An increasing number of users utilize public platforms to communicate. The languages used by general public are diverse and varied. Detection of the offensive words utilized by people on these online platforms is chal...
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Brain tumour detection is an accepted testing task in the beginning phases of life. Yet, presently it has progressed with different AI calculations. Along these lines, to identify the brain tumour of a patient we cons...
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Evaluating the impact of an attack in a system is essential for prioritizing response towards simultaneous threats. While some attacks will impact lower-valued resources of the system, whereas other attacks have an im...
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ISBN:
(数字)9798331509934
ISBN:
(纸本)9798331509941
Evaluating the impact of an attack in a system is essential for prioritizing response towards simultaneous threats. While some attacks will impact lower-valued resources of the system, whereas other attacks have an impact on disrupting critical components. A quantifiable metric for impact would serve as a valuable tool to prioritize attack scenarios based on their potential damage. Our study proposes a structural approach to measure the severity of an attack by integrating a vulnerability assessment framework that includes the Common Vulnerability Scoring System (CVSS) score, the Exploit Prediction Scoring System (EPSS) score, along with economic factors.
Brain tumors are one of the foremost life-threatening diseases, producing serious complications that contribute to a heightened rate of morbidity and mortality. The emergence of deep learning strategies has vastly ref...
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Recent developments in deep-sea exploration, environmental surveillance, and ocean research, accurate segmentation of underwater images is essential. This study pursues this goal by exploring underwater image segmenta...
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An individual’s unwanted and unpleasant reactions that arise from regular drug use are referred to as adverse drug reactions, or ADRs. Mild side effects to serious, potentially fatal diseases can be caused by these r...
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An individual’s unwanted and unpleasant reactions that arise from regular drug use are referred to as adverse drug reactions, or ADRs. Mild side effects to serious, potentially fatal diseases can be caused by these reactions. In order to guarantee the security and effectiveness of pharmacological interventions, it is imperative to track and comprehend ADRs. This paper presents an approach in pharmacovigilance to classify drug reactions caused by different drugs by combining firefly algorithm with different classifiers. The firefly algorithm assigns firefly to each subsets of features and uses the objective function in order to calculate the distance and making cluster of firefly. In the end it gives us the most optimal set of features which we further use in our classification. We also have experimented on three types of classifiers which are: Random Forest, K-Nearest Neighbour, Decision Tree. In the end we have compared the accuracy, Precision, F1 Score of different classifiers and concluded that by using firefly for feature extraction we can increase accuracy, precision and F1-score. We have also compared this Firefly algorithm with Elephant Herding Optimization (EHO) used for feature selection. We dived into the advancements and challenges faced during the prediction of reactions of drugs. There is much scope in this as we can further increase our performance and efficiency by combining multiple classifiers or using different feature extraction techniques. We used a dataset consisting of 1333 entries with 24 features from Kaggle, split into training and testing sets with a 70:30 ratio. The results demonstrate that applying the Firefly Algorithm enhances model performance. Random Forest classifier achieved the highest accuracy of 97.5% with the Firefly Algorithm, compared to 97.2% without it. Similarly, KNN and Decision Tree classifiers also showed improvements in accuracy, with KNN improving from 93.5% to 95.0% and Decision Tree improving from 96.7% to 97.0%. Add
Zero-shot Relation Extraction (ZSRE) aims to predict novel relations from sentences with given entity pairs, where the relations have not been encountered during training. Prototype-based methods, which achieve ZSRE b...
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In node classification tasks, traditional methods like LPA-GCN struggle with scalability and sensitivity to label noise. We propose LPA-GraphSAGE, combining the Label Propagation Algorithm with GraphSAGE’s ...
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Brain MRI segmentation is critical for diagnosis and treatment planning, but existing methods are often limited by their task-specific designs and lack of generalizability. A significant challenge lies in integrating ...
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