ZEHRA GÜL MORÇIMEN¹, ŞEYMA TAŞDEMIR², AYLIN ŞENDEMIR^3,4,*

BIOCELL, Vol.48, No.1, pp. 79-96, 2024, DOI:10.32604/biocell.2023.045361

Abstract Neurodegeneration is a catastrophic process that develops progressive damage leading to functional and structural loss of the cells of the nervous system and is among the biggest unavoidable problems of our age. Animal models do not reflect the pathophysiology observed in humans due to distinct differences between the neural pathways, gene expression patterns, neuronal plasticity, and other disease-related mechanisms in animals and humans. Classical in vitro cell culture models are also not sufficient for pre-clinical drug testing in reflecting the complex pathophysiology of neurodegenerative diseases. Today, modern, engineered techniques are applied to develop multicellular, intricate in vitro models and to… More >

Sumaya S. Sulaiman^1,2,*, Ibraheem Nadher³, Sarab M. Hameed²

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1049-1069, 2024, DOI:10.32604/cmc.2023.046051

Abstract Fraud of credit cards is a major issue for financial organizations and individuals. As fraudulent actions become more complex, a demand for better fraud detection systems is rising. Deep learning approaches have shown promise in several fields, including detecting credit card fraud. However, the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters. This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data, thereby improving fraud detection. Three deep learning models: AutoEncoder (AE), Convolution Neural Network (CNN), and Long Short-Term Memory… More >

Tayyaba Farhat^1,2, Sheeraz Akram^3,*, Hatoon S. AlSagri³, Zulfiqar Ali⁴, Awais Ahmad³, Arfan Jaffar^1,2

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 105-126, 2024, DOI:10.32604/cmc.2023.045022

Abstract Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by significant challenges in social interaction, communication, and repetitive behaviors. Timely and precise ASD detection is crucial, particularly in regions with limited diagnostic resources like Pakistan. This study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local context. The research involves experimentation with VGG16 and MobileNet models, exploring different batch sizes, optimizers, and learning rate schedulers. In addition, the “Orange” machine learning tool is employed to evaluate classifier performance and automated… More >

Fangyi Li^*, Dachang Zhu^*, Huimin Shi

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1981-1999, 2024, DOI:10.32604/cmes.2023.031332

Abstract In time-variant reliability problems, there are a lot of uncertain variables from different sources. Therefore, it is important to consider these uncertainties in engineering. In addition, time-variant reliability problems typically involve a complex multilevel nested optimization problem, which can result in an enormous amount of computation. To this end, this paper studies the time-variant reliability evaluation of structures with stochastic and bounded uncertainties using a mixed probability and convex set model. In this method, the stochastic process of a limit-state function with mixed uncertain parameters is first discretized and then converted into a time-independent reliability problem. Further, to solve the… More >

Mian Muhammad Danyal¹, Muhammad Haseeb¹, Sarwar Shah Khan^2,*, Bilal Khan¹, Subhan Ullah¹

Journal on Artificial Intelligence, Vol.6, pp. 23-42, 2024, DOI:10.32604/jai.2023.045617

Abstract Movies reviews provide valuable insights that can help people decide which movies are worth watching and avoid wasting their time on movies they will not enjoy. Movie reviews may contain spoilers or reveal significant plot details, which can reduce the enjoyment of the movie for those who have not watched it yet. Additionally, the abundance of reviews may make it difficult for people to read them all at once, classifying all of the movie reviews will help in making this decision without wasting time reading them all. Opinion mining, also called sentiment analysis, is the process of identifying and extracting… More >

Mingjing Li^1,*, Leiting Dong¹, Satya N. Atluri²

The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.4, pp. 1-2, 2023, DOI:10.32604/icces.2023.09689

Abstract The Fragile Points Method (FPM) is a discontinuous meshless method based on the Galerkin weak form [1]. In the FPM, the problem domain is discretized by spatial points and subdomains, and the displacement trial function of each subdomain is derived based on the points within the support domain. For this reason, the FPM doesn’t suffer from the mesh distortion and is suitable to model complex structural deformations. Furthermore, similar to the discontinuous Galerkin finite element method, the displacement trial functions used in the FPM is piece-wise continuous, and the numerical flux is introduced across each interior interface to guarantee the… More >

Karim Ragui¹, Mengshuai Chen^1,2, Lin Chen^1,2,3,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.3, pp. 1-2, 2023, DOI:10.32604/icces.2023.010378

Abstract The ambiguous dynamics associated with heat and mass transfer of invading carbon dioxide in sub-critical and supercritical states, as well as the response of pore-scale resident fluids, play a key role in understanding CO₂ capture and storage (CCUS) and the corresponding phase equilibrium mechanisms. To this end, this paper reveals the transport mechanisms of invading supercritical carbon dioxide (sCO₂) in polluted micromodels using a variant of Lattice-Boltzmann Color Fluid model and descriptive experimental data. The breakthrough time is evaluated by characterizing the displacement velocity, the capillary to pressuredifference ratio, and the transient heat and mass diffusion at a series of… More >

Adil Hussain¹, Amna Khatoon^2,*, Ayesha Aslam², Tariq¹, Muhammad Asif Khosa¹

Journal of Cyber Security, Vol.6, pp. 1-23, 2024, DOI:10.32604/jcs.2023.046915

Abstract The importance of cybersecurity in contemporary society cannot be inflated, given the substantial impact of networks on various aspects of daily life. Traditional cybersecurity measures, such as anti-virus software and firewalls, safeguard networks against potential threats. In network security, using Intrusion Detection Systems (IDSs) is vital for effectively monitoring the various software and hardware components inside a given network. However, they may encounter difficulties when it comes to detecting solitary attacks. Machine Learning (ML) models are implemented in intrusion detection widely because of the high accuracy. The present work aims to assess the performance of machine learning algorithms in the… More >

Sicheng Zhu, Dingli Liu^*, Weijun Liu, Ying Li, Tian Zhou

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 483-513, 2024, DOI:10.32604/cmes.2023.044809

Abstract In recent years, frequent fire disasters have led to enormous damage in China. Effective firefighting rescues can minimize the losses caused by fires. During the rescue processes, the travel time of fire trucks can be severely affected by traffic conditions, changing the effective coverage of fire stations. However, it is still challenging to determine the effective coverage of fire stations considering dynamic traffic conditions. This paper addresses this issue by combining the traveling time calculation model with the effective coverage simulation model. In addition, it proposes a new index of total effective coverage area (TECA) based on the time-weighted average… More >

Weisi Chen^1,*, Walayat Hussain^2,*, Francesco Cauteruccio³, Xu Zhang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 187-224, 2024, DOI:10.32604/cmes.2023.031388

Abstract Financial time series prediction, whether for classification or regression, has been a heated research topic over the last decade. While traditional machine learning algorithms have experienced mediocre results, deep learning has largely contributed to the elevation of the prediction performance. Currently, the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking, making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better, what techniques and components are involved, and how the model can be designed and implemented. This review article provides an overview of techniques, components and… More > Graphic Abstract