Mariem Belhor^1,2,3, Adnen El-Amraoui^1,*, Abderrazak Jemai², François Delmotte¹

Computer Systems Science and Engineering, Vol.48, No.1, pp. 271-271, 2024, DOI:10.32604/csse.2023.048573 - 26 January 2024

Abstract This article has no abstract. More >

Mustufa Haider Abidi^*, Hisham Alkhalefah, Mohamed K. Aboudaif

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 977-997, 2024, DOI:10.32604/cmes.2023.044169 - 30 December 2023

Abstract The healthcare data requires accurate disease detection analysis, real-time monitoring, and advancements to ensure proper treatment for patients. Consequently, Machine Learning methods are widely utilized in Smart Healthcare Systems (SHS) to extract valuable features from heterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities. These methods are employed across different domains that are susceptible to adversarial attacks, necessitating careful consideration. Hence, this paper proposes a crossover-based Multilayer Perceptron (CMLP) model. The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on the medical… More >

Xin Liu¹, Jie Li^1,*, Jianwei Zhao², Bin Cao^2,*, Rongge Yan³, Zhihan Lyu⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 143-185, 2024, DOI:10.32604/cmes.2023.030391 - 30 December 2023

Abstract Most of the neural network architectures are based on human experience, which requires a long and tedious trial-and-error process. Neural architecture search (NAS) attempts to detect effective architectures without human intervention. Evolutionary algorithms (EAs) for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures. Using multiobjective EAs for NAS, optimal neural architectures that meet various performance criteria can be explored and discovered efficiently. Furthermore, hardware-accelerated NAS methods can improve the efficiency of the NAS. While existing reviews have mainly focused on different strategies to complete NAS, a… More > Graphic Abstract

Laila M. Halman, Mohammed J. F. Alenazi^*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1469-1483, 2024, DOI:10.32604/cmes.2023.028077 - 17 November 2023

Abstract The healthcare sector holds valuable and sensitive data. The amount of this data and the need to handle, exchange, and protect it, has been increasing at a fast pace. Due to their nature, software-defined networks (SDNs) are widely used in healthcare systems, as they ensure effective resource utilization, safety, great network management, and monitoring. In this sector, due to the value of the data, SDNs face a major challenge posed by a wide range of attacks, such as distributed denial of service (DDoS) and probe attacks. These attacks reduce network performance, causing the degradation of… More > Graphic Abstract

Kristi K. Westphaln^1,2,*, Karen Kay Imagawa^2,3, Lorena Espinosa Smith^1,4, Julia Srivastava⁵, Nancy A. Pike^1,5

Congenital Heart Disease, Vol.18, No.6, pp. 657-670, 2023, DOI:10.32604/chd.2023.044179 - 19 January 2024

Abstract Background: Congenital heart disease (CHD) is a chronic medical condition often diagnosed at birth and requires surgical intervention, multiple hospitalizations, and lifelong care. This can put significant stress on the family, leading to altered maternal mental health, bonding and attachment issues, and the potential for child abuse. The purpose of this study is to explore the characteristics of a sample of young children with CHD who experienced hospitalization with concurrent concern for child abuse in a free-standing pediatric tertiary care hospital. Methods: Electronic medical records were reviewed for children aged 0–5 years old who were… More >

Gustavo Foronda^1,2, Vanessa Ferreira Amorim de Melo^2,3,*, Claudia Regina Pinheiro de Castro Grau⁴, Ingrid Magatti Piva¹, Glaucia Maria Penha Tavares⁴, Ana Cristina Sayuri Tanaka¹, Nana Miura¹

Congenital Heart Disease, Vol.18, No.6, pp. 649-656, 2023, DOI:10.32604/chd.2023.027987 - 19 January 2024

Abstract Background: Children with congenital heart disease (CHD), even after surgical approaches, and especially those who undergo staged procedures in the first months of life, remain vulnerable to readmissions and complications, requiring very close monitoring and differentiated intervention strategies. Methods: Descriptive and exploratory study, of the experience report type, which presents the process of building the high-risk outpatient clinic for complex congenital heart diseases (AAR) at the Instituto do Coração (InCor). Results: Report of the path taken to structure the AAR, demonstrating the organization, interface with the multidisciplinary team, admission and discharge criteria, training, and patient profile.… More >

Mohamed Ammar^1,*, Hamed Al-Raweshidy^2,*

Journal on Artificial Intelligence, Vol.5, pp. 195-218, 2023, DOI:10.32604/jai.2023.045199 - 29 December 2023

Abstract Despite advances in intelligent medical care, difficulties remain. Due to its complicated governance, designing, planning, improving, and managing the cardiac system remains difficult. Oversight, including intelligent monitoring, feedback systems, and management practises, is unsuccessful. Current platforms cannot deliver lifelong personal health management services. Insufficient accuracy in patient crisis warning programmes. No frequent, direct interaction between healthcare workers and patients is visible. Physical medical systems and intelligent information systems are not integrated. This study introduces the Advanced Cardiac Twin (ACT) model integrated with Artificial Neural Network (ANN) to handle real-time monitoring, decision-making, and crisis prediction. THINGSPEAK… More >

Ahmad F Subahi^*, Areej Athama

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3801-3816, 2023, DOI:10.32604/cmc.2023.034522 - 26 December 2023

Abstract With the rapid growth in the availability of digital health-related data, there is a great demand for the utilization of intelligent information systems within the healthcare sector. These systems can manage and manipulate this massive amount of health-related data and encourage different decision-making tasks. They can also provide various sustainable health services such as medical error reduction, diagnosis acceleration, and clinical services quality improvement. The intensive care unit (ICU) is one of the most important hospital units. However, there are limited rooms and resources in most hospitals. During times of seasonal diseases and pandemics, ICUs… More >

Seungwoo Kang¹, Seyha Ros¹, Inseok Song¹, Prohim Tam¹, Sa Math², Seokhoon Kim^1,3,*

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1967-1983, 2023, DOI:10.32604/cmc.2023.045020 - 29 November 2023

Abstract Intelligent healthcare networks represent a significant component in digital applications, where the requirements hold within quality-of-service (QoS) reliability and safeguarding privacy. This paper addresses these requirements through the integration of enabler paradigms, including federated learning (FL), cloud/edge computing, software-defined/virtualized networking infrastructure, and converged prediction algorithms. The study focuses on achieving reliability and efficiency in real-time prediction models, which depend on the interaction flows and network topology. In response to these challenges, we introduce a modified version of federated logistic regression (FLR) that takes into account convergence latencies and the accuracy of the final FL model… More >

Firas Abedi¹, Hayder M. A. Ghanimi², Abeer D. Algarni³, Naglaa F. Soliman^3,*, Walid El-Shafai^4,5, Ali Hashim Abbas⁶, Zahraa H. Kareem⁷, Hussein Muhi Hariz⁸, Ahmed Alkhayyat⁹

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2791-2814, 2023, DOI:10.32604/csse.2023.038762 - 09 November 2023

Abstract Data mining plays a crucial role in extracting meaningful knowledge from large-scale data repositories, such as data warehouses and databases. Association rule mining, a fundamental process in data mining, involves discovering correlations, patterns, and causal structures within datasets. In the healthcare domain, association rules offer valuable opportunities for building knowledge bases, enabling intelligent diagnoses, and extracting invaluable information rapidly. This paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System (MLARMC-HDMS). The MLARMC-HDMS technique integrates classification and association rule mining (ARM) processes. Initially, the chimp… More >