This research addresses the critical need for early crop disease detection to optimize yields and minimize economic losses. Leveraging DenseNet, a deep learning model, the study focuses on automated detection of rice ...
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As social media platforms continue to expand, understanding user behavior and emotions has become essential. This paper introduces a framework for creating detailed user profiles by analyzing the sentiments expressed ...
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Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detecti...
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Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.
This conference paper looks at the pervasive influence of social media on different aspects of our lives and examines four different areas. The first area focuses on realtime detection of events in social media and ad...
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Educational institutions showing interest to find the opinion of the students about their course and the instructors to enhance the teaching-learning *** this,most research uses sentiment analysis to track students’*...
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Educational institutions showing interest to find the opinion of the students about their course and the instructors to enhance the teaching-learning *** this,most research uses sentiment analysis to track students’*** sentence-level sentiment analysis focuses on the whole sentence *** studies show that the sentiments alone are not enough to observe the feeling of the students because different words express different sentiments in a *** is a need to extract the targets in a given sentence which helps to find the sentiment towards those *** extraction is the subtask of targeted sentiment *** this paper,we proposed the innovative model to find the targets of the given sentence using Bi-Integrated Conditional Random Fields(CRF).A Parallel fusion neural network model is designed to perform this *** evaluate the model using the Michigan dataset and we build a dataset for target extraction from student *** experimental results show that our proposed fusion model achieves better results compared to baseline models.
Detection of underwater fish species has become more important through marine science research. Automatic detection of fish species would facilitate the development of marine science. There are several different metho...
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In today's world predicting stock prices remains a challenge due to markets being volatile as it is driven by multiple factors. In the past, investors and business men depended on traditional methods of prediction...
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The integrity of product warranties stands as a critical concern, marked by challenges like data tampering and fabrication within traditional verification systems. This paper explores the transformative potential of b...
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This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented *** proposed approach is a combination of an enhanced grey wolf optimizer(E...
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This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented *** proposed approach is a combination of an enhanced grey wolf optimizer(EGWO)and an extreme learning machine(ELM).EGWO is an augmented form of the classic grey wolf optimizer(GWO).Compared to standard GWO,EGWO has a better hunting mechanism and produces an optimal *** EGWO was used to optimize the ELM structure and a hybrid model,ELM-EGWO,was *** train and validate the proposed ELM-EGWO model,a sum of 361 experimental results featuring five influencing factors was *** on sensitivity analysis,three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive *** consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training(RMSE=0.0959)and testing(RMSE=0.0912)*** outcomes of the ELM-EGWO are significantly superior to those of deep neural networks(DNN),k-nearest neighbors(KNN),long short-term memory(LSTM),and other hybrid ELMs constructed with GWO,particle swarm optimization(PSO),harris hawks optimization(HHO),salp swarm algorithm(SSA),marine predators algorithm(MPA),and colony predation algorithm(CPA).The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness.
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