The detection and monitoring of emotions are important in various applications, e.g., to enable n... more The detection and monitoring of emotions are important in various applications, e.g., to enable naturalistic and personalised human-robot interaction. Emotion detection often require modelling of various data inputs from multiple modalities, including physiological signals (e.g., EEG and GSR), environmental data (e.g., audio and weather), videos (e.g., for capturing facial expressions and gestures) and more recently motion and location data. Many traditional machine learning algorithms have been utilised to capture the diversity of multimodal data at the sensors and features levels for human emotion classification. While the feature engineering processes often embedded in these algorithms are beneficial for emotion modelling, they inherit some critical limitations which may hinder the development of reliable and accurate models. In this work, we adopt a deep learning approach for emotion classification through an iterative process by adding and removing large number of sensor signal...
Semantic Web resources such as DBpedia provide a rich source of structured knowledge about geogra... more Semantic Web resources such as DBpedia provide a rich source of structured knowledge about geographical features such as towns, rivers and historical buildings. Retrieval from these resources of all content that is relevant to a particular spatial query of, for example, containment or proximity is not always straightforward because there is considerable inconsistency in the way in which geographical features are referenced to location. In DBpedia some geographical feature instances have point coordinates, some have qualitative properties that provide explicit or implicit locational information via place names, and some have neither of these. Here we show how structured geo-spatial query, a form of question answering, on DBpedia can be performed with a hybrid strategy that exploits both quantitative and qualitative spatial properties in combination with a high quality reference geo-dataset that can help to support a full range of geo-spatial query operators.
A feature extraction method for automatic web page classification system
Web page classification is a very important topic today; this is due to the increasing volume of ... more Web page classification is a very important topic today; this is due to the increasing volume of data available on the World Wide Web and the heterogeneity in the formats of the data. For that, there exist a need to ways to manage and extract important knowledge from the web and to facilitate indexing and searching. This paper proposes a method for extracting features of web pages using WEKA as a data mining tool. The resulting features are used for building an automatic web page classification system with a specific number of categories (autos, computers & internet, health, sports) based on different web page classification algorithms (page text, page title)with a good accuracy that can be improved using the feature weights.
The web is growing very fast; it has a very large amount of information from different types. Thi... more The web is growing very fast; it has a very large amount of information from different types. This necessitates the need to ways to arrange and organize this vast amount of data. One of these ways is automatic Web page classification; that is used in many other applications. In this paper, the comparison between various page structural elements, which used in the Web page classification task, has been presented. The classification rules and word-weighting algorithms have been used for the significance criteria limitation of page structural elements in web page classification. The obtained results showed that, the page title proved its significance; giving better accuracy over all categories, whereas it s ranged between 84.69% and 93.85% with average 90.52%. Finally, the results also proved that the word-weighting algorithm has improved the accuracy than the classification rules algorithm with all other classification criteria (title, body text, header and URL). Paper Pages: 164-171
Semantic Web resources such as DBpedia provide a rich source of structured knowledge about geogra... more Semantic Web resources such as DBpedia provide a rich source of structured knowledge about geographical features such as towns, rivers and historical buildings. Retrieval from these resources of all content that is relevant to a particular spatial query of, for example, containment or proximity is not always straightforward because there is considerable inconsistency in the way in which geographical features are referenced to location. In DBpedia some geographical feature instances have point coordinates, some have qualitative properties that provide explicit or implicit locational information via place names, and some have neither of these. Here we show how structured geo-spatial query, a form of question answering, on DBpedia can be performed with a hybrid strategy that exploits both quantitative and qualitative spatial properties in combination with a high quality reference geo-dataset that can help to support a full range of geo-spatial query operators.
Over the past few years, there has been a noticeable advancement in environmental models and info... more Over the past few years, there has been a noticeable advancement in environmental models and information fusion systems taking advantage of the recent developments in sensor and mobile technologies. However, little attention has been paid so far to quantifying the relationship between environment changes and their impact on our bodies in real-life settings. In this paper, we identify a data driven approach based on direct and continuous sensor data to assess the impact of the surrounding environment and physiological changes and emotion. We aim at investigating the potential of fusing on-body physiological signals, environmental sensory data and on-line self-report emotion measures in order to achieve the following objectives: (1) model the short term impact of the ambient environment on human body, (2) predict emotions based on-body sensors and environmental data. To achieve this, we have conducted a real-world study 'in the wild' with on-body and mobile sensors. Data was collected from participants walking around Nottingham city centre, in order to develop analytical and predictive models. Multiple regression, after allowing for possible confounders, showed a noticeable correlation between noise exposure and heart rate. Similarly, UV and environmental noise have been shown to have a noticeable effect on changes in ElectroDermal Activity (EDA). Air pressure demonstrated the greatest contribution towards the detected changes in body temperature and motion. Also, significant correlation was found between air pressure and heart rate. Finally, decision fusion of the classification results from different modalities is performed. To the best of our knowledge this work presents the first attempt at fusing and modelling data from environmental and physiological sources collected from sensors in a real-world setting.
Recently, Social media has arisen not only as a personal communication media, but also, as a medi... more Recently, Social media has arisen not only as a personal communication media, but also, as a media to communicate opinions about products and services or even political and general events among its users. Due to its widespread and popularity, there is a massive amount of user reviews or opinions produced and shared daily. Twitter is one of the most widely used social media micro blogging sites. Mining user opinions from social media data is not a straight forward task; it can be accomplished in different ways. In this work, an open source approach is presented, throughout which, twitter Microblogs data has been collected, pre-processed, analyzed and visualized using open source tools to perform text mining and sentiment analysis for analyzing user contributed online reviews about two giant retail stores in the UK namely Tesco and Asda stores over Christmas period 2014. Collecting customer opinions can be expensive and time consuming task using conventional methods such as surveys. The sentiment analysis of the customer opinions makes it easier for businesses to understand their competitive value in a changing market and to understand their customer views about their products and services, which also provide an insight into future marketing strategies and decision making policies.
The detection and monitoring of emotions are important in various applications, e.g., to enable n... more The detection and monitoring of emotions are important in various applications, e.g., to enable naturalistic and personalised human-robot interaction. Emotion detection often require modelling of various data inputs from multiple modalities, including physiological signals (e.g., EEG and GSR), environmental data (e.g., audio and weather), videos (e.g., for capturing facial expressions and gestures) and more recently motion and location data. Many traditional machine learning algorithms have been utilised to capture the diversity of multimodal data at the sensors and features levels for human emotion classification. While the feature engineering processes often embedded in these algorithms are beneficial for emotion modelling, they inherit some critical limitations which may hinder the development of reliable and accurate models. In this work, we adopt a deep learning approach for emotion classification through an iterative process by adding and removing large number of sensor signal...
Semantic Web resources such as DBpedia provide a rich source of structured knowledge about geogra... more Semantic Web resources such as DBpedia provide a rich source of structured knowledge about geographical features such as towns, rivers and historical buildings. Retrieval from these resources of all content that is relevant to a particular spatial query of, for example, containment or proximity is not always straightforward because there is considerable inconsistency in the way in which geographical features are referenced to location. In DBpedia some geographical feature instances have point coordinates, some have qualitative properties that provide explicit or implicit locational information via place names, and some have neither of these. Here we show how structured geo-spatial query, a form of question answering, on DBpedia can be performed with a hybrid strategy that exploits both quantitative and qualitative spatial properties in combination with a high quality reference geo-dataset that can help to support a full range of geo-spatial query operators.
A feature extraction method for automatic web page classification system
Web page classification is a very important topic today; this is due to the increasing volume of ... more Web page classification is a very important topic today; this is due to the increasing volume of data available on the World Wide Web and the heterogeneity in the formats of the data. For that, there exist a need to ways to manage and extract important knowledge from the web and to facilitate indexing and searching. This paper proposes a method for extracting features of web pages using WEKA as a data mining tool. The resulting features are used for building an automatic web page classification system with a specific number of categories (autos, computers & internet, health, sports) based on different web page classification algorithms (page text, page title)with a good accuracy that can be improved using the feature weights.
The web is growing very fast; it has a very large amount of information from different types. Thi... more The web is growing very fast; it has a very large amount of information from different types. This necessitates the need to ways to arrange and organize this vast amount of data. One of these ways is automatic Web page classification; that is used in many other applications. In this paper, the comparison between various page structural elements, which used in the Web page classification task, has been presented. The classification rules and word-weighting algorithms have been used for the significance criteria limitation of page structural elements in web page classification. The obtained results showed that, the page title proved its significance; giving better accuracy over all categories, whereas it s ranged between 84.69% and 93.85% with average 90.52%. Finally, the results also proved that the word-weighting algorithm has improved the accuracy than the classification rules algorithm with all other classification criteria (title, body text, header and URL). Paper Pages: 164-171
Semantic Web resources such as DBpedia provide a rich source of structured knowledge about geogra... more Semantic Web resources such as DBpedia provide a rich source of structured knowledge about geographical features such as towns, rivers and historical buildings. Retrieval from these resources of all content that is relevant to a particular spatial query of, for example, containment or proximity is not always straightforward because there is considerable inconsistency in the way in which geographical features are referenced to location. In DBpedia some geographical feature instances have point coordinates, some have qualitative properties that provide explicit or implicit locational information via place names, and some have neither of these. Here we show how structured geo-spatial query, a form of question answering, on DBpedia can be performed with a hybrid strategy that exploits both quantitative and qualitative spatial properties in combination with a high quality reference geo-dataset that can help to support a full range of geo-spatial query operators.
Over the past few years, there has been a noticeable advancement in environmental models and info... more Over the past few years, there has been a noticeable advancement in environmental models and information fusion systems taking advantage of the recent developments in sensor and mobile technologies. However, little attention has been paid so far to quantifying the relationship between environment changes and their impact on our bodies in real-life settings. In this paper, we identify a data driven approach based on direct and continuous sensor data to assess the impact of the surrounding environment and physiological changes and emotion. We aim at investigating the potential of fusing on-body physiological signals, environmental sensory data and on-line self-report emotion measures in order to achieve the following objectives: (1) model the short term impact of the ambient environment on human body, (2) predict emotions based on-body sensors and environmental data. To achieve this, we have conducted a real-world study 'in the wild' with on-body and mobile sensors. Data was collected from participants walking around Nottingham city centre, in order to develop analytical and predictive models. Multiple regression, after allowing for possible confounders, showed a noticeable correlation between noise exposure and heart rate. Similarly, UV and environmental noise have been shown to have a noticeable effect on changes in ElectroDermal Activity (EDA). Air pressure demonstrated the greatest contribution towards the detected changes in body temperature and motion. Also, significant correlation was found between air pressure and heart rate. Finally, decision fusion of the classification results from different modalities is performed. To the best of our knowledge this work presents the first attempt at fusing and modelling data from environmental and physiological sources collected from sensors in a real-world setting.
Recently, Social media has arisen not only as a personal communication media, but also, as a medi... more Recently, Social media has arisen not only as a personal communication media, but also, as a media to communicate opinions about products and services or even political and general events among its users. Due to its widespread and popularity, there is a massive amount of user reviews or opinions produced and shared daily. Twitter is one of the most widely used social media micro blogging sites. Mining user opinions from social media data is not a straight forward task; it can be accomplished in different ways. In this work, an open source approach is presented, throughout which, twitter Microblogs data has been collected, pre-processed, analyzed and visualized using open source tools to perform text mining and sentiment analysis for analyzing user contributed online reviews about two giant retail stores in the UK namely Tesco and Asda stores over Christmas period 2014. Collecting customer opinions can be expensive and time consuming task using conventional methods such as surveys. The sentiment analysis of the customer opinions makes it easier for businesses to understand their competitive value in a changing market and to understand their customer views about their products and services, which also provide an insight into future marketing strategies and decision making policies.
Uploads
Papers by eman younis