Yun-Ting Lai, Ming-Ho Chang, Yao-Hsin Chou^*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083120 - 13 August 2026

Abstract Portfolio optimization is inherently a multi-objective problem that aims to maximize expected return while minimizing investment risk, while also facing exponential growth in the search space and increasing market complexity. Existing multi-objective optimization approaches often struggle to balance convergence and diversity, particularly under realistic trading conditions such as short-selling. To address these challenges, this paper proposes a novel Multi-objective Quantum-inspired Tabu Search (MoQTS) framework for portfolio optimization with short-selling strategies. The proposed method incorporates a quantum-inspired superposition mechanism to enhance global exploration and introduces an entanglement-driven neighborhood search strategy that systematically generates structured local perturbations… More >

Jordi Vallverdú^*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.082945 - 13 August 2026

Abstract Large rule-based systems—from automated theorem provers to diagnostic engines and expert systems—face a common bottleneck: when many rules are simultaneously applicable, choosing which rule to fire can dominate search effort. We present HL-W, a formally constrained adaptive proof-search control layer over a fixed base calculus. Each inference rule R is assigned a scalar weight w(R,t)∈[0,1] at search stage t; rule applications are scheduled by combining a threshold condition w(R,t)≥θ with an explicit fairness mechanism. Because the underlying inference rules are left unchanged, every derivation produced by the framework… More >

Elena S. Kartashynska^*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.082702 - 13 August 2026

Abstract The discovery of graphene and its unique physicochemical properties has catalyzed intensive research into alternative two-dimensional (2D) materials, with a view to their prospective applications in diverse fields of physics, chemistry, and materials science. In this context, there is a notable scientific interest in developing computationally efficient theoretical approaches capable of reliably estimating key parameters of organic films deposited on 2D surfaces. This objective necessitates a rigorous selection and validation of appropriate computational methods, ensuring an optimal balance between computational cost and predictive accuracy. This study presents a method for evaluating the thermodynamic and structural… More >

Jiaying Li¹, Xiujuan Wang^1,*, Shuhan Han², Liya Xu¹, Changxing Wang¹

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.082631 - 13 August 2026

Abstract Deep neural networks are widely applied in computer vision tasks but remain highly vulnerable to adversarial attacks. Tiny and imperceptible perturbations can cause severe model misclassification. Most existing defense methods improve robustness but significantly reduce model accuracy on clean examples. To address this issue, we propose a defense framework combining pixel value transformation and spatial transformation. The proposed method divides the input image into two complementary regions. Feature compression is applied to one region to reduce model sensitivity to subtle perturbations. Intense reversible pixel transformation is applied to the other region to disrupt the spatial… More >

Changsheng Hou¹, Xionglve Li², Bingnan Hou², Zhiping Cai², Jingtao Hu^1,*, Shuai Ye¹, Hao Li¹

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.082384 - 13 August 2026

Abstract Malicious network attacks pose severe threats to cyberspace, and efficient post-incident traceback and forensics techniques are urgently demanded. Existing payload attribution methods mainly support exact matching, while similar-payload schemes suffer from low efficiency and excessive overhead; most are single-node solutions that fail against IP spoofing and stepping-stone attacks, and the distributed Topology-aware Single Packet IP Traceback System (TOPO) relies on full-node cooperation and flooding forwarding, leading to huge overhead and a nearly 100% false positive rate. To mitigate these issues, we propose Distributed Similar Payload Traceback (DSPT), a distributed system that achieves hop-by-hop traceback via More >

Piotr Musznicki¹, Marek Turzyński¹, Lyu Guanghua², Ghulam E Mustafa Abro^3,*, Viola Gierszewska¹, Arsalan Muhammad Soomar¹, Syed Hadi Hussain Shah²

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.082254 - 13 August 2026

Abstract This paper presents an accurate and efficient methodology for parameter extraction in complex impedance models using Differential Evolution (DE), an evolutionary optimization technique. The proposed approach targets equivalent RLC circuit topologies and aims to match measured impedance characteristics across a wide frequency spectrum. By formulating the extraction process as a global optimization problem, DE enables precise identification of component values, even for high-order models with multiple resonances. The method is implemented in Python using open-source libraries, facilitating reproducibility and integration into broader modeling workflows. Validation is performed on both analytically derived resonant circuits and physically More >

Gen Xu^*, Li Lv, Jiayan Dong

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.081338 - 13 August 2026

Abstract Memory-unsafe languages such as C and C++ remain widely used because they provide low-level control and high performance, but they remain vulnerable to spatial and temporal memory-safety violations such as out-of-bounds accesses, buffer overflows, and use-after-free errors. Prior hardware-assisted defenses reduce software overhead, yet many still rely on CPU-side metadata checks that add latency to the critical path and often miss DMA-originated accesses. We show that metadata-access cost is not dominated solely by DRAM latency: a substantial portion of the delay comes from on-chip traversal and cache-related processing. Motivated by this result, we propose SerMC,… More >

Van-Thuan Nguyen^1,2, Van-Nui Nguyen², Van-Hung Le^3,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.081249 - 13 August 2026

Abstract The vision-language models (VLM) combine the image and text to solve practical applications. Specifically, VLM leverages the results of computer vision in conjunction with natural language processing (NLP), like a large language model (LLM), to address real-world problems such as automating and improving the quality of medical examinations and treatments in healthcare, building autonomous driving systems, image captioning, and generating automated chatbots. To understand the development and application of VLM, we surveyed VLM, classifying it according to model architecture, learning methods, evaluation measures, datasets, challenges, and future development directions of VLM based on the model… More >

Jiyeong Park¹, Sercan Yeşilköy¹, Doyeon Lim¹, Huiryeong Park¹, Eunseo Lee¹, Mohsen Ali Alawami^1,*, Ki-Woong Park^2,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.081091 - 13 August 2026

Abstract The recent increase in deepfake content has significantly increased cyber threats. Although numerous deepfake detection technologies have achieved high accuracy, there are limits to clarifying the rationale behind their detection decisions. To bridge the gap, in our study, we leverage the combination of Explainable Artificial Intelligence (XAI) and Large Language Models (LLMs) to deliver clear, consistent, and understandable interpretations of deepfake detection outcomes. To do that, we integrate XAI and LLMs to visually represent detection rationales and automatically generate coherent natural-language explanations. During the implementation of our method, we developed a multi-task learning framework based… More >

Canan Batur Şahin^1,*, Siti Fatimah Abdul Razak^2,*, Arif Ullah², Ali Fatih Gündüz¹, Nazri Mohd Nawi³

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.080874 - 13 August 2026

Abstract Unmanned Aerial Vehicle (UAV) networks face escalating cybersecurity threats, especially from zero-day attacks that exploit previously unknown vulnerabilities. To address this, we present HADAR-UAV (Hybrid Anomaly Detection with Adaptive Risk-calibration for UAV). This novel intrusion detection framework integrates masked autoencoder representation learning with Deep Support Vector Data Description (Deep SVDD) under conformal prediction guarantees to calibrate risk. Our method overcomes three critical limitations of existing approaches: (i) over-reliance on attack signatures, (ii) lack of statistical guarantees on false alarm rates, and (iii) insufficient robustness in feature extraction under partial observation. Using a rigorous Leave-Two-Attack-Families-Out (L2AFO)… More >