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segmentation</title> <meta name="description" content="Search results for: customer segmentation"> <meta name="keywords" content="customer segmentation"> <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="customer segmentation"> <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> 1423</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: customer segmentation</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1423</span> Business Intelligence for Profiling of Telecommunication Customer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rokhmatul%20Insani">Rokhmatul Insani</a>, <a href="https://publications.waset.org/abstracts/search?q=Hira%20Laksmiwati%20Soemitro"> Hira Laksmiwati Soemitro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Business Intelligence is a methodology that exploits the data to produce information and knowledge systematically, business intelligence can support the decision-making process. Some methods in business intelligence are data warehouse and data mining. A data warehouse can store historical data from transactional data. For data modelling in data warehouse, we apply dimensional modelling by Kimball. While data mining is used to extracting patterns from the data and get insight from the data. Data mining has many techniques, one of which is segmentation. For profiling of telecommunication customer, we use customer segmentation according to customer’s usage of services, customer invoice and customer payment. Customers can be grouped according to their characteristics and can be identified the profitable customers. We apply K-Means Clustering Algorithm for segmentation. The input variable for that algorithm we use RFM (Recency, Frequency and Monetary) model. All process in data mining, we use tools IBM SPSS modeller. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20intelligence" title="business intelligence">business intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20segmentation" title=" customer segmentation"> customer segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20warehouse" title=" data warehouse"> data warehouse</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/46969/business-intelligence-for-profiling-of-telecommunication-customer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46969.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">483</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">1422</span> A Product-Specific/Unobservable Approach to Segmentation for a Value Expressive Credit Card Service</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Manfred%20F.%20Maute">Manfred F. Maute</a>, <a href="https://publications.waset.org/abstracts/search?q=Olga%20Naumenko"> Olga Naumenko</a>, <a href="https://publications.waset.org/abstracts/search?q=Raymond%20T.%20Kong"> Raymond T. Kong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Using data from a nationally representative financial panel of Canadian households, this study develops a psychographic segmentation of the customers of a value-expressive credit card service and tests for effects on relational response differences. The variety of segments elicited by agglomerative and k means clustering and the familiar profiles of individual clusters suggest that the face validity of the psychographic segmentation was quite high. Segmentation had a significant effect on customer satisfaction and relationship depth. However, when socio-demographic characteristics like household size and income were accounted for in the psychographic segmentation, the effect on relational response differences was magnified threefold. Implications for the segmentation of financial services markets are considered. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=customer%20satisfaction" title="customer satisfaction">customer satisfaction</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20services" title=" financial services"> financial services</a>, <a href="https://publications.waset.org/abstracts/search?q=psychographics" title=" psychographics"> psychographics</a>, <a href="https://publications.waset.org/abstracts/search?q=response%20differences" title=" response differences"> response differences</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a> </p> <a href="https://publications.waset.org/abstracts/39282/a-product-specificunobservable-approach-to-segmentation-for-a-value-expressive-credit-card-service" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39282.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">334</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">1421</span> Market Segmentation and Conjoint Analysis for Apple Family Design</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abbas%20Al-Refaie">Abbas Al-Refaie</a>, <a href="https://publications.waset.org/abstracts/search?q=Nour%20Bata"> Nour Bata</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A distributor of Apple products' experiences numerous difficulties in developing marketing strategies for new and existing mobile product entries that maximize customer satisfaction and the firm's profitability. This research, therefore, integrates market segmentation in platform-based product family design and conjoint analysis to identify iSystem combinations that increase customer satisfaction and business profits. First, the enhanced market segmentation grid is created. Then, the estimated demand model is formulated. Finally, the profit models are constructed then used to determine the ideal product family design that maximizes profit. Conjoint analysis is used to explore customer preferences with their satisfaction levels. A total of 200 surveys are collected about customer preferences. Then, simulation is used to determine the importance values for each attribute. Finally, sensitivity analysis is conducted to determine the product family design that maximizes both objectives. In conclusion, the results of this research shall provide great support to Apple distributors in determining the best marketing strategies that enhance their market share. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=market%20segmentation" title="market segmentation">market segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=conjoint%20analysis" title=" conjoint analysis"> conjoint analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=market%20strategies" title=" market strategies"> market strategies</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/39461/market-segmentation-and-conjoint-analysis-for-apple-family-design" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39461.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">1420</span> Receptiveness of Market Segmentation Towards Online Shopping Attitude: A Quality Management Strategy for Online Passenger Car Market</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Noor%20Hasmini%20Abdghani">Noor Hasmini Abdghani</a>, <a href="https://publications.waset.org/abstracts/search?q=Nik%20Kamariah%20Nikmat"> Nik Kamariah Nikmat</a>, <a href="https://publications.waset.org/abstracts/search?q=Nor%20Hayati%20Ahmad"> Nor Hayati Ahmad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Rapid growth of the internet technology led to changes in the consumer lifestyles. This involved customer buying behaviour-based internet that create new kind of buying strategy. Hence, it has summoned many of world firms including Malaysia to generate new quality strategy in preparation to face new customer buying lifestyles. Particularly, this study focused on identifying online customer segment of automobile passenger car customers. Secondly, the objective is to understand online customer’s receptiveness towards internet technologies. This study distributed 700 questionnaires whereby 582 were returned representing 83% response rate. The data were analysed using factor and regression analyses. The result from the factor analysis precipitates four online passenger car segmentations in Malaysia, which are: Segment (1)- Automobile Online shopping Preferences, Segment (2)- Automobile Online Brand Comparison, Segment (3)- Automobile Online Information Seeking and Segment (4)- Automobile Offline Shopping Preferences. In understanding the online customer’s receptiveness towards internet, the regression result shows that there is significant relationship between each of four segments of online passenger car customer with attitude towards automobile online shopping. This implies that, for online customers to have receptiveness toward internet technologies, he or she must have preferences toward online shopping or at least prefer to browse any related information online even if the actual purchase is made at the traditional store. With this proposed segmentation strategy, the firms especially the automobile firms will be able to understand their online customer behavior. At least, the proposed segmentation strategy will help the firms to strategize quality management approach for their online customers’ buying decision making. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Automobile" title="Automobile">Automobile</a>, <a href="https://publications.waset.org/abstracts/search?q=Market%20Segmentation" title=" Market Segmentation"> Market Segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=Online%20Shopping%20Attitude" title=" Online Shopping Attitude"> Online Shopping Attitude</a>, <a href="https://publications.waset.org/abstracts/search?q=Quality%20Management%20Strategy" title=" Quality Management Strategy"> Quality Management Strategy</a> </p> <a href="https://publications.waset.org/abstracts/23966/receptiveness-of-market-segmentation-towards-online-shopping-attitude-a-quality-management-strategy-for-online-passenger-car-market" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23966.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">540</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">1419</span> Comparative Analysis of Predictive Models for Customer Churn Prediction in the Telecommunication Industry</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Deepika%20Christopher">Deepika Christopher</a>, <a href="https://publications.waset.org/abstracts/search?q=Garima%20Anand"> Garima Anand</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To determine the best model for churn prediction in the telecom industry, this paper compares 11 machine learning algorithms, namely Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, XGBoost, LightGBM, Cat Boost, AdaBoost, Extra Trees, Deep Neural Network, and Hybrid Model (MLPClassifier). It also aims to pinpoint the top three factors that lead to customer churn and conducts customer segmentation to identify vulnerable groups. According to the data, the Logistic Regression model performs the best, with an F1 score of 0.6215, 81.76% accuracy, 68.95% precision, and 56.57% recall. The top three attributes that cause churn are found to be tenure, Internet Service Fiber optic, and Internet Service DSL; conversely, the top three models in this article that perform the best are Logistic Regression, Deep Neural Network, and AdaBoost. The K means algorithm is applied to establish and analyze four different customer clusters. This study has effectively identified customers that are at risk of churn and may be utilized to develop and execute strategies that lower customer attrition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=attrition" title="attrition">attrition</a>, <a href="https://publications.waset.org/abstracts/search?q=retention" title=" retention"> retention</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20modeling" title=" predictive modeling"> predictive modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20segmentation" title=" customer segmentation"> customer segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=telecommunications" title=" telecommunications"> telecommunications</a> </p> <a href="https://publications.waset.org/abstracts/184400/comparative-analysis-of-predictive-models-for-customer-churn-prediction-in-the-telecommunication-industry" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184400.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">1418</span> Analysis Customer Loyalty Characteristic and Segmentation Analysis in Mobile Phone Category in Indonesia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20B.%20Robert">A. B. Robert</a>, <a href="https://publications.waset.org/abstracts/search?q=Adam%20Pramadia"> Adam Pramadia</a>, <a href="https://publications.waset.org/abstracts/search?q=Calvin%20Andika"> Calvin Andika</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main purpose of this study is to explore consumer loyalty characteristic of mobile phone category in Indonesia. Second, this research attempts to identify consumer segment and to explore their profile in each segment as the basis of marketing strategy formulation. This study used some tools of multivariate analysis such as discriminant analysis and cluster analysis. Discriminate analysis used to discriminate consumer loyal and not loyal by using particular variables. Cluster analysis used to reveal various segment in mobile phone category. In addition to having better customer understanding in each segment, this study used descriptive analysis and cross tab analysis in each segment defined by cluster analysis. This study expected several findings. First, consumer can be divided into two large group of loyal versus not loyal by set of variables. Second, this study identifies customer segment in mobile phone category. Third, exploring customer profile in each segment that has been identified. This study answer a call for additional empirical research into different product categories. Therefore, a replication research is advisable. By knowing the customer loyalty characteristic, and deep analysis of their consumption behavior and profile for each segment, this study is very advisable for high impact marketing strategy development. This study contributes body of knowledge by adding empirical study of consumer loyalty, segmentation analysis in mobile phone category by multiple brand analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=customer%20loyalty" title="customer loyalty">customer loyalty</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=marketing%20strategy" title=" marketing strategy"> marketing strategy</a>, <a href="https://publications.waset.org/abstracts/search?q=discriminant%20analysis" title=" discriminant analysis"> discriminant analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=cluster%20analysis" title=" cluster analysis"> cluster analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20phone" title=" mobile phone"> mobile phone</a> </p> <a href="https://publications.waset.org/abstracts/18609/analysis-customer-loyalty-characteristic-and-segmentation-analysis-in-mobile-phone-category-in-indonesia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18609.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">596</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">1417</span> Differentiation of Customer Types by Stereotypical Characteristics for Modular and Conventional Construction Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Peter%20Schnell">Peter Schnell</a>, <a href="https://publications.waset.org/abstracts/search?q=Phillip%20Haag"> Phillip Haag</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the course of the structural transformation of the construction industry, the integration of industrialization and digitization has led to the development of construction methods with an increased degree of prefabrication, such as system or modular construction. Compared to conventional construction, these innovative construction methods are characterized by modified structural and procedural properties and expand the range of construction services. Faced with the supply side, it is possible to identify construction-specific customer types with different characteristics and certain preferences as far as the choice of construction method is concerned. The basis for this finding was qualitative expert interviews. By evaluating the stereotypical customer needs, a corresponding segmentation of the demand side can be made along with the basic orientation and decision behavior. This demarcation supports the target- and needs-oriented customer approach and contributes to cooperative and successful project management. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=differentiation%20of%20customer%20types" title="differentiation of customer types">differentiation of customer types</a>, <a href="https://publications.waset.org/abstracts/search?q=modular%20construction%20methods" title=" modular construction methods"> modular construction methods</a>, <a href="https://publications.waset.org/abstracts/search?q=conventional%20construction%20methods" title=" conventional construction methods"> conventional construction methods</a>, <a href="https://publications.waset.org/abstracts/search?q=stereotypical%20customer%20types" title=" stereotypical customer types"> stereotypical customer types</a> </p> <a href="https://publications.waset.org/abstracts/152514/differentiation-of-customer-types-by-stereotypical-characteristics-for-modular-and-conventional-construction-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152514.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">110</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">1416</span> Life Stage Customer Segmentation by Fine-Tuning Large Language Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nikita%20Katyal">Nikita Katyal</a>, <a href="https://publications.waset.org/abstracts/search?q=Shaurya%20Uppal"> Shaurya Uppal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper tackles the significant challenge of accurately classifying customers within a retailer’s customer base. Accurate classification is essential for developing targeted marketing strategies that effectively engage this important demographic. To address this issue, we propose a method that utilizes Large Language Models (LLMs). By employing LLMs, we analyze the metadata associated with product purchases derived from historical data to identify key product categories that act as distinguishing factors. These categories, such as baby food, eldercare products, or family-sized packages, offer valuable insights into the likely household composition of customers, including families with babies, families with kids/teenagers, families with pets, households caring for elders, or mixed households. We segment high-confidence customers into distinct categories by integrating historical purchase behavior with LLM-powered product classification. This paper asserts that life stage segmentation can significantly enhance e-commerce businesses’ ability to target the appropriate customers with tailored products and campaigns, thereby augmenting sales and improving customer retention. Additionally, the paper details the data sources, model architecture, and evaluation metrics employed for the segmentation task. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=LLMs" title="LLMs">LLMs</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=product%20tags" title=" product tags"> product tags</a>, <a href="https://publications.waset.org/abstracts/search?q=fine-tuning" title=" fine-tuning"> fine-tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=target%20segments" title=" target segments"> target segments</a>, <a href="https://publications.waset.org/abstracts/search?q=marketing%20communication" title=" marketing communication"> marketing communication</a> </p> <a href="https://publications.waset.org/abstracts/192572/life-stage-customer-segmentation-by-fine-tuning-large-language-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192572.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">23</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">1415</span> A Framework for Customer Knowledge Management (CKM) as a Key Role in Relationship</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mehrnoosh%20Askarizadeh">Mehrnoosh Askarizadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The customer’s value has become obvious for the leading companies in today’s competitive environment. Therefore they are constantly trying to improve their relationship with customers. Customer Knowledge has been recognized as a strategic resource and a key to the success of any company. Talking about the Customer Knowledge Management is closely associated with Knowledge Management and Customer Relationship Management (CRM). Recent studies conducted in the fields of Knowledge Management (KM) and Customer Relationship Management (CRM) has explained that the two approaches can have great synergies. In this paper, our aim is to provide an understanding of Customer Knowledge Management (CKM) as an integrated management approach and competence it requires. We describe CKM as an ongoing process of generating, disseminating and using customer knowledge within an organization and between an organization and its customers. In addition, we propose a comprehensive framework of CKM, the ability to integrate customer knowledge into customer relationship management processes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=e-commerce" title="e-commerce">e-commerce</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20management%20%28KM%29" title=" knowledge management (KM)"> knowledge management (KM)</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20relationship%20management%20%28CRM%29" title=" customer relationship management (CRM)"> customer relationship management (CRM)</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20knowledge%20management%20%28CKM%29" title=" customer knowledge management (CKM)"> customer knowledge management (CKM)</a> </p> <a href="https://publications.waset.org/abstracts/32892/a-framework-for-customer-knowledge-management-ckm-as-a-key-role-in-relationship" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32892.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">557</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">1414</span> Exploring the Relationships between Experiential Marketing, Customer Satisfaction and Customer Loyalty: An Empirical Examination in Konya</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Resul%20%C3%96zt%C3%BCrk">Resul Öztürk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Experiential marketing is one of the marketing approaches that offers an exceptional framework to integrate elements of experience and entertainment in a product or service. Experiential marketing is defined as a memorable experience that goes deeply into the customer’s mind. Besides that, customer satisfaction is defined as an emotional response to the experiences provided by and associated with particular products or services purchased. Thus, experiential marketing activities can affect the level of customer satisfaction and loyalty. In this context, the research aims to explore the relationship among experiential marketing, customer satisfaction and customer loyalty among the cosmetic products customers in Konya. The partial least squares (PLS) method is used to analyse the survey data. The present study’s findings revealed have that experiential marketing has been a significant predictor of customer satisfaction and customer loyalty, and also experiential marketing has a significantly positive effect on customer satisfaction and customer loyalty. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=experiential%20marketing" title="experiential marketing">experiential marketing</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20satisfaction" title=" customer satisfaction"> customer satisfaction</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20loyalty" title=" customer loyalty"> customer loyalty</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20sciences" title=" social sciences"> social sciences</a> </p> <a href="https://publications.waset.org/abstracts/29338/exploring-the-relationships-between-experiential-marketing-customer-satisfaction-and-customer-loyalty-an-empirical-examination-in-konya" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29338.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">476</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">1413</span> A Comparative Study of Medical Image Segmentation Methods for Tumor Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mayssa%20Bensalah">Mayssa Bensalah</a>, <a href="https://publications.waset.org/abstracts/search?q=Atef%20Boujelben"> Atef Boujelben</a>, <a href="https://publications.waset.org/abstracts/search?q=Mouna%20Baklouti"> Mouna Baklouti</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Abid"> Mohamed Abid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image segmentation has a fundamental role in analysis and interpretation for many applications. The automated segmentation of organs and tissues throughout the body using computed imaging has been rapidly increasing. Indeed, it represents one of the most important parts of clinical diagnostic tools. In this paper, we discuss a thorough literature review of recent methods of tumour segmentation from medical images which are briefly explained with the recent contribution of various researchers. This study was followed by comparing these methods in order to define new directions to develop and improve the performance of the segmentation of the tumour area from medical images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=features%20extraction" title="features extraction">features extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title=" image segmentation"> image segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20images" title=" medical images"> medical images</a>, <a href="https://publications.waset.org/abstracts/search?q=tumor%20detection" title=" tumor detection"> tumor detection</a> </p> <a href="https://publications.waset.org/abstracts/132616/a-comparative-study-of-medical-image-segmentation-methods-for-tumor-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132616.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">167</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">1412</span> Toward Automatic Chest CT Image Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Angely%20Sim%20Jia%20Wun">Angely Sim Jia Wun</a>, <a href="https://publications.waset.org/abstracts/search?q=Sasa%20Arsovski"> Sasa Arsovski</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Numerous studies have been conducted on the segmentation of medical images. Segmenting the lungs is one of the common research topics in those studies. Our research stemmed from the lack of solutions for automatic bone, airway, and vessel segmentation, despite the existence of multiple lung segmentation techniques. Consequently, currently, available software tools used for medical image segmentation do not provide automatic lung, bone, airway, and vessel segmentation. This paper presents segmentation techniques along with an interactive software tool architecture for segmenting bone, lung, airway, and vessel tissues. Additionally, we propose a method for creating binary masks from automatically generated segments. The key contribution of our approach is the technique for automatic image thresholding using adjustable Hounsfield values and binary mask extraction. Generated binary masks can be successfully used as a training dataset for deep-learning solutions in medical image segmentation. In this paper, we also examine the current software tools used for medical image segmentation, discuss our approach, and identify its advantages. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lung%20segmentation" title="lung segmentation">lung segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=binary%20masks" title=" binary masks"> binary masks</a>, <a href="https://publications.waset.org/abstracts/search?q=U-Net" title=" U-Net"> U-Net</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20software%20tools" title=" medical software tools"> medical software tools</a> </p> <a href="https://publications.waset.org/abstracts/168342/toward-automatic-chest-ct-image-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168342.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">98</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">1411</span> Marketing Research and Analysis Improvement Effect on Production</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mina%20Zaky%20Sarofim%20Zaky">Mina Zaky Sarofim Zaky</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Experiential marketing is a form of marketing that offers a unique integration of experiential and entertainment elements into a product or service. Experiential marketing is defined as an unforgettable experience that penetrates the customer's mind. Customer satisfaction is also defined as the emotional response to the experience provided with the purchased product or service. Experiential marketing activities can, therefore, affect the level of customer satisfaction and loyalty. In this context, the study aims to determine the relationship between experiential marketing, customer satisfaction and customer loyalty in cosmetic products in Konya. The least squares method (PLS) was used to analyze the research data. Existing research has shown that experiential marketing is a significant predictor of customer satisfaction and customer loyalty, and that experiential marketing has a positive impact on customer satisfaction and customer loyalty. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=internet" title="internet">internet</a>, <a href="https://publications.waset.org/abstracts/search?q=marketing" title=" marketing"> marketing</a>, <a href="https://publications.waset.org/abstracts/search?q=tourism" title=" tourism"> tourism</a>, <a href="https://publications.waset.org/abstracts/search?q=tourism%20management%20corporate%20responsibility" title=" tourism management corporate responsibility"> tourism management corporate responsibility</a>, <a href="https://publications.waset.org/abstracts/search?q=employee%20organizational%20performance" title=" employee organizational performance"> employee organizational performance</a>, <a href="https://publications.waset.org/abstracts/search?q=internal%20marketing" title=" internal marketing"> internal marketing</a>, <a href="https://publications.waset.org/abstracts/search?q=internal%20customer%20experiential%20marketing" title=" internal customer experiential marketing"> internal customer experiential marketing</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20satisfaction" title=" customer satisfaction"> customer satisfaction</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20loyalty" title=" customer loyalty"> customer loyalty</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20sciences" title=" social sciences"> social sciences</a> </p> <a href="https://publications.waset.org/abstracts/185809/marketing-research-and-analysis-improvement-effect-on-production" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185809.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">1410</span> A Comparison between Different Segmentation Techniques Used in Medical Imaging </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ibtihal%20D.%20Mustafa">Ibtihal D. Mustafa</a>, <a href="https://publications.waset.org/abstracts/search?q=Mawia%20A.%20Hassan"> Mawia A. Hassan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Tumor segmentation from MRI image is important part of medical images experts. This is particularly a challenging task because of the high assorting appearance of tumor tissue among different patients. MRI images are advance of medical imaging because it is give richer information about human soft tissue. There are different segmentation techniques to detect MRI brain tumor. In this paper, different procedure segmentation methods are used to segment brain tumors and compare the result of segmentations by using correlation and structural similarity index (SSIM) to analysis and see the best technique that could be applied to MRI image. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MRI" title="MRI">MRI</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=correlation" title=" correlation"> correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20similarity" title=" structural similarity"> structural similarity</a> </p> <a href="https://publications.waset.org/abstracts/51091/a-comparison-between-different-segmentation-techniques-used-in-medical-imaging" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51091.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">410</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">1409</span> Multidimensional Sports Spectators Segmentation and Social Media Marketing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Schmid">B. Schmid</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Kexel"> C. Kexel</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Djafarova"> E. Djafarova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Understanding consumers is elementary for practitioners in marketing. Consumers of sports events, the sports spectators, are a particularly complex consumer crowd. In order to identify and define their profiles different segmentation approaches can be found in literature, one of them being multidimensional segmentation. Multidimensional segmentation models correspond to the broad range of attitudes, behaviours, motivations and beliefs of sports spectators, other than earlier models. Moreover, in sports there are some well-researched disciplines (e.g. football or North American sports) where consumer profiles and marketing strategies are elaborate and others where no research at all can be found. For example, there is almost no research on athletics spectators. This paper explores the current state of research on sports spectators segmentation. An in-depth literature review provides the framework for a spectators segmentation in athletics. On this basis, additional potential consumer groups and implications for social media marketing will be explored. The findings are the basis for further research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multidimensional%20segmentation" title="multidimensional segmentation">multidimensional segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20media" title=" social media"> social media</a>, <a href="https://publications.waset.org/abstracts/search?q=sports%20marketing" title=" sports marketing"> sports marketing</a>, <a href="https://publications.waset.org/abstracts/search?q=sports%20spectators%20segmentation" title=" sports spectators segmentation"> sports spectators segmentation</a> </p> <a href="https://publications.waset.org/abstracts/47477/multidimensional-sports-spectators-segmentation-and-social-media-marketing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47477.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">307</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">1408</span> Arabic Handwriting Recognition Using Local Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Arif">Mohammed Arif</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdessalam%20Kifouche"> Abdessalam Kifouche</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Optical character recognition (OCR) has a main role in the present time. It's capable to solve many serious problems and simplify human activities. The OCR yields to 70's, since many solutions has been proposed, but unfortunately, it was supportive to nothing but Latin languages. This work proposes a system of recognition of an off-line Arabic handwriting. This system is based on a structural segmentation method and uses support vector machines (SVM) in the classification phase. We have presented a state of art of the characters segmentation methods, after that a view of the OCR area, also we will address the normalization problems we went through. After a comparison between the Arabic handwritten characters & the segmentation methods, we had introduced a contribution through a segmentation algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=OCR" title="OCR">OCR</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=Arabic%20characters" title=" Arabic characters"> Arabic characters</a>, <a href="https://publications.waset.org/abstracts/search?q=PAW" title=" PAW"> PAW</a>, <a href="https://publications.waset.org/abstracts/search?q=post-processing" title=" post-processing"> post-processing</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM" title=" SVM"> SVM</a> </p> <a href="https://publications.waset.org/abstracts/183495/arabic-handwriting-recognition-using-local-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183495.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">71</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">1407</span> Image Segmentation Using 2-D Histogram in RGB Color Space in Digital Libraries </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=El%20Asnaoui%20Khalid">El Asnaoui Khalid</a>, <a href="https://publications.waset.org/abstracts/search?q=Aksasse%20Brahim"> Aksasse Brahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Ouanan%20Mohammed"> Ouanan Mohammed </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an unsupervised color image segmentation method. It is based on a hierarchical analysis of 2-D histogram in RGB color space. This histogram minimizes storage space of images and thus facilitates the operations between them. The improved segmentation approach shows a better identification of objects in a color image and, at the same time, the system is fast. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title="image segmentation">image segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20analysis" title=" hierarchical analysis"> hierarchical analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=2-D%20histogram" title=" 2-D histogram"> 2-D histogram</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/42096/image-segmentation-using-2-d-histogram-in-rgb-color-space-in-digital-libraries" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42096.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">380</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">1406</span> Imp_hist-Si: Improved Hybrid Image Segmentation Technique for Satellite Imagery to Decrease the Segmentation Error Rate</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Neetu%20Manocha">Neetu Manocha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image segmentation is a technique where a picture is parted into distinct parts having similar features which have a place with similar items. Various segmentation strategies have been proposed as of late by prominent analysts. But, after ultimate thorough research, the novelists have analyzed that generally, the old methods do not decrease the segmentation error rate. Then author finds the technique HIST-SI to decrease the segmentation error rates. In this technique, cluster-based and threshold-based segmentation techniques are merged together. After then, to improve the result of HIST-SI, the authors added the method of filtering and linking in this technique named Imp_HIST-SI to decrease the segmentation error rates. The goal of this research is to find a new technique to decrease the segmentation error rates and produce much better results than the HIST-SI technique. For testing the proposed technique, a dataset of Bhuvan – a National Geoportal developed and hosted by ISRO (Indian Space Research Organisation) is used. Experiments are conducted using Scikit-image & OpenCV tools of Python, and performance is evaluated and compared over various existing image segmentation techniques for several matrices, i.e., Mean Square Error (MSE) and Peak Signal Noise Ratio (PSNR). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=satellite%20image" title="satellite image">satellite image</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title=" image segmentation"> image segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=edge%20detection" title=" edge detection"> edge detection</a>, <a href="https://publications.waset.org/abstracts/search?q=error%20rate" title=" error rate"> error rate</a>, <a href="https://publications.waset.org/abstracts/search?q=MSE" title=" MSE"> MSE</a>, <a href="https://publications.waset.org/abstracts/search?q=PSNR" title=" PSNR"> PSNR</a>, <a href="https://publications.waset.org/abstracts/search?q=HIST-SI" title=" HIST-SI"> HIST-SI</a>, <a href="https://publications.waset.org/abstracts/search?q=linking" title=" linking"> linking</a>, <a href="https://publications.waset.org/abstracts/search?q=filtering" title=" filtering"> filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=imp_HIST-SI" title=" imp_HIST-SI"> imp_HIST-SI</a> </p> <a href="https://publications.waset.org/abstracts/149905/imp-hist-si-improved-hybrid-image-segmentation-technique-for-satellite-imagery-to-decrease-the-segmentation-error-rate" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149905.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">140</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">1405</span> Performance Evaluation of Various Segmentation Techniques on MRI of Brain Tissue</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=U.V.%20Suryawanshi">U.V. Suryawanshi</a>, <a href="https://publications.waset.org/abstracts/search?q=S.S.%20Chowhan"> S.S. Chowhan</a>, <a href="https://publications.waset.org/abstracts/search?q=U.V%20Kulkarni"> U.V Kulkarni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Accuracy of segmentation methods is of great importance in brain image analysis. Tissue classification in Magnetic Resonance brain images (MRI) is an important issue in the analysis of several brain dementias. This paper portraits performance of segmentation techniques that are used on Brain MRI. A large variety of algorithms for segmentation of Brain MRI has been developed. The objective of this paper is to perform a segmentation process on MR images of the human brain, using Fuzzy c-means (FCM), Kernel based Fuzzy c-means clustering (KFCM), Spatial Fuzzy c-means (SFCM) and Improved Fuzzy c-means (IFCM). The review covers imaging modalities, MRI and methods for noise reduction and segmentation approaches. All methods are applied on MRI brain images which are degraded by salt-pepper noise demonstrate that the IFCM algorithm performs more robust to noise than the standard FCM algorithm. We conclude with a discussion on the trend of future research in brain segmentation and changing norms in IFCM for better results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title="image segmentation">image segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=preprocessing" title=" preprocessing"> preprocessing</a>, <a href="https://publications.waset.org/abstracts/search?q=MRI" title=" MRI"> MRI</a>, <a href="https://publications.waset.org/abstracts/search?q=FCM" title=" FCM"> FCM</a>, <a href="https://publications.waset.org/abstracts/search?q=KFCM" title=" KFCM"> KFCM</a>, <a href="https://publications.waset.org/abstracts/search?q=SFCM" title=" SFCM"> SFCM</a>, <a href="https://publications.waset.org/abstracts/search?q=IFCM" title=" IFCM"> IFCM</a> </p> <a href="https://publications.waset.org/abstracts/12406/performance-evaluation-of-various-segmentation-techniques-on-mri-of-brain-tissue" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12406.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">331</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1404</span> Maximizing Customer Service through Logistics Service Support in the Automobile Industry in Ghana</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=John%20M.%20Frimpong">John M. Frimpong</a>, <a href="https://publications.waset.org/abstracts/search?q=Matilda%20K.%20Owusu-Bio"> Matilda K. Owusu-Bio</a>, <a href="https://publications.waset.org/abstracts/search?q=Caleb%20%20Annan"> Caleb Annan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Business today is highly competitive, and the automobile industry is no exception. Therefore, it is necessary to determine the customer value and service quality measures that lead to customer satisfaction which in turn lead to customer loyalty. However, in the automobile industry, the role of logistics service support in these relationships cannot be undermined. It could be inferred that logistics service supports and its management has a direct correlation with customer service and or service quality. But this is not always the same for all industries. Therefore, this study was to investigate how automobile companies implement the concept of customer service through logistics service supports. In order to ascertain this, two automobile companies in Ghana were selected, and these are Toyota Ghana Limited and Mechanical Lloyd Company Ltd. The study developed a conceptual model to depict the study’s objectives from which questionnaires were developed from for data collection. Respondents were made up of customers and staff of the two companies. The findings of the study revealed that the automobile industry partly attributes their customer satisfaction to the customer value, service quality or customer value. It shows a positive relationship between logistics service supports and service quality and customer value. However, the results indicate that customer satisfaction is not predicted by logistics services. This implies that in the automobile industry, it is not always the case that when customer service is implemented through logistics service supports, it leads to customer satisfaction. Therefore, there is the need for all players and stakeholders in the automobile industry investigate other factors which help to increase customer satisfaction in addition to logistics service supports. It is recommended that logistics service supports should be geared towards meeting customer expectations and not just based on the organization’s standards and procedures. It is necessary to listen to the voice of the customer to tailor the service package to suit the needs and expectations of the customer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=customer%20loyalty" title="customer loyalty">customer loyalty</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20satisfaction" title=" customer satisfaction"> customer satisfaction</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20service" title=" customer service"> customer service</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20value" title=" customer value"> customer value</a>, <a href="https://publications.waset.org/abstracts/search?q=logistics%20service%20supports" title=" logistics service supports"> logistics service supports</a> </p> <a href="https://publications.waset.org/abstracts/66124/maximizing-customer-service-through-logistics-service-support-in-the-automobile-industry-in-ghana" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66124.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">494</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">1403</span> Strategies in Customer Relationship Management and Customers’ Behavior in Making Decision on Buying Car Insurance of Southeast Insurance Co. Ltd. in Bangkok </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nattapong%20Techarattanased">Nattapong Techarattanased</a>, <a href="https://publications.waset.org/abstracts/search?q=Paweena%20Sribunrueng"> Paweena Sribunrueng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The objective of this study is to investigate strategies in customer relationship management and customers’ behavior in making decision on buying car insurance of Southeast Insurance Co. Ltd. in Bangkok. Subjects in this study included 400 customers with the age over 20 years old to complete questionnaires. The data were analyzed by arithmetic mean and multiple regressions. The results revealed that the customers’ opinions on the strategies in customer relationship management, i.e. customer relationship, customer feedback, customer follow-up, useful service suggestions, customer communication, and service channels were in moderate level but on the customer retention was in high level. Moreover, the strategy in customer relationship management, i.e. customer relationship, and customer feedback had an influence on customers’ buying decision on buying car insurance. The two factors above can be used for the prediction at the rate of 34%. In addition, the strategy in customer relationship management, i.e. customer retention, customer feedback, and useful service suggestions had an influence on the customers’ buying decision on period of being customers. The three factors could be used for the prediction at the rate of 45%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=strategies" title="strategies">strategies</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20relationship%20management" title=" customer relationship management"> customer relationship management</a>, <a href="https://publications.waset.org/abstracts/search?q=behavior%20in%20buying%20decision" title=" behavior in buying decision"> behavior in buying decision</a>, <a href="https://publications.waset.org/abstracts/search?q=car%20insurance" title=" car insurance"> car insurance</a> </p> <a href="https://publications.waset.org/abstracts/11837/strategies-in-customer-relationship-management-and-customers-behavior-in-making-decision-on-buying-car-insurance-of-southeast-insurance-co-ltd-in-bangkok" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11837.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">405</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">1402</span> Measurements of Service Quality vs Customer Satisfaction in Government Owned Retail Store at Kochi</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20S.%20Ajisha">N. S. Ajisha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In today’s competitive world the quality of the service you deliver is one of the important factor that determine customer satisfaction. Service quality is considered to be one important determinant to evaluate customer satisfaction and the relationship between service quality and customer satisfaction is considered as the foundation in researches on customer satisfaction. This research examines to do a gap analysis between the perception and expectation of the services delivered and find relation between the service quality and customer satisfaction. Service quality is found out here using the SERVQUAL model. And it finds out the dimension of service quality which is more important to measure customer satisfaction. The dimensions which we measure using SERVQUAL include the tangibles, reliability, responsiveness, assurance, and empathy. This study involves primary data collection like market survey. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=customer%20satisfaction" title="customer satisfaction">customer satisfaction</a>, <a href="https://publications.waset.org/abstracts/search?q=service%20quality" title=" service quality"> service quality</a>, <a href="https://publications.waset.org/abstracts/search?q=retail%20service%20quality" title=" retail service quality"> retail service quality</a>, <a href="https://publications.waset.org/abstracts/search?q=Kochi" title=" Kochi"> Kochi</a> </p> <a href="https://publications.waset.org/abstracts/29194/measurements-of-service-quality-vs-customer-satisfaction-in-government-owned-retail-store-at-kochi" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29194.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">554</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">1401</span> Sea-Land Segmentation Method Based on the Transformer with Enhanced Edge Supervision</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lianzhong%20Zhang">Lianzhong Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Chao%20Huang"> Chao Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sea-land segmentation is a basic step in many tasks such as sea surface monitoring and ship detection. The existing sea-land segmentation algorithms have poor segmentation accuracy, and the parameter adjustments are cumbersome and difficult to meet actual needs. Also, the current sea-land segmentation adopts traditional deep learning models that use Convolutional Neural Networks (CNN). At present, the transformer architecture has achieved great success in the field of natural images, but its application in the field of radar images is less studied. Therefore, this paper proposes a sea-land segmentation method based on the transformer architecture to strengthen edge supervision. It uses a self-attention mechanism with a gating strategy to better learn relative position bias. Meanwhile, an additional edge supervision branch is introduced. The decoder stage allows the feature information of the two branches to interact, thereby improving the edge precision of the sea-land segmentation. Based on the Gaofen-3 satellite image dataset, the experimental results show that the method proposed in this paper can effectively improve the accuracy of sea-land segmentation, especially the accuracy of sea-land edges. The mean IoU (Intersection over Union), edge precision, overall precision, and F1 scores respectively reach 96.36%, 84.54%, 99.74%, and 98.05%, which are superior to those of the mainstream segmentation models and have high practical application values. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SAR" title="SAR">SAR</a>, <a href="https://publications.waset.org/abstracts/search?q=sea-land%20segmentation" title=" sea-land segmentation"> sea-land segmentation</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=transformer" title=" transformer"> transformer</a> </p> <a href="https://publications.waset.org/abstracts/148759/sea-land-segmentation-method-based-on-the-transformer-with-enhanced-edge-supervision" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148759.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">181</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">1400</span> The Impact of Artificial Intelligence on Marketing Principles and Targets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Felib%20Ayman%20Shawky%20Salem">Felib Ayman Shawky Salem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Experiential marketing means an unforgettable experience that remains deeply anchored in the customer's memory. Furthermore, customer satisfaction is defined as the emotional response to the experiences provided that relate to specific products or services purchased. Therefore, experiential marketing activities can influence the level of customer satisfaction and loyalty. In this context, the study aims to examine the relationship between experiential marketing, customer satisfaction and loyalty of beauty products in Konya. The results of this study showed that experiential marketing is an important indicator of customer satisfaction and loyalty and that experiential marketing has a significant positive impact on customer satisfaction and loyalty. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sponsorship" title="sponsorship">sponsorship</a>, <a href="https://publications.waset.org/abstracts/search?q=marketing%20communication%20theories" title=" marketing communication theories"> marketing communication theories</a>, <a href="https://publications.waset.org/abstracts/search?q=marketing%20communication%20tools%20internet" title=" marketing communication tools internet"> marketing communication tools internet</a>, <a href="https://publications.waset.org/abstracts/search?q=marketing" title=" marketing"> marketing</a>, <a href="https://publications.waset.org/abstracts/search?q=tourism" title=" tourism"> tourism</a>, <a href="https://publications.waset.org/abstracts/search?q=tourism%20management%20corporate%20responsibility" title=" tourism management corporate responsibility"> tourism management corporate responsibility</a>, <a href="https://publications.waset.org/abstracts/search?q=employee%20organizational%20performance" title=" employee organizational performance"> employee organizational performance</a>, <a href="https://publications.waset.org/abstracts/search?q=internal%20marketing" title=" internal marketing"> internal marketing</a>, <a href="https://publications.waset.org/abstracts/search?q=internal%20customer%20experiential%20marketing" title=" internal customer experiential marketing"> internal customer experiential marketing</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20satisfaction" title=" customer satisfaction"> customer satisfaction</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20loyalty" title=" customer loyalty"> customer loyalty</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20sciences." title=" social sciences."> social sciences.</a> </p> <a href="https://publications.waset.org/abstracts/183159/the-impact-of-artificial-intelligence-on-marketing-principles-and-targets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183159.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">68</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">1399</span> Religiosity and Customer Loyalty in Islamic Banking: An Evidence from Pakistan</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Taimoor%20Hassan">Muhammad Taimoor Hassan</a>, <a href="https://publications.waset.org/abstracts/search?q=Kausar%20Abbas"> Kausar Abbas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Islamic Banking Services is one of the growing businesses in financial sector around the globe with 15 to 18 percent growth all over the world and 10-12 percent growth rate in Pakistan. This research study is aimed at measuring the impact of religiosity on the customer loyalty of Islamic Banking in Pakistan. The study has utilized cause and effect research design to assess the impact of religiosity on Islamic Banking. Data from 350 respondents have been collected to meet the purpose of the study. The results revealed that the religiosity has a significant impact on the customer loyalty of Islamic Banking through the customer attitude and customer trust on the sequential model. The results suggest that the religiosity, customer attitude, and customer trust are the interconnected variables which lead to customer loyalty in Islamic Banking of Pakistan. The study is useful in the setting of Pakistan to further increase the customer loyalty for Islamic Banks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Islamic%20banks" title="Islamic banks">Islamic banks</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20loyalty" title=" customer loyalty"> customer loyalty</a>, <a href="https://publications.waset.org/abstracts/search?q=attitude" title=" attitude"> attitude</a>, <a href="https://publications.waset.org/abstracts/search?q=Pakistan" title=" Pakistan "> Pakistan </a> </p> <a href="https://publications.waset.org/abstracts/92887/religiosity-and-customer-loyalty-in-islamic-banking-an-evidence-from-pakistan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92887.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">285</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">1398</span> U-Net Based Multi-Output Network for Lung Disease Segmentation and Classification Using Chest X-Ray Dataset</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jaiden%20X.%20Schraut">Jaiden X. Schraut</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Medical Imaging Segmentation of Chest X-rays is used for the purpose of identification and differentiation of lung cancer, pneumonia, COVID-19, and similar respiratory diseases. Widespread application of computer-supported perception methods into the diagnostic pipeline has been demonstrated to increase prognostic accuracy and aid doctors in efficiently treating patients. Modern models attempt the task of segmentation and classification separately and improve diagnostic efficiency; however, to further enhance this process, this paper proposes a multi-output network that follows a U-Net architecture for image segmentation output and features an additional CNN module for auxiliary classification output. The proposed model achieves a final Jaccard Index of .9634 for image segmentation and a final accuracy of .9600 for classification on the COVID-19 radiography database. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chest%20X-ray" title="chest X-ray">chest X-ray</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=image%20segmentation" title=" image segmentation"> image segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20classification" title=" image classification"> image classification</a> </p> <a href="https://publications.waset.org/abstracts/155537/u-net-based-multi-output-network-for-lung-disease-segmentation-and-classification-using-chest-x-ray-dataset" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155537.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">144</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">1397</span> Digital Retinal Images: Background and Damaged Areas Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eman%20A.%20Gani">Eman A. Gani</a>, <a href="https://publications.waset.org/abstracts/search?q=Loay%20E.%20George"> Loay E. George</a>, <a href="https://publications.waset.org/abstracts/search?q=Faisel%20G.%20Mohammed"> Faisel G. Mohammed</a>, <a href="https://publications.waset.org/abstracts/search?q=Kamal%20H.%20Sager"> Kamal H. Sager</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Digital retinal images are more appropriate for automatic screening of diabetic retinopathy systems. Unfortunately, a significant percentage of these images are poor quality that hinders further analysis due to many factors (such as patient movement, inadequate or non-uniform illumination, acquisition angle and retinal pigmentation). The retinal images of poor quality need to be enhanced before the extraction of features and abnormalities. So, the segmentation of retinal image is essential for this purpose, the segmentation is employed to smooth and strengthen image by separating the background and damaged areas from the overall image thus resulting in retinal image enhancement and less processing time. In this paper, methods for segmenting colored retinal image are proposed to improve the quality of retinal image diagnosis. The methods generate two segmentation masks; i.e., background segmentation mask for extracting the background area and poor quality mask for removing the noisy areas from the retinal image. The standard retinal image databases DIARETDB0, DIARETDB1, STARE, DRIVE and some images obtained from ophthalmologists have been used to test the validation of the proposed segmentation technique. Experimental results indicate the introduced methods are effective and can lead to high segmentation accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=retinal%20images" title="retinal images">retinal images</a>, <a href="https://publications.waset.org/abstracts/search?q=fundus%20images" title=" fundus images"> fundus images</a>, <a href="https://publications.waset.org/abstracts/search?q=diabetic%20retinopathy" title=" diabetic retinopathy"> diabetic retinopathy</a>, <a href="https://publications.waset.org/abstracts/search?q=background%20segmentation" title=" background segmentation"> background segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=damaged%20areas%20segmentation" title=" damaged areas segmentation"> damaged areas segmentation</a> </p> <a href="https://publications.waset.org/abstracts/12289/digital-retinal-images-background-and-damaged-areas-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12289.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">403</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">1396</span> An Image Segmentation Algorithm for Gradient Target Based on Mean-Shift and Dictionary Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yanwen%20Li">Yanwen Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Shuguo%20Xie"> Shuguo Xie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In electromagnetic imaging, because of the diffraction limited system, the pixel values could change slowly near the edge of the image targets and they also change with the location in the same target. Using traditional digital image segmentation methods to segment electromagnetic gradient images could result in lots of errors because of this change in pixel values. To address this issue, this paper proposes a novel image segmentation and extraction algorithm based on Mean-Shift and dictionary learning. Firstly, the preliminary segmentation results from adaptive bandwidth Mean-Shift algorithm are expanded, merged and extracted. Then the overlap rate of the extracted image block is detected before determining a segmentation region with a single complete target. Last, the gradient edge of the extracted targets is recovered and reconstructed by using a dictionary-learning algorithm, while the final segmentation results are obtained which are very close to the gradient target in the original image. Both the experimental results and the simulated results show that the segmentation results are very accurate. The Dice coefficients are improved by 70% to 80% compared with the Mean-Shift only method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gradient%20image" title="gradient image">gradient image</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation%20and%20extract" title=" segmentation and extract"> segmentation and extract</a>, <a href="https://publications.waset.org/abstracts/search?q=mean-shift%20algorithm" title=" mean-shift algorithm"> mean-shift algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=dictionary%20iearning" title=" dictionary iearning"> dictionary iearning</a> </p> <a href="https://publications.waset.org/abstracts/74979/an-image-segmentation-algorithm-for-gradient-target-based-on-mean-shift-and-dictionary-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74979.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">266</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1395</span> The Role of Brand Experience in Customer Satisfaction and Customer Loyalty in Ayandeh Bank Branches in Tehran</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Reza%20Agha%20Seyed%20Hosseini">Seyed Reza Agha Seyed Hosseini</a>, <a href="https://publications.waset.org/abstracts/search?q=Nicolas%20Hamelin"> Nicolas Hamelin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many marketing executives are looking for a comprehensive plan for delivering quality services and products that will create a distinct and unforgettable long-term experience for customers in dealing with their brand. Various brand management experts believe that a company looking to enhance its brand experience in the minds of customers should have a plan to increase customer satisfaction as well as customer loyalty. The purpose of this research was to investigate the role of brand experience in customer satisfaction and customer loyalty in Ayandeh Bank branches in Tehran. The study employed a quantitative methodology. For data gathering, a questionnaire was utilised to measure all the variables of the research. The statistical population of the study consisted of all the customers of Ayandeh Bank branches in Tehran, and the study data was gathered from 400 respondents. The findings indicate that brand experience has a direct and meaningful impact on customer satisfaction and customer loyalty, and, furthermore, that customer satisfaction has a direct and significant effect on customer loyalty in the branches of Ayandeh Bank in Tehran. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brand%20experience" title="brand experience">brand experience</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20satisfaction" title=" customer satisfaction"> customer satisfaction</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20loyalty" title=" customer loyalty"> customer loyalty</a>, <a href="https://publications.waset.org/abstracts/search?q=bank" title=" bank"> bank</a> </p> <a href="https://publications.waset.org/abstracts/131290/the-role-of-brand-experience-in-customer-satisfaction-and-customer-loyalty-in-ayandeh-bank-branches-in-tehran" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131290.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">258</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">1394</span> Effective Texture Features for Segmented Mammogram Images Based on Multi-Region of Interest Segmentation Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ramayanam%20Suresh">Ramayanam Suresh</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Nagaraja%20Rao"> A. Nagaraja Rao</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Eswara%20Reddy"> B. Eswara Reddy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Texture features of mammogram images are useful for finding masses or cancer cases in mammography, which have been used by radiologists. Textures are greatly succeeded for segmented images rather than normal images. It is necessary to perform segmentation for exclusive specification of cancer and non-cancer regions separately. Region of interest (ROI) is most commonly used technique for mammogram segmentation. Limitation of this method is that it is unable to explore segmentation for large collection of mammogram images. Therefore, this paper is proposed multi-ROI segmentation for addressing the above limitation. It supports greatly in finding the best texture features of mammogram images. Experimental study demonstrates the effectiveness of proposed work using benchmarked images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=texture%20features" title="texture features">texture features</a>, <a href="https://publications.waset.org/abstracts/search?q=region%20of%20interest" title=" region of interest"> region of interest</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-ROI%20segmentation" title=" multi-ROI segmentation"> multi-ROI segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=benchmarked%20images" title=" benchmarked images "> benchmarked images </a> </p> <a href="https://publications.waset.org/abstracts/88666/effective-texture-features-for-segmented-mammogram-images-based-on-multi-region-of-interest-segmentation-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88666.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">310</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=customer%20segmentation&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=customer%20segmentation&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=customer%20segmentation&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=customer%20segmentation&page=5">5</a></li> <li class="page-item"><a class="page-link" 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