Maheesha Dhashantha Silva^*, Qi Liu

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2971-2989, 2024, DOI:10.32604/cmc.2024.051289

Abstract In recent years, Non-Intrusive Load Monitoring (NILM) has become an emerging approach that provides affordable energy management solutions using aggregated load obtained from a single smart meter in the power grid. Furthermore, by integrating Machine Learning (ML), NILM can efficiently use electrical energy and offer less of a burden for the energy monitoring process. However, conducted research works have limitations for real-time implementation due to the practical issues. This paper aims to identify the contribution of ML approaches to developing a reliable Energy Management (EM) solution with NILM. Firstly, phases of the NILM are discussed,… More >

Kamlesh Kumar Soothar^1,2, Yuanxiang Chen^1,2,*, Arif Hussain Magsi³, Cong Hu¹, Hussain Shah¹

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2697-2721, 2024, DOI:10.32604/cmc.2024.049607

Abstract Efficient optical network management poses significant importance in backhaul and access network communication for preventing service disruptions and ensuring Quality of Service (QoS) satisfaction. The emerging faults in optical networks introduce challenges that can jeopardize the network with a variety of faults. The existing literature witnessed various partial or inadequate solutions. On the other hand, Machine Learning (ML) has revolutionized as a promising technique for fault detection and prevention. Unlike traditional fault management systems, this research has three-fold contributions. First, this research leverages the ML and Deep Learning (DL) multi-classification system and evaluates their accuracy… More >

Jiajun Yan, Ying Zhou^*, Anchen Dai, Tao Wang

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 563-580, 2024, DOI:10.32604/cmc.2024.048922

Abstract As an emerging network paradigm, the software-defined network (SDN) finds extensive application in areas such as smart grids, the Internet of Things (IoT), and edge computing. The forwarding layer in software-defined networks is susceptible to eavesdropping attacks. Route hopping is a moving target defense (MTD) technology that is frequently employed to resist eavesdropping attacks. In the traditional route hopping technology, both request and reply packets use the same hopping path. If an eavesdropping attacker monitors the nodes along this path, the risk of 100% data leakage becomes substantial. In this paper, we present an effective… More >

Jungpil Shin^1,*, Md. Al Mehedi Hasan², Md. Maniruzzaman¹, Taiki Watanabe¹, Issei Jozume¹

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1205-1222, 2024, DOI:10.32604/cmc.2024.046954

Abstract Person identification is one of the most vital tasks for network security. People are more concerned about their security due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprints and faces have been widely used for person identification, which has the risk of information leakage as a result of reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiable pattern, which will not be reproducible falsely by capturing psychological and behavioral information of a person using vision and sensor-based techniques. In existing studies, most… More >

Hongjun Zhang^1,2, Hao Zhang², Yu Lei³, Hao Ye¹, Peng Li^1,*, Desheng Shi¹

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4109-4128, 2024, DOI:10.32604/cmc.2024.048355

Abstract The study delves into the expanding role of network platforms in our daily lives, encompassing various mediums like blogs, forums, online chats, and prominent social media platforms such as Facebook, Twitter, and Instagram. While these platforms offer avenues for self-expression and community support, they concurrently harbor negative impacts, fostering antisocial behaviors like phishing, impersonation, hate speech, cyberbullying, cyberstalking, cyberterrorism, fake news propagation, spamming, and fraud. Notably, individuals also leverage these platforms to connect with authorities and seek aid during disasters. The overarching objective of this research is to address the dual nature of network platforms… More >

Usman Khan¹, Muhammad Khalid Khan¹, Muhammad Ayub Latif¹, Muhammad Naveed^1,2,*, Muhammad Mansoor Alam^2,3,4, Salman A. Khan¹, Mazliham Mohd Su’ud^2,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 2967-3000, 2024, DOI:10.32604/cmc.2024.045101

Abstract Recently, there has been a notable surge of interest in scientific research regarding spectral images. The potential of these images to revolutionize the digital photography industry, like aerial photography through Unmanned Aerial Vehicles (UAVs), has captured considerable attention. One encouraging aspect is their combination with machine learning and deep learning algorithms, which have demonstrated remarkable outcomes in image classification. As a result of this powerful amalgamation, the adoption of spectral images has experienced exponential growth across various domains, with agriculture being one of the prominent beneficiaries. This paper presents an extensive survey encompassing multispectral and… More >

M. L. Maruthi Prasad^*, R. Santhosh

Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 1-13, 2024, DOI:10.32604/iasc.2023.036628

Abstract This research concentrates to model an efficient thyroid prediction approach, which is considered a baseline for significant problems faced by the women community. The major research problem is the lack of automated model to attain earlier prediction. Some existing model fails to give better prediction accuracy. Here, a novel clinical decision support system is framed to make the proper decision during a time of complexity. Multiple stages are followed in the proposed framework, which plays a substantial role in thyroid prediction. These steps include i) data acquisition, ii) outlier prediction, and iii) multi-stage weight-based ensemble More >

LU ZHANG^*, JINGUO CHU^*, YUSHAN YU

Oncology Research, Vol.32, No.4, pp. 703-716, 2024, DOI:10.32604/or.2023.030988

Abstract Background: Limited research has been conducted on the influence of autophagy-associated long non-coding RNAs (ARLncRNAs) on the prognosis of hepatocellular carcinoma (HCC). Methods: We analyzed 371 HCC samples from TCGA, identifying expression networks of ARLncRNAs using autophagy-related genes. Screening for prognostically relevant ARLncRNAs involved univariate Cox regression, Lasso regression, and multivariate Cox regression. A Nomogram was further employed to assess the reliability of Riskscore, calculated from the signatures of screened ARLncRNAs, in predicting outcomes. Additionally, we compared drug sensitivities in patient groups with differing risk levels and investigated potential biological pathways through enrichment analysis, using… More >

Oulfa Harrat^1,*, Yazid Hadidane¹, S. M. Anas^2,*, Nadhim Hamah Sor^3,4, Ahmed Farouk Deifalla⁵, Paul O. Awoyera⁶, Nadia Gouider¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3435-3465, 2024, DOI:10.32604/cmes.2023.044950

Abstract Given their numerous functional and architectural benefits, such as improved bearing capacity and increased resistance to elastic instability modes, cold-formed steel (CFS) built-up sections have become increasingly developed and used in recent years, particularly in the construction industry. This paper presents an analytical and numerical study of assembled CFS two single channel-shaped columns with different slenderness and configurations (back-to-back, face-to-face, and box). These columns were joined by double-row rivets for the back-to-back and box configurations, whereas they were welded together for the face-to-face design. The built-up columns were filled with ordinary concrete of good strength.… More > Graphic Abstract

Shahriar Md Arman¹, Tao Yang^1,*, Shahadat Shahed², Alanoud Al Mazroa³, Afraa Attiah⁴, Linda Mohaisen⁴

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2087-2110, 2024, DOI:10.32604/cmc.2024.047870

Abstract The rapid growth of smart technologies and services has intensified the challenges surrounding identity authentication techniques. Biometric credentials are increasingly being used for verification due to their advantages over traditional methods, making it crucial to safeguard the privacy of people’s biometric data in various scenarios. This paper offers an in-depth exploration for privacy-preserving techniques and potential threats to biometric systems. It proposes a noble and thorough taxonomy survey for privacy-preserving techniques, as well as a systematic framework for categorizing the field’s existing literature. We review the state-of-the-art methods and address their advantages and limitations in More >