Dmitro Karlov, Ivan Tupitsya, M. Parkhomenko, O. Musienko, A. Lekakh
The subject of the study in this article is data transmission processes of the video information resource in the information communication systems of the air segment under the conditions of errors in the data transmission channel. The purpose of the article is the development of the method of compression coding in order to ensure an increase in the level of reliability of video information resources under the conditions of errors in communication channels. The following tasks are identified: to develop a method of compression coding using structural decomposition of statistical space; analyze the effectiveness of the developed method from the standpoint of ensuring the required level of reliability. The following results are obtained: the developed method of encoding video information allows increasing the level of reliability in the conditions of the transmission of video information resources in the information communication systems of the air segment due to the localization of the action of errors.
{"title":"Compression Coding Method Using Internal Restructuring of Information Space","authors":"Dmitro Karlov, Ivan Tupitsya, M. Parkhomenko, O. Musienko, A. Lekakh","doi":"10.47839/ijc.21.3.2692","DOIUrl":"https://doi.org/10.47839/ijc.21.3.2692","url":null,"abstract":"The subject of the study in this article is data transmission processes of the video information resource in the information communication systems of the air segment under the conditions of errors in the data transmission channel. The purpose of the article is the development of the method of compression coding in order to ensure an increase in the level of reliability of video information resources under the conditions of errors in communication channels. The following tasks are identified: to develop a method of compression coding using structural decomposition of statistical space; analyze the effectiveness of the developed method from the standpoint of ensuring the required level of reliability. The following results are obtained: the developed method of encoding video information allows increasing the level of reliability in the conditions of the transmission of video information resources in the information communication systems of the air segment due to the localization of the action of errors.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84011239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. R, A. Kanavalli, Anshul Gupta, Ashutosh Pattanaik, Sashank Agarwal
Software Defined Network (SDN) is the new era of networking technology based on a centralized controller that separates the switch hardware from its operating software. The most important challenge is the security of SDN and the most prominent attack is the Distributed Denial of Service (DDoS) attack. Some of the research work done so far detects DDoS attacks using a threshold, which is usually assumed without proper scientific reason and hence may not be always accurate. The mitigation techniques used by some researchers block the host from sending the network traffic beyond a threshold, by installing drop rules in the flow table of the switch connected to that host. Doing so will not only block the attack traffic but also the genuine ones from other applications of that host. In this paper, we propose a model that calculates the threshold limit for the type of applications sending data to a particular switch, in real-time using a machine learning (ML) model, and determines whether that application traffic is DDoS traffic. After the detection, only application type sending DDoS traffic is blocked while other genuine applications are allowed to send the network traffic without any interruption. The use of a dynamic threshold, based on the current network traffic, will help in detecting DDoS efficiently.
{"title":"Real-time DDoS Detection and Mitigation in Software Defined Networks using Machine Learning Techniques","authors":"S. R, A. Kanavalli, Anshul Gupta, Ashutosh Pattanaik, Sashank Agarwal","doi":"10.47839/ijc.21.3.2691","DOIUrl":"https://doi.org/10.47839/ijc.21.3.2691","url":null,"abstract":"Software Defined Network (SDN) is the new era of networking technology based on a centralized controller that separates the switch hardware from its operating software. The most important challenge is the security of SDN and the most prominent attack is the Distributed Denial of Service (DDoS) attack. Some of the research work done so far detects DDoS attacks using a threshold, which is usually assumed without proper scientific reason and hence may not be always accurate. The mitigation techniques used by some researchers block the host from sending the network traffic beyond a threshold, by installing drop rules in the flow table of the switch connected to that host. Doing so will not only block the attack traffic but also the genuine ones from other applications of that host. In this paper, we propose a model that calculates the threshold limit for the type of applications sending data to a particular switch, in real-time using a machine learning (ML) model, and determines whether that application traffic is DDoS traffic. After the detection, only application type sending DDoS traffic is blocked while other genuine applications are allowed to send the network traffic without any interruption. The use of a dynamic threshold, based on the current network traffic, will help in detecting DDoS efficiently.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":"142 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72560563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, five objective measures of the quality of speech signals distorted by reverberation are compared with the Speech Transmission Index (STI). The main aim of the comparison is to further test and explain the reasons for the previously discovered phenomenon of an increase in the speech quality and intelligibility with increasing room size. The comparison is performed for three university classrooms of small, medium and large sizes. The correlation coefficients between the quality and intelligibility estimates of speech obtained for 5-6 points of each room are estimated. Speech signal quality is assessed using intrusive measures such as segmental signal-to-noise ratio (SSNR), log-spectral distortion (LSD), frequency-weighted segmental signal-to-noise ratio (FWSNR), bark spectral distortion (BSD), and perceptual evaluation of speech quality (PESQ). For BSD, high correlation coefficients (0.57-0.99) are determined for rooms of all sizes and an increase in the correlation coefficient with the room size increase is found, which can be explained by a decrease in the density of early sound reflections. For FWSNR, high correlation (0.65-0.98) is determined for medium and large rooms. For PESQ, high correlation (0.96-0.99) is obtained for large classroom. SSNR and LSD are found to be uncorrelated with STI for rooms of all sizes.
{"title":"Impact of University Classroom Size on the Relationship between Speech Quality and Intelligibility","authors":"A. Prodeus, M. Didkovska, Kateryna Kukharicheva","doi":"10.47839/ijc.21.3.2690","DOIUrl":"https://doi.org/10.47839/ijc.21.3.2690","url":null,"abstract":"In this paper, five objective measures of the quality of speech signals distorted by reverberation are compared with the Speech Transmission Index (STI). The main aim of the comparison is to further test and explain the reasons for the previously discovered phenomenon of an increase in the speech quality and intelligibility with increasing room size. The comparison is performed for three university classrooms of small, medium and large sizes. The correlation coefficients between the quality and intelligibility estimates of speech obtained for 5-6 points of each room are estimated. Speech signal quality is assessed using intrusive measures such as segmental signal-to-noise ratio (SSNR), log-spectral distortion (LSD), frequency-weighted segmental signal-to-noise ratio (FWSNR), bark spectral distortion (BSD), and perceptual evaluation of speech quality (PESQ). For BSD, high correlation coefficients (0.57-0.99) are determined for rooms of all sizes and an increase in the correlation coefficient with the room size increase is found, which can be explained by a decrease in the density of early sound reflections. For FWSNR, high correlation (0.65-0.98) is determined for medium and large rooms. For PESQ, high correlation (0.96-0.99) is obtained for large classroom. SSNR and LSD are found to be uncorrelated with STI for rooms of all sizes.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":"694 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81727072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Image segmentation is a fundamental and important step in many computer vision applications. One of the most widely used image segmentation techniques is clustering. It is a process of segmenting the intensities of a non-homogeneous image into homogeneous regions based on their similarity property. However, clustering methods require a prior initialization of random clustering centers and often converge to the local optimum, thanks to the choices of the initial centers, which is a major drawback. Therefore, to overcome this problem, we used the improved version of the sine-cosine algorithm to optimize the traditional clustering techniques to improve the image segmentation results. The proposed method provides better exploration of the search space compared to the original SCA algorithm which only focuses on the best solution to generate a new solution. The proposed ISCA algorithm is able to speed up the convergence and avoid falling into local optima by introducing two mechanisms that take into account the first is the given random position of the search space and the second is the position of the best solution found so far to balance the exploration and exploitation. The performance of the proposed approach was evaluated by comparing several clustering algorithms based on metaheuristics such as the original SCA, genetic algorithms (GA) and particle swarm optimization (PSO). Our evaluation results were analyzed based on the best fitness values of several metrics used in this paper, which demonstrates the high performance of the proposed approach that gives satisfactory results compared to other comparison methods.
{"title":"A Performant Clustering Approach Based on An Improved Sine Cosine Algorithm","authors":"Lahbib Khrissi, N. El Akkad, H. Satori, K. Satori","doi":"10.47839/ijc.21.2.2584","DOIUrl":"https://doi.org/10.47839/ijc.21.2.2584","url":null,"abstract":"Image segmentation is a fundamental and important step in many computer vision applications. One of the most widely used image segmentation techniques is clustering. It is a process of segmenting the intensities of a non-homogeneous image into homogeneous regions based on their similarity property. However, clustering methods require a prior initialization of random clustering centers and often converge to the local optimum, thanks to the choices of the initial centers, which is a major drawback. Therefore, to overcome this problem, we used the improved version of the sine-cosine algorithm to optimize the traditional clustering techniques to improve the image segmentation results. The proposed method provides better exploration of the search space compared to the original SCA algorithm which only focuses on the best solution to generate a new solution. The proposed ISCA algorithm is able to speed up the convergence and avoid falling into local optima by introducing two mechanisms that take into account the first is the given random position of the search space and the second is the position of the best solution found so far to balance the exploration and exploitation. The performance of the proposed approach was evaluated by comparing several clustering algorithms based on metaheuristics such as the original SCA, genetic algorithms (GA) and particle swarm optimization (PSO). Our evaluation results were analyzed based on the best fitness values of several metrics used in this paper, which demonstrates the high performance of the proposed approach that gives satisfactory results compared to other comparison methods.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":"111 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82464134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The educational data mining research attempts have contributed in developing policies to improve student learning in different levels of educational institutions. One of the common challenges to building accurate classification and prediction systems is the imbalanced distribution of classes in the data collected. This study investigates data-level techniques and algorithm-level techniques. Six classifiers from each technique are used to explore their effectiveness to handle the imbalanced data problem while predicting students’ graduation grade based on their performance at the first stage. The classifiers are tested using the k-fold cross-validation approach before and after applying the data-level and algorithm-level techniques. For the purpose of evaluation, various evaluation metrics have been used such as accuracy, precision, recall, and f1-score. The results showed that the classifiers do not perform well with imbalanced dataset, and the performance could be improved by using these techniques. As for the level of improvement, it varies from one technique to another. Additionally, the results of the statistical hypothesis testing confirmed that there were no statistically significant differences for classifiers of the two techniques.
{"title":"Examining Techniques to Solving Imbalanced Datasets in Educational Data Mining Systems","authors":"Ahmed Al-Ashoor, S. Abdullah","doi":"10.47839/ijc.21.2.2589","DOIUrl":"https://doi.org/10.47839/ijc.21.2.2589","url":null,"abstract":"The educational data mining research attempts have contributed in developing policies to improve student learning in different levels of educational institutions. One of the common challenges to building accurate classification and prediction systems is the imbalanced distribution of classes in the data collected. This study investigates data-level techniques and algorithm-level techniques. Six classifiers from each technique are used to explore their effectiveness to handle the imbalanced data problem while predicting students’ graduation grade based on their performance at the first stage. The classifiers are tested using the k-fold cross-validation approach before and after applying the data-level and algorithm-level techniques. For the purpose of evaluation, various evaluation metrics have been used such as accuracy, precision, recall, and f1-score. The results showed that the classifiers do not perform well with imbalanced dataset, and the performance could be improved by using these techniques. As for the level of improvement, it varies from one technique to another. Additionally, the results of the statistical hypothesis testing confirmed that there were no statistically significant differences for classifiers of the two techniques.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77028189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The accuracy of photovoltaic (PV) power generation forecast can seriously affect the penetration ability of PV power into the existing power grid, which is one of the key approaches to achieve emission peak, as well as realize carbon neutrality. In the conventional forecasting methods, Global Horizontal Irradiation (GHI), Diffuse Horizontal Irradiance (DHI), temperature, wind speed, rainfall, etc. are considered as the mainly factors to forecast the PV output power, but ignore the impact of PV power generation caused by the whole PV system’s decay over the 25–30 years lifecycle. The ultraviolet (UV) index, which reflects the quantity of 10–400 nm irradiation, has a strong correlation with such decay and power generation. This paper proposes a novel PV power forecasting model that involving UV index in an artificial neural network, using Adam method to optimize the training process with the Keras-tuner employed for optimization of the hyperparameters. Experiments demonstrate that the proposed model achieves more precise performance than conventional methods.
{"title":"Photovoltaic Power Forecasting based on Artificial Neural Network and Ultraviolet Index","authors":"Li Sun, Yanxia Sun","doi":"10.47839/ijc.21.2.2583","DOIUrl":"https://doi.org/10.47839/ijc.21.2.2583","url":null,"abstract":"The accuracy of photovoltaic (PV) power generation forecast can seriously affect the penetration ability of PV power into the existing power grid, which is one of the key approaches to achieve emission peak, as well as realize carbon neutrality. In the conventional forecasting methods, Global Horizontal Irradiation (GHI), Diffuse Horizontal Irradiance (DHI), temperature, wind speed, rainfall, etc. are considered as the mainly factors to forecast the PV output power, but ignore the impact of PV power generation caused by the whole PV system’s decay over the 25–30 years lifecycle. The ultraviolet (UV) index, which reflects the quantity of 10–400 nm irradiation, has a strong correlation with such decay and power generation. This paper proposes a novel PV power forecasting model that involving UV index in an artificial neural network, using Adam method to optimize the training process with the Keras-tuner employed for optimization of the hyperparameters. Experiments demonstrate that the proposed model achieves more precise performance than conventional methods.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79817219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
V. Kozel, Oleksii Ivanchuk, Ievgeniia Drozdova, O. Prykhodko
The Internet of Things is designed to eliminate or minimize human participation in functioning intelligent devices connected to a network for improving human living conditions and their comfort in different spheres. The rapid expansion of the Internet of Things leads to a steady increase in the number of signaling protocols and data structure protocols being developed and used in the IoT, and thus, it complicates their selection when designing the IoT system. In addition, when designing a wireless IoT network, the problem of selecting an energy-efficient protocol arises, as the constant exchange of data depletes the power supply that IoT devices are equipped with. Thus, human intervention for regular battery maintenance is required. A set of rules and criteria for the selection of optimal combination of protocols when designing the IoT system is proposed. The assessment of distributed protocols according to selected criteria based on the Boolean functions has been conducted. The developed program that enables choosing the optimal combination of protocols has been presented. Automation of the protocol selection process at the initial stage will make it possible to reduce the time for designing the IoT system.
{"title":"Automation of the Protocol Selection Process for IoT Systems","authors":"V. Kozel, Oleksii Ivanchuk, Ievgeniia Drozdova, O. Prykhodko","doi":"10.47839/ijc.21.2.2594","DOIUrl":"https://doi.org/10.47839/ijc.21.2.2594","url":null,"abstract":"The Internet of Things is designed to eliminate or minimize human participation in functioning intelligent devices connected to a network for improving human living conditions and their comfort in different spheres. The rapid expansion of the Internet of Things leads to a steady increase in the number of signaling protocols and data structure protocols being developed and used in the IoT, and thus, it complicates their selection when designing the IoT system. In addition, when designing a wireless IoT network, the problem of selecting an energy-efficient protocol arises, as the constant exchange of data depletes the power supply that IoT devices are equipped with. Thus, human intervention for regular battery maintenance is required. A set of rules and criteria for the selection of optimal combination of protocols when designing the IoT system is proposed. The assessment of distributed protocols according to selected criteria based on the Boolean functions has been conducted. The developed program that enables choosing the optimal combination of protocols has been presented. Automation of the protocol selection process at the initial stage will make it possible to reduce the time for designing the IoT system.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":"76 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74638053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dynamic metrics capture the run time features of object-oriented languages, i.e., runtime polymorphism, dynamic binding, etc., that are not covered by static metrics. Therefore, in this paper, we derived a new approach to measuring the software reusability of a design pattern based on dynamic metrics. To achieve this, the authors proposed a model based on five parameters, i.e., polymorphism, inheritance, number of children, coupling, and complexity, to measure the reusability factor by using various soft computing techniques, i.e., Fuzzy, Neural Network, and Neuro-Fuzzy. Further, we also compared the proposed model with four existing machine learning algorithms. Lastly, we found that the proposed model using the neuro-fuzzy technique is trained well and predicts well with MAE (Mean absolute error) 0.003 and RMSE (Root mean square error) 0.009 based on dynamic metrics. Hence, it is concluded that dynamic metrics are a better predictor of the reusability factor than static metrics.
动态度量捕获了面向对象语言的运行时特性，例如，运行时多态性、动态绑定等，这些都不是静态度量所涵盖的。因此，在本文中，我们推导了一种基于动态度量来度量设计模式的软件可重用性的新方法。为了实现这一目标，作者提出了一个基于多态性、继承性、子节点数、耦合性和复杂性五个参数的模型，利用模糊、神经网络和神经模糊等多种软计算技术来衡量可重用性因子。此外，我们还将所提出的模型与四种现有的机器学习算法进行了比较。最后，我们发现使用神经模糊技术的模型训练良好，并且基于动态指标的MAE (Mean absolute error) 0.003和RMSE (Root Mean square error) 0.009的预测效果良好。因此，可以得出结论，动态度量比静态度量更能预测可重用性因素。
{"title":"Software Reusability Estimation based on Dynamic Metrics using Soft Computing Techniques","authors":"Manju Duhan, P. Bhatia","doi":"10.47839/ijc.21.2.2587","DOIUrl":"https://doi.org/10.47839/ijc.21.2.2587","url":null,"abstract":"Dynamic metrics capture the run time features of object-oriented languages, i.e., runtime polymorphism, dynamic binding, etc., that are not covered by static metrics. Therefore, in this paper, we derived a new approach to measuring the software reusability of a design pattern based on dynamic metrics. To achieve this, the authors proposed a model based on five parameters, i.e., polymorphism, inheritance, number of children, coupling, and complexity, to measure the reusability factor by using various soft computing techniques, i.e., Fuzzy, Neural Network, and Neuro-Fuzzy. Further, we also compared the proposed model with four existing machine learning algorithms. Lastly, we found that the proposed model using the neuro-fuzzy technique is trained well and predicts well with MAE (Mean absolute error) 0.003 and RMSE (Root mean square error) 0.009 based on dynamic metrics. Hence, it is concluded that dynamic metrics are a better predictor of the reusability factor than static metrics.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90293376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stéphane Cédric KOUMETIO TEKOUABOU, Walid Cherif, H. Toulni, Elarbi A. Abdelaoui, H. Silkan
Recently, the diversity of data collected on both social networks and digital interfaces is extremely increased. This diversity of data raises the problem of heterogeneous variables that are not favourable to classification algorithms. Although machine learning and predictive analysis have significantly improved the efficiency of the classification in customer relationship management (CRM) systems, their performance remains very limited by heterogeneous data processing. In this paper, we propose a new predictive classification approach well adapted for targeting actual CRM systems. Our approach consists of preprocessing each type of feature and constructing a reduced array. From this reduced array, the class membership computations become very faster and perform the predictive targeting of a new instance great accurately. The results of the experiments carried out on four types of data from the CRMs showed that the proposed algorithm is a good tool for strengthening these systems not only to optimize their loyalty actions but also to efficiently acquire new customers.
{"title":"Using Class Membership based Approach to Improve Predictive Classification in Customer Relationship Management Systems","authors":"Stéphane Cédric KOUMETIO TEKOUABOU, Walid Cherif, H. Toulni, Elarbi A. Abdelaoui, H. Silkan","doi":"10.47839/ijc.21.2.2593","DOIUrl":"https://doi.org/10.47839/ijc.21.2.2593","url":null,"abstract":"Recently, the diversity of data collected on both social networks and digital interfaces is extremely increased. This diversity of data raises the problem of heterogeneous variables that are not favourable to classification algorithms. Although machine learning and predictive analysis have significantly improved the efficiency of the classification in customer relationship management (CRM) systems, their performance remains very limited by heterogeneous data processing. In this paper, we propose a new predictive classification approach well adapted for targeting actual CRM systems. Our approach consists of preprocessing each type of feature and constructing a reduced array. From this reduced array, the class membership computations become very faster and perform the predictive targeting of a new instance great accurately. The results of the experiments carried out on four types of data from the CRMs showed that the proposed algorithm is a good tool for strengthening these systems not only to optimize their loyalty actions but also to efficiently acquire new customers.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86370152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Michael Scholz, S. König, Julia Klein, Judith Gieringer
KINETARIUM is a new platform for interactive, collaborative fulldome shows for hundreds of people. It enables visitors to intervene spontaneously and in real time in what is happening on the dome, by using their own smartphones. Kinetarium introduces interactivity and gamification to the domes, with the visitors becoming fully immersed in the projection, just as if they were right in the middle of things. Everyone in the audience can participate in the show. Together, the players can go on missions, solve puzzles and discover new worlds – or simply try to crack the high score. In addition to dealing with scientific phenomena, the players experience how difficult the simplest tasks can be when joint decision-making, coordination, team work or compromises are required. That way, the games also teach learning processes about group dynamics or social behavior. The planetariums can thus enrich scientific content with playful and group-dynamic elements and make their program more attractive for a young, gaming and 3D savvy audience.
{"title":"KINETARIUM: Interactive Multiplayer Games for Fulldome Projections","authors":"Michael Scholz, S. König, Julia Klein, Judith Gieringer","doi":"10.47839/ijc.21.2.2586","DOIUrl":"https://doi.org/10.47839/ijc.21.2.2586","url":null,"abstract":"KINETARIUM is a new platform for interactive, collaborative fulldome shows for hundreds of people. It enables visitors to intervene spontaneously and in real time in what is happening on the dome, by using their own smartphones. Kinetarium introduces interactivity and gamification to the domes, with the visitors becoming fully immersed in the projection, just as if they were right in the middle of things. Everyone in the audience can participate in the show. Together, the players can go on missions, solve puzzles and discover new worlds – or simply try to crack the high score. In addition to dealing with scientific phenomena, the players experience how difficult the simplest tasks can be when joint decision-making, coordination, team work or compromises are required. That way, the games also teach learning processes about group dynamics or social behavior. The planetariums can thus enrich scientific content with playful and group-dynamic elements and make their program more attractive for a young, gaming and 3D savvy audience.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85024476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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