Pub Date : 2021-10-27DOI: 10.1080/24751839.2021.1989241
Milana Grbić, Vukasin Crnogorac, M. Predojević, Aleksandar Kartelj, Dragan Matic
ABSTRACT A protein complex is a collection of two or more associated proteins that interact with each other in a stable long-term interaction. Protein complexes have essential roles in regulatory processes, cellular functions and signaling cascades. This paper examines how well-known collections of protein complexes are supported in protein–protein interaction (PPI) networks, i.e. whether they form connected subnetworks in a particular PPI network. For that purpose, we apply a variable neighbourhood search (VNS) metaheuristic algorithm for adding the minimum number of interactions in order to support protein complexes. Experimental results obtained on several PPI networks (BioGRID, WI-PHI and String) and four protein complex standards (MIPS, TAP06, SGD and CYC2008) show that considered networks do not include enough PPIs to support all complexes from complex standards. Deeper analysis indicates that there exists common PPIs which are probably missing in the considered networks. These findings can be useful for further biological interpretation and developing of PPI prediction models.
{"title":"Supportness of the protein complex standards in PPI networks","authors":"Milana Grbić, Vukasin Crnogorac, M. Predojević, Aleksandar Kartelj, Dragan Matic","doi":"10.1080/24751839.2021.1989241","DOIUrl":"https://doi.org/10.1080/24751839.2021.1989241","url":null,"abstract":"ABSTRACT A protein complex is a collection of two or more associated proteins that interact with each other in a stable long-term interaction. Protein complexes have essential roles in regulatory processes, cellular functions and signaling cascades. This paper examines how well-known collections of protein complexes are supported in protein–protein interaction (PPI) networks, i.e. whether they form connected subnetworks in a particular PPI network. For that purpose, we apply a variable neighbourhood search (VNS) metaheuristic algorithm for adding the minimum number of interactions in order to support protein complexes. Experimental results obtained on several PPI networks (BioGRID, WI-PHI and String) and four protein complex standards (MIPS, TAP06, SGD and CYC2008) show that considered networks do not include enough PPIs to support all complexes from complex standards. Deeper analysis indicates that there exists common PPIs which are probably missing in the considered networks. These findings can be useful for further biological interpretation and developing of PPI prediction models.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"6 1","pages":"6 - 26"},"PeriodicalIF":2.7,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43348187","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}
Pub Date : 2021-10-17DOI: 10.1080/24751839.2021.1987076
Salmi Cheikh, Bouchema Sara, Zaoui Sara
ABSTRACT Community detection concepts can be encountered in many disciplines such as sociology, biology, and computer science, etc. Nowadays, a huge amount of data is produced by digital social networks and needs to be processed. In fact, the analysis of this data makes it possible to extract new knowledge about groups of individuals, their communication modes, and orientations. This knowledge can be exploited in marketing, security, Web usage, and many other decisional purposes. Community detection problem (CDP) is NP-hard and many algorithms have been designed to solve it but not to a satisfactory level. In this paper, we propose a hybrid heuristic approach based on a combination of genetic algorithms and tabu search that does not need any prior knowledge about the number or the size of each community to tackle the CDP. The method is efficient because it uses an enhanced encoding, which excludes redundant chromosomes while performing genetic operations. This approach is evaluated on a wide range of real-world networks. The result of experiments shows that the proposed algorithm outperforms many other algorithms according to the modularity measure.
{"title":"An enhanced evolutionary approach for solving the community detection problem","authors":"Salmi Cheikh, Bouchema Sara, Zaoui Sara","doi":"10.1080/24751839.2021.1987076","DOIUrl":"https://doi.org/10.1080/24751839.2021.1987076","url":null,"abstract":"ABSTRACT Community detection concepts can be encountered in many disciplines such as sociology, biology, and computer science, etc. Nowadays, a huge amount of data is produced by digital social networks and needs to be processed. In fact, the analysis of this data makes it possible to extract new knowledge about groups of individuals, their communication modes, and orientations. This knowledge can be exploited in marketing, security, Web usage, and many other decisional purposes. Community detection problem (CDP) is NP-hard and many algorithms have been designed to solve it but not to a satisfactory level. In this paper, we propose a hybrid heuristic approach based on a combination of genetic algorithms and tabu search that does not need any prior knowledge about the number or the size of each community to tackle the CDP. The method is efficient because it uses an enhanced encoding, which excludes redundant chromosomes while performing genetic operations. This approach is evaluated on a wide range of real-world networks. The result of experiments shows that the proposed algorithm outperforms many other algorithms according to the modularity measure.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"6 1","pages":"83 - 100"},"PeriodicalIF":2.7,"publicationDate":"2021-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45289137","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}
ABSTRACT Analysing the human voice has always been a challenge to the engineering society for various purposes such as product review, emotional state detection, developing AI, and much more. Two basic grounds of voice or speech analysis are to detect human gender and the geographical region based on accent. This study presents a three-layer feature extraction method from the raw human voice to detect the gender as male or female, as well as the region from where that voice belongs. Fundamental frequency, spectral entropy, spectral flatness, and mode frequency have been calculated in the first layer of feature extraction. On the other hand, Mel Frequency Cepstral Coefficient has been used to extract the features in the second layer and linear predictive coding in the third layer. Regular voice contains some noises which have been removed with multiple audio data filtering processes to get noise-free smooth data. Multi-Output-based 1D Convolutional Neural Network has been used to recognize gender and region from a combined dataset which consists of TIMIT, RAVDESS, and BGC datasets. The model has successfully predicted the gender with 93.01% and region with 97.07% accuracy. This method works better than usual state-of-the-art methods in separate datasets along with the combined dataset on both gender and region classification.
{"title":"Gender and region detection from human voice using the three-layer feature extraction method with 1D CNN","authors":"Mohammad Amaz Uddin, Refat Khan Pathan, Md Sayem Hossain, Munmun Biswas","doi":"10.1080/24751839.2021.1983318","DOIUrl":"https://doi.org/10.1080/24751839.2021.1983318","url":null,"abstract":"ABSTRACT Analysing the human voice has always been a challenge to the engineering society for various purposes such as product review, emotional state detection, developing AI, and much more. Two basic grounds of voice or speech analysis are to detect human gender and the geographical region based on accent. This study presents a three-layer feature extraction method from the raw human voice to detect the gender as male or female, as well as the region from where that voice belongs. Fundamental frequency, spectral entropy, spectral flatness, and mode frequency have been calculated in the first layer of feature extraction. On the other hand, Mel Frequency Cepstral Coefficient has been used to extract the features in the second layer and linear predictive coding in the third layer. Regular voice contains some noises which have been removed with multiple audio data filtering processes to get noise-free smooth data. Multi-Output-based 1D Convolutional Neural Network has been used to recognize gender and region from a combined dataset which consists of TIMIT, RAVDESS, and BGC datasets. The model has successfully predicted the gender with 93.01% and region with 97.07% accuracy. This method works better than usual state-of-the-art methods in separate datasets along with the combined dataset on both gender and region classification.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"6 1","pages":"27 - 42"},"PeriodicalIF":2.7,"publicationDate":"2021-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49539022","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}
Pub Date : 2021-10-06DOI: 10.1080/24751839.2021.1983331
Simge Nur Aslan, A. Uçar, C. Güzelı̇ş
ABSTRACT Humanoid robots are expected to manipulate the objects they have not previously seen in real-life environments. Hence, it is important that the robots have the object recognition capability. However, object recognition is still a challenging problem at different locations and different object positions in real time. The current paper presents four novel models with small structure, based on Convolutional Neural Networks (CNNs) for object recognition with humanoid robots. In the proposed models, a few combinations of convolutions are used to recognize the class labels. The MNIST and CIFAR-10 benchmark datasets are first tested on our models. The performance of the proposed models is shown by comparisons to that of the best state-of-the-art models. The models are then applied on the Robotis-Op3 humanoid robot to recognize the objects of different shapes. The results of the models are compared to those of the models, such as VGG-16 and Residual Network-20 (ResNet-20), in terms of training and validation accuracy and loss, parameter number and training time. The experimental results show that the proposed model exhibits high accurate recognition by the lower parameter number and smaller training time than complex models. Consequently, the proposed models can be considered promising powerful models for object recognition with humanoid robots.
{"title":"New convolutional neural network models for efficient object recognition with humanoid robots","authors":"Simge Nur Aslan, A. Uçar, C. Güzelı̇ş","doi":"10.1080/24751839.2021.1983331","DOIUrl":"https://doi.org/10.1080/24751839.2021.1983331","url":null,"abstract":"ABSTRACT Humanoid robots are expected to manipulate the objects they have not previously seen in real-life environments. Hence, it is important that the robots have the object recognition capability. However, object recognition is still a challenging problem at different locations and different object positions in real time. The current paper presents four novel models with small structure, based on Convolutional Neural Networks (CNNs) for object recognition with humanoid robots. In the proposed models, a few combinations of convolutions are used to recognize the class labels. The MNIST and CIFAR-10 benchmark datasets are first tested on our models. The performance of the proposed models is shown by comparisons to that of the best state-of-the-art models. The models are then applied on the Robotis-Op3 humanoid robot to recognize the objects of different shapes. The results of the models are compared to those of the models, such as VGG-16 and Residual Network-20 (ResNet-20), in terms of training and validation accuracy and loss, parameter number and training time. The experimental results show that the proposed model exhibits high accurate recognition by the lower parameter number and smaller training time than complex models. Consequently, the proposed models can be considered promising powerful models for object recognition with humanoid robots.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"6 1","pages":"63 - 82"},"PeriodicalIF":2.7,"publicationDate":"2021-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48223208","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}
Pub Date : 2021-10-02DOI: 10.1080/24751839.2021.1966236
Z. Vetulani, Grazyna Vetulani, P. Mohanty
ABSTRACT In this paper, based on the example of our early works for Polish, we want to share our experience in the challenging task of developing NLP-based technologies in the situation of initial scarcity of digital language resources that ranked Polish among the Less-Resourced Languages. We present some of our projects aiming at language resources and tools we had to create in order to be able to process texts in Polish and develop real-scale systems with language understanding competence. The case study we present here is the rule-based system POLINT-112-SMS for improving information management in emergency situations. We argue in favour of the lexicon-grammar approach to the formal description of inflecting languages and present our current work on this grammatical paradigm. Our current work is on the implementation of the ideas presented in the first part of the paper on three prominent Indian languages, that is, Hindi, Odia, and Bengali.
{"title":"Development of real size IT systems with language competence as a challenge for a Less-Resourced Language: a methodological proposal for Indo-Aryan languages","authors":"Z. Vetulani, Grazyna Vetulani, P. Mohanty","doi":"10.1080/24751839.2021.1966236","DOIUrl":"https://doi.org/10.1080/24751839.2021.1966236","url":null,"abstract":"ABSTRACT In this paper, based on the example of our early works for Polish, we want to share our experience in the challenging task of developing NLP-based technologies in the situation of initial scarcity of digital language resources that ranked Polish among the Less-Resourced Languages. We present some of our projects aiming at language resources and tools we had to create in order to be able to process texts in Polish and develop real-scale systems with language understanding competence. The case study we present here is the rule-based system POLINT-112-SMS for improving information management in emergency situations. We argue in favour of the lexicon-grammar approach to the formal description of inflecting languages and present our current work on this grammatical paradigm. Our current work is on the implementation of the ideas presented in the first part of the paper on three prominent Indian languages, that is, Hindi, Odia, and Bengali.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"5 1","pages":"514 - 535"},"PeriodicalIF":2.7,"publicationDate":"2021-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44174250","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}
Pub Date : 2021-10-01DOI: 10.1080/24751839.2021.1981684
Z. H. Kilimci
ABSTRACT Customer loyalty is important for many industries, including banking, telecommunications, gaming, and shopping, in terms of sustainability. In mobile applications, it is observed that the demand rises with the usage of mobile devices such as smartphones. Therefore, it is important to predict when players tend to leave an application. Most of the studies so far focus on churn prediction or customer loyalty in mobile applications by analyzing demographic, economic, and behavioral data about customers. In this work, we introduce sentiment analysis-based customer loyalty prediction in mobile applications using word embeddings, deep learning algorithms, and deep contextualized word representations. To our knowledge, this is the first study to evaluate loyalty of customers analyzing sentiments of users from their comments using deep learning, word embedding, and deep contextualized word representation models. For this purpose, CNNs, RNNs, LSTMs, BERT, MBERT, DistilBERT, RoBERT are used for classification purpose. On the other hand, word embedding models such as Word2Vec, GloVe, and FastText are employed for text representation. To demonstrate the impact of proposed model, comprehensive experiments are performed on seven different datasets. The experiment results show sentiment analysis of users in mobile applications can be a powerful indicator in terms of predicting customer loyalty.
{"title":"Prediction of user loyalty in mobile applications using deep contextualized word representations","authors":"Z. H. Kilimci","doi":"10.1080/24751839.2021.1981684","DOIUrl":"https://doi.org/10.1080/24751839.2021.1981684","url":null,"abstract":"ABSTRACT Customer loyalty is important for many industries, including banking, telecommunications, gaming, and shopping, in terms of sustainability. In mobile applications, it is observed that the demand rises with the usage of mobile devices such as smartphones. Therefore, it is important to predict when players tend to leave an application. Most of the studies so far focus on churn prediction or customer loyalty in mobile applications by analyzing demographic, economic, and behavioral data about customers. In this work, we introduce sentiment analysis-based customer loyalty prediction in mobile applications using word embeddings, deep learning algorithms, and deep contextualized word representations. To our knowledge, this is the first study to evaluate loyalty of customers analyzing sentiments of users from their comments using deep learning, word embedding, and deep contextualized word representation models. For this purpose, CNNs, RNNs, LSTMs, BERT, MBERT, DistilBERT, RoBERT are used for classification purpose. On the other hand, word embedding models such as Word2Vec, GloVe, and FastText are employed for text representation. To demonstrate the impact of proposed model, comprehensive experiments are performed on seven different datasets. The experiment results show sentiment analysis of users in mobile applications can be a powerful indicator in terms of predicting customer loyalty.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"6 1","pages":"43 - 62"},"PeriodicalIF":2.7,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60140771","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}
Pub Date : 2021-09-28DOI: 10.1080/24751839.2021.1977066
Nguyen Hoang Nguyen, Duy Thien An Nguyen, Bingkun Ma, Jiang Hu
ABSTRACT Basketball is known for the vast amount of data collected for each player, team, game, and season. As a result, basketball is an ideal domain to work on different data analysis techniques to gain useful insights. In this study, we continued our previous study published in 2020 Computational Collective Intelligence (12th International Conference, ICCCI 2020, Da Nang, Vietnam, November 30 – December 3, 2020, Proceedings) reviewing some important factors to predict players’ future performance and being selected in an All-Star game, one of the most prestigious events, of National Basket Association league. Besides traditional Machine Learning, Deep Learning is also applied in this study for prediction purpose. However, compared to traditional Machine Learning, Deep Learning’s performance is not as good for our dataset. It is understandable when our data are relatively small and structured with a few predictor variables which limited Deep Learning’s ability to deal with a vast amount of Big Data. Our final results, through both Regression and Classification Analysis, indicated that scoring is the most important factor from the primary players for any team and also basketball fan’s favourable style.
{"title":"The application of machine learning and deep learning in sport: predicting NBA players’ performance and popularity","authors":"Nguyen Hoang Nguyen, Duy Thien An Nguyen, Bingkun Ma, Jiang Hu","doi":"10.1080/24751839.2021.1977066","DOIUrl":"https://doi.org/10.1080/24751839.2021.1977066","url":null,"abstract":"ABSTRACT Basketball is known for the vast amount of data collected for each player, team, game, and season. As a result, basketball is an ideal domain to work on different data analysis techniques to gain useful insights. In this study, we continued our previous study published in 2020 Computational Collective Intelligence (12th International Conference, ICCCI 2020, Da Nang, Vietnam, November 30 – December 3, 2020, Proceedings) reviewing some important factors to predict players’ future performance and being selected in an All-Star game, one of the most prestigious events, of National Basket Association league. Besides traditional Machine Learning, Deep Learning is also applied in this study for prediction purpose. However, compared to traditional Machine Learning, Deep Learning’s performance is not as good for our dataset. It is understandable when our data are relatively small and structured with a few predictor variables which limited Deep Learning’s ability to deal with a vast amount of Big Data. Our final results, through both Regression and Classification Analysis, indicated that scoring is the most important factor from the primary players for any team and also basketball fan’s favourable style.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"6 1","pages":"217 - 235"},"PeriodicalIF":2.7,"publicationDate":"2021-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48649784","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}
Pub Date : 2021-09-20DOI: 10.1080/24751839.2021.1975425
Xu Feng, K. Nguyen, Zhiyuan Luo
One of the most popular approaches for indoor positioning is WiFi fingerprinting, which has been intrinsically tackled as a traditional machine learning problem since the beginning, to achieve a few metres of accuracy on average. In recent years, deep learning has emerged as an alternative approach, with a large number of publications reporting sub-metre positioning accuracy. Therefore, this survey presents a timely, comprehensive review of the most interesting deep learning methods being used for WiFi fingerprinting. In doing so, we aim to identify the most efficient neural networks, under a variety of positioning evaluation metrics for different readers. We will demonstrate that despite the new emerging WiFi signal measures (i.e. CSI and RTT), RSS produces competitive performances under deep learning. We will also show that simple neural networks outperform more complex ones in certain environments.
{"title":"A survey of deep learning approaches for WiFi-based indoor positioning","authors":"Xu Feng, K. Nguyen, Zhiyuan Luo","doi":"10.1080/24751839.2021.1975425","DOIUrl":"https://doi.org/10.1080/24751839.2021.1975425","url":null,"abstract":"One of the most popular approaches for indoor positioning is WiFi fingerprinting, which has been intrinsically tackled as a traditional machine learning problem since the beginning, to achieve a few metres of accuracy on average. In recent years, deep learning has emerged as an alternative approach, with a large number of publications reporting sub-metre positioning accuracy. Therefore, this survey presents a timely, comprehensive review of the most interesting deep learning methods being used for WiFi fingerprinting. In doing so, we aim to identify the most efficient neural networks, under a variety of positioning evaluation metrics for different readers. We will demonstrate that despite the new emerging WiFi signal measures (i.e. CSI and RTT), RSS produces competitive performances under deep learning. We will also show that simple neural networks outperform more complex ones in certain environments.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"6 1","pages":"163 - 216"},"PeriodicalIF":2.7,"publicationDate":"2021-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45209632","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}
Pub Date : 2021-08-23DOI: 10.1080/24751839.2021.1962105
T. Dinh, Thanh Thoa Pham Thi, C. Pham-Nguyen, Le Nguyen Hoai Nam
ABSTRACT The advancement of the Internet of Things, big data, and mobile computing leads to the need for smart services that enable the context awareness and the adaptability to their changing contexts. Today, designing a smart service system is a complex task due to the lack of an adequate model support in awareness and pervasive environment. In this paper, we present the concept of a context-aware smart service system and propose a knowledge model for context-aware smart service systems. The proposed model organizes the domain and context-aware knowledge into knowledge components based on the three levels of services: Services, Service system, and Network of service systems. The knowledge model for context-aware smart service systems integrates all the information and knowledge related to smart services, knowledge components, and context awareness that can play a key role for any framework, infrastructure, or applications deploying smart services. In order to demonstrate the approach, two case studies about chatbot as context-aware smart services for customer support are presented.
{"title":"A knowledge-based model for context-aware smart service systems","authors":"T. Dinh, Thanh Thoa Pham Thi, C. Pham-Nguyen, Le Nguyen Hoai Nam","doi":"10.1080/24751839.2021.1962105","DOIUrl":"https://doi.org/10.1080/24751839.2021.1962105","url":null,"abstract":"ABSTRACT The advancement of the Internet of Things, big data, and mobile computing leads to the need for smart services that enable the context awareness and the adaptability to their changing contexts. Today, designing a smart service system is a complex task due to the lack of an adequate model support in awareness and pervasive environment. In this paper, we present the concept of a context-aware smart service system and propose a knowledge model for context-aware smart service systems. The proposed model organizes the domain and context-aware knowledge into knowledge components based on the three levels of services: Services, Service system, and Network of service systems. The knowledge model for context-aware smart service systems integrates all the information and knowledge related to smart services, knowledge components, and context awareness that can play a key role for any framework, infrastructure, or applications deploying smart services. In order to demonstrate the approach, two case studies about chatbot as context-aware smart services for customer support are presented.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"6 1","pages":"141 - 162"},"PeriodicalIF":2.7,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46903272","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}
Pub Date : 2021-08-17DOI: 10.1080/24751839.2021.1955533
S. M. N. Arosha Senanayake, Sisir Joshi
ABSTRACT Domain-specific data service models can retrieve critical features from frequently occurring road accident patterns (RAPs). The aim of this research is to propose scan efficient association rules’ mining-based pattern analysis which provides more accurate RAP prediction in frequent accident locations with the fastest matching pattern search from a RAP database (RAP DB). Association rules’ mining technique derives a correlation between frequent RAP and association among various attributes of a road accident. While the clustering technique discriminates different RAPs, Naïve Bayes Classification classifies and then predicts the severity of accident using Fuzzy Inference Engine (FIE) interfaced with RAP Case Library (RAP CL) using hybrid intelligence. The results of the proposed road accident data service model prove a significant increase in the accuracy of accident prediction compared to the reported results. A novel hybrid learning algorithm, interfaced with Scan Efficient Apriori (SEA) algorithm implemented, leads the fast RAP search from the first scan through RAP CL and retain new RAP in the RAP CL using case-based reasoning (CBR) during subsequent scanning. Thus, the RAP miner built proves road accident prediction using SEA, FIE and CBR with the highest accuracy and fast RAP set processing.
{"title":"A road accident pattern miner (RAP miner)","authors":"S. M. N. Arosha Senanayake, Sisir Joshi","doi":"10.1080/24751839.2021.1955533","DOIUrl":"https://doi.org/10.1080/24751839.2021.1955533","url":null,"abstract":"ABSTRACT Domain-specific data service models can retrieve critical features from frequently occurring road accident patterns (RAPs). The aim of this research is to propose scan efficient association rules’ mining-based pattern analysis which provides more accurate RAP prediction in frequent accident locations with the fastest matching pattern search from a RAP database (RAP DB). Association rules’ mining technique derives a correlation between frequent RAP and association among various attributes of a road accident. While the clustering technique discriminates different RAPs, Naïve Bayes Classification classifies and then predicts the severity of accident using Fuzzy Inference Engine (FIE) interfaced with RAP Case Library (RAP CL) using hybrid intelligence. The results of the proposed road accident data service model prove a significant increase in the accuracy of accident prediction compared to the reported results. A novel hybrid learning algorithm, interfaced with Scan Efficient Apriori (SEA) algorithm implemented, leads the fast RAP search from the first scan through RAP CL and retain new RAP in the RAP CL using case-based reasoning (CBR) during subsequent scanning. Thus, the RAP miner built proves road accident prediction using SEA, FIE and CBR with the highest accuracy and fast RAP set processing.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"5 1","pages":"484 - 498"},"PeriodicalIF":2.7,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47089545","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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