Fangju Zhou¹, Hanbo Zhang², Na Ye¹, Jing Huang¹, Zhu Ren^1,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4539-4561, 2025, DOI:10.32604/cmc.2025.065946 - 30 July 2025

Abstract This paper explores security risks in state estimation based on multi-sensor systems that implement a Kalman filter and a detector. When measurements are transmitted via wireless networks to a remote estimator, the innovation sequence becomes susceptible to interception and manipulation by adversaries. We consider a class of linear deception attacks, wherein the attacker alters the innovation to degrade estimation accuracy while maintaining stealth against the detector. Given the inherent volatility of the detection function based on the detector, we propose broadening the traditional feasibility constraint to accommodate a certain degree of deviation from the distribution… More >

Abdul Ahad^1,2, Ira Puspitasari^1,3,*, Jiangbin Zheng², Shamsher Ullah⁴, Farhan Ullah⁵, Sheikh Tahir Bakhsh⁶, Ivan Miguel Pires^7,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5115-5134, 2025, DOI:10.32604/cmc.2025.062605 - 06 March 2025

Abstract This research explores the use of Fuzzy K-Nearest Neighbor (F-KNN) and Artificial Neural Networks (ANN) for predicting heart stroke incidents, focusing on the impact of feature selection methods, specifically Chi-Square and Best First Search (BFS). The study demonstrates that BFS significantly enhances the performance of both classifiers. With BFS preprocessing, the ANN model achieved an impressive accuracy of 97.5%, precision and recall of 97.5%, and an Receiver Operating Characteristics (ROC) area of 97.9%, outperforming the Chi-Square-based ANN, which recorded an accuracy of 91.4%. Similarly, the F-KNN model with BFS achieved an accuracy of 96.3%, precision More >

Muhammad Ali Ilyas¹, Abdul Rehman², Assad Abbas¹, Dongsun Kim^3,*, Muhammad Tahir Naseem^4,*, Nasro Min Allah⁵

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4525-4549, 2024, DOI:10.32604/cmc.2024.056291 - 19 December 2024

Abstract In an era dominated by information dissemination through various channels like newspapers, social media, radio, and television, the surge in content production, especially on social platforms, has amplified the challenge of distinguishing between truthful and deceptive information. Fake news, a prevalent issue, particularly on social media, complicates the assessment of news credibility. The pervasive spread of fake news not only misleads the public but also erodes trust in legitimate news sources, creating confusion and polarizing opinions. As the volume of information grows, individuals increasingly struggle to discern credible content from false narratives, leading to widespread… More >

Salem Alhayani^*, Diane R. Murphy

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2235-2259, 2023, DOI:10.32604/cmc.2023.038003 - 30 August 2023

Abstract The Internet service provider (ISP) is the heart of any country’s Internet infrastructure and plays an important role in connecting to the World Wide Web. Internet exchange point (IXP) allows the interconnection of two or more separate network infrastructures. All Internet traffic entering a country should pass through its IXP. Thus, it is an ideal location for performing malicious traffic analysis. Distributed denial of service (DDoS) attacks are becoming a more serious daily threat. Malicious actors in DDoS attacks control numerous infected machines known as botnets. Botnets are used to send numerous fake requests to… More >

Mohamed Abouhawwash^1,2, S. Sridevi³, Suma Christal Mary Sundararajan⁴, Rohit Pachlor⁵, Faten Khalid Karim⁶, Doaa Sami Khafaga^6,*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 239-253, 2023, DOI:10.32604/csse.2023.037812 - 26 May 2023

Abstract One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome (PCOS). Consequently, timely screening of polycystic ovarian syndrome can help in the process of recovery. Finding a method to aid doctors in this procedure was crucial due to the difficulties in detecting this condition. This research aimed to determine whether it is possible to optimize the detection of PCOS utilizing Deep Learning algorithms and methodologies. Additionally, feature selection methods that produce the most important subset of features can speed up calculation and enhance… More >

Lingwu Qian¹, Jianxiang Li², Qi Tang¹, Mengfei Liu¹, Bingjie Yuan¹, Guoli Ji^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1441-1455, 2023, DOI:10.32604/cmes.2023.024534 - 06 February 2023

Abstract In recent years, a number of wireless indoor positioning (WIP), such as Bluetooth, Wi-Fi, and Ultra-Wideband (UWB) technologies, are emerging. However, the indoor environment is complex and changeable. Walls, pillars, and even pedestrians may block wireless signals and produce non-line-of-sight (NLOS) deviations, resulting in decreased positioning accuracy and the inability to provide people with real-time continuous indoor positioning. This work proposed a strong tracking particle filter based on the chi-square test (SPFC) for indoor positioning. SPFC can fuse indoor wireless signals and the information of the inertial sensing unit (IMU) in the smartphone and detect More >

Vaibhav Rupapara¹, Furqan Rustam², Abid Ishaq², Ernesto Lee³, Imran Ashraf^4,*

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1931-1949, 2023, DOI:10.32604/iasc.2023.028257 - 05 January 2023

Abstract Diabetes mellitus is a metabolic disease that is ranked among the top 10 causes of death by the world health organization. During the last few years, an alarming increase is observed worldwide with a 70% rise in the disease since 2000 and an 80% rise in male deaths. If untreated, it results in complications of many vital organs of the human body which may lead to fatality. Early detection of diabetes is a task of significant importance to start timely treatment. This study introduces a methodology for the classification of diabetic and normal people using… More >

Dalel Ben Ismail¹, Hela Elmannai^2,*, Souham Meshoul², Mohamed Saber Naceur¹

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2219-2236, 2023, DOI:10.32604/iasc.2023.028944 - 19 July 2022

Abstract Imagery assessment is an efficient method for detecting craniofacial anomalies. A cephalometric landmark matching approach may help in orthodontic diagnosis, craniofacial growth assessment and treatment planning. Automatic landmark matching and anomalies detection helps face the manual labelling limitations and optimize preoperative planning of maxillofacial surgery. The aim of this study was to develop an accurate Cephalometric Landmark Matching method as well as an automatic system for anatomical anomalies classification. First, the Active Appearance Model (AAM) was used for the matching process. This process was achieved by the Ant Colony Optimization (ACO) algorithm enriched with proximity… More >

Yabin Xu^1,2,*, Peiyuan Gu², Xiaowei Xu³

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 113-127, 2021, DOI:10.32604/iasc.2021.018895 - 26 July 2021

Abstract In order to effectively detect interest flooding attack (IFA) in Named Data Networking (NDN), this paper proposes a detection method of interest flooding attack based on chi-square test and similarity test. Firstly, it determines the detection window size based on the distribution of information name prefixes (that is information entropy) in the current network traffic. The attackers may append arbitrary random suffix to a certain prefix in the network traffic, and then send a large number of interest packets that cannot get the response. Targeted at this problem, the sensitivity of chi-square test is used… More >

Kun Fang^{1, 2}, Jianquan Ouyang^{1, *}

Journal on Artificial Intelligence, Vol.2, No.1, pp. 1-15, 2020, DOI:10.32604/jai.2020.09738 - 15 July 2020

Abstract Generating an Adversarial network (GAN) has shown great development prospects in image generation and semi-supervised learning and has evolved into TripleGAN. However, there are still two problems that need to be solved in Triple-GAN: based on the KL divergence distribution structure, gradients are easy to disappear and training instability occurs. Since Triple-GAN tags the samples manually, the manual marking workload is too large. Marked uneven and so on. This article builds on this improved Triple-GAN model (Improved Triple-GAN), which uses Random Forests to classify real samples, automate tagging of leaf nodes, and use Least Squares More >