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

Zuriati Ahmad Zukarnain^1,*, Amgad Muneer^2,3, Nur Atirah Mohamad Nassir¹, Akram A. Almohammedi^4,5

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2725-2740, 2023, DOI:10.32604/csse.2023.034448

Abstract With the advancements in the era of artificial intelligence, blockchain, cloud computing, and big data, there is a need for secure, decentralized medical record storage and retrieval systems. While cloud storage solves storage issues, it is challenging to realize secure sharing of records over the network. Medi-block record in the healthcare system has brought a new digitalization method for patients’ medical records. This centralized technology provides a symmetrical process between the hospital and doctors when patients urgently need to go to a different or nearby hospital. It enables electronic medical records to be available with the correct authentication and restricts… More >

Naveed Khan¹, Jianbiao Zhang^1,*, Ghulam Ali Mallah², Shehzad Ashraf Chaudhry³

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3877-3896, 2023, DOI:10.32604/cmc.2023.032553

Abstract The mobile cellular network provides internet connectivity for heterogeneous Internet of Things (IoT) devices. The cellular network consists of several towers installed at appropriate locations within a smart city. These cellular towers can be utilized for various tasks, such as e-healthcare systems, smart city surveillance, traffic monitoring, infrastructure surveillance, or sidewalk checking. Security is a primary concern in data broadcasting, particularly authentication, because the strength of a cellular network’s signal is much higher frequency than the associated one, and their frequencies can sometimes be aligned, posing a significant challenge. As a result, that requires attention, and without information authentication, such… More >

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 >

Ali A. Altalbe^1,2,*, Aamir Shahzad³, Muhammad Nasir Khan⁴

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1569-1585, 2023, DOI:10.32604/iasc.2023.039124

Abstract The development of human-robot interaction has been continuously increasing for the last decades. Through this development, it has become simpler and safe interactions using a remotely controlled telepresence robot in an insecure and hazardous environment. The audio-video communication connection or data transmission stability has already been well handled by fast-growing technologies such as 5G and 6G. However, the design of the physical parameters, e.g., maneuverability, controllability, and stability, still needs attention. Therefore, the paper aims to present a systematic, controlled design and implementation of a telepresence mobile robot. The primary focus of this paper is to perform the computational analysis… More >

Sultan Mesfer Aldossary^*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2199-2212, 2023, DOI:10.32604/iasc.2023.038243

Abstract The challenge of encrypting sensitive information of a medical image in a healthcare system is still one that requires a high level of computing complexity, despite the ongoing development of cryptography. After looking through the previous research, it has become clear that the security issues still need to be looked into further because there is room for expansion in the research field. Recently, neural networks have emerged as a cost-effective and effective optimization strategy in terms of providing security for images. This revelation came about as a result of current developments. Nevertheless, such an implementation is a technique that is… More >

Md. Moddassir Alam¹, Md Mottahir Alam², Muhammad Moinuddin^2,3, Mohammad Tauheed Ahmad⁴, Jabir Hakami⁵, Anis Ahmad Chaudhary⁶, Asif Irshad Khan⁷, Tauheed Khan Mohd^8,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4553-4571, 2023, DOI:10.32604/cmc.2023.032118

Abstract Artificial Intelligence (AI) is finding increasing application in healthcare monitoring. Machine learning systems are utilized for monitoring patient health through the use of IoT sensor, which keep track of the physiological state by way of various health data. Thus, early detection of any disease or derangement can aid doctors in saving patients’ lives. However, there are some challenges associated with predicting health status using the common algorithms, such as time requirements, chances of errors, and improper classification. We propose an Artificial Krill Herd based on the Random Forest (AKHRF) technique for monitoring patients’ health and eliciting an optimal prescription based… More >