RETRACTED: An optimization based feature extraction and machine learning techniques for named entity identification

Govindarajan, Saravanan; Mustafa, Mohammed Ahmed; Kiyosov, Sherzod; Duong, Nguyen Phuc; Raju, M. Naga; Gola, Kamal Kumar

doi:10.1016/j.ijleo.2022.170348

Cited by 10 publications

(4 citation statements)

References 24 publications

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“…Radish is an annual plant that has tuberous, wedge-shaped or spherical roots depending on the variety, and the medicinal part in it (seeds, leaves, and roots), and the active ingredients in radish are starch, sugar, Raphaiol, Raphanin, Reticol and Sinapine, the compound Raphaiol is from Alkaloids, which are the most important medicinal substances in plants, and from the chemical view are complex organic chemicals, and the roots and leaves of radish have an effective effect on the body's resistance to bacteria [19][20][21][22][23][24].…”

Section: Resultsmentioning

confidence: 99%

Study Effect of the Aqueous and Alcoholic Extracts of Radish and Ginger on the Gram Negative Bacteria that Cause Urinary Tract Infection in Balad City

Mutlek,

Jumaa,

Mahdi

2023

IJDNE

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This study aimed to investigate the inhibitory effect of aqueous and alcoholic extracts of radish and ginger on bacterial species isolated from urinary tract and auditory system infections. Prior research has shown the potential of natural plant extracts as alternatives to synthetic antibiotics. In this study, extracts were tested at concentrations of 10, 25, 50, and 100 mg/ml for each extract against six different bacterial species. Results demonstrated that the hot alcoholic extract was more efficient than the hot water extracts of the two plants, with 100 mg/ml concentration having the highest inhibition ability. In addition, the inhibitory effect increased with increasing extract concentration. The hot alcoholic extracts of both ginger and radish were found to have higher inhibitory effects against the bacterial species, with the hot alcoholic ginger extract being more effective than the radish water extract. Furthermore, the synergistic effects of the hot alcoholic and water extracts of radish and ginger were tested against all of the identified bacterial species. Finally, sensitivity testing demonstrated that the isolated bacteria showed differences in their sensitivity and resistance to different antibiotics. Overall, this study highlights the potential of natural plant extracts as a source of new antibiotics for the treatment of bacterial infections. Existing research has shown the potential of natural plant extracts as alternatives to synthetic antibiotics for the treatment of bacterial infections, including UTIs. However, there is a lack of studies investigating the inhibitory effect of specific plant extracts, such as radish and ginger, on bacterial species isolated from UTIs and auditory system infections. This study aims to address this research gap by investigating the inhibitory effect of aqueous and alcoholic extracts of radish and ginger on six different bacterial species. The importance of this research gap lies in the increasing prevalence of antibiotic resistance and the need for alternative treatments. The research motivation is to explore the potential of natural plant extracts as a source of new antibiotics for the treatment of bacterial infections.

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Section: Resultsmentioning

confidence: 99%

Study Effect of the Aqueous and Alcoholic Extracts of Radish and Ginger on the Gram Negative Bacteria that Cause Urinary Tract Infection in Balad City

Mutlek,

Jumaa,

Mahdi

2023

IJDNE

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“…Under the same experimental settings, we compared the performance of our EMLB model with the methods proposed by Nasar et al ( 2021 ), Fabregat et al ( 2023 ), Govindarajan et al ( 2023 ), Ke et al ( 2023 ), Laursen et al ( 2023 ), Tang et al ( 2023 ). After conducting multiple independent experiments, we used the average F1 score as the evaluation metric.…”

Section: Methodsmentioning

confidence: 99%

Integration of natural and deep artificial cognitive models in medical images: BERT-based NER and relation extraction for electronic medical records

Guo,

Liu,

Niu

2023

Front. Neurosci.

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IntroductionMedical images and signals are important data sources in the medical field, and they contain key information such as patients' physiology, pathology, and genetics. However, due to the complexity and diversity of medical images and signals, resulting in difficulties in medical knowledge acquisition and decision support.MethodsIn order to solve this problem, this paper proposes an end-to-end framework based on BERT for NER and RE tasks in electronic medical records. Our framework first integrates NER and RE tasks into a unified model, adopting an end-to-end processing manner, which removes the limitation and error propagation of multiple independent steps in traditional methods. Second, by pre-training and fine-tuning the BERT model on large-scale electronic medical record data, we enable the model to obtain rich semantic representation capabilities that adapt to the needs of medical fields and tasks. Finally, through multi-task learning, we enable the model to make full use of the correlation and complementarity between NER and RE tasks, and improve the generalization ability and effect of the model on different data sets.Results and discussionWe conduct experimental evaluation on four electronic medical record datasets, and the model significantly out performs other methods on different datasets in the NER task. In the RE task, the EMLB model also achieved advantages on different data sets, especially in the multi-task learning mode, its performance has been significantly improved, and the ETE and MTL modules performed well in terms of comprehensive precision and recall. Our research provides an innovative solution for medical image and signal data.

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“…Although several large open-domain knowledge graphs exist, such as Wikidata, Freebase, OpenCyc, and Geonames, they are not yet complete [8]. This is particularly true in specialized domains such as cybersecurity, where, despite appearing confined, constant evolution occurs with the emergence of new knowledge [9]. An almost complete knowledge graph is significant for higher-level applications such as question-answering systems, where the ability to provide accurate answers depends on the knowledge graph that contains the relevant information [10].…”

Section: Introductionmentioning

confidence: 99%

A Knowledge Graph Completion Algorithm Based on the Fusion of Neighborhood Features and vBiLSTM Encoding for Network Security

Zhang,

Wang,

Han

et al. 2024

Electronics

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Knowledge graphs in the field of network security can integrate diverse, heterogeneous, and fragmented network security data, further explore the relationships between data, and provide support for deep analysis. Currently, there is sparse security information in the field of network security knowledge graphs. The limited information provided by traditional text encoding models leads to insufficient reasoning ability, greatly restricting the development of this field. Starting from text encoding, this paper first addresses the issue of the inadequate capabilities of traditional models using a deep learning model for assistance. It designs a vBiLSTM model based on a word2vec and BiLSTM combination to process network security texts. By utilizing word vector models to retain semantic information in entities and extract key features to input processed data into BiLSTM networks for extracting higher-level features that better capture and express their deeper meanings, this design significantly enhances understanding and expression capabilities toward complex semantics in long sentences before inputting final feature vectors into the KGC-N model. The KGC-N model uses feature vectors combined with graph structure information to fuse forward and reverse domain features and then utilizes a Transformer decoder to decode predictions and complete missing information within the network security knowledge map. Compared with other models using evaluation metrics such as MR, MRR demonstrates that employing our proposed method effectively improves performance on completion tasks and increases comprehension abilities toward complex relations, thereby enhancing accuracy and efficiency when completing knowledge graphs.

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RETRACTED: An optimization based feature extraction and machine learning techniques for named entity identification

Cited by 10 publications

References 24 publications

Study Effect of the Aqueous and Alcoholic Extracts of Radish and Ginger on the Gram Negative Bacteria that Cause Urinary Tract Infection in Balad City

Study Effect of the Aqueous and Alcoholic Extracts of Radish and Ginger on the Gram Negative Bacteria that Cause Urinary Tract Infection in Balad City

Integration of natural and deep artificial cognitive models in medical images: BERT-based NER and relation extraction for electronic medical records

A Knowledge Graph Completion Algorithm Based on the Fusion of Neighborhood Features and vBiLSTM Encoding for Network Security

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