Moshe Dayan Sirapangi¹, S. Gopikrishnan^1,*

CMC-Computers, Materials & Continua, Vol., , 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 monitoring and disease detection. By… More >

Shuyi Li, Hongchao Hu^*, Xiaohan Yang, Guozhen Cheng, Wenyan Liu, Wei Guo

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.047275

Abstract Adversarial distillation (AD) has emerged as a potential solution to tackle the challenging optimization problem of loss with hard labels in adversarial training. However, fixed sample-agnostic and student-egocentric attack strategies are unsuitable for distillation. Additionally, the reliability of guidance from static teachers diminishes as target models become more robust. This paper proposes an AD method called Learnable Distillation Attack Strategies and Evolvable Teachers Adversarial Distillation (LDAS&ET-AD). Firstly, a learnable distillation attack strategies generating mechanism is developed to automatically generate sample-dependent attack strategies tailored for distillation. A strategy model is introduced to produce attack strategies that enable adversarial examples (AEs) to… More >

Ruchun Jia¹, Jianwei Zhang^1,*, Lin Yi¹, Yunxiang Han¹, Feike Yang²

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.047543

Abstract In order to enhance the accuracy of Air Traffic Control (ATC) cybersecurity attack detection, in this paper, a new clustering detection method is designed for air traffic control network security attacks. The feature set for ATC cybersecurity attacks is constructed by setting the feature states, adding recursive features, and determining the feature criticality. The expected information gain and entropy of the feature data are computed to determine the information gain of the feature data and reduce the interference of similar feature data. An autoencoder is introduced into the AI (artificial intelligence) algorithm to encode and decode the characteristics of ATC… More >

Halil Ibrahim Okur^1,2,*, Kadir Tohma¹, Ahmet Sertbas²

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.050585

Abstract Text classification, by automatically categorizing texts, is one of the foundational elements of natural language processing applications. This study investigates how text classification performance can be improved through the integration of entity-relation information obtained from the Wikidata (Wikipedia database) database and BERT-based pre-trained Named Entity Recognition (NER) models. Focusing on a significant challenge in the field of natural language processing (NLP), the research evaluates the potential of using entity and relational information to extract deeper meaning from texts. The adopted methodology encompasses a comprehensive approach that includes text preprocessing, entity detection, and the integration of relational information. Experiments conducted on… More >

Hao Sun^*, Xiubo Chen, Kaiguo Yuan

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.049159

Abstract Federated learning ensures data privacy and security by sharing models among multiple computing nodes instead of plaintext data. However, there is still a potential risk of privacy leakage, for example, attackers can obtain the original data through model inference attacks. Therefore, safeguarding the privacy of model parameters becomes crucial. One proposed solution involves incorporating homomorphic encryption algorithms into the federated learning process. However, the existing federated learning privacy protection scheme based on homomorphic encryption will greatly reduce the efficiency and robustness when there are performance differences between parties or abnormal nodes. To solve the above problems, this paper proposes a… More >

Danyang Zheng, Jinchen Xv, Feng Yue, Qiming Du, Zhiheng Wang, Zheng Shan^*

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.048956

Abstract As cloud quantum computing gains broader acceptance, a growing quantity of researchers are directing their focus towards this domain. Nevertheless, the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity, which in turn hampers users from achieving optimal satisfaction. Therefore, cloud quantum computing service providers require a unified analysis and scheduling framework for their quantum resources and user jobs to meet the ever-growing usage demands. This paper introduces a new multi-programming scheduling framework for quantum computing in a cloud environment. The framework addresses the issue of limited quantum computing resources in cloud environments and ensures… More >

Shuai Zhou¹, Dazhi Wang^1,*, Yongliang Ni², Keling Song², Yanming Li²

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.048859

Abstract In the process of identifying parameters for a permanent magnet synchronous motor, the particle swarm optimization method is prone to being stuck in local optima in the later stages of iteration, resulting in low parameter accuracy. This work proposes a fuzzy particle swarm optimization approach based on the transformation function and the filled function. This approach addresses the topic of particle swarm optimization in parameter identification from two perspectives. Firstly, the algorithm uses a transformation function to change the form of the fitness function without changing the position of the extreme point of the fitness function, making the extreme point… More >

Husam Ahmad Al Hamad^*, Mohammad Shehab^*

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.048527

Abstract Recognizing handwritten characters remains a critical and formidable challenge within the realm of computer vision. Although considerable strides have been made in enhancing English handwritten character recognition through various techniques, deciphering Arabic handwritten characters is particularly intricate. This complexity arises from the diverse array of writing styles among individuals, coupled with the various shapes that a single character can take when positioned differently within document images, rendering the task more perplexing. In this study, a novel segmentation method for Arabic handwritten scripts is suggested. This work aims to locate the local minima of the vertical and diagonal word image densities… More >

Dmitry Gura^1,2, Bo Dong^3,*, Duaa Mehiar⁴, Nidal Al Said⁵

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.048238

Abstract The motivation for this study is that the quality of deep fakes is constantly improving, which leads to the need to develop new methods for their detection. The proposed Customized Convolutional Neural Network method involves extracting structured data from video frames using facial landmark detection, which is then used as input to the CNN. The customized Convolutional Neural Network method is the date augmented-based CNN model to generate ‘fake data’ or ‘fake images’. This study was carried out using Python and its libraries. We used 242 films from the dataset gathered by the Deep Fake Detection Challenge, of which 199… More >

Chunhua Wang^1,2, Wenqian Shang^1,2,*, Tong Yi^3,*, Haibin Zhu⁴

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.048135

Abstract The advent of self-attention mechanisms within Transformer models has significantly propelled the advancement of deep learning algorithms, yielding outstanding achievements across diverse domains. Nonetheless, self-attention mechanisms falter when applied to datasets with intricate semantic content and extensive dependency structures. In response, this paper introduces a Diffusion Sampling and Label-Driven Co-attention Neural Network (DSLD), which adopts a diffusion sampling method to capture more comprehensive semantic information of the data. Additionally, the model leverages the joint correlation information of labels and data to introduce the computation of text representation, correcting semantic representation biases in the data, and increasing the accuracy of semantic… More >