Xiaozhong Yu^1,2,*, Jinhua Zheng^1,2

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 197-225, 2024, DOI:10.32604/cmc.2023.043943 - 30 January 2024

Abstract In rice production, the prevention and management of pests and diseases have always received special attention. Traditional methods require human experts, which is costly and time-consuming. Due to the complexity of the structure of rice diseases and pests, quickly and reliably recognizing and locating them is difficult. Recently, deep learning technology has been employed to detect and identify rice diseases and pests. This paper introduces common publicly available datasets; summarizes the applications on rice diseases and pests from the aspects of image recognition, object detection, image segmentation, attention mechanism, and few-shot learning methods according to More >

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 - 30 December 2023

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 More >

Ammar Saeed^1,*, Eesa Al Solami²

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2079-2096, 2023, DOI:10.32604/cmc.2023.030551 - 29 November 2023

Abstract The evolution of the internet and its accessibility in the twenty-first century has resulted in a tremendous increase in the use of social media platforms. Some social media sources contribute to the propagation of fake news that has no real validity, but they accumulate over time and begin to appear in the feed of every consumer producing even more ambiguity. To sustain the value of social media, such stories must be distinguished from the true ones. As a result, an automated system is required to save time and money. The classification of fake news and… More >

Mozamel M. Saeed^1,*, Zaher Al Aghbari²

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 325-348, 2023, DOI:10.32604/cmc.2023.036894 - 31 October 2023

Abstract Emailing is among the cheapest and most easily accessible platforms, and covers every idea of the present century like banking, personal login database, academic information, invitation, marketing, advertisement, social engineering, model creation on cyber-based technologies, etc. The uncontrolled development and easy access to the internet are the reasons for the increased insecurity in email communication. Therefore, this review paper aims to investigate deep learning approaches for detecting the threats associated with e-mail security. This study compiles the literature related to the deep learning methodologies, which are applicable for providing safety in the field of cyber… More >

K. Vignesh^1,*, A. Askarunisa², A. M. Abirami³

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1051-1067, 2023, DOI:10.32604/csse.2023.024475 - 15 June 2022

Abstract Crop yield has been predicted using environmental, land, water, and crop characteristics in a prospective research design. When it comes to predicting crop production, there are a number of factors to consider, including weather conditions, soil qualities, water levels and the location of the farm. A broad variety of algorithms based on deep learning are used to extract useful crops for forecasting. The combination of data mining and deep learning creates a whole crop yield prediction system that is able to connect raw data to predicted crop yields. The suggested study uses a Discrete Deep… More >