Karshiev Sanjar¹, Soyoun Bang¹, Sookhee Ryue², Heechul Jung^1,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3569-3583, 2024, DOI:10.32604/cmc.2024.048312

Abstract The advancement of navigation systems for the visually impaired has significantly enhanced their mobility by mitigating the risk of encountering obstacles and guiding them along safe, navigable routes. Traditional approaches primarily focus on broad applications such as wayfinding, obstacle detection, and fall prevention. However, there is a notable discrepancy in applying these technologies to more specific scenarios, like identifying distinct food crop types or recognizing faces. This study proposes a real-time application designed for visually impaired individuals, aiming to bridge this research-application gap. It introduces a system capable of detecting 20 different food crop types… More >

Yifan Liu¹, Shancang Li^1,*, Xinheng Wang², Li Xu³

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1233-1261, 2024, DOI:10.32604/cmes.2024.046473

Abstract The Industrial Internet of Things (IIoT) has brought numerous benefits, such as improved efficiency, smart analytics, and increased automation. However, it also exposes connected devices, users, applications, and data generated to cyber security threats that need to be addressed. This work investigates hybrid cyber threats (HCTs), which are now working on an entirely new level with the increasingly adopted IIoT. This work focuses on emerging methods to model, detect, and defend against hybrid cyber attacks using machine learning (ML) techniques. Specifically, a novel ML-based HCT modelling and analysis framework was proposed, in which regularisation and Random Forest were More >

Yang Wang^#, Xun Yang^#, Mingtang Ye, Yuhang Zhao, Runsen Chen, Min Da, Zhiqi Wang, Xuming Mo, Jirong Qi^*

Congenital Heart Disease, Vol.19, No.2, pp. 219-231, 2024, DOI:10.32604/chd.2024.048314

Abstract Congenital heart disease (CHD), the most prevalent congenital ailment, has seen advancements in the “dual indicator” screening program. This facilitates the early-stage diagnosis and treatment of children with CHD, subsequently enhancing their survival rates. While cardiac auscultation offers an objective reflection of cardiac abnormalities and function, its evaluation is significantly influenced by personal experience and external factors, rendering it susceptible to misdiagnosis and omission. In recent years, continuous progress in artificial intelligence (AI) has enabled the digital acquisition, storage, and analysis of heart sound signals, paving the way for intelligent CHD auscultation-assisted diagnostic technology. Although More > Graphic Abstract

Xiong Zhang^1,2, Minqing Zhang^1,2,3,*, Xu An Wang^1,2,3, Wen Jiang^1,2, Chao Jiang^1,2, Pan Yang^1,4

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1925-1938, 2024, DOI:10.32604/cmc.2024.050899

Abstract This paper proposes an artificial intelligence-based robust information hiding algorithm to address the issue of confidential information being susceptible to noise attacks during transmission. The algorithm we designed aims to mitigate the impact of various noise attacks on the integrity of secret information during transmission. The method we propose involves encoding secret images into stylized encrypted images and applies adversarial transfer to both the style and content features of the original and embedded data. This process effectively enhances the concealment and imperceptibility of confidential information, thereby improving the security of such information during transmission and… More >

Muchang Rao, Hang Qin^*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2647-2672, 2024, DOI:10.32604/cmc.2024.050380

Abstract More devices in the Intelligent Internet of Things (AIoT) result in an increased number of tasks that require low latency and real-time responsiveness, leading to an increased demand for computational resources. Cloud computing’s low-latency performance issues in AIoT scenarios have led researchers to explore fog computing as a complementary extension. However, the effective allocation of resources for task execution within fog environments, characterized by limitations and heterogeneity in computational resources, remains a formidable challenge. To tackle this challenge, in this study, we integrate fog computing and cloud computing. We begin by establishing a fog-cloud environment… More >

Mahmood Ul Haq¹, Muhammad Athar Javed Sethi¹, Najib Ben Aoun^2,3, Ala Saleh Alluhaidan^4,*, Sadique Ahmad^5,6, Zahid farid⁷

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2169-2186, 2024, DOI:10.32604/cmc.2024.049645

Abstract Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security, authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neural networks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since they do not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networks as a more robust design capable of retaining pose information and spatial correlations to recognize objects more like the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, and so on, which are… More >

Ahmed Alhussen¹, Arshiya S. Ansari^2,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1903-1923, 2024, DOI:10.32604/cmc.2024.049260

Abstract Traffic in today’s cities is a serious problem that increases travel times, negatively affects the environment, and drains financial resources. This study presents an Artificial Intelligence (AI) augmented Mobile Ad Hoc Networks (MANETs) based real-time prediction paradigm for urban traffic challenges. MANETs are wireless networks that are based on mobile devices and may self-organize. The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts. This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network (CSFPNN) technique to assess real-time data… More >

Moshe Dayan Sirapangi¹, S. Gopikrishnan^1,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2229-2251, 2024, DOI:10.32604/cmc.2024.047438

Abstract Medical Internet of Things (IoT) devices are becoming more and more common in healthcare. This has created a huge need for advanced predictive health modeling strategies that can make good use of the growing amount of multimodal data to find potential health risks early and help individuals in a personalized way. Existing methods, while useful, have limitations in predictive accuracy, delay, personalization, and user interpretability, requiring a more comprehensive and efficient approach to harness modern medical IoT devices. MAIPFE is a multimodal approach integrating pre-emptive analysis, personalized feature selection, and explainable AI for real-time health… More >

Umar Isa Abdulkadir^*, Anil Fernando^*

Journal on Artificial Intelligence, Vol.6, pp. 71-83, 2024, DOI:10.32604/jai.2024.045332

Abstract One of the significant issues the insurance industry faces is its ability to predict future claims related to individual policyholders. As risk varies from one policyholder to another, the industry has faced the challenge of using various risk factors to accurately predict the likelihood of claims by policyholders using historical data. Traditional machine-learning models that use neural networks are recognized as exceptional algorithms with predictive capabilities. This study aims to develop a deep learning model using sequential deep regression techniques for insurance claim prediction using historical data obtained from Kaggle with 1339 cases and eight… More >

Xianwei Jiang¹, Yanqiong Zhang^1,*, Juan Lei¹, Yudong Zhang^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1-40, 2024, DOI:10.32604/cmes.2024.047649

Abstract Research on Chinese Sign Language (CSL) provides convenience and support for individuals with hearing impairments to communicate and integrate into society. This article reviews the relevant literature on Chinese Sign Language Recognition (CSLR) in the past 20 years. Hidden Markov Models (HMM), Support Vector Machines (SVM), and Dynamic Time Warping (DTW) were found to be the most commonly employed technologies among traditional identification methods. Benefiting from the rapid development of computer vision and artificial intelligence technology, Convolutional Neural Networks (CNN), 3D-CNN, YOLO, Capsule Network (CapsNet) and various deep neural networks have sprung up. Deep Neural… More >