1 10 6 https://repository.horizon.ac.id/files/original/84e7b2c7bd4d09414563ba1180f1a112.pdf 44180f6c024b12067b5b90b91c505b7a Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 10 NO. 3 (2021) MAY 2021 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource The Impact of Coporate Website on Dissemination of Research Information Among Stakeholders in Nigeria Subject The topic of the resource Website, Health Institute, Economics/social Institute, web portal and authority. Description An account of the resource This study accesses the influence of an official website as one that has been licensed by using an authority to signify itself or its houses online. Individuals, companies, governments, and different organizations can be such an authority. An internet portal is a web-based platform that gives employees, clients and suppliers with a single get right of entry to factor to information.8 A web portal can be used to supply the consumer with customized data such as employee training, protection manuals or a customer profile. A web portal can additionally be used to beautify the collaboration of information and improve the way employees, customers and suppliers interact with your commercial enterprise [7]. There are couple of reasons why an MSMEs will seem toward net portal development. This study was once made in two classes of lookup institutes: Health institutes and economic/social institutes. Comparison figures point out that there is no sizable difference in phrases of presence of a respectable website of Health Institutes and Economic/social institutes. Health Institutes have extra capability to diffuse their improvements to public than Economic/social institutes, using their website as a verbal exchange device of lookup findings dissemination. The comparisons of use of professional website, goal audiences were carried out as well as reliability check in percentages to allow conclusive results. Creator An entity primarily responsible for making the resource Chinedum Eunice Chibudike1,Haruna Abdu2,Henry Okwudili Chibudike3 Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource May 2021 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/eb7973618e1ad9fa21255a66434af0c9.pdf c65598d5f1e57f573f407e1ead6319a6 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 10 NO. 3 (2021) MAY 2021 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Dhouib-Matrix-TSP1 Method to Optimize Octagonal Fuzzy Travelling Salesman Problem Using α-Cut Technique Subject The topic of the resource component; Optimization; Dhouib-Matrix-TSP1; Fuzzy Numbers; Travelling Salesman Problem; Approximation Method; Ranking Function; α-Cut Technique Description An account of the resource This paper proposes the optimization of the fuzzy travel salesman problem by using the α-Cut technique as a ranking function and the Dhouib-Matrix-TSP1 as an approximation method. This method is enhanced by the standard deviation metric and obtains a minimal tour in fuzzy environment where all parameters are octagonal fuzzy numbers. Fuzzy numbers are converted into a crisp number thanks to the ranking function α-Cut. The proposed approach in details is discussed and illustrated by a numerical example. This method helps in designing successfully the tour to a salesman on navigation through the distance matrix so that it minimizes the total fuzzy distance. Creator An entity primarily responsible for making the resource Mariem Miledi,Souhail Dhouib,Taicir Loukil Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource May 2021 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/ce841592d42597975ea5bcdecb95cd84.pdf 1b6171f9614b7c70587257f8ca9d9f47 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 10 NO. 3 (2021) MAY 2021 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Deep Learning Classification of Building Types in Northern Cyprus Subject The topic of the resource Deep learning; AlexNet; Neural Network; Northern Cyprus; Apartment; Villa, Real Estate Description An account of the resource Among the areas where AI studies centered on developing models that provide real-time solutions for the real estate industry are real estate price forecasting, building age, and types and design of the building (villa, apartment, floor number). Nevertheless, within the ML sector, DL is an emerging region with an Interest increases every year. As a result, a growing number of DL research are in conferences and papers, models for real estate have begun to emerge. In this study, we present a deep learning method for classification of houses in Northern Cyprus using Convolutional neural network. This work proposes the use of Convolutional neural networks in the classification of houses images. The classification will be based on the house age, house price, number of floors in the house, house type i.e. Villa and Apartment. The first category is Villa versus Apartments class; based on the training dataset of 362 images the class result shows the overall accuracy of 96.40%. The second category is split into two classes according to age of the buildings, namely 0 to 5 years Apartments 6 to 10 years Apartments. This class is to classify the building based on their age and the result shows the accuracy of 87.42%. The third category is villa with roof versus Villa without roof apartments class which also shows the overall accuracy of 87.60%. The fourth category is Villa Price from 10,000 euro to 200,000 Versus Villa Price from 200,000 Euro to above and the result shows the accuracy of 81.84%. The last category consists of three classes namely 2 floor Apartment versus 3 floor Apartment, 2 floor Apartment versus 4 floor Apartment and 2 floor Apartment versus 5 floor Apartment which all shows the accuracy of 83.54%, 82.48% and 84.77% respectively. From the experiments carried out in this thesis and the results obtained we conclude that the main aims and objectives of this thesis which is to used Deep learning in Classification and detection of houses in Northern Cyprus and to test the performance of AlexNet for houses classification was successful. This study will be very significant in creation of smart cities and digitization of real estate sector as the world embrace the used of the vast power of Artificial Intelligence, machine learning and machine vision. Creator An entity primarily responsible for making the resource Mubarak Ahmad Muhammad,Sertan Serte Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource May 2021 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/b1365c4af0e49c3859f43bbf97035d50.pdf 40225bab9a7fc125a865ef13d33ba851 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 10 NO. 3 (2021) MAY 2021 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Web and Social Media Presence in the Hospitality Industry: A Greek Island Case Subject The topic of the resource Social Media, Website, Facebook, Hotels, Greece, Rhodes island, Agritourism I. INTRODUCTION Description An account of the resource The new opportunities offered by emerging technologies for better tourist services have affected the hospitality sector. Specifically, the use of the Web, in general, and the social media influence travelers’ choices. Therefore, it is important for modern hotel businesses to be actively involved and present on the Web and social media. Moreover, COVID-19 outbreak has highlighted the importance for better choices that guarantee safety that must be made in advance. This study tries to investigate the use of the Web and social media by the hospitality sector in Greece using automated evaluation tools. The case study of the Rhodes island is selected as one of the most popular destinations in Greece for both internal and external tourists. Agritourism was also taken into account. Results show that the websites and Facebook are the most preferred tools for online presence, whereas there is low use of Instagram, LinkedIn and Twitter. Creator An entity primarily responsible for making the resource Sotirios Karetsos Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource May 2021 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/594061a40150ec0c1dc13704e7bcac0b.pdf 4216f0f369efa5f1a3224105db79f81f Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 10 NO. 3 (2021) MAY 2021 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Automated Chronic Kidney Disease Detection Model with Knearest Neighbor Subject The topic of the resource Chronic kidney disease, Kidney disease; kidney disease detection; KNN; machine learning Description An account of the resource chronic kidney disease is one of the most common disease in the world today. Kidney disease causes death if the patient is not threated at early stage. One of the challenge in kidney disease treatment is accurate identification of kidney disease at an early stage. Moreover, detecting kidney disease requires experienced nephrologist. However, in developing nations lack of medical specialist or nephrologist for identifying chronic kidney disease makes the problem more challenging. As alternative solution to kidney disease identification, researchers have developed many intelligent models using K-nearest Neighbors (KNN) algorithm. However, the accuracy of the existing KNN model has scope for improvement. Thus, this study proposed KNN based model for accurate identification of kidney disease at early stage. To develop optimized KNN model, we have employed error plot to find most favorable K value to obtain more accurate result than the existing models. To conduct experiments, study employed kidney disease dataset collected form publically available Kaggle data repository for training and testing the proposed model. Finally, we have evaluated the proposed model against predictive accuracy. The experimental result on the proposed model appears to prove that the predictive accuracy of the model is 99.86%. Creator An entity primarily responsible for making the resource Tsehay Admassu Assegie Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource May 2021 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/ef863423d901df2ebedc90786940b2ab.pdf d29c25efbd846b516b6f31aa5b4a73ac Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 10 NO. 3 (2021) MAY 2021 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Comparative Evaluation for Effectiveness Analysis of Policy Based Deep Reinforcement Learning Approaches Subject The topic of the resource eep Reinforcement Learning; Deep Learning; Multi Agent Description An account of the resource Deep Reinforcement Learning (DRL) has proven to be a very strong technique with results in various applications in recent years. Especially the achievements in the studies in the field of robotics show that much more progress will be made in this field. Undoubtedly, policy choices and parameter settings play an active role in the success of DRL. In this study, an analysis has been made on the policies used by examining the DRL studies conducted in recent years. Policies used in the literature are grouped under three different headings: value-based, policy-based and actor-critic. However, the problem of moving a common target using Newton's law of motion of collaborative agents is presented. Trainings are carried out in a frictionless environment with two agents and one object using four different policies. Agents try to force an object in the environment by colliding it and try to move it out of the area it is in. Two-dimensional surface is used during the training phase. As a result of the training, each policy is reported separately and its success is observed. Test results are discussed in section 5. Thus, policies are tested together with an application by providing information about the policies used in deep reinforcement learning approaches. Creator An entity primarily responsible for making the resource Ziya Tan, Mehmet Karaköse Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource May 2021 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text