Wariyo Godana Arero¹, Yaqin Zhao¹, Mudasir Ahmad Wani², Pir Noman Ahmad³, Kashish Ara Shakil^4,*, Sadique Ahmad⁵, Sidrak Habtemariam Teredda⁶, Merhawit Berhane Teklu⁷, Longwen Wu^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.080279 - 27 April 2026

Abstract Lung cancer accounts for the highest number of cancer deaths globally, underscoring the urgent need for early and precise detection to enhance patient outcomes. While deep learning has made remarkable strides in analyzing medical images, current approaches face a fundamental challenge. They cannot adequately capture detailed local patterns and broader contextual relationships within lung Computed tomography (CT) scans. To address this limitation, we introduce AMVT-NMN (adaptive multi-scale vision transformer with neuromorphic memory networks), which combines three complementary mechanisms. The dynamic adaptive kernel networks component intelligently adjusts receptive field sizes based on input characteristics, enabling flexible… More >

Mamoona Jabbar, Uzma Jamil^*, Muhammad Younas, Bushra Zafar

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.080241 - 27 April 2026

Abstract Magnetic Resonance Imaging is one of the most commonly used neuro-oncology imaging modalities, which is a non-invasive mode of imaging and helps in detecting brain abnormalities in an effective way. Earlier researchers have demonstrated that brain tumor segmentation and classification can be effectively performed using deep learning techniques. Existing studies are primarily aimed at increasing prediction accuracy and provide insignificant consideration to model interpretability, limiting their practical application in clinical practice. To address this limitation, this research presents a two-stage explainable deep learning model, which combines transformer-based segmentation with an ensemble classification model that is… More >

Sumin Yeon, Minjae Lee, Jiho Bae, Suwon Lee^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079954 - 27 April 2026

Abstract In everyday life, people effectively convey their intentions through pointing gestures without explicitly naming objects. In particular, pointing gestures used in conjunction with linguistic expressions such as “this” and “that” play a crucial role in intuitively indicating objects or locations in space. Although research on the recognition of such nonverbal gestures has been actively pursued within the field of human-computer interaction (HCI), accurately interpreting a user’s intent remains challenging in situations where the pointing gesture is ambiguous. This paper proposes an integrated system that combines a large language model (LLM), capable of understanding complex human… More > Graphic Abstract

Yue Su¹, Shukuan Zhang^1,*, Jinghao Jiao¹, Jiankang Zhong², Qianxi Zhao¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079927 - 27 April 2026

Abstract This paper introduces a novel dual-layer optimization fault diagnosis framework for inter-turn short-circuit (ITSC) faults in permanent magnet synchronous motors (PMSMs). The synergistic of a SABO-optimized VMD for enhanced feature extraction and an MFO-optimized SVM for intelligent classification is proposed. Firstly, mathematical and simulation models of ITSC faults in PMSMs are established to obtain fault phase currents and motor electromagnetic torques as characteristic fault signals. Then, the SABO algorithm is used to optimize the VMD parameters, followed by VMD decomposition of the characteristic fault signals to obtain Intrinsic Mode Functions (IMFs), and the time-domain parameters More >

M. A. El-Shorbagy^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079859 - 27 April 2026

Abstract This paper provides a thorough examination of Genetic Algorithms (GAs), a category of evolutionary computation methods derived from the concepts of natural selection and genetics. The main concept and operational principle of GAs are elucidated, highlighting the evolution of populations of candidate solutions across multiple generations to get optimal or near-optimal solutions for complicated problems. The paper delineates the sequential phases of a conventional GA, encompassing problem formulation, solution encoding, initialization of population, fitness evaluation, selection, crossover, mutation, and termination criteria, so offering a coherent framework for comprehending the algorithm’s functionality. Moreover, numerous prominent genetic… More > Graphic Abstract

Zelong Liang^1,2, Tinh Quoc Bui^3,4, Zhichao Dong⁵, Weihua Li^1,*, Yingjun Wang^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079830 - 27 April 2026

Abstract Topology optimization (TO) has become a core computational paradigm for structural design by defining optimality through physics-based objectives and constraints. However, practical engineering design often involves incomplete and imperfect physical modeling due to multi-physics coupling, manufacturing uncertainty, and computational constraints, leaving critical design factors insufficiently captured in purely physics-driven formulations. In parallel, data-driven and generative methods have enabled rapid topology generation and intent-aware design exploration, yet often weaken explicit optimality guarantees. This review argues that these seemingly divergent developments can be organized under a unified information–physics perspective. We term this emerging field Topology Optimization Informatics… More > Graphic Abstract

Safaa Zaman¹, Marwa M. Eid^2,*, Ebrahim A. Mattar³, Doaa Sami Khafaga⁴, El-Sayed M. El-Kenawy^5,6

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079827 - 27 April 2026

Abstract The rapid growth of Internet of Things (IoT) technologies has transformed modern urban environments into complex smart cities, generating vast amounts of high-dimensional, heterogeneous data. Effectively analyzing this data is crucial for optimizing urban infrastructure, enhancing quality of life, and supporting sustainable development. However, smart city data presents significant challenges, including non-linear dependencies, noisy signals, and high dimensionality. To address these challenges, this study proposes the Dynamic Leader Sibha Algorithm (DLSA), a novel metaheuristic optimization technique inspired by the structured counting dynamics of the Sibha. The DLSA was applied to the Smart Cities Index dataset,… More >

Ali S. Alghamdi^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079763 - 27 April 2026

Abstract The integration of wind-based DG introduces significant variability and uncertainty into the operation of distribution networks, which complicates the planning and decision-making process. This paper presents a dual-objective stochastic optimization framework for the optimal allocation of wind DG, considering dynamic network reconfiguration across multiple loading conditions. Probabilistic modeling of wind speed is integrated using the Weibull distribution and the associated wind power uncertainty is discretized through a scenario-based point estimation method. Variability in load is accounted for by considering multiple loading levels, and the integrated uncertainty space is constructed as the Cartesian product of wind… More >

Mustaqeem Khan¹, Ufaq Khan², Mamoun Awad¹, Nazar Zaki¹, Guiyoung Son³, Soonil Kwon^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079697 - 27 April 2026

Abstract Knowledge distillation has shown impressive results in different fields, including detection, recognition, and generation. These models are excellent at tasks such as speech recognition, but they need to be shrunk down using adaptive knowledge distillation (AKD). The use of AKD can improve human-computer interactions and streamline data collection in the field of Speech Emotion Recognition (SER). This study presents a high-level approach that employs a novel adaptive knowledge distillation (AKD) with spatio-temporal transformers to acquire advanced semantic features from the input signal. This method uses an instance-by-instance correlation between the teacher and a student to determine the More >

Ahmed Ibrahim Hassanin Mohamed^1,2,*, Nourhan M. Amin^2,3, Heba Elsaid Matter², Ibrahim F. Eldemary², Ahmed F. Oan²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079630 - 27 April 2026

Abstract The design of foundations for high-voltage electrical network lattice towers depends on reliable prediction of resistance to uplift and lateral forces. Because foundation works contribute substantially to the total project cost, a clear understanding of ultimate pullout capacity and the associated failure mechanism is required to support safe and economical design. This paper presents a three-dimensional finite element investigation using PLAXIS 3D to quantify the influence of soil type (pure sand and sand with 8% fines), footing dimensions ((3.5 × 7), (5 × 10), (7.5 × 15)), relative compaction RC are 92% and 100%, and… More >