Yang Li, Fei Yin, Xianghui Hui^*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4047-4063, 2023, DOI:10.32604/cmc.2023.036945 - 31 March 2023

Abstract With the rapid development of the Internet globally since the 21st century, the amount of data information has increased exponentially. Data helps improve people’s livelihood and working conditions, as well as learning efficiency. Therefore, data extraction, analysis, and processing have become a hot issue for people from all walks of life. Traditional recommendation algorithm still has some problems, such as inaccuracy, less diversity, and low performance. To solve these problems and improve the accuracy and variety of the recommendation algorithms, the research combines the convolutional neural networks (CNN) and the attention model to design a… More >

B. Divya^*, M. Santhi

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1881-1898, 2023, DOI:10.32604/iasc.2023.031573 - 05 January 2023

Abstract Pest detection in agricultural crop fields is the most challenging task, so an effective pest detection technique is required to detect insects automatically. Image processing techniques are widely preferred in agricultural science because they offer multiple advantages like maximal crop protection, improved crop management and productivity. On the other hand, developing the automatic pest monitoring system dramatically reduces the workforce and errors. Existing image processing approaches are limited due to the disadvantages like poor efficiency and less accuracy. Therefore, a successful image processing technique based on FF-GWO-CNN classification algorithm is introduced for effective pest monitoring… More >

Fikret Yalcinkaya^*, Ali Erbas

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1961-1977, 2022, DOI:10.32604/iasc.2022.024961 - 24 March 2022

Abstract Early stage melanoma detection (ESMD) is crucial as late detection kills. Computer aided diagnosis systems (CADS) integrated with high level algorithms are major tools capable of ESMD with high degree of accuracy, specificity, and sensitivity. CADS use the image and the information within the pixels of the image. Pixels’ characteristics and orientations determine the colour and shapes of the images as the pixels and associated environment are closely interrelated with the lesion. CADS integrated with Convolutional Neural Networks (CNN) specifically play a major role for ESMD with high degree of accuracy. The proposed system has… More >

Jiaxiu Dong^1,2,3, Jianhua Liu⁴, Niannian Wang^1,2,3,*, Hongyuan Fang^1,2,3, Jinping Zhang¹, Haobang Hu^1,2,3, Duo Ma^1,2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 541-564, 2021, DOI:10.32604/cmes.2021.015875 - 22 July 2021

Abstract Nowadays, asphalt road has dominated highways around the world. Among various defects of asphalt road, cracks have been paid more attention, since cracks often cause major engineering and personnel safety incidents. Current manual crack inspection methods are time-consuming and labor-intensive, and most segmentation methods cannot detect cracks at the pixel level. This paper proposes an intelligent segmentation and measurement model based on the modified Mask R-CNN algorithm to automatically and accurately detect asphalt road cracks. The model proposed in this paper mainly includes a convolutional neural network (CNN), an optimized region proposal network (RPN), a… More >