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  • Open Access

    ARTICLE

    An Enhanced Lung Cancer Detection Approach Using Dual-Model Deep Learning Technique

    Sumaia Mohamed Elhassan1, Saad Mohamed Darwish1,*, Saleh Mesbah Elkaffas2

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 835-867, 2025, DOI:10.32604/cmes.2024.058770 - 17 December 2024

    Abstract Lung cancer continues to be a leading cause of cancer-related deaths worldwide, emphasizing the critical need for improved diagnostic techniques. Early detection of lung tumors significantly increases the chances of successful treatment and survival. However, current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung tissue. Single-model deep learning technologies for lung cancer detection, while beneficial, cannot capture the full range of features present in medical imaging data, leading to incomplete or inaccurate detection. Furthermore, it may not be robust enough to handle the… More >

  • Open Access

    ARTICLE

    A Scalable and Generalized Deep Ensemble Model for Road Anomaly Detection in Surveillance Videos

    Sarfaraz Natha1,2,*, Fareed A. Jokhio1, Mehwish Laghari1, Mohammad Siraj3,*, Saif A. Alsaif3, Usman Ashraf4, Asghar Ali5

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3707-3729, 2024, DOI:10.32604/cmc.2024.057684 - 19 December 2024

    Abstract Surveillance cameras have been widely used for monitoring in both private and public sectors as a security measure. Close Circuits Television (CCTV) Cameras are used to surveillance and monitor the normal and anomalous incidents. Real-world anomaly detection is a significant challenge due to its complex and diverse nature. It is difficult to manually analyze because vast amounts of video data have been generated through surveillance systems, and the need for automated techniques has been raised to enhance detection accuracy. This paper proposes a novel deep-stacked ensemble model integrated with a data augmentation approach called Stack… More >

  • Open Access

    ARTICLE

    Augmenting Internet of Medical Things Security: Deep Ensemble Integration and Methodological Fusion

    Hamad Naeem1, Amjad Alsirhani2,*, 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 >

  • Open Access

    ARTICLE

    Stroke Electroencephalogram Data Synthesizing through Progressive Efficient Self-Attention Generative Adversarial Network

    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 >

  • Open Access

    ARTICLE

    DeepBio: A Deep CNN and Bi-LSTM Learning for Person Identification Using Ear Biometrics

    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 >

  • Open Access

    ARTICLE

    Resilience Augmentation in Unmanned Weapon Systems via Multi-Layer Attention Graph Convolutional Neural Networks

    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 >

  • Open Access

    ARTICLE

    Improving Diversity with Multi-Loss Adversarial Training in Personalized News Recommendation

    Ruijin Xue1,2, Shuang Feng1,2,*, Qi Wang1,2

    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 >

  • Open Access

    ARTICLE

    Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

    Atif Mehmood1,2, Zhonglong Zheng1,*, Rizwan Khan1, Ahmad Al Smadi3, Farah Shahid1,2, 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 >

  • Open Access

    ARTICLE

    YOLO-Based Damage Detection with StyleGAN3 Data Augmentation for Parcel Information-Recognition System

    Seolhee Kim1, Sang-Duck Lee2,*

    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 >

  • Open Access

    ARTICLE

    An Experimental and Numerical Thermal Flow Analysis in a Solar Air Collector with Different Delta Wing Height Ratios

    Ghobad Shafiei Sabet1,*, Ali Sari1, Ahmad Fakhari2,*, 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 >

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