Xin Liu¹, Jie Li^1,*, Jianwei Zhao², Bin Cao^2,*, Rongge Yan³, Zhihan Lyu⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 143-185, 2024, DOI:10.32604/cmes.2023.030391

Abstract Most of the neural network architectures are based on human experience, which requires a long and tedious trial-and-error process. Neural architecture search (NAS) attempts to detect effective architectures without human intervention. Evolutionary algorithms (EAs) for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures. Using multiobjective EAs for NAS, optimal neural architectures that meet various performance criteria can be explored and discovered efficiently. Furthermore, hardware-accelerated NAS methods can improve the efficiency of the NAS. While existing reviews have mainly focused on different strategies to complete NAS, a few studies have explored the… More > Graphic Abstract

Narayanan Ganesh¹, Rajendran Shankar², Miroslav Mahdal³, Janakiraman Senthil Murugan⁴, Jasgurpreet Singh Chohan⁵, Kanak Kalita^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 103-141, 2024, DOI:10.32604/cmes.2023.028018

Abstract Computer vision (CV) was developed for computers and other systems to act or make recommendations based on visual inputs, such as digital photos, movies, and other media. Deep learning (DL) methods are more successful than other traditional machine learning (ML) methods in CV. DL techniques can produce state-of-the-art results for difficult CV problems like picture categorization, object detection, and face recognition. In this review, a structured discussion on the history, methods, and applications of DL methods to CV problems is presented. The sector-wise presentation of applications in this paper may be particularly useful for researchers in niche fields who have… More >

Shuqin Zhang, Xinyu Su^*, Yunfei Han, Tianhui Du, Peiyu Shi

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3993-4023, 2023, DOI:10.32604/cmc.2023.044526

Abstract Advanced Persistent Threat (APT) is now the most common network assault. However, the existing threat analysis models cannot simultaneously predict the macro-development trend and micro-propagation path of APT attacks. They cannot provide rapid and accurate early warning and decision responses to the present system state because they are inadequate at deducing the risk evolution rules of network threats. To address the above problems, firstly, this paper constructs the multi-source threat element analysis ontology (MTEAO) by integrating multi-source network security knowledge bases. Subsequently, based on MTEAO, we propose a two-layer threat prediction model (TL-TPM) that combines the knowledge graph and the… More >

Doaa Sami Khafaga¹, Zongming Lv², Imran Khan^3,4, Shebnam M. Sefat⁵, Amel Ali Alhussan^1,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3723-3740, 2023, DOI:10.32604/cmc.2023.039956

Abstract With the capacities of self-learning, acquainted capacities, high-speed looking for ideal arrangements, solid nonlinear fitting, and mapping self-assertively complex nonlinear relations, neural systems have made incredible advances and accomplished broad application over the final half-century. As one of the foremost conspicuous methods for fake insights, neural systems are growing toward high computational speed and moo control utilization. Due to the inborn impediments of electronic gadgets, it may be troublesome for electronic-implemented neural systems to make the strides these two exhibitions encourage. Optical neural systems can combine optoelectronic procedures and neural organization models to provide ways to break the bottleneck. This… More >

Ahmed Hassan Abdou^1,*, Omar Abdullah Alkhateeb², Hossam Eldin Hamed Mansour³, Hesham S. Ghazzawy⁴, Muayad Saud Albadrani⁵, Nadi Awad Al-harbi⁶, Wasimah B. Al-Shammari⁷, Khaled Abdelaal^8,*

Phyton-International Journal of Experimental Botany, Vol.92, No.12, pp. 3305-3321, 2023, DOI:10.32604/phyton.2023.044780

Abstract Plant growth-promoting bacteria (PGPB) play an important role in improving agricultural production under several abiotic stress factors. PGPB can be used to increase crop growth and development through hormonal balance and increase nutrient uptake. The positive effect of PGPB may be due to its pivotal role in morphophysiological and biochemical characteristics like leaf number, leaf area, and stem length. Furthermore, relative water content, chlorophyll content, carotenoids, antioxidant enzymes, and plant hormones were improved with PGPB treatment. Crop yield and yield components were also increased with PGPB treatment in numerous crops. The anatomical structure of plant organs was increased such as… More >

Caixia Tao, Shize Yang^*, Taiguo Li

Energy Engineering, Vol.121, No.1, pp. 187-201, 2024, DOI:10.32604/ee.2023.042421

Abstract With the current integration of distributed energy resources into the grid, the structure of distribution networks is becoming more complex. This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms. Consequently, traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima. To tackle this issue, a more advanced particle swarm optimization algorithm is proposed. To address the varying emphases at different stages of the optimization process, a dynamic strategy is implemented to regulate the social and self-learning factors. The Metropolis criterion is introduced into the simulated annealing algorithm to… More >

Danial Jahed Armaghani^1,*, Ahmed Salih Mohammed^2,3, Ramesh Murlidhar Bhatawdekar⁴, Pouyan Fakharian⁵, Ashutosh Kainthola⁶, Wael Imad Mahmood⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2023-2027, 2024, DOI:10.32604/cmes.2023.031701

Abstract This article has no abstract. More >

Longteng Liang^1,2,3, Zhouquan Feng^2,4,*, Hongyi Zhang^2,4, Zhengqing Chen^2,4, Changzhao Qian^1,3

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2403-2419, 2024, DOI:10.32604/cmes.2023.031260

Abstract Eddy current dampers (ECDs) have emerged as highly desirable solutions for vibration control due to their exceptional damping performance and durability. However, the existing constitutive models present challenges to the widespread implementation of ECD technology, and there is limited availability of finite element analysis (FEA) software capable of accurately modeling the behavior of ECDs. This study addresses these issues by developing a new constitutive model that is both easily understandable and user-friendly for FEA software. By utilizing numerical results obtained from electromagnetic FEA, a novel power law constitutive model is proposed to capture the nonlinear behavior of ECDs. The effectiveness… More >

Shude Fu^1,2, Xinye Wu^1,2,*, Wenjie Wang³, Yixin Hu^1,3,*, Zhengke Li¹, Feng Jiang³

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2357-2382, 2024, DOI:10.32604/cmes.2023.031118

Abstract In this paper, given the shortcomings of jellyfish search algorithm with low search ability in the early stage and easy to fall into local optimal solution, this paper introduces adaptive weight function and elite strategy, improving the global search scope in the early stage and the ability to refine the local development in the later stage. In the numerical study, the benchmark problem of dimensional optimization with a 10-bar truss structure and simultaneous dimensional shape optimization with a 15-bar truss structure is adopted, and the corresponding penalty method is used for constraint treatment. The test results show that the improved… More > Graphic Abstract

Hong Zhang^1,*, Haijian Shao²

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2061-2102, 2024, DOI:10.32604/cmes.2023.030649

Abstract OpenAI and ChatGPT, as state-of-the-art language models driven by cutting-edge artificial intelligence technology, have gained widespread adoption across diverse industries. In the realm of computer vision, these models have been employed for intricate tasks including object recognition, image generation, and image processing, leveraging their advanced capabilities to fuel transformative breakthroughs. Within the gaming industry, they have found utility in crafting virtual characters and generating plots and dialogues, thereby enabling immersive and interactive player experiences. Furthermore, these models have been harnessed in the realm of medical diagnosis, providing invaluable insights and support to healthcare professionals in the realm of disease detection.… More > Graphic Abstract