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 >

Weiyu Hao¹, Jingyi Wang², Huimin Lu^3,*

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4419-4433, 2024, DOI:10.32604/cmc.2024.055478 - 19 December 2024

Abstract While traditional Convolutional Neural Network (CNN)-based semantic segmentation methods have proven effective, they often encounter significant computational challenges due to the requirement for dense pixel-level predictions, which complicates real-time implementation. To address this, we introduce an advanced real-time semantic segmentation strategy specifically designed for autonomous driving, utilizing the capabilities of Visual Transformers. By leveraging the self-attention mechanism inherent in Visual Transformers, our method enhances global contextual awareness, refining the representation of each pixel in relation to the overall scene. This enhancement is critical for quickly and accurately interpreting the complex elements within driving scenarios—a fundamental… More >

Xudong Yang¹, Yifan Wang^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.4, pp. 1-1, 2024, DOI:10.32604/icces.2024.011275

Abstract Shape-morphing materials present promising avenues for mimicking the adaptive characteristics of biological organisms capable of transitioning between diverse morphologies. However, existing morphing strategies through pre-arranged localized strain and/or cut/fold patterns have a limited range of achievable geometries, and the morphed structures usually have low stiffness due to the intrinsic softness of underlying materials. To overcome these challenges, we are inspired by the inherently non-monolithic architectures in living organisms, e.g., the nacre or bone consisting of stiff building blocks joined by the weak interfaces, which endow creatures ingenious shape-morphing abilities and tunable mechanical properties through collectively… More >

Bojana Z. Jadranin¹, Marija V. Ćosić¹, Djordje P. Božović¹, Milorad M. Vujičić^1,2, Beáta Papp³, Aneta D. Sabovljević^1,2, Marko S. Sabovljević^1,2,4,*

Phyton-International Journal of Experimental Botany, Vol.93, No.11, pp. 3039-3054, 2024, DOI:10.32604/phyton.2024.058469 - 30 November 2024

Abstract The rare and threatened semi-aquatic moss Drepanocladus lycopodioides (Amblystegiaceae) was the subject of growth optimization under ex situ axenic laboratory conditions. The positioning of the plantlets on media, media types as well as selected growth regulators and sugars were parameters tested in optimizing growth promotion of this species in captivity. Out of the tested media types, the KNOP medium and the upright positioning of the explants were the best for propagation and biomass production of D. lycopodioides. The addition of sugars had no significant effect on this moss development axenically, while exogenously applied Benzylaminopurine (BAP) at a… More >

Suneel Kumar Rath¹, Madhusmita Sahu¹, Shom Prasad Das², Junali Jasmine Jena³, Chitralekha Jena⁴, Baseem Khan^5,6,7,*, Ahmed Ali⁷, Pitshou Bokoro⁷

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1513-1536, 2024, DOI:10.32604/csse.2024.057067 - 22 November 2024

Abstract Redundancy, correlation, feature irrelevance, and missing samples are just a few problems that make it difficult to analyze software defect data. Additionally, it might be challenging to maintain an even distribution of data relating to both defective and non-defective software. The latter software class’s data are predominately present in the dataset in the majority of experimental situations. The objective of this review study is to demonstrate the effectiveness of combining ensemble learning and feature selection in improving the performance of defect classification. Besides the successful feature selection approach, a novel variant of the ensemble learning… More >

Farida Asriani^1,2, Azhari Azhari^1,*, Wahyono Wahyono¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3079-3096, 2024, DOI:10.32604/cmc.2024.058193 - 18 November 2024

Abstract Incredible progress has been made in human action recognition (HAR), significantly impacting computer vision applications in sports analytics. However, identifying dynamic and complex movements in sports like badminton remains challenging due to the need for precise recognition accuracy and better management of complex motion patterns. Deep learning techniques like convolutional neural networks (CNNs), long short-term memory (LSTM), and graph convolutional networks (GCNs) improve recognition in large datasets, while the traditional machine learning methods like SVM (support vector machines), RF (random forest), and LR (logistic regression), combined with handcrafted features and ensemble approaches, perform well but… More >

Tao Yan¹, Javed Rashid^2,3, Muhammad Shoaib Saleem^3,4, Sajjad Ahmad⁴, Muhammad Faheem^5,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2685-2708, 2024, DOI:10.32604/cmc.2024.058186 - 18 November 2024

Abstract Electricity is essential for keeping power networks balanced between supply and demand, especially since it costs a lot to store. The article talks about different deep learning methods that are used to guess how much green energy different Asian countries will produce. The main goal is to make reliable and accurate predictions that can help with the planning of new power plants to meet rising demand. There is a new deep learning model called the Green-electrical Production Ensemble (GP-Ensemble). It combines three types of neural networks: convolutional neural networks (CNNs), gated recurrent units (GRUs), and… More >

Hira Akhtar Butt¹, Khoula Said Al Harthy², Mumtaz Ali Shah³, Mudassar Hussain^2,*, Rashid Amin^4,*, Mujeeb Ur Rehman¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3003-3031, 2024, DOI:10.32604/cmc.2024.057185 - 18 November 2024

Abstract Detecting sophisticated cyberattacks, mainly Distributed Denial of Service (DDoS) attacks, with unexpected patterns remains challenging in modern networks. Traditional detection systems often struggle to mitigate such attacks in conventional and software-defined networking (SDN) environments. While Machine Learning (ML) models can distinguish between benign and malicious traffic, their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent retraining. In this paper, we propose a novel DDoS detection framework that combines Machine Learning (ML) and Ensemble Learning (EL) techniques to improve DDoS attack detection and mitigation in SDN environments. Our model… More >

Chen Chen, Kai Qiao, Jie Yang, Jian Chen, Bin Yan^*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2629-2642, 2024, DOI:10.32604/cmc.2024.056038 - 18 November 2024

Abstract Computed Tomography (CT) is a commonly used technology in Printed Circuit Boards (PCB) non-destructive testing, and element segmentation of CT images is a key subsequent step. With the development of deep learning, researchers began to exploit the “pre-training and fine-tuning” training process for multi-element segmentation, reducing the time spent on manual annotation. However, the existing element segmentation model only focuses on the overall accuracy at the pixel level, ignoring whether the element connectivity relationship can be correctly identified. To this end, this paper proposes a PCB CT image element segmentation model optimizing the semantic perception… More >

Chenchen Zhou^1,2, Shuaishuai Liang³, Bin Qi³, Chenxu Liu², Nam-Joon Cho^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.3, pp. 1-2, 2024, DOI:10.32604/icces.2024.012380

Abstract Micro tools/parts are attracting increasing attention due to the miniaturization evolutionary tendency in many fields, whose functionalities are critically determined by their materials and shapes [1- 5]. Sharp-edged ceramic microparticles have great prospects to be used as micromachining tools and micro components. However, it remains a huge challenge to fabricate nontransparent ceramic sharp-edged microparticles in a high-throughput way while taking their shape complexity, precision, and strength into account [6-8]. Herein, we present an online mixing and in-situ polymerization strategy: “one-pot microfluidic fabrication” along with two novel microfluidic device fabrication methods: “groove & tongue” and sliding More >