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 >

Umi Salma Basha¹, Shashi Kant Gupta², Wedad Alawad³, SeongKi Kim^4,*, Salil Bharany^5,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3397-3416, 2024, DOI:10.32604/cmc.2024.046265 - 15 May 2024

Abstract A new era of data access and management has begun with the use of cloud computing in the healthcare industry. Despite the efficiency and scalability that the cloud provides, the security of private patient data is still a major concern. Encryption, network security, and adherence to data protection laws are key to ensuring the confidentiality and integrity of healthcare data in the cloud. The computational overhead of encryption technologies could lead to delays in data access and processing rates. To address these challenges, we introduced the Enhanced Parallel Multi-Key Encryption Algorithm (EPM-KEA), aiming to bolster… 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 >

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 >

Musheer Ahmad¹, Reem Ibrahim Alkanhel^2,*, Naglaa F. Soliman², Abeer D. Algarni², Fathi E. Abd El-Samie³, Walid El-Shafai^3,4

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2361-2380, 2023, DOI:10.32604/csse.2023.038439 - 28 July 2023

Abstract Patient privacy and data protection have been crucial concerns in E-healthcare systems for many years. In modern-day applications, patient data usually holds clinical imagery, records, and other medical details. Lately, the Internet of Medical Things (IoMT), equipped with cloud computing, has come out to be a beneficial paradigm in the healthcare field. However, the openness of networks and systems leads to security threats and illegal access. Therefore, reliable, fast, and robust security methods need to be developed to ensure the safe exchange of healthcare data generated from various image sensing and other IoMT-driven devices in… More >

Jiwon Jeon, Junho Kim, Mincheol Shin, Mucheol Kim^*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3403-3421, 2023, DOI:10.32604/csse.2023.036658 - 03 April 2023

Abstract The development of information technology allows the collaborative business process to be run across multiple enterprises in a larger market environment. However, while collaborative business expands the realm of businesses, it also causes various hazards in collaborative Interaction, such as data falsification, inconstancy, and misuse. To solve these issues, a blockchain-based collaborative business modeling approach was proposed and analyzed. However, the existing studies lack the blockchain risk problem-solving specification, and there is no verification technique to examine the process. Consequently, it is difficult to confirm the appropriateness of the approach. Thus, here, we propose and… More >

V. Gowri^1,*, V. Vijaya Chamundeeswari²

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3675-3690, 2023, DOI:10.32604/iasc.2023.035817 - 15 March 2023

Abstract Decision forest is a well-renowned machine learning technique to address the detection and prediction problems related to clinical data. But, the traditional decision forest (DF) algorithms have lower classification accuracy and cannot handle high-dimensional feature space effectively. In this work, we propose a bootstrap decision forest using penalizing attributes (BFPA) algorithm to predict heart disease with higher accuracy. This work integrates a significance-based attribute selection (SAS) algorithm with the BFPA classifier to improve the performance of the diagnostic system in identifying cardiac illness. The proposed SAS algorithm is used to determine the correlation among attributes… More >

Hasanien K. Kuba¹, Mustafa A. Azzawi², Saad M. Darwish^3,*, Oday A. Hassen⁴, Ansam A. Abdulhussein⁵

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4119-4133, 2023, DOI:10.32604/cmc.2023.033182 - 31 October 2022

Abstract It is crucial, while using healthcare data, to assess the advantages of data privacy against the possible drawbacks. Data from several sources must be combined for use in many data mining applications. The medical practitioner may use the results of association rule mining performed on this aggregated data to better personalize patient care and implement preventive measures. Historically, numerous heuristics (e.g., greedy search) and metaheuristics-based techniques (e.g., evolutionary algorithm) have been created for the positive association rule in privacy preserving data mining (PPDM). When it comes to connecting seemingly unrelated diseases and drugs, negative association… More >

Marwan H. Hassan¹, Saad M. Darwish^2,*, Saleh M. Elkaffas³

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4417-4434, 2023, DOI:10.32604/cmc.2023.033175 - 31 October 2022

Abstract Electronic patient data gives many advantages, but also new difficulties. Deadlocks may delay procedures like acquiring patient information. Distributed deadlock resolution solutions introduce uncertainty due to inaccurate transaction properties. Soft computing-based solutions have been developed to solve this challenge. In a single framework, ambiguous, vague, incomplete, and inconsistent transaction attribute information has received minimal attention. The work presented in this paper employed type-2 neutrosophic logic, an extension of type-1 neutrosophic logic, to handle uncertainty in real-time deadlock-resolving systems. The proposed method is structured to reflect multiple types of knowledge and relations among transactions’ features that… More >

Ahmed H. Almulihi¹, Fawaz Alassery², Asif Irshad Khan³, Sarita Shukla⁴, Bineet Kumar Gupta⁴, Rajeev Kumar^4,*

Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1763-1779, 2022, DOI:10.32604/iasc.2022.023460 - 09 December 2021

Abstract The contributions of the Internet of Medical Things (IoMT), cloud services, information systems, and smart devices are useful for the healthcare industry. With the help of digital healthcare, our lives have been made much more secure and effortless and provide more convenient and accessible treatment. In current, the modern healthcare sector has become more significant and convenient for the purpose of both external and internal threats. Big data breaches affect clients, stakeholders, organisations, and businesses, and they are a source of concern and complication for security professionals. This research examines the many types and categories… More >