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of future records</title> <meta name="description" content="Search results for: prediction of future records"> <meta name="keywords" content="prediction of future records"> <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 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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="prediction of future records"> <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> 10185</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: prediction of future records</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10185</span> Prediction of Marine Ecosystem Changes Based on the Integrated Analysis of Multivariate Data Sets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prozorkevitch%20D.">Prozorkevitch D.</a>, <a href="https://publications.waset.org/abstracts/search?q=Mishurov%20A."> Mishurov A.</a>, <a href="https://publications.waset.org/abstracts/search?q=Sokolov%20K."> Sokolov K.</a>, <a href="https://publications.waset.org/abstracts/search?q=Karsakov%20L."> Karsakov L.</a>, <a href="https://publications.waset.org/abstracts/search?q=Pestrikova%20L."> Pestrikova L.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The current body of knowledge about the marine environment and the dynamics of marine ecosystems includes a huge amount of heterogeneous data collected over decades. It generally includes a wide range of hydrological, biological and fishery data. Marine researchers collect these data and analyze how and why the ecosystem changes from past to present. Based on these historical records and linkages between the processes it is possible to predict future changes. Multivariate analysis of trends and their interconnection in the marine ecosystem may be used as an instrument for predicting further ecosystem evolution. A wide range of information about the components of the marine ecosystem for more than 50 years needs to be used to investigate how these arrays can help to predict the future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=barents%20sea%20ecosystem" title="barents sea ecosystem">barents sea ecosystem</a>, <a href="https://publications.waset.org/abstracts/search?q=abiotic" title=" abiotic"> abiotic</a>, <a href="https://publications.waset.org/abstracts/search?q=biotic" title=" biotic"> biotic</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20sets" title=" data sets"> data sets</a>, <a href="https://publications.waset.org/abstracts/search?q=trends" title=" trends"> trends</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a> </p> <a href="https://publications.waset.org/abstracts/148270/prediction-of-marine-ecosystem-changes-based-on-the-integrated-analysis-of-multivariate-data-sets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148270.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">116</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">10184</span> Comparison of Different k-NN Models for Speed Prediction in an Urban Traffic Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seyoung%20Kim">Seyoung Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeongmin%20Kim"> Jeongmin Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Kwang%20Ryel%20Ryu"> Kwang Ryel Ryu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (<em>k</em>-NN) as predictive models is that it does not require any explicit model building. Instead, <em>k</em>-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up <em>k</em>-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different <em>k</em>-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=k-NN" title=" k-NN"> k-NN</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20speed%20prediction" title=" traffic speed prediction"> traffic speed prediction</a> </p> <a href="https://publications.waset.org/abstracts/43415/comparison-of-different-k-nn-models-for-speed-prediction-in-an-urban-traffic-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43415.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">363</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">10183</span> Estimation of Location and Scale Parameters of Extended Exponential Distribution Based on Record Statistics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=E.%20Krishna">E. Krishna</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An Extended form of exponential distribution using Marshall and Olkin method is introduced.The location scale family of these distributions is considered. For location scale free family, exact expressions for single and product moments of upper record statistics are derived. The mean, variance and covariance of record values are computed for various values of the shape parameter. Using these the BLUE's of location and scale parameters are derived.The variances and covariance of estimates are obtained.Through Monte Carlo simulation the condence intervals for location and scale parameters are constructed.The Best liner unbiased Predictor (BLUP) of future records are also discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BLUE" title="BLUE">BLUE</a>, <a href="https://publications.waset.org/abstracts/search?q=BLUP" title=" BLUP"> BLUP</a>, <a href="https://publications.waset.org/abstracts/search?q=con%0Cdence%20interval" title=" condence interval"> condence interval</a>, <a href="https://publications.waset.org/abstracts/search?q=Marshall-Olkin%20distribution" title=" Marshall-Olkin distribution"> Marshall-Olkin distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20simulation" title=" Monte Carlo simulation"> Monte Carlo simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction%20of%20future%20records" title=" prediction of future records"> prediction of future records</a>, <a href="https://publications.waset.org/abstracts/search?q=record%20statistics" title=" record statistics"> record statistics</a> </p> <a href="https://publications.waset.org/abstracts/15639/estimation-of-location-and-scale-parameters-of-extended-exponential-distribution-based-on-record-statistics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15639.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">417</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">10182</span> The Best Prediction Data Mining Model for Breast Cancer Probability in Women Residents in Kabul</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mina%20Jafari">Mina Jafari</a>, <a href="https://publications.waset.org/abstracts/search?q=Kobra%20Hamraee"> Kobra Hamraee</a>, <a href="https://publications.waset.org/abstracts/search?q=Saied%20Hossein%20Hosseini"> Saied Hossein Hosseini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The prediction of breast cancer disease is one of the challenges in medicine. In this paper we collected 528 records of women’s information who live in Kabul including demographic, life style, diet and pregnancy data. There are many classification algorithm in breast cancer prediction and tried to find the best model with most accurate result and lowest error rate. We evaluated some other common supervised algorithms in data mining to find the best model in prediction of breast cancer disease among afghan women living in Kabul regarding to momography result as target variable. For evaluating these algorithms we used Cross Validation which is an assured method for measuring the performance of models. After comparing error rate and accuracy of three models: Decision Tree, Naive Bays and Rule Induction, Decision Tree with accuracy of 94.06% and error rate of %15 is found the best model to predicting breast cancer disease based on the health care records. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title="decision tree">decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title=" breast cancer"> breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=probability" title=" probability"> probability</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a> </p> <a href="https://publications.waset.org/abstracts/128692/the-best-prediction-data-mining-model-for-breast-cancer-probability-in-women-residents-in-kabul" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/128692.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">138</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">10181</span> A Deep-Learning Based Prediction of Pancreatic Adenocarcinoma with Electronic Health Records from the State of Maine</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiaodong%20Li">Xiaodong Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Peng%20Gao"> Peng Gao</a>, <a href="https://publications.waset.org/abstracts/search?q=Chao-Jung%20Huang"> Chao-Jung Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Shiying%20Hao"> Shiying Hao</a>, <a href="https://publications.waset.org/abstracts/search?q=Xuefeng%20B.%20Ling"> Xuefeng B. Ling</a>, <a href="https://publications.waset.org/abstracts/search?q=Yongxia%20Han">Yongxia Han</a>, <a href="https://publications.waset.org/abstracts/search?q=Yaqi%20Zhang"> Yaqi Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Le%20Zheng"> Le Zheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Chengyin%20Ye"> Chengyin Ye</a>, <a href="https://publications.waset.org/abstracts/search?q=Modi%20Liu"> Modi Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Minjie%20Xia"> Minjie Xia</a>, <a href="https://publications.waset.org/abstracts/search?q=Changlin%20Fu"> Changlin Fu</a>, <a href="https://publications.waset.org/abstracts/search?q=Bo%20Jin"> Bo Jin</a>, <a href="https://publications.waset.org/abstracts/search?q=Karl%20G.%20Sylvester"> Karl G. Sylvester</a>, <a href="https://publications.waset.org/abstracts/search?q=Eric%20Widen"> Eric Widen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Predicting the risk of Pancreatic Adenocarcinoma (PA) in advance can benefit the quality of care and potentially reduce population mortality and morbidity. The aim of this study was to develop and prospectively validate a risk prediction model to identify patients at risk of new incident PA as early as 3 months before the onset of PA in a statewide, general population in Maine. The PA prediction model was developed using Deep Neural Networks, a deep learning algorithm, with a 2-year electronic-health-record (EHR) cohort. Prospective results showed that our model identified 54.35% of all inpatient episodes of PA, and 91.20% of all PA that required subsequent chemoradiotherapy, with a lead-time of up to 3 months and a true alert of 67.62%. The risk assessment tool has attained an improved discriminative ability. It can be immediately deployed to the health system to provide automatic early warnings to adults at risk of PA. It has potential to identify personalized risk factors to facilitate customized PA interventions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cancer%20prediction" title="cancer prediction">cancer prediction</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=electronic%20health%20records" title=" electronic health records"> electronic health records</a>, <a href="https://publications.waset.org/abstracts/search?q=pancreatic%20adenocarcinoma" title=" pancreatic adenocarcinoma"> pancreatic adenocarcinoma</a> </p> <a href="https://publications.waset.org/abstracts/129535/a-deep-learning-based-prediction-of-pancreatic-adenocarcinoma-with-electronic-health-records-from-the-state-of-maine" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129535.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">155</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">10180</span> Assessment of Records Management in Registry Department of Kebbi State University of Science and Technology, Aliero Nigeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Murtala%20Aminu">Murtala Aminu</a>, <a href="https://publications.waset.org/abstracts/search?q=Salisu%20Adamu%20Aliero"> Salisu Adamu Aliero</a>, <a href="https://publications.waset.org/abstracts/search?q=Adamu%20Muhammed"> Adamu Muhammed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Records are a vital asset in ensuring that the institution is governed effectively and efficiently, and is accountable to its staff, students and the community that it serves. The major purpose of this study was to assess record management of the registry department of Kebbi state University of science and technology Aliero. To be able to achieve this objective, research questions were formulated and answers obtained, which centered on records creation, record management policy, challenges facing records management. The review of related literature revealed that there is need for records to be properly managed and in doing so there is need for good records management policy that clearly spells out the various programs required for effective records management. Survey research method was used involving questionnaire, and observation. The findings revealed that the registry department of the University still has a long way to go with respect to day-today records management. The study recommended provision for adequate, modern, safe and functional storage facilities, sufficient and regular funding, recruitment of trained personnel, on the job training for existing staff, computerization of all units records, and uninterrupted power supply to all parts of the unit as a means of ensuring proper records management. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=records" title="records">records</a>, <a href="https://publications.waset.org/abstracts/search?q=management" title=" management"> management</a>, <a href="https://publications.waset.org/abstracts/search?q=records%20management%20policy" title=" records management policy"> records management policy</a>, <a href="https://publications.waset.org/abstracts/search?q=registry" title=" registry"> registry</a> </p> <a href="https://publications.waset.org/abstracts/83567/assessment-of-records-management-in-registry-department-of-kebbi-state-university-of-science-and-technology-aliero-nigeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83567.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">316</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">10179</span> Big Data in Telecom Industry: Effective Predictive Techniques on Call Detail Records</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sara%20ElElimy">Sara ElElimy</a>, <a href="https://publications.waset.org/abstracts/search?q=Samir%20Moustafa"> Samir Moustafa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mobile network operators start to face many challenges in the digital era, especially with high demands from customers. Since mobile network operators are considered a source of big data, traditional techniques are not effective with new era of big data, Internet of things (IoT) and 5G; as a result, handling effectively different big datasets becomes a vital task for operators with the continuous growth of data and moving from long term evolution (LTE) to 5G. So, there is an urgent need for effective Big data analytics to predict future demands, traffic, and network performance to full fill the requirements of the fifth generation of mobile network technology. In this paper, we introduce data science techniques using machine learning and deep learning algorithms: the autoregressive integrated moving average (ARIMA), Bayesian-based curve fitting, and recurrent neural network (RNN) are employed for a data-driven application to mobile network operators. The main framework included in models are identification parameters of each model, estimation, prediction, and final data-driven application of this prediction from business and network performance applications. These models are applied to Telecom Italia Big Data challenge call detail records (CDRs) datasets. The performance of these models is found out using a specific well-known evaluation criteria shows that ARIMA (machine learning-based model) is more accurate as a predictive model in such a dataset than the RNN (deep learning model). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data%20analytics" title="big data analytics">big data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=CDRs" title=" CDRs"> CDRs</a>, <a href="https://publications.waset.org/abstracts/search?q=5G" title=" 5G"> 5G</a> </p> <a href="https://publications.waset.org/abstracts/115530/big-data-in-telecom-industry-effective-predictive-techniques-on-call-detail-records" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/115530.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">139</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">10178</span> Multilayer Neural Network and Fuzzy Logic Based Software Quality Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sadaf%20Sahar">Sadaf Sahar</a>, <a href="https://publications.waset.org/abstracts/search?q=Usman%20Qamar"> Usman Qamar</a>, <a href="https://publications.waset.org/abstracts/search?q=Sadaf%20Ayaz"> Sadaf Ayaz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the software development lifecycle, the quality prediction techniques hold a prime importance in order to minimize future design errors and expensive maintenance. There are many techniques proposed by various researchers, but with the increasing complexity of the software lifecycle model, it is crucial to develop a flexible system which can cater for the factors which in result have an impact on the quality of the end product. These factors include properties of the software development process and the product along with its operation conditions. In this paper, a neural network (perceptron) based software quality prediction technique is proposed. Using this technique, the stakeholders can predict the quality of the resulting software during the early phases of the lifecycle saving time and resources on future elimination of design errors and costly maintenance. This technique can be brought into practical use using successful training. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=software%20quality" title="software quality">software quality</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=perception" title=" perception"> perception</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a> </p> <a href="https://publications.waset.org/abstracts/78014/multilayer-neural-network-and-fuzzy-logic-based-software-quality-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78014.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">317</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">10177</span> Reasons for Non-Applicability of Software Entropy Metrics for Bug Prediction in Android </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arvinder%20Kaur">Arvinder Kaur</a>, <a href="https://publications.waset.org/abstracts/search?q=Deepti%20Chopra"> Deepti Chopra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Software Entropy Metrics for bug prediction have been validated on various software systems by different researchers. In our previous research, we have validated that Software Entropy Metrics calculated for Mozilla subsystem’s predict the future bugs reasonably well. In this study, the Software Entropy metrics are calculated for a subsystem of Android and it is noticed that these metrics are not suitable for bug prediction. The results are compared with a subsystem of Mozilla and a comparison is made between the two software systems to determine the reasons why Software Entropy metrics are not applicable for Android. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=android" title="android">android</a>, <a href="https://publications.waset.org/abstracts/search?q=bug%20prediction" title=" bug prediction"> bug prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=mining%20software%20repositories" title=" mining software repositories"> mining software repositories</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20entropy" title=" software entropy"> software entropy</a> </p> <a href="https://publications.waset.org/abstracts/49619/reasons-for-non-applicability-of-software-entropy-metrics-for-bug-prediction-in-android" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49619.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">578</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">10176</span> Stock Price Prediction Using Time Series Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sumit%20Sen">Sumit Sen</a>, <a href="https://publications.waset.org/abstracts/search?q=Sohan%20Khedekar"> Sohan Khedekar</a>, <a href="https://publications.waset.org/abstracts/search?q=Umang%20Shinde"> Umang Shinde</a>, <a href="https://publications.waset.org/abstracts/search?q=Shivam%20Bhargava"> Shivam Bhargava</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study has been undertaken to investigate whether the deep learning models are able to predict the future stock prices by training the model with the historical stock price data. Since this work required time series analysis, various models are present today to perform time series analysis such as Recurrent Neural Network LSTM, ARIMA and Facebook Prophet. Applying these models the movement of stock price of stocks are predicted and also tried to provide the future prediction of the stock price of a stock. Final product will be a stock price prediction web application that is developed for providing the user the ease of analysis of the stocks and will also provide the predicted stock price for the next seven days. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Autoregressive%20Integrated%20Moving%20Average" title="Autoregressive Integrated Moving Average">Autoregressive Integrated Moving Average</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=Long%20Short%20Term%20Memory" title=" Long Short Term Memory"> Long Short Term Memory</a>, <a href="https://publications.waset.org/abstracts/search?q=Time-series" title=" Time-series"> Time-series</a> </p> <a href="https://publications.waset.org/abstracts/137402/stock-price-prediction-using-time-series-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137402.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">141</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">10175</span> SEMCPRA-Sar-Esembled Model for Climate Prediction in Remote Area</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kamalpreet%20Kaur">Kamalpreet Kaur</a>, <a href="https://publications.waset.org/abstracts/search?q=Renu%20Dhir"> Renu Dhir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Climate prediction is an essential component of climate research, which helps evaluate possible effects on economies, communities, and ecosystems. Climate prediction involves short-term weather prediction, seasonal prediction, and long-term climate change prediction. Climate prediction can use the information gathered from satellites, ground-based stations, and ocean buoys, among other sources. The paper's four architectures, such as ResNet50, VGG19, Inception-v3, and Xception, have been combined using an ensemble approach for overall performance and robustness. An ensemble of different models makes a prediction, and the majority vote determines the final prediction. The various architectures such as ResNet50, VGG19, Inception-v3, and Xception efficiently classify the dataset RSI-CB256, which contains satellite images into cloudy and non-cloudy. The generated ensembled S-E model (Sar-ensembled model) provides an accuracy of 99.25%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=climate" title="climate">climate</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20images" title=" satellite images"> satellite images</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/178864/semcpra-sar-esembled-model-for-climate-prediction-in-remote-area" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/178864.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">10174</span> Traffic Prediction with Raw Data Utilization and Context Building</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhou%20Yang">Zhou Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Heli%20Sun"> Heli Sun</a>, <a href="https://publications.waset.org/abstracts/search?q=Jianbin%20Huang"> Jianbin Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jizhong%20Zhao"> Jizhong Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Shaojie%20Qiao"> Shaojie Qiao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traffic prediction is essential in a multitude of ways in modern urban life. The researchers of earlier work in this domain carry out the investigation chiefly with two major focuses: (1) the accurate forecast of future values in multiple time series and (2) knowledge extraction from spatial-temporal correlations. However, two key considerations for traffic prediction are often missed: the completeness of raw data and the full context of the prediction timestamp. Concentrating on the two drawbacks of earlier work, we devise an approach that can address these issues in a two-phase framework. First, we utilize the raw trajectories to a greater extent through building a VLA table and data compression. We obtain the intra-trajectory features with graph-based encoding and the intertrajectory ones with a grid-based model and the technique of back projection that restore their surrounding high-resolution spatial-temporal environment. To the best of our knowledge, we are the first to study direct feature extraction from raw trajectories for traffic prediction and attempt the use of raw data with the least degree of reduction. In the prediction phase, we provide a broader context for the prediction timestamp by taking into account the information that are around it in the training dataset. Extensive experiments on several well-known datasets have verified the effectiveness of our solution that combines the strength of raw trajectory data and prediction context. In terms of performance, our approach surpasses several state-of-the-art methods for traffic prediction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=traffic%20prediction" title="traffic prediction">traffic prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=raw%20data%20utilization" title=" raw data utilization"> raw data utilization</a>, <a href="https://publications.waset.org/abstracts/search?q=context%20building" title=" context building"> context building</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20reduction" title=" data reduction"> data reduction</a> </p> <a href="https://publications.waset.org/abstracts/150300/traffic-prediction-with-raw-data-utilization-and-context-building" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150300.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">127</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">10173</span> Automatic Flood Prediction Using Rainfall Runoff Model in Moravian-Silesian Region</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Sir">B. Sir</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Podhoranyi"> M. Podhoranyi</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Kuchar"> S. Kuchar</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Kocyan"> T. Kocyan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Rainfall-runoff models play important role in hydrological predictions. However, the model is only one part of the process for creation of flood prediction. The aim of this paper is to show the process of successful prediction for flood event (May 15–May 18 2014). The prediction was performed by rainfall runoff model HEC–HMS, one of the models computed within Floreon+ system. The paper briefly evaluates the results of automatic hydrologic prediction on the river Olše catchment and its gages Český Těšín and Věřňovice. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=flood" title="flood">flood</a>, <a href="https://publications.waset.org/abstracts/search?q=HEC-HMS" title=" HEC-HMS"> HEC-HMS</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=rainfall" title=" rainfall"> rainfall</a>, <a href="https://publications.waset.org/abstracts/search?q=runoff" title=" runoff "> runoff </a> </p> <a href="https://publications.waset.org/abstracts/20151/automatic-flood-prediction-using-rainfall-runoff-model-in-moravian-silesian-region" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20151.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">394</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">10172</span> Applying Pre-Accident Observational Methods for Accident Assessment and Prediction at Intersections in Norrkoping City in Sweden</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ghazwan%20Al-Haji">Ghazwan Al-Haji</a>, <a href="https://publications.waset.org/abstracts/search?q=Adeyemi%20Adedokun"> Adeyemi Adedokun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traffic safety at intersections is highly represented, given the fact that accidents occur randomly in time and space. It is necessary to judge whether the intersection is dangerous or not based on short-term observations, and not waiting for many years of assessing historical accident data. There are active and pro-active road infrastructure safety methods for assessing safety at intersections. This study aims to investigate the use of quantitative and qualitative pre-observational methods as the best practice for accident prediction, future black spot identification, and treatment. Historical accident data from STRADA (the Swedish Traffic Accident Data Acquisition) was used within Norrkoping city in Sweden. The ADT (Average Daily Traffic), capacity and speed were used to predict accident rates. Locations with the highest accident records and predicted accident counts were identified and hence audited qualitatively by using Street Audit. The results from these quantitative and qualitative methods were analyzed, validated and compared. The paper provides recommendations on the used methods as well as on how to reduce the accident occurrence at the chosen intersections. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intersections" title="intersections">intersections</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20conflict" title=" traffic conflict"> traffic conflict</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20safety" title=" traffic safety"> traffic safety</a>, <a href="https://publications.waset.org/abstracts/search?q=street%20audit" title=" street audit"> street audit</a>, <a href="https://publications.waset.org/abstracts/search?q=accidents%20predictions" title=" accidents predictions"> accidents predictions</a> </p> <a href="https://publications.waset.org/abstracts/75125/applying-pre-accident-observational-methods-for-accident-assessment-and-prediction-at-intersections-in-norrkoping-city-in-sweden" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75125.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">233</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">10171</span> Model-Driven and Data-Driven Approaches for Crop Yield Prediction: Analysis and Comparison</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiangtuo%20Chen">Xiangtuo Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Paul-Henry%20Courn%C3%A9de"> Paul-Henry Cournéde</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Crop yield prediction is a paramount issue in agriculture. The main idea of this paper is to find out efficient way to predict the yield of corn based meteorological records. The prediction models used in this paper can be classified into model-driven approaches and data-driven approaches, according to the different modeling methodologies. The model-driven approaches are based on crop mechanistic modeling. They describe crop growth in interaction with their environment as dynamical systems. But the calibration process of the dynamic system comes up with much difficulty, because it turns out to be a multidimensional non-convex optimization problem. An original contribution of this paper is to propose a statistical methodology, Multi-Scenarios Parameters Estimation (MSPE), for the parametrization of potentially complex mechanistic models from a new type of datasets (climatic data, final yield in many situations). It is tested with CORNFLO, a crop model for maize growth. On the other hand, the data-driven approach for yield prediction is free of the complex biophysical process. But it has some strict requirements about the dataset. A second contribution of the paper is the comparison of these model-driven methods with classical data-driven methods. For this purpose, we consider two classes of regression methods, methods derived from linear regression (Ridge and Lasso Regression, Principal Components Regression or Partial Least Squares Regression) and machine learning methods (Random Forest, k-Nearest Neighbor, Artificial Neural Network and SVM regression). The dataset consists of 720 records of corn yield at county scale provided by the United States Department of Agriculture (USDA) and the associated climatic data. A 5-folds cross-validation process and two accuracy metrics: root mean square error of prediction(RMSEP), mean absolute error of prediction(MAEP) were used to evaluate the crop prediction capacity. The results show that among the data-driven approaches, Random Forest is the most robust and generally achieves the best prediction error (MAEP 4.27%). It also outperforms our model-driven approach (MAEP 6.11%). However, the method to calibrate the mechanistic model from dataset easy to access offers several side-perspectives. The mechanistic model can potentially help to underline the stresses suffered by the crop or to identify the biological parameters of interest for breeding purposes. For this reason, an interesting perspective is to combine these two types of approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=crop%20yield%20prediction" title="crop yield prediction">crop yield prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=crop%20model" title=" crop model"> crop model</a>, <a href="https://publications.waset.org/abstracts/search?q=sensitivity%20analysis" title=" sensitivity analysis"> sensitivity analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=paramater%20estimation" title=" paramater estimation"> paramater estimation</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=random%20forest" title=" random forest"> random forest</a> </p> <a href="https://publications.waset.org/abstracts/72860/model-driven-and-data-driven-approaches-for-crop-yield-prediction-analysis-and-comparison" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72860.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">231</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">10170</span> Access to Health Data in Medical Records in Indonesia in Terms of Personal Data Protection Principles: The Limitation and Its Implication</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anny%20Retnowati">Anny Retnowati</a>, <a href="https://publications.waset.org/abstracts/search?q=Elisabeth%20Sundari"> Elisabeth Sundari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research aims to elaborate the meaning of personal data protection principles on patient access to health data in medical records in Indonesia and its implications. The method uses normative legal research by examining health law in Indonesia regarding the patient's right to access their health data in medical records. The data will be analysed qualitatively using the interpretation method to elaborate on the limitation of the meaning of personal data protection principles on patients' access to their data in medical records. The results show that patients only have the right to obtain copies of their health data in medical records. There is no right to inspect directly at any time. Indonesian health law limits the principle of patients' right to broad access to their health data in medical records. This restriction has implications for the reduction of personal data protection as part of human rights. This research contribute to show that a limitaion of personal data protection may abuse the human rights. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=access" title="access">access</a>, <a href="https://publications.waset.org/abstracts/search?q=health%20data" title=" health data"> health data</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20records" title=" medical records"> medical records</a>, <a href="https://publications.waset.org/abstracts/search?q=personal%20data" title=" personal data"> personal data</a>, <a href="https://publications.waset.org/abstracts/search?q=protection" title=" protection"> protection</a> </p> <a href="https://publications.waset.org/abstracts/163628/access-to-health-data-in-medical-records-in-indonesia-in-terms-of-personal-data-protection-principles-the-limitation-and-its-implication" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163628.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">93</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">10169</span> Predicting the Human Impact of Natural Onset Disasters Using Pattern Recognition Techniques and Rule Based Clustering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sara%20Hasani">Sara Hasani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research focuses on natural sudden onset disasters characterised as ‘occurring with little or no warning and often cause excessive injuries far surpassing the national response capacities’. Based on the panel analysis of the historic record of 4,252 natural onset disasters between 1980 to 2015, a predictive method was developed to predict the human impact of the disaster (fatality, injured, homeless) with less than 3% of errors. The geographical dispersion of the disasters includes every country where the data were available and cross-examined from various humanitarian sources. The records were then filtered into 4252 records of the disasters where the five predictive variables (disaster type, HDI, DRI, population, and population density) were clearly stated. The procedure was designed based on a combination of pattern recognition techniques and rule-based clustering for prediction and discrimination analysis to validate the results further. The result indicates that there is a relationship between the disaster human impact and the five socio-economic characteristics of the affected country mentioned above. As a result, a framework was put forward, which could predict the disaster’s human impact based on their severity rank in the early hours of disaster strike. The predictions in this model were outlined in two worst and best-case scenarios, which respectively inform the lower range and higher range of the prediction. A necessity to develop the predictive framework can be highlighted by noticing that despite the existing research in literature, a framework for predicting the human impact and estimating the needs at the time of the disaster is yet to be developed. This can further be used to allocate the resources at the response phase of the disaster where the data is scarce. <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=natural%20disaster" title=" natural disaster"> natural disaster</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a> </p> <a href="https://publications.waset.org/abstracts/109152/predicting-the-human-impact-of-natural-onset-disasters-using-pattern-recognition-techniques-and-rule-based-clustering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/109152.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">153</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">10168</span> Uplink Throughput Prediction in Cellular Mobile Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Engin%20Eyceyurt">Engin Eyceyurt</a>, <a href="https://publications.waset.org/abstracts/search?q=Josko%20Zec"> Josko Zec</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The current and future cellular mobile communication networks generate enormous amounts of data. Networks have become extremely complex with extensive space of parameters, features and counters. These networks are unmanageable with legacy methods and an enhanced design and optimization approach is necessary that is increasingly reliant on machine learning. This paper proposes that machine learning as a viable approach for uplink throughput prediction. LTE radio metric, such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal to Noise Ratio (SNR) are used to train models to estimate expected uplink throughput. The prediction accuracy with high determination coefficient of 91.2% is obtained from measurements collected with a simple smartphone application. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=drive%20test" title="drive test">drive test</a>, <a href="https://publications.waset.org/abstracts/search?q=LTE" title=" LTE"> LTE</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=uplink%20throughput%20prediction" title=" uplink throughput prediction"> uplink throughput prediction</a> </p> <a href="https://publications.waset.org/abstracts/127005/uplink-throughput-prediction-in-cellular-mobile-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127005.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">156</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">10167</span> Prediction on Housing Price Based on Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Li%20Yu">Li Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Chenlu%20Jiao"> Chenlu Jiao</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongrun%20Xin"> Hongrun Xin</a>, <a href="https://publications.waset.org/abstracts/search?q=Yan%20Wang"> Yan Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaiyang%20Wang"> Kaiyang Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry. <p class="card-text"><strong>Keywords:</strong> <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=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=LSTM" title=" LSTM"> LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=housing%20prediction" title=" housing prediction"> housing prediction</a> </p> <a href="https://publications.waset.org/abstracts/84747/prediction-on-housing-price-based-on-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84747.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">10166</span> Monthly River Flow Prediction Using a Nonlinear Prediction Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20H.%20Adenan">N. H. Adenan</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20S.%20M.%20Noorani"> M. S. M. Noorani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> River flow prediction is an essential to ensure proper management of water resources can be optimally distribute water to consumers. This study presents an analysis and prediction by using nonlinear prediction method involving monthly river flow data in Tanjung Tualang from 1976 to 2006. Nonlinear prediction method involves the reconstruction of phase space and local linear approximation approach. The phase space reconstruction involves the reconstruction of one-dimensional (the observed 287 months of data) in a multidimensional phase space to reveal the dynamics of the system. Revenue of phase space reconstruction is used to predict the next 72 months. A comparison of prediction performance based on correlation coefficient (CC) and root mean square error (RMSE) have been employed to compare prediction performance for nonlinear prediction method, ARIMA and SVM. Prediction performance comparisons show the prediction results using nonlinear prediction method is better than ARIMA and SVM. Therefore, the result of this study could be used to developed an efficient water management system to optimize the allocation water resources. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=river%20flow" title="river flow">river flow</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20prediction%20method" title=" nonlinear prediction method"> nonlinear prediction method</a>, <a href="https://publications.waset.org/abstracts/search?q=phase%20space" title=" phase space"> phase space</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20linear%20approximation" title=" local linear approximation"> local linear approximation</a> </p> <a href="https://publications.waset.org/abstracts/2867/monthly-river-flow-prediction-using-a-nonlinear-prediction-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2867.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">412</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">10165</span> Artificial Neural Network in FIRST Robotics Team-Based Prediction System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cedric%20Leong">Cedric Leong</a>, <a href="https://publications.waset.org/abstracts/search?q=Parth%20Desai"> Parth Desai</a>, <a href="https://publications.waset.org/abstracts/search?q=Parth%20Patel"> Parth Patel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this project was to develop a neural network based on qualitative team data to predict alliance scores to determine winners of matches in the FIRST Robotics Competition (FRC). The game for the competition changes every year with different objectives and game objects, however the idea was to create a prediction system which can be reused year by year using some of the statistics that are constant through different games, making our system adaptable to future games as well. Aerial Assist is the FRC game for 2014, and is played in alliances of 3 teams going against one another, namely the Red and Blue alliances. This application takes any 6 teams paired into 2 alliances of 3 teams and generates the prediction for the final score between them. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artifical%20neural%20network" title="artifical neural network">artifical neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction%20system" title=" prediction system"> prediction system</a>, <a href="https://publications.waset.org/abstracts/search?q=qualitative%20team%20data" title=" qualitative team data"> qualitative team data</a>, <a href="https://publications.waset.org/abstracts/search?q=FIRST%20Robotics%20Competition%20%28FRC%29" title=" FIRST Robotics Competition (FRC)"> FIRST Robotics Competition (FRC)</a> </p> <a href="https://publications.waset.org/abstracts/10302/artificial-neural-network-in-first-robotics-team-based-prediction-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10302.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">513</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">10164</span> Using Combination of Sets of Features of Molecules for Aqueous Solubility Prediction: A Random Forest Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammet%20Baldan">Muhammet Baldan</a>, <a href="https://publications.waset.org/abstracts/search?q=Emel%20Timu%C3%A7in"> Emel Timuçin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Generally, absorption and bioavailability increase if solubility increases; therefore, it is crucial to predict them in drug discovery applications. Molecular descriptors and Molecular properties are traditionally used for the prediction of water solubility. There are various key descriptors that are used for this purpose, namely Drogan Descriptors, Morgan Descriptors, Maccs keys, etc., and each has different prediction capabilities with differentiating successes between different data sets. Another source for the prediction of solubility is structural features; they are commonly used for the prediction of solubility. However, there are little to no studies that combine three or more properties or descriptors for prediction to produce a more powerful prediction model. Unlike available models, we used a combination of those features in a random forest machine learning model for improved solubility prediction to better predict and, therefore, contribute to drug discovery systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=solubility" title="solubility">solubility</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20descriptors" title=" molecular descriptors"> molecular descriptors</a>, <a href="https://publications.waset.org/abstracts/search?q=maccs%20keys" title=" maccs keys"> maccs keys</a> </p> <a href="https://publications.waset.org/abstracts/186736/using-combination-of-sets-of-features-of-molecules-for-aqueous-solubility-prediction-a-random-forest-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186736.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">46</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">10163</span> Programming Language Extension Using Structured Query Language for Database Access</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chapman%20Eze%20Nnadozie">Chapman Eze Nnadozie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Relational databases constitute a very vital tool for the effective management and administration of both personal and organizational data. Data access ranges from a single user database management software to a more complex distributed server system. This paper intends to appraise the use a programming language extension like structured query language (SQL) to establish links to a relational database (Microsoft Access 2013) using Visual C++ 9 programming language environment. The methodology used involves the creation of tables to form a database using Microsoft Access 2013, which is Object Linking and Embedding (OLE) database compliant. The SQL command is used to query the tables in the database for easy extraction of expected records inside the visual C++ environment. The findings of this paper reveal that records can easily be accessed and manipulated to filter exactly what the user wants, such as retrieval of records with specified criteria, updating of records, and deletion of part or the whole records in a table. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20access" title="data access">data access</a>, <a href="https://publications.waset.org/abstracts/search?q=database" title=" database"> database</a>, <a href="https://publications.waset.org/abstracts/search?q=database%20management%20system" title=" database management system"> database management system</a>, <a href="https://publications.waset.org/abstracts/search?q=OLE" title=" OLE"> OLE</a>, <a href="https://publications.waset.org/abstracts/search?q=programming%20language" title=" programming language"> programming language</a>, <a href="https://publications.waset.org/abstracts/search?q=records" title=" records"> records</a>, <a href="https://publications.waset.org/abstracts/search?q=relational%20database" title=" relational database"> relational database</a>, <a href="https://publications.waset.org/abstracts/search?q=software" title=" software"> software</a>, <a href="https://publications.waset.org/abstracts/search?q=SQL" title=" SQL"> SQL</a>, <a href="https://publications.waset.org/abstracts/search?q=table" title=" table"> table</a> </p> <a href="https://publications.waset.org/abstracts/83796/programming-language-extension-using-structured-query-language-for-database-access" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83796.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">187</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">10162</span> On Improving Breast Cancer Prediction Using GRNN-CP</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kefaya%20Qaddoum">Kefaya Qaddoum</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this study is to predict breast cancer and to construct a supportive model that will stimulate a more reliable prediction as a factor that is fundamental for public health. In this study, we utilize general regression neural networks (GRNN) to replace the normal predictions with prediction periods to achieve a reasonable percentage of confidence. The mechanism employed here utilises a machine learning system called conformal prediction (CP), in order to assign consistent confidence measures to predictions, which are combined with GRNN. We apply the resulting algorithm to the problem of breast cancer diagnosis. The results show that the prediction constructed by this method is reasonable and could be useful in practice. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title="neural network">neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=conformal%20prediction" title=" conformal prediction"> conformal prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=cancer%20classification" title=" cancer classification"> cancer classification</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a> </p> <a href="https://publications.waset.org/abstracts/74483/on-improving-breast-cancer-prediction-using-grnn-cp" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74483.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">291</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">10161</span> Intelligent Earthquake Prediction System Based On Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emad%20Amar">Emad Amar</a>, <a href="https://publications.waset.org/abstracts/search?q=Tawfik%20Khattab"> Tawfik Khattab</a>, <a href="https://publications.waset.org/abstracts/search?q=Fatma%20Zada"> Fatma Zada</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Predicting earthquakes is an important issue in the study of geography. Accurate prediction of earthquakes can help people to take effective measures to minimize the loss of personal and economic damage, such as large casualties, destruction of buildings and broken of traffic, occurred within a few seconds. United States Geological Survey (USGS) science organization provides reliable scientific information of Earthquake Existed throughout history & Preliminary database from the National Center Earthquake Information (NEIC) show some useful factors to predict an earthquake in a seismic area like Aleutian Arc in the U.S. state of Alaska. The main advantage of this prediction method that it does not require any assumption, it makes prediction according to the future evolution of object's time series. The article compares between simulation data result from trained BP and RBF neural network versus actual output result from the system calculations. Therefore, this article focuses on analysis of data relating to real earthquakes. Evaluation results show better accuracy and higher speed by using radial basis functions (RBF) neural network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BP%20neural%20network" title="BP neural network">BP neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=RBF%20neural%20network" title=" RBF neural network"> RBF neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=earthquake" title=" earthquake"> earthquake</a> </p> <a href="https://publications.waset.org/abstracts/18470/intelligent-earthquake-prediction-system-based-on-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18470.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">496</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">10160</span> Student Records Management System Using Smart Cards and Biometric Technology for Educational Institutions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Patrick%20O.%20Bobbie">Patrick O. Bobbie</a>, <a href="https://publications.waset.org/abstracts/search?q=Prince%20S.%20Attrams"> Prince S. Attrams</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent times, the rapid change in new technologies has spurred up the way and manner records are handled in educational institutions. Also, there is a need for reliable access and ease-of use to these records, resulting in increased productivity in organizations. In academic institutions, such benefits help in quality assessments, institutional performance, and assessments of teaching and evaluation methods. Students in educational institutions benefit the most when advanced technologies are deployed in accessing records. This research paper discusses the use of biometric technologies coupled with smartcard technologies to provide a unique way of identifying students and matching their data to financial records to grant them access to restricted areas such as examination halls. The system developed in this paper, has an identity verification component as part of its main functionalities. A systematic software development cycle of analysis, design, coding, testing and support was used. The system provides a secured way of verifying student’s identity and real time verification of financial records. An advanced prototype version of the system has been developed for testing purposes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biometrics" title="biometrics">biometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=smartcards" title=" smartcards"> smartcards</a>, <a href="https://publications.waset.org/abstracts/search?q=identity-verification" title=" identity-verification"> identity-verification</a>, <a href="https://publications.waset.org/abstracts/search?q=fingerprints" title=" fingerprints"> fingerprints</a> </p> <a href="https://publications.waset.org/abstracts/17960/student-records-management-system-using-smart-cards-and-biometric-technology-for-educational-institutions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17960.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">419</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">10159</span> Algorithm and Software Based on Multilayer Perceptron Neural Networks for Estimating Channel Use in the Spectral Decision Stage in Cognitive Radio Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Danilo%20L%C3%B3pez">Danilo López</a>, <a href="https://publications.waset.org/abstracts/search?q=Johana%20Hern%C3%A1ndez"> Johana Hernández</a>, <a href="https://publications.waset.org/abstracts/search?q=Edwin%20Rivas"> Edwin Rivas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of the Multilayer Perceptron Neural Networks (MLPNN) technique is presented to estimate the future state of use of a licensed channel by primary users (PUs); this will be useful at the spectral decision stage in cognitive radio networks (CRN) to determine approximately in which time instants of future may secondary users (SUs) opportunistically use the spectral bandwidth to send data through the primary wireless network. To validate the results, sequences of occupancy data of channel were generated by simulation. The results show that the prediction percentage is greater than 60% in some of the tests carried out. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cognitive%20radio" title="cognitive radio">cognitive radio</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=primary%20user" title=" primary user"> primary user</a> </p> <a href="https://publications.waset.org/abstracts/61993/algorithm-and-software-based-on-multilayer-perceptron-neural-networks-for-estimating-channel-use-in-the-spectral-decision-stage-in-cognitive-radio-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61993.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">371</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">10158</span> Status of Herpetofauna of Trans-Himalayan Region of Ladakh, India</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dimpi%20A.%20Patel">Dimpi A. Patel</a>, <a href="https://publications.waset.org/abstracts/search?q=Pankaj%20Raina"> Pankaj Raina</a>, <a href="https://publications.waset.org/abstracts/search?q=Ramesh%20Chinnasamy"> Ramesh Chinnasamy</a>, <a href="https://publications.waset.org/abstracts/search?q=Sunetro%20Ghosal"> Sunetro Ghosal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The herpetological fauna of Ladakh has been surveyed few times till 1999. In 2019, a rapid survey to document current herpetofaunal composition was undertaken in which a total of 6 species belonging to 2 orders and five families along with their altitudinal ranges were recorded. We present a revised checklist of reptiles found in Ladakh trans Himalayas based on historical records and recent field surveys. Records for erroneously reported species in literature are discussed and recommended for removal from the list from this region. For several species, new elevation range records have been recorded. This paper contributes to the present status of the richness of reptiles and amphibians in the region by documenting the composition and ecological distribution of the herpetofauna of unstudied sites. Species-specific temperature and humidity regimes were also recorded during the survey periods. Our study creates baseline information for future ecological and behavioral studies on the herpetofauna of the region by providing habitat preferences and distribution in detail. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=amphibians" title="amphibians">amphibians</a>, <a href="https://publications.waset.org/abstracts/search?q=distribution" title=" distribution"> distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=diversity" title=" diversity"> diversity</a>, <a href="https://publications.waset.org/abstracts/search?q=reptiles" title=" reptiles"> reptiles</a>, <a href="https://publications.waset.org/abstracts/search?q=trans-Himalaya" title=" trans-Himalaya"> trans-Himalaya</a> </p> <a href="https://publications.waset.org/abstracts/137701/status-of-herpetofauna-of-trans-himalayan-region-of-ladakh-india" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137701.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">169</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">10157</span> Analysis on Prediction Models of TBM Performance and Selection of Optimal Input Parameters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hang%20Lo%20Lee">Hang Lo Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Ki%20Il%20Song"> Ki Il Song</a>, <a href="https://publications.waset.org/abstracts/search?q=Hee%20Hwan%20Ryu"> Hee Hwan Ryu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An accurate prediction of TBM(Tunnel Boring Machine) performance is very difficult for reliable estimation of the construction period and cost in preconstruction stage. For this purpose, the aim of this study is to analyze the evaluation process of various prediction models published since 2000 for TBM performance, and to select the optimal input parameters for the prediction model. A classification system of TBM performance prediction model and applied methodology are proposed in this research. Input and output parameters applied for prediction models are also represented. Based on these results, a statistical analysis is performed using the collected data from shield TBM tunnel in South Korea. By performing a simple regression and residual analysis utilizinFg statistical program, R, the optimal input parameters are selected. These results are expected to be used for development of prediction model of TBM performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=TBM%20performance%20prediction%20model" title="TBM performance prediction model">TBM performance prediction model</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20system" title=" classification system"> classification system</a>, <a href="https://publications.waset.org/abstracts/search?q=simple%20regression%20analysis" title=" simple regression analysis"> simple regression analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=residual%20analysis" title=" residual analysis"> residual analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20input%20parameters" title=" optimal input parameters"> optimal input parameters</a> </p> <a href="https://publications.waset.org/abstracts/52738/analysis-on-prediction-models-of-tbm-performance-and-selection-of-optimal-input-parameters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52738.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">309</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">10156</span> A Type-2 Fuzzy Model for Link Prediction in Social Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mansoureh%20Naderipour">Mansoureh Naderipour</a>, <a href="https://publications.waset.org/abstracts/search?q=Susan%20Bastani"> Susan Bastani</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Fazel%20Zarandi"> Mohammad Fazel Zarandi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Predicting links that may occur in the future and missing links in social networks is an attractive problem in social network analysis. Granular computing can help us to model the relationships between human-based system and social sciences in this field. In this paper, we present a model based on granular computing approach and Type-2 fuzzy logic to predict links regarding nodes’ activity and the relationship between two nodes. Our model is tested on collaboration networks. It is found that the accuracy of prediction is significantly higher than the Type-1 fuzzy and crisp approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=social%20network" title="social network">social network</a>, <a href="https://publications.waset.org/abstracts/search?q=link%20prediction" title=" link prediction"> link prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=granular%20computing" title=" granular computing"> granular computing</a>, <a href="https://publications.waset.org/abstracts/search?q=type-2%20fuzzy%20sets" title=" type-2 fuzzy sets"> type-2 fuzzy sets</a> </p> <a href="https://publications.waset.org/abstracts/49749/a-type-2-fuzzy-model-for-link-prediction-in-social-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49749.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 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