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1
Miah, M. Saef Ullah, Junaida Sulaiman, Talha Bin Sarwar, Kamal Z. Zamli, and Rajan Jose. "Study of Keyword Extraction Techniques for Electric Double-Layer Capacitor Domain Using Text Similarity Indexes: An Experimental Analysis." Complexity 2021 (December 2, 2021): 1–12. http://dx.doi.org/10.1155/2021/8192320.
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Keywords perform a significant role in selecting various topic-related documents quite easily. Topics or keywords assigned by humans or experts provide accurate information. However, this practice is quite expensive in terms of resources and time management. Hence, it is more satisfying to utilize automated keyword extraction techniques. Nevertheless, before beginning the automated process, it is necessary to check and confirm how similar expert-provided and algorithm-generated keywords are. This paper presents an experimental analysis of similarity scores of keywords generated by different supervised and unsupervised automated keyword extraction algorithms with expert-provided keywords from the electric double layer capacitor (EDLC) domain. The paper also analyses which texts provide better keywords such as positive sentences or all sentences of the document. From the unsupervised algorithms, YAKE, TopicRank, MultipartiteRank, and KPMiner are employed for keyword extraction. From the supervised algorithms, KEA and WINGNUS are employed for keyword extraction. To assess the similarity of the extracted keywords with expert-provided keywords, Jaccard, Cosine, and Cosine with word vector similarity indexes are employed in this study. The experiment shows that the MultipartiteRank keyword extraction technique measured with cosine with word vector similarity index produces the best result with 92% similarity with expert-provided keywords. This study can help the NLP researchers working with the EDLC domain or recommender systems to select more suitable keyword extraction and similarity index calculation techniques.
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Corrin, Linda, Kate Thompson, Gwo-Jen Hwang, and Jason M. Lodge. "The importance of choosing the right keywords for educational technology publications." Australasian Journal of Educational Technology 38, no. 2 (June 6, 2022): 1–8. http://dx.doi.org/10.14742/ajet.8087.
Full textAbstract:
Keywords refer to important words or concepts that represent the research foci and theoretical backgrounds of an academic study. They enable readers to glean a quick impression about what they are going to read from an academic article. Keywords also provide valuable information for researchers who intend to search for articles related to a particular field or conduct a survey related to a specific topic. Therefore, in selected academic journals, detailed guidelines are provided to help authors choose appropriate keywords for highlighting their research. In this editorial, we examine the role of keywords from several perspectives by reviewing the keywords adopted by AJET authors in recent years. Accordingly, we attempt to provide recommendations to AJET authors for their future submissions. It is expected that, via using proper keywords, the authors and readers as well as the journal can be benefited.
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Lindner, James R. "International Agricultural and Extension Education: Meeting the Unknown Challenges of Tomorrow." Journal of International Agricultural and Extension Education 25, no. 4 (December 15, 2018): 99–106. http://dx.doi.org/10.5191/jiaee.2018.25408.
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The purpose of this paper is to provide philosophical observations and reflections over 25 years of the Journal of International Agricultural and Extension Education (JIAEE) as a reader, author, and editor. The paper provides a brief history of JIAEE including changes that occurred over the years. This paper honors those that contributed to JIAEE and stood as its caretakers over the past 25 years. This main body of the paper is divided into three sections: early years, middle years, and contemporary years. The paper explores the development and use of JIAEE keywords and provides visual depictions using wordclouds. The purpose of keywords is discussed and suggestions for future use are provided. Final recommendations and well wishes for the future are provided. Recommendations include: Considering the hiring of a permanent editor or publishing firm such as Taylor & Francis; creating a purposeful research agenda in conjunction with AIAEE; and redressing the developing and use of keywords
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Utiyama, Masao, Masaki Murata, and Hitoshi Isahara. "Using author keywords for automatic term recognition." Terminology 6, no. 2 (December 31, 2000): 313–26. http://dx.doi.org/10.1075/term.6.2.10uti.
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This paper proposes a method which regards the keywords provided by the authors of technical papers as terms and learns the statistics which distinguish terms from non-terms. Since it uses keywords as training data, it requires no hand-labeled training corpora manually annotated with terms. The proposed method was used to extract terms from the NTCIR morphologically tagged corpus and achieved 0.800 recall and 0.431 precision. The effectiveness of the proposed method has thus been demonstrated.
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Yeom, Sanghui, Seungyeon Choi, Jeonghee Chi, and Soyoung Park. "Blockchain-Based Employment Contract System Architecture Allowing Encrypted Keyword Searches." Electronics 10, no. 9 (May 4, 2021): 1086. http://dx.doi.org/10.3390/electronics10091086.
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We introduce a blockchain-based online employment contract system to protect the rights and interests of both employees and employers. In the proposed model, an employer and a worker can interactively create a new electronic online contract, and the mutually signed contract is saved on a contract blockchain so that the contract becomes certifiable but cannot be forged by the contract signers. In particular, the blockchain in our system provides transactional privacy to protect sensitive personal information such as social identifier, contact information, income, and so forth, contained in the contract. Since a remote cloud server must provide not only secure storage, accessibility, and availability of all signed contracts, but also increased security in the server, we propose a new encrypted keyword search mechanism with enhanced search accuracy. Each contract is associated with encrypted keywords generated from the names of contractual parties and must remain confidential and anonymous even to the server. Although, the contracts must always be accessible by the contract signers, only the cloud server should be able to retrieve each user’s contract without decrypting the contract or identifying the contract signer. To meet these requirements, we propose a new encrypted keyword search mechanism based on Gentry’s homomorphic encryption technology; the server can find each user’s contract when two encrypted arbitrary keywords are homomorphic to each other. Since the keywords in the proposed system are based on person names or business names, they are easily predictable, and, thereby, many synonyms for a keyword can exist. Therefore, the proposed encrypted keyword search takes into account not only the keywords but also the ownership of each contract; in this way, the proposed search scheme is secure against a keyword guessing attack and provides strong search accuracy against the keyword synonyms. As a result, users can only access their own contracts, and the cloud server can exactly retrieve the requester’s contracts. Implementations for the proposed system and corresponding analysis on its security and simulated performance are provided.
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McCallum, Jenn, Paul Elliott, and Terese McIntosh. "Searching For Signs of Life in Ontario Universities: An Innovative Method for Evaluating Biodiversity Integration within University Curricula." Canadian Journal of Higher Education 47, no. 2 (August 27, 2017): 87–105. http://dx.doi.org/10.47678/cjhe.v47i2.186549.
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This study investigates the degree to which biodiversity concepts are included within university curricula in Ontario and provides a baseline for tracking this. A keyword search of undergraduate and graduate academic calendars from six Ontario universities was conducted. A list of 28 relevant keywords was developed, and university program descriptors were searched for these keywords, while considering core and elective courses within each program. Almost half (49.5%) of the 386 undergraduate programs, and 29.4% of the 327 graduate programs featured biodiversity keywords. Science programs showed the highest degree of integration (74.5% for undergraduate and 37.4% for graduate programs), followed by business programs (57.6% and 38.4%, respectively). The arts and social sciences showed the least biodiversity integration (25.8% of undergraduate and 21.0% of graduate programs). This research method provides a depth of understanding of biodiversity integration within university curricula, although the analysis is limited to the content provided in academic calendars.
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Gur, Tamer. "Biobtree: A tool to search and map bioinformatics identifiers and special keywords." F1000Research 8 (January 7, 2020): 145. http://dx.doi.org/10.12688/f1000research.17927.3.
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Biobtree is a bioinformatics tool to search and map bioinformatics datasets via identifiers or special keywords such as species name. It processes large bioinformatics datasets using a specialized MapReduce-based solution with optimum computational and storage resource usage. It provides uniform and B+ tree-based database output, a web interface, web services and allows performing chain mapping queries between datasets. It can be used via a single executable file or alternatively it can be used via the R or Python-based wrapper packages which are additionally provided for easier integration into existing pipelines. Biobtree is open source and available at GitHub.
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Gur, Tamer. "Biobtree: A tool to search and map bioinformatics identifiers and special keywords." F1000Research 8 (January 20, 2020): 145. http://dx.doi.org/10.12688/f1000research.17927.4.
Full textAbstract:
Biobtree is a bioinformatics tool to search and map bioinformatics datasets via identifiers or special keywords such as species name. It processes large bioinformatics datasets using a specialized MapReduce-based solution with optimum computational and storage resource usage. It provides uniform and B+ tree-based database output, a web interface, web services and allows performing chain mapping queries between datasets. It can be used via a single executable file or alternatively it can be used via the R or Python-based wrapper packages which are additionally provided for easier integration into existing pipelines. Biobtree is open source and available at GitHub.
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D’Auria, Anna, Marco Tregua, Tiziana Russo Spena, and Francesco Bifulco. "Multiple Context of Innovation: Insights from Literature." International Journal of Innovation and Technology Management 14, no. 02 (March 22, 2017): 1740007. http://dx.doi.org/10.1142/s0219877017400077.
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The aim of this paper is to investigate the most relevant conceptualizations of innovation taken into account by scholars, and the linkages among them. Over time, the increasing interest in this area has involved a great variety of actors and contexts in establishing the definition of different innovation labels; we chose to focus the analysis on “system innovation”, “network innovation” and “ecosystem innovation”. In order to observe the most relevant elements of each research stream and common aspects, a two-step analysis of literature contributions has been performed through a bibliometric analysis comparing the evidence emerging from one survey of keywords provided by authors and a second of keywords provided by editors. Through this kind of investigation the editors’ keywords emerged as more objective, but nevertheless, by considering some of the different evidences obtained from the keywords provided by authors, the two-step analysis is useful to provide more complete results.
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I, Pratheek, and Joy Paulose. "Prediction of Answer Keywords using Char-RNN." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 3 (June 1, 2019): 2164. http://dx.doi.org/10.11591/ijece.v9i3.pp2164-2176.
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<p>Generating sequences of characters using a Recurrent Neural Network (RNN) is a tried and tested method for creating unique and context aware words, and is fundamental in Natural Language Processing tasks. These type of Neural Networks can also be used a question-answering system. The main drawback of most of these systems is that they work from a factoid database of information, and when queried about new and current information, the responses are usually bleak. In this paper, the author proposes a novel approach to finding answer keywords from a given body of news text or headline, based on the query that was asked, where the query would be of the nature of current affairs or recent news, with the use of Gated Recurrent Unit (GRU) variant of RNNs. Thus, this ensures that the answers provided are relevant to the content of query that was put forth.</p>
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Perdana, Novario Jaya. "IMPLEMENTASI ALGORITMA GOOGLE LATENT SEMANTIC DISTANCE UNTUK EKSTRAKSI RANGKAIAN KATA KUNCI ARTIKEL JURNAL ILMIAH." Computatio : Journal of Computer Science and Information Systems 2, no. 2 (October 31, 2018): 186. http://dx.doi.org/10.24912/computatio.v2i2.2569.
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The accuracy of search result using search engine depends on the keywords that are used. Lack of the information provided on the keywords can lead to reduced accuracy of the search result. This means searching information on the internet is a hard work. In this research, a software has been built to create document keywords sequences. The software uses Google Latent Semantic Distance which can extract relevant information from the document. The information is expressed in the form of specific words sequences which could be used as keyword recommendations in search engines. The result shows that the implementation of the method for creating document keyword recommendation achieved high accuracy and could finds the most relevant information in the top search results.
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Khan, Muhammad Qasim, Abdul Shahid, M. Irfan Uddin, Muhammad Roman, Abdullah Alharbi, Wael Alosaimi, Jameel Almalki, and Saeed M. Alshahrani. "Impact analysis of keyword extraction using contextual word embedding." PeerJ Computer Science 8 (May 30, 2022): e967. http://dx.doi.org/10.7717/peerj-cs.967.
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A document’s keywords provide high-level descriptions of the content that summarize the document’s central themes, concepts, ideas, or arguments. These descriptive phrases make it easier for algorithms to find relevant information quickly and efficiently. It plays a vital role in document processing, such as indexing, classification, clustering, and summarization. Traditional keyword extraction approaches rely on statistical distributions of key terms in a document for the most part. According to contemporary technological breakthroughs, contextual information is critical in deciding the semantics of the work at hand. Similarly, context-based features may be beneficial in the job of keyword extraction. For example, simply indicating the previous or next word of the phrase of interest might be used to describe the context of a phrase. This research presents several experiments to validate that context-based key extraction is significant compared to traditional methods. Additionally, the KeyBERT proposed methodology also results in improved results. The proposed work relies on identifying a group of important words or phrases from the document’s content that can reflect the authors’ main ideas, concepts, or arguments. It also uses contextual word embedding to extract keywords. Finally, the findings are compared to those obtained using older approaches such as Text Rank, Rake, Gensim, Yake, and TF-IDF. The Journals of Universal Computer (JUCS) dataset was employed in our research. Only data from abstracts were used to produce keywords for the research article, and the KeyBERT model outperformed traditional approaches in producing similar keywords to the authors’ provided keywords. The average similarity of our approach with author-assigned keywords is 51%.
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Oh, Yun-Kyoung. "Analysis of Research Trends in Skin Care using Keyword Network Analysis." Journal of the Korean Society of Cosmetology 28, no. 6 (December 31, 2022): 1308–17. http://dx.doi.org/10.52660/jksc.2022.28.6.1308.
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This study tried to examine the research trends related to skin care by using keyword network analysis on data extracted from the KCI journal D/B provided by the National Research Foundation of Korea. Text refinement was performed by extracting key words from a total of 504 papers published from 2011 to August 2022. The tools used for analysis are Textom and UCINET6. As a result, first, the keyword frequency was ‘cosmetics’ the most, followed by‘appearance management behavior’, ‘skin care shop’, ‘satisfaction’, ‘female’, ‘medical’, ‘service’, and ‘acne’. Second, as a result of examining the frequency of skin care related keywords according to the study period, the 1st period (2011-2016) and the 2nd period (2017-2022) both included ‘cosmetics’ as the keyword the most. In the second period, keywords such as ‘skin care shop’, ‘medical’, ‘aging’, and ‘acne’ appeared in high rankings. Third, keywords such as ‘cosmetics’, ‘satisfaction’, ‘skin care room’, ‘appearance management behavior’ and ‘service’ are keywords with high connection centrality, proximity centrality, and dominance centrality. It was confirmed that it was actively progressing.
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Wu, Huai Guang, Qing Lin, and Zhong Ju Fu. "Intelligent Match of Emergency Plan Based on Chinese Keywords Extraction." Applied Mechanics and Materials 263-266 (December 2012): 1652–58. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.1652.
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This paper introduced an intelligent match method of emergency plan based on keywords extraction. Words frequency, part of speech and position of framework are taken as the keyword weight factors. Least-squares error linear estimate method is used to regulate the factors and calculate keywords weight. And Vector Space Model is set up to calculate the maximum similarity between plan texts to complete design of plan match. The expansible practical parameters adjustment module is provided to adapt to diversity of match plan with emphasis part. Compare with tf*idf, the experimental results show that the presented method is more promising in intelligent match method of emergency plan.
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Tahir Suleymanov, Tahir Suleymanov, and Vazzuha Valiyeva Vazzuha Valiyeva. "TUDY OF HIGH PRESSURE PLUG VALVES, THE SEAL OF WHICH IS PROVIDED WITH OIL." ETM - Equipment, Technologies, Materials 10, no. 02 (April 2, 2022): 04–10. http://dx.doi.org/10.36962/etm10022022-04.
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In this paper, high-pressure valves, the seal of which is provided with oil, are investigated. It is known that such valves are widely used for pumping cement into a well under high pressure in offshore conditions. A number of advantages of such valves over other valves are noted. Keywords: high pressure, tightness, valve, wear, design, internal diameter, barrel, crack, maintenance.
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Handoko, Luqman Hakim, Ai Nur Bayinah, and Firmansyah Firmansyah. "A Bibliometric Analysis of Research on Zakat: Past Trends and Future Directions." al-Uqud : Journal of Islamic Economics 6, no. 2 (July 3, 2022): 191–210. http://dx.doi.org/10.26740/aluqud.v6n2.p191-210.
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Zakat is a part of Islamic social finance that aimed at the social purpose and has been recently received attention from researchers globally. Therefore, the study aimed to explore the current state of zakat literature, identify the bibliometrics in the field, and provide future research direction. To achieve this goal, this study utilised bibliometric analysis using VOSviewer software. The documents were extracted from the Scopus database on 5th March 2021 with the search keyword Zakat and its variations. The result showed 635 documents in 267 journals and revealed approximately 1289 authors. The papers came from 53 countries and 160 affiliations roughly. Based on co-occurrence, the analysis identified 1488 keywords and the word zakat, Islam, Malaysia, Poverty, and zakat institution as the top five frequent keywords. The study also revealed the current trend of zakat studies related to education, performance, SME, and compliance. The most important of this study, seventeen zakat issues, were provided for future research agenda. Henceforth, the following research might focus on this study's suggestions to enrich and enhance zakat literature.
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Handoko, Luqman Hakim, Ai Nur Bayinah, and Firmansyah Firmansyah. "A bibliometric analysis of research on zakat: Past trends and future directions." al-Uqud : Journal of Islamic Economics 6, no. 2 (July 3, 2022): 160–76. http://dx.doi.org/10.26740/aluqud.v6n2.p160-176.
Full textAbstract:
Zakat is a part of Islamic social finance that aimed at the social purpose and has been recently received attention from researchers globally. Therefore, the study aimed to explore the current state of zakat literature, identify the bibliometrics in the field, and provide future research direction. To achieve this goal, this study utilised bibliometric analysis using VOSviewer software. The documents were extracted from the Scopus database on 5th March 2021 with the search keyword Zakat and its variations. The result showed 635 documents in 267 journals and revealed approximately 1289 authors. The papers came from 53 countries and 160 affiliations roughly. Based on co-occurrence, the analysis identified 1488 keywords and the word zakat, Islam, Malaysia, Poverty, and zakat institution as the top five frequent keywords. The study also revealed the current trend of zakat studies related to education, performance, SME, and compliance. The most important of this study, seventeen zakat issues, were provided for future research agenda. Henceforth, the following research might focus on this study's suggestions to enrich and enhance zakat literature.
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Carrillo-Larco, Rodrigo M., Manuel Castillo-Cara, and Jesús Lovón-Melgarejo. "Government plans in the 2016 and 2021 Peruvian presidential elections: A natural language processing analysis of the health chapters." Wellcome Open Research 6 (December 20, 2021): 177. http://dx.doi.org/10.12688/wellcomeopenres.16867.3.
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Background: While clinical medicine has exploded, electronic health records for Natural Language Processing (NLP) analyses, public health, and health policy research have not yet adopted these algorithms. We aimed to dissect the health chapters of the government plans of the 2016 and 2021 Peruvian presidential elections, and to compare different NLP algorithms. Methods: From the government plans (18 in 2016; 19 in 2021) we extracted each sentence from the health chapters. We used five NLP algorithms to extract keywords and phrases from each plan: Term Frequency–Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), TextRank, Keywords Bidirectional Encoder Representations from Transformers (KeyBERT), and Rapid Automatic Keywords Extraction (Rake). Results: In 2016 we analysed 630 sentences, whereas in 2021 there were 1,685 sentences. The TF-IDF algorithm showed that in 2016, 22 terms appeared with a frequency of 0.05 or greater, while in 2021 27 terms met this criterion. The LDA algorithm defined two groups. The first included terms related to things the population would receive (e.g., ’insurance’), while the second included terms about the health system (e.g., ’capacity’). In 2021, most of the government plans belonged to the second group. The TextRank analysis provided keywords showing that ’universal health coverage’ appeared frequently in 2016, while in 2021 keywords about the COVID-19 pandemic were often found. The KeyBERT algorithm provided keywords based on the context of the text. These keywords identified some underlying characteristics of the political party (e.g., political spectrum such as left-wing). The Rake algorithm delivered phrases, in which we found ’universal health coverage’ in 2016 and 2021. Conclusion: The NLP analysis could be used to inform on the underlying priorities in each government plan. NLP analysis could also be included in research of health policies and politics during general elections and provide informative summaries for the general population.
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Carrillo-Larco, Rodrigo M., Manuel Castillo-Cara, and Jesús Lovón-Melgarejo. "Government plans in the 2016 and 2021 Peruvian presidential elections: A natural language processing analysis of the health chapters." Wellcome Open Research 6 (July 8, 2021): 177. http://dx.doi.org/10.12688/wellcomeopenres.16867.1.
Full textAbstract:
Background: While clinical medicine has exploded, electronic health records for Natural Language Processing (NLP) analyses, public health, and health policy research have not yet adopted these algorithms. We aimed to dissect the health chapters of the government plans of the 2016 and 2021 Peruvian presidential elections, and to compare different NLP algorithms. Methods: From the government plans (18 in 2016; 19 in 2021) we extracted each sentence from the health chapters. We used five NLP algorithms to extract keywords and phrases from each plan: Term Frequency–Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), TextRank, Keywords Bidirectional Encoder Representations from Transformers (KeyBERT), and Rapid Automatic Keywords Extraction (Rake). Results: In 2016 we analysed 630 sentences, whereas in 2021 there were 1,685 sentences. The TF-IDF algorithm showed that in 2016, nine terms appeared with a frequency of 0.10 or greater, while in 2021 43 terms met this criterion. The LDA algorithm defined two groups. The first included terms related to things the population would receive (e.g., ’insurance’), while the second included terms about the health system (e.g., ’capacity’). In 2021, most of the government plans belonged to the second group. The TextRank analysis provided keywords showing that ’universal health coverage’ appeared frequently in 2016, while in 2021 keywords about the COVID-19 pandemic were often found. The KeyBERT algorithm provided keywords based on the context of the text. These keywords identified some underlying characteristics of the political party (e.g., political spectrum such as left-wing). The Rake algorithm delivered phrases, in which we found ’universal health coverage’ in 2016 and 2021. Conclusion: The NLP analysis could be used to inform on the underlying priorities in each government plan. NLP analysis could also be included in research of health policies and politics during general elections and provide informative summaries for the general population.
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Carrillo-Larco, Rodrigo M., Manuel Castillo-Cara, and Jesús Lovón-Melgarejo. "Government plans in the 2016 and 2021 Peruvian presidential elections: A natural language processing analysis of the health chapters." Wellcome Open Research 6 (January 18, 2022): 177. http://dx.doi.org/10.12688/wellcomeopenres.16867.4.
Full textAbstract:
Background: While clinical medicine has exploded, electronic health records for Natural Language Processing (NLP) analyses, public health, and health policy research have not yet adopted these algorithms. We aimed to dissect the health chapters of the government plans of the 2016 and 2021 Peruvian presidential elections, and to compare different NLP algorithms. Methods: From the government plans (18 in 2016; 19 in 2021) we extracted each sentence from the health chapters. We used five NLP algorithms to extract keywords and phrases from each plan: Term Frequency–Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), TextRank, Keywords Bidirectional Encoder Representations from Transformers (KeyBERT), and Rapid Automatic Keywords Extraction (Rake). Results: In 2016 we analysed 630 sentences, whereas in 2021 there were 1,685 sentences. The TF-IDF algorithm showed that in 2016, 22 terms appeared with a frequency of 0.05 or greater, while in 2021 27 terms met this criterion. The LDA algorithm defined two groups. The first included terms related to things the population would receive (e.g., ’insurance’), while the second included terms about the health system (e.g., ’capacity’). In 2021, most of the government plans belonged to the second group. The TextRank analysis provided keywords showing that ’universal health coverage’ appeared frequently in 2016, while in 2021 keywords about the COVID-19 pandemic were often found. The KeyBERT algorithm provided keywords based on the context of the text. These keywords identified some underlying characteristics of the political party (e.g., political spectrum such as left-wing). The Rake algorithm delivered phrases, in which we found ’universal health coverage’ in 2016 and 2021. Conclusion: The NLP analysis could be used to inform on the underlying priorities in each government plan. NLP analysis could also be included in research of health policies and politics during general elections and provide informative summaries for the general population.
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Carrillo-Larco, Rodrigo M., Manuel Castillo-Cara, and Jesús Lovón-Melgarejo. "Government plans in the 2016 and 2021 Peruvian presidential elections: A natural language processing analysis of the health chapters." Wellcome Open Research 6 (November 17, 2021): 177. http://dx.doi.org/10.12688/wellcomeopenres.16867.2.
Full textAbstract:
Background: While clinical medicine has exploded, electronic health records for Natural Language Processing (NLP) analyses, public health, and health policy research have not yet adopted these algorithms. We aimed to dissect the health chapters of the government plans of the 2016 and 2021 Peruvian presidential elections, and to compare different NLP algorithms. Methods: From the government plans (18 in 2016; 19 in 2021) we extracted each sentence from the health chapters. We used five NLP algorithms to extract keywords and phrases from each plan: Term Frequency–Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), TextRank, Keywords Bidirectional Encoder Representations from Transformers (KeyBERT), and Rapid Automatic Keywords Extraction (Rake). Results: In 2016 we analysed 630 sentences, whereas in 2021 there were 1,685 sentences. The TF-IDF algorithm showed that in 2016, nine terms appeared with a frequency of 0.10 or greater, while in 2021 43 terms met this criterion. The LDA algorithm defined two groups. The first included terms related to things the population would receive (e.g., ’insurance’), while the second included terms about the health system (e.g., ’capacity’). In 2021, most of the government plans belonged to the second group. The TextRank analysis provided keywords showing that ’universal health coverage’ appeared frequently in 2016, while in 2021 keywords about the COVID-19 pandemic were often found. The KeyBERT algorithm provided keywords based on the context of the text. These keywords identified some underlying characteristics of the political party (e.g., political spectrum such as left-wing). The Rake algorithm delivered phrases, in which we found ’universal health coverage’ in 2016 and 2021. Conclusion: The NLP analysis could be used to inform on the underlying priorities in each government plan. NLP analysis could also be included in research of health policies and politics during general elections and provide informative summaries for the general population.
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Carrillo-Larco, Rodrigo M., Manuel Castillo-Cara, and Jesús Lovón-Melgarejo. "Government plans in the 2016 and 2021 Peruvian presidential elections: A natural language processing analysis of the health chapters." Wellcome Open Research 6 (October 25, 2022): 177. http://dx.doi.org/10.12688/wellcomeopenres.16867.5.
Full textAbstract:
Background: While clinical medicine has exploded, electronic health records for Natural Language Processing (NLP) analyses, public health, and health policy research have not yet adopted these algorithms. We aimed to dissect the health chapters of the government plans of the 2016 and 2021 Peruvian presidential elections, and to compare different NLP algorithms. Methods: From the government plans (18 in 2016; 19 in 2021) we extracted each sentence from the health chapters. We used five NLP algorithms to extract keywords and phrases from each plan: Term Frequency–Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), TextRank, Keywords Bidirectional Encoder Representations from Transformers (KeyBERT), and Rapid Automatic Keywords Extraction (Rake). Results: In 2016 we analysed 630 sentences, whereas in 2021 there were 1,685 sentences. The TF-IDF algorithm showed that in 2016, 26 terms appeared with a frequency of 0.08 or greater, while in 2021 27 terms met this criterion. The LDA algorithm defined two groups. The first included terms related to things the population would receive (e.g., ’insurance’), while the second included terms about the health system (e.g., ’capacity’). In 2021, most of the government plans belonged to the second group. The TextRank analysis provided keywords showing that ’universal health coverage’ appeared frequently in 2016, while in 2021 keywords about the COVID-19 pandemic were often found. The KeyBERT algorithm provided keywords based on the context of the text. These keywords identified some underlying characteristics of the political party (e.g., political spectrum such as left-wing). The Rake algorithm delivered phrases, in which we found ’universal health coverage’ in 2016 and 2021. Conclusion: The NLP analysis could be used to inform on the underlying priorities in each government plan. NLP analysis could also be included in research of health policies and politics during general elections and provide informative summaries for the general population.
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Fakihammed, Wael A., Rehab A. Hussien, Zohal B. Ahmed Atbara, Shiraz O. Higaz, Ashwag A. Mirghani, Osama E. Sir Elkhatim, Moneer A. Abdalla, and Almoeiz Yousif Hammad. "Assessing service providers’ satisfaction with the chronic diseases package provided through the National Health Insurance Fund in Sudan." Journal of Drug Delivery and Therapeutics 12, no. 4 (July 15, 2022): 91–100. http://dx.doi.org/10.22270/jddt.v12i4.5565.
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Background: The prevalence and burden of chronic diseases is growing significantly with direct impact on health and financial security of population. In Sudan, there is a continuous effort to improve access for good and integrated health services but still the utilization of these services is low. Therefore, there is an urgent need to assess the availability and integration of benefit package to ensure better and effective coverage of services.Aim: This is a descriptive cross sectional based study aimed to assess the opinion of service providers about the chronic diseases package provided through the national health insurance fund.Methods: Form390 participating, data was collected via closed ended standardized questionnaires. Pre testing was done to ensure quality survey instruments and fieldwork procedures were conducted. A structured questionnaire with closed-ended questions was developed based on literature and previous studies. The questionnaire made up of 30 questions and organized in the following sections: background characteristics, medical characteristics, health professionals’ knowledge and practice related to NHIF ’chronic diseases package and perception about NHIF ‘chronic diseases package.Results: Our results showed that despite the existence of a number of issues, the National Health Insurance Fund is rather accepted amongst our respondents. Issues manifested were in regards to health education and supply issues of drugs. Conclusion: Our study concluded that, there is an obvious weakness in both training of providers and implementation of national protocols for chronic diseases. Moreover, despite the challenges of drug availability, 70% of our participants expressed positivity when questioned about the presentation of the drug package, bearing in mind the importance of reviewing the list to ensure its inclusion of new items. There is a need for training medical providers about chronic diseases with more focusing on health promotion and application of national guidelines. Additionally, integration of chronic diseases services in necessary for achieving universal health coverage.
Keywords: national protocols for chronic diseases, national health insurance fund, health promotion
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Huang, Yue, Hu Liu, and Jing Pan. "Identification of data mining research frontier based on conference papers." International Journal of Crowd Science 5, no. 2 (May 21, 2021): 143–53. http://dx.doi.org/10.1108/ijcs-01-2021-0001.
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Purpose Identifying the frontiers of a specific research field is one of the most basic tasks in bibliometrics and research published in leading conferences is crucial to the data mining research community, whereas few research studies have focused on it. The purpose of this study is to detect the intellectual structure of data mining based on conference papers. Design/methodology/approach This study takes the authoritative conference papers of the ranking 9 in the data mining field provided by Google Scholar Metrics as a sample. According to paper amount, this paper first detects the annual situation of the published documents and the distribution of the published conferences. Furthermore, from the research perspective of keywords, CiteSpace was used to dig into the conference papers to identify the frontiers of data mining, which focus on keywords term frequency, keywords betweenness centrality, keywords clustering and burst keywords. Findings Research showed that the research heat of data mining had experienced a linear upward trend during 2007 and 2016. The frontier identification based on the conference papers showed that there were five research hotspots in data mining, including clustering, classification, recommendation, social network analysis and community detection. The research contents embodied in the conference papers were also very rich. Originality/value This study detected the research frontier from leading data mining conference papers. Based on the keyword co-occurrence network, from four dimensions of keyword term frequency, betweeness centrality, clustering analysis and burst analysis, this paper identified and analyzed the research frontiers of data mining discipline from 2007 to 2016.
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Ping, Yuan. "Towards two decades of journalistic translation research (2000-2019): a corpus-based bibliometric study of the Translation Studies Bibliography." Meta 66, no. 2 (November 2, 2021): 406–26. http://dx.doi.org/10.7202/1083185ar.
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This paper provides a bibliometric analysis of journalistic translation research (JTR) from the past two decades. It is based on a corpus of 396 entries on journalistic translation and interpreting from the Translation Studies Bibliography (TSB) between 2000 and 2019. This study first consists of a bibliometric analysis of these entries from the aspects of author/editor, year, language, type, journal and publisher. It then explores prominent research topics and areas of focus based both on keywords provided by annotated entries in the TSB and on keywords and high-frequency nouns extracted from the abstracts as a result of corpus analysis. The study elaborates on various methodological approaches to studying journalistic translation according to the keywords provided in the entries of the TSB. The study found that: (1) most of the JTR was published by prestigious journals and publishers in English around 2010; (2) multimedia news about socio-cultural issues, published on various platforms, attracted the most attention from scholars; and (3) socio-cultural approaches have been the most prominent type of approach to journalistic translation over the past two decades. JTR research trends are also predicated according to the current development of the field.
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Kawaura, Takayuki, Jun Endo, and YasukoDoi. "Comparison of Keywords Freely Inputted by Users and the Compendium of “Keywords for Children,” Which is Programmed into the Book Searching System for Children." Journal of Advanced Computational Intelligence and Intelligent Informatics 16, no. 2 (March 20, 2012): 239–46. http://dx.doi.org/10.20965/jaciii.2012.p0239.
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When children look for a book to read, they usually ask an adult to recommend something suitable. In many of such cases, librarians or teachers play an important role. It could also be parents or adults close to the children. The objective of the OPAC system provided in libraries is to offer a tool to help users to find books by themselves but it is not an easy system for children to use. In this study, we aim to construct a book searching system that is easy for children to use. To this end, we studied cases in which children used the book search engine “Adventure in the Sea of Books” we developed in 2002 for “International Institute for Children’s Literature, Osaka.” This is installed with “Subject Heading Lists for Children,” a system we uniquely developed to enable users to search a book by the title, author, or any words they can think of. In this study we will make a comparison between the keywords installed in “Subject Heading Lists for Children” and the index items which the users themselves inputted. This comparison will show what kinds of keywords are most requested by children, and lead to the development of a more userfriendly book searching system.
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Surendran, Divya, and Sasikala K. "A Scheduling Genetic Algorithm For Real-Time Data Freshness And Cloud Data Security Over Keywords Searching." International Journal of Computer and Information System (IJCIS) 3, no. 3 (September 15, 2022): 111–20. http://dx.doi.org/10.29040/ijcis.v3i3.80.
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Cloud storage services allow customers to ingress data stored from any device at any time. The growth of the Internet helps the number of users who need to access online databases without a deep understanding of the schema or query. The languages have risen dramatically, allowing users to search secured data and retrieve desired data from cloud storage using keywords. On the other hand, there are fundamental difficulties such as security, which must be provided to secure user'spersonal information. A hybrid scheduling genetic algorithm (SGA) is proposed in this research. SGA technique enhances the security level and provides data freshness. For evaluation and comparison, parameters such as execution time throughputs are used. According to experimental results, the proposed technique ensures the security of user data from unauthorized parties. Furthermore, SGA is strong and more effective when compared to a set of parameters to the existing algorithm like Data Encryption Standard (DES), Blowfish, and AdvancedEncryption Standard (AES).
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Mamta, Brij B. Gupta, and Syed Taqi Ali. "ISEkFT." Journal of Information Technology Research 12, no. 3 (July 2019): 133–53. http://dx.doi.org/10.4018/jitr.2019070108.
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Public-key encryption with keyword search (PEKS) is a well-known technique which allows searching on encrypted data using the public key system. However, this technique suffers from the keyword guessing attack (KGA). To address this problem, a modified version of PEKS called public key encryption with fuzzy keyword search (PEFKS) has been introduced where each keyword is associated with an exact search trapdoor (EST) and a fuzzy search trapdoor (FST) which is provided to the cloud server. PEFKS prevents KGA in such a way that two or maximum three keywords share the same FST. Hence, even if the cloud server knows the FST it cannot link it to the corresponding keyword. But, with a probability of 1/3 the malicious cloud server can still guess the keyword corresponding to FST. Therefore, in this article, the authors present an approach which can improve the security of the PEFKS technique by reducing the probability of guessing the keyword to 1/k where k is the number of keywords that share the same FST, thus enhancing the overall reliability. In addition, the authors have used an identity-based encryption (IBE) as an underlying technique to construct the searchable encryption scheme and proved its security in the standard model.
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Chae, Chungil, Jeong-Ha Yim, Jaeeun Lee, Sung Jun Jo, and Jeong Rok Oh. "The Bibliometric Keywords Network Analysis of Human Resource Management Research Trends: The Case of Human Resource Management Journals in South Korea." Sustainability 12, no. 14 (July 15, 2020): 5700. http://dx.doi.org/10.3390/su12145700.
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Although previous studies on human resource management (HRM) research trends in the global context provided very useful information about the structures of HRM research trends, they have critical limitations. Despite the growing contribution and significance of HRM practices in Korea, a dominant group of scholars has rarely focused on what is going on in the research community in the country. To overcome the limitations and fill the gaps found in studies on the global HRM trends, the purpose of this study is to conduct the keyword network analysis investigating the semantic network structure composed of Korean HRM studies. A total of 1158 research papers published by three top peer-reviewed HRM journals in Korea that were published from 2007 to 2018 were analyzed. The result shows that the whole network structure of Korean HRM has a complex semantic structure that is socially constructed. Additionally, this study identified the top 10 prominent keywords and its ego-centric networks, and nine thematic clusters. By adopting keywords network analysis in bibliometric methods, this study provides an accurate structural interpretation of Korean HRM research practice to facilitate the sustainable development of the studies on the global HRM trends.
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Zhakibaev, Askhat, and Arailym Beisbekova. "Optimization of the Provided Medical Services in а Medical Organization." Journal of Health Development 1, no. 41 (2021): 93–98. http://dx.doi.org/10.32921/2225-9929-2021-1-41-93-98.
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The aim: To develop recommendations aimed at optimizing the work of the Shymkent city multidisciplinary hospital by studying the main performance indicators. Methods.The research was carried out according to the traditional scheme and included the classical stages: drawing up a plan and program, collecting material, processing and analyzing it. Results. To improve the quality of medical care, targeted measures are being implemented. Thus, the design capacity of Primary Healthcare: polyclinic – 500 people per shift, actual attendance – 703 people per shift. The hospital has 330 beds. However, the rate of bed coverage per 10 000 people is low at 28.4. The overall incidence has decreased, and in 2019 it was 75 652. It was found that the number of visits to doctors has increased, thus, this figure in 2019 reached 66.5%. It was found that the volume of provision of free medicines increased and reached 47 420. The indicator of provision with medical technical means as a whole also increased and amounted to 82%. Conclusions. A set of measures aimed at improving the hospital's activities using the existing organizational structure will allow us to respond in a timely manner to changes in all areas of the hospital's activities, and the KPI system will help assess the effectiveness of managerial actions in the field of quality management to improve the quality of medical care and improve the weaknesses of healthcare facilities. Keywords: medical organization, medical care, KPI system.
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Yamaguchi, Yoichiro, Deokcheol Lee, Takuya Nagai, Taro Funamoto, Takuya Tajima, and Etsuo Chosa. "Googling Musculoskeletal-Related Pain and Ranking of Medical Associations’ Patient Information Pages: Google Ads Keyword Planner Analysis." Journal of Medical Internet Research 22, no. 8 (August 14, 2020): e18684. http://dx.doi.org/10.2196/18684.
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Background Most people currently use the internet to obtain information about many subjects, including health information. Thus, medical associations need to provide accurate medical information websites. Although medical associations have their own patient education pages, it is not clear if these websites actually show up in search results. Objective The aim of this study was to evaluate how well medical associations function as online information providers by searching for information about musculoskeletal-related pain online and determining the ranking of the websites of medical associations. Methods We conducted a Google search for frequently searched keywords. Keywords were extracted using Google Ads Keyword Planner associated with “pain” relevant to the musculoskeletal system from June 2016 to December 2019. The top 20 search queries were extracted and searched using the Google search engine in Japan and the United States. Results The number of suggested queries for “pain” provided by Google Ads Keyword Planner was 930 in the United States and 2400 in Japan. Among the top 20 musculoskeletal-related pain queries chosen, the probability that the medical associations’ websites would appear in the top 10 results was 30% in the United States and 45% in Japan. In five queries each, the associations’ websites did not appear among the top 100 results. No significant difference was found in the rank of the associations’ website search results (P=.28). Conclusions To provide accurate medical information to patients, it is essential to undertake effective measures for search engine optimization. For orthopedic associations, it is necessary that their websites should appear among the top search results.
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Kowalski, Pawel. "Information retrieval system iSybislaw and a synergic effect: some reflections on terminology and a language of keywords from the linguistic perspective." Terminological Bulletin, no. 5 (2019): 140–47. http://dx.doi.org/10.37919/2221-8807-2019-5-18.
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Digitization of academic discourse causes changes that influence the work of researchers and flow of information in the scientific community. One can observe strong globalization and internationalization processes in the current terminological system wordlwide. The need to index the emerging works in the bibliographic databases, which have the goal to provide the necessary information for their users and to preserve the reflection of science and culture for future generations, does not disappear. This means that indexers are forced to skillfully maneuver between the quality and the amount of information provided. The paper presents some thougts on including and combining the international and native terms in the classes of equivalence in the iSybislaw system. To provide a complete and relevant information retrieval they both should be considered as an accesses point to the knowledge offered by the system.
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P, Deekshitha, Dhivya K, Lakshmi Prasanna S, and Anusha T. "Impact of pharmacist provided counseling on mental health status of postmenopausal Women." International Journal of Research in Pharmaceutical Sciences 9, no. 1 (March 12, 2018): 109. http://dx.doi.org/10.26452/ijrps.v9i1.1199.
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Menopause is described as the point of time when the last menstruation occurs in a woman. This is the period during which changes in the bleeding patterns due to alterations in the hormone levels were noticed. Due to alterations in these hormone levels, physical as well as mental health changes occur in women resulting in menopausal symptoms. Hence, this study was conducted to analyze the impact of counseling on MHS of postmenopausal women. This simple prospective interventional study was conducted in the Gynecology department of Tertiary Care Hospital for a period of 4 months in 200 patients divided into two groups. Mental health status (MHS) scores were calculated by using a validated questionnaire. Statistically significant difference in the score of baseline MHS was found between the two groups. Reduction in the score of MHS was seen in both the groups after counseling. Thus, this study revealed that providing counseling to postmenopausal women has positive impact on their mental health. Therefore, rendering effectual and repeated counseling to menopausal women depends on their need assist in leading a happy moreover healthy life.
Keywords: Post menopause; Estrogen deficiency Mental health; Counseling
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Alshawaf, Sahar Mohammed. "The electronic research and academic services provided to graduate students in light of the digital transformation." Cypriot Journal of Educational Sciences 17, no. 12 (December 31, 2022): 4809–22. http://dx.doi.org/10.18844/cjes.v17i12.8052.
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The current study aimed to identify the status of the academic and electronic research services provided to graduate students in light of the digital transformation from their perspectives. To accomplish the aims of the study, the researcher used the descriptive analytical method by administering a questionnaire to 303 male and female graduate students who were randomly selected using the smile random sampling method. The results of the study revealed that the electronic academic services provided to graduate students were medium, while the electronic research services provided to graduate students were poor. The results also showed that there are many obstacles that prevent the provision of electronic academic and research services to graduate students in light of the digital transformation from their perspectives. Keywords: electronic services, academic services, research services, KKU
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Gea, Asaziduhu, and Lisda Junita. "PENERAPAN CRM BERBASIS WEB DAN SMS GATEWAY PELAYANAN ADMINISTRASI UMUM DI DISDUKCAPIL KABUPATEN LANGKAT.pdf." JURNAL ARMADA INFORMATIKA 3, no. 1 (August 1, 2019): 23–35. http://dx.doi.org/10.36520/jai.v3i1.42.
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Service to the community provided by each agency is currently a major concern. The speed of service provided will increase public confidence in the institution. Therefore, all institutions both private and government make every effort to provide the best service in various ways including the use of Digital services. The Department of Population and Civil Registration of Langkat Regency is one of the institutions that provides various public administration services to the public such as making KTP, Birth Certificate, Marriage Certificate, and others. The method of service is still not optimal. Therefore it is necessary to have a media that is a system that integrates the Department of Population and Civil Registration of Langkat Regency with the community with the concept of Customer Relationship Management (CRM) supported by SMS Gateway technology that can help people get the information they need easily and quickly and saving time.
Keywords: CRM (Customer Relationship Management), PHP, SQL Server, Web, SMS Gateway
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Meng, Xiangcheng, and Alan H. S. Chan. "Current States and Future Trends in Safety Research of Construction Personnel: A Quantitative Analysis Based on Social Network Approach." International Journal of Environmental Research and Public Health 18, no. 3 (January 20, 2021): 883. http://dx.doi.org/10.3390/ijerph18030883.
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The construction industry is recognized as a high-risk industry given that safety accidents and personnel injuries frequently occur. This study provided a systematic and quantitative review of existing research achievements by conducting social network approach to identify current states and future trends for the occupational safety of construction personnel. A total of 250 peer-reviewed articles were collected to examine the research on safety issues of workers in construction industry. Social network approach was applied to analyze the interrelationship among authors, keywords, and citations of these articles using VOS viewer and CitNetExplorer. A knowledge structure map was drawn using main path analysis (MPA) towards the collected papers, which was implemented by Pajek. In line with the findings of social network analysis, five research groups, and six keyword themes were identified in accordance with the times of cooperation of researchers and correlation among keywords of the papers. Core papers were identified by using main path analysis for each research domain to represent the key process and backbone for the corresponding area. Based on the finding of the research, significant implications and insights in terms of current research status and further research trends were provided for the scholars, thus helping generate a targeted development plan for occupational safety in construction industry.
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Tomo, Andrea, and Giovanni Landi. "Behavioral Issues for Sustainable Investment Decision-Making: A Literature Review." International Journal of Business and Management 12, no. 1 (December 28, 2016): 1. http://dx.doi.org/10.5539/ijbm.v12n1p1.
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The aim of this work is to understand the role of the Environmental, Social and Governance (ESG) paradigm in the corporate assessment by investors and the use of this paradigm as guide for managerial decision-making process by corporations. A review of the international literature is provided using five different couples of keywords on Thomson Reuters ISI Web of Knowledge research engine. The literature production increased only after the 2007 crisis and the median year of the results is 2011, thus highlighting just a recent attention to themes as ethics and corporate social responsibility. Main limitations are related to the classic limitations of literature reviews, as the choice of number and type of keywords and journals, the resulting selection of studies, the choice of relevant outcomes and the interpretation, generalization and application of results. The study provides both theoretical and practical implications: a complete review of contributions on the theme is provided; then, some insights in investors and corporations behaviors through the ESG lens, thus suggesting a more ethical and responsible behavior in investment decision-making processes.
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Akanni, J., A. A. Isa, R. A. Alao, and C. T. Thomas. "Assessment of Internet Service Provided using UMTS operators at the University of Ilorin main campus." Nigerian Journal of Technology 39, no. 2 (July 16, 2020): 500–505. http://dx.doi.org/10.4314/njt.v39i2.20.
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Over the years the need for mobile broadband had seen a great increment due to the demand of internet mobile users. In this research work, data size for Internet operation on UMTS mobile network operators on the University of Ilorin Campus was assessed. The study assessed three UMTS network operators using an Infinix T627 android smart phone and RantCell App. to measure the download and upload at the selected points on the University of Ilorin Campus. It was observed that the number of active subscribers with their relative position in a cell have a great effect on the performance of the networks and therefore, the authors recommend the need for the operators to improve their data sizes so as to enhance users’ satisfaction.
Keywords: GSM, CDMA, ISP, PS, UMTS.
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Jang, Joonhyouk. "Open API-based Conversational Voice Interaction Scheme for Intelligent IoT Applications for the Digital Underprivileged." Korean Institute of Smart Media 11, no. 10 (November 30, 2022): 22–29. http://dx.doi.org/10.30693/smj.2022.11.10.22.
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Voice interactions are particularly effective in applications targeting the digital underprivileged who are not proficient in the use of smart devices. However, applications based on open APIs are using voice signals only for short, fragmentary input and output due to the limitations of existing touchscreen-oriented UI and API provided. In this paper, we design a conversational voice interaction model for interactions between users and intelligent mobile/IoT applications and propose a keyword detection algorithm based on the edit distance. The proposed model and scheme were implemented in an Android environment, and the edit distance-based keyword detection algorithm showed a higher recognition rate than the existing algorithm for keywords that were incorrectly recognized through speech recognition.
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Dai, Ai Ni. "Design and Analysis of CAN User Layer Communication Protocol." Applied Mechanics and Materials 325-326 (June 2013): 1669–75. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1669.
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A new protocol for CAN user layer communication based on CAN2.0B which is named Qucan_A is presented, at the same time, combined with the feature of control network, the design ideas and protocol structure have been provided, and the examples of how to use it are also given. (Abstract)Keywords-Field bus; Control network; CAN; User layer communication protocol (keywords)
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Bok, Kyoungsoo, Jinwoo Song, Jongtae Lim, and Jaesoo Yoo. "Personalized Search Using User Preferences on Social Media." Electronics 11, no. 19 (September 24, 2022): 3049. http://dx.doi.org/10.3390/electronics11193049.
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In contrast to traditional web search, personalized search provides search results that take into account the user’s preferences. However, the existing personalized search methods have limitations in providing appropriate search results for the individual’s preferences, because they do not consider the user’s recent preferences or the preferences of other users. In this paper, we propose a new search method considering the user’s recent preferences and similar users’ preferences on social media analysis. Since the user expresses personal opinions on social media, it is possible to grasp the user preferences when analyzing the records of social media activities. The proposed method collects user social activity records and determines keywords of interest using TF-IDF. Since user preferences change continuously over time, we assign time weights to keywords of interest, giving many high values to state-of-the-art user preferences. We identify users with similar preferences to extend the search results to be provided to users because considering only user preferences in personalized searches can provide narrow search results. The proposed method provides personalized search results considering social characteristics by applying a ranking algorithm that considers similar user preferences as well as user preferences. It is shown through various performance evaluations that the proposed personalized search method outperforms the existing methods.
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Gupta, Teena, and Rohit K.V.S.S. "Fuzzy Keyword Search Over Encrypted Data using Cloud Computing." International Journal of Engineering and Advanced Technology 12, no. 1 (October 30, 2022): 63–67. http://dx.doi.org/10.35940/ijeat.a3842.1012122.
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“Cloud” is a collective term for a large number of developments and possibilities. It is not an invention, but more of a “practical innovation”, combining several earlier inventions into something new and compelling. A cloud computing platform dynamically provisions, configures, reconfigures, and de provisions servers as needed [8]. Servers in the cloud can be physical machines or virtual machines. Security is a critical issue in cloud computing due to the variety of IT services that can be provided through a cloud environment. This paper focuses on the aspect of searching keywords over encrypted data while maintaining integrity of the data. Using a traditional algorithm like AES. Unlike traditional searching algorithms, here we try to implement a fuzzy logic which is based on a NLP technique called N gram. This fuzzy keyword searching significantly increases the efficiency and safety over cloud. This will keep the searching time efficient and acquire great results. The n-gram logic will be used to make sets of keywords which will used in the search implementation. To achieve more accurate results, Jaccard Coefficient will be used to find the similarity between the sets of keywords and rank them based on that. The purpose of this paper is to improve the traditional keyword search over encrypted data using cloud computing using advanced algorithms without compromising over security. Through rigorous security analysis, we show that our proposed solution is secure and maintains the privacy of the file server while efficiently using the fuzzy logic
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Ilik, Serife Senay. "Views of parents regarding the services provided to the students with attention deficit hyperactivity disorder." Cypriot Journal of Educational Sciences 14, no. 3 (September 30, 2019): 445–56. http://dx.doi.org/10.18844/cjes.v14i3.4327.
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This study adopted a descriptive survey method to investigate the views of parents on education and healthcare services provided to the children with attention deficit hyperactivity disorder (ADHD) and help them explain their views in their own words. The sample was composed of 25 parents of children diagnosed with ADHD, who were attending primary schools in Konya city center. A semi-structured interview form was used for parent interviews. The data were analysed through content analysis. The results revealed that according to the parents’ views, the service provided to the children mostly included either healthcare or education services, but a very little number of children received both services. The great majority of the families stated that the medicines that their children were prescribed increased their attention; however, negatively affected their social development. The parents expressed their opinion that a training programme for parents should be arranged and multimodal interventions should be provided.
Keywords: Education, healthcare, attention deficit, hyperactivity disorder.
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Harro-Loit, Halliki. "Cost effectiveness of journalism education in a small nation state." Žurnalistikos Tyrimai 2 (January 1, 2009): 138–55. http://dx.doi.org/10.15388/zt/jr.2009.2.78.
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The article discusses the role of journalism as a discipline in a small nation state and provides analysis of the economic factors that influence the journalism programs. The article discusses some of the economic aspects of journalism education at the academic university and provides a case study on the basis of the two programs provided by the University of Tartu: Bachelor (BA) and Master’s (MA) programs. The curricula are held by the Institute of Journalism and Communication. Keywords: journalism curriculum, education, cost, small market. p>
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Tambunan, Irma Chairani, and Zuhrinal M. Nawawi. "Analisis Value for Money Dalam Meningkatkan Mutu Pelayanan Politik pada Program Pembinaan Atlet KONI Asahan." ManBiz: Journal of Management and Business 1, no. 2 (June 7, 2022): 116–21. http://dx.doi.org/10.47467/manbiz.v1i2.1735.
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Value for Money is a performance indicator in a public sector that provides information on whether the budget (funds) provided provides a certain value for the community. The indicators referred to in this case are economic, efficient, and effective. Measuring athlete satisfaction with the services provided by KONI Asahan was carried out on 25 athletes in the Muaythai sport. This study aims to analyze Valur for Money in improving public services at the Asahan Indonesian National Sports Committee (KONI) in the design of athlete development with Valur for Money analysis. The measurement of athlete satisfaction with the services provided by the Indonesian National Sports Committee (KONI) Asahan is carried out by means of the IKM (Community Satisfaction Index). The results of the study show that all budget funds financed by the government have been used properly through the allocation of funds for coaching athletes and coaches.
Keywords: Service, Value for Money, Athlete
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Fonte, C. C., J. Patriarca, J. Estima, J. P. de Almeida, and A. Cardoso. "CONSISTENCY AND RELEVANCE OF VGI AVAILABLE FROM SOCIAL NETWORKS FOR EMERGENCY MITIGATION AND MUNICIPAL MANAGEMENT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4 (September 19, 2018): 217–24. http://dx.doi.org/10.5194/isprs-archives-xlii-4-217-2018.
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<p><strong>Abstract.</strong> Volunteered geographical information (VGI) is an increasing source of data for many applications. In order to explore some of these sources of data, an algorithm was conceived and implemented in the ExploringVGI platform enabling the collection of georeferenced data from collaborative projects that provide an Application Programming Interface (API). This paper presents a preliminary study to evaluate the consistency and relevance of VGI extracted from Flickr platform for emergency mitigation and municipal management. The study carried out was based on data extraction and analysis with keywords related to emergency events (“Accident”, “Flood” and “Fire apartment”), and municipal management (“Graffiti” and “Homeless”) in four European cities (Frankfurt, Lisbon, London, and Rome). The proposed approach sets up a region of interest on a map, selects one or more keywords for the search, and carries out a search using the Flickr API. Data detected and extracted were then loaded into a database and further analysed to verify whether they were consistently obtained through consecutive searches at different locations. A statistical analysis performed on data collected for each case provided us with: the total number of data collected for each keyword and location; their relevance in terms of search goal; and the quality of the associate geolocation of the post. Results obtained illustrate the effectiveness of the approach when applied to different scenarios, which contributes to assess the role that VGI available on the Web may have in different events depending on the specific context of a geolocation/keyword(s) combination.</p>
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Kumar, Akhilesh, Anuradha Thakare, Manisha Bhende, Amit Kumar Sinha, Arnold C. Alguno, and Yekula Prasanna Kumar. "Identification and Classification of Depressed Mental State for End-User over Social Media." Computational Intelligence and Neuroscience 2022 (April 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/8755922.
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In researching social network data and depression, it is often necessary to manually label depressed and non-depressed users, which is time-consuming and labor-intensive. The aim of this study is that it explores the relationship between social network data and depression. It can also contribute to detecting and identifying depression. Through collecting and analyzing college students’ microblog social data, a preliminary screening algorithm for college students’ suspected depression microblogs based on depression keywords, and semantic expansion is researched; a comprehensive lexical grammar was proposed. This research provided has a preliminary screening method based on depression keywords and semantic expansion for college students’ suspected depression microblogs, with a screening accuracy. This method forms a depression keyword table by constructing the basic keyword table and the semantic expansion based on the word embedding learning model Word2Vec. Finally, the word table is used to calculate the semantic similarity of the tested microblogs and then identify whether it is a suspected depression microblog. The experimental results on the microblog dataset of college students show that the comprehensive lexical method is better than the SDS questionnaire segmentation method and the expert lexical method in terms of screening accuracy; the comprehensive lexical approach can quickly and automatically screen out a tiny proportion of suspected doubts from a large number of college students’ microblogs. Depression Weibo can reduce the workload of experts’ annotation, improve annotation efficiency, and provide a suitable data processing basis for the subsequent accurate identification (classification problem) of patients with depression.
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Abdulsada, Ayad I., Dhafer G. Honi, and Salah Al-Darraji. "Efficient multi-keyword similarity search over encrypted cloud documents." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 1 (July 1, 2021): 510. http://dx.doi.org/10.11591/ijeecs.v23.i1.pp510-518.
Full textAbstract:
Many organizations and individuals are attracted to outsource their data into remote cloud service providers. To ensure privacy, sensitive data should be encrypted be-fore being hosted. However, encryption disables the direct application of the essential data management operations like searching and indexing. Searchable encryption is acryptographic tool that gives users the ability to search the encrypted data while being encrypted. However, the existing schemes either serve a single exact search that loss the ability to handle the misspelled keywords or multi-keyword search that generate very long trapdoors. In this paper, we address the problem of designing a practical multi-keyword similarity scheme that provides short trapdoors and returns the correct results according to their similarity scores. To do so, each document is translated intoa compressed trapdoor. Trapdoors are generated using key based hash functions to en-sure their privacy. Only authorized users can issue valid trapdoors. Similarity scores of two textual documents are evaluated by computing the Hamming distance between their corresponding trapdoors. A robust security definition is provided together withits proof. Our experimental results illustrate that the proposed scheme improves thesearch efficiency compared to the existing schemes. Further more, it shows a high level of performance.
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Yang, Wenhsien. "A diachronic keyword analysis in research article titles and cited article titles in applied linguistics from 1990 to 2016." English Text Construction 12, no. 1 (May 27, 2019): 84–102. http://dx.doi.org/10.1075/etc.00019.yan.
Full textAbstract:
Abstract Due to the competitiveness in academic publication and the emerging heavy reliance on Internet search engines to expand visibility and readership and to promote publications, writing an attractive and appropriate research article title is essential. In addition, titles may be the first aspect of papers evaluated by journal editors in their screening of submissions in order to meet the requirements of the target audience. These demands call for the use of various academic writing skills, and thus make writing titles challenging. Research on structuring journal article titles has been extensively conducted across disciplines, but what keywords (i.e. lexical items highly distinctive of the titles) are employed to reflect current knowledge and receive high citations is still under-researched. To bridge this gap, the present corpus-driven research collected and investigated titles written in leading applied linguistics journals over 25 years to identify their keywords. These were compared to different time spans to study the significance and relevance of the domain knowledge. The most frequently cited articles were also selected to study how their titles relate to keyword use and to investigate their impact on the domain knowledge. The results reveal that keywords vary in accordance with the research trends over time. In addition, while titles are becoming longer, more keywords are employed by authors in order to expand the paper’s visibility and enhance the citations. The pedagogical implications for teaching academic writing, and suggestions for researching this appendant genre are provided.
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AZCARRAGA, ARNULFO P., TEDDY YAP, and TAT SENG CHUA. "COMPARING KEYWORD EXTRACTION TECHNIQUES FOR WEBSOM TEXT ARCHIVES." International Journal on Artificial Intelligence Tools 11, no. 02 (June 2002): 219–32. http://dx.doi.org/10.1142/s0218213002000861.
Full textAbstract:
The WEBSOM methodology for building very large text archives has a very slow method for extracting meaningful unit labels. This is due to the fact that the method computes for the relative frequencies of all the words of all the documents associated to each unit and then compares these to the relative frequencies of all the words of other units in the map. Since maps may have more than 100,000 units and the archieve may contain up to 7 million documents, the existing WEBSOM method is not practical. A fast alternative method, referred to as the liGHtSOM method, is based on the distribution of weights in the weight vectors of the trained map, plus a simple manipulation of the random projection matrix used for input data compression. Comparison made using a WEBSOM archieve of the Reuters text collection reveal that a high percentage of keywords extracted using this method match the keywords extracted for the same map units using the original WEBSOM method. A detailed time complexity analysis of the two methods is also provided.