Rabia Abid¹, Muhammad Rizwan², Abdulatif Alabdulatif^3,*, Abdullah Alnajim⁴, Meznah Alamro⁵, Mourade Azrour⁶

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3413-3429, 2024, DOI:10.32604/cmc.2024.046880

Abstract Explainable Artificial Intelligence (XAI) has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning (ML) and Deep Learning (DL) based algorithms. In this paper, we chose e-healthcare systems for efficient decision-making and data classification, especially in data security, data handling, diagnostics, laboratories, and decision-making. Federated Machine Learning (FML) is a new and advanced technology that helps to maintain privacy for Personal Health Records (PHR) and handle a large amount of medical data effectively. In this context, XAI, along with FML, increases efficiency and improves the security of e-healthcare systems. The… More >

Dinesh Mavaluru^1,*, Chettupally Anil Carie², Ahmed I. Alutaibi³, Satish Anamalamudi², Bayapa Reddy Narapureddy⁴, Murali Krishna Enduri², Md Ezaz Ahmed¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1487-1503, 2024, DOI:10.32604/cmes.2023.045277

Abstract In this paper, we present a comprehensive system model for Industrial Internet of Things (IIoT) networks empowered by Non-Orthogonal Multiple Access (NOMA) and Mobile Edge Computing (MEC) technologies. The network comprises essential components such as base stations, edge servers, and numerous IIoT devices characterized by limited energy and computing capacities. The central challenge addressed is the optimization of resource allocation and task distribution while adhering to stringent queueing delay constraints and minimizing overall energy consumption. The system operates in discrete time slots and employs a quasi-static approach, with a specific focus on the complexities of task partitioning and the management… 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

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 records of patients. Once an… More >

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

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 different key performance indicators (KPIs)… More > Graphic Abstract

Apurva Sonavane¹, Aditya Khamparia^2,*, Deepak Gupta³

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1525-1550, 2023, DOI:10.32604/cmes.2023.028203

Abstract IoT usage in healthcare is one of the fastest growing domains all over the world which applies to every age group. Internet of Medical Things (IoMT) bridges the gap between the medical and IoT field where medical devices communicate with each other through a wireless communication network. Advancement in IoMT makes human lives easy and better. This paper provides a comprehensive detailed literature survey to investigate different IoMT-driven applications, methodologies, and techniques to ensure the sustainability of IoMT-driven systems. The limitations of existing IoMT frameworks are also analyzed concerning their applicability in real-time driven systems or applications. In addition to… More >

Ahyoung Lee¹, Jui Mhatre¹, Rupak Kumar Das², Min Hong^3,*

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 435-452, 2023, DOI:10.32604/cmc.2023.029340

Abstract Healthcare is a fundamental part of every individual’s life. The healthcare industry is developing very rapidly with the help of advanced technologies. Many researchers are trying to build cloud-based healthcare applications that can be accessed by healthcare professionals from their premises, as well as by patients from their mobile devices through communication interfaces. These systems promote reliable and remote interactions between patients and healthcare professionals. However, there are several limitations to these innovative cloud computing-based systems, namely network availability, latency, battery life and resource availability. We propose a hybrid mobile cloud computing (HMCC) architecture to address these challenges. Furthermore, we… More >

Munayfah Alanazi^*, Shadi Nashwan

Computer Systems Science and Engineering, Vol.42, No.2, pp. 703-725, 2022, DOI:10.32604/csse.2022.022962

Abstract Wireless medical sensor networks (WMSNs) play a significant role in increasing the availability of remote healthcare systems. The vital and physiological data of the patient can be collected using the WMSN via sensor nodes that are placed on his/her body and then transmitted remotely to a healthcare professional for proper diagnosis. The protection of the patient’s privacy and their data from unauthorized access is a major concern in such systems. Therefore, an authentication scheme with a high level of security is one of the most effective mechanisms by which to address these security concerns. Many authentication schemes for remote patient… More >

Taher M. Ghazal^1,2,*, Mohammad Kamrul Hasan¹, Siti Norul Huda Abdullah¹, Khairul Azmi Abubakkar¹, Mohammed A. M. Afifi²

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2579-2597, 2022, DOI:10.32604/cmc.2022.019706

Abstract Smart healthcare applications depend on data from wearable sensors (WSs) mounted on a patient’s body for frequent monitoring information. Healthcare systems depend on multi-level data for detecting illnesses and consequently delivering correct diagnostic measures. The collection of WS data and integration of that data for diagnostic purposes is a difficult task. This paper proposes an Errorless Data Fusion (EDF) approach to increase posture recognition accuracy. The research is based on a case study in a health organization. With the rise in smart healthcare systems, WS data fusion necessitates careful attention to provide sensitive analysis of the recognized illness. As a… More >

S. Benila^1,*, N. Usha Bhanu²

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1277-1291, 2022, DOI:10.32604/iasc.2022.021920

Abstract Many physical objects are connected to the internet in this modern day to make things easier to work based on the convenience of the user, which reduces human involvement with the help of Internet of Things (IoT) technology.This aids in the capture of large amounts of data, the interchange of information via the internet, and the remote operation of machines. IoT health data is typically in the form of big data and is frequently coupled with the cloud for secure storage. Cloud technology provides a wide range of technological services via the internet, and it is a highly interoperable and… More >

Wafaa Alsaggaf^1,*, Felwa Abukhodair¹, Amani Tariq Jamal², Sayed Abdel-Khalek³, Romany F. Mansour⁴

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1189-1203, 2022, DOI:10.32604/cmc.2022.022469

Abstract In recent days, advancements in the Internet of Things (IoT) and cloud computing (CC) technologies have emerged in different application areas, particularly healthcare. The use of IoT devices in healthcare sector often generates large amount of data and also spent maximum energy for data transmission to the cloud server. Therefore, energy efficient clustering mechanism is needed to effectively reduce the energy consumption of IoT devices. At the same time, the advent of deep learning (DL) models helps to analyze the healthcare data in the cloud server for decision making. With this motivation, this paper presents an intelligent disease diagnosis model… More >