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 >

Jianing Zhang¹, Zhikui Chen^1,*, Jing Gao¹, Peng Li²

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.076717 - 08 May 2026

Abstract With the increasing development of ocean information technology, the multi-view fuzzy clustering is attracting increasing attention in pattern mining for massive multi-view ocean data of heterogeneous distributions, owing to its superior performance. However, the previous multi-view fuzzy clustering methods cannot fully consider informative topologies hidden in data distributions, which are crucial to recognize partitions of data. Moreover, they fail to capture invariant structures of multi-view ocean data in learning clustering-specific fusion representation. In addition, they do not take into consideration consistencies contained in the manifolds of data generation in mining soft patterns. To address those… More >

Sarmad Dheyaa Azeez¹, Saadaldeen Rashid Ahmed^2,3, Muhammad Ilyas^4,*, Abu Saleh Musa Miah⁵, Fahmid Al Farid^6,7,*, Md. Hezerul Abdul Karim^6,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.076283 - 08 May 2026

Abstract Traditional Intrusion Detection Systems (IDSs) that rely on fixed signatures or basic machine learning often struggle with sophisticated, multi-stage cyberattacks and previously unknown threats. To fix these problems, this paper introduces IntrusionNet, a mixed deep learning system that combines Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Autoencoders in a two-part design. Differing from typical stacked models, IntrusionNet works on two levels at the same time. First, a supervised CNN-RNN process pulls spatial-temporal data from traffic flows to sort well-known attack patterns. Second, an unsupervised Autoencoder process spots new anomalies by looking at reconstruction… More >

Armaghan Azam^1,#, Arshad Iqbal^1,2,#,*, M. Mohsin Khan^1,2, Naveed Ahmad³, Mohamad Ladan³

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.075960 - 08 May 2026

Abstract Public safety and security remain critical concerns in urban environments. Detecting suspicious activities in densely populated areas poses significant challenges for modern smart cities due to occlusions, limited fixed-camera coverage, and the dynamic nature of large crowds. To address this problem, this paper proposes a Artificial Intelligence (AI)-driven unmanned aerial surveillance framework for proactive monitoring and abnormal activity recognition. The system leverages an Long-term Recurrent Convolutional Network (LRCN)-enabled architecture capable of extracting spatiotemporal patterns from aerial video streams, allowing it to detect suspicious behavior with high precision. Three deep learning models are comparatively evaluated: (i)… More >

Mohamed ElKashouty^1,*, Mohd Yawar Ali Khan^1,*, Samyah Salem Refadah²

Revue Internationale de Géomatique, Vol.35, pp. 161-177, 2026, DOI:10.32604/rig.2026.079702 - 30 April 2026

Abstract Water scarcity is a significant challenge in arid and semi-arid countries, underscoring the importance of thoroughly studying groundwater resources. Egypt, especially in the Darb El-Arbaein region of the southern Western Desert, faces various water challenges and relies primarily on groundwater from the Nubian Sandstone aquifer. Proper management of this groundwater is essential for addressing these challenges. The study examines the spatial and temporal variations in the hydrogeochemistry of the Nubian sandstone aquifer. Data collected from the aquifer’s monitoring network include key hydrogeochemical parameters, such as total dissolved solid (TDS) and piezometric heads, over different periods.… More >

Dongmei Lian^#, Zhou Li^#, Bizhen Lin, Shaoping Zhang, Susu Yuan, Yunfa Yao, Yudong Ju, Zhengfeng Lai^*

Phyton-International Journal of Experimental Botany, Vol.95, No.4, 2026, DOI:10.32604/phyton.2026.078378 - 28 April 2026

Abstract Pepper (Capsicum annuum L.) is highly susceptible to chilling stress, which severely constrains its growth and productivity. Although the eco-friendly biostimulant γ-polyglutamic acid (γ-PGA) has shown promise in enhancing plant tolerance to abiotic stresses, its specific role and underlying mechanisms in alleviating chilling injury in pepper remain poorly understood. This study systematically investigated the physiological and molecular mechanisms by which foliar application of 100 mg·L⁻¹ γ-PGA enhances chilling tolerance in pepper seedlings. Our results demonstrated that γ-PGA pretreatment significantly mitigated chilling-induced growth inhibition and promoted root development, evidenced by a 110.8% increase in the number of root… More >

Nishu Gupta^1,*, Manuel J. C. S. Reis²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.082568 - 27 April 2026

Abstract This article has no abstract. More >