Qianshu Yang, Shuangxi Liu^*, Xianyu Wu, Wei Zhao

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079895 - 08 May 2026

Abstract Path planning for unmanned systems in complex environments must simultaneously satisfy safety, kinematic feasibility, and real-time performance requirements. Monte Carlo Tree Search (MCTS) offers advantages such as model-free operation, strong interpretability, and anytime planning capability, but it suffers from large branching factors, excessive search depths, and poor convergence under sparse reward conditions in high-dimensional state spaces. To address these challenges, this paper proposes a Heuristic Rolling Monte Carlo Tree Search (HRMCTS) framework. First, the path planning problem is formulated as a constrained Markov decision process, where the state consists of position and heading, and actions… More >

Minhyeok Choi¹, Imran Ahsan², Hyunwook Yu¹, Taeyoung Choe¹, Mucheol Kim^1,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079504 - 08 May 2026

Abstract Large language models (LLMs) are increasingly integrated into recommender systems to support semantic reasoning, natural language understanding, and user-adaptive personalization. However, their reliance on static parametric knowledge and fixed representations limits robustness in dynamic environments, particularly under long-tail and cold-start conditions. Retrieval-augmented architectures have emerged to address these limitations by grounding LLMs in external, non-parametric knowledge sources. This systematic literature review synthesizes 138 peer-reviewed studies published between 2023 and 2025 in conferences and journals, focusing on retrieval-augmented and LLM-enhanced recommendation. We analyze these works through a three-dimensional framework covering: (i) domain application, (ii) semantic feature… More >

Soumalya De¹, Rahil Akhtar², Saiyed Umer², Ranjeet Kumar Rout³, G. G. Md. Nawaz Ali^4,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079340 - 08 May 2026

Abstract This work presents a comprehensive sustainable sentiment analysis system utilizing textual data, designed within a structured client-server architecture for real-time deployment. The system integrates dual feature representations Bag-of-Words (BoW) and Term Frequency–Inverse Document Frequency (TF-IDF) whose prediction scores are combined through a parameter-free score-level fusion strategy. The implementation of the proposed system consists of five major components. The first component involves the acquisition of textual data from various sources, followed by rigorous text preprocessing to eliminate noise and enhance data quality. The second component focuses on feature extraction, ensuring that the extracted features not only… More >

Gaeul Kim¹, Dohoon Kim^2,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078931 - 08 May 2026

Abstract Manned-Unmanned Teaming (MUM-T) is an operational system where manned and unmanned systems perform missions through a collaboration interface, expanding beyond defense into civilian domains. The core of MUM-T lies in the organic interaction between manned and unmanned systems. The Collaboration Interface enabling this interaction becomes a primary target for cyber attacks due to its reliance on wireless networks. Compromising the reliability of the collaboration interface goes beyond simple communication failures; it directly leads to mission failure and aircraft safety issues. Therefore, systematic threat analysis and assessment tailored to this specific domain are essential. This study… More >

Abdullah Alshammari^*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078634 - 08 May 2026

Abstract The proliferation of Internet of Things (IoT) devices in the infrastructure of smart cities has posed cybersecurity risks like never before, which have direct implications on the sustainability and energy consumption of cities. In this paper, a multi-faceted Threat-Resilient Energy-Conscious Security Framework (TRECSF) is introduced that combines intrusion detection methods powered by deep learning, blockchain-driven data integrity verification mechanism, and energy-aware security protocols in smart city ecosystems to achieve their sustainability. The new Hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model is introduced to the proposed architecture, which fulfills the purpose of the study to… More >

Liping Wang¹, Jiang Wu^1,2,*, Liang Wang³

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078591 - 08 May 2026

Abstract The rapid growth of networked systems and the increasing diversity of cyberattack behaviors have posed significant challenges to intrusion detection, particularly in scenarios characterized by high-dimensional features and severe class imbalance. Conventional detection approaches based on handcrafted rules or shallow representations often exhibit limited robustness under such conditions. To address these issues, this paper presents a hybrid deep learning framework for network intrusion detection that integrates complementary feature learning mechanisms within a dual-branch architecture. Specifically, a Transformer branch is employed to model long-range temporal dependencies in network traffic, while a convolutional neural network branch (CNN)… More >

Noor Mueen Mohammed Ali Hayder^1,2, Seyed Amin Hosseini Seno^2,*, Mehdi Ebady Manaa^3,4, Hamid Noori², Davood Zabihzadeh⁵

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078546 - 08 May 2026

Abstract With the rapid expansion of networked systems, Distributed Denial-of-Service (DDoS) attacks have become a major threat to Internet security and service availability. Due to their limited scalability, incapacity to capture temporal and relational relationships, and decreased detection accuracy under dynamic and high-volume network traffic, traditional machine learning algorithms frequently fail in large-scale DDoS scenarios. This encourages the application of deep learning techniques that can simulate intricate relationships. This survey systematically reviews graph-based deep learning and Transformer models for DDoS detection. We categorize methods for transforming network traffic into graph representations and analyze key architectures, including… More >

Hafiz Arslan Ramzan^1,*, Kamrul Islam², Md Ahbab Hussain³, Raiyan Muntasir Monim⁴, Sabit Md Asad⁴, Sadia Ramzan⁵

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077782 - 08 May 2026

Abstract Regression testing of large-scale, data-intensive software systems demands efficient test-case prioritization strategies to detect faults early while minimizing computational cost. Conventional prioritization methods, such as coverage-based and risk-based approaches, lack adaptability to evolving project dynamics and fail to leverage the rich test-execution data accumulated over continuous integration cycles. This study presents a Data-Driven Test-Case Prioritization (DD-TCP) Framework that incorporates statistical and machine-learning techniques to model the relationship between test-case features and historical fault detection outcomes. The framework extracts multidimensional attributes including code-change frequency, dependency metrics, execution duration, and past failure density, which are normalized and… More >

Muhammet Ali Karabulut¹, A. F. M. Shahen Shah², Al-Sakib Khan Pathan^3,*, Phillip G. Bradford⁴

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077625 - 08 May 2026

Abstract Today, technological progress is broad and deep. The next generation networks and systems will integrate features, technologies, and models requiring smooth cooperation between new and old technologies. This survey’s uniqueness is that it considers an integrated, hybrid and heterogeneous future where Internet of Things (IoT), Sixth-Generation (6G) mobile communications technology, and Artificial Intelligence (AI) will work together, providing a smart and connected Intelligent Transportation System (ITS). This smart ITS will give better road safety and optimized travel. Currently, there is a scarcity of surveys focusing particularly on smart ITS that is expected soon. In this More >

Shoeb Ali Syed¹, Zohaib Mushtaq^2,*, Akbare Yaqub³, Saifur Rahman⁴, Muhammad Irfan⁴, Saleh Al Dawsari^4,5,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.076869 - 08 May 2026

Abstract Blockchain networks are under mounting pressure from emerging complex zero-day attacks that cannot be prevented with conventional security measures. In this paper, we introduce NeuroChain Sentinel, a new bio-inspired cybersecurity model based on spiking neural networks for detecting anomalies in a distributed ledger system in real time. The main innovations are: a Temporal Spike Pattern Recognition algorithm for simulating the biological timing of the neural system to detect malicious transaction patterns; a distributed consensus-verification topology combined with blockchain algorithms; and small-scale neuromorphic engineering, resulting in an 87% reduction in computational load over conventional deep neural… More >