (THRPT), packet delivery ratio (PDR), network lifetime (NLT), end to end delay (ETED) and energy consumption (ECM). The experimental validation of the proposed model is accomplished, and the outcomes are studied in numerous aspects with existing models under several vehicles and measures. The simulation outcomes reported the enhanced performance of the proposed approach over the recent technologies. As a result, it has resulted in maximal NLT (4480), minimal ECM (65.6), maximal THRPT (81.6), maximal PDR (84.5), and minimal ETED (6.7) as average values over the other methods under all vehicle numbers. 

/pdf/intelligent-energy-aware-optimization-protocol-for-vehicular-ql9ymgvy.pdf

Mohamed Elhoseny

Papers

Sustainable Yield Prediction in Agricultural Areas Based on Fruit Counting Approach

Detecting distributed DoS attacks in autonomous vehicles external environment using machine learning techniques

Retraction Note: Energy consumption analysis of Virtual Machine migration in cloud using hybrid swarm optimization (ABC–BA)

Optimal Lightweight Encryption Based Secret Share Creation Scheme for Digital Images in Wireless Sensor Networks

Intelligent energy aware optimization protocol for vehicular adhoc networks