Borja Bordel^a, Ramon Alcarria^b, Diego Martín^a, Álvaro Sánchez-Picot^a

Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 155-170, 2019, DOI:10.31209/2018.100000052

Abstract The Internet-of-Things (IoT) paradigm faces new and genuine challenges and problems associated, mainly, with the ubiquitous access to the Internet, the huge number of devices involved and the heterogeneity of the components making up this new global network. In this context, protecting these systems against cyberattacks and cybercrimes has turn into a basic issue. In relation to this topic, most proposed solutions in the literature are focused on security; however other aspects have to be considered (such as privacy or trust). Therefore, in this paper we define a theoretical framework for trust in IoT scenarios, More >

Jianfeng Guan^*, Jiawei Li, Zhongbai Jiang

Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 131-141, 2019, DOI:10.31209/2018.100000050

Abstract The traditional web anomaly detection systems face the challenges derived from the constantly evolving of the web malicious attacks, which therefore result in high false positive rate, poor adaptability, easy over-fitting, and high time complexity. Due to these limitations, we need a new anomaly detection system to satisfy the requirements of enterprise-level anomaly detection. There are lots of anomaly detection systems designed for different application domains. However, as for web anomaly detection, it has to describe the network accessing behaviours characters from as many dimensions as possible to improve the performance. In this paper we… More >

Ilsun You, Chang Choi, Vishal Sharma, Isaac Woungang, Bharat K. Bhargava

Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 117-119, 2019, DOI:10.31209/2018.100000048

Abstract This article has no abstract. More >

Belayat Hossain^a, Takatoshi Morooka^b, Makiko Okuno^b, Manabu Nii^a, Shinichi Yoshiya^b, Syoji Kobashi^a

Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 105-115, 2019, DOI:10.31209/2018.100000034

Abstract This work aimed to predict postoperative knee functions of a new patient prior to total knee arthroplasty (TKA) surgery using machine learning, because such prediction is essential for surgical planning and for patients to better understand the TKA outcome. However, the main difficulty is to determine the relationships among individual varieties of preoperative and postoperative knee kinematics. The problem was solved by constructing predictive models from the knee kinematics data of 35 osteoarthritis patients, operated by posterior stabilized implant, based on generalized linear regression (GLR) analysis. Two prediction methods (without and with principal component analysis… More >

Yu-Cheng Chou¹, Wei-Chieh Liao², Yan-Liang Chen², Ming Chang², Po Ting Lin³

Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 79-90, 2019, DOI:10.31209/2018.100000011

Abstract This paper presents the Distributed Heterogeneous Inspection System (DHIS), which comprises two CUDA workstations and is equipped with CPU distributed computing, CPU concurrent computing, and GPU concurrent computing functions. Thirty-two grayscale images, each with 5,000× 12,288 pixels and simulated defect patterns, were created to evaluate the performances of three system configurations: (1) DHIS; (2) two CUDA workstations with CPU distributed computing and GPU concurrent computing; (3) one CUDA workstation with GPU concurrent computing. Experimental results indicated that: (1) only DHIS can satisfy the time limit, and the average turnaround time of DHIS is 37.65% of More >

Juan Li

Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 35-48, 2019, DOI:10.31209/2018.100000003

Abstract K-Nearest Neighbor algorithm (KNN) is a simple and mature classification method. However there are susceptible factors influencing the classification performance, such as k value determination, the overlarge search space, unbalanced and multi-class patterns, etc. To deal with the above problems, a new classification algorithm that absorbs tree structure, tree pruning and adaptive k value method was proposed. The proposed algorithm can overcome the shortcoming of KNN, improve the performance of multi-class and unbalanced classification, reduce the scale of dataset maintaining the comparable classification accuracy. The simulations are conducted and the proposed algorithm is compared with several existing More >

Amy E. Delaney¹, Nina M. Dadlez², Audrey C. Marshall²

Congenital Heart Disease, Vol.14, No.4, pp. 665-670, 2019, DOI:10.1111/chd.12821

Abstract Background: In pediatric cardiac care, many centers participate in multiple, national, domain‐specific registries, as a major component of their quality assessment and im‐ provement efforts. Small cardiac programs, whose clinical activities and scale may not be well‐suited to this approach, need alternative methods to assess and track quality.
Methods: We conceived of and piloted a rapid‐approach cardiac quality assessment, intended to encompass multiple aspects of the service line, in a low‐volume program. The assessment incorporated previously identified measures, drawn from multiple sources, and ultimately relied on retrospective chart review.
Results: A collaborative, multidisciplinary team formed and came to… More >

Jennifer Gerardin¹, Cheryl Raskind‐Hood², Fred H. Rodriguez^3,4, Trenton Hoffman², Andreas Kalogeropoulos⁵, Carol Hogue², Wendy Book³

Congenital Heart Disease, Vol.14, No.4, pp. 541-548, 2019, DOI:10.1111/chd.12780

Abstract Objective: Transfer of congenital heart disease care from the pediatric to adult set‐ ting has been identified as a priority and is associated with better outcomes. Our objective is to determine what percentage of patients with congenital heart disease transferred to adult congenital cardiac care.
Design: A retrospective cohort study.
Setting: Referrals to a tertiary referral center for adult congenital heart disease pa‐ tients from its pediatric referral base.
Patients: This resulted in 1514 patients age 16‐30, seen at least once in three pediat‐ ric Georgia health care systems during 2008‐2010.
Interventions: We analyzed for protective factors associated with age‐appropriate… More >

Lisa X. Deng¹, Lacey P. Gleason², Katherine Awh², Abigail May Khan³, David Drajpuch^2,4, Stephanie Fuller⁵, Leah A. Goldberg², Christopher E. Mascio⁵, Sara L. Partington^2,4, Lynda Tobin^2,4, Adrienne H. Kovacs³, Yuli Y. Kim^2,4

Congenital Heart Disease, Vol.14, No.4, pp. 534-540, 2019, DOI:10.1111/chd.12778

Abstract Objective: To investigate the experiences and communication preferences of adult patients with congenital heart disease (CHD) in the domains of employment, insur‐ ance, and family planning.
Design: Patients ≥ 18 years of age completed a questionnaire about experiences and communication preferences regarding employment, health insurance, and family planning.
Results: Of 152 patients (median age = 33 years, 50% female, 35% with CHD of great complexity), one in four reported work‐related problems due to CHD and a quar‐ ter also recalled a previous gap in health insurance. Of females, 29% experienced an unplanned pregnancy. The median importance of discussion… More >

Fatima M. Ezzeddine¹, Tabitha Moe², Georges Ephrem³, William Aaron Kay³

Congenital Heart Disease, Vol.14, No.4, pp. 511-516, 2019, DOI:10.1111/chd.12771

Abstract Background: Delivery of care to the adult congenital heart disease (ACHD) popula‐ tion has been limited by a shortage in the ACHD physician resources. There is limited data regarding the adequacy of the ACHD physician resources in the United States and our population estimates are extrapolated from Canadian data. Therefore, we proposed to evaluate the adequacy of ACHD physician: patient ratios in the United States at both national and regional levels.
Methods: Data from the Adult Congenital Heart Association (ACHA) website along with metropolitan area and statewide population data from 2016 US Census Bureau estimates were analyzed.… More >