Background: The identification of banded patterns in N-dimensional (ND) 0-1 dataset is one where all the elements in the dimensions are arranged such that the one’s entries are ordered about the center of dimensions. Reordering 0-1 datasets in order to determine banded patterns in data, allows for the identification of interesting pattern that are hidden in the data. The challenge is whether or not the identify banded patterns are significant. Previous work on the significance of banded patterns using parametric test was aimed at 2D and 3D banding algorithms, using a single banding algorithm in each case, which meant no work on the significance of banded patterns using different banding algorithms with different dimensions. Aim: To this end, this paper presents a comparison between ND banding algorithms for 2D and 3D. Method: The approach is to use parametric test on synthetic data, UCI and the real datasets taken from the cattle tracing system (CTS). The ND banding algorithms considered for 2D are:2D banding, barycentric (BC) and 2D sort, and for 3D are: exact-Euclidean, exact-Manhattan variations and the approximate. Results: The experimental results presented shows the significance of banded patterns with p value less than 0.05. However, the post-hoc test result shows a statistically significant difference between 2D banding and BC, 2D banding and 2D sort, exact-Euclidean and exact-Manhattan, exact-Euclidean and the approximate but no significant difference between BC and 2D sort as well as exact-Manhattan and the approximate.
{"title":"Using Parametric Test to determine the Significance of Banded patterns in N-dimensional 0-1 dataset","authors":"F. Abdullahi","doi":"10.56471/slujst.v4i.278","DOIUrl":"https://doi.org/10.56471/slujst.v4i.278","url":null,"abstract":"Background: The identification of banded patterns in N-dimensional (ND) 0-1 dataset is one where all the elements in the dimensions are arranged such that the one’s entries are ordered about the center of dimensions. Reordering 0-1 datasets in order to determine banded patterns in data, allows for the identification of interesting pattern that are hidden in the data. The challenge is whether or not the identify banded patterns are significant. Previous work on the significance of banded patterns using parametric test was aimed at 2D and 3D banding algorithms, using a single banding algorithm in each case, which meant no work on the significance of banded patterns using different banding algorithms with different dimensions. Aim: To this end, this paper presents a comparison between ND banding algorithms for 2D and 3D. Method: The approach is to use parametric test on synthetic data, UCI and the real datasets taken from the cattle tracing system (CTS). The ND banding algorithms considered for 2D are:2D banding, barycentric (BC) and 2D sort, and for 3D are: exact-Euclidean, exact-Manhattan variations and the approximate. Results: The experimental results presented shows the significance of banded patterns with p value less than 0.05. However, the post-hoc test result shows a statistically significant difference between 2D banding and BC, 2D banding and 2D sort, exact-Euclidean and exact-Manhattan, exact-Euclidean and the approximate but no significant difference between BC and 2D sort as well as exact-Manhattan and the approximate.","PeriodicalId":299818,"journal":{"name":"SLU Journal of Science and Technology","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125403791","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}
Background: The use of data augmentation techniques to addressing the challenge of network overfitting and classification error is important in deep learning. Insufficient sample data for training have the tendency to bias the trained model so that it fails to generalize well. Several studies have proposed different augmentation techniques to solve this problem. But there are some peculiarities identified with the nature of datasets when applying augmentation methods. The subtle nature of some abnormalities in digital mammography often makes it difficult to transform such datasets into different form, while preserving the structure of the abnormality. Aim: To address this, this study aims to apply a combination of carefully selected data augmentation operations on digital mammography.
{"title":"Data Augmentation-aided Convolutional Neural Network for Detection of Abnormalities in Digital Mammography","authors":"O. N. Oyelade, Ahmed Aminu Sambo","doi":"10.56471/slujst.v4i.270","DOIUrl":"https://doi.org/10.56471/slujst.v4i.270","url":null,"abstract":"Background: The use of data augmentation techniques to addressing the challenge of network overfitting and classification error is important in deep learning. Insufficient sample data for training have the tendency to bias the trained model so that it fails to generalize well. Several studies have proposed different augmentation techniques to solve this problem. But there are some peculiarities identified with the nature of datasets when applying augmentation methods. The subtle nature of some abnormalities in digital mammography often makes it difficult to transform such datasets into different form, while preserving the structure of the abnormality. Aim: To address this, this study aims to apply a combination of carefully selected data augmentation operations on digital mammography.","PeriodicalId":299818,"journal":{"name":"SLU Journal of Science and Technology","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123981910","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}
Background: A Smart City leverages on Information and Communications Technologies (ICTs), and several other infrastructures for improvement of citizens’ quality of life, efficiency in managing all aspects of city’s operations and services. Having the right architecture in developing smart city applications is paramount to achieving the minimum set of Quality Attributes (QAs). Several architectures and frameworks were proposed that are aimed at satisfying different set of QAs. However, there is a little or no effort in developing a product line architecture that satisfies all QAs that are considered common and essential to smart city applications. Aim: This work is aimed at reviewing existing smart city architectures and frameworks to identify the QAs each of these architecture and frameworks satisfy, categorizing these QAs into high level QAs as well as proposing key QAs for smart city. Method: To achieve this objective, a Systematic Literature Review (SLR) was conducted and two research questions (RQs) were defined, and the result was analyzed using descriptive statistics techniques. Results: Sixteen (16) architectures/frameworks were reviewed, and identified eight (8) high-level QAs, among which four (4) were proposed as key Quality Attributes for smart city.
背景:智慧城市利用信息通信技术(ict)和其他一些基础设施来改善市民的生活质量，提高管理城市运营和服务各个方面的效率。在开发智慧城市应用程序时，拥有正确的架构对于实现最小质量属性集(qa)至关重要。提出了几种体系结构和框架，旨在满足不同的qa集。然而，在开发满足所有被认为是智能城市应用程序常见和必要的qa的产品线架构方面，很少或根本不需要付出努力。目的:这项工作旨在回顾现有的智慧城市架构和框架，以确定这些架构和框架所满足的qa，将这些qa分类为高级qa，并提出智慧城市的关键qa。方法:采用系统文献综述(Systematic Literature Review, SLR)，确定两个研究问题(rq)，并采用描述性统计技术对结果进行分析。结果:对16个架构/框架进行了审查，并确定了8个高级qa，其中4个被提出为智慧城市的关键质量属性。
{"title":"Software Architecture for Smart Cities: A Systematic Literature Review of Quality Attributes","authors":"Muhammad Shaheed Abdullahi","doi":"10.56471/slujst.v4i.275","DOIUrl":"https://doi.org/10.56471/slujst.v4i.275","url":null,"abstract":"Background: A Smart City leverages on Information and Communications Technologies (ICTs), and several other infrastructures for improvement of citizens’ quality of life, efficiency in managing all aspects of city’s operations and services. Having the right architecture in developing smart city applications is paramount to achieving the minimum set of Quality Attributes (QAs). Several architectures and frameworks were proposed that are aimed at satisfying different set of QAs. However, there is a little or no effort in developing a product line architecture that satisfies all QAs that are considered common and essential to smart city applications. Aim: This work is aimed at reviewing existing smart city architectures and frameworks to identify the QAs each of these architecture and frameworks satisfy, categorizing these QAs into high level QAs as well as proposing key QAs for smart city. Method: To achieve this objective, a Systematic Literature Review (SLR) was conducted and two research questions (RQs) were defined, and the result was analyzed using descriptive statistics techniques. Results: Sixteen (16) architectures/frameworks were reviewed, and identified eight (8) high-level QAs, among which four (4) were proposed as key Quality Attributes for smart city.","PeriodicalId":299818,"journal":{"name":"SLU Journal of Science and Technology","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127662953","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 performance of electromagnetic wave links in sand and dust storms has received considerable interest of researchers in recent time, especially with emphasis on the signal attenuation. However, phase rotation and cross-polarization have not been sufficiently treated. This work investigates the cross-polarization discrimination as a result of sand and dust storms at high frequency such as the millimeter wave band. The paper introduces simple mathematical models of wave propagation in sand and dust storms, and developed based on the forward scattering amplitude of sand and dust particles using the Rayleigh technique. The suitability of the Rayleigh approximation for the models are validated by setting three different conditions. The results show that that the technique is valid for determining the scattering of ellipsoidal sand and dust particles for the sizes considered and frequency range. The scattering coefficients are thus derived and models for attenuation and phase rotation are proposed in terms of visibility. The results obtained from the proposed models show close agreement with some earlier published results when compared. Differential attenuation and differential phase rotation are computed and the cross-polarization discriminations are then predicted using the parameters from the models as inputs. The attenuation during dry sand and dust storms becomes significant only when the visibility is low and severe. At such visibility, the cross-polarization discriminations also become low (i.e. significant) and the same trend and pattern is found as the frequency is increased.
{"title":"Investigation of Millimeter Wave’s Cross Polarization Discrimination in Sand and Dust Storms","authors":"A. Musa","doi":"10.56471/slujst.v4i.255","DOIUrl":"https://doi.org/10.56471/slujst.v4i.255","url":null,"abstract":"The performance of electromagnetic wave links in sand and dust storms has received considerable interest of researchers in recent time, especially with emphasis on the signal attenuation. However, phase rotation and cross-polarization have not been sufficiently treated. This work investigates the cross-polarization discrimination as a result of sand and dust storms at high frequency such as the millimeter wave band. The paper introduces simple mathematical models of wave propagation in sand and dust storms, and developed based on the forward scattering amplitude of sand and dust particles using the Rayleigh technique. The suitability of the Rayleigh approximation for the models are validated by setting three different conditions. The results show that that the technique is valid for determining the scattering of ellipsoidal sand and dust particles for the sizes considered and frequency range. The scattering coefficients are thus derived and models for attenuation and phase rotation are proposed in terms of visibility. The results obtained from the proposed models show close agreement with some earlier published results when compared. Differential attenuation and differential phase rotation are computed and the cross-polarization discriminations are then predicted using the parameters from the models as inputs. The attenuation during dry sand and dust storms becomes significant only when the visibility is low and severe. At such visibility, the cross-polarization discriminations also become low (i.e. significant) and the same trend and pattern is found as the frequency is increased.","PeriodicalId":299818,"journal":{"name":"SLU Journal of Science and Technology","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131836665","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}
Spectrum handoff is a crucial function of Cognitive Radio (CR) which is the change of operating frequency. The main problem in spectrum handoff is the time taken in the searching, selection, and switching to a new available channel which can cause a significant amount of delay during spectrum handoff. This research aims to minimize the delay that occurs during spectrum handoff. A Proactive Fuzzy-Based Backup Channel Selection Scheme (PFBBCSS) was proposed where the Secondary User (SU) gathers backup channels in advance before the return of the Primary User (PU), then fuzzy logic would be used for the selection of the best channel out of the available backup channels. The proposed scheme was simulated and evaluated using the MATLAB Simulation tool and the result was compared with a Pure Proactive Spectrum Handoff Scheme. Results showed, in terms of throughput and efficient time utilization under a varying number of licensed channels, that the proposed scheme performed better, making it a good mechanism to be used for handoff decisions by the Secondary User (SU).
{"title":"Intelligent Process of Spectrum Handoff in Cognitive Radio Network","authors":"Emmanuel Alozie, N. Faruk","doi":"10.56471/slujst.v4i.268","DOIUrl":"https://doi.org/10.56471/slujst.v4i.268","url":null,"abstract":"Spectrum handoff is a crucial function of Cognitive Radio (CR) which is the change of operating frequency. The main problem in spectrum handoff is the time taken in the searching, selection, and switching to a new available channel which can cause a significant amount of delay during spectrum handoff. This research aims to minimize the delay that occurs during spectrum handoff. A Proactive Fuzzy-Based Backup Channel Selection Scheme (PFBBCSS) was proposed where the Secondary User (SU) gathers backup channels in advance before the return of the Primary User (PU), then fuzzy logic would be used for the selection of the best channel out of the available backup channels. The proposed scheme was simulated and evaluated using the MATLAB Simulation tool and the result was compared with a Pure Proactive Spectrum Handoff Scheme. Results showed, in terms of throughput and efficient time utilization under a varying number of licensed channels, that the proposed scheme performed better, making it a good mechanism to be used for handoff decisions by the Secondary User (SU).","PeriodicalId":299818,"journal":{"name":"SLU Journal of Science and Technology","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114953501","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}
Background: Machine learning (ML) techniques have proven to be very effective in providing security in a cloud environment considering the continuous evolving nature of threats. Some of the factors that influence the accuracies of ML models include the specific ML algorithm used, sample size, the number of features selected and portion of dataset used for training. Many studies have conducted empirical analyses of the effects of one or more combination of these factors on predicted accuracies of ML models. However, the effect of the portion of the entire dataset that is used for training the ML model as well as the number of features extracted from the dataset in predicting the accuracy of an ML model is yet to be investigated.AimThis study uses Ordinary Least Square (OLS) regression to investigate if the number of features selected and the size of training data are useful in predicting the accuracies obtained in ML based approaches to cloud security.Method: For this research, wehave two independent variables (number of features selected and the size of training data) and one dependent variable (accuracy). We initially selected 16 (sixteen) studies conducted within the last 5 (five) years for our study. We extracted the number offeatures used, the size of the training data and the accuracies obtained from these studies. After identifying and discarding outliers from the extracted values, we were left with 12 (twelve) studies. We conducted our analysis on these 12 studies. Results: The result of our analysis shows that there exist a weak positive and negative relationships among the dependent and independent variables.Although, our analysis shows weak positive and negative relationships among the variables, our model is useful in predicting the accuracies of ML models given the number of features selected and the size of the training data
{"title":"Impact of Number of Features Selected and Size of Training Data on the Accuracy of Machine Learning Based Cloud Security Algorithms – An Empirical Analysis","authors":"Tanko Y. Mohammed","doi":"10.56471/slujst.v4i.279","DOIUrl":"https://doi.org/10.56471/slujst.v4i.279","url":null,"abstract":"Background: Machine learning (ML) techniques have proven to be very effective in providing security in a cloud environment considering the continuous evolving nature of threats. Some of the factors that influence the accuracies of ML models include the specific ML algorithm used, sample size, the number of features selected and portion of dataset used for training. Many studies have conducted empirical analyses of the effects of one or more combination of these factors on predicted accuracies of ML models. However, the effect of the portion of the entire dataset that is used for training the ML model as well as the number of features extracted from the dataset in predicting the accuracy of an ML model is yet to be investigated.AimThis study uses Ordinary Least Square (OLS) regression to investigate if the number of features selected and the size of training data are useful in predicting the accuracies obtained in ML based approaches to cloud security.Method: For this research, wehave two independent variables (number of features selected and the size of training data) and one dependent variable (accuracy). We initially selected 16 (sixteen) studies conducted within the last 5 (five) years for our study. We extracted the number offeatures used, the size of the training data and the accuracies obtained from these studies. After identifying and discarding outliers from the extracted values, we were left with 12 (twelve) studies. We conducted our analysis on these 12 studies. Results: The result of our analysis shows that there exist a weak positive and negative relationships among the dependent and independent variables.Although, our analysis shows weak positive and negative relationships among the variables, our model is useful in predicting the accuracies of ML models given the number of features selected and the size of the training data","PeriodicalId":299818,"journal":{"name":"SLU Journal of Science and Technology","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114925587","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}
Globally agriculture has remained a key factor in food security, employment, and several other favorable economic indices. However, factors like rising world population, trade globalization, and climate variabilities have created the need for modernization and optimization to boost production and livelihood. Machine learning allows machines to read from a pool of available data and provide data-centric results. This has opened up a new and promising perspective. The paper examines recent proven works in machine learning technology application in agriculture to establish the modest contribution of machine learning and emerging deep learning technologies in this field to highlight the need for its adoption in the Nigerian agricultural ecosystem. Therefore, a systematic review was carried out using a categorization model of key agricultural subsectors/activities. Findings have shown a widespread of its application with significant positive impact in almost every aspect of agriculture with new works showing higher result efficiency in deep learning technologies application. Insightful recommendation from these technologies has proven capable of boosting agriculture on various fronts. Thus, the adoption of ML/DL technologies in Nigeria’s Agriculture will go a long way in helping the country attain food sufficiency.
{"title":"An Overview of Machine and Deep Learning Technologies Application in Agriculture: Opportunities and Challenges in Nigeria","authors":"M. Umar, Bashir Muhammad Sani, Usman Suleiman","doi":"10.56471/slujst.v4i.273","DOIUrl":"https://doi.org/10.56471/slujst.v4i.273","url":null,"abstract":"Globally agriculture has remained a key factor in food security, employment, and several other favorable economic indices. However, factors like rising world population, trade globalization, and climate variabilities have created the need for modernization and optimization to boost production and livelihood. Machine learning allows machines to read from a pool of available data and provide data-centric results. This has opened up a new and promising perspective. The paper examines recent proven works in machine learning technology application in agriculture to establish the modest contribution of machine learning and emerging deep learning technologies in this field to highlight the need for its adoption in the Nigerian agricultural ecosystem. Therefore, a systematic review was carried out using a categorization model of key agricultural subsectors/activities. Findings have shown a widespread of its application with significant positive impact in almost every aspect of agriculture with new works showing higher result efficiency in deep learning technologies application. Insightful recommendation from these technologies has proven capable of boosting agriculture on various fronts. Thus, the adoption of ML/DL technologies in Nigeria’s Agriculture will go a long way in helping the country attain food sufficiency.","PeriodicalId":299818,"journal":{"name":"SLU Journal of Science and Technology","volume":"335 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122917465","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}
Identifying and selecting the most consistent subset of metrics which improves the performance of software defect prediction model is paramount but challenging problem as it receives little attention in literature. The current research aimed at investigating the consistency of subsets of metrics that are produced by embedded feature selection techniques. Ten (10) feature selection techniques used from the families of filter and wrapper-based feature selection techniques commonly used in the defect prediction domain. Ten (10) publicly available defect datasets were studied which span both proprietary and open source domains. SVM-RFE-RF presented 42-93% consistent metrics across datasets. While the prior study on non-Embedded produced 56.5% consistent metrics at median. SVM-RFE-LF approach of Embedded Feature Selection Technique produced 54-80% consistent metrics across datasets and 42.5% at median. To state the purpose of tittle has been achieved Embedded based Feature Selection Techniques produced most efficient consistent subset selection across the entire datasets and amongst the feature selection techniques as compared with counterpart filter and wrapper-based feature selection techniques
{"title":"Comparisons of Filter, Wrapper and Embedded-Based Feature Selection Techniques for Consistency of Software Metrics Analysis","authors":"S. Abubakar, Zahraddeen Sufyanu","doi":"10.56471/slujst.v4i.238","DOIUrl":"https://doi.org/10.56471/slujst.v4i.238","url":null,"abstract":"Identifying and selecting the most consistent subset of metrics which improves the performance of software defect prediction model is paramount but challenging problem as it receives little attention in literature. The current research aimed at investigating the consistency of subsets of metrics that are produced by embedded feature selection techniques. Ten (10) feature selection techniques used from the families of filter and wrapper-based feature selection techniques commonly used in the defect prediction domain. Ten (10) publicly available defect datasets were studied which span both proprietary and open source domains. SVM-RFE-RF presented 42-93% consistent metrics across datasets. While the prior study on non-Embedded produced 56.5% consistent metrics at median. SVM-RFE-LF approach of Embedded Feature Selection Technique produced 54-80% consistent metrics across datasets and 42.5% at median. To state the purpose of tittle has been achieved Embedded based Feature Selection Techniques produced most efficient consistent subset selection across the entire datasets and amongst the feature selection techniques as compared with counterpart filter and wrapper-based feature selection techniques","PeriodicalId":299818,"journal":{"name":"SLU Journal of Science and Technology","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125992278","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}
Representation of results/data graphically depicts a better understanding of the behavior of the results/data. Contour plotting is an easy way of representing results/data. Contouring algorithms use linear interpolation in determining the point of the intersection between contour lines and grid segments when drawing contour lines. Using linear interpolation is not very precise and results in discontinuities at end points. This paper presents and examines the contouring algorithm that uses inverse distance weighting interpolation in determining the point of the intersection between contour lines and grid segments of randomly generated data. Comparison made between the maps produced by these algorithms that use linear, cubic and inverse distance weighting interpolation, showed that the map produced by inverse distance weighting interpolation is wrong because different contour lines cross each other, the map produced by cubic interpolation depicts less information because some contour lines are missing when compared with the map produced by linear interpolation
{"title":"Examining Inverse Distance Weighting Interpolation Method in Contouring Algorithm","authors":"Abdulrazaq Abdulrahim","doi":"10.56471/slujst.v4i.274","DOIUrl":"https://doi.org/10.56471/slujst.v4i.274","url":null,"abstract":"Representation of results/data graphically depicts a better understanding of the behavior of the results/data. Contour plotting is an easy way of representing results/data. Contouring algorithms use linear interpolation in determining the point of the intersection between contour lines and grid segments when drawing contour lines. Using linear interpolation is not very precise and results in discontinuities at end points. This paper presents and examines the contouring algorithm that uses inverse distance weighting interpolation in determining the point of the intersection between contour lines and grid segments of randomly generated data. Comparison made between the maps produced by these algorithms that use linear, cubic and inverse distance weighting interpolation, showed that the map produced by inverse distance weighting interpolation is wrong because different contour lines cross each other, the map produced by cubic interpolation depicts less information because some contour lines are missing when compared with the map produced by linear interpolation","PeriodicalId":299818,"journal":{"name":"SLU Journal of Science and Technology","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116785250","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}
Nowadays online reviews by hotel customers greatly influence business as potential new consumers seek unbiased information while making their hotel booking decisions. Hotel management and marketers are more aware of the impact of online reviews on financial performance. This awareness arises from the universal consensus that internet consumer reviews have a significant impact on hotel business performance. Customers use social media to share information about products and services, and online reviews have a substantial influence on customer purchasing decisions. The goal of this study is to provide formative assessment feedback on Maldives hotels using word cloud technique. This include investigating the hotel that is mostly used by guests, finding out the percentage of positive and negative comments made about the hotel, and also assessing the type of comments the majority of customers give about the services rendered to them. Data from 104 distinct Maldives hotels were utilized in this case study to provide quick visual insight using a word cloud approach with R programming language. The result shows that, more than 80% of the comments are positive, implying that the vast majority of these hotels' customers are pleased with their accommodations and services.
{"title":"Assessment of Hotel Guest Satisfaction Using Sentiment Analysis: A Case Study of Maldives Hotels","authors":"Hauwa’u Uraifa Shehu, A. Kana, Fatima Sulaiman","doi":"10.56471/slujst.v4i.272","DOIUrl":"https://doi.org/10.56471/slujst.v4i.272","url":null,"abstract":"Nowadays online reviews by hotel customers greatly influence business as potential new consumers seek unbiased information while making their hotel booking decisions. Hotel management and marketers are more aware of the impact of online reviews on financial performance. This awareness arises from the universal consensus that internet consumer reviews have a significant impact on hotel business performance. Customers use social media to share information about products and services, and online reviews have a substantial influence on customer purchasing decisions. The goal of this study is to provide formative assessment feedback on Maldives hotels using word cloud technique. This include investigating the hotel that is mostly used by guests, finding out the percentage of positive and negative comments made about the hotel, and also assessing the type of comments the majority of customers give about the services rendered to them. Data from 104 distinct Maldives hotels were utilized in this case study to provide quick visual insight using a word cloud approach with R programming language. The result shows that, more than 80% of the comments are positive, implying that the vast majority of these hotels' customers are pleased with their accommodations and services.","PeriodicalId":299818,"journal":{"name":"SLU Journal of Science and Technology","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125399258","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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