Sang-Hoon Choi¹, Ki-Woong Park^2,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4021-4039, 2025, DOI:10.32604/cmc.2025.061267 - 06 March 2025

Abstract The proliferation of Internet of Things (IoT) devices has established edge computing as a critical paradigm for real-time data analysis and low-latency processing. Nevertheless, the distributed nature of edge computing presents substantial security challenges, rendering it a prominent target for sophisticated malware attacks. Existing signature-based and behavior-based detection methods are ineffective against the swiftly evolving nature of malware threats and are constrained by the availability of resources. This paper suggests the Genetic Encoding for Novel Optimization of Malware Evaluation (GENOME) framework, a novel solution that is intended to improve the performance of malware detection and… More >

Tarak Dhaouadi¹, Hichem Mrabet^1,2,*, Adeeb Alhomoud³, Abderrazak Jemai^1,4

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4535-4554, 2025, DOI:10.32604/cmc.2025.060935 - 06 March 2025

Abstract The increasing adoption of Industrial Internet of Things (IIoT) systems in smart manufacturing is leading to raise cyberattack numbers and pressing the requirement for intrusion detection systems (IDS) to be effective. However, existing datasets for IDS training often lack relevance to modern IIoT environments, limiting their applicability for research and development. To address the latter gap, this paper introduces the HiTar-2024 dataset specifically designed for IIoT systems. As a consequence, that can be used by an IDS to detect imminent threats. Likewise, HiTar-2024 was generated using the AREZZO simulator, which replicates realistic smart manufacturing scenarios.… More >

Dongdong Zhang, Chunping Wang, Huiying Wang, Qiang Fu^*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4319-4338, 2025, DOI:10.32604/cmc.2025.060653 - 06 March 2025

Abstract Video camouflaged object detection (VCOD) has become a fundamental task in computer vision that has attracted significant attention in recent years. Unlike image camouflaged object detection (ICOD), VCOD not only requires spatial cues but also needs motion cues. Thus, effectively utilizing spatiotemporal information is crucial for generating accurate segmentation results. Current VCOD methods, which typically focus on exploring motion representation, often ineffectively integrate spatial and motion features, leading to poor performance in diverse scenarios. To address these issues, we design a novel spatiotemporal network with an encoder-decoder structure. During the encoding stage, an adjacent space-time More >

Bin Sun¹, Yinuo Wang¹, Tao Shen^1,*, Lu Zhang¹, Renkang Geng²

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4219-4236, 2025, DOI:10.32604/cmc.2025.060567 - 06 March 2025

Abstract Traffic datasets exhibit complex spatiotemporal characteristics, including significant fluctuations in traffic volume and intricate periodical patterns, which pose substantial challenges for the accurate forecasting and effective management of traffic conditions. Traditional forecasting models often struggle to adequately capture these complexities, leading to suboptimal predictive performance. While neural networks excel at modeling intricate and nonlinear data structures, they are also highly susceptible to overfitting, resulting in inefficient use of computational resources and decreased model generalization. This paper introduces a novel heuristic feature extraction method that synergistically combines the strengths of non-neural network algorithms with neural networks… More >

Jalil Ahmad^1,*, Muhammad Munir^2,*, Nashi Alqahtani^2,3, Tahira Alyas⁴, Muhammad Ahmad⁵, Sadia Bashir⁶, Fasiha Qurashi⁷, Abdul Ghafoor⁸, Hassan Ali–Dinar²

Phyton-International Journal of Experimental Botany, Vol.94, No.2, pp. 281-302, 2025, DOI:10.32604/phyton.2025.061534 - 06 March 2025

Abstract A steady rise in the overall population is creating an overburden on crops due to their global demand. On the other hand, given the current climate change and population growth, agricultural practices established during the Green Revolution are no longer viable. Consequently, innovative practices are the prerequisite of the time struggle with the rising global food demand. The potential of nanotechnology to reduce the phytotoxic effects of these ecological restrictions has shown significant promise. Nanoparticles (NPs) typically enhance plant resilience to stressors by fortifying the physical barrier, optimizing photosynthesis, stimulating enzymatic activity for defense, elevating More >

Syed Zain Ul Abideen¹, Mian Muhammad Kamal^2,*, Eaman Alharbi³, Ashfaq Ahmad Malik⁴, Wadee Alhalabi⁵, Muhammad Shahid Anwar^6,*, Liaqat Ali⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2191-2210, 2025, DOI:10.32604/cmes.2025.061744 - 03 March 2025

Abstract The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet of Things (IoT) applications, particularly in terms of ultra-reliable, secure, and energy-efficient communication. This study explores the integration of Reconfigurable Intelligent Surfaces (RIS) into IoT networks to enhance communication performance. Unlike traditional passive reflector-based approaches, RIS is leveraged as an active optimization tool to improve both backscatter and direct communication modes, addressing critical IoT challenges such as energy efficiency, limited communication range, and double-fading effects in backscatter communication. We propose a novel computational framework that combines… More >

Abdullah M. Alqahtani^1,*, Kamran Ahmad Awan², Abdulaziz Almaleh³, Osama Aletri⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3155-3179, 2025, DOI:10.32604/cmes.2025.061472 - 03 March 2025

Abstract Efficient resource management within Internet of Things (IoT) environments remains a pressing challenge due to the increasing number of devices and their diverse functionalities. This study introduces a neural network-based model that uses Long-Short-Term Memory (LSTM) to optimize resource allocation under dynamically changing conditions. Designed to monitor the workload on individual IoT nodes, the model incorporates long-term data dependencies, enabling adaptive resource distribution in real time. The training process utilizes Min-Max normalization and grid search for hyperparameter tuning, ensuring high resource utilization and consistent performance. The simulation results demonstrate the effectiveness of the proposed method, More >

Sonia Khan¹, Naqash Younas², Musaed Alhussein³, Wahib Jamal Khan², Muhammad Shahid Anwar^4,*, Khursheed Aurangzeb³

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2641-2660, 2025, DOI:10.32604/cmes.2025.060973 - 03 March 2025

Abstract Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks. However, existing methods often fail in dynamic and high-demand environments, leading to resource bottlenecks and increased energy consumption. This study aims to address these limitations by proposing the Quantum Inspired Adaptive Resource Management (QIARM) model, which introduces novel algorithms inspired by quantum principles for enhanced resource allocation. QIARM employs a quantum superposition-inspired technique for multi-state resource representation and an adaptive learning component to adjust resources in real time dynamically. In addition, an energy-aware scheduling module minimizes power More >

Adil Yousif^*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2869-2892, 2025, DOI:10.32604/cmes.2025.059786 - 03 March 2025

Abstract The Internet of Things (IoT) has emerged as an important future technology. IoT-Fog is a new computing paradigm that processes IoT data on servers close to the source of the data. In IoT-Fog computing, resource allocation and independent task scheduling aim to deliver short response time services demanded by the IoT devices and performed by fog servers. The heterogeneity of the IoT-Fog resources and the huge amount of data that needs to be processed by the IoT-Fog tasks make scheduling fog computing tasks a challenging problem. This study proposes an Adaptive Firefly Algorithm (AFA) for… More >

Francesco Triggiano^#, Giusy Diella^#, Mara Lorusso, Paolo Veneziani^*, Marilena D’Ambrosio, Daniela Nesta, Luigi Santacroce^*

BIOCELL, Vol.49, No.2, pp. 199-220, 2025, DOI:10.32604/biocell.2025.059322 - 28 February 2025

Abstract The composition of the vaginal microbiota (VMB) influences the health of the female reproductive tract. Several studies have shown how the absence of lactobacilli causes an imbalance in the vaginal microbial community, favoring the development of infections. The present study aims to evaluate the relationship between the VMB and human papillomavirus (HPV) infection to clarify the role of the vaginal microbiota in the persistence and clearance of HPV. Many researchers have provided the scientific community with information on the composition of the microbiota and how it may also influence HPV infection and the development of… More >