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summarization</title> <meta name="description" content="Search results for: summarization"> <meta name="keywords" content="summarization"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="summarization"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 32</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: summarization</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">32</span> Video Summarization: Techniques and Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zaynab%20El%20Khattabi">Zaynab El Khattabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Youness%20Tabii"> Youness Tabii</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelhamid%20Benkaddour"> Abdelhamid Benkaddour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, huge amount of multimedia repositories make the browsing, retrieval and delivery of video contents very slow and even difficult tasks. Video summarization has been proposed to improve faster browsing of large video collections and more efficient content indexing and access. In this paper, we focus on approaches to video summarization. The video summaries can be generated in many different forms. However, two fundamentals ways to generate summaries are static and dynamic. We present different techniques for each mode in the literature and describe some features used for generating video summaries. We conclude with perspective for further research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20summarization" title="video summarization">video summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=static%20summarization" title=" static summarization"> static summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20skimming" title=" video skimming"> video skimming</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20features" title=" semantic features"> semantic features</a> </p> <a href="https://publications.waset.org/abstracts/27644/video-summarization-techniques-and-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27644.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">400</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31</span> Surveillance Video Summarization Based on Histogram Differencing and Sum Conditional Variance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nada%20Jasim%20Habeeb">Nada Jasim Habeeb</a>, <a href="https://publications.waset.org/abstracts/search?q=Rana%20Saad%20Mohammed"> Rana Saad Mohammed</a>, <a href="https://publications.waset.org/abstracts/search?q=Muntaha%20Khudair%20Abbass"> Muntaha Khudair Abbass </a> </p> <p class="card-text"><strong>Abstract:</strong></p> For more efficient and fast video summarization, this paper presents a surveillance video summarization method. The presented method works to improve video summarization technique. This method depends on temporal differencing to extract most important data from large video stream. This method uses histogram differencing and Sum Conditional Variance which is robust against to illumination variations in order to extract motion objects. The experimental results showed that the presented method gives better output compared with temporal differencing based summarization techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=temporal%20differencing" title="temporal differencing">temporal differencing</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20summarization" title=" video summarization"> video summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=histogram%20differencing" title=" histogram differencing"> histogram differencing</a>, <a href="https://publications.waset.org/abstracts/search?q=sum%20conditional%20variance" title=" sum conditional variance"> sum conditional variance</a> </p> <a href="https://publications.waset.org/abstracts/54404/surveillance-video-summarization-based-on-histogram-differencing-and-sum-conditional-variance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54404.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">348</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">30</span> Linguistic Summarization of Structured Patent Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=E.%20Y.%20Igde">E. Y. Igde</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Aydogan"> S. Aydogan</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20E.%20Boran"> F. E. Boran</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Akay"> D. Akay </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Patent data have an increasingly important role in economic growth, innovation, technical advantages and business strategies and even in countries competitions. Analyzing of patent data is crucial since patents cover large part of all technological information of the world. In this paper, we have used the linguistic summarization technique to prove the validity of the hypotheses related to patent data stated in the literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20sets" title=" fuzzy sets"> fuzzy sets</a>, <a href="https://publications.waset.org/abstracts/search?q=linguistic%20summarization" title=" linguistic summarization"> linguistic summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=patent%20data" title=" patent data"> patent data</a> </p> <a href="https://publications.waset.org/abstracts/74491/linguistic-summarization-of-structured-patent-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74491.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">272</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29</span> Optimized Text Summarization Model on Mobile Screens for Sight-Interpreters: An Empirical Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jianhua%20Wang">Jianhua Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To obtain key information quickly from long texts on small screens of mobile devices, sight-interpreters need to establish optimized summarization model for fast information retrieval. Four summarization models based on previous studies were studied including title+key words (TKW), title+topic sentences (TTS), key words+topic sentences (KWTS) and title+key words+topic sentences (TKWTS). Psychological experiments were conducted on the four models for three different genres of interpreting texts to establish the optimized summarization model for sight-interpreters. This empirical study shows that the optimized summarization model for sight-interpreters to quickly grasp the key information of the texts they interpret is title+key words (TKW) for cultural texts, title+key words+topic sentences (TKWTS) for economic texts and topic sentences+key words (TSKW) for political texts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=different%20genres" title="different genres">different genres</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20screens" title=" mobile screens"> mobile screens</a>, <a href="https://publications.waset.org/abstracts/search?q=optimized%20summarization%20models" title=" optimized summarization models"> optimized summarization models</a>, <a href="https://publications.waset.org/abstracts/search?q=sight-interpreters" title=" sight-interpreters"> sight-interpreters</a> </p> <a href="https://publications.waset.org/abstracts/57345/optimized-text-summarization-model-on-mobile-screens-for-sight-interpreters-an-empirical-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57345.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">314</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28</span> Simulation Data Summarization Based on Spatial Histograms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jing%20Zhao">Jing Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Yoshiharu%20Ishikawa"> Yoshiharu Ishikawa</a>, <a href="https://publications.waset.org/abstracts/search?q=Chuan%20Xiao"> Chuan Xiao</a>, <a href="https://publications.waset.org/abstracts/search?q=Kento%20Sugiura"> Kento Sugiura</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to analyze large-scale scientific data, research on data exploration and visualization has gained popularity. In this paper, we focus on the exploration and visualization of scientific simulation data, and define a spatial V-Optimal histogram for data summarization. We propose histogram construction algorithms based on a general binary hierarchical partitioning as well as a more specific one, the l-grid partitioning. For effective data summarization and efficient data visualization in scientific data analysis, we propose an optimal algorithm as well as a heuristic algorithm for histogram construction. To verify the effectiveness and efficiency of the proposed methods, we conduct experiments on the massive evacuation simulation data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=simulation%20data" title="simulation data">simulation data</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20summarization" title=" data summarization"> data summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20histograms" title=" spatial histograms"> spatial histograms</a>, <a href="https://publications.waset.org/abstracts/search?q=exploration" title=" exploration"> exploration</a>, <a href="https://publications.waset.org/abstracts/search?q=visualization" title=" visualization"> visualization</a> </p> <a href="https://publications.waset.org/abstracts/98571/simulation-data-summarization-based-on-spatial-histograms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98571.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">176</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27</span> Key Frame Based Video Summarization via Dependency Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Janya%20Sainui">Janya Sainui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As a rapid growth of digital videos and data communications, video summarization that provides a shorter version of the video for fast video browsing and retrieval is necessary. Key frame extraction is one of the mechanisms to generate video summary. In general, the extracted key frames should both represent the entire video content and contain minimum redundancy. However, most of the existing approaches heuristically select key frames; hence, the selected key frames may not be the most different frames and/or not cover the entire content of a video. In this paper, we propose a method of video summarization which provides the reasonable objective functions for selecting key frames. In particular, we apply a statistical dependency measure called quadratic mutual informaion as our objective functions for maximizing the coverage of the entire video content as well as minimizing the redundancy among selected key frames. The proposed key frame extraction algorithm finds key frames as an optimization problem. Through experiments, we demonstrate the success of the proposed video summarization approach that produces video summary with better coverage of the entire video content while less redundancy among key frames comparing to the state-of-the-art approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20summarization" title="video summarization">video summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=key%20frame%20extraction" title=" key frame extraction"> key frame extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=dependency%20measure" title=" dependency measure"> dependency measure</a>, <a href="https://publications.waset.org/abstracts/search?q=quadratic%20mutual%20information" title=" quadratic mutual information"> quadratic mutual information</a> </p> <a href="https://publications.waset.org/abstracts/75218/key-frame-based-video-summarization-via-dependency-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75218.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">266</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">26</span> An Experiential Learning of Ontology-Based Multi-document Summarization by Removal Summarization Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pranjali%20Avinash%20Yadav-Deshmukh">Pranjali Avinash Yadav-Deshmukh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Remarkable development of the Internet along with the new technological innovation, such as high-speed systems and affordable large storage space have led to a tremendous increase in the amount and accessibility to digital records. For any person, studying of all these data is tremendously time intensive, so there is a great need to access effective multi-document summarization (MDS) systems, which can successfully reduce details found in several records into a short, understandable summary or conclusion. For semantic representation of textual details in ontology area, as a theoretical design, our system provides a significant structure. The stability of using the ontology in fixing multi-document summarization problems in the sector of catastrophe control is finding its recommended design. Saliency ranking is usually allocated to each phrase and phrases are rated according to the ranking, then the top rated phrases are chosen as the conclusion. With regards to the conclusion quality, wide tests on a selection of media announcements are appropriate for “Jammu Kashmir Overflow in 2014” records. Ontology centered multi-document summarization methods using “NLP centered extraction” outshine other baselines. Our participation in recommended component is to implement the details removal methods (NLP) to enhance the results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=disaster%20management" title="disaster management">disaster management</a>, <a href="https://publications.waset.org/abstracts/search?q=extraction%20technique" title=" extraction technique"> extraction technique</a>, <a href="https://publications.waset.org/abstracts/search?q=k-means" title=" k-means"> k-means</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-document%20summarization" title=" multi-document summarization"> multi-document summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=NLP" title=" NLP"> NLP</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology" title=" ontology"> ontology</a>, <a href="https://publications.waset.org/abstracts/search?q=sentence%20extraction" title=" sentence extraction"> sentence extraction</a> </p> <a href="https://publications.waset.org/abstracts/32426/an-experiential-learning-of-ontology-based-multi-document-summarization-by-removal-summarization-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32426.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">386</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25</span> Feature-Based Summarizing and Ranking from Customer Reviews</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dim%20En%20Nyaung">Dim En Nyaung</a>, <a href="https://publications.waset.org/abstracts/search?q=Thin%20Lai%20Lai%20Thein"> Thin Lai Lai Thein</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the rapid increase of Internet, web opinion sources dynamically emerge which is useful for both potential customers and product manufacturers for prediction and decision purposes. These are the user generated contents written in natural languages and are unstructured-free-texts scheme. Therefore, opinion mining techniques become popular to automatically process customer reviews for extracting product features and user opinions expressed over them. Since customer reviews may contain both opinionated and factual sentences, a supervised machine learning technique applies for subjectivity classification to improve the mining performance. In this paper, we dedicate our work is the task of opinion summarization. Therefore, product feature and opinion extraction is critical to opinion summarization, because its effectiveness significantly affects the identification of semantic relationships. The polarity and numeric score of all the features are determined by Senti-WordNet Lexicon. The problem of opinion summarization refers how to relate the opinion words with respect to a certain feature. Probabilistic based model of supervised learning will improve the result that is more flexible and effective. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=opinion%20mining" title="opinion mining">opinion mining</a>, <a href="https://publications.waset.org/abstracts/search?q=opinion%20summarization" title=" opinion summarization"> opinion summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20mining" title=" text mining"> text mining</a> </p> <a href="https://publications.waset.org/abstracts/25595/feature-based-summarizing-and-ranking-from-customer-reviews" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25595.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">332</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">24</span> Programmed Speech to Text Summarization Using Graph-Based Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamsini%20Pulugurtha">Hamsini Pulugurtha</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20V.%20S.%20L.%20Jagadamba"> P. V. S. L. Jagadamba</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Programmed Speech to Text and Text Summarization Using Graph-based Algorithms can be utilized in gatherings to get the short depiction of the gathering for future reference. This gives signature check utilizing Siamese neural organization to confirm the personality of the client and convert the client gave sound record which is in English into English text utilizing the discourse acknowledgment bundle given in python. At times just the outline of the gathering is required, the answer for this text rundown. Thus, the record is then summed up utilizing the regular language preparing approaches, for example, solo extractive text outline calculations <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Siamese%20neural%20network" title="Siamese neural network">Siamese neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=English%20speech" title=" English speech"> English speech</a>, <a href="https://publications.waset.org/abstracts/search?q=English%20text" title=" English text"> English text</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20extractive%20text%20summarization" title=" unsupervised extractive text summarization"> unsupervised extractive text summarization</a> </p> <a href="https://publications.waset.org/abstracts/143079/programmed-speech-to-text-summarization-using-graph-based-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143079.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">217</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">23</span> Graph-Based Semantical Extractive Text Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mina%20Samizadeh">Mina Samizadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the past few decades, there has been an explosion in the amount of available data produced from various sources with different topics. The availability of this enormous data necessitates us to adopt effective computational tools to explore the data. This leads to an intense growing interest in the research community to develop computational methods focused on processing this text data. A line of study focused on condensing the text so that we are able to get a higher level of understanding in a shorter time. The two important tasks to do this are keyword extraction and text summarization. In keyword extraction, we are interested in finding the key important words from a text. This makes us familiar with the general topic of a text. In text summarization, we are interested in producing a short-length text which includes important information about the document. The TextRank algorithm, an unsupervised learning method that is an extension of the PageRank (algorithm which is the base algorithm of Google search engine for searching pages and ranking them), has shown its efficacy in large-scale text mining, especially for text summarization and keyword extraction. This algorithm can automatically extract the important parts of a text (keywords or sentences) and declare them as a result. However, this algorithm neglects the semantic similarity between the different parts. In this work, we improved the results of the TextRank algorithm by incorporating the semantic similarity between parts of the text. Aside from keyword extraction and text summarization, we develop a topic clustering algorithm based on our framework, which can be used individually or as a part of generating the summary to overcome coverage problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=keyword%20extraction" title="keyword extraction">keyword extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=n-gram%20extraction" title=" n-gram extraction"> n-gram extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20summarization" title=" text summarization"> text summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20clustering" title=" topic clustering"> topic clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20analysis" title=" semantic analysis"> semantic analysis</a> </p> <a href="https://publications.waset.org/abstracts/160526/graph-based-semantical-extractive-text-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160526.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">70</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22</span> Fuzzy Inference-Assisted Saliency-Aware Convolution Neural Networks for Multi-View Summarization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tanveer%20Hussain">Tanveer Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=Khan%20Muhammad"> Khan Muhammad</a>, <a href="https://publications.waset.org/abstracts/search?q=Amin%20Ullah"> Amin Ullah</a>, <a href="https://publications.waset.org/abstracts/search?q=Mi%20Young%20Lee"> Mi Young Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Sung%20Wook%20Baik"> Sung Wook Baik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Big Data generated from distributed vision sensors installed on large scale in smart cities create hurdles in its efficient and beneficial exploration for browsing, retrieval, and indexing. This paper presents a three-folded framework for effective video summarization of such data and provide a compact and representative format of Big Video Data. In the first fold, the paper acquires input video data from the installed cameras and collect clues such as type and count of objects and clarity of the view from a chunk of pre-defined number of frames of each view. The decision of representative view selection for a particular interval is based on fuzzy inference system, acquiring a precise and human resembling decision, reinforced by the known clues as a part of the second fold. In the third fold, the paper forwards the selected view frames to the summary generation mechanism that is supported by a saliency-aware convolution neural network (CNN) model. The new trend of fuzzy rules for view selection followed by CNN architecture for saliency computation makes the multi-view video summarization (MVS) framework a suitable candidate for real-world practice in smart cities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20video%20data%20analysis" title="big video data analysis">big video data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-view%20video%20summarization" title=" multi-view video summarization"> multi-view video summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=saliency%20detection" title=" saliency detection"> saliency detection</a> </p> <a href="https://publications.waset.org/abstracts/135176/fuzzy-inference-assisted-saliency-aware-convolution-neural-networks-for-multi-view-summarization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135176.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">188</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21</span> Deep Learning-Based Approach to Automatic Abstractive Summarization of Patent Documents</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sakshi%20V.%20Tantak">Sakshi V. Tantak</a>, <a href="https://publications.waset.org/abstracts/search?q=Vishap%20K.%20Malik"> Vishap K. Malik</a>, <a href="https://publications.waset.org/abstracts/search?q=Neelanjney%20Pilarisetty"> Neelanjney Pilarisetty</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A patent is an exclusive right granted for an invention. It can be a product or a process that provides an innovative method of doing something, or offers a new technical perspective or solution to a problem. A patent can be obtained by making the technical information and details about the invention publicly available. The patent owner has exclusive rights to prevent or stop anyone from using the patented invention for commercial uses. Any commercial usage, distribution, import or export of a patented invention or product requires the patent owner’s consent. It has been observed that the central and important parts of patents are scripted in idiosyncratic and complex linguistic structures that can be difficult to read, comprehend or interpret for the masses. The abstracts of these patents tend to obfuscate the precise nature of the patent instead of clarifying it via direct and simple linguistic constructs. This makes it necessary to have an efficient access to this knowledge via concise and transparent summaries. However, as mentioned above, due to complex and repetitive linguistic constructs and extremely long sentences, common extraction-oriented automatic text summarization methods should not be expected to show a remarkable performance when applied to patent documents. Other, more content-oriented or abstractive summarization techniques are able to perform much better and generate more concise summaries. This paper proposes an efficient summarization system for patents using artificial intelligence, natural language processing and deep learning techniques to condense the knowledge and essential information from a patent document into a single summary that is easier to understand without any redundant formatting and difficult jargon. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=abstractive%20summarization" title="abstractive summarization">abstractive summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20Processing" title=" natural language Processing"> natural language Processing</a>, <a href="https://publications.waset.org/abstracts/search?q=patent%20document" title=" patent document"> patent document</a> </p> <a href="https://publications.waset.org/abstracts/135403/deep-learning-based-approach-to-automatic-abstractive-summarization-of-patent-documents" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135403.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">123</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20</span> Improvement Image Summarization using Image Processing and Particle swarm optimization Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hooman%20Torabifard">Hooman Torabifard</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the last few years, with the progress of technology and computers and artificial intelligence entry into all kinds of scientific and industrial fields, the lifestyles of human life have changed and in general, the way of humans live on earth has many changes and development. Until now, some of the changes has occurred in the context of digital images and image processing and still continues. However, besides all the benefits, there have been disadvantages. One of these disadvantages is the multiplicity of images with high volume and data; the focus of this paper is on improving and developing a method for summarizing and enhancing the productivity of these images. The general method used for this purpose in this paper consists of a set of methods based on data obtained from image processing and using the PSO (Particle swarm optimization) algorithm. In the remainder of this paper, the method used is elaborated in detail. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20summarization" title="image summarization">image summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20threshold" title=" image threshold"> image threshold</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a> </p> <a href="https://publications.waset.org/abstracts/138289/improvement-image-summarization-using-image-processing-and-particle-swarm-optimization-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138289.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">133</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">19</span> Improoving Readability for Tweet Contextualization Using Bipartite Graphs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amira%20Dhokar">Amira Dhokar</a>, <a href="https://publications.waset.org/abstracts/search?q=Lobna%20Hlaoua"> Lobna Hlaoua</a>, <a href="https://publications.waset.org/abstracts/search?q=Lotfi%20Ben%20Romdhane"> Lotfi Ben Romdhane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Tweet contextualization (TC) is a new issue that aims to answer questions of the form 'What is this tweet about?' The idea of this task was imagined as an extension of a previous area called multi-document summarization (MDS), which consists in generating a summary from many sources. In both TC and MDS, the summary should ideally contain the most relevant information of the topic that is being discussed in the source texts (for MDS) and related to the query (for TC). Furthermore of being informative, a summary should be coherent, i.e. well written to be readable and grammatically compact. Hence, coherence is an essential characteristic in order to produce comprehensible texts. In this paper, we propose a new approach to improve readability and coherence for tweet contextualization based on bipartite graphs. The main idea of our proposed method is to reorder sentences in a given paragraph by combining most expressive words detection and HITS (Hyperlink-Induced Topic Search) algorithm to make up a coherent context. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bipartite%20graphs" title="bipartite graphs">bipartite graphs</a>, <a href="https://publications.waset.org/abstracts/search?q=readability" title=" readability"> readability</a>, <a href="https://publications.waset.org/abstracts/search?q=summarization" title=" summarization"> summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=tweet%20contextualization" title=" tweet contextualization"> tweet contextualization</a> </p> <a href="https://publications.waset.org/abstracts/87337/improoving-readability-for-tweet-contextualization-using-bipartite-graphs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/87337.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">193</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18</span> Summarizing Data Sets for Data Mining by Using Statistical Methods in Coastal Engineering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yunus%20Do%C4%9Fan">Yunus Doğan</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmet%20Durap"> Ahmet Durap</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Coastal regions are the one of the most commonly used places by the natural balance and the growing population. In coastal engineering, the most valuable data is wave behaviors. The amount of this data becomes very big because of observations that take place for periods of hours, days and months. In this study, some statistical methods such as the wave spectrum analysis methods and the standard statistical methods have been used. The goal of this study is the discovery profiles of the different coast areas by using these statistical methods, and thus, obtaining an instance based data set from the big data to analysis by using data mining algorithms. In the experimental studies, the six sample data sets about the wave behaviors obtained by 20 minutes of observations from Mersin Bay in Turkey and converted to an instance based form, while different clustering techniques in data mining algorithms were used to discover similar coastal places. Moreover, this study discusses that this summarization approach can be used in other branches collecting big data such as medicine. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering%20algorithms" title="clustering algorithms">clustering algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=coastal%20engineering" title=" coastal engineering"> coastal engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20summarization" title=" data summarization"> data summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20methods" title=" statistical methods"> statistical methods</a> </p> <a href="https://publications.waset.org/abstracts/61856/summarizing-data-sets-for-data-mining-by-using-statistical-methods-in-coastal-engineering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61856.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">361</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17</span> Bayesian System and Copula for Event Detection and Summarization of Soccer Videos</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dhanuja%20S.%20Patil">Dhanuja S. Patil</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanjay%20B.%20Waykar"> Sanjay B. Waykar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Event detection is a standout amongst the most key parts for distinctive sorts of area applications of video data framework. Recently, it has picked up an extensive interest of experts and in scholastics from different zones. While detecting video event has been the subject of broad study efforts recently, impressively less existing methodology has considered multi-model data and issues related efficiency. Start of soccer matches different doubtful circumstances rise that can't be effectively judged by the referee committee. A framework that checks objectively image arrangements would prevent not right interpretations because of some errors, or high velocity of the events. Bayesian networks give a structure for dealing with this vulnerability using an essential graphical structure likewise the probability analytics. We propose an efficient structure for analysing and summarization of soccer videos utilizing object-based features. The proposed work utilizes the t-cherry junction tree, an exceptionally recent advancement in probabilistic graphical models, to create a compact representation and great approximation intractable model for client’s relationships in an interpersonal organization. There are various advantages in this approach firstly; the t-cherry gives best approximation by means of junction trees class. Secondly, to construct a t-cherry junction tree can be to a great extent parallelized; and at last inference can be performed utilizing distributed computation. Examination results demonstrates the effectiveness, adequacy, and the strength of the proposed work which is shown over a far reaching information set, comprising more soccer feature, caught at better places. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=summarization" title="summarization">summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20network" title=" Bayesian network"> Bayesian network</a>, <a href="https://publications.waset.org/abstracts/search?q=t-cherry%20tree" title=" t-cherry tree"> t-cherry tree</a> </p> <a href="https://publications.waset.org/abstracts/32460/bayesian-system-and-copula-for-event-detection-and-summarization-of-soccer-videos" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32460.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">323</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16</span> EduEasy: Smart Learning Assistant System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Karunasena">A. Karunasena</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Bandara"> P. Bandara</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20A.%20T.%20P.%20Jayasuriya"> J. A. T. P. Jayasuriya</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20D.%20Gallage"> P. D. Gallage</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20M.%20S.%20D.%20Jayasundara"> J. M. S. D. Jayasundara</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20A.%20P.%20Y.%20P.%20Nuwanjaya"> L. A. P. Y. P. Nuwanjaya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Usage of smart learning concepts has increased rapidly all over the world recently as better teaching and learning methods. Most educational institutes such as universities are experimenting those concepts with their students. Smart learning concepts are especially useful for students to learn better in large classes. In large classes, the lecture method is the most popular method of teaching. In the lecture method, the lecturer presents the content mostly using lecture slides, and the students make their own notes based on the content presented. However, some students may find difficulties with the above method due to various issues such as speed in delivery. The purpose of this research is to assist students in large classes in the following content. The research proposes a solution with four components, namely note-taker, slide matcher, reference finder, and question presenter, which are helpful for the students to obtain a summarized version of the lecture note, easily navigate to the content and find resources, and revise content using questions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20summarization" title="automatic summarization">automatic summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=extractive%20text%20summarization" title=" extractive text summarization"> extractive text summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20recognition%20library" title=" speech recognition library"> speech recognition library</a>, <a href="https://publications.waset.org/abstracts/search?q=sentence%20extraction" title=" sentence extraction"> sentence extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=automatic%20web%20search" title=" automatic web search"> automatic web search</a>, <a href="https://publications.waset.org/abstracts/search?q=automatic%20question%20generator" title=" automatic question generator"> automatic question generator</a>, <a href="https://publications.waset.org/abstracts/search?q=sentence%20scoring" title=" sentence scoring"> sentence scoring</a>, <a href="https://publications.waset.org/abstracts/search?q=the%20term%20weight" title=" the term weight"> the term weight</a> </p> <a href="https://publications.waset.org/abstracts/131469/edueasy-smart-learning-assistant-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131469.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">148</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">15</span> Graph-Oriented Summary for Optimized Resource Description Framework Graphs Streams Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amadou%20Fall%20Dia">Amadou Fall Dia</a>, <a href="https://publications.waset.org/abstracts/search?q=Maurras%20Ulbricht%20Togbe"> Maurras Ulbricht Togbe</a>, <a href="https://publications.waset.org/abstracts/search?q=Aliou%20Boly"> Aliou Boly</a>, <a href="https://publications.waset.org/abstracts/search?q=Zakia%20Kazi%20Aoul"> Zakia Kazi Aoul</a>, <a href="https://publications.waset.org/abstracts/search?q=Elisabeth%20Metais"> Elisabeth Metais</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Existing RDF (Resource Description Framework) Stream Processing (RSP) systems allow continuous processing of RDF data issued from different application domains such as weather station measuring phenomena, geolocation, IoT applications, drinking water distribution management, and so on. However, processing window phase often expires before finishing the entire session and RSP systems immediately delete data streams after each processed window. Such mechanism does not allow optimized exploitation of the RDF data streams as the most relevant and pertinent information of the data is often not used in a due time and almost impossible to be exploited for further analyzes. It should be better to keep the most informative part of data within streams while minimizing the memory storage space. In this work, we propose an RDF graph summarization system based on an explicit and implicit expressed needs through three main approaches: (1) an approach for user queries (SPARQL) in order to extract their needs and group them into a more global query, (2) an extension of the closeness centrality measure issued from Social Network Analysis (SNA) to determine the most informative parts of the graph and (3) an RDF graph summarization technique combining extracted user query needs and the extended centrality measure. Experiments and evaluations show efficient results in terms of memory space storage and the most expected approximate query results on summarized graphs compared to the source ones. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=centrality%20measures" title="centrality measures">centrality measures</a>, <a href="https://publications.waset.org/abstracts/search?q=RDF%20graphs%20summary" title=" RDF graphs summary"> RDF graphs summary</a>, <a href="https://publications.waset.org/abstracts/search?q=RDF%20graphs%20stream" title=" RDF graphs stream"> RDF graphs stream</a>, <a href="https://publications.waset.org/abstracts/search?q=SPARQL%20query" title=" SPARQL query"> SPARQL query</a> </p> <a href="https://publications.waset.org/abstracts/88106/graph-oriented-summary-for-optimized-resource-description-framework-graphs-streams-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88106.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">203</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">14</span> A Method for Clinical Concept Extraction from Medical Text</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Moshe%20Wasserblat">Moshe Wasserblat</a>, <a href="https://publications.waset.org/abstracts/search?q=Jonathan%20Mamou"> Jonathan Mamou</a>, <a href="https://publications.waset.org/abstracts/search?q=Oren%20Pereg"> Oren Pereg</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Natural Language Processing (NLP) has made a major leap in the last few years, in practical integration into medical solutions; for example, extracting clinical concepts from medical texts such as medical condition, medication, treatment, and symptoms. However, training and deploying those models in real environments still demands a large amount of annotated data and NLP/Machine Learning (ML) expertise, which makes this process costly and time-consuming. We present a practical and efficient method for clinical concept extraction that does not require costly labeled data nor ML expertise. The method includes three steps: Step 1- the user injects a large in-domain text corpus (e.g., PubMed). Then, the system builds a contextual model containing vector representations of concepts in the corpus, in an unsupervised manner (e.g., Phrase2Vec). Step 2- the user provides a seed set of terms representing a specific medical concept (e.g., for the concept of the symptoms, the user may provide: ‘dry mouth,’ ‘itchy skin,’ and ‘blurred vision’). Then, the system matches the seed set against the contextual model and extracts the most semantically similar terms (e.g., additional symptoms). The result is a complete set of terms related to the medical concept. Step 3 –in production, there is a need to extract medical concepts from the unseen medical text. The system extracts key-phrases from the new text, then matches them against the complete set of terms from step 2, and the most semantically similar will be annotated with the same medical concept category. As an example, the seed symptom concepts would result in the following annotation: “The patient complaints on fatigue [symptom], dry skin [symptom], and Weight loss [symptom], which can be an early sign for Diabetes.” Our evaluations show promising results for extracting concepts from medical corpora. The method allows medical analysts to easily and efficiently build taxonomies (in step 2) representing their domain-specific concepts, and automatically annotate a large number of texts (in step 3) for classification/summarization of medical reports. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clinical%20concepts" title="clinical concepts">clinical concepts</a>, <a href="https://publications.waset.org/abstracts/search?q=concept%20expansion" title=" concept expansion"> concept expansion</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20records%20annotation" title=" medical records annotation"> medical records annotation</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20records%20summarization" title=" medical records summarization "> medical records summarization </a> </p> <a href="https://publications.waset.org/abstracts/116135/a-method-for-clinical-concept-extraction-from-medical-text" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/116135.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">135</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13</span> Analyzing Semantic Feature Using Multiple Information Sources for Reviews Summarization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yu%20Hung%20Chiang">Yu Hung Chiang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hei%20Chia%20Wang"> Hei Chia Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, tourism has become a part of life. Before reserving hotels, customers need some information, which the most important source is online reviews, about hotels to help them make decisions. Due to the dramatic growing of online reviews, it is impossible for tourists to read all reviews manually. Therefore, designing an automatic review analysis system, which summarizes reviews, is necessary for them. The main purpose of the system is to understand the opinion of reviews, which may be positive or negative. In other words, the system would analyze whether the customers who visited the hotel like it or not. Using sentiment analysis methods will help the system achieve the purpose. In sentiment analysis methods, the targets of opinion (here they are called the feature) should be recognized to clarify the polarity of the opinion because polarity of the opinion may be ambiguous. Hence, the study proposes an unsupervised method using Part-Of-Speech pattern and multi-lexicons sentiment analysis to summarize all reviews. We expect this method can help customers search what they want information as well as make decisions efficiently. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=text%20mining" title="text mining">text mining</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=product%20feature%20extraction" title=" product feature extraction"> product feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-lexicons" title=" multi-lexicons"> multi-lexicons</a> </p> <a href="https://publications.waset.org/abstracts/41662/analyzing-semantic-feature-using-multiple-information-sources-for-reviews-summarization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41662.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">331</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12</span> Detecting Paraphrases in Arabic Text</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amal%20Alshahrani">Amal Alshahrani</a>, <a href="https://publications.waset.org/abstracts/search?q=Allan%20Ramsay"> Allan Ramsay</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Paraphrasing is one of the important tasks in natural language processing; i.e. alternative ways to express the same concept by using different words or phrases. Paraphrases can be used in many natural language applications, such as Information Retrieval, Machine Translation, Question Answering, Text Summarization, or Information Extraction. To obtain pairs of sentences that are paraphrases we create a system that automatically extracts paraphrases from a corpus, which is built from different sources of news article since these are likely to contain paraphrases when they report the same event on the same day. There are existing simple standard approaches (e.g. TF-IDF vector space, cosine similarity) and alignment technique (e.g. Dynamic Time Warping (DTW)) for extracting paraphrase which have been applied to the English. However, the performance of these approaches could be affected when they are applied to another language, for instance Arabic language, due to the presence of phenomena which are not present in English, such as Free Word Order, Zero copula, and Pro-dropping. These phenomena will affect the performance of these algorithms. Thus, if we can analysis how the existing algorithms for English fail for Arabic then we can find a solution for Arabic. The results are promising. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title="natural language processing">natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=TF-IDF" title=" TF-IDF"> TF-IDF</a>, <a href="https://publications.waset.org/abstracts/search?q=cosine%20similarity" title=" cosine similarity"> cosine similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20time%20warping%20%28DTW%29" title=" dynamic time warping (DTW)"> dynamic time warping (DTW)</a> </p> <a href="https://publications.waset.org/abstracts/35776/detecting-paraphrases-in-arabic-text" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35776.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">386</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11</span> User Modeling from the Perspective of Improvement in Search Results: A Survey of the State of the Art</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Samira%20Karimi-Mansoub">Samira Karimi-Mansoub</a>, <a href="https://publications.waset.org/abstracts/search?q=Rahem%20Abri"> Rahem Abri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Currently, users expect high quality and personalized information from search results. To satisfy user’s needs, personalized approaches to web search have been proposed. These approaches can provide the most appropriate answer for user’s needs by using user context and incorporating information about query provided by combining search technologies. To carry out personalized web search, there is a need to make different techniques on whole of user search process. There are the number of possible deployment of personalized approaches such as personalized web search, personalized recommendation, personalized summarization and filtering systems and etc. but the common feature of all approaches in various domains is that user modeling is utilized to provide personalized information from the Web. So the most important work in personalized approaches is user model mining. User modeling applications and technologies can be used in various domains depending on how the user collected information may be extracted. In addition to, the used techniques to create user model is also different in each of these applications. Since in the previous studies, there was not a complete survey in this field, our purpose is to present a survey on applications and techniques of user modeling from the viewpoint of improvement in search results by considering the existing literature and researches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=filtering%20systems" title="filtering systems">filtering systems</a>, <a href="https://publications.waset.org/abstracts/search?q=personalized%20web%20search" title=" personalized web search"> personalized web search</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20modeling" title=" user modeling"> user modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20search%20behavior" title=" user search behavior"> user search behavior</a> </p> <a href="https://publications.waset.org/abstracts/73551/user-modeling-from-the-perspective-of-improvement-in-search-results-a-survey-of-the-state-of-the-art" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73551.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">279</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10</span> A Study on Sentiment Analysis Using Various ML/NLP Models on Historical Data of Indian Leaders</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sarthak%20Deshpande">Sarthak Deshpande</a>, <a href="https://publications.waset.org/abstracts/search?q=Akshay%20Patil"> Akshay Patil</a>, <a href="https://publications.waset.org/abstracts/search?q=Pradip%20Pandhare"> Pradip Pandhare</a>, <a href="https://publications.waset.org/abstracts/search?q=Nikhil%20Wankhede"> Nikhil Wankhede</a>, <a href="https://publications.waset.org/abstracts/search?q=Rushali%20Deshmukh"> Rushali Deshmukh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Among the highly significant duties for any language most effective is the sentiment analysis, which is also a key area of NLP, that recently made impressive strides. There are several models and datasets available for those tasks in popular and commonly used languages like English, Russian, and Spanish. While sentiment analysis research is performed extensively, however it is lagging behind for the regional languages having few resources such as Hindi, Marathi. Marathi is one of the languages that included in the Indian Constitution’s 8th schedule and is the third most widely spoken language in the country and primarily spoken in the Deccan region, which encompasses Maharashtra and Goa. There isn’t sufficient study on sentiment analysis methods based on Marathi text due to lack of available resources, information. Therefore, this project proposes the use of different ML/NLP models for the analysis of Marathi data from the comments below YouTube content, tweets or Instagram posts. We aim to achieve a short and precise analysis and summary of the related data using our dataset (Dates, names, root words) and lexicons to locate exact information. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multilingual%20sentiment%20analysis" title="multilingual sentiment analysis">multilingual sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Marathi" title=" Marathi"> Marathi</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20summarization" title=" text summarization"> text summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=lexicon-based%20approaches" title=" lexicon-based approaches"> lexicon-based approaches</a> </p> <a href="https://publications.waset.org/abstracts/181045/a-study-on-sentiment-analysis-using-various-mlnlp-models-on-historical-data-of-indian-leaders" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/181045.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">73</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9</span> Unlocking the Potential of Short Texts with Semantic Enrichment, Disambiguation Techniques, and Context Fusion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mouheb%20Mehdoui">Mouheb Mehdoui</a>, <a href="https://publications.waset.org/abstracts/search?q=Amel%20Fraisse"> Amel Fraisse</a>, <a href="https://publications.waset.org/abstracts/search?q=Mounir%20Zrigui"> Mounir Zrigui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper explores the potential of short texts through semantic enrichment and disambiguation techniques. By employing context fusion, we aim to enhance the comprehension and utility of concise textual information. The methodologies utilized are grounded in recent advancements in natural language processing, which allow for a deeper understanding of semantics within limited text formats. Specifically, topic classification is employed to understand the context of the sentence and assess the relevance of added expressions. Additionally, word sense disambiguation is used to clarify unclear words, replacing them with more precise terms. The implications of this research extend to various applications, including information retrieval and knowledge representation. Ultimately, this work highlights the importance of refining short text processing techniques to unlock their full potential in real-world applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=information%20traffic" title="information traffic">information traffic</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20summarization" title=" text summarization"> text summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=word-sense%20disambiguation" title=" word-sense disambiguation"> word-sense disambiguation</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20enrichment" title=" semantic enrichment"> semantic enrichment</a>, <a href="https://publications.waset.org/abstracts/search?q=ambiguity%20resolution" title=" ambiguity resolution"> ambiguity resolution</a>, <a href="https://publications.waset.org/abstracts/search?q=short%20text%20enhancement" title=" short text enhancement"> short text enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20retrieval" title=" information retrieval"> information retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=contextual%20understanding" title=" contextual understanding"> contextual understanding</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=ambiguity" title=" ambiguity"> ambiguity</a> </p> <a href="https://publications.waset.org/abstracts/193872/unlocking-the-potential-of-short-texts-with-semantic-enrichment-disambiguation-techniques-and-context-fusion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193872.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">8</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8</span> Exploration of Slow-Traffic System Strategies for New Urban Areas Under the Integration of Industry and City - Taking Qianfeng District of Guang’an City as an Example</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qikai%20Guan">Qikai Guan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the deepening of China's urbanization process, the development of urban industry has entered a new period, due to the gradual compounding and diversification of urban industrial functions, urban planning has shifted from the previous single industrial space arrangement and functional design to focusing on the upgrading of the urban structure, and on the diversified needs of people. As an important part of urban activity space, ‘slow moving space’ is of great significance in alleviating urban traffic congestion, optimizing residents' travel experience and improving urban ecological space. Therefore, this paper takes the slow-moving transportation system under the perspective of industry-city integration as the starting point, through sorting out the development needs of the city in the process of industry-city integration, analyzing the characteristics of the site base, sorting out a series of compatibility between the layout of the new industrial zone and the urban slow-moving system, and integrating the design concepts. At the same time, through the analysis and summarization of domestic and international experience, the construction ideas are proposed. Finally, the following aspects of planning strategy optimization are proposed: industrial layout, urban vitality, ecological pattern, regional characteristics and landscape image. In terms of specific design, on the one hand, it builds a regional slow-moving network, puts forward a diversified design strategy for the industry-oriented and multi-functional composite central area, realizes the coexistence of pedestrian-oriented and multiple transportation modes, basically covers the public facilities, and enhances the vitality of the city. On the other hand, it improves the landscape ecosystem, creates a healthy, diversified and livable superline landscape system, helps the construction of the ‘green core’ of the central city, and improves the travel experience of the residents. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=industry-city%20integration" title="industry-city integration">industry-city integration</a>, <a href="https://publications.waset.org/abstracts/search?q=slow-moving%20system" title=" slow-moving system"> slow-moving system</a>, <a href="https://publications.waset.org/abstracts/search?q=public%20space" title=" public space"> public space</a>, <a href="https://publications.waset.org/abstracts/search?q=functional%20integration" title=" functional integration"> functional integration</a> </p> <a href="https://publications.waset.org/abstracts/194665/exploration-of-slow-traffic-system-strategies-for-new-urban-areas-under-the-integration-of-industry-and-city-taking-qianfeng-district-of-guangan-city-as-an-example" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/194665.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">7</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7</span> A Semantic and Concise Structure to Represent Human Actions </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tobias%20Str%C3%BCbing">Tobias Strübing</a>, <a href="https://publications.waset.org/abstracts/search?q=Fatemeh%20Ziaeetabar"> Fatemeh Ziaeetabar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Humans usually manipulate objects with their hands. To represent these actions in a simple and understandable way, we need to use a semantic framework. For this purpose, the Semantic Event Chain (SEC) method has already been presented which is done by consideration of touching and non-touching relations between manipulated objects in a scene. This method was improved by a computational model, the so-called enriched Semantic Event Chain (eSEC), which incorporates the information of static (e.g. top, bottom) and dynamic spatial relations (e.g. moving apart, getting closer) between objects in an action scene. This leads to a better action prediction as well as the ability to distinguish between more actions. Each eSEC manipulation descriptor is a huge matrix with thirty rows and a massive set of the spatial relations between each pair of manipulated objects. The current eSEC framework has so far only been used in the category of manipulation actions, which eventually involve two hands. Here, we would like to extend this approach to a whole body action descriptor and make a conjoint activity representation structure. For this purpose, we need to do a statistical analysis to modify the current eSEC by summarizing while preserving its features, and introduce a new version called Enhanced eSEC or (e2SEC). This summarization can be done from two points of the view: 1) reducing the number of rows in an eSEC matrix, 2) shrinking the set of possible semantic spatial relations. To achieve these, we computed the importance of each matrix row in an statistical way, to see if it is possible to remove a particular one while all manipulations are still distinguishable from each other. On the other hand, we examined which semantic spatial relations can be merged without compromising the unity of the predefined manipulation actions. Therefore by performing the above analyses, we made the new e2SEC framework which has 20% fewer rows, 16.7% less static spatial and 11.1% less dynamic spatial relations. This simplification, while preserving the salient features of a semantic structure in representing actions, has a tremendous impact on the recognition and prediction of complex actions, as well as the interactions between humans and robots. It also creates a comprehensive platform to integrate with the body limbs descriptors and dramatically increases system performance, especially in complex real time applications such as human-robot interaction prediction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=enriched%20semantic%20event%20chain" title="enriched semantic event chain">enriched semantic event chain</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20action%20representation" title=" semantic action representation"> semantic action representation</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20relations" title=" spatial relations"> spatial relations</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20analysis" title=" statistical analysis"> statistical analysis</a> </p> <a href="https://publications.waset.org/abstracts/129003/a-semantic-and-concise-structure-to-represent-human-actions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129003.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">126</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6</span> Towards Creative Movie Title Generation Using Deep Neural Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Simon%20Espigol%C3%A9">Simon Espigolé</a>, <a href="https://publications.waset.org/abstracts/search?q=Igor%20Shalyminov"> Igor Shalyminov</a>, <a href="https://publications.waset.org/abstracts/search?q=Helen%20Hastie"> Helen Hastie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Deep machine learning techniques including deep neural networks (DNN) have been used to model language and dialogue for conversational agents to perform tasks, such as giving technical support and also for general chit-chat. They have been shown to be capable of generating long, diverse and coherent sentences in end-to-end dialogue systems and natural language generation. However, these systems tend to imitate the training data and will only generate the concepts and language within the scope of what they have been trained on. This work explores how deep neural networks can be used in a task that would normally require human creativity, whereby the human would read the movie description and/or watch the movie and come up with a compelling, interesting movie title. This task differs from simple summarization in that the movie title may not necessarily be derivable from the content or semantics of the movie description. Here, we train a type of DNN called a sequence-to-sequence model (seq2seq) that takes as input a short textual movie description and some information on e.g. genre of the movie. It then learns to output a movie title. The idea is that the DNN will learn certain techniques and approaches that the human movie titler may deploy that may not be immediately obvious to the human-eye. To give an example of a generated movie title, for the movie synopsis: ‘A hitman concludes his legacy with one more job, only to discover he may be the one getting hit.’; the original, true title is ‘The Driver’ and the one generated by the model is ‘The Masquerade’. A human evaluation was conducted where the DNN output was compared to the true human-generated title, as well as a number of baselines, on three 5-point Likert scales: ‘creativity’, ‘naturalness’ and ‘suitability’. Subjects were also asked which of the two systems they preferred. The scores of the DNN model were comparable to the scores of the human-generated movie title, with means m=3.11, m=3.12, respectively. There is room for improvement in these models as they were rated significantly less ‘natural’ and ‘suitable’ when compared to the human title. In addition, the human-generated title was preferred overall 58% of the time when pitted against the DNN model. These results, however, are encouraging given the comparison with a highly-considered, well-crafted human-generated movie title. Movie titles go through a rigorous process of assessment by experts and focus groups, who have watched the movie. This process is in place due to the large amount of money at stake and the importance of creating an effective title that captures the audiences’ attention. Our work shows progress towards automating this process, which in turn may lead to a better understanding of creativity itself. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=creativity" title="creativity">creativity</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20machine%20learning" title=" deep machine learning"> deep machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20generation" title=" natural language generation"> natural language generation</a>, <a href="https://publications.waset.org/abstracts/search?q=movies" title=" movies"> movies</a> </p> <a href="https://publications.waset.org/abstracts/85450/towards-creative-movie-title-generation-using-deep-neural-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85450.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">326</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5</span> Green Organic Chemistry, a New Paradigm in Pharmaceutical Sciences</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pesaru%20Vigneshwar%20Reddy">Pesaru Vigneshwar Reddy</a>, <a href="https://publications.waset.org/abstracts/search?q=Parvathaneni%20Pavan"> Parvathaneni Pavan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Green organic chemistry which is the latest and one of the most researched topics now-a- days has been in demand since 1990’s. Majority of the research in green organic chemistry chemicals are some of the important starting materials for greater number of major chemical industries. The production of organic chemicals has raw materials (or) reagents for other application is major sector of manufacturing polymers, pharmaceuticals, pesticides, paints, artificial fibers, food additives etc. organic synthesis on a large scale compound to the labratory scale, involves the use of energy, basic chemical ingredients from the petro chemical sectors, catalyst and after the end of the reaction, seperation, purification, storage, packing distribution etc. During these processes there are many problems of health and safety for workers in addition to the environmental problems caused there by use and deposition as waste. Green chemistry with its 12 principles would like to see changes in conventional way that were used for decades to make synthetic organic chemical and the use of less toxic starting materials. Green chemistry would like to increase the efficiency of synthetic methods, to use less toxic solvents, reduce the stage of synthetic routes and minimize waste as far as practically possible. In this way, organic synthesis will be part of the effort for sustainable development Green chemistry is also interested for research and alternatives innovations on many practical aspects of organic synthesis in the university and research labaratory of institutions. By changing the methodologies of organic synthesis, health and safety will be advanced in the small scale laboratory level but also will be extended to the industrial large scale production a process through new techniques. The three key developments in green chemistry include the use of super critical carbondioxide as green solvent, aqueous hydrogen peroxide as an oxidising agent and use of hydrogen in asymmetric synthesis. It also focuses on replacing traditional methods of heating with that of modern methods of heating like microwaves traditions, so that carbon foot print should reduces as far as possible. Another beneficiary of this green chemistry is that it will reduce environmental pollution through the use of less toxic reagents, minimizing of waste and more bio-degradable biproducts. In this present paper some of the basic principles, approaches, and early achievements of green chemistry has a branch of chemistry that studies the laws of passing of chemical reactions is also considered, with the summarization of green chemistry principles. A discussion about E-factor, old and new synthesis of ibuprofen, microwave techniques, and some of the recent advancements also considered. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy" title="energy">energy</a>, <a href="https://publications.waset.org/abstracts/search?q=e-factor" title=" e-factor"> e-factor</a>, <a href="https://publications.waset.org/abstracts/search?q=carbon%20foot%20print" title=" carbon foot print"> carbon foot print</a>, <a href="https://publications.waset.org/abstracts/search?q=micro-wave" title=" micro-wave"> micro-wave</a>, <a href="https://publications.waset.org/abstracts/search?q=sono-chemistry" title=" sono-chemistry"> sono-chemistry</a>, <a href="https://publications.waset.org/abstracts/search?q=advancement" title=" advancement"> advancement</a> </p> <a href="https://publications.waset.org/abstracts/18830/green-organic-chemistry-a-new-paradigm-in-pharmaceutical-sciences" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18830.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">306</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4</span> Artificial Law: Legal AI Systems and the Need to Satisfy Principles of Justice, Equality and the Protection of Human Rights</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Begum%20Koru">Begum Koru</a>, <a href="https://publications.waset.org/abstracts/search?q=Isik%20Aybay"> Isik Aybay</a>, <a href="https://publications.waset.org/abstracts/search?q=Demet%20Celik%20Ulusoy"> Demet Celik Ulusoy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The discipline of law is quite complex and has its own terminology. Apart from written legal rules, there is also living law, which refers to legal practice. Basic legal rules aim at the happiness of individuals in social life and have different characteristics in different branches such as public or private law. On the other hand, law is a national phenomenon. The law of one nation and the legal system applied on the territory of another nation may be completely different. People who are experts in a particular field of law in one country may have insufficient expertise in the law of another country. Today, in addition to the local nature of law, international and even supranational law rules are applied in order to protect basic human values and ensure the protection of human rights around the world. Systems that offer algorithmic solutions to legal problems using artificial intelligence (AI) tools will perhaps serve to produce very meaningful results in terms of human rights. However, algorithms to be used should not be developed by only computer experts, but also need the contribution of people who are familiar with law, values, judicial decisions, and even the social and political culture of the society to which it will provide solutions. Otherwise, even if the algorithm works perfectly, it may not be compatible with the values of the society in which it is applied. The latest developments involving the use of AI techniques in legal systems indicate that artificial law will emerge as a new field in the discipline of law. More AI systems are already being applied in the field of law, with examples such as predicting judicial decisions, text summarization, decision support systems, and classification of documents. Algorithms for legal systems employing AI tools, especially in the field of prediction of judicial decisions and decision support systems, have the capacity to create automatic decisions instead of judges. When the judge is removed from this equation, artificial intelligence-made law created by an intelligent algorithm on its own emerges, whether the domain is national or international law. In this work, the aim is to make a general analysis of this new topic. Such an analysis needs both a literature survey and a perspective from computer experts' and lawyers' point of view. In some societies, the use of prediction or decision support systems may be useful to integrate international human rights safeguards. In this case, artificial law can serve to produce more comprehensive and human rights-protective results than written or living law. In non-democratic countries, it may even be thought that direct decisions and artificial intelligence-made law would be more protective instead of a decision "support" system. Since the values of law are directed towards "human happiness or well-being", it requires that the AI algorithms should always be capable of serving this purpose and based on the rule of law, the principle of justice and equality, and the protection of human rights. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AI%20and%20law" title="AI and law">AI and law</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20law" title=" artificial law"> artificial law</a>, <a href="https://publications.waset.org/abstracts/search?q=protection%20of%20human%20rights" title=" protection of human rights"> protection of human rights</a>, <a href="https://publications.waset.org/abstracts/search?q=AI%20tools%20for%20legal%20systems" title=" AI tools for legal systems"> AI tools for legal systems</a> </p> <a href="https://publications.waset.org/abstracts/174646/artificial-law-legal-ai-systems-and-the-need-to-satisfy-principles-of-justice-equality-and-the-protection-of-human-rights" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/174646.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">73</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3</span> Knowledge Creation Environment in the Iranian Universities: A Case Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahdi%20%20Shaghaghi">Mahdi Shaghaghi</a>, <a href="https://publications.waset.org/abstracts/search?q=Amir%20Ghaebi"> Amir Ghaebi</a>, <a href="https://publications.waset.org/abstracts/search?q=Fariba%20Ahmadi"> Fariba Ahmadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Purpose: The main purpose of the present research is to analyze the knowledge creation environment at a Iranian University (Alzahra University) as a typical University in Iran, using a combination of the i-System and Ba models. This study is necessary for understanding the determinants of knowledge creation at Alzahra University as a typical University in Iran. Methodology: To carry out the present research, which is an applied study in terms of purpose, a descriptive survey method was used. In this study, a combination of the i-System and Ba models has been used to analyze the knowledge creation environment at Alzahra University. i-System consists of 5 constructs including intervention (input), intelligence (process), involvement (process), imagination (process), and integration (output). The Ba environment has three pillars, namely the infrastructure, the agent, and the information. The integration of these two models resulted in 11 constructs which were as follows: intervention (input), infrastructure-intelligence, agent-intelligence, information-intelligence (process); infrastructure-involvement, agent-involvement, information-involvement (process); infrastructure-imagination, agent-imagination, information-imagination (process); and integration (output). These 11 constructs were incorporated into a 52-statement questionnaire and the validity and reliability of the questionnaire were examined and confirmed. The statistical population included the faculty members of Alzahra University (344 people). A total of 181 participants were selected through the stratified random sampling technique. The descriptive statistics, binomial test, regression analysis, and structural equation modeling (SEM) methods were also utilized to analyze the data. Findings: The research findings indicated that among the 11 research constructs, the levels of intervention, information-intelligence, infrastructure-involvement, and agent-imagination constructs were average and not acceptable. The levels of infrastructure-intelligence and information-imagination constructs ranged from average to low. The levels of agent-intelligence and information-involvement constructs were also completely average. The level of infrastructure-imagination construct was average to high and thus was considered acceptable. The levels of agent-involvement and integration constructs were above average and were in a highly acceptable condition. Furthermore, the regression analysis results indicated that only two constructs, viz. the information-imagination and agent-involvement constructs, positively and significantly correlate with the integration construct. The results of the structural equation modeling also revealed that the intervention, intelligence, and involvement constructs are related to the integration construct with the complete mediation of imagination. Discussion and conclusion: The present research suggests that knowledge creation at Alzahra University relatively complies with the combination of the i-System and Ba models. Unlike this model, the intervention, intelligence, and involvement constructs are not directly related to the integration construct and this seems to have three implications: 1) the information sources are not frequently used to assess and identify the research biases; 2) problem finding is probably of less concern at the end of studies and at the time of assessment and validation; 3) the involvement of others has a smaller role in the summarization, assessment, and validation of the research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=i-System" title="i-System">i-System</a>, <a href="https://publications.waset.org/abstracts/search?q=Ba%20model" title=" Ba model "> Ba model </a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20creation" title=" knowledge creation "> knowledge creation </a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20management" title=" knowledge management"> knowledge management</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20creation%20environment" title=" knowledge creation environment"> knowledge creation environment</a>, <a href="https://publications.waset.org/abstracts/search?q=Iranian%20Universities" title=" Iranian Universities"> Iranian Universities</a> </p> <a href="https://publications.waset.org/abstracts/112544/knowledge-creation-environment-in-the-iranian-universities-a-case-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112544.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">101</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" 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