Jasem Almotiri^*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2109-2142, 2025, DOI:10.32604/cmc.2025.062923 - 16 April 2025

Abstract The evolving field of Alzheimer’s disease (AD) diagnosis has greatly benefited from deep learning models for analyzing brain magnetic resonance (MR) images. This study introduces Dynamic GradNet, a novel deep learning model designed to increase diagnostic accuracy and interpretability for multiclass AD classification. Initially, four state-of-the-art convolutional neural network (CNN) architectures, the self-regulated network (RegNet), residual network (ResNet), densely connected convolutional network (DenseNet), and efficient network (EfficientNet), were comprehensively compared via a unified preprocessing pipeline to ensure a fair evaluation. Among these models, EfficientNet consistently demonstrated superior performance in terms of accuracy, precision, recall, and… More >

Feng Zhu^*, Kailin Wu, Jie Ding

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2573-2598, 2025, DOI:10.32604/cmc.2025.062007 - 16 April 2025

Abstract With the widespread application of Internet of Things (IoT) technology, the processing of massive real-time streaming data poses significant challenges to the computational and data-processing capabilities of systems. Although distributed streaming data processing frameworks such as Apache Flink and Apache Spark Streaming provide solutions, meeting stringent response time requirements while ensuring high throughput and resource utilization remains an urgent problem. To address this, the study proposes a formal modeling approach based on Performance Evaluation Process Algebra (PEPA), which abstracts the core components and interactions of cloud-based distributed streaming data processing systems. Additionally, a generic service… More >

Meng Zhu^1,2, Xingyue Wang³, Honge Ren^3,4,*, Abeer Hakeem⁵, Linda Mohaisen^5,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2889-2905, 2025, DOI:10.32604/cmc.2025.061421 - 16 April 2025

Abstract Changing a person’s posture and low resolution are the key challenges for person re-identification (ReID) in various deep learning applications. In this paper, we introduce an innovative architecture using a dual attention network that includes an attention module and a joint measurement module of spatial-temporal information. The proposed approach can be classified into two main tasks. Firstly, the spatial attention feature map is formed by aggregating features in the spatial dimension. Additionally, the same operation is carried out on the channel dimension to form channel attention feature maps. Therefore, the receptive field size is adjusted… More >

Sungbum Kim¹, Gwangsik Lee², Jian Song², Insoo Lee², Taeshik Shon^3,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 661-683, 2025, DOI:10.32604/cmc.2025.063687 - 26 March 2025

Abstract The smart home platform integrates with Internet of Things (IoT) devices, smartphones, and cloud servers, enabling seamless and convenient services. It gathers and manages extensive user data, including personal information, device operations, and patterns of user behavior. Such data plays an essential role in criminal investigations, highlighting the growing importance of specialized smart home forensics. Given the rapid advancement in smart home software and hardware technologies, many companies are introducing new devices and services that expand the market. Consequently, scalable and platform-specific forensic research is necessary to support efficient digital investigations across diverse smart home… More >

Faten S. Alamri¹, Tariq Sadad^2,*, Ahmed S. Almasoud³, Raja Atif Aurangzeb⁴, Amjad Khan³

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1023-1039, 2025, DOI:10.32604/cmc.2025.061890 - 26 March 2025

Abstract Mango farming significantly contributes to the economy, particularly in developing countries. However, mango trees are susceptible to various diseases caused by fungi, viruses, and bacteria, and diagnosing these diseases at an early stage is crucial to prevent their spread, which can lead to substantial losses. The development of deep learning models for detecting crop diseases is an active area of research in smart agriculture. This study focuses on mango plant diseases and employs the ConvNeXt and Vision Transformer (ViT) architectures. Two datasets were used. The first, MangoLeafBD, contains data for mango leaf diseases such as… More >

Shih-Lin Lin^*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 537-557, 2025, DOI:10.32604/cmc.2025.061379 - 26 March 2025

Abstract Tires are integral to vehicular systems, directly influencing both safety and overall performance. Traditional tire pressure inspection methods—such as manual or gauge-based approaches—are often time-consuming, prone to inconsistency, and lack the flexibility needed to meet diverse operational demands. In this research, we introduce an AI-driven tire pressure detection system that leverages an enhanced GoogLeNet architecture incorporating a novel Softplus-LReLU activation function. By combining the smooth, non-saturating characteristics of Softplus with a linear adjustment term, this activation function improves computational efficiency and helps stabilize network gradients, thereby mitigating issues such as gradient vanishing and neuron death.… More >

Rifat Sarker Aoyon¹, Ismail Hossain², M. Abdullah-Al-Wadud³, Jia Uddin^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2617-2640, 2025, DOI:10.32604/cmes.2025.060917 - 03 March 2025

Abstract This research introduces a unique approach to segmenting breast cancer images using a U-Net-based architecture. However, the computational demand for image processing is very high. Therefore, we have conducted this research to build a system that enables image segmentation training with low-power machines. To accomplish this, all data are divided into several segments, each being trained separately. In the case of prediction, the initial output is predicted from each trained model for an input, where the ultimate output is selected based on the pixel-wise majority voting of the expected outputs, which also ensures data privacy.… More >

Ahlem Walha¹, Amel Ksibi^2,*, Mohammed Zakariah^3,*, Manel Ayadi², Tagrid Alshalali², Oumaima Saidani², Leila Jamel², Nouf Abdullah Almujally²

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2959-3001, 2025, DOI:10.32604/cmes.2025.060545 - 03 March 2025

Abstract Dementia is a neurological disorder that affects the brain and its functioning, and women experience its effects more than men do. Preventive care often requires non-invasive and rapid tests, yet conventional diagnostic techniques are time-consuming and invasive. One of the most effective ways to diagnose dementia is by analyzing a patient’s speech, which is cheap and does not require surgery. This research aims to determine the effectiveness of deep learning (DL) and machine learning (ML) structures in diagnosing dementia based on women’s speech patterns. The study analyzes data drawn from the Pitt Corpus, which contains… More >

Gaoshuai Su^1,2, Hongyang Li^1,*, Huafeng Chen^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2527-2545, 2025, DOI:10.32604/cmes.2025.059452 - 03 March 2025

Abstract Brain tumors, one of the most lethal diseases with low survival rates, require early detection and accurate diagnosis to enable effective treatment planning. While deep learning architectures, particularly Convolutional Neural Networks (CNNs), have shown significant performance improvements over traditional methods, they struggle to capture the subtle pathological variations between different brain tumor types. Recent attention-based models have attempted to address this by focusing on global features, but they come with high computational costs. To address these challenges, this paper introduces a novel parallel architecture, ParMamba, which uniquely integrates Convolutional Attention Patch Embedding (CAPE) and the… More >

Jawad Ahmad Ansari¹, Mohamad Khairi Ishak^2,*, Khalid Ammar²

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2311-2330, 2025, DOI:10.32604/cmc.2025.057848 - 17 February 2025

Abstract Cyber-Physical System (CPS) devices are increasing exponentially. Lacking confidentiality creates a vulnerable network. Thus, demanding the overall system with the latest and robust solutions for the defence mechanisms with low computation cost, increased integrity, and surveillance. The proposal of a mechanism that utilizes the features of authenticity measures using the Destination Sequence Distance Vector (DSDV) routing protocol which applies to the multi-WSN (Wireless Sensor Network) of IoT devices in CPS which is developed for the Device-to-Device (D2D) authentication developed from the local-chain and public chain respectively combined with the Software Defined Networking (SDN) control and… More >