A system's ability to adjust and adapt to different events and surroundings is known as variability. Variability is required to increase the product's flexibility and potential for reuse. Researchers have so f...
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There is a huge amount of digitalized medical documents which are valuable for many applications. The text in a medical document is needed to collect knowledges and information about diseases and are used to make medi...
There is a huge amount of digitalized medical documents which are valuable for many applications. The text in a medical document is needed to collect knowledges and information about diseases and are used to make medical decisions. An automatic processing to extract information and recognizing named entities in these texts is very useful to facilitate many medical tasks. However, the text is not structured and is written in natural language, which makes this task a challenge. In fact, recognizing entities in Arabic language is not well studied in this field. In our work, we propose a Named Entity Recognition (NER) model to recognize entities such as: of disease, diagnosis, treatment, and symptom. For that, we perform a classification task using a dataset of 27 real medical documents based on a Support Vector Machine (SVM). We have pre-processed the text and selected candidate words to be classified. Then, we have represented each word by multiple features such as: FastText embedding, Part of Speech (PoS), Term Frequency-Inverse Document Frequency TF-IDF embedding, and non medical word matching. To classify the word, we take into account the surrounding words to capture its context. We have used PCA to reduce the size of TF-IDF vectors and we don’t use a hand-crafted supplemental resource. Thus, we have classified each word into its entity class by the SVM model. Evaluation results show that our model is stable and provides high and balanced results. In addition, we outperform a state-of-the-art model by an F1-score of +6.56% although the model requires a big manual effort for preparation.
many educational institutions employ data mining to keep track of their students' records, particularly their academic achievements, which is more significant. To improve their achievements, students' academic...
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this paper proposes a more desirable semantic analysis for capacity recommender structures for large facts management. The proposed approach is primarily based on ontologies, text-mining, and semantic relatedness proc...
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In large-scale informationsystems, storage device performance continues to improve while workloads expand in size and access characteristics. This growth puts tremendous pressure on caches and storage hierarchy in te...
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In service recommender system, graph neural networks (GNNs) perform message passing through diffusion mechanism based on user-service relationship graph. However, existing GNN-based service recommendation models suffe...
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Today, water contamination affects a large portion of the world's population. Manually collecting water samples from various sites and analyzing them in a lab as part of the routine monitoring technique. These app...
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Given a graph G and an integer k, Max Min FVS asks whether there exists a minimal set of vertices of size at least k whose deletion destroys all cycles. We present several results that improve upon the state of the ar...
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The use of terahertz, extremely large-scale multiple-input multiple-output (XL-MIMO) systems in 6G is driven by its potential to deliver unprecedented data rates, alleviate spectrum congestion, and improve network eff...
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Using Babenko's approach, multivariate Mittag–Leffler (MM–L) function, and Krasnoselskii's fixed point theorem, we first investigate the existence of solutions to a Liouville–Caputo nonlinear integro-differ...
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