• Title/Summary/Keyword: Search engines

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Reinterpretation of the protein identification process for proteomics data

  • Kwon, Kyung-Hoon;Lee, Sang-Kwang;Cho, Kun;Park, Gun-Wook;Kang, Byeong-Soo;Park, Young-Mok
    • Interdisciplinary Bio Central
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    • v.1 no.3
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    • pp.9.1-9.6
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    • 2009
  • Introduction: In the mass spectrometry-based proteomics, biological samples are analyzed to identify proteins by mass spectrometer and database search. Database search is the process to select the best matches to the experimental mass spectra among the amino acid sequence database and we identify the protein as the matched sequence. The match score is defined to find the matches from the database and declare the highest scored hit as the most probable protein. According to the score definition, search result varies. In this study, the difference among search results of different search engines or different databases was investigated, in order to suggest a better way to identify more proteins with higher reliability. Materials and Methods: The protein extract of human mesenchymal stem cell was separated by several bands by one-dimensional electrophorysis. One-dimensional gel was excised one by one, digested by trypsin and analyzed by a mass spectrometer, FT LTQ. The tandem mass (MS/MS) spectra of peptide ions were applied to the database search of X!Tandem, Mascot and Sequest search engines with IPI human database and SwissProt database. The search result was filtered by several threshold probability values of the Trans-Proteomic Pipeline (TPP) of the Institute for Systems Biology. The analysis of the output which was generated from TPP was performed. Results and Discussion: For each MS/MS spectrum, the peptide sequences which were identified from different conditions such as search engines, threshold probability, and sequence database were compared. The main difference of peptide identification at high threshold probability was caused by not the difference of sequence database but the difference of the score. As the threshold probability decreases, the missed peptides appeared. Conversely, in the extremely high threshold level, we missed many true assignments. Conclusion and Prospects: The different identification result of the search engines was mainly caused by the different scoring algorithms. Usually in proteomics high-scored peptides are selected and low-scored peptides are discarded. Many of them are true negatives. By integrating the search results from different parameter and different search engines, the protein identification process can be improved.

A Hybrid Query Disambiguation Adaptive Approach for Web Information Retrieval

  • Ibrahim, Roliana;Kamal, Shahid;Ghani, Imran;Jeong, Seung Ryul
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.9 no.7
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    • pp.2468-2487
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    • 2015
  • In web searching, trustable and precise results are greatly affected by the inherent uncertainty in the input queries. Queries submitted to search engines are by nature ambiguous and constitute a significant proportion of the instances given to web search engines. Ambiguous queries pose real challenges for the web search engines due to versatility of information. Temporal based approaches whereas somehow reduce the uncertainty in queries but still lack to provide results according to users aspirations. Web search science has created an interest for the researchers to incorporate contextual information for resolving the uncertainty in search results. In this paper, we propose an Adaptive Disambiguation Approach (ADA) of hybrid nature that makes use of both the temporal and contextual information to improve user experience. The proposed hybrid approach presents the search results to the users based on their location and temporal information. A Java based prototype of the systems is developed and evaluated using standard dataset to determine its efficacy in terms of precision, accuracy, recall, and F1-measure. Supported by experimental results, ADA demonstrates better results along all the axes as compared to temporal based approaches.

Identifying Influencing Factors on the Price Per Click of Keyword Advertising : Focusing on Keyword Type, Search Number and Competition (온라인 키워드 광고 시장에서 광고 단가에 영향을 미치는 요인 분석 : 키워드 유형, 검색 횟수와 경쟁업체의 수를 중심으로)

  • Lee, Hong Joo
    • Journal of Information Technology Services
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    • v.11 no.3
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    • pp.257-267
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    • 2012
  • Many advertisers utilize sponsored search in search engines since customers want to find relevant information on their purchases from the search engines. Many factors have influences on price per click of the sponsored search. These influences are different based on the types of keywords such as search/experience or prominent/specific. However, differences of the influences have not been studied well. Thus, this study wants to identify the differences of the influences according the type of keywords. One month data of keyword advertising were collected from Naver. The influences of search number, click through rate, and competition on price per click were different according to the keyword types.

Knowledge-based Semantic Meta-Search Engine (지식기반 의미 메타 검색엔진)

  • Lee, In-K.;Son, Seo-H.;Kwon, Soon-H.
    • Journal of the Korean Institute of Intelligent Systems
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    • v.14 no.6
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    • pp.737-744
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    • 2004
  • Retrieving relevant information well corresponding to the user`s request from web is a crucial task of search engines. However, most of conventional search engines based on pattern matching schemes to queries have a limitation that is not easy to provide results corresponding to the user`s request due to the uncertainty of queries. To overcome the limitation in this paper, we propose a framework for knowledge-based semantic meta-search engines with the following five processes: (i) Query formation, (ii) Query expansion, (iii) Searching, (iv) Ranking recreation, and (v) Knowledge base. From simulation results on english-based web documents, we can see that the Proposed knowledge-based semantic meta-search engine provides more correct and better searching results than those obtained by using the Google.

Development of A Plagiarism Detection System Using Web Search and Morpheme Analysis (인터넷 검색과 형태소분석을 이용한 표절검사시스템의 개발에 관한 연구)

  • Hwang, In-Soo
    • Journal of Information Technology Applications and Management
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    • v.16 no.1
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    • pp.21-36
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    • 2009
  • As the World Wide Web (WWW) has become a major channel for information delivery, the data accumulated in the Internet increases at an incredible speed, and it derives the advances of information search technologies. It is the search engine that solves the problem of information overloading and helps people to identify relevant information. However, as search engines become a powerful tool for finding information, the opportunities of plagiarizing have increased significantly in e-Learning. In this paper, we developed an online plagiarism detection system for detecting plagiarized documents that incorporates the functions of search engines and acts in exactly the same way of plagiarizing. The plagiarism detection system uses morpheme analysis to improve the performance and sentence-based comparison to investigate document comes from multiple sources. As a result of applying this system in e-Learning, the performance of plagiarism detection was improved.

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Analysis of Cultural Context of Image Search with Deep Transfer Learning (심층 전이 학습을 이용한 이미지 검색의 문화적 특성 분석)

  • Kim, Hyeon-sik;Jeong, Jin-Woo
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.24 no.5
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    • pp.674-677
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    • 2020
  • The cultural background of users utilizing image search engines has a significant impact on the satisfaction of the search results. Therefore, it is important to analyze and understand the cultural context of images for more accurate image search. In this paper, we investigate how the cultural context of images can affect the performance of image classification. To this end, we first collected various types of images (e.g,. food, temple, etc.) with various cultural contexts (e.g., Korea, Japan, etc.) from web search engines. Afterwards, a deep transfer learning approach using VGG19 and MobileNetV2 pre-trained with ImageNet was adopted to learn the cultural features of the collected images. Through various experiments we show the performance of image classification can be differently affected according to the cultural context of images.

RepWeb: A Web-Based Search Tool for Repeat-Related Literatures

  • Woo, Tae-Ha;Kim, Young-Uk;Kwon, Je-Keun;Seo, Jung-Min
    • Genomics & Informatics
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    • v.5 no.2
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    • pp.88-91
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    • 2007
  • Repetitive sequences such as SINE, LINE, and LTR elements form a major part of eukaryotic genomes. A literature search tool that summarizes the information contained within repeat elements would provide biologists in the field of genomics with a useful tool for analyzing genomic sequence features. We developed a java program designed to make literature access easier by using two search engines simultaneously. RepWeb is a web-based search system that provides a user friendly interface for searching the reference data and journals for information related to repeat elements by using the search engines, Google Scholar and PubMed, simultaneously. It provides an interface that displays the repeat element- related biological information, and includes useful functions such as the production of a repeat tree, clickable links to PubMed and Google Scholar, exporting, and sorting a field into date, author, journal and title.

Current State of the Art and Review of Google and Baidu Search Engines' Privacy Policies Using Sentiment Analysis and Opinion Mining (구글과 바이두 검색엔진의 개인정보에 대한 감성분석과 마이닝)

  • Li, Jiapei;Li, Xiaomeng;Xiam, Xiam;Kang, Sun-kyung;Lee, Hyun Chang;Shin, Seong-yoon
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2017.05a
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    • pp.158-159
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    • 2017
  • Sentiment analysis is the review of written or verbal communications to determine some measure of emotion or feeling in the communication. Search engines are one of the most popular sites visited on the Internet generating hundreds of billions of hits per month worldwide. Obviously privacy policies related to these search sites are extremely important. Our study reviews the privacy policies of the two largest search engines, Google and Baidu to determine the overall sentiment of their privacy policies. Significant individual findings and significant differences were found using several sentiment and opinion analysis methods.

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A Study on Personal Information Hacking using Domestic Search Engines (국내 검색엔진을 이용한 개인정보 해킹에 관한 연구)

  • Yang, Hyung-Kyu;Lee, Kang-Ho;Choi, Jong-Ho
    • Journal of the Korea Society of Computer and Information
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    • v.12 no.3
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    • pp.195-201
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    • 2007
  • ARecent advances of network technologies and internet infrastructures construct a fast and useful information-oriented society. However, these nay infringe on privacy and expose sensitive information such as user id, secret number and credit card number. Therefore, we need countermeasures for solving these problems. In this paper we try to hack personal information using Google and domestic search engines, Naver and Empas. After analyze the result, we suggest solutions to prevent personal information hacking based on these search engines.

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The Evaluation and Measurement of Customer Satisfaction for Search Engines (검색엔진의 고객만족도 측정 및 평가)

  • 최성운;이락구
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.24 no.67
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    • pp.83-92
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    • 2001
  • This paper is to measure and evaluate the degree of customer satisfaction for internet search engines. The service quality scale is developed after testing validity and reliability through the result of inquiring into the literature and the interview of university students. The stepwise regression analysis using MINITAB is used to analyze the survey results.

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