Jiaqi Wang¹, Pengfei Sun¹, Leilei Chen², Jianfeng Yang³, Zhenghe Liu¹, Haojie Lian^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1381-1418, 2023, DOI:10.32604/cmes.2023.023693

Abstract Geological hazard is an adverse geological condition that can cause loss of life and property. Accurate prediction and analysis of geological hazards is an important and challenging task. In the past decade, there has been a great expansion of geohazard detection data and advancement in data-driven simulation techniques. In particular, great efforts have been made in applying deep learning to predict geohazards. To understand the recent progress in this field, this paper provides an overview of the commonly used data sources and deep neural networks in the prediction of a variety of geological hazards. More >

Loc Vu-Quoc^1,*, Alexander Humer²

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1069-1343, 2023, DOI:10.32604/cmes.2023.028130

Abstract Three recent breakthroughs due to AI in arts and science serve as motivation: An award winning digital image, protein folding, fast matrix multiplication. Many recent developments in artificial neural networks, particularly deep learning (DL), applied and relevant to computational mechanics (solid, fluids, finite-element technology) are reviewed in detail. Both hybrid and pure machine learning (ML) methods are discussed. Hybrid methods combine traditional PDE discretizations with ML methods either (1) to help model complex nonlinear constitutive relations, (2) to nonlinearly reduce the model order for efficient simulation (turbulence), or (3) to accelerate the simulation by predicting… More >

Peng Geng¹, Ji Lu¹, Ying Zhang^2,*, Simin Ma¹, Zhanzhong Tang², Jianhua Liu³

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 2001-2023, 2023, DOI:10.32604/cmes.2023.027127

Abstract In medical image segmentation task, convolutional neural networks (CNNs) are difficult to capture long-range dependencies, but transformers can model the long-range dependencies effectively. However, transformers have a flexible structure and seldom assume the structural bias of input data, so it is difficult for transformers to learn positional encoding of the medical images when using fewer images for training. To solve these problems, a dual branch structure is proposed. In one branch, Mix-Feed-Forward Network (Mix-FFN) and axial attention are adopted to capture long-range dependencies and keep the translation invariance of the model. Mix-FFN whose depth-wise convolutions… More >

Bin Li^*

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.10, pp. 2729-2748, 2023, DOI:10.32604/fdmp.2023.029649

Abstract In order to overcome the deficiencies of current methods for the prediction of the productivity of shale gas horizontal wells after fracturing, a new sophisticated approach is proposed in this study. This new model stems from the combination several techniques, namely, artificial neural network (ANN), particle swarm optimization (PSO), Imperialist Competitive Algorithms (ICA), and Ant Clony Optimization (ACO). These are properly implemented by using the geological and engineering parameters collected from 317 wells. The results show that the optimum PSO-ANN model has a high accuracy, obtaining a R² of 0.847 on the testing. The partial dependence More >

Tusongjiang Kari¹, Sun Guoliang², Lei Kesong¹, Ma Xiaojing^1,*, Wu Xian¹

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1437-1452, 2023, DOI:10.32604/iasc.2023.037012

Abstract Wind power volatility not only limits the large-scale grid connection but also poses many challenges to safe grid operation. Accurate wind power prediction can mitigate the adverse effects of wind power volatility on wind power grid connections. For the characteristics of wind power antecedent data and precedent data jointly to determine the prediction accuracy of the prediction model, the short-term prediction of wind power based on a combined neural network is proposed. First, the Bi-directional Long Short Term Memory (BiLSTM) network prediction model is constructed, and the bi-directional nature of the BiLSTM network is used… More >

A. Manju¹, R. kaladevi², Shanmugasundaram Hariharan³, Shih-Yu Chen^4,5,*, Vinay Kukreja⁶, Pradip Kumar Sharma⁷, Fayez Alqahtani⁸, Amr Tolba⁹, Jin Wang¹⁰

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 993-1007, 2023, DOI:10.32604/cmc.2023.039567

Abstract The medical community has more concern on lung cancer analysis. Medical experts’ physical segmentation of lung cancers is time-consuming and needs to be automated. The research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning techniques. Computer-Aided Diagnostic (CAD) system aids in the diagnosis and shortens the time necessary to detect the tumor detected. The application of Deep Neural Networks (DNN) has also been exhibited as an excellent and effective method in classification and segmentation tasks. This research aims to separate lung cancers from… More >

Inès Hilali-Jaghdam^1,*, Azhari A Elhag², Anis Ben Ishak³, Bushra M. Elamin Elnaim⁴, Omer Eltag Mohammed Elhag⁵, Feda Muhammed Abuhaimed¹, S. Abdel-Khalek^2,6

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1127-1150, 2023, DOI:10.32604/cmc.2023.038915

Abstract In 2019, the novel coronavirus disease 2019 (COVID-19) ravaged the world. As of July 2021, there are about 192 million infected people worldwide and 4.1365 million deaths. At present, the new coronavirus is still spreading and circulating in many places around the world, especially since the emergence of Delta variant strains has increased the risk of the COVID-19 pandemic again. The symptoms of COVID-19 are diverse, and most patients have mild symptoms, with fever, dry cough, and fatigue as the main manifestations, and about 15.7% to 32.0% of patients will develop severe symptoms. Patients are… More >

Ouail Mjahed^1,*, Salah El Hadaj¹, El Mahdi El Guarmah^1,2, Soukaina Mjahed¹

Computer Systems Science and Engineering, Vol.47, No.1, pp. 757-775, 2023, DOI:10.32604/csse.2023.039111

Abstract Denial of Service (DoS/DDoS) intrusions are damaging cyber-attacks, and their identification is of great interest to the Intrusion Detection System (IDS). Existing IDS are mainly based on Machine Learning (ML) methods including Deep Neural Networks (DNN), but which are rarely hybridized with other techniques. The intrusion data used are generally imbalanced and contain multiple features. Thus, the proposed approach aims to use a DNN-based method to detect DoS/DDoS attacks using CICIDS2017, CSE-CICIDS2018 and CICDDoS 2019 datasets, according to the following key points. a) Three imbalanced CICIDS2017-2018-2019 datasets, including Benign and DoS/DDoS attack classes, are used.… More >

Jaehwan Lee¹, Sangoh Park^2,*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 171-186, 2023, DOI:10.32604/csse.2023.038139

Abstract Recently, various mobile apps have included more features to improve user convenience. Mobile operating systems load as many apps into memory for faster app launching and execution. The least recently used (LRU)-based termination of cached apps is a widely adopted approach when free space of the main memory is running low. However, the LRU-based cached app termination does not distinguish between frequently or infrequently used apps. The app launch performance degrades if LRU terminates frequently used apps. Recent studies have suggested the potential of using users’ app usage patterns to predict the next app launch… More >

Zhifang Liao¹, Fangying Fu¹, Yiqi Zhao¹, Sui Tan^2,3,*, Zhiwu Yu^2,3, Yan Zhang⁴

Computer Systems Science and Engineering, Vol.47, No.1, pp. 431-452, 2023, DOI:10.32604/csse.2023.038087

Abstract With the rapid development of Open-Source (OS), more and more software projects are maintained and developed in the form of OS. These Open-Source projects depend on and influence each other, gradually forming a huge OS project network, namely an Open-Source Software ECOsystem (OSSECO). Unfortunately, not all OS projects in the open-source ecosystem can be healthy and stable in the long term, and more projects will go from active to inactive and gradually die. In a tightly connected ecosystem, the death of one project can potentially cause the collapse of the entire ecosystem network. How can… More >