Abdulaziz A. Alsulami^1,*, Badraddin Alturki², Ahmad J. Tayeb², Rayan A. Alsemmeari², Raed Alsini¹

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076413 - 09 April 2026

Abstract Intrusion Detection Systems (IDS) play a critical role in protecting networked environments from cyberattacks. They have become increasingly important in smart environments such as the Internet of Things (IoT) systems. However, IDS for IoT networks face critical challenges due to hardware constraints, including limited computational resources and storage capacity, which lead to high feature dimensionality, prediction uncertainty, and increased processing cost. These factors make many conventional detection approaches unsuitable for real-time IoT deployment. To address these challenges, this paper proposes an adaptive intrusion detection framework that intelligently balances detection accuracy and computational efficiency. The proposed… More >

Sultan Kahla¹, Zuping Zhang^1,*, Majed Alsafyani², Ahmed Emara^3,*, Mohammod Abdullah Bin Hossain⁴, Abdulwahab Osman Sheikhdon¹

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076402 - 09 April 2026

Abstract The effective diagnosis and treatment planning require the correct classification of the cerebral neoplasia, such as glioma, meningioma, and pituitary tumors. The recent developments in the deep learning field have made a significant contribution to the field of image analysis in medicine; however, Vision Transformers (ViTs) have achieved good results but are computationally complex. This paper presents NeuroTriad-ViT, a proprietary large-scale Vision Transformer of 235 million parameters, which is represented as a high-performance teacher model to classify brain tumors. Knowledge distillation is applied in an attempt to transfer the representations that the teacher learned to… More >

Hafiz Safdar Sultan¹, Labiba Gillani Fahad¹, Noshina Tariq², Noor Zaman Jahnjhi^3,4, Mamoona Humayun^5,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076379 - 09 April 2026

Abstract Smart homes enable elderly individuals and people with impairments to live independently through remote monitoring of their activities. Sequences of sensor activations are mapped with their associated labels to recognize different activities. Activity recognition in a multi-resident environment is challenging due to multiple activities performed by different residents in parallel. A novel multi-resident activity recognition approach is proposed to separate the sensor events based on their location. A spatial matrix is generated to capture the spatial and temporal patterns of the activities, and activations of sensors are recorded as binary values. The spatial matrix is More >

Qiuxiao Mou, Haoyu Gui, Xianghong Tang^*, Jianguang Lu

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076251 - 09 April 2026

Abstract Electrocardiogram (ECG) is a widely used non-invasive tool for diagnosing cardiovascular diseases. ECG zero-shot classification involves pre-training a model on a large dataset to classify unknown disease categories. However, existing ECG feature extraction networks often neglect key lead signals and spatial topology dependencies during cross-modal alignment. To address these issues, we propose a multimodal channel compression graph attention alignment network (MCCGAA). MCCGAA incorporates a channel attention module (CAM) to effectively integrate key lead features and a graph attention-based alignment network to capture spatial dependencies, enhancing cross-modal alignment. Additionally, MCCGAA employs a log-sum-exp loss function, improving More >

Muhammad Mukhtar^1,2, Farizah Yunus¹, Ahmad Shukri Mohd Noor^1,*, Zulfiqar Ali³, Muhammad Junaid^4,*, Mehmood Ahmed⁴

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076246 - 09 April 2026

Abstract The increasing adoption of 5G cellular networks has introduced significant challenges for network operators. The main challenge lies in the management of seamless handoff (HO), which occurs owing to the rapid expansion of equipment, data, and network complexity. To address this challenge, a model named optimal HO management deep learning neural network (OHMDLNN) is proposed. The model is trained on network activity data, and it uses KPIs (key performance indicators) and system-level parameters to make HO decisions. As demonstrated in the article, OHMDLNN is successful in analyzing the effect and interdependence of KPIs from both… More >

Zhi Zhang^1,2, Bingyu Sun^1,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076236 - 09 April 2026

Abstract In facial landmark detection, shape deviations induced by large poses and exaggerated expressions often prevent existing algorithms from simultaneously achieving fine-grained local accuracy and holistic global shape constraints. To address this, we propose a Transformer-Conv Attention-based Method (TCAM). Built upon a hybrid coordinate-heatmap regression backbone, TCAM integrates the long-range dependency modeling of Transformers with the local feature extraction advantages of Depthwise Convolution (DWConv). Specifically, by partitioning feature maps into sub-regions and applying Transformer modeling, the module enforces sparse linear constraints on global information, effectively mitigating the issues caused by discontinuous landmark distributions. Experimental results on More >

Jie Wang, Deming Lei^*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076202 - 09 April 2026

Abstract Both flexible jobshop scheduling and parallel batch processing machine scheduling have been extensively considered; however, the flexible jobshop and parallel batch processing machine scheduling problem (FJPBPMSP) is prevalent in real-life manufacturing processes and is seldom investigated. In this study, FJPBPMSP is examined, where flexible processing and batch processing are performed sequentially. An adaptive imperialist competitive algorithm with cooperation (CAICA) is proposed to minimize makespan and total energy consumption simultaneously. In CAICA, a four-string representation is adopted, and initial empires with novel structures are formed by uniformly dividing the population. An adaptive assimilation and revolution are More >

Zahra Farhadpour^1,*, Tan Fong Ang^1,*, Chee Sun Liew²

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076163 - 09 April 2026

Abstract The heterogeneity and dynamic behavior of fog computing environments introduce major challenges to achieving optimal application placement. Limited fog resources and varying workloads often necessitate offloading applications beyond their local clusters, making it difficult to maintain the required level of service quality under varying conditions. In this context, placement methods must ensure a balanced trade-off between multiple objectives, such as time and cost, while maintaining reliable adherence to constraints like application deadlines and limited fog-node memory. Existing solutions, including heuristic, metaheuristic, learning-based, and hybrid optimization approaches, have been proposed to address these challenges. However, many… More >

Raed Alotaibi^1,*, Muhammad Atta Othman Ahmed², Omar Reyad^3,4,*, Nahla Fathy Omran⁵

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076156 - 09 April 2026

Abstract The widespread use of social media has made assessing users’ tastes and preferences increasingly complex and important. At the same time, the rapid dissemination of misinformation on these platforms poses a critical challenge, driving significant efforts to develop effective detection methods. This study offers a comprehensive analysis leveraging advanced Machine Learning (ML) techniques to classify news articles as fake or true, contributing to discourse on media integrity and combating misinformation. The suggested method employed a diverse dataset encompassing a wide range of topics. The method evaluates the performance of five ML models: Artificial Neural Networks… More >

Qian Hu, Lei Tan^*, Qingjun Yuan, Zong Zuo, Yan Li

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076137 - 09 April 2026

Abstract Multi-behavior recommendation methods leverage various types of user interaction behaviors to make personalized recommendations. Behavior paths formed by diverse user interactions reveal distinctive patterns between users and items. Modeling these behavioral paths captures multidimensional behavioral features, which enables accurate learning of user preferences and improves recommendation accuracy. However, existing methods share two critical limitations: (1) Lack of modeling for the diversity of behavior paths; (2) Ignoring the impact of item attribute information on user behavior paths. To address these issues, we propose a Directed Behavior path graph-based Multi-behavior Recommendation method (DBMR). Specifically, we first construct… More >