Fang Xu^1,2,3, Songhao Jiang^1,2, Yi Ma^1,2,3,*, Manzoor Ahmed^1,3,*, Zenggang Xiong^1,2,3, Yuanlin Lyu^1,2

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1095-1113, 2024, DOI:10.32604/cmc.2023.046577

Abstract Effective data communication is a crucial aspect of the Social Internet of Things (SIoT) and continues to be a significant research focus. This paper proposes a data forwarding algorithm based on Multidimensional Social Relations (MSRR) in SIoT to solve this problem. The proposed algorithm separates message forwarding into intra- and cross-community forwarding by analyzing interest traits and social connections among nodes. Three new metrics are defined: the intensity of node social relationships, node activity, and community connectivity. Within the community, messages are sent by determining which node is most similar to the sender by weighing the strength of social connections… More >

Liufeng Du^1,*, Shaoru Shang¹, Linghua Zhang², Chong Li¹, Jianing Yang³, Xiyan Tian¹

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1749-1767, 2024, DOI:10.32604/cmes.2023.030144

Abstract Due to the fine-grained communication scenarios characterization and stability, Wi-Fi channel state information (CSI) has been increasingly applied to indoor sensing tasks recently. Although spatial variations are explicitly reflected in CSI measurements, the representation differences caused by small contextual changes are easily submerged in the fluctuations of multipath effects, especially in device-free Wi-Fi sensing. Most existing data solutions cannot fully exploit the temporal, spatial, and frequency information carried by CSI, which results in insufficient sensing resolution for indoor scenario changes. As a result, the well-liked machine learning (ML)-based CSI sensing models still struggling with stable performance. This paper formulates a… More >

Feiyan Zhou^1,*, Hui Yin¹, Chen Luo², Haixin Tong², Kun Yu², Zewen Li², Xiangjun Zeng²

Energy Engineering, Vol.120, No.9, pp. 1979-1990, 2023, DOI:10.32604/ee.2023.029480

Abstract The load types in low-voltage distribution systems are diverse. Some loads have current signals that are similar to series fault arcs, making it difficult to effectively detect fault arcs during their occurrence and sustained combustion, which can easily lead to serious electrical fire accidents. To address this issue, this paper establishes a fault arc prototype experimental platform, selects multiple commonly used loads for fault arc experiments, and collects data in both normal and fault states. By analyzing waveform characteristics and selecting fault discrimination feature indicators, corresponding feature values are extracted for qualitative analysis to explore changes in time-frequency characteristics of… More > Graphic Abstract

Yuyan Luo^1,2, Xiaoxu Zhang^1,*, Tao Tong¹, Yong Qin^3,*, Zheng Yang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 2049-2076, 2023, DOI:10.32604/cmes.2023.025413

Abstract Graduate education is the main way to train high-level innovative talents, the basic layout to cope with the global talent competition, and the important cornerstone for implementing the innovation-driven development strategy and building an innovation-driven country. Therefore, graduate education is of great remarkably to the development of national education. As an important manifestation of graduate education, the quality of a graduate thesis should receive more attention. It is conducive to promoting the quality of graduates by supervising and examining the quality of the graduate thesis. For this purpose, this work is based on text mining, expert interviews, and questionnaire surveys… More >

Jing He^1,2, Haonan Chen^3,*, Lingxiao Li⁴, Yebin Zou⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 211-237, 2023, DOI:10.32604/cmes.2022.020597

Abstract There are many sources of geographic big data, and most of them come from heterogeneous environments. The data sources obtained in this case contain attribute information of different spatial scales, different time scales and different complexity levels. It is worth noting that the emergence of new high-dimensional trajectory data types and the increasing number of details are becoming more difficult. In this case, visualizing high-dimensional spatiotemporal trajectory data is extremely challenging. Therefore, i-tStar and its extension i-tStar (3D) proposed, a trajectory behavior feature for moving objects that are integrated into a view with less effort to display and extract spatiotemporal… More > Graphic Abstract

Nithya Rekha Sivakumar^1,*, Sara Abdelwahab Ghorashi¹, Faten Khalid Karim¹, Eatedal Alabdulkreem¹, Amal Al-Rasheed²

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6253-6267, 2022, DOI:10.32604/cmc.2022.029266

Abstract Mobile clouds are the most common medium for aggregating, storing, and analyzing data from the medical Internet of Things (MIoT). It is employed to monitor a patient’s essential health signs for earlier disease diagnosis and prediction. Among the various disease, skin cancer was the wide variety of cancer, as well as enhances the endurance rate. In recent years, many skin cancer classification systems using machine and deep learning models have been developed for classifying skin tumors, including malignant melanoma (MM) and other skin cancers. However, accurate cancer detection was not performed with minimum time consumption. In order to address these… More >

Huizhong Sun¹, Guosheng Xu^1,*, Hewei Yu², Minyan Ma³, Yanhui Guo¹, Ruijie Quan⁴

Journal of Quantum Computing, Vol.3, No.2, pp. 55-63, 2021, DOI:10.32604/jqc.2021.017365

Abstract Language detection models based on system calls suffer from certain false negatives and detection blind spots. Hence, the normal behavior sequences of some malware applications for a short period can become malicious behavior within a certain time window. To detect such behaviors, we extract a multidimensional time distribution feature matrix on the basis of statistical analysis. This matrix mainly includes multidimensional time distribution features, multidimensional word pair correlation features, and multidimensional word frequency distribution features. A multidimensional time distribution model based on neural networks is built to detect the overall abnormal behavior within a given time window. Experimental evaluation is… More >

Yue Li¹, Guangquan Xu^2,3, Hequn Xian^1,*, Longlong Rao³, Jiangang Shi^4,*

Intelligent Automation & Soft Computing, Vol.25, No.3, pp. 637-647, 2019, DOI:10.31209/2019.100000118

Abstract In order to prevent the spread of Android malware and protect privacy information from being compromised, this study proposes a novel multidimensional hybrid features extraction and analysis method for Android malware detection. This method is based primarily on a multidimensional hybrid features vector by extracting the information of permission requests, API calls, and runtime behaviors. The innovation of this study is to extract greater amounts of static and dynamic features information and combine them, that renders the features vector for training completer and more comprehensive. In addition, the feature selection algorithm is used to further optimize the extracted information to… More >

Jianfeng Guan^*, Jiawei Li, Zhongbai Jiang

Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 131-141, 2019, DOI:10.31209/2018.100000050

Abstract The traditional web anomaly detection systems face the challenges derived from the constantly evolving of the web malicious attacks, which therefore result in high false positive rate, poor adaptability, easy over-fitting, and high time complexity. Due to these limitations, we need a new anomaly detection system to satisfy the requirements of enterprise-level anomaly detection. There are lots of anomaly detection systems designed for different application domains. However, as for web anomaly detection, it has to describe the network accessing behaviours characters from as many dimensions as possible to improve the performance. In this paper we design and implement a Multidimensional… More >

Y.C. Hon¹, T. Wei²

CMES-Computer Modeling in Engineering & Sciences, Vol.7, No.2, pp. 119-132, 2005, DOI:10.3970/cmes.2005.007.119

Abstract We propose in this paper an effective meshless and integration-free method for the numerical solution of multidimensional inverse heat conduction problems. Due to the use of fundamental solutions as basis functions, the method leads to a global approximation scheme in both the spatial and time domains. To tackle the ill-conditioning problem of the resultant linear system of equations, we apply the Tikhonov regularization method based on the generalized cross-validation criterion for choosing the regularization parameter to obtain a stable approximation to the solution. The effectiveness of the algorithm is illustrated by several numerical two- and three-dimensional examples. More >