Medhat A. Tawfeek^1,2, Nacim Yanes^3,4, Leila Jamel^5,*, Ghadah Aldehim⁵, Mahmood A. Mahmood^1,6

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2545-2564, 2023, DOI:10.32604/cmc.2023.042179 - 29 November 2023

Abstract The trend towards smart greenhouses stems from various factors, including a lack of agricultural land area owing to population concentration and housing construction on agricultural land, as well as water shortages. This study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research centers. The proposed model uses a one-dimensional convolutional neural network (CNN) deep learning model to control the growth of strategic crops, including cucumber, pepper, tomato, and bean. The proposed model uses the Internet of Things (IoT) to collect data on agricultural operations… More >

S. Sivapurnima^*, D. Manjula

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1233-1248, 2023, DOI:10.32604/csse.2023.025991 - 03 November 2022

Abstract Software is unavoidable in software development and maintenance. In literature, many methods are discussed which fails to achieve efficient software bug detection and classification. In this paper, efficient Adaptive Deep Learning Model (ADLM) is developed for automatic duplicate bug report detection and classification process. The proposed ADLM is a combination of Conditional Random Fields decoding with Long Short-Term Memory (CRF-LSTM) and Dingo Optimizer (DO). In the CRF, the DO can be consumed to choose the efficient weight value in network. The proposed automatic bug report detection is proceeding with three stages like pre-processing, feature extraction… More >

Sathit Prasomphan^*

Computer Systems Science and Engineering, Vol.44, No.2, pp. 991-1007, 2023, DOI:10.32604/csse.2023.025452 - 15 June 2022

Abstract Establishing a system for measuring plant health and bacterial infection is critical in agriculture. Previously, the farmers themselves, who observed them with their eyes and relied on their experience in analysis, which could have been incorrect. Plant inspection can determine which plants reflect the quantity of green light and near-infrared using infrared light, both visible and eye using a drone. The goal of this study was to create algorithms for assessing bacterial infections in rice using images from unmanned aerial vehicles (UAVs) with an ensemble classification technique. Convolution neural networks in unmanned aerial vehicles image… More >

Sathit Prasomphan^*

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1295-1307, 2023, DOI:10.32604/csse.2023.025293 - 15 June 2022

Abstract Fine-grained image classification is a challenging research topic because of the high degree of similarity among categories and the high degree of dissimilarity for a specific category caused by different poses and scales. A cultural heritage image is one of the fine-grained images because each image has the same similarity in most cases. Using the classification technique, distinguishing cultural heritage architecture may be difficult. This study proposes a cultural heritage content retrieval method using adaptive deep learning for fine-grained image retrieval. The key contribution of this research was the creation of a retrieval model that… More >

Guanghao Jin¹, Chunmei Pei¹, Na Zhao¹, Hengguang Li², Qingzeng Song³, Jing Yu^1,*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5185-5196, 2022, DOI:10.32604/cmc.2022.027307 - 28 July 2022

Abstract When utilizing the deep learning models in some real applications, the distribution of the labels in the environment can be used to increase the accuracy. Generally, to compute this distribution, there should be the validation set that is labeled by the ground truths. On the other side, the dependency of ground truths limits the utilization of the distribution in various environments. In this paper, we carried out a novel system for the deep learning-based classification to solve this problem. Firstly, our system only uses one validation set with ground truths to compute some hyper parameters,… More >