Hamad Naeem¹, Amjad Alsirhani^2,*, Faeiz M. Alserhani³, Farhan Ullah⁴, Ondrej Krejcar¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2185-2223, 2024, DOI:10.32604/cmes.2024.056308 - 31 October 2024

Abstract When it comes to smart healthcare business systems, network-based intrusion detection systems are crucial for protecting the system and its networks from malicious network assaults. To protect IoMT devices and networks in healthcare and medical settings, our proposed model serves as a powerful tool for monitoring IoMT networks. This study presents a robust methodology for intrusion detection in Internet of Medical Things (IoMT) environments, integrating data augmentation, feature selection, and ensemble learning to effectively handle IoMT data complexity. Following rigorous preprocessing, including feature extraction, correlation removal, and Recursive Feature Elimination (RFE), selected features are standardized… More >

Suzhe Wang^*, Xueying Zhang, Fenglian Li, Zelin Wu

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1177-1196, 2024, DOI:10.32604/cmc.2024.056016 - 15 October 2024

Abstract Early and timely diagnosis of stroke is critical for effective treatment, and the electroencephalogram (EEG) offers a low-cost, non-invasive solution. However, the shortage of high-quality patient EEG data often hampers the accuracy of diagnostic classification methods based on deep learning. To address this issue, our study designed a deep data amplification model named Progressive Conditional Generative Adversarial Network with Efficient Approximating Self Attention (PCGAN-EASA), which incrementally improves the quality of generated EEG features. This network can yield full-scale, fine-grained EEG features from the low-scale, coarse ones. Specially, to overcome the limitations of traditional generative models… More >

Anshul Mahajan^*, Sunil K. Singla

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1623-1649, 2024, DOI:10.32604/cmes.2024.054468 - 27 September 2024

Abstract The identification of individuals through ear images is a prominent area of study in the biometric sector. Facial recognition systems have faced challenges during the COVID-19 pandemic due to mask-wearing, prompting the exploration of supplementary biometric measures such as ear biometrics. The research proposes a Deep Learning (DL) framework, termed DeepBio, using ear biometrics for human identification. It employs two DL models and five datasets, including IIT Delhi (IITD-I and IITD-II), annotated web images (AWI), mathematical analysis of images (AMI), and EARVN1. Data augmentation techniques such as flipping, translation, and Gaussian noise are applied to More >

Kexin Wang^*, Yingdong Gou, Dingrui Xue^*, Jiancheng Liu, Wanlong Qi, Gang Hou, Bo Li

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2941-2962, 2024, DOI:10.32604/cmc.2024.052893 - 15 August 2024

Abstract The collective Unmanned Weapon System-of-Systems (UWSOS) network represents a fundamental element in modern warfare, characterized by a diverse array of unmanned combat platforms interconnected through heterogeneous network architectures. Despite its strategic importance, the UWSOS network is highly susceptible to hostile infiltrations, which significantly impede its battlefield recovery capabilities. Existing methods to enhance network resilience predominantly focus on basic graph relationships, neglecting the crucial higher-order dependencies among nodes necessary for capturing multi-hop meta-paths within the UWSOS. To address these limitations, we propose the Enhanced-Resilience Multi-Layer Attention Graph Convolutional Network (E-MAGCN), designed to augment the adaptability of More >

Ruijin Xue^1,2, Shuang Feng^1,2,*, Qi Wang^1,2

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3107-3122, 2024, DOI:10.32604/cmc.2024.052600 - 15 August 2024

Abstract Users’ interests are often diverse and multi-grained, with their underlying intents even more so. Effectively capturing users’ interests and uncovering the relationships between diverse interests are key to news recommendation. Meanwhile, diversity is an important metric for evaluating news recommendation algorithms, as users tend to reject excessive homogeneous information in their recommendation lists. However, recommendation models themselves lack diversity awareness, making it challenging to achieve a good balance between the accuracy and diversity of news recommendations. In this paper, we propose a news recommendation algorithm that achieves good performance in both accuracy and diversity. Unlike… More >

Atif Mehmood^1,2, Zhonglong Zheng^1,*, Rizwan Khan¹, Ahmad Al Smadi³, Farah Shahid^1,2, Shahid Iqbal⁴, Mutasem K. Alsmadi⁵, Yazeed Yasin Ghadi⁶, Syed Aziz Shah⁸, Mostafa M. Ibrahim⁷

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2081-2098, 2024, DOI:10.32604/cmc.2024.046869 - 15 August 2024

Abstract Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease (AD). Mild cognitive impairment (MCI) is a condition that falls between the spectrum of normal cognitive function and AD. However, previous studies have mainly used handcrafted features to classify MCI, AD, and normal control (NC) individuals. This paper focuses on using gray matter (GM) scans obtained through magnetic resonance imaging (MRI) for the diagnosis of individuals with MCI, AD, and NC. To improve classification performance, we developed two transfer learning strategies with data augmentation (i.e., shear range, rotation, zoom… More >

Seolhee Kim¹, Sang-Duck Lee^2,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 195-215, 2024, DOI:10.32604/cmc.2024.052070 - 18 July 2024

Abstract Damage to parcels reduces customer satisfaction with delivery services and increases return-logistics costs. This can be prevented by detecting and addressing the damage before the parcels reach the customer. Consequently, various studies have been conducted on deep learning techniques related to the detection of parcel damage. This study proposes a deep learning-based damage detection method for various types of parcels. The method is intended to be part of a parcel information-recognition system that identifies the volume and shipping information of parcels, and determines whether they are damaged; this method is intended for use in the… More >

Ghobad Shafiei Sabet^1,*, Ali Sari¹, Ahmad Fakhari^2,*, Nasrin Afsarimanesh³, Dominic Organ⁴, Seyed Mehran Hoseini¹

Frontiers in Heat and Mass Transfer, Vol.22, No.2, pp. 491-509, 2024, DOI:10.32604/fhmt.2024.048290 - 20 May 2024

Abstract This study conducts both numerical and empirical assessments of thermal transfer and fluid flow characteristics in a Solar Air Collector (SAC) using a Delta Wing Vortex Generator (DWVG), and the effects of different height ratios (R = 0.6, 0.8, 1, 1.2 and 1.4) in delta wing vortex generators, which were not considered in the earlier studies, are investigated. Energy and exergy analyses are performed to gain maximum efficiency. The Reynolds number based on the outlet velocity and hydraulic diameter falls between 4400 and 22000, corresponding to the volume flow rate of 5.21–26.07 m/h. It is More >

Do Nang Toan^1,*, Lam Thanh Hien², Ha Manh Toan¹, Nguyen Trong Vinh², Pham Trung Hieu¹

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 319-335, 2024, DOI:10.32604/iasc.2024.046925 - 21 May 2024

Abstract Cancer is one of the most dangerous diseases with high mortality. One of the principal treatments is radiotherapy by using radiation beams to destroy cancer cells and this workflow requires a lot of experience and skill from doctors and technicians. In our study, we focused on the 3D dose prediction problem in radiotherapy by applying the deep-learning approach to computed tomography (CT) images of cancer patients. Medical image data has more complex characteristics than normal image data, and this research aims to explore the effectiveness of data preprocessing and augmentation in the context of the… More >

Jiakai Li, Jianpeng Hu^*, Geng Zhang

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2481-2503, 2024, DOI:10.32604/cmc.2024.050005 - 15 May 2024

Abstract In the process of constructing domain-specific knowledge graphs, the task of relational triple extraction plays a critical role in transforming unstructured text into structured information. Existing relational triple extraction models face multiple challenges when processing domain-specific data, including insufficient utilization of semantic interaction information between entities and relations, difficulties in handling challenging samples, and the scarcity of domain-specific datasets. To address these issues, our study introduces three innovative components: Relation semantic enhancement, data augmentation, and a voting strategy, all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks. We first… More >