Yunhao Yu¹, Meiling Dizha¹, Boda Zhang¹, Ruibin Wen¹, Fuhua Luo¹, Xiang Guo¹, Junjie Song², Bingdong Wang², Zhenyong Zhang^2,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3861-3877, 2025, DOI:10.32604/cmc.2025.066097 - 23 September 2025

Abstract The load profile is a key characteristic of the power grid and lies at the basis for the power flow control and generation scheduling. However, due to the wide adoption of internet-of-things (IoT)-based metering infrastructure, the cyber vulnerability of load meters has attracted the adversary’s great attention. In this paper, we investigate the vulnerability of manipulating the nodal prices by injecting false load data into the meter measurements. By taking advantage of the changing properties of real-world load profile, we propose a deeply hidden load data attack (i.e., DH-LDA) that can evade bad data detection,… More >

Dang Hung Tran, Van Tinh Nguyen^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4679-4698, 2025, DOI:10.32604/cmc.2025.063228 - 19 May 2025

Abstract It is known that long non-coding RNAs (lncRNAs) play vital roles in biological processes and contribute to the progression, development, and treatment of various diseases. Obviously, understanding associations between diseases and lncRNAs significantly enhances our ability to interpret disease mechanisms. Nevertheless, the process of determining lncRNA-disease associations is costly, labor-intensive, and time-consuming. Hence, it is expected to foster computational strategies to uncover lncRNA-disease relationships for further verification to save time and resources. In this study, a collaborative filtering and graph attention network-based LncRNA-Disease Association (CFGANLDA) method was nominated to expose potential lncRNA-disease associations. First, it… More >

KUN FENG^1,#, HAO PENG^2,#, QINGPENG LV¹, YEWEI ZHANG^1,*

Oncology Research, Vol.32, No.6, pp. 1063-1078, 2024, DOI:10.32604/or.2024.047078 - 23 May 2024

Abstract Hepatocellular carcinoma (HCC) is a malignancy known for its unfavorable prognosis. The dysregulation of the tumor microenvironment (TME) can affect the sensitivity to immunotherapy or chemotherapy, leading to treatment failure. The elucidation of PHLDA2’s involvement in HCC is imperative, and the clinical value of PHLDA2 is also underestimated. Here, bioinformatics analysis was performed in multiple cohorts to explore the phenotype and mechanism through which PHLDA2 may affect the progression of HCC. Then, the expression and function of PHLDA2 were examined via the qRT-PCR, Western Blot, and MTT assays. Our findings indicate a substantial upregulation of… More >

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

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2331-2359, 2024, DOI:10.32604/cmc.2024.047275 - 15 May 2024

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… More >

Mehdi Hassan^1,2, Safdar Ali³, Jin Young Kim^2,*, Muhammad Sanaullah⁴, Hani Alquhayz⁵, Khushbakht Safdar⁶

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1643-1663, 2023, DOI:10.32604/cmc.2023.038578 - 30 August 2023

Abstract Breast cancer resistance protein (BCRP) is an important resistance protein that significantly impacts anticancer drug discovery, treatment, and rehabilitation. Early identification of BCRP substrates is quite a challenging task. This study aims to predict early substrate structure, which can help to optimize anticancer drug development and clinical diagnosis. For this study, a novel intelligent approach-based methodology is developed by modifying the ResNet101 model using transfer learning (TL) for automatic deep feature (DF) extraction followed by classification with linear discriminant analysis algorithm (TLRNDF-LDA). This study utilized structural fingerprints, which are exploited by DF contrary to conventional More >

Amel Ali Alhussan¹, Fatma M. Talaat², El-Sayed M. El-kenawy³, Abdelaziz A. Abdelhamid^4,5, Abdelhameed Ibrahim⁶, Doaa Sami Khafaga^1,*, Mona Alnaggar⁷

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 499-515, 2023, DOI:10.32604/cmc.2023.039368 - 08 June 2023

Abstract In computer vision, emotion recognition using facial expression images is considered an important research issue. Deep learning advances in recent years have aided in attaining improved results in this issue. According to recent studies, multiple facial expressions may be included in facial photographs representing a particular type of emotion. It is feasible and useful to convert face photos into collections of visual words and carry out global expression recognition. The main contribution of this paper is to propose a facial expression recognition model (FERM) depending on an optimized Support Vector Machine (SVM). To test the… More >

Xueqing Liao^*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 677-690, 2023, DOI:10.32604/iasc.2023.037219 - 29 April 2023

Abstract In order to improve the consistency between the recommended retrieval results and user needs, improve the recommendation efficiency, and reduce the average absolute deviation of resource retrieval, a design method of intelligent recommendation retrieval model for Fujian intangible cultural heritage digital archive resources based on knowledge atlas is proposed. The TG-LDA (Tag-granularity LDA) model is proposed on the basis of the standard LDA (Linear Discriminant Analysis) model. The model is used to mine archive resource topics. The Pearson correlation coefficient is used to measure the relevance between topics. Based on the measurement results, the FastText… More >

Khalid M. O. Nahar^1,*, Mohammad Alauthman², Saud Yonbawi³, Ammar Almomani^4,5

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5307-5319, 2023, DOI:10.32604/cmc.2023.031848 - 29 April 2023

Abstract Social media networks are becoming essential to our daily activities, and many issues are due to this great involvement in our lives. Cyberbullying is a social media network issue, a global crisis affecting the victims and society as a whole. It results from a misunderstanding regarding freedom of speech. In this work, we proposed a methodology for detecting such behaviors (bullying, harassment, and hate-related texts) using supervised machine learning algorithms (SVM, Naïve Bayes, Logistic regression, and random forest) and for predicting a topic associated with these text data using unsupervised natural language processing, such as More >

B. Yasotha^1,*, T. Sasikala², M. Krishnamurthy³

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1625-1640, 2023, DOI:10.32604/csse.2023.025669 - 03 November 2022

Abstract The internet has become a part of every human life. Also, various devices that are connected through the internet are increasing. Nowadays, the Industrial Internet of things (IIoT) is an evolutionary technology interconnecting various industries in digital platforms to facilitate their development. Moreover, IIoT is being used in various industrial fields such as logistics, manufacturing, metals and mining, gas and oil, transportation, aviation, and energy utilities. It is mandatory that various industrial fields require highly reliable security and preventive measures against cyber-attacks. Intrusion detection is defined as the detection in the network of security threats… More >

A. Gautami^1,*, J. Shanthini², S. Karthik³

Computer Systems Science and Engineering, Vol.45, No.1, pp. 427-443, 2023, DOI:10.32604/csse.2023.026688 - 16 August 2022

Abstract In Wireless Sensor Networks (WSN), attacks mostly aim in limiting or eliminating the capability of the network to do its normal function. Detecting this misbehaviour is a demanding issue. And so far the prevailing research methods show poor performance. AQN3 centred efficient Intrusion Detection Systems (IDS) is proposed in WSN to ameliorate the performance. The proposed system encompasses Data Gathering (DG) in WSN as well as Intrusion Detection (ID) phases. In DG, the Sensor Nodes (SN) is formed as clusters in the WSN and the Distance-based Fruit Fly Fuzzy c-means (DFFF) algorithm chooses the Cluster… More >