Huda Talib Najm^1,2, Ahmed Sabah Al-Araji³, Nur Syazreen Ahmad^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.071837 - 29 January 2026

Abstract Mobile service robots (MSRs) in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions, including model uncertainties and external disturbances. This study presents a cognitive control strategy that integrates a Numerical Feedforward Inverse Dynamic Controller (NFIDC) with a Feedback Radial Basis Function Neural Network (FRBFNN). The robot’s mechanical structure was designed in SolidWorks 2022 SP2.0 and validated under operational loads using finite element analysis in ANSYS 2022 R1. The NFIDC-FRBFNN framework merges proactive inverse dynamic compensation with adaptive neural learning to achieve smooth torque responses and accurate motion control.… More >

Zia Ur Rahman¹, Nigar Ali^1,2, Dragan Pamucar³, Imtiaz Ahmad^1,2, Haci Mehmet Baskonus^2,*, Naseer Ul Haq¹, Zeeshan Ali⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.070268 - 29 January 2026

Abstract Hepatitis B Virus (HBV) infection and heavy alcohol consumption are the two primary pathogenic causes of liver cirrhosis. In this paper, we proposed a deterministic mathematical model and a logistic equation to investigate the dynamics of liver cirrhosis progression as well as to explain the implications of variations in alcohol consumption on chronic hepatitis B patients, respectively. The intricate interactions between liver cirrhosis, recovery, and treatment dynamics are captured by the model. This study aims to show that alcohol consumption by Hepatitis B-infected individuals accelerates liver cirrhosis progression while treatment of acutely infected individuals reduces… More >

Sandeerah Choudhary¹, Qaisar Abbas², Tallha Akram^3,*, Irshad Qureshi⁴, Mutlaq B. Aldajani², Hammad Salahuddin¹

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1755-1787, 2025, DOI:10.32604/cmes.2025.072200 - 26 November 2025

Abstract The increasing demand for sustainable construction practices has led to growing interest in recycled aggregate concrete (RAC) as an eco-friendly alternative to conventional concrete. However, predicting its compressive strength remains a challenge due to the variability in recycled materials and mix design parameters. This study presents a robust machine learning framework for predicting the compressive strength of recycled aggregate concrete using feedforward neural networks (FFNN), Random Forest (RF), and XGBoost. A literature-derived dataset of 502 samples was enriched via interpolation-based data augmentation and modeled using five distinct optimization techniques within MATLAB’s Neural Net Fitting module:… More >

Kentaro Kogawa^1,2,*, Reiji Ito², Daishi Hirano², Kenji Hoshino^1,2

Congenital Heart Disease, Vol.20, No.4, pp. 451-461, 2025, DOI:10.32604/chd.2025.068006 - 18 September 2025

Abstract Objective: To investigate the usefulness of the descending aortic retrograde flow area to forward flow area (dAO RF) ratio using echocardiography in managing pulmonary blood flow during the perioperative period following Blalock-Thomas-Taussig shunt (BTTS) surgery. Methods: This retrospective study analyzed patient characteristics, surgical procedures, and perioperative courses. The dAO RF ratio was measured upon admission to the pediatric intensive care unit (PICU) and 12, 24, 48, and 72 h post-surgery. Blood pressure, percutaneous oxygen saturation, arterial blood gas values, and lactate levels were measured at the same time as the dAO RF ratio. Results: Seventy-one cases were… More >

Ahmed El Youssefi¹, Abdelaaziz Hessane^1,2, Imad Zeroual¹, Yousef Farhaoui^1,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3411-3433, 2025, DOI:10.32604/cmc.2025.063218 - 16 April 2025

Abstract This study provides a systematic investigation into the influence of feature selection methods on cryptocurrency price forecasting models employing technical indicators. In this work, over 130 technical indicators—covering momentum, volatility, volume, and trend-related technical indicators—are subjected to three distinct feature selection approaches. Specifically, mutual information (MI), recursive feature elimination (RFE), and random forest importance (RFI). By extracting an optimal set of 20 predictors, the proposed framework aims to mitigate redundancy and overfitting while enhancing interpretability. These feature subsets are integrated into support vector regression (SVR), Huber regressors, and k-nearest neighbors (KNN) models to forecast the… More >

Hamayun Khan^1,*, Muhammad Atif Imtiaz², Hira Siddique³, Muhammad Tausif Afzal Rana⁴, Arshad Ali⁵, Muhammad Zeeshan Baig⁶, Saif ur Rehman⁷, Yazed Alsaawy⁵

Computer Systems Science and Engineering, Vol.49, pp. 317-331, 2025, DOI:10.32604/csse.2025.061118 - 19 March 2025

Abstract The migration of tasks aided by machine learning (ML) predictions IN (DPM) is a system-level design technique that is used to reduce energy by enhancing the overall performance of the processor. In this paper, we address the issue of system-level higher task dissipation during the execution of parallel workloads with common deadlines by introducing a machine learning-based framework that includes task migration using energy-efficient earliest deadline first scheduling (EA-EDF). ML-based EA-EDF enhances the overall throughput and optimizes the energy to avoid delay and performance degradation in a multiprocessor system. The proposed system model allocates processors… More >

Kaiqiao Wang^1,2, Peng Liu^1,2,*, Chun Zhang^1,2

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4931-4948, 2025, DOI:10.32604/cmc.2025.060547 - 06 March 2025

Abstract Underwater target detection in forward-looking sonar (FLS) images is a challenging but promising endeavor. The existing neural-based methods yield notable progress but there remains room for improvement due to overlooking the unique characteristics of underwater environments. Considering the problems of low imaging resolution, complex background environment, and large changes in target imaging of underwater sonar images, this paper specifically designs a sonar images target detection Network based on Progressive sensitivity capture, named ProNet. It progressively captures the sensitive regions in the current image where potential effective targets may exist. Guided by this basic idea, the… More >

Gang Xu^1,2,6, Xinyu Fan¹, Xiu-Bo Chen², Xin Liu⁴, Zongpeng Li⁵, Yanhui Mao^6,7, Kejia Zhang^3,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2293-2309, 2025, DOI:10.32604/cmc.2024.057882 - 17 February 2025

Abstract Blockchain-enabled Internet of Medical Things (BIoMT) has attracted significant attention from academia and healthcare organizations. However, the large amount of medical data involved in BIoMT has also raised concerns about data security and personal privacy protection. To alleviate these concerns, blind signature technology has emerged as an effective method to solve blindness and unforgeability. Unfortunately, most existing blind signature schemes suffer from the security risk of key leakage. In addition, traditional blind signature schemes are also vulnerable to quantum computing attacks. Therefore, it remains a crucial and ongoing challenge to explore the construction of key-secure,… More >

Slim Chaoui^1,*, Omar Alruwaili¹, Faeiz Alserhani¹, Haifa Harrouch²

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1933-1954, 2025, DOI:10.32604/cmes.2025.059346 - 27 January 2025

Abstract In this paper, we propose a hybrid decode-and-forward and soft information relaying (HDFSIR) strategy to mitigate error propagation in coded cooperative communications. In the HDFSIR approach, the relay operates in decode-and-forward (DF) mode when it successfully decodes the received message; otherwise, it switches to soft information relaying (SIR) mode. The benefits of the DF and SIR forwarding strategies are combined to achieve better performance than deploying the DF or SIR strategy alone. Closed-form expressions for the outage probability and symbol error rate (SER) are derived for coded cooperative communication with HDFSIR and energy-harvesting relays. Additionally,… More >

Naeem Ali Askar^1,*, Adib Habbal^1,*, Hassen Hamouda², Abdullah Mohammad Alnajim³, Sheroz Khan⁴

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4625-4658, 2024, DOI:10.32604/cmc.2024.058607 - 19 December 2024

Abstract Named Data Networking (NDN) has emerged as a promising communication paradigm, emphasizing content-centric access rather than location-based access. This model offers several advantages for Internet of Healthcare Things (IoHT) environments, including efficient content distribution, built-in security, and natural support for mobility and scalability. However, existing NDN-based IoHT systems face inefficiencies in their forwarding strategy, where identical Interest packets are forwarded across multiple nodes, causing broadcast storms, increased collisions, higher energy consumption, and delays. These issues negatively impact healthcare system performance, particularly for individuals with disabilities and chronic diseases requiring continuous monitoring. To address these challenges,… More >