Md. Mottahir Alam¹, Mohammed K. Al Mesfer^2,3, Haroonhaider Sidhwa⁴, Mohd Danish^2,3, Asif Irshad Khan⁵, Tauheed Khan Mohd^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.077236 - 30 March 2026

Abstract Ground water is a crucial ecological resource and source of drinking water to a great percentage of the world population. The quality of groundwater in an area with industrial emission and air pollution is an especially important issue that requires proper evaluation. This paper introduces a spatiotemporal deep learning model that incorporates the use of metaheuristic optimization in predicting groundwater quality in various pollution contexts. The given method is a combination of the Spatial–Temporal-Assisted Deep Belief Network (StaDBN) and a hybrid Whale Optimization Algorithm and Tiki-Taka Algorithms (WOA–TTA) that would model intricate patterns of contamination.… More >

Shahab Ali Khan¹, Tehseen Mazhar^2,3,*, Syed Faisal Abbas Shah⁴, Wasim Ahmad¹, Sunawar Khan², Afsha BiBi⁵, Usama Shah¹, Habib Hamam^6,7,8,9

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.075882 - 30 March 2026

Abstract The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has enabled Artificial Intelligence of Things (AIoT) systems that support intelligent and responsive smart societies, but it also introduces major security and privacy concerns across domains such as healthcare, transportation, and smart cities. This Systemic Literature Review (SLR) addresses three research questions: identifying major threats and challenges in AIoT ecosystems, reviewing state-of-the-art security and privacy techniques, and evaluating their effectiveness. An SLR covering the period from 2020 to 2025 was conducted using major academic digital libraries, including IEEE Xplore, ACM Digital Library, ScienceDirect, More >

Abiodun Abiola^*, Francisca Segura, José Manuel Andújar, Antonio Javier Barragán

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2025.070788 - 30 March 2026

Abstract This paper presents an artificial intelligence (AI)-based observer that combines fuzzy logic and neural networks to detect abnormalities in sensors embedded in an electrolyser. Electrolysers are hydrogen production plants that require effective maintenance to guarantee suitable operation, prevent degradation, and avoid loss of efficiency. In this sense, predictive maintenance arises as one of the most advisable techniques for maintenance in electrolysers by using sensor data to predict potential abnormalities. However, if the sensor fails, there will be an incorrect forecasting of abnormalities. Among the different types of operational faults that sensors can present are drift-related… More >

Mohammad Nauman^*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.077259 - 12 March 2026

Abstract Large Language Models (LLMs) are increasingly utilized for semantic understanding and reasoning, yet their use in sensitive settings is limited by privacy concerns. This paper presents In-Mig, a mobile-agent architecture that integrates LLM reasoning within agents that can migrate across organizational venues. Unlike centralized approaches, In-Mig performs reasoning in situ, ensuring that raw data remains within institutional boundaries while allowing for cross-venue synthesis. The architecture features a policy-scoped memory model, utility-driven route planning, and cryptographic trust enforcement. A prototype using JADE for mobility and quantized Mistral-7B demonstrates practical feasibility. Evaluation across various scenarios shows that In-Mig achieves More >

Shasha Li, Bin Ji^*, Xiaodong Liu, Jun Ma, Jie Yu^*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076083 - 12 March 2026

Abstract Aligning natural language with operating system (OS) commands allows users to perform complex computer tasks through simple natural language descriptions. However, due to the complex nature of natural language, it still remains challenging to achieve precise alignment. In this paper, we present ComAlign, a Chinese benchmark dataset that pairs Chinese natural language descriptions with corresponding OS commands. ComAlign covers a broad range of 82 distinct OS command types with a total of 1811 natural language descriptions. We elaborate on the construction of ComAlign and construct three baselines to evaluate the alignment accuracy on ComAlign. Experimental More >

Shih-Ming Cho¹, Sung-Wen Wang¹, Min-Chie Chiu^2,*, Shao-Chun Chen¹

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.074911 - 12 March 2026

Abstract To address crop depredation by intelligent species (e.t, macaques) and the habituation from traditional methods, this study proposes an intelligent, closed-loop, adaptive laser deterrence system. A core contribution is an efficient multi-stage Semi-Supervised Learning (SSL) and incremental fine-tuning (IFT) framework, which reduced manual annotation by ~60% and training time by ~68%. This framework was benchmarked against YOLOv8n, v10n, and v11n. Our analysis revealed that YOLOv12n’s high Signal-to-Noise Ratio (SNR) (47.1% retention) pseudo-labels made it the only model to gain performance (+0.010 mAP) from SSL, allowing it to overtake competitors. Subsequently, in the IFT stress test,… More >

HeeSeok Choi¹, Joon-Min Gil^2,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074511 - 12 March 2026

Abstract Given the growing number of vehicle accidents caused by unintended acceleration and braking failure, verifying Sudden Unintended Acceleration (SUA) incidents has become a persistent challenge. A central issue of debate is whether such events stem from mechanical malfunctions or driver pedal misapplications. However, existing verification procedures implemented by vehicle manufacturers often involve closed tests after vehicle recalls; thus raising ongoing concerns about reliability and transparency. Consequently, there is a growing need for a user-driven framework that enables independent data acquisition and verification. Although previous studies have addressed SUA detection using deep learning, few have explored… More >

Changxu Zhao¹, Jianping Liu^1,*, Xiaofeng Wang¹, Wei Sun², Libo Liu³, Haiyu Ren¹, Pan Liu¹, Qiantong Wang¹

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.074409 - 12 March 2026

Abstract Foundation models are reshaping artificial intelligence, yet their deployment in specialised domains such as agricultural question answering (AQA) still faces challenges including data scarcity and barriers to domain-specific knowledge. To systematically review recent progress in this area, this paper adopts a task–paradigm perspective and examines applications across three major AQA task families. For text-based QA, we analyse the strengths and limitations of retrieval-based, generative, and hybrid approaches built on large language models, revealing a clear trend toward hybrid paradigms that balance precision and flexibility. For visual diagnosis, we discuss techniques such as cross-modal alignment and More >

Mekhled Alharbi^1,*, Khalid Haseeb², Mamoona Humayun^3,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.073832 - 12 March 2026

Abstract The Internet of Things (IoT) and cloud computing have significantly contributed to the development of smart cities, enabling real-time monitoring, intelligent decision-making, and efficient resource management. These systems, particularly in IoT networks, rely on numerous interconnected devices that handle time-sensitive data for critical applications. In related approaches, trusted communication and reliable device interaction have been overlooked, thereby lowering security when sharing sensitive IoT data. Moreover, it incurs additional energy consumption and overhead while addressing potential threats in the dynamic environment. In this research, an Artificial Intelligence (AI) recommended fault-tolerant framework is proposed that leverages blockchain More >

Mingwei Zhan^1,#, Zhaokai Zhou^2,#, Jianpeng Zhang^3,#, Xin Wang⁴, Canxuan Li⁵, Bochen Pan⁶, Zhanyang Luo⁷, Wenjie Shi⁸, Yongjie Wang⁹, Minglun Li¹⁰, Weizhuo Wang^11,*, Run Shi^12,*, Jingyu Zhu^1,13,*

Canadian Journal of Urology, Vol.33, No.1, pp. 35-49, 2026, DOI:10.32604/cju.2026.076084 - 28 February 2026

Abstract Artificial intelligence (AI) is transforming the diagnostic landscape of malignant tumors in the urinary system, including prostate cancer, bladder cancer, and renal cell carcinoma (RCC). By integrating imaging, pathology, and molecular data, AI enhances the precision and reproducibility of tumor detection, grading, and risk stratification. In prostate cancer, AI-assisted multiparametric Magnetic resonance imaging (MRI) and digital pathology systems improve lesion localization and Gleason scoring. For bladder cancer, deep learning-based cystoscopy and radiomics models from Computed tomography/magnetic resonance imaging (CT/MRI) enable real-time lesion segmentation and non-invasive biomarker prediction, such as Programmed Cell Death-Ligand 1 (PD-L1) expression. More >