Jiawei Qin^1,2, Hua Zhang^1,*, Hanbing Yan², Tian Zhu², Song Hu¹, Dingyu Yan²

Intelligent Automation & Soft Computing, Vol., , DOI:10.32604/iasc.2024.047509

Abstract By the analysis of vulnerabilities of Android native system services, we find that some vulnerabilities are caused by inconsistent data transmission and inconsistent data processing logic between client and server. The existing research cannot find the above two types of vulnerabilities and the test cases of them face the problem of low coverage. In this paper, we propose an extraction method of test cases based on the native system services of the client and design a case construction method that supports multi-parameter mutation based on genetic algorithm and priority strategy. Based on the above method, we implement a detection tool-BArcherFuzzer… More >

Sujithra Sankar^1,*, S. Sathyalakshmi²

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.048408

Abstract In the era of advanced machine learning techniques, the development of accurate predictive models for complex medical conditions, such as thyroid cancer, has shown remarkable progress. Accurate predictive models for thyroid cancer enhance early detection, improve resource allocation, and reduce overtreatment. However, the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and transparency. This paper proposes a novel association-rule based feature-integrated machine learning model which shows better classification and prediction accuracy than present state-of-the-art models. Our study also focuses on the application of SHapley Additive exPlanations (SHAP) values as a powerful tool for explaining… More >

Chuyuan Wei^*, Jinzhe Li, Zhiyuan Wang, Shanshan Wan, Maozu Guo

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.047811

Abstract Deep neural network-based relational extraction research has made significant progress in recent years, and it provides data support for many natural language processing downstream tasks such as building knowledge graph, sentiment analysis and question-answering systems. However, previous studies ignored much unused structural information in sentences that could enhance the performance of the relation extraction task. Moreover, most existing dependency-based models utilize self-attention to distinguish the importance of context, which hardly deals with multiple-structure information. To efficiently leverage multiple structure information, this paper proposes a dynamic structure attention mechanism model based on textual structure information, which deeply integrates word embedding, named… More >

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 >

LING MA^1,#, ZIRUI JIANG^1,#, XIAO HOU¹, YUTING XU¹, ZIYUN CHEN¹, SIYI ZHANG¹, HANXUE LI¹, SHAOJIE MA¹, GENG ZHANG², XIUJUN WANG^1,*, JING JI^1,*

BIOCELL, Vol., , DOI:10.32604/biocell.2024.050868

Abstract Background: Regulatory proteins involved in human cellular division and proliferation, cyclin-dependent kinases 4 and 6 (CDK4/6) are overexpressed in numerous cancers, including triple-negative breast cancer (TNBC). TNBC is a common pathological subtype of breast cancer that is prone to recurrence and metastasis, and has a single treatment method. As one of the CDK4/6 inhibitors, abemaciclib can effectively inhibit the growth of breast tumors. In this study, we synthesized LA-D-B1, a derivative of Abemaciclib, and investigated its anti-tumor effects in breast cancer. Methods: Cellular viability was assessed using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Cell cloning and migration abilities were determined by… More >

YUNBAO GUO^1,#, XU LIU^1,#, QI XU², XIAOTONG ZHOU³, JIAWEI LIU³, YANYAN XU², YAN LU^2,*, HAIYAN LIU^2,*

BIOCELL, Vol., , DOI:10.32604/biocell.2024.049748

Abstract Background: Glioma is a kind of tumor that easily deteriorates and originates from glial cells in nerve tissue. Honokiol is a bisphenol compound that is an essential monomeric compound extracted from the roots and bark of Magnoliaceae plants. It also has anti-infection, antitumor, and immunomodulatory effects. In this study, we found that honokiol induces cell apoptosis in the human glioma cell lines U87-MG and U251-MG. However, the mechanism through which honokiol regulates glioma cell apoptosis is still unknown. Methods: We performed RNA-seq analysis of U251-MG cells treated with honokiol and control cells. Protein-protein interaction (PPI) network analysis was performed, and… More > Graphic Abstract

HONGBIAO WU^*, DONGFANG LIU

BIOCELL, Vol., , DOI:10.32604/biocell.2024.049466

Abstract Background: Colorectal cancer (CRC) represents a substantial risk to public health. Bevacizumab, the first US FDA-approved antiangiogenic drug (AAD) for human CRC treatment, faces resistance in patients. The role of lipid metabolism, particularly through OPA3-regulated lipid droplet production, in overcoming this resistance is under investigation. Methods: The protein expression pattern of OPA3 in CRC primary/normal tissues was evaluated by bioinformatics analysis. OPA3-overexpressed SW-480 and HCT-116 cell lines were established, and bevacizumab resistance and OPA3 effects on cell malignancy were examined. OPA3 protein/mRNA expression and lipid droplet-related genes were measured with Western blot and qRT-PCR. OPA3 subcellular localization was detected using… More > Graphic Abstract

Fatimah Mudhhi Alanazi^*, Bothina Abdelmeneem Elsobky, Shaimaa Aly Elmorsy

Computer Systems Science and Engineering, Vol., , DOI:10.32604/csse.2024.042401

Abstract Proliferation of technology, coupled with networking growth, has catapulted cybersecurity to the forefront of modern security concerns. In this landscape, the precise detection of cyberattacks and anomalies within networks is crucial, necessitating the development of efficient intrusion detection systems (IDS). This article introduces a framework utilizing the fusion of fuzzy sets with support vector machines (SVM), named FSVM. The core strategy of FSVM lies in calculating the significance of network features to determine their relative importance. Features with minimal significance are prudently disregarded, a method akin to feature selection. This process not only curtails the computational burden of the classification… More >