Mudassir Khalil¹, Muhammad Imran Sharif^2,*, Ahmed Naeem³, Muhammad Umar Chaudhry¹, Hafiz Tayyab Rauf^4,*, Adham E. Ragab⁵

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1461-1461, 2025, DOI:10.32604/cmc.2024.061589 - 03 January 2025

Abstract This article has no abstract. More >

Sameh Zarif^1,2,*, Hadier Moawad², Khalid Amin², Abdullah Alharbi³, Wail S. Elkilani⁴, Shouze Tang⁵, Marian Wagdy⁶

Computer Systems Science and Engineering, Vol.49, pp. 79-98, 2025, DOI:10.32604/csse.2024.057439 - 03 January 2025

Abstract Authentication is the most crucial aspect of security and a predominant measure employed in cybersecurity. Cloud computing provides a shared electronic device resource for users via the internet, and the authentication techniques used must protect data from attacks. Previous approaches failed to resolve the challenge of making passwords secure, memorable, usable, and time-saving. Graphical Password (GP) is still not widely utilized in reality because consumers suffer from multiple login stages. This paper proposes an Indexed Choice-Based Graphical Password (ICGP) scheme for improving the authentication part. ICGP consists of two stages: registration and authentication. At the… More >

Annan Yang¹, Jiawei Zhang^2,*, Haojie Yang^3,*, Keyi Tao¹, Mengna Xu¹, Yuyu Zhao¹

Journal on Big Data, Vol.6, pp. 21-41, 2024, DOI:10.32604/jbd.2024.057612 - 31 December 2024

Abstract This study introduces an electric vehicle charging station layout optimization method utilizing Point of Interest (POI) data, addressing traditional design limitations. It details the acquisition and visualization of POI data for Yancheng’s key locations and charging stations. Employing a hybrid K-Means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm, the study determines areas requiring optimization through location entropy and overlap analysis. The research shows that the integrated clustering approach can efficiently guide the fair distribution of charging stations, enhancing service quality and supporting the sustainable growth of the electric vehicle sector. More >

Pengda Niu¹, Ruidi Li^1,*, Tiechui Yuan¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.012243

Abstract In terms of grain refinement and component shape complexity, laser additive manufacturing (AM) is unmatched. This is exemplified by laser powder bed fusion (LPBF), whose rapid solidification and non-equilibrium metallurgy have expanded the understanding of ultra-fine grains and sub-stable organization among academics. At present, the reliability of LPBF components is being questioned by the industry due to the rapid heating and cooling cycles in AM processing, coupled with the extreme non-equilibrium heat-fluid-mass process, which renders LPBF printing vulnerable to metallurgical defects like microcracks and porosity. A significant impediment to the development of LPBF lies in… More >

Kee-Ahn Lee^1,*, So-Yeon Park¹, Soo-Bin Kim¹, Young-Kyun Kim^1,2

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.4, pp. 1-1, 2024, DOI:10.32604/icces.2024.012196

Abstract NiCoCr medium-entropy alloys (MEAs) with controlled interstitial C contents were fabricated by using powder bed fusion-type additive manufacturing (AM) process. And the microstructure, mechanical properties, and high temperature oxidation resistance of in-situ carbide-reinforced NiCoCr Medium alloy were investigated. The initial microstructure shows that both AM-built interstitial C-doped MEAs had a heterogeneous grain structure and epitaxial growth grains along the building direction. The analysis of electron channeling contrast images showed a large amount of nano-sized precipitates (in-situ precipitates) distributed at the sub-structure boundaries formed by a dislocation network, and a large number of stacking faults were simultaneously observed inside the sub-structure. A… More >

Chenze Li¹, Manish Jain^1,2, Qian Liu¹, Zhuohan Cao¹, Michael Ferry³, Jamie J. Kruzic¹, Bernd Gludovatz¹, Xiaopeng Li^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.4, pp. 1-2, 2024, DOI:10.32604/icces.2024.011290

Abstract Laser powder bed fusion (LPBF) additive manufacturing (AM) technology has become a versatile tool for producing new microstructures in metal components, offering novel mechanical properties for different applications. In this work, enhanced ductility (~55% elongation) and tunable mechanical anisotropy (ratio of ductility along vertical to horizontal orientation from ~0.2 to ~1) were achieved for a CoCrNi medium entropy alloy (MEA) by multi-scale synergistic microstructure manipulation (i.e., melt pool boundary, grain morphology and crystallographic texture) through adjusting key LPBF processing parameters (e.g., laser power and scan speed). By increasing the volumetric energy density (VED) from 68.3… More >

Li Wang^1,*, Xiaoyong Wang²

Energy Engineering, Vol.121, No.12, pp. 3953-3979, 2024, DOI:10.32604/ee.2024.056705 - 22 November 2024

Abstract Plug-in Hybrid Electric Vehicles (PHEVs) represent an innovative breed of transportation, harnessing diverse power sources for enhanced performance. Energy management strategies (EMSs) that coordinate and control different energy sources is a critical component of PHEV control technology, directly impacting overall vehicle performance. This study proposes an improved deep reinforcement learning (DRL)-based EMS that optimizes real-time energy allocation and coordinates the operation of multiple power sources. Conventional DRL algorithms struggle to effectively explore all possible state-action combinations within high-dimensional state and action spaces. They often fail to strike an optimal balance between exploration and exploitation, and… More >

Juhwan Kim, Baehoon Son, Jihyeon Yu, Joobeom Yun^*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3371-3393, 2024, DOI:10.32604/cmc.2024.057234 - 18 November 2024

Abstract Digital forensics aims to uncover evidence of cybercrimes within compromised systems. These cybercrimes are often perpetrated through the deployment of malware, which inevitably leaves discernible traces within the compromised systems. Forensic analysts are tasked with extracting and subsequently analyzing data, termed as artifacts, from these systems to gather evidence. Therefore, forensic analysts must sift through extensive datasets to isolate pertinent evidence. However, manually identifying suspicious traces among numerous artifacts is time-consuming and labor-intensive. Previous studies addressed such inefficiencies by integrating artificial intelligence (AI) technologies into digital forensics. Despite the efforts in previous studies, artifacts were… More >

Noura Alsedais¹, Mohamed Ahmed Mansour², Abdelraheem M. Aly³, Sara I. Abdelsalam^4,5,*

Frontiers in Heat and Mass Transfer, Vol.22, No.5, pp. 1277-1307, 2024, DOI:10.32604/fhmt.2024.056087 - 30 October 2024

Abstract The research examines fluid behavior in a porous box-shaped enclosure. The fluid contains nanoscale particles and swimming microbes and is subject to magnetic forces at an angle. Natural circulation driven by biological factors is investigated. The analysis combines a traditional numerical approach with machine learning techniques. Mathematical equations describing the system are transformed into a dimensionless form and then solved using computational methods. The artificial neural network (ANN) model, trained with the Levenberg-Marquardt method, accurately predicts values, showing high correlation (R = 1), low mean squared error (MSE), and minimal error clustering. Parametric analysis reveals significant… More >

Jiachang Liu^1,*, Zhengwei Huang², Junfeng Xiang¹, Lu Liu¹, Manlin Hu¹

Energy Engineering, Vol.121, No.11, pp. 3461-3486, 2024, DOI:10.32604/ee.2024.054514 - 21 October 2024

Abstract To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance, this paper proposes a seasonal short-term load combination prediction model based on modal decomposition and a feature-fusion multi-algorithm hybrid neural network model. Specifically, the characteristics of load components are analyzed for different seasons, and the corresponding models are established. First, the improved complete ensemble empirical modal decomposition with adaptive noise (ICEEMDAN) method is employed to decompose the system load for all four seasons, and the new sequence is obtained through reconstruction based on the… More >