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- The successful development of an effective machine learning model for detecting malicious queries is a challenging task that requires domain expertise. Malicious queries are complex and constantly evolving, and their detection is further complicated by variations in database server architectures. To tackle this challenge, domain experts with a deep understanding of attack vectors, techniques, and trends are crucial. Their insights into the patterns, behaviour, and characteristics of malicious queries provide invaluable guidance for designing appropriate features and training the model. This study aims to leverage this domain expertise to propose a robust machine-learning model capable of accurately identifying and categorizing malicious queries. By incorporating expert knowledge and using advanced techniques like machine learning, the model can be optimized to address the dynamic nature of these threats, leading to improved accuracy in real-world scenarios. After the extensive experiment, using 88,213 malicious and normal queries from GitHub and other sources, the Random Forest model yielded the highest accuracy of 98.4% and 97% sensitivity compared to other machine learning models experimented in the study. The performance comparison of the study with existing works indicates that the machine learning model achieved promising results.
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- In recent years, the agricultural sector has increasingly embraced advanced technologies to tackle food security and animal health issues. Poultry farming, a crucial part of agriculture, faces significant challenges from diseases affecting poultry health and economic sustainability. This project employs Convolutional Neural Networks (CNNs), a form of deep learning, to enhance poultry disease prediction accuracy, using chicken diseases as a case study. CNNs have revolutionised various fields, including disease prediction, by extracting meaningful patterns from data like images. This project leverages CNNs to analyse a diverse dataset of chicken disease images, creating a robust prediction model. The process involves compiling an extensive dataset of high-resolution chicken disease images, designing a CNN architecture with convolutional and pooling layers, and exploring transfer learning from pre-trained models. Rigorous training, validation, hyperparameter tuning, and data augmentation ensure model reliability. The project’s goals are twofold: demonstrating the feasibility of using CNNs for poultry disease prediction and offering a comprehensive poultry disease prediction framework. The latter could enable early disease detection and target interventions, reducing economic losses and enhancing food security. The proposed model achieved an overall accuracy of 96.5% and an F1 score of 96.8% respectively, on the tested dataset of poultry disease, indicating its high performance in poultry disease prediction.
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