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  • Open Access


    Adaptive Deep Learning Model for Software Bug Detection and Classification

    S. Sivapurnima*, D. Manjula

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

    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 in addition bug detection with… More >

  • Open Access


    Environment Adaptive Deep Learning Classification System Based on One-shot Guidance

    Guanghao Jin1, Chunmei Pei1, Na Zhao1, Hengguang Li2, Qingzeng Song3, Jing Yu1,*

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

    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, which is named as one-shot… More >

  • Open Access


    Rice Bacterial Infection Detection Using Ensemble Technique on Unmanned Aerial Vehicles Images

    Sathit Prasomphan*

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

    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 were used. To convey this… More >

  • Open Access


    Toward Fine-grained Image Retrieval with Adaptive Deep Learning for Cultural Heritage Image

    Sathit Prasomphan*

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

    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 could handle incremental streams of… More >

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