Increasing demand for personalized tours for tourists travel in an urban area motivates more attention to points of interest (POI) and tour recommendation services. Recently, the granularity of POI has been discussed to provide more detailed information for tour planning, which supports both inside and outside routes that would improve tourists' travel experience. Such tour recommendation systems require a predefined POI database with different granularities, but existing POI discovery methods do not consider the granularity of POI well and treat all POIs as the same scale. On the other hand, the parameters also need to be tuned for different cities, which is not a trivial process. To this end, we propose a city adaptive clustering framework for discovering POIs with different granularities in this article. Our proposed method takes advantage of two clustering algorithms and is adaptive to different cities due to automatic identification of suitable parameters for different datasets. Experiments on two real-world social image datasets reveal the effectiveness of our proposed framework. Finally, the discovered POIs with two levels of granularity are successfully applied on inner and outside tour planning.
{"title":"Discovery of Points of Interest with Different Granularities for Tour Recommendation Using a City Adaptive Clustering Framework","authors":"Junjie Sun, T. Kinoue, Qiang Ma","doi":"10.18267/j.aip.161","DOIUrl":"https://doi.org/10.18267/j.aip.161","url":null,"abstract":"Increasing demand for personalized tours for tourists travel in an urban area motivates more attention to points of interest (POI) and tour recommendation services. Recently, the granularity of POI has been discussed to provide more detailed information for tour planning, which supports both inside and outside routes that would improve tourists' travel experience. Such tour recommendation systems require a predefined POI database with different granularities, but existing POI discovery methods do not consider the granularity of POI well and treat all POIs as the same scale. On the other hand, the parameters also need to be tuned for different cities, which is not a trivial process. To this end, we propose a city adaptive clustering framework for discovering POIs with different granularities in this article. Our proposed method takes advantage of two clustering algorithms and is adaptive to different cities due to automatic identification of suitable parameters for different datasets. Experiments on two real-world social image datasets reveal the effectiveness of our proposed framework. Finally, the discovered POIs with two levels of granularity are successfully applied on inner and outside tour planning.","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43251526","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}
Insurance is a crucial mechanism used to lighten the financial burden as it provides protection against financial losses resulting from unexpected events. Insurers adopt various approaches, such as machine learning, to attract the uninsured. By using machine learning, a company is able to tap into the wealth of information of its potential customers. The main objective of this study is to apply artificial neural networks (ANNs) to predict the propensity of consumers to purchase an insurance policy by using the dataset from the Computational Intelligence and Learning (CoIL) Challenge 2000. In addition, this study also aims to identify factors that affect the propensity of customers to purchase insurance policies via feature selection. The dataset is pre-processed with feature construction and three feature selection methods, which are the neighbourhood component analysis (NCA), sequential forward selection (SFS) and sequential backward selection (SBS). Sampling techniques are carried out to address the issue of imbalanced class distributions. The results obtained are found to be comparable with the top few entries of the CoIL Challenge 2000, which shows the efficiency of the proposed model in predicting consumers’ intention of purchasing insurance policies.
{"title":"A Neural Network-Based Approach in Predicting Consumers' Intentions of Purchasing Insurance Policies","authors":"Wen Teng Chang, Kee Huong Lai","doi":"10.18267/j.aip.152","DOIUrl":"https://doi.org/10.18267/j.aip.152","url":null,"abstract":"Insurance is a crucial mechanism used to lighten the financial burden as it provides protection against financial losses resulting from unexpected events. Insurers adopt various approaches, such as machine learning, to attract the uninsured. By using machine learning, a company is able to tap into the wealth of information of its potential customers. The main objective of this study is to apply artificial neural networks (ANNs) to predict the propensity of consumers to purchase an insurance policy by using the dataset from the Computational Intelligence and Learning (CoIL) Challenge 2000. In addition, this study also aims to identify factors that affect the propensity of customers to purchase insurance policies via feature selection. The dataset is pre-processed with feature construction and three feature selection methods, which are the neighbourhood component analysis (NCA), sequential forward selection (SFS) and sequential backward selection (SBS). Sampling techniques are carried out to address the issue of imbalanced class distributions. The results obtained are found to be comparable with the top few entries of the CoIL Challenge 2000, which shows the efficiency of the proposed model in predicting consumers’ intention of purchasing insurance policies.","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46660210","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}
Sentiment analysis is one of the most frequently used aspects of Natural Language Processing (NLP), which utilizes the polarity classification of reviews expressed at the aspect, sentence or document level. Several businesses and organizations utilize this technique to improve production, as well as employee and service efficiency. However, the users’ reviews in our study were expressed in an unstructured data form, which contained spelling errors, leading to complex classifications for both the users and the machine. To solve the problem, a supervised technique of Machine Learning (ML) algorithms can be applied to the data extraction, where classification polarity can be categorized into a positive, negative or neutral class. In this research, we compared nine ML algorithms to determine the most suitable ML algorithm for creating sentiment polarity classification of customer reviews in Thai, which is a low-resource language. The dataset was collected manually from two online agencies (Agoda.com and Booking.com) utilizing a special Thai language. We employed 11 preprocessing steps to clean and handle the large amount of noise data. Next, the Delta TF-IDF, TF-IDF, N-Gram, and Word2Vec techniques were applied to convert the text reviews into vectors, processed with different ML algorithms, to determine sentiment polarity classification and to make accurate comparisons. All ML algorithms were evaluated for sentiment polarity classification with ten-fold cross-validation, with which to compare the values of recall, precision, F1-score and accuracy. The experiment results show that the Support Vector Machine (SVM) using the Delta TF-IDF technique was the best ML algorithm for polarity classification of hotel reviews in the Thai language with the highest accuracy of 89.96%. The results of this research can be applied as the tool for small and medium-sized enterprises within the field of sentiment analysis of the Thai language in the hotel domain.
{"title":"Sentiment Analysis for Thai Language in Hotel Domain Using Machine Learning Algorithms","authors":"Nattawat Khamphakdee, Pusadee Seresangtakul","doi":"10.18267/j.aip.155","DOIUrl":"https://doi.org/10.18267/j.aip.155","url":null,"abstract":"Sentiment analysis is one of the most frequently used aspects of Natural Language Processing (NLP), which utilizes the polarity classification of reviews expressed at the aspect, sentence or document level. Several businesses and organizations utilize this technique to improve production, as well as employee and service efficiency. However, the users’ reviews in our study were expressed in an unstructured data form, which contained spelling errors, leading to complex classifications for both the users and the machine. To solve the problem, a supervised technique of Machine Learning (ML) algorithms can be applied to the data extraction, where classification polarity can be categorized into a positive, negative or neutral class. In this research, we compared nine ML algorithms to determine the most suitable ML algorithm for creating sentiment polarity classification of customer reviews in Thai, which is a low-resource language. The dataset was collected manually from two online agencies (Agoda.com and Booking.com) utilizing a special Thai language. We employed 11 preprocessing steps to clean and handle the large amount of noise data. Next, the Delta TF-IDF, TF-IDF, N-Gram, and Word2Vec techniques were applied to convert the text reviews into vectors, processed with different ML algorithms, to determine sentiment polarity classification and to make accurate comparisons. All ML algorithms were evaluated for sentiment polarity classification with ten-fold cross-validation, with which to compare the values of recall, precision, F1-score and accuracy. The experiment results show that the Support Vector Machine (SVM) using the Delta TF-IDF technique was the best ML algorithm for polarity classification of hotel reviews in the Thai language with the highest accuracy of 89.96%. The results of this research can be applied as the tool for small and medium-sized enterprises within the field of sentiment analysis of the Thai language in the hotel domain.","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45669421","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}
Ahmad Termidzi Bin Serojai, Hamimah Binti Ujir, Irwandi Hipni Bin Mohamad Hipiny
This study explores the factors of e-commerce adoption among Sarawakians. One of the factors is the level of cybersecurity awareness. We aim to assess the readiness for e-commerce among Sarawakians due to the lack of study conducted on the subject. A research model based on the perceived risk (PR), perceived usefulness (PU) and perceived quality of products (PQ), and the intention (I) of adoption of e-commerce services in Sarawak is proposed. The validity of the proposed model is then tested using various validity tests such as item reliability, construct validity, convergent validity and discriminant validity via the SmartPLS software. Once the validity of the model has been determined, a structural equation model is used to study the strength of the model before the test of the hypothesis can be done. The effect size, f2, is calculated by using SmartPLS. The index value of each variable is also plotted in the importance-performance matrix analysis (IPMA). Based on the survey data from 128 end users in Sarawak, the study finds that PU is the most crucial factor in adopting e-commerce services, followed by PQ. Surprisingly, PR does not play any role in the intention of Sarawakians to adopt e-commerce services. The results suggest several important key points as follows: (i) the Sarawak Government and its e-commerce partners should focus on educating the people of Sarawak on the importance of cybersecurity to prevent cyber-related crimes from occurring and causing massive damage in Sarawak’s attempt to digitise its economy; (ii)Sarawakians prefer functional e-commerce services; (iii) the quality of e-commerce products should also be maintained; and (iv) the developers should focus on the usefulness of their products to ensure that their service can be adopted by the people of Sarawak.
{"title":"E-Commerce Readiness Assessment in Sarawak","authors":"Ahmad Termidzi Bin Serojai, Hamimah Binti Ujir, Irwandi Hipni Bin Mohamad Hipiny","doi":"10.18267/j.aip.153","DOIUrl":"https://doi.org/10.18267/j.aip.153","url":null,"abstract":"This study explores the factors of e-commerce adoption among Sarawakians. One of the factors is the level of cybersecurity awareness. We aim to assess the readiness for e-commerce among Sarawakians due to the lack of study conducted on the subject. A research model based on the perceived risk (PR), perceived usefulness (PU) and perceived quality of products (PQ), and the intention (I) of adoption of e-commerce services in Sarawak is proposed. The validity of the proposed model is then tested using various validity tests such as item reliability, construct validity, convergent validity and discriminant validity via the SmartPLS software. Once the validity of the model has been determined, a structural equation model is used to study the strength of the model before the test of the hypothesis can be done. The effect size, f2, is calculated by using SmartPLS. The index value of each variable is also plotted in the importance-performance matrix analysis (IPMA). Based on the survey data from 128 end users in Sarawak, the study finds that PU is the most crucial factor in adopting e-commerce services, followed by PQ. Surprisingly, PR does not play any role in the intention of Sarawakians to adopt e-commerce services. The results suggest several important key points as follows: (i) the Sarawak Government and its e-commerce partners should focus on educating the people of Sarawak on the importance of cybersecurity to prevent cyber-related crimes from occurring and causing massive damage in Sarawak’s attempt to digitise its economy; (ii)Sarawakians prefer functional e-commerce services; (iii) the quality of e-commerce products should also be maintained; and (iv) the developers should focus on the usefulness of their products to ensure that their service can be adopted by the people of Sarawak.","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48038738","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}
Shabnam Shadroo, Mohsen Yoosefi Nejad, Samira Tavanaiee Yosefian, M. Naserbakht, M. Hosseinzadeh
Mental health is an important issue for university students. The objective of this article was to apply and compare the different classification methods for students’ mental health problems. Furthermore, it presents an ensemble classification method to improve the accuracy of classifiers and assist psychologists in the decision making process. For this, 10 different classifiers were applied for classifying students into two groups. In addition, two methods of combining the classifiers are presented. In the first proposed method, the classifiers were selected based on their accuracy, and then voting was carried out based on maximum probability. In the second proposed method, the methods were combined based on the fields of the confusion table, and the voting was carried out based on majority voting scheme. These two methods were evaluated in two ways. Focusing on the accuracy and the maximum probability voting, the accuracy of the first method was 92.24%, whereas in the second method, it was 95.97%. Further, using confusion table and majority voting applied to the entire dataset, the accuracy reached 96.66%. The results are promising to assist the process of mental health assessment of students.
{"title":"Proposing Two Hybrid Data Mining Models for Discovering Students' Mental Health Problems","authors":"Shabnam Shadroo, Mohsen Yoosefi Nejad, Samira Tavanaiee Yosefian, M. Naserbakht, M. Hosseinzadeh","doi":"10.18267/j.aip.148","DOIUrl":"https://doi.org/10.18267/j.aip.148","url":null,"abstract":"Mental health is an important issue for university students. The objective of this article was to apply and compare the different classification methods for students’ mental health problems. Furthermore, it presents an ensemble classification method to improve the accuracy of classifiers and assist psychologists in the decision making process. For this, 10 different classifiers were applied for classifying students into two groups. In addition, two methods of combining the classifiers are presented. In the first proposed method, the classifiers were selected based on their accuracy, and then voting was carried out based on maximum probability. In the second proposed method, the methods were combined based on the fields of the confusion table, and the voting was carried out based on majority voting scheme. These two methods were evaluated in two ways. Focusing on the accuracy and the maximum probability voting, the accuracy of the first method was 92.24%, whereas in the second method, it was 95.97%. Further, using confusion table and majority voting applied to the entire dataset, the accuracy reached 96.66%. The results are promising to assist the process of mental health assessment of students.","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42890229","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 article deals with the history of the formation, current state and prospects for the development of social informatics as a current direction in science and education in Russia. The article offers mainly a personal view of the author, who has been involved in shaping social informatics in Russia for the last three decades. The article presents the distinctive features of the Russian scientific school of social informatics and its priorities in the formation of this field. The main directions of research in the field of social informatics in Russia in the context of the formation of the global information society are determined. Particular emphasis is placed on the interdisciplinary nature of many issues related to social informatics and their systematic study. Finally, the article summarizes the current necessity for the deep study of issues related to social informatics, e.g., information inequality, information crime, cyberbullies, manipulation of consciousness, virtualization of society, information wars, information poverty, information culture, and using computers to analyse social phenomena such as communication via social media. It is important not only in the area of scientific research but also in the system of secondary and higher education and training of scholars.
{"title":"Social Informatics: 30 Years of Development of Russian Scientific School","authors":"Konstantin Konstantinovich Kolin","doi":"10.18267/j.aip.150","DOIUrl":"https://doi.org/10.18267/j.aip.150","url":null,"abstract":"The article deals with the history of the formation, current state and prospects for the development of social informatics as a current direction in science and education in Russia. The article offers mainly a personal view of the author, who has been involved in shaping social informatics in Russia for the last three decades. The article presents the distinctive features of the Russian scientific school of social informatics and its priorities in the formation of this field. The main directions of research in the field of social informatics in Russia in the context of the formation of the global information society are determined. Particular emphasis is placed on the interdisciplinary nature of many issues related to social informatics and their systematic study. Finally, the article summarizes the current necessity for the deep study of issues related to social informatics, e.g., information inequality, information crime, cyberbullies, manipulation of consciousness, virtualization of society, information wars, information poverty, information culture, and using computers to analyse social phenomena such as communication via social media. It is important not only in the area of scientific research but also in the system of secondary and higher education and training of scholars.","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46209908","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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