Altaf Hussain¹, Shuaiyong Li², Tariq Hussain³, Xianxuan Lin^4,*, Farman Ali^5,*, Ahmad Ali AlZubi⁶

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 1999-2051, 2024, DOI:10.32604/cmc.2024.056183 - 18 November 2024

Abstract Devices and networks constantly upgrade, leading to rapid technological evolution. Three-dimensional (3D) point cloud transmission plays a crucial role in aerial computing terminology, facilitating information exchange. Various network types, including sensor networks and 5G mobile networks, support this transmission. Notably, Flying Ad hoc Networks (FANETs) utilize Unmanned Aerial Vehicles (UAVs) as nodes, operating in a 3D environment with Six Degrees of Freedom (6DoF). This study comprehensively surveys UAV networks, focusing on models for Light Detection and Ranging (LiDAR) 3D point cloud compression/transmission. Key topics covered include autonomous navigation, challenges in video streaming infrastructure, motivations for More >

Sonia Goel^1,#, Meena Tushir¹, Jyoti Arora², Tripti Sharma², Deepali Gupta³, Ali Nauman^4,#, Ghulam Muhammad^5,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3125-3145, 2024, DOI:10.32604/cmc.2024.054476 - 18 November 2024

Abstract In numerous real-world healthcare applications, handling incomplete medical data poses significant challenges for missing value imputation and subsequent clustering or classification tasks. Traditional approaches often rely on statistical methods for imputation, which may yield suboptimal results and be computationally intensive. This paper aims to integrate imputation and clustering techniques to enhance the classification of incomplete medical data with improved accuracy. Conventional classification methods are ill-suited for incomplete medical data. To enhance efficiency without compromising accuracy, this paper introduces a novel approach that combines imputation and clustering for the classification of incomplete data. Initially, the linear More >

Attiya Khan¹, Muhammad Rizwan², Ovidiu Bagdasar^2,3, Abdulatif Alabdulatif^4,*, Sulaiman Alamro⁴, Abdullah Alnajim⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2121-2141, 2024, DOI:10.32604/cmes.2024.054380 - 31 October 2024

Abstract The Internet of Medical Things (IoMT) is an emerging technology that combines the Internet of Things (IoT) into the healthcare sector, which brings remarkable benefits to facilitate remote patient monitoring and reduce treatment costs. As IoMT devices become more scalable, Smart Healthcare Systems (SHS) have become increasingly vulnerable to cyberattacks. Intrusion Detection Systems (IDS) play a crucial role in maintaining network security. An IDS monitors systems or networks for suspicious activities or potential threats, safeguarding internal networks. This paper presents the development of an IDS based on deep learning techniques utilizing benchmark datasets. We propose More >

Sameh M. Said^1,2,*, Yasin Essa¹

Congenital Heart Disease, Vol.19, No.4, pp. 407-417, 2024, DOI:10.32604/chd.2024.055636 - 31 October 2024

Abstract Minimally invasive approaches for cardiac surgery in children have been lagging in comparison to the adult world. A wide range of the most common congenital heart defects in infants and children can be repaired successfully through a variety of non-sternotomy incisions. This has been shown to be associated with superior cosmetic results, shorter hospital stays, and rapid return to full activity compared to sternotomy. These approaches have been around for decades, but they have not been widely adopted for a variety of reasons. Right axillary thoracotomy is one of these approaches that we believe should More >

Hamed Taherdoost^1,2,3,4,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 79-104, 2024, DOI:10.32604/cmc.2024.054378 - 15 October 2024

Abstract Technical and accessibility issues in hospitals often prevent patients from receiving optimal mental and physical health care, which is essential for independent living, especially as societies age and chronic diseases like diabetes and cardiovascular disease become more common. Recent advances in the Internet of Things (IoT)-enabled wearable devices offer potential solutions for remote health monitoring and everyday activity recognition, gaining significant attention in personalized healthcare. This paper comprehensively reviews wearable healthcare technology integrated with the IoT for continuous vital sign monitoring. Relevant papers were extracted and analyzed using a systematic numerical review method, covering various More >

Mahesh Thyluru Ramakrishna¹, Kuppusamy Pothanaicker², Padma Selvaraj³, Surbhi Bhatia Khan^4,7,*, Vinoth Kumar Venkatesan⁵, Saeed Alzahrani⁶, Mohammad Alojail⁶

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 867-883, 2024, DOI:10.32604/cmc.2024.053563 - 15 October 2024

Abstract Brain tumor is a global issue due to which several people suffer, and its early diagnosis can help in the treatment in a more efficient manner. Identifying different types of brain tumors, including gliomas, meningiomas, pituitary tumors, as well as confirming the absence of tumors, poses a significant challenge using MRI images. Current approaches predominantly rely on traditional machine learning and basic deep learning methods for image classification. These methods often rely on manual feature extraction and basic convolutional neural networks (CNNs). The limitations include inadequate accuracy, poor generalization of new data, and limited ability… More >

Nahed Tawfik^1,*, Heba M. Emara², Walid El-Shafai³, Naglaa F. Soliman⁴, Abeer D. Algarni⁴, Fathi E. Abd El-Samie⁴

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 271-307, 2024, DOI:10.32604/cmc.2024.052404 - 15 October 2024

Abstract Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins, including hereditary factors and various clinical changes. It stands as the deadliest type of cancer and a significant cause of cancer-related deaths globally. Early diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately, leading to improved prognosis and higher survival rates. The significant increase in both the incidence and mortality rates of lung cancer, particularly its ranking as the second most prevalent cancer among women worldwide, underscores the need for comprehensive research into efficient… More >

Stephen Ojo¹, Moez Krichen^2,3,*, Meznah A. Alamro⁴, Alaeddine Mihoub⁵, Gabriel Avelino Sampedro⁶, Jaroslava Kniezova^7,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 643-661, 2024, DOI:10.32604/cmc.2024.052147 - 15 October 2024

Abstract Myelin damage and a wide range of symptoms are caused by the immune system targeting the central nervous system in Multiple Sclerosis (MS), a chronic autoimmune neurological condition. It disrupts signals between the brain and body, causing symptoms including tiredness, muscle weakness, and difficulty with memory and balance. Traditional methods for detecting MS are less precise and time-consuming, which is a major gap in addressing this problem. This gap has motivated the investigation of new methods to improve MS detection consistency and accuracy. This paper proposed a novel approach named FAD consisting of Deep Neural Network… More >

Chia-Hui Hou^1,*, Po-Lin Chen²

International Journal of Mental Health Promotion, Vol.26, No.9, pp. 697-708, 2024, DOI:10.32604/ijmhp.2024.051751 - 20 September 2024

Abstract Background: As the elderly population grows, the demand for long-term care services is increasing. Despite significant investments in care quality and workforce training, long-term care workers often face challenges such as work fatigue, heavy workloads, and inadequate support. These issues can impact job satisfaction, mental health, and care quality, leading to staff turnover. This study examines how optimism, social support, and psychological resilience relate to caregiving burden, aiming to understand their effects on caregivers’ well-being and performance to enhance the quality of long-term care services. Methods: The participants were 542 long-term care workers. Descriptive statistics, t-tests,… More >

Amit Singhal¹, Krishna Kant Agrawal², Angeles Quezada³, Adrian Rodriguez Aguiñaga⁴, Samantha Jiménez⁴, Satya Prakash Yadav^5,,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 401-441, 2024, DOI:10.32604/cmes.2024.051363 - 20 August 2024

Abstract The use of Explainable Artificial Intelligence (XAI) models becomes increasingly important for making decisions in smart healthcare environments. It is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these algorithms. These models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial intelligence. Nevertheless, the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer images. This research presents an advanced investigation of XAI models to classify cancer images. It describes the different levels of explainability… More >