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bus system</title> <meta name="description" content="Search results for: organizing bus system"> <meta name="keywords" content="organizing bus system"> <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 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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="organizing bus system"> <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> 17723</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: organizing bus system</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17723</span> Self-Organizing Control Systems for Unstable and Deterministic Chaotic Processes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mamyrbek%20A.%20Beisenbi">Mamyrbek A. Beisenbi</a>, <a href="https://publications.waset.org/abstracts/search?q=Nurgul%20M.%20Kissikova"> Nurgul M. Kissikova</a>, <a href="https://publications.waset.org/abstracts/search?q=Saltanat%20E.%20Beisembina"> Saltanat E. Beisembina</a>, <a href="https://publications.waset.org/abstracts/search?q=Salamat%20T.%20Suleimenova"> Salamat T. Suleimenova</a>, <a href="https://publications.waset.org/abstracts/search?q=Samal%20A.%20Kaliyeva"> Samal A. Kaliyeva</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper proposes a method for constructing a self-organizing control system for unstable and deterministic chaotic processes in the class of catastrophe “hyperbolic umbilic” for objects with m-inputs and n-outputs. The self-organizing control system is investigated by the universal gradient-velocity method of Lyapunov vector functions. The conditions for self-organization of the control system in the class of catastrophes “hyperbolic umbilic” are shown in the form of a system of algebraic inequalities that characterize the aperiodic robust stability in the stationary states of the system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gradient-velocity%20method%20of%20Lyapunov%20vector-functions" title="gradient-velocity method of Lyapunov vector-functions">gradient-velocity method of Lyapunov vector-functions</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperbolic%20umbilic" title=" hyperbolic umbilic"> hyperbolic umbilic</a>, <a href="https://publications.waset.org/abstracts/search?q=self-organizing%20control%20system" title=" self-organizing control system"> self-organizing control system</a>, <a href="https://publications.waset.org/abstracts/search?q=stability" title=" stability"> stability</a> </p> <a href="https://publications.waset.org/abstracts/147574/self-organizing-control-systems-for-unstable-and-deterministic-chaotic-processes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147574.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">137</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">17722</span> Optimization of Structures Subjected to Earthquake</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alireza%20%20Lavaei">Alireza Lavaei</a>, <a href="https://publications.waset.org/abstracts/search?q=Alireza%20%20Lohrasbi"> Alireza Lohrasbi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammadali%20M.%20Shahlaei"> Mohammadali M. Shahlaei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To reduce the overall time of structural optimization for earthquake loads two strategies are adopted. In the first strategy, a neural system consisting self-organizing map and radial basis function neural networks, is utilized to predict the time history responses. In this case, the input space is classified by employing a self-organizing map neural network. Then a distinct RBF neural network is trained in each class. In the second strategy, an improved genetic algorithm is employed to find the optimum design. A 72-bar space truss is designed for optimal weight using exact and approximate analysis for the El Centro (S-E 1940) earthquake loading. The numerical results demonstrate the computational advantages and effectiveness of the proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimization" title="optimization">optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=self-organizing%20map" title=" self-organizing map"> self-organizing map</a> </p> <a href="https://publications.waset.org/abstracts/53234/optimization-of-structures-subjected-to-earthquake" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53234.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">311</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">17721</span> Using Self Organizing Feature Maps for Classification in RGB Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hassan%20Masoumi">Hassan Masoumi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahad%20Salimi"> Ahad Salimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Nazanin%20Barhemmat"> Nazanin Barhemmat</a>, <a href="https://publications.waset.org/abstracts/search?q=Babak%20Gholami"> Babak Gholami</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Artificial neural networks have gained a lot of interest as empirical models for their powerful representational capacity, multi input and output mapping characteristics. In fact, most feed-forward networks with nonlinear nodal functions have been proved to be universal approximates. In this paper, we propose a new supervised method for color image classification based on self organizing feature maps (SOFM). This algorithm is based on competitive learning. The method partitions the input space using self-organizing feature maps to introduce the concept of local neighborhoods. Our image classification system entered into RGB image. Experiments with simulated data showed that separability of classes increased when increasing training time. In additional, the result shows proposed algorithms are effective for color image classification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification" title="classification">classification</a>, <a href="https://publications.waset.org/abstracts/search?q=SOFM%20algorithm" title=" SOFM algorithm"> SOFM algorithm</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=neighborhood" title=" neighborhood"> neighborhood</a>, <a href="https://publications.waset.org/abstracts/search?q=RGB%20image" title=" RGB image"> RGB image</a> </p> <a href="https://publications.waset.org/abstracts/26819/using-self-organizing-feature-maps-for-classification-in-rgb-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26819.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">478</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">17720</span> Finding the Longest Common Subsequence in Normal DNA and Disease Affected Human DNA Using Self Organizing Map</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=G.%20Tamilpavai">G. Tamilpavai</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Vishnuppriya"> C. Vishnuppriya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Bioinformatics is an active research area which combines biological matter as well as computer science research. The longest common subsequence (LCSS) is one of the major challenges in various bioinformatics applications. The computation of the LCSS plays a vital role in biomedicine and also it is an essential task in DNA sequence analysis in genetics. It includes wide range of disease diagnosing steps. The objective of this proposed system is to find the longest common subsequence which presents in a normal and various disease affected human DNA sequence using Self Organizing Map (SOM) and LCSS. The human DNA sequence is collected from National Center for Biotechnology Information (NCBI) database. Initially, the human DNA sequence is separated as k-mer using k-mer separation rule. Mean and median values are calculated from each separated k-mer. These calculated values are fed as input to the Self Organizing Map for the purpose of clustering. Then obtained clusters are given to the Longest Common Sub Sequence (LCSS) algorithm for finding common subsequence which presents in every clusters. It returns nx(n-1)/2 subsequence for each cluster where n is number of k-mer in a specific cluster. Experimental outcomes of this proposed system produce the possible number of longest common subsequence of normal and disease affected DNA data. Thus the proposed system will be a good initiative aid for finding disease causing sequence. Finally, performance analysis is carried out for different DNA sequences. The obtained values show that the retrieval of LCSS is done in a shorter time than the existing system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering" title="clustering">clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=k-mers" title=" k-mers"> k-mers</a>, <a href="https://publications.waset.org/abstracts/search?q=longest%20common%20subsequence" title=" longest common subsequence"> longest common subsequence</a>, <a href="https://publications.waset.org/abstracts/search?q=SOM" title=" SOM"> SOM</a> </p> <a href="https://publications.waset.org/abstracts/90640/finding-the-longest-common-subsequence-in-normal-dna-and-disease-affected-human-dna-using-self-organizing-map" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90640.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">267</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">17719</span> Kohonen Self-Organizing Maps as a New Method for Determination of Salt Composition of Multi-Component Solutions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sergey%20A.%20Burikov">Sergey A. Burikov</a>, <a href="https://publications.waset.org/abstracts/search?q=Tatiana%20A.%20Dolenko"> Tatiana A. Dolenko</a>, <a href="https://publications.waset.org/abstracts/search?q=Kirill%20A.%20Gushchin"> Kirill A. Gushchin</a>, <a href="https://publications.waset.org/abstracts/search?q=Sergey%20A.%20Dolenko"> Sergey A. Dolenko</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper presents the results of clusterization by Kohonen self-organizing maps (SOM) applied for analysis of array of Raman spectra of multi-component solutions of inorganic salts, for determination of types of salts present in the solution. It is demonstrated that use of SOM is a promising method for solution of clusterization and classification problems in spectroscopy of multi-component objects, as attributing a pattern to some cluster may be used for recognition of component composition of the object. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kohonen%20self-organizing%20maps" title="Kohonen self-organizing maps">Kohonen self-organizing maps</a>, <a href="https://publications.waset.org/abstracts/search?q=clusterization" title=" clusterization"> clusterization</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-component%20solutions" title=" multi-component solutions"> multi-component solutions</a>, <a href="https://publications.waset.org/abstracts/search?q=Raman%20spectroscopy" title=" Raman spectroscopy"> Raman spectroscopy</a> </p> <a href="https://publications.waset.org/abstracts/14544/kohonen-self-organizing-maps-as-a-new-method-for-determination-of-salt-composition-of-multi-component-solutions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14544.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">443</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">17718</span> Self-Organizing Maps for Credit Card Fraud Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=ChunYi%20Peng">ChunYi Peng</a>, <a href="https://publications.waset.org/abstracts/search?q=Wei%20Hsuan%20CHeng"> Wei Hsuan CHeng</a>, <a href="https://publications.waset.org/abstracts/search?q=Shyh%20Kuang%20Ueng"> Shyh Kuang Ueng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=self-organizing%20map%20technology" title="self-organizing map technology">self-organizing map technology</a>, <a href="https://publications.waset.org/abstracts/search?q=fraud%20detection" title=" fraud detection"> fraud detection</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20visualization" title=" information visualization"> information visualization</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analysis" title=" data analysis"> data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=composite%20intelligent%20system%20technologies" title=" composite intelligent system technologies"> composite intelligent system technologies</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20support%20technologies" title=" decision support technologies"> decision support technologies</a> </p> <a href="https://publications.waset.org/abstracts/183639/self-organizing-maps-for-credit-card-fraud-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183639.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">57</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">17717</span> Self-Organizing Maps for Credit Card Fraud Detection and Visualization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Peng%20Chun-Yi">Peng Chun-Yi</a>, <a href="https://publications.waset.org/abstracts/search?q=Chen%20Wei-Hsuan"> Chen Wei-Hsuan</a>, <a href="https://publications.waset.org/abstracts/search?q=Ueng%20Shyh-Kuang"> Ueng Shyh-Kuang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=self-organizing%20map%20technology" title="self-organizing map technology">self-organizing map technology</a>, <a href="https://publications.waset.org/abstracts/search?q=fraud%20detection" title=" fraud detection"> fraud detection</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20visualization" title=" information visualization"> information visualization</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analysis" title=" data analysis"> data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=composite%20intelligent%20system%20technologies" title=" composite intelligent system technologies"> composite intelligent system technologies</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20support%20technologies" title=" decision support technologies"> decision support technologies</a> </p> <a href="https://publications.waset.org/abstracts/183172/self-organizing-maps-for-credit-card-fraud-detection-and-visualization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183172.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">59</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">17716</span> Unsupervised Learning with Self-Organizing Maps for Named Entity Recognition in the CONLL2003 Dataset</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Assel%20Jaxylykova">Assel Jaxylykova</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexnder%20Pak"> Alexnder Pak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study utilized a Self-Organizing Map (SOM) for unsupervised learning on the CONLL-2003 dataset for Named Entity Recognition (NER). The process involved encoding words into 300-dimensional vectors using FastText. These vectors were input into a SOM grid, where training adjusted node weights to minimize distances. The SOM provided a topological representation for identifying and clustering named entities, demonstrating its efficacy without labeled examples. Results showed an F1-measure of 0.86, highlighting SOM's viability. Although some methods achieve higher F1 measures, SOM eliminates the need for labeled data, offering a scalable and efficient alternative. The SOM's ability to uncover hidden patterns provides insights that could enhance existing supervised methods. Further investigation into potential limitations and optimization strategies is suggested to maximize benefits. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=named%20entity%20recognition" title="named entity recognition">named entity recognition</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=self-organizing%20map" title=" self-organizing map"> self-organizing map</a>, <a href="https://publications.waset.org/abstracts/search?q=CONLL-2003" title=" CONLL-2003"> CONLL-2003</a>, <a href="https://publications.waset.org/abstracts/search?q=semantics" title=" semantics"> semantics</a> </p> <a href="https://publications.waset.org/abstracts/188422/unsupervised-learning-with-self-organizing-maps-for-named-entity-recognition-in-the-conll2003-dataset" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/188422.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">47</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">17715</span> Evaluation of Groundwater Quality and Its Suitability for Drinking and Agricultural Purposes Using Self-Organizing Maps</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=L.%20Belkhiri">L. Belkhiri</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Mouni"> L. Mouni</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Tiri"> A. Tiri</a>, <a href="https://publications.waset.org/abstracts/search?q=T.S.%20Narany"> T.S. Narany</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the present study, the self-organizing map (SOM) clustering technique was applied to identify homogeneous clusters of hydrochemical parameters in El Milia plain, Algeria, to assess the quality of groundwater for potable and agricultural purposes. The visualization of SOM-analysis indicated that 35 groundwater samples collected in the study area were classified into three clusters, which showed progressive increase in electrical conductivity from cluster one to cluster three. Samples belonging to cluster one are mostly located in the recharge zone showing hard fresh water type, however, water type gradually changed to hard-brackish type in the discharge zone, including clusters two and three. Ionic ratio studies indicated the role of carbonate rock dissolution in increases on groundwater hardness, especially in cluster one. However, evaporation and evapotranspiration are the main processes increasing salinity in cluster two and three. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=groundwater%20quality" title="groundwater quality">groundwater quality</a>, <a href="https://publications.waset.org/abstracts/search?q=self-organizing%20maps" title=" self-organizing maps"> self-organizing maps</a>, <a href="https://publications.waset.org/abstracts/search?q=drinking%20water" title=" drinking water"> drinking water</a>, <a href="https://publications.waset.org/abstracts/search?q=irrigation%20water" title=" irrigation water"> irrigation water</a> </p> <a href="https://publications.waset.org/abstracts/62284/evaluation-of-groundwater-quality-and-its-suitability-for-drinking-and-agricultural-purposes-using-self-organizing-maps" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62284.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">256</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">17714</span> Comparing the Motion of Solar System with Water Droplet Motion to Predict the Future of Solar System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Areena%20Bhatti">Areena Bhatti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The geometric arrangement of planet and moon is the result of a self-organizing system. In our solar system, the planets and moons are constantly orbiting around the sun. The aim of this theory is to compare the motion of a solar system with the motion of water droplet when poured into a water body. The basic methodology is to compare both motions to know how they are related to each other. The difference between both systems will be that one is extremely fast, and the other is extremely slow. The role of this theory is that by looking at the fast system we can conclude how slow the system will get to an end. Just like ripples are formed around water droplet that move away from the droplet and water droplet forming those ripples become small in size will tell us how solar system will behave in the same way. So it is concluded that large and small systems can work under the same process but with different motions of time, and motion of the solar system is the slowest form of water droplet motion. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=motion" title="motion">motion</a>, <a href="https://publications.waset.org/abstracts/search?q=water" title=" water"> water</a>, <a href="https://publications.waset.org/abstracts/search?q=sun" title=" sun"> sun</a>, <a href="https://publications.waset.org/abstracts/search?q=time" title=" time"> time</a> </p> <a href="https://publications.waset.org/abstracts/111769/comparing-the-motion-of-solar-system-with-water-droplet-motion-to-predict-the-future-of-solar-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/111769.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">151</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">17713</span> Parallel Self Organizing Neural Network Based Estimation of Archie’s Parameters and Water Saturation in Sandstone Reservoir</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=G.%20M.%20Hamada">G. M. Hamada</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20A.%20Al-Gathe"> A. A. Al-Gathe</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20M.%20Al-Khudafi"> A. M. Al-Khudafi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Determination of water saturation in sandstone is a vital question to determine the initial oil or gas in place in reservoir rocks. Water saturation determination using electrical measurements is mainly on Archie’s formula. Consequently accuracy of Archie’s formula parameters affects water saturation values rigorously. Determination of Archie’s parameters a, m, and n is proceeded by three conventional techniques, Core Archie-Parameter Estimation (CAPE) and 3-D. This work introduces the hybrid system of parallel self-organizing neural network (PSONN) targeting accepted values of Archie’s parameters and, consequently, reliable water saturation values. This work focuses on Archie’s parameters determination techniques; conventional technique, CAPE technique, and 3-D technique, and then the calculation of water saturation using current. Using the same data, a hybrid parallel self-organizing neural network (PSONN) algorithm is used to estimate Archie’s parameters and predict water saturation. Results have shown that estimated Arche’s parameters m, a, and n are highly accepted with statistical analysis, indicating that the PSONN model has a lower statistical error and higher correlation coefficient. This study was conducted using a high number of measurement points for 144 core plugs from a sandstone reservoir. PSONN algorithm can provide reliable water saturation values, and it can supplement or even replace the conventional techniques to determine Archie’s parameters and thereby calculate water saturation profiles. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=water%20saturation" title="water saturation">water saturation</a>, <a href="https://publications.waset.org/abstracts/search?q=Archie%E2%80%99s%20parameters" title=" Archie’s parameters"> Archie’s parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=PSONN" title=" PSONN"> PSONN</a>, <a href="https://publications.waset.org/abstracts/search?q=sandstone%20reservoir" title=" sandstone reservoir"> sandstone reservoir</a> </p> <a href="https://publications.waset.org/abstracts/149641/parallel-self-organizing-neural-network-based-estimation-of-archies-parameters-and-water-saturation-in-sandstone-reservoir" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149641.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">128</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">17712</span> Process Monitoring Based on Parameterless Self-Organizing Map</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Young%20Jae%20Choung">Young Jae Choung</a>, <a href="https://publications.waset.org/abstracts/search?q=Seoung%20Bum%20Kim"> Seoung Bum Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Statistical Process Control (SPC) is a popular technique for process monitoring. A widely used tool in SPC is a control chart, which is used to detect the abnormal status of a process and maintain the controlled status of the process. Traditional control charts, such as Hotelling’s T2 control chart, are effective techniques to detect abnormal observations and monitor processes. However, many complicated manufacturing systems exhibit nonlinearity because of the different demands of the market. In this case, the unregulated use of a traditional linear modeling approach may not be effective. In reality, many industrial processes contain the nonlinear and time-varying properties because of the fluctuation of process raw materials, slowing shift of the set points, aging of the main process components, seasoning effects, and catalyst deactivation. The use of traditional SPC techniques with time-varying data will degrade the performance of the monitoring scheme. To address these issues, in the present study, we propose a parameterless self-organizing map (PLSOM)-based control chart. The PLSOM-based control chart not only can manage a situation where the distribution or parameter of the target observations changes, but also address the nonlinearity of modern manufacturing systems. The control limits of the proposed PLSOM chart are established by estimating the empirical level of significance on the percentile using a bootstrap method. Experimental results with simulated data and actual process data from a thin-film transistor-liquid crystal display process demonstrated the effectiveness and usefulness of the proposed chart. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=control%20chart" title="control chart">control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter-less%20self-organizing%20map" title=" parameter-less self-organizing map"> parameter-less self-organizing map</a>, <a href="https://publications.waset.org/abstracts/search?q=self-organizing%20map" title=" self-organizing map"> self-organizing map</a>, <a href="https://publications.waset.org/abstracts/search?q=time-varying%20property" title=" time-varying property"> time-varying property</a> </p> <a href="https://publications.waset.org/abstracts/52108/process-monitoring-based-on-parameterless-self-organizing-map" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52108.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">275</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">17711</span> Clustering the Wheat Seeds Using SOM Artificial Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Salah%20Ghamari">Salah Ghamari </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, the ability of self organizing map artificial (SOM) neural networks in clustering the wheat seeds varieties according to morphological properties of them was considered. The SOM is one type of unsupervised competitive learning. Experimentally, five morphological features of 300 seeds (including three varieties: gaskozhen, Md and sardari) were obtained using image processing technique. The results show that the artificial neural network has a good performance (90.33% accuracy) in classification of the wheat varieties despite of high similarity in them. The highest classification accuracy (100%) was achieved for sardari. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title="artificial neural networks">artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=self%20organizing%20map" title=" self organizing map"> self organizing map</a>, <a href="https://publications.waset.org/abstracts/search?q=wheat%20variety" title=" wheat variety"> wheat variety</a> </p> <a href="https://publications.waset.org/abstracts/33833/clustering-the-wheat-seeds-using-som-artificial-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33833.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">656</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">17710</span> Hyperspectral Data Classification Algorithm Based on the Deep Belief and Self-Organizing Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Li%20Qingjian">Li Qingjian</a>, <a href="https://publications.waset.org/abstracts/search?q=Li%20Ke"> Li Ke</a>, <a href="https://publications.waset.org/abstracts/search?q=He%20Chun"> He Chun</a>, <a href="https://publications.waset.org/abstracts/search?q=Huang%20Yong"> Huang Yong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the method of combining the Pohl Seidman's deep belief network with the self-organizing neural network is proposed to classify the target. This method is mainly aimed at the high nonlinearity of the hyperspectral image, the high sample dimension and the difficulty in designing the classifier. The main feature of original data is extracted by deep belief network. In the process of extracting features, adding known labels samples to fine tune the network, enriching the main characteristics. Then, the extracted feature vectors are classified into the self-organizing neural network. This method can effectively reduce the dimensions of data in the spectrum dimension in the preservation of large amounts of raw data information, to solve the traditional clustering and the long training time when labeled samples less deep learning algorithm for training problems, improve the classification accuracy and robustness. Through the data simulation, the results show that the proposed network structure can get a higher classification precision in the case of a small number of known label samples. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DBN" title="DBN">DBN</a>, <a href="https://publications.waset.org/abstracts/search?q=SOM" title=" SOM"> SOM</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20classification" title=" pattern classification"> pattern classification</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperspectral" title=" hyperspectral"> hyperspectral</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20compression" title=" data compression"> data compression</a> </p> <a href="https://publications.waset.org/abstracts/89759/hyperspectral-data-classification-algorithm-based-on-the-deep-belief-and-self-organizing-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89759.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">341</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">17709</span> Self-Organizing Maps for Exploration of Partially Observed Data and Imputation of Missing Values in the Context of the Manufacture of Aircraft Engines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sara%20Rejeb">Sara Rejeb</a>, <a href="https://publications.waset.org/abstracts/search?q=Catherine%20Duveau"> Catherine Duveau</a>, <a href="https://publications.waset.org/abstracts/search?q=Tabea%20Rebafka"> Tabea Rebafka</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To monitor the production process of turbofan aircraft engines, multiple measurements of various geometrical parameters are systematically recorded on manufactured parts. Engine parts are subject to extremely high standards as they can impact the performance of the engine. Therefore, it is essential to analyze these databases to better understand the influence of the different parameters on the engine's performance. Self-organizing maps are unsupervised neural networks which achieve two tasks simultaneously: they visualize high-dimensional data by projection onto a 2-dimensional map and provide clustering of the data. This technique has become very popular for data exploration since it provides easily interpretable results and a meaningful global view of the data. As such, self-organizing maps are usually applied to aircraft engine condition monitoring. As databases in this field are huge and complex, they naturally contain multiple missing entries for various reasons. The classical Kohonen algorithm to compute self-organizing maps is conceived for complete data only. A naive approach to deal with partially observed data consists in deleting items or variables with missing entries. However, this requires a sufficient number of complete individuals to be fairly representative of the population; otherwise, deletion leads to a considerable loss of information. Moreover, deletion can also induce bias in the analysis results. Alternatively, one can first apply a common imputation method to create a complete dataset and then apply the Kohonen algorithm. However, the choice of the imputation method may have a strong impact on the resulting self-organizing map. Our approach is to address simultaneously the two problems of computing a self-organizing map and imputing missing values, as these tasks are not independent. In this work, we propose an extension of self-organizing maps for partially observed data, referred to as missSOM. First, we introduce a criterion to be optimized, that aims at defining simultaneously the best self-organizing map and the best imputations for the missing entries. As such, missSOM is also an imputation method for missing values. To minimize the criterion, we propose an iterative algorithm that alternates the learning of a self-organizing map and the imputation of missing values. Moreover, we develop an accelerated version of the algorithm by entwining the iterations of the Kohonen algorithm with the updates of the imputed values. This method is efficiently implemented in R and will soon be released on CRAN. Compared to the standard Kohonen algorithm, it does not come with any additional cost in terms of computing time. Numerical experiments illustrate that missSOM performs well in terms of both clustering and imputation compared to the state of the art. In particular, it turns out that missSOM is robust to the missingness mechanism, which is in contrast to many imputation methods that are appropriate for only a single mechanism. This is an important property of missSOM as, in practice, the missingness mechanism is often unknown. An application to measurements on one type of part is also provided and shows the practical interest of missSOM. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=imputation%20method%20of%20missing%20data" title="imputation method of missing data">imputation method of missing data</a>, <a href="https://publications.waset.org/abstracts/search?q=partially%20observed%20data" title=" partially observed data"> partially observed data</a>, <a href="https://publications.waset.org/abstracts/search?q=robustness%20to%20missingness%20mechanism" title=" robustness to missingness mechanism"> robustness to missingness mechanism</a>, <a href="https://publications.waset.org/abstracts/search?q=self-organizing%20maps" title=" self-organizing maps"> self-organizing maps</a> </p> <a href="https://publications.waset.org/abstracts/142434/self-organizing-maps-for-exploration-of-partially-observed-data-and-imputation-of-missing-values-in-the-context-of-the-manufacture-of-aircraft-engines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142434.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">151</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">17708</span> Evaluation of the Self-Organizing Map and the Adaptive Neuro-Fuzzy Inference System Machine Learning Techniques for the Estimation of Crop Water Stress Index of Wheat under Varying Application of Irrigation Water Levels for Efficient Irrigation Scheduling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aschalew%20C.%20Workneh">Aschalew C. Workneh</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20S.%20Hari%20Prasad"> K. S. Hari Prasad</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20S.%20P.%20Ojha"> C. S. P. Ojha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The crop water stress index (CWSI) is a cost-effective, non-destructive, and simple technique for tracking the start of crop water stress. This study investigated the feasibility of CWSI derived from canopy temperature to detect the water status of wheat crops. Artificial intelligence (AI) techniques have become increasingly popular in recent years for determining CWSI. In this study, the performance of two AI techniques, adaptive neuro-fuzzy inference system (ANFIS) and self-organizing maps (SOM), are compared while determining the CWSI of paddy crops. Field experiments were conducted for varying irrigation water applications during two seasons in 2022 and 2023 at the irrigation field laboratory at the Civil Engineering Department, Indian Institute of Technology Roorkee, India. The ANFIS and SOM-simulated CWSI values were compared with the experimentally calculated CWSI (EP-CWSI). Multiple regression analysis was used to determine the upper and lower CWSI baselines. The upper CWSI baseline was found to be a function of crop height and wind speed, while the lower CWSI baseline was a function of crop height, air vapor pressure deficit, and wind speed. The performance of ANFIS and SOM were compared based on mean absolute error (MAE), mean bias error (MBE), root mean squared error (RMSE), index of agreement (d), Nash-Sutcliffe efficiency (NSE), and coefficient of correlation (R²). Both models successfully estimated the CWSI of the paddy crop with higher correlation coefficients and lower statistical errors. However, the ANFIS (R²=0.81, NSE=0.73, d=0.94, RMSE=0.04, MAE= 0.00-1.76 and MBE=-2.13-1.32) outperformed the SOM model (R²=0.77, NSE=0.68, d=0.90, RMSE=0.05, MAE= 0.00-2.13 and MBE=-2.29-1.45). Overall, the results suggest that ANFIS is a reliable tool for accurately determining CWSI in wheat crops compared to SOM. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20neuro-fuzzy%20inference%20system" title="adaptive neuro-fuzzy inference system">adaptive neuro-fuzzy inference system</a>, <a href="https://publications.waset.org/abstracts/search?q=canopy%20temperature" title=" canopy temperature"> canopy temperature</a>, <a href="https://publications.waset.org/abstracts/search?q=crop%20water%20stress%20index" title=" crop water stress index"> crop water stress index</a>, <a href="https://publications.waset.org/abstracts/search?q=self-organizing%20map" title=" self-organizing map"> self-organizing map</a>, <a href="https://publications.waset.org/abstracts/search?q=wheat" title=" wheat"> wheat</a> </p> <a href="https://publications.waset.org/abstracts/184504/evaluation-of-the-self-organizing-map-and-the-adaptive-neuro-fuzzy-inference-system-machine-learning-techniques-for-the-estimation-of-crop-water-stress-index-of-wheat-under-varying-application-of-irrigation-water-levels-for-efficient-irrigation-scheduling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184504.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">55</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">17707</span> Development of a Context Specific Planning Model for Achieving a Sustainable Urban City</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jothilakshmy%20Nagammal">Jothilakshmy Nagammal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research paper deals with the different case studies, where the Form-Based Codes are adopted in general and the different implementation methods in particular are discussed to develop a method for formulating a new planning model. The organizing principle of the Form-Based Codes, the transect is used to zone the city into various context specific transects. An approach is adopted to develop the new planning model, city Specific Planning Model (CSPM), as a tool to achieve sustainability for any city in general. A case study comparison method in terms of the planning tools used, the code process adopted and the various control regulations implemented in thirty two different cities are done. The analysis shows that there are a variety of ways to implement form-based zoning concepts: Specific plans, a parallel or optional form-based code, transect-based code /smart code, required form-based standards or design guidelines. The case studies describe the positive and negative results from based zoning, Where it is implemented. From the different case studies on the method of the FBC, it is understood that the scale for formulating the Form-Based Code varies from parts of the city to the whole city. The regulating plan is prepared with the organizing principle as the transect in most of the cases. The various implementation methods adopted in these case studies for the formulation of Form-Based Codes are special districts like the Transit Oriented Development (TOD), traditional Neighbourhood Development (TND), specific plan and Street based. The implementation methods vary from mandatory, integrated and floating. To attain sustainability the research takes the approach of developing a regulating plan, using the transect as the organizing principle for the entire area of the city in general in formulating the Form-Based Codes for the selected Special Districts in the study area in specific, street based. Planning is most powerful when it is embedded in the broader context of systemic change and improvement. Systemic is best thought of as holistic, contextualized and stake holder-owned, While systematic can be thought of more as linear, generalisable, and typically top-down or expert driven. The systemic approach is a process that is based on the system theory and system design principles, which are too often ill understood by the general population and policy makers. The system theory embraces the importance of a global perspective, multiple components, interdependencies and interconnections in any system. In addition, the recognition that a change in one part of a system necessarily alters the rest of the system is a cornerstone of the system theory. The proposed regulating plan taking the transect as an organizing principle and Form-Based Codes to achieve sustainability of the city has to be a hybrid code, which is to be integrated within the existing system - A Systemic Approach with a Systematic Process. This approach of introducing a few form based zones into a conventional code could be effective in the phased replacement of an existing code. It could also be an effective way of responding to the near-term pressure of physical change in “sensitive” areas of the community. With this approach and method the new Context Specific Planning Model is created towards achieving sustainability is explained in detail this research paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=context%20based%20planning%20model" title="context based planning model">context based planning model</a>, <a href="https://publications.waset.org/abstracts/search?q=form%20based%20code" title=" form based code"> form based code</a>, <a href="https://publications.waset.org/abstracts/search?q=transect" title=" transect"> transect</a>, <a href="https://publications.waset.org/abstracts/search?q=systemic%20approach" title=" systemic approach"> systemic approach</a> </p> <a href="https://publications.waset.org/abstracts/27077/development-of-a-context-specific-planning-model-for-achieving-a-sustainable-urban-city" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27077.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">338</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">17706</span> Automatic Queuing Model Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fahad%20Suleiman">Fahad Suleiman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Queuing, in medical system is the process of moving patients in a specific sequence to a specific service according to the patients’ nature of illness. The term scheduling stands for the process of computing a schedule. This may be done by a queuing based scheduler. This paper focuses on the medical consultancy system, the different queuing algorithms that are used in healthcare system to serve the patients, and the average waiting time. The aim of this paper is to build automatic queuing system for organizing the medical queuing system that can analyses the queue status and take decision which patient to serve. The new queuing architecture model can switch between different scheduling algorithms according to the testing results and the factor of the average waiting time. The main innovation of this work concerns the modeling of the average waiting time is taken into processing, in addition with the process of switching to the scheduling algorithm that gives the best average waiting time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=queuing%20systems" title="queuing systems">queuing systems</a>, <a href="https://publications.waset.org/abstracts/search?q=queuing%20system%20models" title=" queuing system models"> queuing system models</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduling%20algorithms" title=" scheduling algorithms"> scheduling algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=patients" title=" patients"> patients</a> </p> <a href="https://publications.waset.org/abstracts/38535/automatic-queuing-model-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38535.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">354</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">17705</span> Developing a Smart Card Using Internet of Things-Uni-C</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Enji%20E.%20Alzamzami">Enji E. Alzamzami</a>, <a href="https://publications.waset.org/abstracts/search?q=Kholod%20A.%20Almwallad"> Kholod A. Almwallad</a>, <a href="https://publications.waset.org/abstracts/search?q=Rahaf%20J.%20Alwafi"> Rahaf J. Alwafi</a>, <a href="https://publications.waset.org/abstracts/search?q=Roaa%20H.%20Alansari"> Roaa H. Alansari</a>, <a href="https://publications.waset.org/abstracts/search?q=Shatha%20S.%20Alshehri"> Shatha S. Alshehri</a>, <a href="https://publications.waset.org/abstracts/search?q=Aeshah%20A.%20Alsiyami"> Aeshah A. Alsiyami</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper demonstrates a system that helps solve the congestion problem at the entrance gates and limits the spread of viruses among people in crowded environments, such as COVID-19, using the IoT (Internet of Things). This system may assist in organizing the campus entry process efficiently by developing a smart card application supported by NFC (Near Field Communication) technology through which users' information could be sent to a reader to share it with the server and allow the server to perform its tasks and send a confirmation response for the request either by acceptance or rejection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=COVID-19" title="COVID-19">COVID-19</a>, <a href="https://publications.waset.org/abstracts/search?q=IoT" title=" IoT"> IoT</a>, <a href="https://publications.waset.org/abstracts/search?q=NFC%20technology" title=" NFC technology"> NFC technology</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20card" title=" smart card"> smart card</a> </p> <a href="https://publications.waset.org/abstracts/149076/developing-a-smart-card-using-internet-of-things-uni-c" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149076.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">136</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">17704</span> Atmospheric Circulation Drivers Of Nationally-Aggregated Wind Energy Production Over Greece</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kostas%20Philippopoulos">Kostas Philippopoulos</a>, <a href="https://publications.waset.org/abstracts/search?q=Chris%20G.%20Tzanis"> Chris G. Tzanis</a>, <a href="https://publications.waset.org/abstracts/search?q=Despina%20Deligiorgi"> Despina Deligiorgi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Climate change adaptation requires the exploitation of renewable energy sources such as wind. However, climate variability can affect the regional wind energy potential and consequently the available wind power production. The goal of the research project is to examine the impact of atmospheric circulation on wind energy production over Greece. In the context of synoptic climatology, the proposed novel methodology employs Self-Organizing Maps for grouping and classifying the atmospheric circulation and nationally-aggregated capacity factor time series for a 30-year period. The results indicate the critical effect of atmospheric circulation on the national aggregated wind energy production values and therefore address the issue of optimum distribution of wind farms for a specific region. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wind%20energy" title="wind energy">wind energy</a>, <a href="https://publications.waset.org/abstracts/search?q=atmospheric%20circulation" title=" atmospheric circulation"> atmospheric circulation</a>, <a href="https://publications.waset.org/abstracts/search?q=capacity%20factor" title=" capacity factor"> capacity factor</a>, <a href="https://publications.waset.org/abstracts/search?q=self-organizing%20maps" title=" self-organizing maps"> self-organizing maps</a> </p> <a href="https://publications.waset.org/abstracts/146188/atmospheric-circulation-drivers-of-nationally-aggregated-wind-energy-production-over-greece" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146188.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">162</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">17703</span> Scheduling of Bus Fleet Departure Time Based on Mathematical Model of Number of Bus Stops for Municipality Bus Organization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Abdi%20Kordani">Ali Abdi Kordani</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamid%20Bigdelirad"> Hamid Bigdelirad</a>, <a href="https://publications.waset.org/abstracts/search?q=Sid%20Mohammad%20Boroomandrad"> Sid Mohammad Boroomandrad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Operating Urban Bus Transit System is a phenomenon that has a major role in transporting passengers in cities. There are many factors involved in planning and operating an Urban Bus Transit System, one of which is selecting optimized number of stops and scheduling of bus fleet departure. In this paper, we tried to introduce desirable methodology to select number of stops and schedule properly. Selecting the right number of stops causes convenience in accessibility and reduction in travel time and finally increase in public preference of this transportation mode. The achieved results revealed that number of stops must reduce from 33 to 25. Also according to scheduling and conducted economic analysis, the number of buses must decrease from 17 to 11 to have the most appropriate status for the Bus Organization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=number%20of%20optimized%20stops" title="number of optimized stops">number of optimized stops</a>, <a href="https://publications.waset.org/abstracts/search?q=organizing%20bus%20system" title=" organizing bus system"> organizing bus system</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduling" title=" scheduling"> scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20transit" title=" urban transit"> urban transit</a> </p> <a href="https://publications.waset.org/abstracts/111521/scheduling-of-bus-fleet-departure-time-based-on-mathematical-model-of-number-of-bus-stops-for-municipality-bus-organization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/111521.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">17702</span> Rule Insertion Technique for Dynamic Cell Structure Neural Network </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Osama%20Elsarrar">Osama Elsarrar</a>, <a href="https://publications.waset.org/abstracts/search?q=Marjorie%20Darrah"> Marjorie Darrah</a>, <a href="https://publications.waset.org/abstracts/search?q=Richard%20Devin"> Richard Devin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper discusses the idea of capturing an expert’s knowledge in the form of human understandable rules and then inserting these rules into a dynamic cell structure (DCS) neural network. The DCS is a form of self-organizing map that can be used for many purposes, including classification and prediction. This particular neural network is considered to be a topology preserving network that starts with no pre-structure, but assumes a structure once trained. The DCS has been used in mission and safety-critical applications, including adaptive flight control and health-monitoring in aerial vehicles. The approach is to insert expert knowledge into the DCS before training. Rules are translated into a pre-structure and then training data are presented. This idea has been demonstrated using the well-known Iris data set and it has been shown that inserting the pre-structure results in better accuracy with the same training. <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=self-organizing%20map" title=" self-organizing map"> self-organizing map</a>, <a href="https://publications.waset.org/abstracts/search?q=rule%20extraction" title=" rule extraction"> rule extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=rule%20insertion" title=" rule insertion"> rule insertion</a> </p> <a href="https://publications.waset.org/abstracts/116302/rule-insertion-technique-for-dynamic-cell-structure-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/116302.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">172</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">17701</span> Problems in Lifelong Education Course in Information and Communication Technology</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hisham%20Md.Suhadi">Hisham Md.Suhadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Faaizah%20Shahbodin"> Faaizah Shahbodin</a>, <a href="https://publications.waset.org/abstracts/search?q=Jamaluddin%20Hashim"> Jamaluddin Hashim</a>, <a href="https://publications.waset.org/abstracts/search?q=Nurul%20Huda%20Mahsudi"> Nurul Huda Mahsudi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahathir%20Mohd%20Sarjan"> Mahathir Mohd Sarjan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study is the way to identify the problems that occur in organizing short courses lifelong learning in the information and communication technology (ICT) education which are faced by the lecturer and staff at the Mara Skill Institute and Industrial Training Institute in Pahang, Malaysia. The important aspects of these issues are classified to five which are selecting the courses administrative. Fifty lecturers and staff were selected as a respondent. The sample is selected by using the non-random sampling method purpose sampling. The questionnaire is used as a research instrument and divided into five main parts. All the data that gain from the questionnaire are analyzed by using the SPSS in term of mean, standard deviation and percentage. The findings showed that there are the problems occur in organizing the short course for lifelong learning in ICT education. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lifelong%20Education" title="lifelong Education">lifelong Education</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20and%20communication%20technology" title=" information and communication technology"> information and communication technology</a>, <a href="https://publications.waset.org/abstracts/search?q=short%20course" title=" short course"> short course</a>, <a href="https://publications.waset.org/abstracts/search?q=ICT%20education" title=" ICT education"> ICT education</a>, <a href="https://publications.waset.org/abstracts/search?q=courses%20administrative" title=" courses administrative"> courses administrative</a> </p> <a href="https://publications.waset.org/abstracts/31799/problems-in-lifelong-education-course-in-information-and-communication-technology" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31799.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">455</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">17700</span> Stochastic Programming and C-Somga: Animal Ration Formulation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pratiksha%20Saxena">Pratiksha Saxena</a>, <a href="https://publications.waset.org/abstracts/search?q=Dipti%20Singh"> Dipti Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Neha%20Khanna"> Neha Khanna</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A self-organizing migrating genetic algorithm(C-SOMGA) is developed for animal diet formulation. This paper presents animal diet formulation using stochastic and genetic algorithm. Tri-objective models for cost minimization and shelf life maximization are developed. These objectives are achieved by combination of stochastic programming and C-SOMGA. Stochastic programming is used to introduce nutrient variability for animal diet. Self-organizing migrating genetic algorithm provides exact and quick solution and presents an innovative approach towards successful application of soft computing technique in the area of animal diet formulation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=animal%20feed%20ration" title="animal feed ration">animal feed ration</a>, <a href="https://publications.waset.org/abstracts/search?q=feed%20formulation" title=" feed formulation"> feed formulation</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20programming" title=" linear programming"> linear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20programming" title=" stochastic programming"> stochastic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=self-migrating%20genetic%20algorithm" title=" self-migrating genetic algorithm"> self-migrating genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=C-SOMGA%20technique" title=" C-SOMGA technique"> C-SOMGA technique</a>, <a href="https://publications.waset.org/abstracts/search?q=shelf%20life%20maximization" title=" shelf life maximization"> shelf life maximization</a>, <a href="https://publications.waset.org/abstracts/search?q=cost%20minimization" title=" cost minimization"> cost minimization</a>, <a href="https://publications.waset.org/abstracts/search?q=nutrient%20maximization" title=" nutrient maximization"> nutrient maximization</a> </p> <a href="https://publications.waset.org/abstracts/35795/stochastic-programming-and-c-somga-animal-ration-formulation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35795.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">442</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">17699</span> Self-Organizing Map Network for Wheeled Robot Movement Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Boguslaw%20Schreyer">Boguslaw Schreyer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper investigates the application of the Kohonen’s Self-Organizing Map (SOM) to the wheeled robot starting and braking dynamic states. In securing wheeled robot stability as well as minimum starting and braking time, it is important to ensure correct torque distribution as well as proper slope of braking and driving moments. In this paper, a correct movement distribution has been formulated, securing optimum adhesion coefficient and good transversal stability of a wheeled robot. A neural tuner has been proposed to secure the above properties, although most of the attention is attached to the SOM network application. If the delay of the torque application or torque release is not negligible, it is important to change the rising and falling slopes of the torque. The road/surface condition is also paramount in robot dynamic states control. As the road conditions may randomly change in time, application of the SOM network has been suggested in order to classify the actual road conditions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=slip%20control" title="slip control">slip control</a>, <a href="https://publications.waset.org/abstracts/search?q=SOM%20network" title=" SOM network"> SOM network</a>, <a href="https://publications.waset.org/abstracts/search?q=torque%20distribution" title=" torque distribution"> torque distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=wheeled%20Robot" title=" wheeled Robot"> wheeled Robot</a> </p> <a href="https://publications.waset.org/abstracts/123015/self-organizing-map-network-for-wheeled-robot-movement-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/123015.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">17698</span> Efficient Passenger Counting in Public Transport Based on Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chonlakorn%20Wiboonsiriruk">Chonlakorn Wiboonsiriruk</a>, <a href="https://publications.waset.org/abstracts/search?q=Ekachai%20Phaisangittisagul"> Ekachai Phaisangittisagul</a>, <a href="https://publications.waset.org/abstracts/search?q=Chadchai%20Srisurangkul"> Chadchai Srisurangkul</a>, <a href="https://publications.waset.org/abstracts/search?q=Itsuo%20Kumazawa"> Itsuo Kumazawa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Public transportation is a crucial aspect of passenger transportation, with buses playing a vital role in the transportation service. Passenger counting is an essential tool for organizing and managing transportation services. However, manual counting is a tedious and time-consuming task, which is why computer vision algorithms are being utilized to make the process more efficient. In this study, different object detection algorithms combined with passenger tracking are investigated to compare passenger counting performance. The system employs the EfficientDet algorithm, which has demonstrated superior performance in terms of speed and accuracy. Our results show that the proposed system can accurately count passengers in varying conditions with an accuracy of 94%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title="computer vision">computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title=" object detection"> object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=passenger%20counting" title=" passenger counting"> passenger counting</a>, <a href="https://publications.waset.org/abstracts/search?q=public%20transportation" title=" public transportation"> public transportation</a> </p> <a href="https://publications.waset.org/abstracts/167734/efficient-passenger-counting-in-public-transport-based-on-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167734.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">154</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">17697</span> A Geospatial Consumer Marketing Campaign Optimization Strategy: Case of Fuzzy Approach in Nigeria Mobile Market</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adeolu%20O.%20Dairo">Adeolu O. Dairo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Getting the consumer marketing strategy right is a crucial and complex task for firms with a large customer base such as mobile operators in a competitive mobile market. While empirical studies have made efforts to identify key constructs, no geospatial model has been developed to comprehensively assess the viability and interdependency of ground realities regarding the customer, competition, channel and the network quality of mobile operators. With this research, a geo-analytic framework is proposed for strategy formulation and allocation for mobile operators. Firstly, a fuzzy analytic network using a self-organizing feature map clustering technique based on inputs from managers and literature, which depicts the interrelationships amongst ground realities is developed. The model is tested with a mobile operator in the Nigeria mobile market. As a result, a customer-centric geospatial and visualization solution is developed. This provides a consolidated and integrated insight that serves as a transparent, logical and practical guide for strategic, tactical and operational decision making. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=geospatial" title="geospatial">geospatial</a>, <a href="https://publications.waset.org/abstracts/search?q=geo-analytics" title=" geo-analytics"> geo-analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=self-organizing%20map" title=" self-organizing map"> self-organizing map</a>, <a href="https://publications.waset.org/abstracts/search?q=customer-centric" title=" customer-centric"> customer-centric</a> </p> <a href="https://publications.waset.org/abstracts/93024/a-geospatial-consumer-marketing-campaign-optimization-strategy-case-of-fuzzy-approach-in-nigeria-mobile-market" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93024.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">183</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">17696</span> Innovative Design Considerations for Adaptive Spacecraft</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Parandhama%20Gowd">K. Parandhama Gowd</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Space technologies have changed the way we live in the present day society and manage many aspects of our daily affairs through Remote sensing, Navigation & Communications. Further, defense and military usage of spacecraft has increased tremendously along with civilian purposes. The number of satellites deployed in space in Low Earth Orbit (LEO), Medium Earth Orbit (MEO), and the Geostationary Orbit (GEO) has gone up. The dependency on remote sensing and operational capabilities are most invariably to be exploited more and more in future. Every country is acquiring spacecraft in one way or other for their daily needs, and spacecraft numbers are likely to increase significantly and create spacecraft traffic problems. The aim of this research paper is to propose innovative design concepts for adaptive spacecraft. The main idea here is to improve existing design methods of spacecraft design and development to further improve upon design considerations for futuristic adaptive spacecraft with inbuilt features for automatic adaptability and self-protection. In other words, the innovative design considerations proposed here are to have future spacecraft with self-organizing capabilities for orbital control and protection from anti-satellite weapons (ASAT). Here, an attempt is made to propose design and develop futuristic spacecraft for 2030 and beyond due to tremendous advancements in VVLSI, miniaturization, and nano antenna array technologies, including nano technologies are expected. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=satellites" title="satellites">satellites</a>, <a href="https://publications.waset.org/abstracts/search?q=low%20earth%20orbit%20%28LEO%29" title=" low earth orbit (LEO)"> low earth orbit (LEO)</a>, <a href="https://publications.waset.org/abstracts/search?q=medium%20earth%20orbit%20%28MEO%29" title=" medium earth orbit (MEO)"> medium earth orbit (MEO)</a>, <a href="https://publications.waset.org/abstracts/search?q=geostationary%20earth%20orbit%20%28GEO%29" title=" geostationary earth orbit (GEO)"> geostationary earth orbit (GEO)</a>, <a href="https://publications.waset.org/abstracts/search?q=self-organizing%20control%20system" title=" self-organizing control system"> self-organizing control system</a>, <a href="https://publications.waset.org/abstracts/search?q=anti-satellite%20weapons%20%28ASAT%29" title=" anti-satellite weapons (ASAT)"> anti-satellite weapons (ASAT)</a>, <a href="https://publications.waset.org/abstracts/search?q=orbital%20control" title=" orbital control"> orbital control</a>, <a href="https://publications.waset.org/abstracts/search?q=radar%20warning%20receiver" title=" radar warning receiver"> radar warning receiver</a>, <a href="https://publications.waset.org/abstracts/search?q=missile%20warning%20receiver" title=" missile warning receiver"> missile warning receiver</a>, <a href="https://publications.waset.org/abstracts/search?q=laser%20warning%20receiver" title=" laser warning receiver"> laser warning receiver</a>, <a href="https://publications.waset.org/abstracts/search?q=attitude%20and%20orbit%20control%20systems%20%28AOCS%29" title=" attitude and orbit control systems (AOCS)"> attitude and orbit control systems (AOCS)</a>, <a href="https://publications.waset.org/abstracts/search?q=command%20and%20data%20handling%20%28CDH%29" title=" command and data handling (CDH)"> command and data handling (CDH)</a> </p> <a href="https://publications.waset.org/abstracts/48866/innovative-design-considerations-for-adaptive-spacecraft" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48866.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">296</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">17695</span> Self-Regulation in Composition Writing: The Case of Variation of Self-Regulation Dispositions in Opinion Essay and Technical Writing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dave%20Kenneth%20Tayao%20Cayado">Dave Kenneth Tayao Cayado</a>, <a href="https://publications.waset.org/abstracts/search?q=Carlo%20P.%20Magno"> Carlo P. Magno</a>, <a href="https://publications.waset.org/abstracts/search?q=Venice%20Cristine%20Dangaran"> Venice Cristine Dangaran</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present study determines whether there will be differences in the self-regulation dispositions that learners utilize when writing different types of composition. There were 7 self-regulation factors that were used to develop a scale in this study such as memory strategy, goal setting, self-evaluation, seeking assistance, learning responsibility, environmental structuring, and organizing. The scale was made specific for writing a composition. The researcher-made scale was administered to 150 participants who all came from a university in the Philippines. The participants were asked to write two compositions namely opinion essay and research introduction/review of related literature. The zero-order correlation revealed that all the factors of self-regulation are correlated with one another. However, only seeking assistance and self-evaluation are correlated with opinion essay and technical writing is not correlated to any of the self-regulation factors. However, when path analysis was used, it was shown that seeking assistance can predict opinion essay scores whereas memory strategy, self-evaluation, and organizing can predict technical writing scores. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=opinion%20essay" title="opinion essay">opinion essay</a>, <a href="https://publications.waset.org/abstracts/search?q=self-regulation" title=" self-regulation"> self-regulation</a>, <a href="https://publications.waset.org/abstracts/search?q=technical%20writing" title=" technical writing"> technical writing</a>, <a href="https://publications.waset.org/abstracts/search?q=writing%20skills" title=" writing skills"> writing skills</a> </p> <a href="https://publications.waset.org/abstracts/98869/self-regulation-in-composition-writing-the-case-of-variation-of-self-regulation-dispositions-in-opinion-essay-and-technical-writing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98869.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">182</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">17694</span> Smart Trash Can Interface between Origin and Destination Waste Management</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatemeh%20Ghorbani">Fatemeh Ghorbani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The increase in population in the cities has also led to the increase and accumulation of urban waste. Managing and organizing waste is an action that must be taken to prevent environmental pollution. Separation of waste from the source is the first step that must be taken to determine whether the waste should be buried, burned, recycled, or used in the industry according to its type. Separation of trash is a cultural work that the general public must learn the necessity of doing; then, it is necessary to provide suitable conditions for collecting this waste. It is necessary to put segregated garbage cans in the city so that people can put the garbage in the right place. In this research, a smart trash can has been designed, which is connected to the central system of the municipality and has information on the units of each neighborhood separately. By entering the postal code on the page connected to each bin and entering the type of waste, the section related to the waste in the existing bin is opened and the person places the waste in the desired section. In addition, all the bins are connected to the municipal system, and the sensors in it warn each relevant body about the fullness and emptiness of the bins. Also, people can know how full and empty the bins around their building are through the designed application connected to the system. In this way, each organization collects its desired waste, wet and dry waste are separated from the beginning, and city pollution and unpleasant odors are also prevented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=connector" title="connector">connector</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20trash%20can" title=" smart trash can"> smart trash can</a>, <a href="https://publications.waset.org/abstracts/search?q=waste%20management" title=" waste management"> waste management</a> </p> <a href="https://publications.waset.org/abstracts/165235/smart-trash-can-interface-between-origin-and-destination-waste-management" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165235.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">66</span> </span> 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