Computational linguistics is an engineering-based scientific discipline.It deals with understanding written and spoken language from a computational viewpoint.Further,the domain also helps construct the artefacts that...Computational linguistics is an engineering-based scientific discipline.It deals with understanding written and spoken language from a computational viewpoint.Further,the domain also helps construct the artefacts that are useful in processing and producing a language either in bulk or in a dialogue setting.Named Entity Recognition(NER)is a fundamental task in the data extraction process.It concentrates on identifying and labelling the atomic components from several texts grouped under different entities,such as organizations,people,places,and times.Further,the NER mechanism identifies and removes more types of entities as per the requirements.The significance of the NER mechanism has been well-established in Natural Language Processing(NLP)tasks,and various research investigations have been conducted to develop novel NER methods.The conventional ways of managing the tasks range from rule-related and hand-crafted feature-related Machine Learning(ML)techniques to Deep Learning(DL)techniques.In this aspect,the current study introduces a novel Dart Games Optimizer with Hybrid Deep Learning-Driven Computational Linguistics(DGOHDL-CL)model for NER.The presented DGOHDL-CL technique aims to determine and label the atomic components from several texts as a collection of the named entities.In the presented DGOHDL-CL technique,the word embed-ding process is executed at the initial stage with the help of the word2vec model.For the NER mechanism,the Convolutional Gated Recurrent Unit(CGRU)model is employed in this work.At last,the DGO technique is used as a hyperparameter tuning strategy for the CGRU algorithm to boost the NER’s outcomes.No earlier studies integrated the DGO mechanism with the CGRU model for NER.To exhibit the superiority of the proposed DGOHDL-CL technique,a widespread simulation analysis was executed on two datasets,CoNLL-2003 and OntoNotes 5.0.The experimental outcomes establish the promising performance of the DGOHDL-CL technique over other models.展开更多
目的将实时直接分析离子源(direct analysis in real time,DART)与三重四极杆质谱联用,建立食品中非法添加PDE-5型抑制剂的快速筛查方法。方法3种不同基质样品加入乙腈振摇10 s后,设进样速率0.2 mm/s,进样体积4μl,选择DART离子源,离子...目的将实时直接分析离子源(direct analysis in real time,DART)与三重四极杆质谱联用,建立食品中非法添加PDE-5型抑制剂的快速筛查方法。方法3种不同基质样品加入乙腈振摇10 s后,设进样速率0.2 mm/s,进样体积4μl,选择DART离子源,离子化温度450℃,在正离子、多反应监测(MRM)下进行检测。结果该方法7种物质的检出限为0.025~12.5μg/g,检出阳性样品4种,分别非法添加羟基卡巴地那非、环己基去甲他达拉非、N-苄基他达拉非和N-苯基丙氧苯基卡巴地那非。结论该方法样品前处理简单,分析速度快,定性准确,环境污染小,能满足食品中非法添加PDE-5型抑制剂的快速筛查要求。展开更多
目的建立快速定性检测减肥胶囊中添加的西布曲明的DART-MS/MS方法。方法 :采用DART(实时直接分析)离子源结合三重串联四级杆质谱,采取子离子扫描(Product Ion Scan)对固体胶囊样品进行直接分析,通过对照品与样品的质谱图进行定性检测。...目的建立快速定性检测减肥胶囊中添加的西布曲明的DART-MS/MS方法。方法 :采用DART(实时直接分析)离子源结合三重串联四级杆质谱,采取子离子扫描(Product Ion Scan)对固体胶囊样品进行直接分析,通过对照品与样品的质谱图进行定性检测。结果4批样品中有3批检出西布曲明。结论该方法可以用于减肥胶囊中添加西布曲明的快速定性检测,检出限达1ppm,可用于药品快速检验中批量样品的初筛。展开更多
基金Princess Nourah Bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R281)Princess Nourah Bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4331004DSR10).
文摘Computational linguistics is an engineering-based scientific discipline.It deals with understanding written and spoken language from a computational viewpoint.Further,the domain also helps construct the artefacts that are useful in processing and producing a language either in bulk or in a dialogue setting.Named Entity Recognition(NER)is a fundamental task in the data extraction process.It concentrates on identifying and labelling the atomic components from several texts grouped under different entities,such as organizations,people,places,and times.Further,the NER mechanism identifies and removes more types of entities as per the requirements.The significance of the NER mechanism has been well-established in Natural Language Processing(NLP)tasks,and various research investigations have been conducted to develop novel NER methods.The conventional ways of managing the tasks range from rule-related and hand-crafted feature-related Machine Learning(ML)techniques to Deep Learning(DL)techniques.In this aspect,the current study introduces a novel Dart Games Optimizer with Hybrid Deep Learning-Driven Computational Linguistics(DGOHDL-CL)model for NER.The presented DGOHDL-CL technique aims to determine and label the atomic components from several texts as a collection of the named entities.In the presented DGOHDL-CL technique,the word embed-ding process is executed at the initial stage with the help of the word2vec model.For the NER mechanism,the Convolutional Gated Recurrent Unit(CGRU)model is employed in this work.At last,the DGO technique is used as a hyperparameter tuning strategy for the CGRU algorithm to boost the NER’s outcomes.No earlier studies integrated the DGO mechanism with the CGRU model for NER.To exhibit the superiority of the proposed DGOHDL-CL technique,a widespread simulation analysis was executed on two datasets,CoNLL-2003 and OntoNotes 5.0.The experimental outcomes establish the promising performance of the DGOHDL-CL technique over other models.
文摘目的将实时直接分析离子源(direct analysis in real time,DART)与三重四极杆质谱联用,建立食品中非法添加PDE-5型抑制剂的快速筛查方法。方法3种不同基质样品加入乙腈振摇10 s后,设进样速率0.2 mm/s,进样体积4μl,选择DART离子源,离子化温度450℃,在正离子、多反应监测(MRM)下进行检测。结果该方法7种物质的检出限为0.025~12.5μg/g,检出阳性样品4种,分别非法添加羟基卡巴地那非、环己基去甲他达拉非、N-苄基他达拉非和N-苯基丙氧苯基卡巴地那非。结论该方法样品前处理简单,分析速度快,定性准确,环境污染小,能满足食品中非法添加PDE-5型抑制剂的快速筛查要求。
文摘目的建立快速定性检测减肥胶囊中添加的西布曲明的DART-MS/MS方法。方法 :采用DART(实时直接分析)离子源结合三重串联四级杆质谱,采取子离子扫描(Product Ion Scan)对固体胶囊样品进行直接分析,通过对照品与样品的质谱图进行定性检测。结果4批样品中有3批检出西布曲明。结论该方法可以用于减肥胶囊中添加西布曲明的快速定性检测,检出限达1ppm,可用于药品快速检验中批量样品的初筛。
