To promote behavioral change among adolescents in Zambia, the National HIV/AIDS/STI/TB Council, in collaboration with UNICEF, developed the Zambia U-Report platform. This platform provides young people with improved a...To promote behavioral change among adolescents in Zambia, the National HIV/AIDS/STI/TB Council, in collaboration with UNICEF, developed the Zambia U-Report platform. This platform provides young people with improved access to information on various Sexual Reproductive Health topics through Short Messaging Service (SMS) messages. Over the years, the platform has accumulated millions of incoming and outgoing messages, which need to be categorized into key thematic areas for better tracking of sexual reproductive health knowledge gaps among young people. The current manual categorization process of these text messages is inefficient and time-consuming and this study aims to automate the process for improved analysis using text-mining techniques. Firstly, the study investigates the current text message categorization process and identifies a list of categories adopted by counselors over time which are then used to build and train a categorization model. Secondly, the study presents a proof of concept tool that automates the categorization of U-report messages into key thematic areas using the developed categorization model. Finally, it compares the performance and effectiveness of the developed proof of concept tool against the manual system. The study used a dataset comprising 206,625 text messages. The current process would take roughly 2.82 years to categorise this dataset whereas the trained SVM model would require only 6.4 minutes while achieving an accuracy of 70.4% demonstrating that the automated method is significantly faster, more scalable, and consistent when compared to the current manual categorization. These advantages make the SVM model a more efficient and effective tool for categorizing large unstructured text datasets. These results and the proof-of-concept tool developed demonstrate the potential for enhancing the efficiency and accuracy of message categorization on the Zambia U-report platform and other similar text messages-based platforms.展开更多
In the 21st century, the surge in natural and human-induced disasters necessitates robust disaster managementframeworks. This research addresses a critical gap, exploring dynamics in the successful implementation andp...In the 21st century, the surge in natural and human-induced disasters necessitates robust disaster managementframeworks. This research addresses a critical gap, exploring dynamics in the successful implementation andperformance monitoring of disaster management. Focusing on eleven key elements like Vulnerability and RiskAssessment, Training, Disaster Preparedness, Communication, and Community Resilience, the study utilizesScopus Database for secondary data, employing Text Mining and MS-Excel for analysis and data management.IBM SPSS (26) and IBM AMOS (20) facilitate Exploratory Factor Analysis (EFA) and Structural Equation Modeling(SEM) for model evaluation.The research raises questions about crafting a comprehensive, adaptable model, understanding the interplaybetween vulnerability assessment, training, and disaster preparedness, and integrating effective communicationand collaboration. Findings offer actionable insights for policy, practice, and community resilience against disasters. By scrutinizing each factor's role and interactions, the research lays the groundwork for a flexible model.Ultimately, the study aspires to cultivate more resilient communities amid the escalating threats of an unpredictable world, fostering effective navigation and thriving.展开更多
Purpose–Document retrieval has become a hot research topic over the past few years,and has been paid more attention in browsing and synthesizing information from different documents.The purpose of this paper is to de...Purpose–Document retrieval has become a hot research topic over the past few years,and has been paid more attention in browsing and synthesizing information from different documents.The purpose of this paper is to develop an effective document retrieval method,which focuses on reducing the time needed for the navigator to evoke the whole document based on contents,themes and concepts of documents.Design/methodology/approach–This paper introduces an incremental learning approach for text categorization using Monarch Butterfly optimization–FireFly optimization based Neural Network(MB–FF based NN).Initially,the feature extraction is carried out on the pre-processed data using Term Frequency–Inverse Document Frequency(TF–IDF)and holoentropy to find the keywords of the document.Then,cluster-based indexing is performed using MB–FF algorithm,and finally,by matching process with the modified Bhattacharya distance measure,the document retrieval is done.In MB–FF based NN,the weights in the NN are chosen using MB–FF algorithm.Findings–The effectiveness of the proposed MB–FF based NN is proven with an improved precision value of 0.8769,recall value of 0.7957,F-measure of 0.8143 and accuracy of 0.7815,respectively.Originality/value–The experimental results show that the proposed MB–FF based NN is useful to companies,which have a large workforce across the country.展开更多
理解蛋白质的生物学功能是定量合成生物学成功的前提。然而,除了少数模式生物外,大多数生物中有许多蛋白质的功能尚未通过实验进行解析。因此,开发自动、准确的蛋白质功能预测算法尤为重要。近年来,以深度学习为代表的人工智能算法成为...理解蛋白质的生物学功能是定量合成生物学成功的前提。然而,除了少数模式生物外,大多数生物中有许多蛋白质的功能尚未通过实验进行解析。因此,开发自动、准确的蛋白质功能预测算法尤为重要。近年来,以深度学习为代表的人工智能算法成为蛋白质生物信息学发展的主流。在蛋白质功能预测领域,深度学习尤为显著。例如,在最近几届国际蛋白质功能预测大赛(Critical Assessment of Function Annotation,CAFA)中,排名靠前的算法使用深度学习模型(主要是大语言模型)实现基于文本数据挖掘的蛋白质功能预测。具体而言,这些方法或直接利用从科学文献中提取的文本特征来预测基因本体(Gene Ontology,GO),或通过具有相似文献的模板蛋白质来预测GO。尽管在开发更强大的深度学习模型用于基于文本挖掘的蛋白质功能注释方面已有大量研究,基于文本挖掘的蛋白质功能预测算法在处理科学文献数据时仍存在一些长期被忽视的问题。本文首先回顾了蛋白质功能注释中现有的方法和挑战:第一,大多数基于文本挖掘的蛋白质功能预测器仅使用由UniProt数据库管理员为目标蛋白手工收集的PubMed摘要,忽略了尚未被UniProt收录的文献;第二,几乎所有方法都只处理摘要,而忽略了PubMed Central和Europe PMC等数据库中可获得的更详尽的全文文献;第三,鲜有研究工作能自动区分低通量实验、高通量研究和计算预测等不同类别的科研文献,这大大增加了基于文本进行功能注释的难度。此外,本文还提出了利用人工智能最新发展的有前景的方法,以改进基于文本挖掘的蛋白质功能注释。这有助于开发下一代文本挖掘工具,针对性攻克文本数据处理的现有困难,以实现更准确的功能注释。展开更多
With the dramatically development of Internet, the information processing and management technology onWWW have become a great important branch of data mining and data warehouse. Especially, nowadays, Text Miningis mar...With the dramatically development of Internet, the information processing and management technology onWWW have become a great important branch of data mining and data warehouse. Especially, nowadays, Text Miningis marvelously emerging and plays an important role in interrelated fields. So it is worth summarizing the contentabout text mining from its definition to relational methods and techniques. In this paper, combined to comparativelymature data mining technology, we present the definition of text mining and the multi-stage text mining process mod-el. Moreover, this paper roundly introduces the key areas of text mining and some of the powerful text analysis tech-niques, including: Word Automatic Segmenting, Feature Representation, Feature Extraction, Text Categorization,Text Clustering, Text Summarization, Information Extraction, Pattern Quality Evaluation, etc. These techniquescover the whole process from information preprocessing to knowledge obtaining.展开更多
文摘To promote behavioral change among adolescents in Zambia, the National HIV/AIDS/STI/TB Council, in collaboration with UNICEF, developed the Zambia U-Report platform. This platform provides young people with improved access to information on various Sexual Reproductive Health topics through Short Messaging Service (SMS) messages. Over the years, the platform has accumulated millions of incoming and outgoing messages, which need to be categorized into key thematic areas for better tracking of sexual reproductive health knowledge gaps among young people. The current manual categorization process of these text messages is inefficient and time-consuming and this study aims to automate the process for improved analysis using text-mining techniques. Firstly, the study investigates the current text message categorization process and identifies a list of categories adopted by counselors over time which are then used to build and train a categorization model. Secondly, the study presents a proof of concept tool that automates the categorization of U-report messages into key thematic areas using the developed categorization model. Finally, it compares the performance and effectiveness of the developed proof of concept tool against the manual system. The study used a dataset comprising 206,625 text messages. The current process would take roughly 2.82 years to categorise this dataset whereas the trained SVM model would require only 6.4 minutes while achieving an accuracy of 70.4% demonstrating that the automated method is significantly faster, more scalable, and consistent when compared to the current manual categorization. These advantages make the SVM model a more efficient and effective tool for categorizing large unstructured text datasets. These results and the proof-of-concept tool developed demonstrate the potential for enhancing the efficiency and accuracy of message categorization on the Zambia U-report platform and other similar text messages-based platforms.
文摘In the 21st century, the surge in natural and human-induced disasters necessitates robust disaster managementframeworks. This research addresses a critical gap, exploring dynamics in the successful implementation andperformance monitoring of disaster management. Focusing on eleven key elements like Vulnerability and RiskAssessment, Training, Disaster Preparedness, Communication, and Community Resilience, the study utilizesScopus Database for secondary data, employing Text Mining and MS-Excel for analysis and data management.IBM SPSS (26) and IBM AMOS (20) facilitate Exploratory Factor Analysis (EFA) and Structural Equation Modeling(SEM) for model evaluation.The research raises questions about crafting a comprehensive, adaptable model, understanding the interplaybetween vulnerability assessment, training, and disaster preparedness, and integrating effective communicationand collaboration. Findings offer actionable insights for policy, practice, and community resilience against disasters. By scrutinizing each factor's role and interactions, the research lays the groundwork for a flexible model.Ultimately, the study aspires to cultivate more resilient communities amid the escalating threats of an unpredictable world, fostering effective navigation and thriving.
文摘Purpose–Document retrieval has become a hot research topic over the past few years,and has been paid more attention in browsing and synthesizing information from different documents.The purpose of this paper is to develop an effective document retrieval method,which focuses on reducing the time needed for the navigator to evoke the whole document based on contents,themes and concepts of documents.Design/methodology/approach–This paper introduces an incremental learning approach for text categorization using Monarch Butterfly optimization–FireFly optimization based Neural Network(MB–FF based NN).Initially,the feature extraction is carried out on the pre-processed data using Term Frequency–Inverse Document Frequency(TF–IDF)and holoentropy to find the keywords of the document.Then,cluster-based indexing is performed using MB–FF algorithm,and finally,by matching process with the modified Bhattacharya distance measure,the document retrieval is done.In MB–FF based NN,the weights in the NN are chosen using MB–FF algorithm.Findings–The effectiveness of the proposed MB–FF based NN is proven with an improved precision value of 0.8769,recall value of 0.7957,F-measure of 0.8143 and accuracy of 0.7815,respectively.Originality/value–The experimental results show that the proposed MB–FF based NN is useful to companies,which have a large workforce across the country.
文摘理解蛋白质的生物学功能是定量合成生物学成功的前提。然而,除了少数模式生物外,大多数生物中有许多蛋白质的功能尚未通过实验进行解析。因此,开发自动、准确的蛋白质功能预测算法尤为重要。近年来,以深度学习为代表的人工智能算法成为蛋白质生物信息学发展的主流。在蛋白质功能预测领域,深度学习尤为显著。例如,在最近几届国际蛋白质功能预测大赛(Critical Assessment of Function Annotation,CAFA)中,排名靠前的算法使用深度学习模型(主要是大语言模型)实现基于文本数据挖掘的蛋白质功能预测。具体而言,这些方法或直接利用从科学文献中提取的文本特征来预测基因本体(Gene Ontology,GO),或通过具有相似文献的模板蛋白质来预测GO。尽管在开发更强大的深度学习模型用于基于文本挖掘的蛋白质功能注释方面已有大量研究,基于文本挖掘的蛋白质功能预测算法在处理科学文献数据时仍存在一些长期被忽视的问题。本文首先回顾了蛋白质功能注释中现有的方法和挑战:第一,大多数基于文本挖掘的蛋白质功能预测器仅使用由UniProt数据库管理员为目标蛋白手工收集的PubMed摘要,忽略了尚未被UniProt收录的文献;第二,几乎所有方法都只处理摘要,而忽略了PubMed Central和Europe PMC等数据库中可获得的更详尽的全文文献;第三,鲜有研究工作能自动区分低通量实验、高通量研究和计算预测等不同类别的科研文献,这大大增加了基于文本进行功能注释的难度。此外,本文还提出了利用人工智能最新发展的有前景的方法,以改进基于文本挖掘的蛋白质功能注释。这有助于开发下一代文本挖掘工具,针对性攻克文本数据处理的现有困难,以实现更准确的功能注释。
文摘With the dramatically development of Internet, the information processing and management technology onWWW have become a great important branch of data mining and data warehouse. Especially, nowadays, Text Miningis marvelously emerging and plays an important role in interrelated fields. So it is worth summarizing the contentabout text mining from its definition to relational methods and techniques. In this paper, combined to comparativelymature data mining technology, we present the definition of text mining and the multi-stage text mining process mod-el. Moreover, this paper roundly introduces the key areas of text mining and some of the powerful text analysis tech-niques, including: Word Automatic Segmenting, Feature Representation, Feature Extraction, Text Categorization,Text Clustering, Text Summarization, Information Extraction, Pattern Quality Evaluation, etc. These techniquescover the whole process from information preprocessing to knowledge obtaining.
