2021 32nd Irish Signals and Systems Conference (ISSC)
Quality assurance is often a time-consuming and error prone process for organisations. However, i... more Quality assurance is often a time-consuming and error prone process for organisations. However, it is increasingly important for companies that produce fabricated products for integration into safety critical environments. For example, creating pipe systems for the pharmaceutical industry will include additional risks. As a result, increased regulation is required, which has resulted in further paperwork and validation for companies operating in this sector.A lot of the isometric drawings provided to companies for fabrication remain in paper format (or scanned paper documents). This provides an administrative burden on these companies as the average project could generate up to 5,000 isometric drawings. This research explores techniques that could be utilised to automatically extract Bill of Materials (BOM) information from these isometric drawings.Tesseract has failed to perform OCR accurately on the extracted Region of Interest (ROI) data containing the BOM information, achieving a mean average of 43.8%. This paper explores different pre-processing techniques to increase the accuracy of recognition. Techniques such as binarisation, erosion, noise reduction and contouring were employed to increase this accuracy. In the study, the accuracy increased to a mean average of 81.2%. This has demonstrated that effective use of pre-processing can have an impact on character recognition.
2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC)
Artificial intelligence solutions in the healthcare sector are a fundamental phenomenon. It has e... more Artificial intelligence solutions in the healthcare sector are a fundamental phenomenon. It has enabled medical practitioners to perform high quality and precision treatments to prevent diseases or cure a patient. While it is essential to use such solutions, it is also more important to make these solutions transparent to medical professionals. Doctors rely on the cause behind a prognosis rather than just the binary result. This study provides an insight into the feasibility and importance of explainable artificial intelligence solutions for the healthcare sector. A case-study on diabetes in Pima Indian females aids this research motive. The study has maintained good explainability of the predictions and high accuracy by the machine learning models used. This study used a white-box machine learning framework, local interpretable model-agnostic explanations, to prove the cause. The framework successfully interpreted case-by-case predictions of some machine learning models. The machine learning models, while being interpretable, also provided high accuracy in prediction. The highest accuracy, 80.5%, was shown by a random forest model. The study found out glucose levels as the most contributing factors for the outcome of diabetes. The results from this study can be used by researchers to reevaluate their position on white-box machine-learning solutions in the healthcare sector.
2021 32nd Irish Signals and Systems Conference (ISSC), 2021
Computer Vision (CV) is an area within the field of Artificial Intelligence (AI) which analyses i... more Computer Vision (CV) is an area within the field of Artificial Intelligence (AI) which analyses images and video and attempts to identify and interpret the data contained in these. It aims to match or better the results a human could achieve given the same dataset. CV technology has major applications within the area of assistive technology. It has the potential to make the lives of disabled people easier by making the objects and systems they interact with more accessible. The aim of this project is to create a Convolutional Neural Network (CNN) suitable for use in a mobile bank note recognition application which can alleviate the struggle visually impaired people experience when trying to identify different bank note values. Limited previous studies have attempted to tackle this problem using a CNN. Additionally, these past studies have often neglected to include partial currency images in their datasets. This study uses data augmentation techniques to simulate partial currency im...
2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), 2020
The face is the most dominant part of the human body, we can get a lot of information from facial... more The face is the most dominant part of the human body, we can get a lot of information from facial features such as detecting the face of person, gender classification and even age prediction. In current times, Computer Vision (CV) has been used to train machines to comprehend and envision the real world. In this research, a novel artifact has been presented to detect face, classify genders, and predict age from the human facial images all in real-time using a live stream from a camera source. Convolutional Neural Networks (CNN) have been used for training purposes along with the CV library, Keras. To power this novel study, each model has been trained separately and finely tuned before merging them into the final system. The use of the careful modern architecture of CNN and current regularisation methods have been properly evaluated and implemented.The accuracy of the developed model has been calculated manually achieving an overall accuracy of 85%. All the testing has been performe...
2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT), 2020
This research investigates a method for performing manual classification of the terrain in imager... more This research investigates a method for performing manual classification of the terrain in imagery from the on-board camera of an unmanned aerial vehicle, to develop classifiers for systematic terrain classification. Drone images were captured across rural County Donegal in Ireland, and software was developed to manually label the terrain in these images, labelled in a lattice of 30 x30-pixel tiles. This dataset was used to train both a classic computer vision model and a Convolutional Neural Net model to classify the type of terrain under the UAV. The accuracy of the computer vision approach to the classification was compared to that of a Convolutional Neural Network trained using the Semantic Segmentation approach. The Convolutional Neural Network classifier was found to be the most accurate approach, achieving an fl score of 0.95.
2020 31st Irish Signals and Systems Conference (ISSC)
Increasingly content sharing websites such as social media have become very popular in many count... more Increasingly content sharing websites such as social media have become very popular in many countries across the world. Classifying the gender of a person based on these short messages is an interesting research area that could benefit legal investigation, forensics, marketing analysis, advertising and recommendation. This research will explore the use of Natural Language Processing (NLP) techniques and tweets in a gender classification system. This investigation will compare multiple techniques such as Bag of Words (Term Frequency - Inverse Document Frequency), Word Embedding (W2Vec, GloVe) and traditional Machine Learning techniques (Logistic Regression, Support Vector Machine and Naïve Bayes) in this context. A new dataset has been generated to be used as part of this study comprising of the user gender and associated tweets. This dataset was developed due to the unavailability of any public standard dataset with the volume required to perform this investigation. The results have determined that the traditional Bag of Words model did not provide any significant results in classification. However, word embedding models have significantly performed better using multiple machine learning techniques. Therefore, the word embedding models have been proven to be the most effective technique in classifying gender based on twitter text data.
The distribution of misleading information or fake news has become a problem for society in recen... more The distribution of misleading information or fake news has become a problem for society in recent times. In the world of social media, where anyone can share their opinions, beliefs and make it sound like these are fact, fake news becomes a threat to the reputation of companies and to people. In 2016, the USA Presidential elections gathered more attention from the generation of fake news articles, leading to a huge number of researchers and scientists to explore this Natural Language Processing research area with a sense of urgency and keen interest. However, investigation regarding what people are consuming from social media is in early stages and efforts are in progress to explore how people can separate disinformation from truthful content. The primary challenge in fake news detection is determining how to detect it. Supervised learning methods help us to detect these stories using labelled data to determine if text is real or fake. This research aims to develop and compare supe...
Purpose Increasingly manufacturers of smartphone devices are utilising a diverse range of sensors... more Purpose Increasingly manufacturers of smartphone devices are utilising a diverse range of sensors. This innovation has enabled developers to accurately determine a user’s current context. One area that has been significantly enhanced by the increased use of context in mobile applications is tourism. Traditionally tour guide applications rely heavily on location and essentially ignore other types of context. This has led to problems of inappropriate suggestions and tourists experiencing information overload. These problems can be mitigated if appropriate personalisation and content filtering is performed. This research proposes an intelligent context aware recommender system that aims to minimise the highlighted problems. Design / Methodology / Approach Intelligent reasoning was performed to determine the weight or importance of each different type of environmental and temporal context. Environmental context such as the weather outside can have an impact on the suitability of tourist...
In recent years ‘smartphone’ devices are becoming increasingly aware of their surroundings using ... more In recent years ‘smartphone’ devices are becoming increasingly aware of their surroundings using sensors to detect a user’s current context. Due to this increase in the use of sensors, there is currently a lot of potential for developing context-aware mobile applications. One of the key uses of sensor data in context-aware applications is determining location, as this can greatly improve the potential functionality available to the user. However, there are other types of context that can be determined when developing these applications. For example time, weather and even the person using the device. An activity that can be significantly improved with the introduction of context is mobile tourism. Traditional mobile tourism applications depend heavily on location, and for the most part ignore other types of context. This can lead to the delivery of irrelevant information to the user as there is a lack of adequate content filtering. If the user is inundated with irrelevant or only par...
2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC)
Drink driving or driving under the influence of alcohol is major problem throughout the world. In... more Drink driving or driving under the influence of alcohol is major problem throughout the world. In Ireland, drink driving has increased in its occurrences and the amount of people who still take the chance and drink and drive is high. According to reports released from the Road Safety Authority, the first two months of 2019 alone in Ireland, there has been a 17% increase in people being caught while driving under the influence since the preceding first two months of 2018. It was found that the main method in Ireland at least of catching intoxicated drivers was the use of checkpoints. Although, due to manpower and rural areas, it is difficult for police to be in the right place at the right time. This paper explores the option of using a Raspberry Pi as a device that could be used to prevent or stop a driver under the influence of alcohol from operating their vehicle. In order to test the sensor, a series of sober breath samples and a series of alcohol-potent products were used. The results have proven that the Raspberry Pi along with a MQ-3 gas sensor can accurately evaluate Blood Alcohol Concentration.
2021 International Conference on Smart Applications, Communications and Networking (SmartNets)
The ongoing COVID-19 pandemic has changed people’s lives in ways that many would not have predict... more The ongoing COVID-19 pandemic has changed people’s lives in ways that many would not have predicted. In the days, weeks and months since mandatory lockdowns and restrictions came into effect worldwide, people have had to adjust their daily lives in an effort to slow and restrict the spread of the virus -- like regularly sanitising their hands, maintaining social distancing in crowded places, and wearing facemasks. The latter is contentious for some but has been a necessary deterrent in slowing the spread of this virus. There is potential for utilising technology as a supplementary deterrent and monitoring tool to help detect non-compliance of mask wearing. This research investigates the efficacy of AI for such purposes, exploring the applicability of a Convolutional Neural Network (CNN), for predicting if a person in a real time video feed is wearing a facemask. A dataset of over 10,000 images was created to effectively evaluate this research. The CNN developed was tested against the validation dataset to evaluate its performance, the model demonstrated 98.47% accuracy on a varied and balanced dataset.
Journal of Hospitality and Tourism Technology, 2016
Purpose Manufacturers of smartphone devices are increasingly utilising a diverse range of sensors... more Purpose Manufacturers of smartphone devices are increasingly utilising a diverse range of sensors. This innovation has enabled developers to accurately determine a user’s current context. One area that has been significantly enhanced by the increased use of context in mobile applications is tourism. Traditionally, tour guide applications rely heavily on location and essentially ignore other types of context. This has led to problems of inappropriate suggestions and tourists experiencing information overload. These problems can be mitigated if appropriate personalisation and content filtering is performed. This research proposes an intelligent context-aware recommender system that aims to minimise the highlighted problems. Design/methodology/approach Intelligent reasoning was performed to determine the weight or importance of different types of environmental and temporal context. Environmental context such as the weather outside can have an impact on the suitability of tourist attrac...
Smartphones are now equipped with various location sensing capabilities that allow the phone to a... more Smartphones are now equipped with various location sensing capabilities that allow the phone to accurately detect a user’s physical location. Combined with other context data, this has provided a great deal of potential for developing context-aware mobile applications, which will greatly improve and change how users interact with mobile devices. An activity that can be greatly enhanced with the addition of context awareness is mobile tourism. Context-aware applications currently exist for a number of major cities worldwide. However, a common characteristic of these applications is the provision of too much irrelevant information. This is due to an over-reliance on location as a context and the lack of adequate information content filtering. A deluge of irrelevant or only partially relevant information can cause information overload for tourists using the application. Other context data can assist these applications in reducing information overload, but traditional implementations have tended to lack an adequate degree of personalisation. This research focuses on implementing context by building a level of intelligence into tourist based context-aware applications. This facilitates personalisation and provides focused and timely updates related to the tourist’s current environment. This will be achieved through the application implicitly learning over time and dynamically updating personalisation settings. The application will present points of potential interest to the tourist based on their current personalisation settings. This should significantly improve the tourist experience when interacting with a context-aware mobile device. When the user initially launches the application there will be no historic data available for personalisation and so this paper explores the possibility of using social network data to build an initial information based stereotype for personalisation purposes. Another benefit of this approach is integrating the social aspect of tourism between willing participants. This facilitates real-time interaction between tourists as they traverse their current environment. This will in turn lead to valuable synergistic information flows between tourists as they explore their surroundings and hence lead to a better, more informed and rewarding tourist experience. These information flows can also be valuable for the tourist information service when analysing qualitative data regarding tourists visiting their city. Therefore, it is also imperative that mobile technology and social media are embraced by the tourism industry.
... Kevin Meehan, Tom Lunney, Kevin Curran, Aidan McCaughey ... [13] Bart Van Wissen, Nicholas Pa... more ... Kevin Meehan, Tom Lunney, Kevin Curran, Aidan McCaughey ... [13] Bart Van Wissen, Nicholas Palmer, Roelof Kemp, Thilo Kielmann, and Henri Bal, "ContextDroid: an Expression-Based Context Framework for Android," in PhoneSense, Zurich, Switzerland, 2010. ...
2021 32nd Irish Signals and Systems Conference (ISSC)
Quality assurance is often a time-consuming and error prone process for organisations. However, i... more Quality assurance is often a time-consuming and error prone process for organisations. However, it is increasingly important for companies that produce fabricated products for integration into safety critical environments. For example, creating pipe systems for the pharmaceutical industry will include additional risks. As a result, increased regulation is required, which has resulted in further paperwork and validation for companies operating in this sector.A lot of the isometric drawings provided to companies for fabrication remain in paper format (or scanned paper documents). This provides an administrative burden on these companies as the average project could generate up to 5,000 isometric drawings. This research explores techniques that could be utilised to automatically extract Bill of Materials (BOM) information from these isometric drawings.Tesseract has failed to perform OCR accurately on the extracted Region of Interest (ROI) data containing the BOM information, achieving a mean average of 43.8%. This paper explores different pre-processing techniques to increase the accuracy of recognition. Techniques such as binarisation, erosion, noise reduction and contouring were employed to increase this accuracy. In the study, the accuracy increased to a mean average of 81.2%. This has demonstrated that effective use of pre-processing can have an impact on character recognition.
2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC)
Artificial intelligence solutions in the healthcare sector are a fundamental phenomenon. It has e... more Artificial intelligence solutions in the healthcare sector are a fundamental phenomenon. It has enabled medical practitioners to perform high quality and precision treatments to prevent diseases or cure a patient. While it is essential to use such solutions, it is also more important to make these solutions transparent to medical professionals. Doctors rely on the cause behind a prognosis rather than just the binary result. This study provides an insight into the feasibility and importance of explainable artificial intelligence solutions for the healthcare sector. A case-study on diabetes in Pima Indian females aids this research motive. The study has maintained good explainability of the predictions and high accuracy by the machine learning models used. This study used a white-box machine learning framework, local interpretable model-agnostic explanations, to prove the cause. The framework successfully interpreted case-by-case predictions of some machine learning models. The machine learning models, while being interpretable, also provided high accuracy in prediction. The highest accuracy, 80.5%, was shown by a random forest model. The study found out glucose levels as the most contributing factors for the outcome of diabetes. The results from this study can be used by researchers to reevaluate their position on white-box machine-learning solutions in the healthcare sector.
2021 32nd Irish Signals and Systems Conference (ISSC), 2021
Computer Vision (CV) is an area within the field of Artificial Intelligence (AI) which analyses i... more Computer Vision (CV) is an area within the field of Artificial Intelligence (AI) which analyses images and video and attempts to identify and interpret the data contained in these. It aims to match or better the results a human could achieve given the same dataset. CV technology has major applications within the area of assistive technology. It has the potential to make the lives of disabled people easier by making the objects and systems they interact with more accessible. The aim of this project is to create a Convolutional Neural Network (CNN) suitable for use in a mobile bank note recognition application which can alleviate the struggle visually impaired people experience when trying to identify different bank note values. Limited previous studies have attempted to tackle this problem using a CNN. Additionally, these past studies have often neglected to include partial currency images in their datasets. This study uses data augmentation techniques to simulate partial currency im...
2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), 2020
The face is the most dominant part of the human body, we can get a lot of information from facial... more The face is the most dominant part of the human body, we can get a lot of information from facial features such as detecting the face of person, gender classification and even age prediction. In current times, Computer Vision (CV) has been used to train machines to comprehend and envision the real world. In this research, a novel artifact has been presented to detect face, classify genders, and predict age from the human facial images all in real-time using a live stream from a camera source. Convolutional Neural Networks (CNN) have been used for training purposes along with the CV library, Keras. To power this novel study, each model has been trained separately and finely tuned before merging them into the final system. The use of the careful modern architecture of CNN and current regularisation methods have been properly evaluated and implemented.The accuracy of the developed model has been calculated manually achieving an overall accuracy of 85%. All the testing has been performe...
2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT), 2020
This research investigates a method for performing manual classification of the terrain in imager... more This research investigates a method for performing manual classification of the terrain in imagery from the on-board camera of an unmanned aerial vehicle, to develop classifiers for systematic terrain classification. Drone images were captured across rural County Donegal in Ireland, and software was developed to manually label the terrain in these images, labelled in a lattice of 30 x30-pixel tiles. This dataset was used to train both a classic computer vision model and a Convolutional Neural Net model to classify the type of terrain under the UAV. The accuracy of the computer vision approach to the classification was compared to that of a Convolutional Neural Network trained using the Semantic Segmentation approach. The Convolutional Neural Network classifier was found to be the most accurate approach, achieving an fl score of 0.95.
2020 31st Irish Signals and Systems Conference (ISSC)
Increasingly content sharing websites such as social media have become very popular in many count... more Increasingly content sharing websites such as social media have become very popular in many countries across the world. Classifying the gender of a person based on these short messages is an interesting research area that could benefit legal investigation, forensics, marketing analysis, advertising and recommendation. This research will explore the use of Natural Language Processing (NLP) techniques and tweets in a gender classification system. This investigation will compare multiple techniques such as Bag of Words (Term Frequency - Inverse Document Frequency), Word Embedding (W2Vec, GloVe) and traditional Machine Learning techniques (Logistic Regression, Support Vector Machine and Naïve Bayes) in this context. A new dataset has been generated to be used as part of this study comprising of the user gender and associated tweets. This dataset was developed due to the unavailability of any public standard dataset with the volume required to perform this investigation. The results have determined that the traditional Bag of Words model did not provide any significant results in classification. However, word embedding models have significantly performed better using multiple machine learning techniques. Therefore, the word embedding models have been proven to be the most effective technique in classifying gender based on twitter text data.
The distribution of misleading information or fake news has become a problem for society in recen... more The distribution of misleading information or fake news has become a problem for society in recent times. In the world of social media, where anyone can share their opinions, beliefs and make it sound like these are fact, fake news becomes a threat to the reputation of companies and to people. In 2016, the USA Presidential elections gathered more attention from the generation of fake news articles, leading to a huge number of researchers and scientists to explore this Natural Language Processing research area with a sense of urgency and keen interest. However, investigation regarding what people are consuming from social media is in early stages and efforts are in progress to explore how people can separate disinformation from truthful content. The primary challenge in fake news detection is determining how to detect it. Supervised learning methods help us to detect these stories using labelled data to determine if text is real or fake. This research aims to develop and compare supe...
Purpose Increasingly manufacturers of smartphone devices are utilising a diverse range of sensors... more Purpose Increasingly manufacturers of smartphone devices are utilising a diverse range of sensors. This innovation has enabled developers to accurately determine a user’s current context. One area that has been significantly enhanced by the increased use of context in mobile applications is tourism. Traditionally tour guide applications rely heavily on location and essentially ignore other types of context. This has led to problems of inappropriate suggestions and tourists experiencing information overload. These problems can be mitigated if appropriate personalisation and content filtering is performed. This research proposes an intelligent context aware recommender system that aims to minimise the highlighted problems. Design / Methodology / Approach Intelligent reasoning was performed to determine the weight or importance of each different type of environmental and temporal context. Environmental context such as the weather outside can have an impact on the suitability of tourist...
In recent years ‘smartphone’ devices are becoming increasingly aware of their surroundings using ... more In recent years ‘smartphone’ devices are becoming increasingly aware of their surroundings using sensors to detect a user’s current context. Due to this increase in the use of sensors, there is currently a lot of potential for developing context-aware mobile applications. One of the key uses of sensor data in context-aware applications is determining location, as this can greatly improve the potential functionality available to the user. However, there are other types of context that can be determined when developing these applications. For example time, weather and even the person using the device. An activity that can be significantly improved with the introduction of context is mobile tourism. Traditional mobile tourism applications depend heavily on location, and for the most part ignore other types of context. This can lead to the delivery of irrelevant information to the user as there is a lack of adequate content filtering. If the user is inundated with irrelevant or only par...
2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC)
Drink driving or driving under the influence of alcohol is major problem throughout the world. In... more Drink driving or driving under the influence of alcohol is major problem throughout the world. In Ireland, drink driving has increased in its occurrences and the amount of people who still take the chance and drink and drive is high. According to reports released from the Road Safety Authority, the first two months of 2019 alone in Ireland, there has been a 17% increase in people being caught while driving under the influence since the preceding first two months of 2018. It was found that the main method in Ireland at least of catching intoxicated drivers was the use of checkpoints. Although, due to manpower and rural areas, it is difficult for police to be in the right place at the right time. This paper explores the option of using a Raspberry Pi as a device that could be used to prevent or stop a driver under the influence of alcohol from operating their vehicle. In order to test the sensor, a series of sober breath samples and a series of alcohol-potent products were used. The results have proven that the Raspberry Pi along with a MQ-3 gas sensor can accurately evaluate Blood Alcohol Concentration.
2021 International Conference on Smart Applications, Communications and Networking (SmartNets)
The ongoing COVID-19 pandemic has changed people’s lives in ways that many would not have predict... more The ongoing COVID-19 pandemic has changed people’s lives in ways that many would not have predicted. In the days, weeks and months since mandatory lockdowns and restrictions came into effect worldwide, people have had to adjust their daily lives in an effort to slow and restrict the spread of the virus -- like regularly sanitising their hands, maintaining social distancing in crowded places, and wearing facemasks. The latter is contentious for some but has been a necessary deterrent in slowing the spread of this virus. There is potential for utilising technology as a supplementary deterrent and monitoring tool to help detect non-compliance of mask wearing. This research investigates the efficacy of AI for such purposes, exploring the applicability of a Convolutional Neural Network (CNN), for predicting if a person in a real time video feed is wearing a facemask. A dataset of over 10,000 images was created to effectively evaluate this research. The CNN developed was tested against the validation dataset to evaluate its performance, the model demonstrated 98.47% accuracy on a varied and balanced dataset.
Journal of Hospitality and Tourism Technology, 2016
Purpose Manufacturers of smartphone devices are increasingly utilising a diverse range of sensors... more Purpose Manufacturers of smartphone devices are increasingly utilising a diverse range of sensors. This innovation has enabled developers to accurately determine a user’s current context. One area that has been significantly enhanced by the increased use of context in mobile applications is tourism. Traditionally, tour guide applications rely heavily on location and essentially ignore other types of context. This has led to problems of inappropriate suggestions and tourists experiencing information overload. These problems can be mitigated if appropriate personalisation and content filtering is performed. This research proposes an intelligent context-aware recommender system that aims to minimise the highlighted problems. Design/methodology/approach Intelligent reasoning was performed to determine the weight or importance of different types of environmental and temporal context. Environmental context such as the weather outside can have an impact on the suitability of tourist attrac...
Smartphones are now equipped with various location sensing capabilities that allow the phone to a... more Smartphones are now equipped with various location sensing capabilities that allow the phone to accurately detect a user’s physical location. Combined with other context data, this has provided a great deal of potential for developing context-aware mobile applications, which will greatly improve and change how users interact with mobile devices. An activity that can be greatly enhanced with the addition of context awareness is mobile tourism. Context-aware applications currently exist for a number of major cities worldwide. However, a common characteristic of these applications is the provision of too much irrelevant information. This is due to an over-reliance on location as a context and the lack of adequate information content filtering. A deluge of irrelevant or only partially relevant information can cause information overload for tourists using the application. Other context data can assist these applications in reducing information overload, but traditional implementations have tended to lack an adequate degree of personalisation. This research focuses on implementing context by building a level of intelligence into tourist based context-aware applications. This facilitates personalisation and provides focused and timely updates related to the tourist’s current environment. This will be achieved through the application implicitly learning over time and dynamically updating personalisation settings. The application will present points of potential interest to the tourist based on their current personalisation settings. This should significantly improve the tourist experience when interacting with a context-aware mobile device. When the user initially launches the application there will be no historic data available for personalisation and so this paper explores the possibility of using social network data to build an initial information based stereotype for personalisation purposes. Another benefit of this approach is integrating the social aspect of tourism between willing participants. This facilitates real-time interaction between tourists as they traverse their current environment. This will in turn lead to valuable synergistic information flows between tourists as they explore their surroundings and hence lead to a better, more informed and rewarding tourist experience. These information flows can also be valuable for the tourist information service when analysing qualitative data regarding tourists visiting their city. Therefore, it is also imperative that mobile technology and social media are embraced by the tourism industry.
... Kevin Meehan, Tom Lunney, Kevin Curran, Aidan McCaughey ... [13] Bart Van Wissen, Nicholas Pa... more ... Kevin Meehan, Tom Lunney, Kevin Curran, Aidan McCaughey ... [13] Bart Van Wissen, Nicholas Palmer, Roelof Kemp, Thilo Kielmann, and Henri Bal, "ContextDroid: an Expression-Based Context Framework for Android," in PhoneSense, Zurich, Switzerland, 2010. ...
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