Danial Jahed Armaghani^1,*, Zida Liu², Hadi Khabbaz¹, Hadi Fattahi³, Diyuan Li², Mohammad Afrazi⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2421-2451, 2024, DOI:10.32604/cmes.2024.052210 - 31 October 2024

Abstract Tunnel Boring Machines (TBMs) are vital for tunnel and underground construction due to their high safety and efficiency. Accurately predicting TBM operational parameters based on the surrounding environment is crucial for planning schedules and managing costs. This study investigates the effectiveness of tree-based machine learning models, including Random Forest, Extremely Randomized Trees, Adaptive Boosting Machine, Gradient Boosting Machine, Extreme Gradient Boosting Machine (XGBoost), Light Gradient Boosting Machine, and CatBoost, in predicting the Penetration Rate (PR) of TBMs by considering rock mass and material characteristics. These techniques are able to provide a good relationship between input(s)… More >

Nadir Omer^1,*, Ahmed H. Samak², Ahmed I. Taloba^3,4, Rasha M. Abd El-Aziz^3,5

Computer Systems Science and Engineering, Vol.48, No.1, pp. 77-95, 2024, DOI:10.32604/csse.2023.039265 - 26 January 2024

Abstract Today’s world depends on the Internet to meet all its daily needs. The usage of the Internet is growing rapidly. The world is using the Internet more frequently than ever. The hazards of harmful attacks have also increased due to the growing reliance on the Internet. Hazards to cyber security are actions taken by someone with malicious intent to steal data, destroy computer systems, or disrupt them. Due to rising cyber security concerns, cyber security has emerged as the key component in the fight against all online threats, forgeries, and assaults. A device capable of… More >

Manas Ranjan Prusty¹, Rishi Dinesh², Hariket Sukesh Kumar Sheth², Alapati Lakshmi Viswanath², Sandeep Kumar Satapathy^2,3,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3077-3094, 2023, DOI:10.32604/cmc.2023.042718 - 26 December 2023

Abstract This paper presents a novel computerized technique for the segmentation of nuclei in hematoxylin and eosin (H&E) stained histopathology images. The purpose of this study is to overcome the challenges faced in automated nuclei segmentation due to the diversity of nuclei structures that arise from differences in tissue types and staining protocols, as well as the segmentation of variable-sized and overlapping nuclei. To this extent, the approach proposed in this study uses an ensemble of the UNet architecture with various Convolutional Neural Networks (CNN) architectures as encoder backbones, along with stain normalization and test time… More >

Jaime Ortiz Córdoba, Alejandro Molina Zarca^*, Antonio Skármeta

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2307-2333, 2023, DOI:10.32604/iasc.2023.039607 - 21 June 2023

Abstract 5G/Beyond 5G (B5G) networks provide connectivity to many heterogeneous devices, raising significant security and operational issues and making traditional infrastructure management increasingly complex. In this regard, new frameworks such as Anastacia-H2020 or INSPIRE-5GPlus automate the management of next-generation infrastructures, especially regarding policy-based security, abstraction, flexibility, and extensibility. This paper presents the design, workflow, and implementation of a security solution based on Unmanned Aerial Vehicles (UAVs), able to extend 5G/B5G security framework capabilities with UAV features like dynamic service provisioning in specific geographic areas. The proposed solution allows enforcing UAV security policies in proactive and reactive… 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 - 08 June 2023

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 >

Nilkanth Mukund Deshpande^1,2, Shilpa Gite^6,7,*, Biswajeet Pradhan^3,4,5, Mazen Ebraheem Assiri⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.3, pp. 843-872, 2022, DOI:10.32604/cmes.2022.021225 - 03 August 2022

Abstract Machine learning (ML) has emerged as a critical enabling tool in the sciences and industry in recent years. Today’s machine learning algorithms can achieve outstanding performance on an expanding variety of complex tasks–thanks to advancements in technique, the availability of enormous databases, and improved computing power. Deep learning models are at the forefront of this advancement. However, because of their nested nonlinear structure, these strong models are termed as “black boxes,” as they provide no information about how they arrive at their conclusions. Such a lack of transparencies may be unacceptable in many applications, such… More >

Norah Saleh Alghamdi¹, Sami Bourouis^2,*, Nizar Bouguila³

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1139-1156, 2022, DOI:10.32604/cmc.2022.022959 - 24 February 2022

Abstract Interest in automated data classification and identification systems has increased over the past years in conjunction with the high demand for artificial intelligence and security applications. In particular, recognizing human activities with accurate results have become a topic of high interest. Although the current tools have reached remarkable successes, it is still a challenging problem due to various uncontrolled environments and conditions. In this paper two statistical frameworks based on nonparametric hierarchical Bayesian models and Gamma distribution are proposed to solve some real-world applications. In particular, two nonparametric hierarchical Bayesian models based on Dirichlet process… More >

Chengcheng Wang^1,2,*, Dawei Luo¹, Bingxue Hou³, Mortaza Gholizadeh⁴, Zanxiong Tan¹, Xiaojie Han¹

Journal of Renewable Materials, Vol.10, No.5, pp. 1337-1348, 2022, DOI:10.32604/jrm.2022.018770 - 22 December 2021

Abstract It is critical to study efficient, stable oxygen reduction reaction (ORR) electrocatalysts due to insufficient stability and expensive price of Pt/C catalysts for Zn-air batteries. Fe–N–C electrocatalysts was synthesized by a facile solvent-green method and the efficiency of Fe–N–C optimized was studied as potential ORR electrocatalysts under alkaline condition. Results indicated that it had excellent ORR activity with E_1/2 of 0.93 V, which was competitive to that of Pt/C-JM under the same conditions. Moreover, the assembled Zn-air battery exhibited discharge potential and charge potential of 1.2 V, 2.32 V at 5 mA cm⁻² with high stability, respectively. More > Graphic Abstract

Wael Said¹, Elsayed Mostafa^1,*, M. M. Hassan¹, Ayman Mohamed Mostafa²

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3193-3209, 2022, DOI:10.32604/cmc.2022.023554 - 07 December 2021

Abstract User's data is considered as a vital asset of several organizations. Migrating data to the cloud computing is not an easy decision for any organization due to the privacy and security concerns. Service providers must ensure that both data and applications that will be stored on the cloud should be protected in a secure environment. The data stored on the public cloud will be vulnerable to outside and inside attacks. This paper provides interactive multi-layer authentication frameworks for securing user identities on the cloud. Different access control policies are applied for verifying users on the… More >

DIPESHKUMAR D. KACHHADIYA, Z.V.P. MURTHY^*

Journal of Polymer Materials, Vol.38, No.1-2, pp. 49-61, 2021, DOI:10.32381/JPM.2021.38.1.5

Abstract The conventional process for methanol-water separation like distillation consumes about 60 % of total energy. As an alternative, researchers have developed a membrane-based separation process for alcohol-water mixtures separation. However, there is a big challenge for researches to separate alcohol-water aqueous mixtures using a polymeric membrane because of swelling. In the present work, the aim is to separate methanol from water by pervaporation using polymeric membranes made up of polyvinylidenefluroide (PVDF) and polyethylene glycol (PEG200) modified PVDF membranes. The membranes were characterized by thermogravimetry analysis (TGA), field emission scanning electron microscopy (FE-SEM), and Fourier-transform infrared… More >